RMCP Vol. 11, Num 4 (2020): October-December [english version] by Revista Mexicana de Ciencias Pecuarias

Edición Bilingüe Bilingual Edition

Revista Mexicana de Ciencias Pecuarias Rev. Mex. Cienc. Pecu. Vol. 11 Núm 4, pp. 933-1230, OCTUBRE-DICIEMBRE-2020

ISSN: 2448-6698

Rev. Mex. Cienc. Pecu. Vol. 11 Núm. 4, pp. 933-1230, OCTUBRE-DICIEMBRE-2020

Conejo de 35 días de edad, raza California del Instituto de Ciencias Agropecuarias, Universidad Autónoma del Estado de Hidalgo. Fotografía: Dra. Maricela Ayala Martínez

REVISTA MEXICANA DE CIENCIAS PECUARIAS Volumen 11 Número 4, OctubreDiciembre 2020. Es una publicación trimestral de acceso abierto, revisada por pares y arbitrada, editada por el Instituto Nacional de Investigaciones Forestales, Agrícolas y Pecuarias (INIFAP). Avenida Progreso No. 5, Barrio de Santa Catarina, Delegación Coyoacán, C.P. 04010, Cuidad de México, www.inifap.gob.mx Distribuida por el Centro Nacional de Investigación Disciplinaria en Salud Animal e Inocuidad, Km 15.5 Carretera México-Toluca, Colonia Palo Alto, Cuidad de México, C.P. 05110. Editor responsable: Arturo García Fraustro. Reservas de Derechos al Uso Exclusivo número 04-2016-060913393200-203. ISSN: 2448-6698, otorgados por el Instituto Nacional del Derecho de Autor (INDAUTOR). Responsable de la última actualización de este número: Arturo García Fraustro, Centro Nacional de Investigación Disciplinaria en Salud Animal e Inocuidad, Km. 15.5 Carretera México-Toluca, Colonia Palo Alto, Ciudad de México, C.P. 015110. http://cienciaspecuarias. inifap.gob.mx, la presente publicación tuvo su última actualización en octubre de 2020.

DIRECTORIO FUNDADOR John A. Pino EDITORES ADJUNTOS Oscar L. Rodríguez Rivera Alfonso Arias Medina

EDITOR EN JEFE Arturo García Fraustro

EDITORES POR DISCIPLINA Dra. Yolanda Beatriz Moguel Ordóñez, INIFAP, México Dr. Ramón Molina Barrios, Instituto Tecnológico de Sonora, México Dra. Maria Cristina Schneider, Universidad de Georgetown, Estados Unidos Dra. Elisa Margarita Rubí Chávez, UNAM, México Dr. Feliciano Milian Suazo, Universidad Autónoma de Querétaro, México Dr. Javier F. Enríquez Quiroz, INIFAP, México Dra. Martha Hortencia Martín Rivera, Universidad de Sonora URN, México Dr. Fernando Arturo Ibarra Flores, Universidad de Sonora URN, México Dr. James A. Pfister, USDA, Estados Unidos Dr. Eduardo Daniel Bolaños Aguilar, INIFAP, México Dr. Sergio Iván Román-Ponce, INIFAP, México Dr. Jesús Fernández Martín, INIA, España Dr. Sergio D. Rodríguez Camarillo, INIFAP, México Dr. Martin Talavera Rojas, Universidad Autónoma del Estado de México, México Dra. Maria Salud Rubio Lozano, Facultad de Medicina Veterinaria y Zootecnia, UNAM, México Dra. Elizabeth Loza-Rubio, INIFAP, México Dr. Juan Carlos Saiz Calahorra, Instituto Nacional de Investigaciones Agrícolas, España Dra. Silvia Elena Buntinx Dios, Facultad de Medicina Veterinaria y Zootecnia, UNAM, México Dr. José Armando Partida de la Peña, INIFAP, México Dr. José Luis Romano Muñoz, INIFAP, México

Dr. Alejandro Plascencia Jorquera, Universidad Autónoma de Baja California, México Dr. Juan Ku Vera, Universidad Autónoma de Yucatán, México Dr. Ricardo Basurto Gutiérrez, INIFAP, México Dr. Luis Corona Gochi, Facultad de Medicina Veterinaria y Zootecnia, UNAM, México Dr. Juan Manuel Pinos Rodríguez, Facultad de Medicina Veterinaria y Zootecnia, Universidad Veracruzana, México Dr. Carlos López Coello, Facultad de Medicina Veterinaria y Zootecnia, UNAM, México Dr. Arturo Francisco Castellanos Ruelas, Facultad de Química. UADY Dra. Guillermina Ávila Ramírez, UNAM, México Dr. Emmanuel Camuus, CIRAD, Francia. Dr. Héctor Jiménez Severiano, INIFAP., México Dr. Juan Hebert Hernández Medrano, UNAM, México Dr. Adrian Guzmán Sánchez, Universidad Autónoma Metropolitana-Xochimilco, México Dr. Eugenio Villagómez Amezcua Manjarrez, INIFAP, CENID Salud Animal e Inocuidad, México Dr. Fernando Cervantes Escoto, Universidad Autónoma Chapingo, México Dr. Adolfo Guadalupe Álvarez Macías, Universidad Autónoma Metropolitana Xochimilco, México Dr. Alfredo Cesín Vargas, UNAM, México Dra. Marisela Leal Hernández, INIFAP, México Dra. Nydia Edith Reyes Rodríguez, UAEH, México Dr. Efrén Ramírez Bribiesca, Colegio de Postgraduados, México

TIPOGRAFÍA Y FORMATO: Oscar L. Rodríguez Rivera

Indizada en el “Journal Citation Report” Science Edition del ISI . Inscrita en el Sistema de Clasificación de Revistas Científicas y Tecnológicas de CONACyT; en EBSCO Host y la Red de Revistas Científicas de América Latina y el Caribe, España y Portugal (RedALyC) (www.redalyc.org); en la Red Iberoamericana de Revistas Científicas de Veterinaria de Libre Acceso (www.veterinaria.org/revistas/ revivec); en los Índices SCOPUS y EMBASE de Elsevier (www.elsevier. com).

REVISTA MEXICANA DE CIENCIAS PECUARIAS La Revista Mexicana de Ciencias Pecuarias es un órgano de difusión científica y técnica de acceso abierto, revisada por pares y arbitrada. Su objetivo es dar a conocer los resultados de las investigaciones realizadas por cualquier institución científica, relacionadas particularmente con las distintas disciplinas de la Medicina Veterinaria y la Zootecnia. Además de trabajos de las disciplinas indicadas en su Comité Editorial, se aceptan también para su evaluación y posible publicación, trabajos de otras disciplinas, siempre y cuando estén relacionados con la investigación pecuaria.

total por publicar es de $ 5,600.00 más IVA por manuscrito ya editado. Se publica en formato digital en acceso abierto, por lo que se autoriza la reproducción total o parcial del contenido de los artículos si se cita la fuente. El envío de los trabajos de debe realizar directamente en el sitio oficial de la revista. Correspondencia adicional deberá dirigirse al Editor Adjunto a la siguiente dirección: Calle 36 No. 215 x 67 y 69 Colonia Montes de Amé, C.P. 97115 Mérida, Yucatán, México. Tel/Fax +52 (999) 941-5030. Correo electrónico (C-ele): rodriguez_oscar@prodigy.net.mx.

Se publican en la revista tres categorías de trabajos: Artículos Científicos, Notas de Investigación y Revisiones Bibliográficas (consultar las Notas al autor); la responsabilidad de cada trabajo recae exclusivamente en los autores, los cuales, por la naturaleza misma de los experimentos pueden verse obligados a referirse en algunos casos a los nombres comerciales de ciertos productos, ello sin embargo, no implica preferencia por los productos citados o ignorancia respecto a los omitidos, ni tampoco significa en modo alguno respaldo publicitario hacia los productos mencionados.

La correspondencia relativa a suscripciones, asuntos de intercambio o distribución de números impresos anteriores, deberá dirigirse al Editor en Jefe de la Revista Mexicana de Ciencias Pecuarias, CENID Salud Animal e Inocuidad, Km 15.5 Carretera México-Toluca, Col. Palo Alto, D.F. C.P. 05110, México; Tel: +52(55) 3871-8700 ext. 80316; garcia.arturo@inifap.gob.mx o arias.alfonso@inifap.gob.mx. Inscrita en la base de datos de EBSCO Host y la Red de Revistas Científicas de América Latina y el Caribe, España y Portugal (RedALyC) (www.redalyc.org), en la Red Iberoamericana de Revistas Científicas de Veterinaria de Libre Acceso (www.veterinaria.org/revistas/ revivec), indizada en el “Journal Citation Report” Science Edition del ISI (http://thomsonreuters. com/) y en los Índices SCOPUS y EMBASE de Elsevier (www.elsevier.com)

Todas las contribuciones serán cuidadosamente evaluadas por árbitros, considerando su calidad y relevancia académica. Queda entendido que el someter un manuscrito implica que la investigación descrita es única e inédita. La publicación de Rev. Mex. Cienc. Pecu. es trimestral en formato bilingüe Español e Inglés. El costo

VISITE NUESTRA PÁGINA EN INTERNET Artículos completos desde 1963 a la fecha y Notas al autor en: http://cienciaspecuarias.inifap.gob.mx Revista Mexicana de Ciencias Pecuarias is an open access peer-reviewed and refereed scientific and technical journal, which publishes results of research carried out in any scientific or academic institution, especially related to different areas of veterinary medicine and animal production. Papers on disciplines different from those shown in Editorial Committee can be accepted, if related to livestock research.

Part of, or whole articles published in this Journal may be reproduced, stored in a retrieval system or transmitted in any form or by any means, electronic, mechanical, photocopying or otherwise, provided the source is properly acknowledged. Manuscripts should be submitted directly in the official web site. Additional information may be mailed to Associate Editor, Revista Mexicana de Ciencias Pecuarias, Calle 36 No. 215 x 67 y 69 Colonia Montes de Amé, C.P. 97115 Mérida, Yucatán, México. Tel/Fax +52 (999) 941-5030. E-mail: rodriguez_oscar@prodigy.net.mx.

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For subscriptions, exchange or distribution of previous printed issues, please contact: Editor-in-Chief of Revista Mexicana de Ciencias Pecuarias, CENID Salud Animal e Inocuidad, Km 15.5 Carretera México-Toluca, Col. Palo Alto, D.F. C.P. 05110, México; Tel: +52(55) 3871-8700 ext. 80316; garcia.arturo@inifap.gob.mx or arias.alfonso@inifap.gob.mx. Registered in the EBSCO Host database. The Latin American and the Caribbean Spain and Portugal Scientific Journals Network (RedALyC) (www.redalyc.org). The Iberoamerican Network of free access Veterinary Scientific Journals (www.veterinaria.org/ revistas/ revivec). Thomson Reuter´s “Journal Citation Report” Science Edition (http://thomsonreuters.com/). Elsevier´s SCOPUS and EMBASE (www.elsevier.com) and the Essential Electronic Agricultural Library (www.teeal.org).

All contributions will be carefully refereed for academic relevance and quality. Submission of an article is understood to imply that the research described is unique and unpublished. Rev. Mex. Cien. Pecu. is published quarterly in original lenguage Spanish or English. Total fee charges are US $ 325.00 per article in both printed languages.

VISIT OUR SITE IN THE INTERNET Full articles from year 1963 to date and Instructions for authors can be accessed via the site http://cienciaspecuarias.inifap.gob.mx

REVISTA MEXICANA DE CIENCIAS PECUARIAS REV. MEX. CIENC. PECU.

VOL. 11 No. 4

OCTUBRE-DICIEMBRE-2020

CONTENIDO ARTÍCULOS

Pág. Detección molecular de coronavirus bovino asociado al complejo respiratorio bovino en ganado de engorda del valle de Mexicali, Baja California, México Molecular detection of bovine coronavirus associated with the bovine respiratory complex in beef cattle in the Mexicali Valley, Baja California, Mexico Carolina Orozco-Cabrera, Gilberto López-Valencia, Luis Mario Muñoz-Del Real, Soila Maribel Gaxiola-Camacho, Nohemí Castro-del Campo, Sergio Arturo Cueto-González, José Guadalupe Guerrero-Velázquez, Kattya MorenoTorres, Kelvin Orlando Espinoza-Blandón, Sergio Daniel Gómez-Gómez, Enrique Trasviña-Muñoz, Francisco Javier Monge-Navarro …………………………………………………………………………………………………………………….………………….933

Frecuencia de M. hyopneumoniae, M. hyorhinis y M. hyosynoviae en muestras nasales y de pulmón de cerdos con síntomas de neumonía enzoótica porcina Frequency of M. hyopneumoniae, M. hyorhinis and M. hyosynoviae in nasal and lung samples from pigs with symptoms of porcine enzootic pneumonia

Rosa Elena Miranda Morales, Verónica Rojas Trejo, Luis Enrique López-Cerino, Erika Margarita Carrillo Casas, Rosa Elena Sarmiento Silva, María Elena Trujillo Ortega, Rolando Beltrán Figueroa, Francisco José Trigo Tavera………………………………………………………………………………………………………………………………………………….….946

Presencia de Hymenolepis nana y diminuta en roedores de la ciudadela las Piñas, MilagroEcuador y su riesgo en salud pública Presence of Hymenolepis nana and diminuta in rodents of the Las Pinas citadel, in Milagro, Ecuador, and its risk for public health Roberto Darwin Coello-Peralta, Galo Ernesto Martínez-Cepeda, Douglas Pinela-Castro, Enrique Omar ReyesEcheverria, Enrique Xavier Rodríguez-Burnham, Maria de Lourdes Salazar Mazamba, Betty Pazmiño-Gómez, Antonella Ramírez-Tigrero, Manuel Bernstein, Pedro Cedeño-Reyes…………………………………………………………….…961

Frecuencia de contaminación y de serotipos de Salmonella enterica y Escherichia coli en una operación integrada de matanza y deshuese de bovinos Frequency of contamination and serovars of Salmonella enterica and Escherichia coli in an integrated cattle slaughtering and deboning operation Jorge Alfredo de la Garza-García, María Salud Rubio Lozano, María del Carmen Wacher-Rodarte, Armando Navarro Ocaña, Rigoberto Hernández-Castro, Juan Xicohtencatl-Cortes, Enrique Jesús Delgado Suárez..........................................................................................................................................................971

III

Antimicrobial resistance of Escherichia coli isolated from cattle carcasses and feces in Center of Mexico La resistencia antimicrobiana en Escherichia coli aislada de canales y heces bovinas de rastros en el centro de México Vicente Vega Sánchez, Martín Talavera Rojas, Jeannette Barba León, Andrea Paloma Zepeda Velázquez, Nydia Edith Reyes Rodríguez…………………………………………………………………………………………………………………………………….…991

Resistencia antimicrobiana de Salmonella spp aisladas de canales de cerdo obtenidas de dos tipos de rastros en Jalisco, México Antimicrobial resistance in Salmonella spp. isolated from pig carcasses in two slaughterhouse types in Jalisco, Mexico Vicente Vega-Sánchez, Jeannette Barba-León, Delia Guillermina González-Aguilar, Elisa Cabrera-Díaz, Carlos Pacheco-Gallardo, Adriana Guadalupe Orozco-García…..………………………………………………………………….………….1004

Influence of milking method, storage conditions and somatic cell counts on the milk quality form tanks La influencia del método de ordeño, las condiciones de almacenamiento y el recuento de células somáticas en la calidad de la leche cruda en tanques Joadilza da Silva Bezerra, Juliana Paula Felipe de Oliveira, Danielle Cavalcanti Sales, Yhêlda Maria de Oliveira Silva, Stela Antas Urbano, Luis Henrique Fernandes Borba, Lisandra Murmann, Adriano Henrique do Nascimento Rangel………………………………………………………………………………………………………………………………………………...…1016

Serotypes and Stx2 subtyping of Shiga toxin producing Escherichia coli isolates from cattle carcasses and feces Serotipos y aislamientos de Escherichia coli productora del subtipo Stx2 de la toxina Shiga provenientes de canales y heces de ganado bovino Nydia Edith Reyes-Rodríguez, Jeannette Barba-León, Armando Navarro-Ocaña, Vicente Vega-Sánchez, Fabián Ricardo Gómez De Anda, Juan Martín Talavera-González, Martín Talavera-Rojas……………………………………………1030

Impacto de la inclusión de información extranjera sobre la evaluación genética mexicana de sementales Holstein Impact of the inclusion of foreign information on Mexican genetic evaluation of Holstein sires Gustavo Javier Martínez Marín, Felipe de Jesús Ruiz López, Carlos Gustavo Vásquez Peláez, Sergio Iván Román Ponce, Adriana García Ruiz……………………………………………………………………………………………………………………….1045

Análisis genómico de diversidad y estructura genómica de las poblaciones bovinas de la raza mexicana de Lidia Genomic diversity and structure of Lidia breed cattle in Mexico Paulina G. Eusebi, Oscar Cortés, Susana Dunner, Javier Cañón……………………………………………………………..…..1059

Análisis del pedigrí en diez poblaciones mexicanas de ovinos Pedigree analysis in ten sheep populations in Mexico Joel Domínguez-Viveros, Felipe Alonso Rodríguez-Almeida, Adán Medellín-Cázares, Juan Pablo GutiérrezGarcía……………………………………………………………………………………………………………..……………………………………..1071

Acumulación de forraje de Lotus corniculatus L., en función a diferentes estrategias de cosecha Forage accumulation in Lotus corniculatus L. as a function of harvest strategy Perpetuo Álvarez Vázquez. Juan de Dios Guerrero Rodríguez, Gabino Garcia De Los Santos, Maria Esther Ortega Cerrilla, Sergio Iban Mendoza Pedroza, Santiago Joaquín Cancino……………………………………………………………………………………………………………………………………............….1087

Presence of the yeast Kodamaea ohmeri associated with Aethina tumida (Coleoptera: Nitidulidae) collected in Africanized honey bee colonies from two apiaries of Yucatan, Mexico Presencia de la levadura Kodamaea ohmeri en escarabajos Aethina tumida (Coleoptera: Nitidulidae) colectados de colonias de abeja melífera africanizada en dos apiarios en Yucatán, México Azucena Canto, Luis A. Medina-Medina, Elisa Chan, Rosalina Rodríguez…………………………..….……………………..…1101

Factores determinantes del uso de sorgo para alimentación de ganado bovino en el noroeste de México Determining factors for the use of sorghum as fodder for bovines in Northwestern Mexico Venancio Cuevas-Reyes, Blanca Isabel Sánchez Toledano, Roselia Servín Juárez, Juan Esteban Reyes Jiménez, Alfredo Loaiza Meza, Tomas Moreno Gallegos……………………………………….………………………………………………....1113

Mejoramiento genético de la biomasa aérea y sus componentes en alfalfa: selección familial de medios hermanos Genetic improvement of aerial alfalfa biomass and its components: half-sib family selection Milton Javier Luna-Guerrero, Cándido López-Castañeda, Alfonso Hernández-Garay……………………………………..…1126

REVISIONES DE LITERATURA

Post vitrification pregnancy rate of in vivo produced embryos derived from equids. Review Tasa de preñez post vitrificación en los embriones de équidos producidos in vivo. Revisión Christian Urías-Castro, Ana Myriam Boeta……………………………………………………………………………..……1142

Herramientas moleculares utilizadas para el análisis metagenómico. Revisión Molecular tools used for metagenomic analysis. Review Nohemí Gabriela Cortés-López, Perla Lucía Ordóñez-Baquera, Joel Domínguez-Viveros…………………..1150

Origen metabólico y propiedades bioactivas de ácidos grasos ramificados e impares en leche de rumiantes. Revisión Metabolic origin and bioactive properties of odd and branched-chain fatty acids in ruminants’ milk. Review… Pilar Gómez-Cortés, Miguel Ángel de la Fuente………………………………………………………………………….1174

NOTAS DE INVESTIGACIÓN

Análisis de QTL asociados a polimorfismos de nucleótido único (SNP) involucrados en el fenotipo lechero del ganado Holstein QTL analysis associated to single nucleotide polymorphisms (SNP) involved in the dairy phenotype of Holstein cattle José Manuel Valdez-Torres, Juan Alberto Grado Ahuir, Beatriz Elena Castro-Valenzuela, M. Eduviges BurrolaBarraza……………………………………………………………………………………………………………….……….1192

Efecto de la adición de clorhidrato de zilpaterol genérico en el perfil bioquímico y hematológico de ovinos de pelo engordados en corral Evaluation of the biochemical and hematological profiles of feedlot hair sheep after the supplementation with generic zilpaterol hydrochloride Arnulfo Vicente Pérez, Leonel Avendaño Reyes, Ulises Macías Cruz, Antonio Aguilar Quiñones, Ricardo Vicente Pérez, Miguel Mellado Bosque, Miguel Ángel Gastélum Delgado, Abelardo Correa Calderón, G. López-Rincón, Juan Eulogio Guerra Liera…………………………………………………………………….………………1208

Dietary supplementation effects with Ruta graveolens on performance, carcass traits and meat quality on rabbits Efectos de la suplementación dietética con Ruta graveolens en el desempeño, las características de la canal y la calidad de la carne de conejo Maricela Ayala Martínez, Armando Zepeda-Bastida, Sergio Soto-Simental………………………….…………..1220

Actualización: marzo, 2020 NOTAS AL AUTOR La Revista Mexicana de Ciencias Pecuarias se edita completa en dos idiomas (español e inglés) y publica tres categorías de trabajos: Artículos científicos, Notas de investigación y Revisiones bibliográficas.

Los autores interesados en publicar en esta revista deberán ajustarse a los lineamientos que más adelante se indican, los cuales en términos generales, están de acuerdo con los elaborados por el Comité Internacional de Editores de Revistas Médicas (CIERM) Bol Oficina Sanit Panam 1989;107:422-437. 1.

Página del título Resumen en español Resumen en inglés Texto Agradecimientos y conflicto de interés Literatura citada

Sólo se aceptarán trabajos inéditos. No se admitirán si están basados en pruebas de rutina, ni datos experimentales sin estudio estadístico cuando éste sea indispensable. Tampoco se aceptarán trabajos que previamente hayan sido publicados condensados o in extenso en Memorias o Simposio de Reuniones o Congresos (a excepción de Resúmenes). Todos los trabajos estarán sujetos a revisión de un Comité Científico Editorial, conformado por Pares de la Disciplina en cuestión, quienes desconocerán el nombre e Institución de los autores proponentes. El Editor notificará al autor la fecha de recepción de su trabajo. El manuscrito deberá someterse a través del portal de la Revista en la dirección electrónica: http://cienciaspecuarias.inifap.gob.mx, consultando el “Instructivo para envío de artículos en la página de la Revista Mexicana de Ciencias Pecuarias”. Para su elaboración se utilizará el procesador de Microsoft Word, con letra Times New Roman a 12 puntos, a doble espacio. Asimismo se deberán llenar los formatos de postulación, carta de originalidad y no duplicidad y disponibles en el propio sitio oficial de la revista.

Por ser una revista con arbitraje, y para facilitar el trabajo de los revisores, todos los renglones de cada página deben estar numerados; asimismo cada página debe estar numerada, inclusive cuadros, ilustraciones y gráficas.

Los artículos tendrán una extensión máxima de 20 cuartillas a doble espacio, sin incluir páginas de Título, y cuadros o figuras (los cuales no deberán exceder de ocho y ser incluidos en el texto). Las Notas de investigación tendrán una extensión máxima de 15 cuartillas y 6 cuadros o figuras. Las Revisiones bibliográficas una extensión máxima de 30 cuartillas y 5 cuadros.

Los manuscritos de las tres categorías de trabajos que se publican en la Rev. Mex. Cienc. Pecu. deberán contener los componentes que a continuación se indican, empezando cada uno de ellos en página aparte.

Página del Título. Solamente debe contener el título del trabajo, que debe ser conciso pero informativo; así como el título traducido al idioma inglés. En el manuscrito no es necesaria información como nombres de autores, departamentos, instituciones, direcciones de correspondencia, etc., ya que estos datos tendrán que ser registrados durante el proceso de captura de la solicitud en la plataforma del OJS (http://ciencias pecuarias.inifap.gob.mx).

Resumen en español. En la segunda página se debe incluir un resumen que no pase de 250 palabras. En él se indicarán los propósitos del estudio o investigación; los procedimientos básicos y la metodología empleada; los resultados más importantes encontrados, y de ser posible, su significación estadística y las conclusiones principales. A continuación del resumen, en punto y aparte, agregue debidamente rotuladas, de 3 a 8 palabras o frases cortas clave que ayuden a los indizadores a clasificar el trabajo, las cuales se publicarán junto con el resumen.

Resumen en inglés. Anotar el título del trabajo en inglés y a continuación redactar el “abstract” con las mismas instrucciones que se señalaron para el resumen en español. Al final en punto y aparte, se deberán escribir las correspondientes palabras clave (“key words”).

10. Texto. Las tres categorías de trabajos que se publican en la Rev. Mex. Cienc. Pecu. consisten en lo siguiente: a) Artículos científicos. Deben ser informes de trabajos originales derivados de resultados parciales o finales de investigaciones. El texto del Artículo científico se divide en secciones que llevan estos encabezamientos:

VII

Introducción Materiales y Métodos Resultados Discusión Conclusiones e implicaciones Literatura citada

referencias, aunque pueden insertarse en el texto (entre paréntesis).

Reglas básicas para la Literatura citada Nombre de los autores, con mayúsculas sólo las iniciales, empezando por el apellido paterno, luego iniciales del materno y nombre(s). En caso de apellidos compuestos se debe poner un guión entre ambos, ejemplo: Elías-Calles E. Entre las iniciales de un autor no se debe poner ningún signo de puntuación, ni separación; después de cada autor sólo se debe poner una coma, incluso después del penúltimo; después del último autor se debe poner un punto.

En los artículos largos puede ser necesario agregar subtítulos dentro de estas divisiones a fin de hacer más claro el contenido, sobre todo en las secciones de Resultados y de Discusión, las cuales también pueden presentarse como una sola sección. b) Notas de investigación. Consisten en modificaciones a técnicas, informes de casos clínicos de interés especial, preliminares de trabajos o investigaciones limitadas, descripción de nuevas variedades de pastos; así como resultados de investigación que a juicio de los editores deban así ser publicados. El texto contendrá la misma información del método experimental señalado en el inciso a), pero su redacción será corrida del principio al final del trabajo; esto no quiere decir que sólo se supriman los subtítulos, sino que se redacte en forma continua y coherente.

El título del trabajo se debe escribir completo (en su idioma original) luego el título abreviado de la revista donde se publicó, sin ningún signo de puntuación; inmediatamente después el año de la publicación, luego el número del volumen, seguido del número (entre paréntesis) de la revista y finalmente el número de páginas (esto en caso de artículo ordinario de revista). Puede incluir en la lista de referencias, los artículos aceptados aunque todavía no se publiquen; indique la revista y agregue “en prensa” (entre corchetes).

c) Revisiones bibliográficas. Consisten en el tratamiento y exposición de un tema o tópico de relevante actualidad e importancia; su finalidad es la de resumir, analizar y discutir, así como poner a disposición del lector información ya publicada sobre un tema específico. El texto se divide en: Introducción, y las secciones que correspondan al desarrollo del tema en cuestión.

En el caso de libros de un solo autor (o más de uno, pero todos responsables del contenido total del libro), después del o los nombres, se debe indicar el título del libro, el número de la edición, el país, la casa editorial y el año. Cuando se trate del capítulo de un libro de varios autores, se debe poner el nombre del autor del capítulo, luego el título del capítulo, después el nombre de los editores y el título del libro, seguido del país, la casa editorial, año y las páginas que abarca el capítulo.

11. Agradecimientos y conflicto de interés. Siempre que corresponda, se deben especificar las colaboraciones que necesitan ser reconocidas, tales como a) la ayuda técnica recibida; b) el agradecimiento por el apoyo financiero y material, especificando la índole del mismo; c) las relaciones financieras que pudieran suscitar un conflicto de intereses. Las personas que colaboraron pueden ser citadas por su nombre, añadiendo su función o tipo de colaboración; por ejemplo: “asesor científico”, “revisión crítica de la propuesta para el estudio”, “recolección de datos”, etc. Siempre que corresponda, los autores deberán mencionar si existe algún conflicto de interés.

En el caso de tesis, se debe indicar el nombre del autor, el título del trabajo, luego entre corchetes el grado (licenciatura, maestría, doctorado), luego el nombre de la ciudad, estado y en su caso país, seguidamente el nombre de la Universidad (no el de la escuela), y finalmente el año. Emplee el estilo de los ejemplos que aparecen a continuación, los cuales están parcialmente basados en el formato que la Biblioteca Nacional de Medicina de los Estados Unidos usa en el Index Medicus.

12. Literatura citada. Numere las referencias consecutivamente en el orden en que se mencionan por primera vez en el texto. Las referencias en el texto, en los cuadros y en las ilustraciones se deben identificar mediante números arábigos entre paréntesis, sin señalar el año de la referencia. Evite hasta donde sea posible, el tener que mencionar en el texto el nombre de los autores de las referencias. Procure abstenerse de utilizar los resúmenes como referencias; las “observaciones inéditas” y las “comunicaciones personales” no deben usarse como

Revistas

Artículo ordinario, con volumen y número. (Incluya el nombre de todos los autores cuando sean seis o menos; si son siete o más, anote sólo el nombre de los seis primeros y agregue “et al.”).

VIII

Basurto GR, Garza FJD. Efecto de la inclusión de grasa o proteína de escape ruminal en el comportamiento de toretes Brahman en engorda. Téc Pecu Méx 1998;36(1):35-48.

XI)

Sólo número sin indicar volumen. II) Stephano HA, Gay GM, Ramírez TC. Encephalomielitis, reproductive failure and corneal opacity (blue eye) in pigs associated with a paramyxovirus infection. Vet Rec 1988;(122):6-10.

XII) Cunningham EP. Genetic diversity in domestic animals: strategies for conservation and development. In: Miller RH et al. editors. Proc XX Beltsville Symposium: Biotechnology’s role in genetic improvement of farm animals. USDA. 1996:13.

III) Chupin D, Schuh H. Survey of present status ofthe use of artificial insemination in developing countries. World Anim Rev 1993;(74-75):26-35.

Tesis.

No se indica el autor.

XIII) Alvarez MJA. Inmunidad humoral en la anaplasmosis y babesiosis bovinas en becerros mantenidos en una zona endémica [tesis maestría]. México, DF: Universidad Nacional Autónoma de México; 1989.

IV) Cancer in South Africa [editorial]. S Afr Med J 1994;84:15.

Suplemento de revista.

XIV) Cairns RB. Infrared spectroscopic studies of solid oxigen [doctoral thesis]. Berkeley, California, USA: University of California; 1965.

V) Hall JB, Staigmiller RB, Short RE, Bellows RA, Bartlett SE. Body composition at puberty in beef heifers as influenced by nutrition and breed [abstract]. J Anim Sci 1998;71(Suppl 1):205.

Organización como autor. XV) NRC. National Research Council. The nutrient requirements of beef cattle. 6th ed. Washington, DC, USA: National Academy Press; 1984.

Organización, como autor. VI) The Cardiac Society of Australia and New Zealand. Clinical exercise stress testing. Safety and performance guidelines. Med J Aust 1996;(164):282-284.

XVI) SAGAR. Secretaría de Agricultura, Ganadería y Desarrollo Rural. Curso de actualización técnica para la aprobación de médicos veterinarios zootecnistas responsables de establecimientos destinados al sacrificio de animales. México. 1996.

En proceso de publicación. VII) Scifres CJ, Kothmann MM. Differential grazing use of herbicide treated area by cattle. J Range Manage [in press] 2000.

XVII) AOAC. Oficial methods of analysis. 15th ed. Arlington, VA, USA: Association of Official Analytical Chemists. 1990.

Libros y otras monografías

XVIII) SAS. SAS/STAT User’s Guide (Release 6.03). Cary NC, USA: SAS Inst. Inc. 1988.

Autor total. VIII) Steel RGD, Torrie JH. Principles and procedures of statistics: A biometrical approach. 2nd ed. New York, USA: McGraw-Hill Book Co.; 1980.

XIX) SAS. SAS User´s Guide: Statistics (version 5 ed.). Cary NC, USA: SAS Inst. Inc. 1985.

Publicaciones electrónicas

Autor de capítulo. IX)

XX) Jun Y, Ellis M. Effect of group size and feeder type on growth performance and feeding patterns in growing pigs. J Anim Sci 2001;79:803-813. http://jas.fass.org/cgi/reprint/79/4/803.pdf. Accessed Jul 30, 2003.

Roberts SJ. Equine abortion. In: Faulkner LLC editor. Abortion diseases of cattle. 1rst ed. Springfield, Illinois, USA: Thomas Books; 1968:158-179.

Memorias de reuniones. X)

Olea PR, Cuarón IJA, Ruiz LFJ, Villagómez AE. Concentración de insulina plasmática en cerdas alimentadas con melaza en la dieta durante la inducción de estro lactacional [resumen]. Reunión nacional de investigación pecuaria. Querétaro, Qro. 1998:13.

Loeza LR, Angeles MAA, Cisneros GF. Alimentación de cerdos. En: Zúñiga GJL, Cruz BJA editores. Tercera reunión anual del centro de investigaciones forestales y agropecuarias del estado de Veracruz. Veracruz. 1990:51-56.

XXII) Sanh MV, Wiktorsson H, Ly LV. Effect of feeding level on milk production, body weight change, feed conversion and postpartum oestrus of crossbred lactating cows in tropical conditions. Livest Prod Sci 2002;27(2-3):331-338. http://www.sciencedirect. com/science/journal/03016226. Accessed Sep 12, 2003.

ha hectárea (s) h hora (s) i.m. intramuscular (mente) i.v. intravenosa (mente) J joule (s) kg kilogramo (s) km kilómetro (s) L litro (s) log logaritmo decimal Mcal megacaloría (s) MJ megajoule (s) m metro (s) msnm metros sobre el nivel del mar µg microgramo (s) µl microlitro (s) µm micrómetro (s)(micra(s)) mg miligramo (s) ml mililitro (s) mm milímetro (s) min minuto (s) ng nanogramo (s)Pprobabilidad (estadística) p página PC proteína cruda PCR reacción en cadena de la polimerasa pp páginas ppm partes por millón % por ciento (con número) rpm revoluciones por minuto seg segundo (s) t tonelada (s) TND total de nutrientes digestibles UA unidad animal UI unidades internacionales

13. Cuadros, Gráficas e Ilustraciones. Es preferible que sean pocos, concisos, contando con los datos necesarios para que sean autosuficientes, que se entiendan por sí mismos sin necesidad de leer el texto. Para las notas al pie se deberán utilizar los símbolos convencionales. 14 Versión final. Es el documento en el cual los autores ya integraron las correcciones y modificaciones indicadas por el Comité Revisor. Los trabajos deberán ser elaborados con Microsoft Word. Las fotografías e imágenes deberán estar en formato jpg (o compatible) con al menos 300 dpi de resolución. Tanto las fotografías, imágenes, gráficas, cuadros o tablas deberán incluirse en el mismo archivo del texto. Los cuadros no deberán contener ninguna línea vertical, y las horizontales solamente las que delimitan los encabezados de columna, y la línea al final del cuadro. 15. Una vez recibida la versión final, ésta se mandará para su traducción al idioma inglés o español, según corresponda. Si los autores lo consideran conveniente podrán enviar su manuscrito final en ambos idiomas. 16. Tesis. Se publicarán como Artículo o Nota de Investigación, siempre y cuando se ajusten a las normas de esta revista. 17. Los trabajos no aceptados para su publicación se regresarán al autor, con un anexo en el que se explicarán los motivos por los que se rechaza o las modificaciones que deberán hacerse para ser reevaluados.

versus

gravedades

Cualquier otra abreviatura se pondrá entre paréntesis inmediatamente después de la(s) palabra(s) completa(s).

18. Abreviaturas de uso frecuente: cal cm °C DL50 g

caloría (s) centímetro (s) grado centígrado (s) dosis letal 50% gramo (s)

19. Los nombres científicos y otras locuciones latinas se deben escribir en cursivas.

Updated: March, 2020 INSTRUCTIONS FOR AUTHORS Revista Mexicana de Ciencias Pecuarias is a scientific journal published in a bilingual format (Spanish and English) which carries three types of papers: Research Articles, Technical Notes, and Reviews. Authors interested in publishing in this journal, should follow the belowmentioned directives which are based on those set down by the International Committee of Medical Journal Editors (ICMJE) Bol Oficina Sanit Panam 1989;107:422-437. 1.

Title page Abstract Text Acknowledgments and conflict of interest Literature cited

Only original unpublished works will be accepted. Manuscripts based on routine tests, will not be accepted. All experimental data must be subjected to statistical analysis. Papers previously published condensed or in extenso in a Congress or any other type of Meeting will not be accepted (except for Abstracts). All contributions will be peer reviewed by a scientific editorial committee, composed of experts who ignore the name of the authors. The Editor will notify the author the date of manuscript receipt. Papers will be submitted in the Web site http://cienciaspecuarias.inifap.gob.mx, according the â&#x20AC;&#x153;Guide for submit articles in the Web site of the Revista Mexicana de Ciencias Pecuariasâ&#x20AC;?. Manuscripts should be prepared, typed in a 12 points font at double space (including the abstract and tables), At the time of submission a signed agreement co-author letter should enclosed as complementary file; coauthors at different institutions can mail this form independently. The corresponding author should be indicated together with his address (a post office box will not be accepted), telephone and Email.

Title page. It should only contain the title of the work, which should be concise but informative; as well as the title translated into English language. In the manuscript is not necessary information as names of authors, departments, institutions and correspondence addresses, etc.; as these data will have to be registered during the capture of the application process on the OJS platform (http://cienciaspecuarias.inifap.gob.mx).

Abstract. On the second page a summary of no more than 250 words should be included. This abstract should start with a clear statement of the objectives and must include basic procedures and methodology. The more significant results and their statistical value and the main conclusions should be elaborated briefly. At the end of the abstract, and on a separate line, a list of up to 10 key words or short phrases that best describe the nature of the research should be stated.

Text. The three categories of articles which are published in Revista Mexicana de Ciencias Pecuarias are the following:

a) Research Articles. They should originate in primary works and may show partial or final results of research. The text of the article must include the following parts:

To facilitate peer review all pages should be numbered consecutively, including tables, illustrations and graphics, and the lines of each page should be numbered as well.

Introduction Materials and Methods Results Discussion Conclusions and implications Literature cited

Research articles will not exceed 20 double spaced pages, without including Title page and Tables and Figures (8 maximum and be included in the text). Technical notes will have a maximum extension of 15 pages and 6 Tables and Figures. Reviews should not exceed 30 pages and 5 Tables and Figures.

In lengthy articles, it may be necessary to add other sections to make the content clearer. Results and Discussion can be shown as a single section if considered appropriate.

Manuscripts of all three type of articles published in Revista Mexicana de Ciencias Pecuarias should contain the following sections, and each one should begin on a separate page.

b) Technical Notes. They should be brief and be

evidence for technical changes, reports of clinical cases of special interest, complete description of a limited investigation, or research results which

should be published as a note in the opinion of the editors. The text will contain the same information presented in the sections of t he research article but without section titles.

names(s), the number of the edition, the country, the printing house and the year. e. When a reference is made of a chapter of book written by several authors; the name of the author(s) of the chapter should be quoted, followed by the title of the chapter, the editors and the title of the book, the country, the printing house, the year, and the initial and final pages.

c) Reviews. The purpose of these papers is to summarize, analyze and discuss an outstanding topic. The text of these articles should include the following sections: Introduction, and as many sections as needed that relate to the description of the topic in question.

f. In the case of a thesis, references should be made of the author’s name, the title of the research, the degree obtained, followed by the name of the City, State, and Country, the University (not the school), and finally the year.

10. Acknowledgements. Whenever appropriate, collaborations that need recognition should be specified: a) Acknowledgement of technical support; b) Financial and material support, specifying its nature; and c) Financial relationships that could be the source of a conflict of interest.

Examples The style of the following examples, which are partly based on the format the National Library of Medicine of the United States employs in its Index Medicus, should be taken as a model.

People which collaborated in the article may be named, adding their function or contribution; for example: “scientific advisor”, “critical review”, “data collection”, etc. 11. Literature cited. All references should be quoted in their original language. They should be numbered consecutively in the order in which they are first mentioned in the text. Text, tables and figure references should be identified by means of Arabic numbers. Avoid, whenever possible, mentioning in the text the name of the authors. Abstain from using abstracts as references. Also, “unpublished observations” and “personal communications” should not be used as references, although they can be inserted in the text (inside brackets).

Journals

Standard journal article (List the first six authors followed by et al.) I)

Basurto GR, Garza FJD. Efecto de la inclusión de grasa o proteína de escape ruminal en el comportamiento de toretes Brahman en engorda. Téc Pecu Méx 1998;36(1):35-48.

Issue with no volume

Key rules for references

II) Stephano HA, Gay GM, Ramírez TC. Encephalomielitis, reproductive failure and corneal opacity (blue eye) in pigs associated with a paramyxovirus infection. Vet Rec 1988;(122):6-10.

a. The names of the authors should be quoted beginning with the last name spelt with initial capitals, followed by the initials of the first and middle name(s). In the presence of compound last names, add a dash between both, i.e. Elias-Calles E. Do not use any punctuation sign, nor separation between the initials of an author; separate each author with a comma, even after the last but one.

III) Chupin D, Schuh H. Survey of present status of the use of artificial insemination in developing countries. World Anim Rev 1993;(74-75):26-35.

No author given

b. The title of the paper should be written in full, followed by the abbreviated title of the journal without any punctuation sign; then the year of the publication, after that the number of the volume, followed by the number (in brackets) of the journal and finally the number of pages (this in the event of ordinary article).

IV) Cancer in South Africa [editorial]. S Afr Med J 1994;84:15.

Journal supplement V) Hall JB, Staigmiller RB, Short RE, Bellows RA, Bartlett SE. Body composition at puberty in beef heifers as influenced by nutrition and breed [abstract]. J Anim Sci 1998;71(Suppl 1):205.

c. Accepted articles, even if still not published, can be included in the list of references, as long as the journal is specified and followed by “in press” (in brackets). d. In the case of a single author’s book (or more than one, but all responsible for the book’s contents), the title of the book should be indicated after the

XII

Organization, as author

Organization as author XV) NRC. National Research Council. The nutrient requirements of beef cattle. 6th ed. Washington, DC, USA: National Academy Press; 1984.

VI) The Cardiac Society of Australia and New Zealand. Clinical exercise stress testing. Safety and performance guidelines. Med J Aust 1996;(164):282284.

In press VII) Scifres CJ, Kothmann MM. Differential grazing use of herbicide-treated area by cattle. J Range Manage [in press] 2000.

XVII) AOAC. Official methods of analysis. 15th ed. Arlington, VA, USA: Association of Official Analytical Chemists. 1990.

Books and other monographs

Author(s)

XVIII) SAS. SAS/STAT User’s Guide (Release 6.03). Cary NC, USA: SAS Inst. Inc. 1988.

VIII) Steel RGD, Torrie JH. Principles and procedures of statistics: A biometrical approach. 2nd ed. New York, USA: McGraw-Hill Book Co.; 1980.

XIX) SAS. SAS User´s Guide: Statistics (version 5 ed.). Cary NC, USA: SAS Inst. Inc. 1985.

Chapter in a book IX)

Electronic publications

Roberts SJ. Equine abortion. In: Faulkner LLC editor. Abortion diseases of cattle. 1rst ed. Springfield, Illinois, USA: Thomas Books; 1968:158-179.

Conference paper X)

XI)

XXI) Villalobos GC, González VE, Ortega SJA. Técnicas para estimar la degradación de proteína y materia orgánica en el rumen y su importancia en rumiantes en pastoreo. Téc Pecu Méx 2000;38(2): 119-134. http://www.tecnicapecuaria.org/trabajos/20021217 5725.pdf. Consultado 30 Jul, 2003. XXII) Sanh MV, Wiktorsson H, Ly LV. Effect of feeding level on milk production, body weight change, feed conversion and postpartum oestrus of crossbred lactating cows in tropical conditions. Livest Prod Sci 2002;27(2-3):331-338. http://www.sciencedirect.com/science/journal/030 16226. Accesed Sep 12, 2003.

12. Tables, Graphics and Illustrations. It is preferable that they should be few, brief and having the necessary data so they could be understood without reading the text. Explanatory material should be placed in footnotes, using conventional symbols.

Thesis XIII) Alvarez MJA. Inmunidad humoral en la anaplasmosis y babesiosis bovinas en becerros mantenidos en una zona endémica [tesis maestría]. México, DF: Universidad Nacional Autónoma de México; 1989.

13. Final version. This is the document in which the authors have already integrated the corrections and modifications indicated by the Review Committee. The works will have to be elaborated with Microsoft Word. Photographs and images must be in jpg (or compatible) format with at least 300 dpi resolution. Photographs, images, graphs, charts or tables must be included in the same text file. The boxes should not contain any vertical lines, and the horizontal ones only those that delimit the column headings, and the line at the end of the box.

XIV) Cairns RB. Infrared spectroscopic studies of solid oxigen [doctoral thesis]. Berkeley, California, USA: University of California; 1965.

XIII

14. Once accepted, the final version will be translated into Spanish or English, although authors should feel free to send the final version in both languages. No charges will be made for style or translation services.

MJ m Âľl Âľm mg ml mm min ng

mega joule (s) meter (s) micro liter (s) micro meter (s) milligram (s) milliliter (s) millimeter (s) minute (s) nanogram (s) P probability (statistic) p page CP crude protein PCR polymerase chain reaction pp pages ppm parts per million % percent (with number) rpm revolutions per minute sec second (s) t metric ton (s) TDN total digestible nutrients AU animal unit IU international units

15. Thesis will be published as a Research Article or as a Technical Note, according to these guidelines. 16. Manuscripts not accepted for publication will be returned to the author together with a note explaining the cause for rejection, or suggesting changes which should be made for re-assessment.

17. List of abbreviations: cal cm Â°C DL50 g ha h i.m. i.v. J kg km L log Mcal

calorie (s) centimeter (s) degree Celsius lethal dose 50% gram (s) hectare (s) hour (s) intramuscular (..ly) intravenous (..ly) joule (s) kilogram (s) kilometer (s) liter (s) decimal logarithm mega calorie (s)

versus

gravidity

The full term for which an abbreviation stands should precede its first use in the text. 18. Scientific names and other Latin terms should be written in italics.

XIV

https://doi.org/10.22319/rmcp.v11i4.5137 Article

Molecular detection of bovine coronavirus associated with the bovine respiratory complex in beef cattle in the Mexicali Valley, Baja California, Mexico

Carolina Orozco-Cabrera a Gilberto López-Valencia a Luis Mario Muñoz-Del Real a Soila Maribel Gaxiola-Camacho b Nohemí Castro-del Campo b Sergio Arturo Cueto-González a José Guadalupe Guerrero-Velázquez a Kattya Moreno-Torres a Kelvin Orlando Espinoza-Blandón a Sergio Daniel Gómez-Gómez a Enrique Trasviña-Muñoz a Francisco Javier Monge-Navarro a*

Universidad Autónoma de Baja California. Instituto de Investigaciones en Ciencias Veterinarias. Km. 3.5 Carretera a San Felipe, Fraccionamiento Campestre, 21386, Mexicali, Baja California, México. b

Universidad Autónoma de Sinaloa. Facultad de Medicina Veterinaria y Zootecnia, Culiacán Sinaloa, México.

* Corresponding author: fmonge@uabc.edu.mx

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Abstract: The bovine respiratory complex (BRC) is the leading cause of disease and death in beef cattle worldwide. It is a multifactorial infectious syndrome caused by different viruses and bacteria that reduce the productive efficiency and cause economic losses. In Mexico, BRC has been reported in all regions where cattle are fattened; however, these reports lack information on the presence of bovine respiratory coronavirus (BCV). This makes it necessary to have reliable and accurate diagnostic tools for detecting the presence of BCV in beef cattle fattened in Mexico, in order to propose appropriate sanitary measures for their clinical management. In this work, a real-time-PCR molecular diagnostic platform (rt-PCR) was developed to amplify a fragment of the BCV S protein in nasal exudate samples. When applying the rt-PCR platform for BCV in seemingly healthy beef cattle with signs of respiratory disease associated to BRC, 19/50 (38 %) were found to be positive, confirming the presence of this virus in the cattle of the region. The results of this work constitute the first report on the presence of the BCV associated to the BRC in the cattle region of northwestern Mexico and establish the bases for future research about the role that this virus plays in the presentation of the pathology of the BRC in beef cattle exploitation systems in this region and across the country. Key words: Bovine Respiratory Coronavirus, Bovine Respiratory Complex, PCR, Protein S, Beef cattle.

Received: 30/10/2018 Accepted: 09/09/2019

Introduction Bovine Respiratory Complex (BRC) is considered to be the main cause of clinical disease and death in feedlot cattle worldwide, and thereby, of economic losses for the farmers(1). Traditionally, BRC is associated with single or combined disease of bovine respiratory syncytial virus (RSV), type 1 bovine herpes virus (BHV), bovine parainfluenza virus 3 (BPI3) and bovine viral diarrhea virus (BVDV), which produce a primary infection with mild clinical signs(2). The BRC is also associated with the presence of the bacteria Pasteurella multocida, Mannheimia haemolytica, Histophilus somni, and Mycoplasma bovis, which act as opportunistic pathogens during conditions of stress or primary viral infection(3,4). Besides the mentioned viruses, the bovine coronavirus (BCV) has also been reported as a viral agent associated to BRC, causing respiratory disease and reduction in weight gain in beef cattle(5-9). The infection produced by BVC has been reported all over the world and is

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considered an endemic disease in dairy and beef cattle farms(7). BVC is transmitted mainly through the fecal-oral route, although it has also been shown to be transmitted through the respiratory route, by inhalation of aerosols containing the viral particles. When BCV enters the gastrointestinal tract, it triggers a clinical picture of diarrhea, dehydration, acidosis and hypoglycemia in young animals(10). When BVC is admitted through inhalation, it infects the respiratory epithelium of the nasal turbinates, trachea and lungs. Replication leads to the elimination of the virus in nasal secretions, and the disease produces a picture with clinical signs ranging from absent to severe, including depression, fever, conjunctivitis, respiratory distress, and mild to severe cough(11). In Mexico, the BRC has been reported in all regions where cattle are fattened (12,13,14). However, these reports lack information regarding the presence of the BVC in beef cattle farms, being of the greatest importance for developing diagnostic tools that allow to confirm the presence of the BVC in the cattle. The diagnosis of BVC is achieved using different serological techniques(10,15) or viral isolation from nasal exudate and biopsies from different tissues(10,16,17). The application of molecular techniques for the detection of BVC associated with BRCâ&#x20AC;&#x2022;including conventional PCR (PCR) and real-time (rt-PCR)â&#x20AC;&#x2022;has recently been reported(6,10,14). Prominent among these are the platforms that amplify the gene which encodes for the BCV S protein, the most important viral structure for the production of neutralizing antibodies(7,18). The S-protein gene is highly conserved among the BCV strains and has been widely used as a target gene for molecular tests for diagnosing this disease in livestock and other animal species, including humans(6,19,20). The aim of this work was the development and use of a platform for molecular diagnosis by rt-PCR to detect a fragment of the gene that codes for the BCV S protein in samples of bovine nasal exudate. The results indicate that the rt-PCR system is highly sensitive and specific for detecting the BVC and can be used in beef cattle exploitation systems in the region and across the country.

Material and methods This study was conducted at the Diagnostic Laboratories Unit (ULADI) of the Institute of Research in Veterinary Sciences of the Autonomous University of Baja California, Mexicali Campus.

Nasal exudate samples Fifty (50) nasal exudate samples were collected from beef cattle in stables, belonging to a technified bovine exploitation system located in the Mexicali Valley, Baja California. The samples were obtained from newly admitted animals aged 18 months on average, after less than 30 d of their arrival at the farm. Thirty samples were collected from animals with nasal

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discharge, cough, depression, or body temperature greater than 38.5°C (Group 1), which were classified as sick, and 20 samples, from apparently healthy animals that showed none of the above(21). The nasal exudate samples were collected by deep intranasal route, using broom-type Dacron swabs. Once the samples were taken, each swab was immersed in a tube containing sterile phosphate-base saline (PBS), pH 7.4, and the handle was cut so that the tube could be closed to protect the sample from possible contamination; the corresponding group was identified with a progressive number. Once collected, the samples were transported to the laboratory for processing.

Removal of RNA from nasal exudate

For RNA extraction, Aurum Total RNA Fibrous Tissue Reagent Kits (Bio Rad, Hercules, California, USA) were used according to the manufacturer's instructions. RNA was recovered from each extraction in a volume of 50 µL of the elution solution provided in the reagent kits. The extracted RNA was stored frozen at -20 °C until the time of the rt-PCR.

Oligonucleotide design for BCV

Oligonucleotides were designed from the sequence of the gene that encodes for the S protein of the bovine coronavirus strain R-AH187 (BCV-S), with access number GenBank EF424620.1. The gene corresponds to a 4,090 base pair molecule published in July 2016. The Primer3Plus oligonucleotide design software, version 2006-2007, was used; it is available at: http://www.bioinformatics.nl/cgi-bin/primer3plus/primer3plus.cgi. From the sequence of the BCV-S gene, the positive band oligonucleotide called BCVf with 5'CTACTTGGAATAGGAGATTG-3' sequence was generated, while for the negative band oligonucleotide called BCVr, the selected sequence was 3'-TACACGGAGAAATTGG-5'; the amplification of these oligonucleotides by rt-PCR generates a product of 132 base pairs and a 36 % GC content, with a dissociation temperature (Tm) of 77.0 ºC for this PCR product. Table 1 shows the characteristics of oligonucleotides. Oligonucleotides were synthesized by GenScript LTD (Piscataway, New Jersey, USA) and were reconstituted with molecular biology grade water equivalent to 10 times the nano molar (nM) concentration value referred to by the manufacturer, to obtain a standard concentration of 100 micro molar (µM). For the rt-PCR tests, the working concentration of the oligonucleotides was set at 10 µM.

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Table 1. Sequences and properties of oligonucleotides designed from the BCV-S gene with the reference GenBank EF424620.1 Oligonucleotide: BCVf Sequence: Start: nucleotide 1337

CTACTTGGAATAGGAGATTTG Length: 21 pb

Tm: 55.4 ºC

Oligonucleotide:

BCVr

Sequence:

TACACGGACAGAAATTTGTG

End: nucleotide 1469

Length: 20 pb

Tm: 54.3 ºC

Product:

132 pb

Tm: 77.0 ºC

GC: 38%

GC: 40%

Master mix In this work it was used the One-Step RT-PCR Script masterbatch I (Bio Rad, Hercules, California, USA) formulated with SYBR Green I fluorophore in a masterbatch solution using both oligonucleotides at a concentration of 400 nM, 2 µl RNA quench and molecular biology grade water for a total reaction volume optimized to 10µL.

Positive RNA controls for BCV (o) As positive RNA control extracted from the liquid fraction of the Scourgard 4 K7C vaccine (Zoetis, New Jersey, USA), which contains inactivated Hansen strain bovine coronavirus and inactivated G6 and G10 bovine rotavirus strains, enterotoxigenic E. coli K 99 and Clostridium perfringens toxoid type C, was utilized. The procedure for RNA extraction was performed according to the protocol of the Bio Rad Aurum Total RNA Fibrous Tissue set of reagents, using 300 µl of the vaccine. The extracted RNA was divided into 10 µl aliquots and stored in a freezer at -20° C until the RT-PCR tests were performed.

RT-PCR test protocols The rt-PCR tests were performed on a Bio Rad CFX96 thermal cycler. Denaturation, hybridization and extension parameters were calculated using the Protocol Autowriter tool of the CFX96 package integrated to the thermal cycler, taking into consideration the size of the PCR product, the oligonucleotide sequence and the type of enzyme in the master mix, resulting in an initial step at 50 °C during 10 min for reverse transcription, followed by a denaturation cycle of 95 °C during 3 min, and continuing with 39 denaturation cycles at 937

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95 °C during 10 sec, 20 sec at 51. 0 °C for oligonucleotide hybridization, and 15 sec at 72 °C for extension. Also, for each run, dissociation curve analysis from 65 °C to 95 °C was performed to identify amplification curves within the estimated temperature of 77.0 °C +/- 1 °C of the 132 bp PCR product and discriminate between artifacts other than the amplification of the expected RNA template.

Interpretation of results rt-PCR test results for BCV were considered positive when the corresponding sample obtained a fluorescent amplification signal before cycle 40, above the threshold control line automatically established by the CFX96 program, and amounting to 10 times the standard deviation of the average fluorescence index generated by all the samples during the first 10 cycles of each run. Results were considered negative when the corresponding sample failed to develop a fluorescent amplification signal above the threshold line of the negative reference control within a maximum of 40 cycles.

Results RT-PCR standardization for BCV (o) The amplification graph and dissociation curve calculated with Bio-Rad’s CFX96 package for the BVC rt-PCR system showed that the optimal combination of reagents for maximum amplification of the oligonucleotides BCVf and BCVr is achieved at a concentration of 400 nM with 2 µL RNA template. Under these conditions the positive controls extracted from the Scourguard 4 K/C vaccine developed a signal above the threshold line with an average amplification cycle (Cq) of 31.09 in 40 total cycles of each amplification run; the negative controls showed no evidence of amplification (Figure 1). Also, analysis of the dissociation curve (Tm) for the positive control RNA showed a dissociation temperature range between 76.0 and 78.0 °C, with an average temperature of 77.0 °C (Figure 2); these parameters allow considering the rt-PCR test for CVB as valid.

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Figure 1: Amplification curve of bovine coronavirus controls extracted from Scourguard 4 K/C vaccine using the oligonucleotides BCVf and BCVr at a concentration of 400 nM with a 2 Âľl RNA template

Figure 2: Dissociation curve of rt-PCR positive controls for bovine coronavirus showing an average temperature of 77.0 Â°C

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RT-PCR results for nasal exudate samples

Fifty RNA samples of nasal exudate from stabled cattle were tested in duplicate, of which 19 (38.0 %) achieved amplification above the threshold line established by CFX96 and were therefore considered positive. Of the samples that were positive, 5 (10.0 %) belonged to Group 1, which corresponds to animals with signs and symptoms associated with the BRC, and 14 (28.0 %), to Group 2, which is made up of animals without signs and symptoms of respiratory disease. The average Cq of the samples of both groups was 34.60 cycles with a range of Cq between 30.87 and 35.95 cycles, and average Tm of 77.0 Â°C (Figure 2).

Discussion BCV is a globally distributed pathogenic virus that causes enteric diseases in young calves and winter dysentery in adult cattle. BVC is also implicated in BRC-associated infections in beef cattle. Although infections by BCV produce a mortality smaller than 2 %, the morbidity of this virus can reach 100 % of the animals of a farm, causing respiratory or digestive syndromes that negatively affect the rate of gain of weight or milk production and increase the costs due to veterinary services, antibiotics and other medicines that altogether cause economic losses for the cattle sector(10,15). The development and implementation of the rt-PCR platform for BCV presented here, arises in response to the need for reliable, accurate and rapid diagnostic tools to detect a disease of viral origin that has been reported as part of the BRC; however, due to the large number of signs and symptoms that common pathogens produce, it is difficult to establish precisely the main causal agent of the pathology in an animal or herd(22,23); especially when the infectious process develops with minimal or imperceptible symptoms, causing a delay in the initiation of the corresponding therapy, extending the time required to recover the state of health and, therefore, negatively affecting the levels of productivity of the sick animals(20,24). In this work, an rt-PCR platform for detecting and amplifying a fragment of the gene that encodes for the BVC S protein was designed, developed and instrumented, which turned out to be a highly sensitive and specific molecular diagnostic platform for the detection of BCV from nasal exudate samples. Although in this work the viral particles were not quantified, the sensitivity of rt-PCR platforms to BCV from nasal exudate samples has been previously reported as having detection ranges of 102 cDNA(25) to 103 cDNA(26) copies per reaction, with amplification curves developed after cycle 34 for both studies. This agrees with the average Cq of 34. 60 developed by the samples analyzed in this work; therefore, can be propose that the rt-PCR platform for CVB reported here has an estimated sensitivity between 102 and 103 cDNA copies per reaction.

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Due to the genetic properties of the S protein, characterized by a high homology between the different viral strains and a high immunological reactivity(22,24), different fragments of the BCV S protein gene have been used as a reference methodological basis for the development of molecular and serological diagnostic platforms for the rapid detection and diagnosis of this virus with high levels of sensitivity and specificity, even in samples containing small amounts of virus, where conventional diagnostic tests may be inconclusive(23). The results obtained indicate that 19 samples (38 %) from both study groups tested positive with the rt-PCR platform for BCV. Notably, while five of the samples from the group of sick animals (n= 30) were positive, 14 of the samples from the group of apparently healthy animals (n= 20) were positive to the tests. Contrary to what was anticipated, 70 % of the samples from the group of apparently healthy animals were positive to the rt-PCR platform for BCV. This may be because BCV can infect up to 45 % of newly arrived cattle without showing obvious signs or symptoms of disease(27,28). However, apparently healthy cattle with nasal shedding of BCV have been shown to be 1.6 times more likely to suffer at least one episode of respiratory disease and 2.2 times more likely to develop lung lesions than animals that do not shed virus by this route(7,29); therefore, these animals may have been incubating the virus while not yet developing the clinical respiratory profile characteristic of BRC. The prevalence of 38 % is similar to that reported in other regions of the world. In a study carried out in Australia in beef cattle for export, nasal exudate samples were analyzed using an rt-PCR platform similar to the one used here, finding a prevalence of 40.1 % for BVC, followed by 0.4 % for BVDV, 0.3 % for IBR, 0.3 % for RSV and 0.3 % for BPI3, evidencing the magnitude of the influence of BCV on the occurrence of respiratory disease of BRC in that country(29). Likewise, these results show a higher positive rate than the one reported in Ireland, where a study was carried out to establish the prevalence of pathogens associated to BRC from samples of nasal exudate using rt-PCR and found a positive rate of 22.9 % for BCV, 11.6 % for BRSV, 7.0 % for BPI3, 6.1 % for IBR, and 5 % for BVDV, highlighting the fact that BCV is the virus associated to BRC that is most frequently diagnosed in the beef cattle of that country(30). BCV is also the most prevalent virus associated with BRC in beef cattle in the United States of America. Prevalence reports of pathogens associated to BRC in nasal exudate samples from beef cattle analyzed with rt-PCR techniques indicate a prevalence of 62.8 % for BCV, followed by BVDV, with 15.7 %; IBR, with 14.9 %; BRSV, with 9.1 %, and BPI3, with 8.3 %(23). The prevalence rate in the USA exceeds that reported for Australia and Ireland, as well as that reported for Mexico through this work, mainly because beef cattle farms in the USA house hundreds of thousands of cattle in a given region, where close contact between healthy and sick animals can favor the transmission and persistence of BCV among cattle(31).

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The results presented here constitute the first report on the presence of the BCV associated to the BRC in the cattle region of northwestern Mexico and place the BCV in the second position of the table of prevalence of virus associated to the BRC previously detected in the area, where the bovine respiratory syncytial virus (BRSV) occupied the first position with 80.6 % of prevalence, followed by the parainfluenza virus 3 (BPI3), with 23.8 %; the bovine herpes virus 1 (BHV)-1, with 20.4 %, and the bovine viral diarrhea virus (BVDV), with 11.3 %(15).

Conclusions and implications It concludes that the BCV is present in the cattle stables of the Mexicali Valley, Baja California. The rt-PCR platform for BCV reported here is a fast, sensitive and specific molecular diagnostic tool to detect BCV in nasal exudate samples from cattle in feedlots. The prevalence of 38.0 % for BCV reported in this work should be the starting point for future researches on the role that this virus plays in the presentation of the BRC pathology in the beef cattle exploitation systems in our region and across the country. The authors of this paper declare that they have no conflict of interests of any kind.

Literature cited: 1. Hay KE, Morton JM, Schibrowski ML, Clements ACA, Mahony TJ, Barnes TS. Associations between prior management of cattle and risk of bovine respiratory disease in feedlot cattle. Prev Vet Med 2016;(127):37-43. 2. Gershwin LJ, Van Eenennaam AL, Anderson ML, McEligot HA, Shao MX, ToaffRosenstein R, et al. Single pathogen challenge with agents of the bovine respiratory disease complex. PLOS ONE 2015;10(11):e0142479. 3. Hilton WM. BRD in 2014: where have we been, where are we now, and where do we want to go? Anim Health Res Rev 2014;(15):120-122. 4. Xue W, Ellis J, Mattick D, Smith L, Brady R, Trigo E. Immunogenicity of a modified-live virus vaccine against bovine viral diarrhea virus types 1 and 2, infectious bovine rhinotracheitis virus, bovine parainfluenza-3 virus, and bovine respiratory syncytial virus when administered intranasally in young calves. Vaccine 2010;(28):3784-3792. 5. Thomas CJ, Hoet AE, Sreevatsan S, Wittum TE, Briggs RE, Duff GC, Saif LJ. Transmission of bovine coronavirus and serologic responses in feedlot calves under field conditions. Am J Vet Res 2006;(67):1412-1420.

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6. Bidokhti MR, Travén M, Ohlson A, Baule C, Hakhverdyan M, Belák S, et al. Tracing the transmission of bovine coronavirus infections in cattle herds based on S gene diversity. Vet J 2012;(193):386-390. 7. Saif LJ. Bovine respiratory coronavirus. Vet Clin North Am Food Anim Pract 2010;(26): 349-364. 8. Hick PM, Read AJ, Lugton I, Busfield F, Dawood KE, Gabor L, et al. Coronavirus infection in intensively managed cattle with respiratory disease. Aust Vet J 2012;(90):381-386. 9. Liu L, Hagglund S, Hakhverdyan M, Alenius S, Larsen LE, Belák S. Molecular epidemiology of bovine coronavirus on the basis of comparative analyses of the S gene. J Clin Microbiol 2006;(44):957-960. 10. Oma VS, Travén M, Alenius S, Myrmel M, Stokstad M. Bovine coronavirus in naturally and experimentally exposed calves; viral shedding and the potential for transmission. Virol J 2016;(13):1-11. 11. Park SJ, Kim GY, Choy HE, Hong YJ, Saif LJ, Jeong JH, et al. Dual enteric and respiratory tropisms of winter dysentery bovine coronavirus in calves. Arch Virol 2007;(152):1885-1900. 12. Figueroa-Chávez D, Segura-Correa JC, García-Márquez LJ, Pescador-Rubio A, Valdivia-Flores AG. Detection of antibodies and risk factors for infection with bovine respiratory syncytial virus and parainfluenza virus 3 in dual-purpose farms in Colima, Mexico. Trop Anim Health Prod 2012;(44):1417-1421. 13. Solís-Calderón JJ, Segura-Correa JC, Aguilar-Romero F, Segura-Correa VM. Detection of antibodies and risk factors for infection with bovine respiratory syncytial virus and parainfluenza virus-3 in beef cattle of Yucatan, Mexico. Prev Vet Med 2007;(82):102110. 14. Rodríguez-Castillo JL, Lopez-Valencia, G, Monge-Navarro FJ, Medina-Basulto GE, Hori-Oshima S, Cueto-González SA, et al. Detection and economic impact related to bovine respiratory disease, shrink, and traveling distance in feedlot cattle in Northwest Mexico. Turk J Vet Anim Sci 2017;(41):294-301. 15. Amer HM, Wahed AAE, Shalaby MA, Almajhdi FN, Hufert FT, Weidmann M. A new approach for diagnosis of bovine coronavirus using a reverse transcription recombinase polymerase amplification assay. J Virol Methods 2013;(193):337-340.

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16. Decaro N, Mari V, Desario C, Campolo M, Elia G, Martella V, et al. Severe outbreak of bovine coronavirus infection in dairy cattle during the warmer season. Vet Microbiol 2008;(126):30-39. 17. Toftaker I, Toft N, Stokstad M, Solverod L, Harkiss G, Watt N, et al. Evaluation of a multiplex immunoassay for bovine respiratory syncytial virus and bovine coronavirus antibodies in bulk tank milk against two indirect ELISAs using latent class analysis. Prev Vet Med 2018;(154):1-8. 18. Workman AM, Kuehn LA, McDaneld TG, Clawson ML, Chitko-Mckown CG, Loy JD. Evaluation of the effect of serum antibody abundance against bovine coronavirus on bovine coronavirus shedding and risk of respiratory tract disease in beef calves from birth through the first five weeks in a feedlot. Am J Vet Res 2017;(78):1065-1076. 19. Singasa K, Songserm T, Lertwatcharasarakul P, Arunvipas P. Molecular and phylogenetic characterization of bovine coronavirus virus isolated from dairy cattle in Central Region, Thailand. Trop Anim Health Prod 2017;(49):1523-1529. 20. Bok M, Miño S, Rodriguez D, Badaracco A, Nuñes I, Souza SP, et al. Molecular and antigenic characterization of bovine Coronavirus circulating in Argentinean cattle during 1994-2010. Vet Microbiol 2015;(181):221-229. 21. Villagómez-Cortés JA, Martínez-Herrera DI. Epidemiological evaluation of clinical bovine respiratory disease complex in a tropical Mexican feedlot. ROAVS 2013;(3):315-321. 22. Belouzard S, Millet JK, Licitra BN, Whittaker GR. Mechanisms of coronavirus cell entry mediated by the viral spike protein. Viruses 2012;(4):1011-1033. 23. Fulton RW, d´Offay JM, Landis C, Miles DG, Smith RA, Saliki JT, et al. Detection and characterization of viruses as field and vaccine strains in feedlot cattle with bovine respiratory disease. Vaccine 2016;(34):3478-3492. 24. Li F. Structure, function, and evolution of coronavirus spike proteins. Annu Rev Virol 2016;(3):237-261. 25. Escutenaire S, Mohamed N, Isaksson M, Thorén P, Klingeborn B, Belák S, et al. SYBR green real-time reverse transcription-polymerase chain reaction assay for the generic detection of coronaviruses. Arch Virol (2007);152(1):41-58. 26. Amer HM, Almajhdi FN. Development of a SYBR Green I based real-time RT-PCR assay for detection and quantification of bovine coronavirus. Mol Cell Probes (2011);25(2):101-107.

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27. Hick PM, Read AJ, Lugton I, Busfield F, Dawood KE, Gabor L, et al. Coronavirus infection in intensively managed cattle with respiratory disease. Aust Vet J 2012;(90):381-386. 28. Lathrop SL, Wittum TE, BrocK KV, Loerch SC, Perino LJ, Bingham HR, et al. Association between infection of the respiratory tract attributable to bovine coronavirus and health and growth performance of cattle in feedlots. Am J Vet Res 2000;(61):10621066. 29. Moore SJ, O'dea MA, Perkins N, O'hara AJ. Estimation of nasal shedding and seroprevalence of organisms known to be associated with bovine respiratory disease in Australian live export cattle. J Vet Diagn Invest 2014;(27):6-17. 30. O'Neill R, Mooney J, Connaghan E, Furphy C, Graham DA. Patterns of detection of respiratory viruses in nasal swabs from calves in Ireland: a retrospective study. Vet Rec 2015;175(14):351. 31. Wolfger B, Timsit E, White BJ, Orsel K. A systematic review of bovine respiratory disease diagnosis focused on diagnostic confirmation, early detection, and prediction of unfavorable outcomes in feedlot cattle. Vet Clin North Am Food Anim Pract 2015;(31):351-65.

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1220-1130https://doi.org/10.22319/rmcp.v11i4.5124 Article

Frequency of M. hyopneumoniae, M. hyorhinis and M. hyosynoviae in nasal and lung samples from pigs with symptoms of porcine enzootic pneumonia

Rosa Elena Miranda Morales a* Verónica Rojas Trejo a Luis Enrique López-Cerino a Erika Margarita Carrillo Casas b Rosa Elena Sarmiento Silva a María Elena Trujillo Ortega c Rolando Beltrán Figueroa c Francisco José Trigo Tavera d

Universidad Nacional Autónoma de México. Facultad de Medicina Veterinaria y Zootecnia, Departamento de Microbiología e Inmunología. UNAM. Ciudad Universitaria, 04519, CDMX, México. Hospital General “Dr. Manuel Gea González”, Departamento de Biología Molecular e Histocompatibilidad. CDMX, México. b

Universidad Nacional Autónoma de México, Facultad de Medicina Veterinaria y Zootecnia, Departamento de Cerdos. CDMX, México. d

Universidad Nacional Autónoma de México, Facultad de Medicina Veterinaria y Zootecnia, Departamento de Patología. CDMX, México.

*Corresponding author: roelmimo@yahoo.com.mx

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Abstract: M. hyopneumoniae, M. hyorhinis and M. hyosynoviae are genetically related species of the genus Mycoplasma that affect pig production. The objective of this work was the isolation and identification by PCR of M. hyopneumoniae, M. hyorhinis and M. hyosynoviae from nasal swabs and lung samples of pigs from different regions of Mexico in order to determine the frequency of these species and to evaluate PCR as a diagnostic tool for PEP. Pigs aged 4 to 8 weeks with clinical diagnosis of PEP were included. Lung samples and nasal swabs were obtained for the isolation of the Mycoplasma in liquid Friis medium and identified by species-specific PCR based on the 16S rRNA subunit. Isolation was achieved in 37.11 % (36/97) of the samples. The three Mycoplasma species were identified in lung and nasal swab samples. Mycoplasma co-infection was identified in 27.77 % (10/36). The bacterial genera associated with Mycoplasma infections were E. coli, Bordetella, Enterobacter, SCN, Corynebacterium, Pasteurella, Streptococcus, Shigella and Klebsiella. Mixed infection was present in 26 nasal swabs (45.61 %) and absent in the lungs. It was concluded that the frequency of Mycoplasma on production farms was higher than expected (40.27 %). It was also identified other Mycoplasma species involved in the development of PEP. Therefore, surveillance through isolation and molecular techniques can be of great help to breeding stock providers, as well as for removing Mycoplasma from pig farms. Key words: Mycoplasmosis, M. hyopneumoniae, M. hyorhinis, M. hyosynoviae, Porcine enzootic pneumonia.

Received: 29/10/2018 Accepted: 09/09/2019

Introduction The Swine Respiratory Disease Complex (PRDC) is a major health problem for the pig industry worldwide(1). It is caused by the association of infections such as Mycoplasma, porcine reproductive and respiratory syndrome virus (PRRSV), porcine circovirus type 2 (PCV2), Pasteurella multocida, Actinobacillus pleuropneumoniae, Streptococcus suis, Haemophilus parasuis, Bordetella bronchiseptica, and Arcanobacterium pyogenes(1,2). A predisposing factor is porcine enzootic pneumonia (PEP), primarily caused by Mycoplasma hyopneumoniae(3), which adheres to the respiratory epithelium, damages the ciliated cells of the trachea, bronchi and bronchioles(4), and suppresses the immune response of the upper respiratory tract that favors the development of PRDC(5,6). 947

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PEP is a high-prevalence chronic respiratory disease with high morbidity and low mortality. between 30 to 80 % of the pig programmed for slaughter exhibit typical consolidation lesions(7,8). Throughout the pig's productive life, the prevalence of M. hyopneumoniae increases until it reaches the age of slaughter, even in vaccinated animals(9). Reproductive females are a reservoir that perpetuates the continuous circulation of respiratory pathogens associated with PEP(10,11). The severity of the disease differs among herds, with a high prevalence in conventional pig farms(12). The most significant clinical sign of PEP is a chronic, dry, non-productive cough that occurs in fattening pigs aged 16 to 22 wk. The main macroscopic lesion is cranioventral pulmonary consolidation(5), which is histologically characterized by bronchointerstitial pneumonia with hyperplasia of the bronchus-associated lymphoid tissue (BALT)(13). The main risk factor for PEP is vertical transmission from sow to piglet during lactation, given that vaccination does not guarantee protection(14) since M. hyopneumoniae can circulate in vaccinated animals(15) and in free-living animals such as wild boar, with which vulnerability to M. hyopneumoniae is shared, and which can be a reservoir of these bacteria(16). The severity of the disease at the time of slaughter may be predicted of the initial prevalence at weaning, based on the variables indicative of infection (average of lung lesions, percentage of lung tissue affected, presence of M. hyopneumoniae in the bronchial epithelium and seroconversion), as there is a positive correlation between these two variables(17). Most Mycoplasma infections remain subclinical(18) and may involve other species of the same bacterial genus such as M. hyorhinis, a commensal inhabitant of the upper respiratory tract mucosa and tonsils(19). M hyosynoviae, a species mainly associated with acute arthritis and, to a lesser extent, with suppurative pneumonia with severe pulmonary consolidation, and pleurisy(20,21,22). M. hyopneumoniae, M. hyorhinis and M. hyosynoviae are genetically related species of porcine interest(23), which can be discriminated by PCR based on the hypervariable regions of the 16S subunit of the genus(23,24). The objective of this work was the isolation and identification by PCR of M. hyopneumoniae, M. hyorhinis and M. hyosynoviae from nasal swabs and samples from pigs of different regions of the Mexican Republic, in order to determine the frequency of these species and to evaluate PCR as a diagnostic tool for enzootic swine pneumonia.

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Material and methods Animals and sampe collection Pigs aged 4 to 8 wk diagnosed with PEP according to clinical signs and with gross lesions in the lung (purple to gray areas of tissue consolidation in the cranio-ventral lung lobe) were included in this study. 40 lung samples and 57 nasal swabs were aseptically obtained by pressing against the structural wall of the tissue(25). Sample collection was conducted on farms in four regions of Mexico, from May 2015 to January 2016 (Table 1). Each sample was collected in duplicate for Mycoplasma isolation and for traditional bacteriology. All animal procedures were approved by the Institutional Committee for the Care and Use of Experimental Animals (CICUAE) of the National Autonomous University of Mexico, following international ethical standards. Table 1: Regions of origin of the lung samples and nasal swabs included in this work Samples Geographic region Number of samples 40 lungs Mexico 12 Veracruz 28 57 nasal swabs Hidalgo 25 Guanajuato 32 Total 97

Mycoplasma isolation For Mycoplasma isolation, nasal swabs were resuspended in 2 ml of Friis medium. Lung samples were frozen at -20°C until they were followed up in the laboratory. Lung samples were routinely processed by maceration in 3 ml of Friis medium for isolation(18,26,27). 200 μl of the suspension of each sample in Friis medium were inoculated in 1.8 ml of Friis medium supplemented with pig serum (10 %), horse serum (10 %), and penicillin (100 μg/mL) to optimize the recovery of M. hyopneumoniae(28), and supplemented with Larginine (0.05 %) for the recovery of M. hyosynoviae(29). Subsequently, up to 10-6 serial dilutions were made, and, finally, 10 μL were plated onto Friis agar(27). Tubes were incubated at 37 °C until a color change was observed in the medium, or up to 30 d, before being discarded. Positive samples were those that developed at least one unit of color change, while samples that had no color change after 30 d were considered negative. The agar plates were incubated at 37 °C with 5 % CO2 for 1 to 2 wk. Each isolated colony was further inoculated into 2 ml of Friis medium and incubated. After observing the color change, the cultures were evaluated to confirm their purity and subsequent use until PCR discrimination of the species.

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Species-specific PCR for the identification of Mycoplasma PCR based on the 16S rRNA subunit for the identification of the three Mycoplasma species was applied to each of the isolates. The reference strains M. hyopneumoniae ATCC 25617, M. hyorhinis ATCC 17981, M. hyosynoviae strain S-16, and M. bovis Donetta PG45—all kindly donated by Aarhus University, Aarhus, Denmark—were cultured in 50 ml medium, concentrated by centrifugation for DNA extraction according to the protocol with guanidinium thiocyanate(30). Each isolate was also processed for DNA extraction and stored at -70 °C until further analysis. Amplification of the 16S rRNA subunit was performed in a total reaction volume of 25 µL containing 0.25 µL of Taq PCR Reaction Mix (Sigma-Aldrich, Austria), 10 pmol of each sense and antisense initiator (Table 2)(24), and 10 µl of DNA(31). The reaction conditions were: initial denaturation at 96 °C, for 5 min, followed by 30 denaturation cycles at 94 °C for 45 s, alignment at 72 °C for 2 min, and extension at 72 °C for 4 min. DNA from pure cultures of M. hyopneumoniae ATCC 25617, M. hyorhinis ATCC 17981 and M. hyosynoviae strain S-16 were applied as positive controls, and M. bovis Donetta PG45, as negative control.

Table 2: PCR initiators based on 16S rRNA from M. hyopneumoniae, M. hyorhinis and M. hyosynoviae Mycoplasma Product species Sequence (5 -3) (bp) Reference M. hyopneumoniae

M. hyorhinis

M. hyosynoviae

F 5'-TTC AAA GGA GCC TTC AAG 1000 CTT C-3' R 5'-GAC GTC AAA TCA TCA TGC CTC T-3' F 5' CGGGATGTAGCAATACATTCAG 1129 3' R 5' GACGTCAAATCATCATGCCTCT 3' F 5' CAGGGCTCAACCCTGGCTCGC 3' 585 R 5' GACGTCAAATCATCATGCCTCT 3'

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This work Gen Bank Access No. NR029183. 1

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Results Mycoplasma isolation 97 samples were collected: 40 from lungs with typical Mycoplasma lesions suggestive of PEP (Figure 1) and 57 nasal swabs from pigs from different geographical regions of Mexico (Table 1). From the lung samples, 22.5 % (9/40) were positive to the isolation of Mycoplasma spp and 77.5 % (31/40) were negative. Of the nasal swabs, 47.36 % (27/57) were positive, and 52.63 % (30/57) were negative. In the positive samples, the color change of the culture medium was observed as early as the 5th d or until the 12th d. On average, the color change was observed on the 7th d. The remaining samples were considered as negative after 30 d without color change. Figure 1: Typical Mycoplasma lesions in the lungs collected for this study

In (A) lung with typical PEP injury, distributed over all lobes of the lung, (B) approach to lung consolidation, (C) lung with higher degree of lung consolidation, (D) lung sequestration resulting from the evolution of the injury, (E) evidence of scarring in the lung tissue.

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PCR results Amplified fragments of 1,000 bp of M. hyopneumoniae, 1,129 bp of M. hyorhinis, and 585 bp of M. hyosynoviae using the reference strains (ATCC 25617, ATCC 17981, and M. hyosynoviae strain S-16), were visualized by 1.5% agarose gel electrophoresis at 80 V for 60 min, stained with ethidium bromide and displayed on a UV transilluminator, as shown in Figure 2. 22 % (2/9) of the lung sample isolates (LSIs) of Mycoplasma tested positive for M. hyopneumoniae; 55.5 % (5/9), for M. hyorhinis, and 44 % (4/9), for M. hyosynoviae. 44 % (4/9) of the LSIs tested negative with species-specific PCR. 7.40 % (2/27) of the nasal swab isolates (NSIs) tested positive for M. hyopneumoniae; 51.85% (14/27), for M. hyorhinis, and 33.3 % (9/27), for M. hyosynoviae. 22.22 % (6/27) of the NSIs tested negative with species-specific PCR (6/27) (Table 3). Despite having been successfully isolated, four LSIs and six NSIs remained unidentified with the species-specific PCR. Figure 2: Electrophoretic profiles of the amplified fragments of M. hyopneumoniae, M. hyorhinis and 16S rRNA

Lane 1, Molecular Weight Marker (1 Kb plus Invitrogen), Lane 2, M. hyopneumoniae ATCC 25617, 1000 bp; Lane 3. M. bovis Donetta PG45, donated by the University of Aarhus, Denmark, Lane 4, M. hyorhinis, ATCC17981, 585 bp, Lane 6, Unrelated product with 685 bp of p97 protein from M. hyopneumoniae, ATCC25617, Lane 7, M. hyosynoviae, strain S-16, 1129 bp, also donated by the University of Aarhus, Denmark, Lane 8, Molecular Weight Marker (1 Kb plus Invitrogen). 952

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Table 3: List of isolates identified by species-specific PCR for M. hyopneumoniae, M. hyorhinis, and M. hyosynoviae Positive isolation (%) M. M. hyopneumoniae hyorhinis 2/9 (22.0) 5/9 (55.5) 2/27 (7.4) 14/27 (51.8) 4/36 (11.1) 19/36 (52.7)

Sample Lung Nasal swab Total

M. hyosynoviae 4/9 (44.0) 9/27 (33.3) 13/36 (36.1)

Mycoplasma spp isolates 4/9 (44.0) 6/27 (22.2) 10/36 (27.7)

The coexistence of M. hyopneumoniae, M. hyorhinis and M. hyosynoviae was detected in ten samples representing 27.77 % (10/36): in two lungs all three species, in two other lungs and five nasal swabs M. hyorhinis and M. hyosynoviae were identified, and only one swab contained M. hyopneumoniae and M. hyorhinis (Complementary Table 1). Additionally, the associated bacterial genera identified by general bacteriology in nasal swabs were E. coli, Enterobacter, coagulase-negative Staphylococcus, Klebsiella, Bordetella, Corynebacterium, Pasteurella, Shigella and Streptococcus. No bacterial growth was identified in lung samples. Supplementary table 1: Identification of Mycoplasma isolates by species-specific PCR Number Description

Type of sample*

M. hyop

M. hyor

M. hyos

1 2 3 4 5 6

111 112 113 114 115 116

NS NS NS NS NS NS

+ + + +

+ + -

7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24

117 118 119 120 121 122 123 124 125 126 127 130 133 148 159 160 161 162

NS NS NS NS NS NS NS NS NS NS NS NS NS NS NS NS NS NS

+ + -

+ + + + + + + + +

+ + + + + +

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Bacterial Genera CNS E. coli , Shigella Pasteurella Klebsiella, CNS Klebsiella, E. coli, CNS E. coli, CNS, Bordetella, Corynebacterium CNS, Corynebacterium CNS, Corynebacterium Enterobacter Corynebacterium Klebsiella, CNS Corynebacterium Klebsiella, CNS CNS Corynebacterium Corynebacterium Klebsiella, Corynebacterium CNS E. coli CNS No bacterial growth CNS Pasteurella E. coli, CNS

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25 165 NS + Streptococcus, CNS 26 168 NS + E. coli 27 170 NS + + CNS 28 182 L + + No bacterial growth 29 183 L + + No bacterial growth 30 186 L No bacterial growth 31 188 L No bacterial growth 32 194 L + + + No bacterial growth 33 206 L + + + No bacterial growth 34 207 L + No bacterial growth 35 208 L No bacterial growth 36 210 L No bacterial growth M. hyop = M hyopneumoniae: M. hyor= M. hyorhinis; M. hyos= M. hyosynoviae; *NS= nasal swab, L= lung, CNS= Coagulase-negative Staphylococcus

Discussion In Mexico there are few studies on the association of these three Mycoplasma species with PEP, mainly due to the difficulties for their isolation and to those inherent in the biological sample. The concentration of microorganisms is often below the detection limit as a result of the widespread use of antibiotics for the control of porcine mycoplasmosis. Therefore, isolation procedures are necessary to encourage their growth and identification for research and surveillance purposes. The procedure used allowed the association identification of the three species related to pig production. M. hyopneumoniae is the most frequently isolated species of Mycoplasma from pigs with clinical signs of pneumonia and has a low transmission rate. However, in association can increase the severity of infections caused by viruses and bacteria(32). M. hyorhinis has gone from being a secondary pathogen(33,34), to being considered a causal agent of PEP and PRDC(35). In this study, this species of Mycoplasma is the most prevalent in nasal swabs 51.85% (14/57); this observation can be explained by the success of the control measures that have been implemented in pig production farms. This study reports herein that M. hyosynoviae is in close interaction with the other two Mycoplasma species in lungs with typical PEP lesions. M. hyosynoviae was present in nasal swabs as a microorganism associated in a high percentage of the cases (33%, i.e. 9/27). Therefore, this commensal Mycoplasma species may have pathogenic potential, and further studies will be required to assess its role in the development of PEP. Bacteriological culture is the "gold standard" for diagnosis. However, among its drawbacks, it is very laborious, it is seldom used as a routine method, and it does not distinguish between species associated with PEP. PCR based on the 16S rRNA subunit allowed to discern, quickly and precisely, between M. hyopneumoniae and M. hyorhinis. On the other 954

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hand, 10 cases were identified in which the species evaluated in this work were not involved. This result raises the possibility that other Mycoplasma species may be involved. The collection method (nasal swab, tracheobronchial mucus, deep postmortem swab, bronchoalveolar lavage or lung tissue) has a significant effect on the frequency of M. hyopneumoniae, since the reported frequency varies between 3 and 40 %, according to the method used(36). Hitherto, nasal swabs have been the method of antemortem sample collection in piglets at herd level(28,37). Pieters et al(38) suggest that laryngeal swabs are useful in the early stage of infection in piglets. Other authors state that the optimal sampling site for detection by molecular methods for M. hyopneumoniae is tracheobronchial mucus collection (TBMC), since its sensitivity is 3.5 times more sensitive in piglets aged under 25 d(36). The upper respiratory tract (nasal cavity and pharynx) plays an important role in monitoring and cleansing pathogenic microorganisms and also in inducing the appropriate immune response. M. hyopneumoniae mainly colonizes the cilia of the respiratory tract of the pigs(39). In adult animals from production farms, TBMC becomes difficult and expensive to obtain. Animal handling is restricted in keeping with current swine influenza prevention measures, and, based on this experience, it is recommend the use of nasal swabs as the appropriate sampling technique. The prevalence of M. hyopneumoniae in naturally infected sows is 36.4 %(40); in piglets, it can vary from 3.6 to 16 %. The frequency of Mycoplasma determined here was higher than expected, namely: 37.11 % (36/97). Mycoplasma was present in the lungs of 22.50 % of the animals, (9/40), and in nasal swabs, in 47.36 % (27/57). Infection by a single Mycoplasma species was 44.44 % (16/36): in LSIs 11.11 % (1/9) and in NSIs 55.55 % (15/27). The association of more than one Mycoplasma species was present in 27.77 % (10/36): the association of the three species represented 2 % (2/10), and the association of M. hyorhinis and M. hyosynoviae 5.15% (5/10). Co-infection of M. hyopneumoniae and M. hyorhinis, and M. hyosynoviae and M. hyorhinis have previously been associated with joint problems. In this work, both associations were identified in the respiratory tract of animals in pig farms with PEP. The rate of Mycoplasma associated with PEP is variable, regardless of whether the rate of mixed infections remains constant(35). In this study, the bacterial genera associated in mixed infections were similar to those previously reported(41). PCR can be complementary or alternative to histopathological diagnosis and represents an option for epidemiological surveillance and research. In addition, it can assist in the elimination of Mycoplasma spp from swine production farms, as it is the best long-term control strategy, so far, for many swine producers and breeding stock suppliers(42).

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Conclusions and implications The frequency of Mycoplasma in pig farms in the states of Hidalgo, Guanajuato, Veracruz and Mexico was higher than expected (40.27 %). There are other Mycoplasma species that may be involved in the development of PEP, and this paper adds evidence of M. hyorhinis as a causal agent of PEP.

Acknowledgements This work was supported by the Project DGAPA UNAM PAPITT IN 222515.

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8. Ross RF, Mycoplasmal diseases. In: Leman AD, Straw B, Mengeling W, D’Allaire S, Taylor D, editors. Diseases of swine,7 th ed. Ames, Iowa, USA: Iowa State University Press; 1992:537-551. 9. Sibila M, Nofrarias M, López-Soria S, Segalés J, Valero O, Espinal A, et al. Chronological study of Mycoplasma hyopneumoniae infection, seroconversion and associated lung lesions in vaccinated and non-vaccinated pigs. Vet Microbiol 2007;122(1-2):97-107. 10. Fablet C, Marois C, Kuntz-Simon G, Rose N, Dorenlor V, Eono F, et al. Longitudinal study of respiratory infection patterns of breeding sows in five farrow-to-finish herds. Vet Microbiol 2011;147:329-339. 11. Takeuti K, De Barcellos D, de Lara A, Kunrath C, Pieters M. Detection of Mycoplasma hyopneumoniae in naturally infected gilts over time. Vet Microbiol 2007;203:215-220. 12. Sibila M, Calsamiglia M, Vidal D, Badiella L, Aldaz A, Jensen JC. Dynamics of Mycoplasma hyopneumoniae infection in 12 farms with different production systems. Can J Vet Res 2004;68(1):12-18. 13. Caswell JL, Williams K. The respiratory system. In: Maxie MG et al. editors. Jubb, Kennedy, and Palmer’s Pathology of Domestic Animals, 5th ed. Edinburgh, UK: Elsevier; 2007:523-655. 14. Meyns T, Dewulf J, de Kruif A, Calus D, Haesebrouck F, Maes D. Comparison of transmission of Mycoplasma hyopneumoniae in vaccinated and non-vaccinated populations. Vaccine 2006:24(49-50):7081-7086. 15. Maes D, Deluyker H, Verdonck M, Castryck F, Miry C, Vrijens B, et al. Effect of vaccination against Mycoplasma hyopneumoniae in pig herds with an all-in/all-out production system. Vaccine 1999;17(9):1024-1034. 16. Sibila M, Mentaberre G, Boadella M, Huerta E, Casas-Díaz E, Vicente J, et al. Serological, pathological and polymerase chain reaction studies on Mycoplasma hyopneumoniae infection in the wild boar. Vet Miocrobiol 2010;144(1-2):214-218. 17. Fano E, Pijoan C, Dee S, Deen J. Effect of Mycoplasma hyopneumoniae colonization at weaning on the disease severity in growing pigs. Can J Vet Res 2007;71(3):195-200. 18. Friis FN. A selective medium for Mycoplasma suipneumoniae. Acta Vet Scand 1971;12(3):454-466.

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19. MarĂŠ CJ, Switzer WP. Virus pneumonia of pigs: propagation and characterization of a causative agent. Am J Vet Res 1966;27(121):1687-1693. 20. Ross R, Hill M, Wood RL. Swine Arthritis. Pork Industry Handbook. Herd Health. USA, Michigan State University Cooperative Extension Service; 2003:1-4. 21. Hagedorn-Olsen T, Nielsen NC, Friis NF. Induction of arthritis with Mycoplasma hyosynoviae in pigs: Clinical response and re-isolation of the organism from body fluids and organs. Zentralbl Veterinarmed A 1999;46(6):317-325. 22. Dahlia H, Tan LJ, Zarrahimah Z, Maria J. Isolation of Mycoplasma hyosynoviae from pneumonic lung of swine. Trop Biomed 2009;26(3):341-345. 23. Stemke GM, Laigret F, Grau O, Bove JM. Phylogenetic relationships of three porcine mycoplasmas; Mycoplasma hyopneumoniae, Mycoplasma flocculare, and Mycoplasma hyorhinis, and complete 16S rRNA sequence of M. flocculare. Int J Syst Bacteriol 1992;42(2):220-225. 24. Stakenborg T, Vicca J, Butaye P, Imberechts H, Peeters J, De Kruif A, et al. A Multiplex PCR to identify porcine Mycoplasmas present in broth cultures. Vet Res Comm 2006;30(3):239-247. 25. Pieters M, Pijoan C, Fano E, Dee S. An assessment of the duration of Mycoplasma hyopneumoniae infection in an experimentally infected population of pigs. Vet Microbiol 2009;134(3-4):261-266. 26. Cook BS, Beddow JG, Manso-SilvĂĄn L, Maglennon GA, Rycroft AN. Selective medium for culture of Mycoplasma hyopneumoniae. Vet Microbiol 2016;195:158-164. 27. Friis NF. Some recommendations concerning primary isolation of Mycoplasma suipneumoniae and Mycoplasma flocculare a survey. Nord Vet Med 1975;27(6):337339. 28. Marois C, Le Carrou J, Kobisch M, Gautier-Bouchardon AV. Isolation of Mycoplasma hyopneumoniae from different sampling sites in experimentally infected and contact SFP piglets. Vet Microbiol 2007;120(1-2):96-104. 29. Kobisch M, Friis NF. Swine Mycoplasmoses. Rev Sci Tech Off Int Epiz 1996;15(4):1569-1605. 30. Nelson JE, Krawetz SA. Purification of cloned and genomic DNA by guanidine thiocyanate/isobutyl alcohol fractionation. Ann Biochem 1992;207(1):197-201.

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31. Nathues H, Beilage E, Kreienbrock L, Rosengarten R, Spergser J. RAPD and VNTR analyses demonstrate genotypic heterogeneity of Mycoplasma hyopneumoniae isolates from pigs housed in a region with high pig density. Vet Microbiol 2011;152(3-4):338345. 32. Opriessnig T, Thacker EL, Yu S, Fenaux M, Meng XJ, Halbur PG. Experimental reproduction of postweaning multisystemic wasting syndrome in pigs by dual infection with Mycoplasma hyopneumoniae and porcine circovirus type 2. Vet Pathol 2004;41(6):624-640. 33. Charlebois A, Marois-CrĂŠhan C, HĂŠlie P, Gagnon CA, Gottschalk M, Archambault M. Genetic diversity of Mycoplasma hyopneumoniae isolates of abattoir pigs. Vet Microbiol 2014;168(2-4):348-356. 34. Kawsashima K, Yamada S, Kobayashi H, Narita M. Detection of porcine reproductive and respiratory syndrome virus and Mycoplasma hyorhinis antigens in pulmonary lesions of pigs suffering from respiratory distress. J Comp Pathol 1996;114(3):315323. 35. Lin J, Chen S, Yeh K, Weng C. Mycoplasma hyorhinis in Taiwan: Diagnosis and isolation of swine pneumonia pathogen. Vet Microbiol 2006;115(1-3):111-116. 36. Vangroenweghe F, Karriker L, Main R, Christianson E, Marsteller T, Hammen K, et al. Assessment of litter prevalence of Mycoplasma hyopneumoniae in preweaned piglets utilizing an antemortem tracheobronchial mucus collection technique and a real-time polymerase chain reaction assay. J Vet Diagn Invest 2015;27(5):606-610. 37. Kurth KT, Hsu T, Snook ER, Thacker EL, Thacker BJ, Minion FC. Use of a Mycoplasma hyopneumoniae nested polymerase chain reaction test to determine the optimal sampling sites in swine. J Vet Diagn Invest 2002;14(6):463-469. 38. Pieters M, Daniels J, Rovira A. Comparison of sample types and diagnostic methods for in vivo detection of Mycoplasma hyopneumoniae during early stages of infection. Vet Microbiol 2017;203:103-109. 39. Maes D, Verdonck M, Deluyker H, de Kruif A. Enzootic pneumonia in pigs. Vet Q 1996;18(3):104-109. 40. Takeuti KL, de Barcellos DESN, de Lara AC, Kunrath CF, Pieters M. Detection of Mycoplasma hyopneumoniae in naturally infected gilts over time. Vet Microbiol 2017;203:215-220.

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41. Siquiera FM, PĂŠrez-Wohlfeil E, Carvalho FM, Trelles O, Schrank IS, Vasconcelos ATR, et al. Microbiome overview in swine lungs. PLosONE 2017;12(7):e0181503. 42. Holst S, Yeske P, Pieters M. Elimination of Mycoplasma hyopneumoniae from breedto-wean farms: a review of current protocols with emphasis on herd closure and medication. J Swine Health Prod 2015;23(6):321-230.

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https://doi.org/10.22319/rmcp.v11i4.5182 Article

Presence of Hymenolepis nana and diminuta in rodents of the Las Pinas citadel, in Milagro, Ecuador, and its risk for public health

Roberto Darwin Coello-Peralta a Galo Ernesto Martínez-Cepeda a* Douglas Pinela-Castro a Enrique Omar Reyes-Echeverria a Enrique Xavier Rodríguez-Burnham a Maria de Lourdes Salazar Mazamba a Betty Pazmiño-Gómez b Antonella Ramírez-Tigrero a Manuel Bernstein a Pedro Cedeño-Reyes a

Universidad de Guayaquil. Facultad de Medicina Veterinaria y Zootecnia. Km 27 ½ vía a Daule, Guayaquil, Ecuador. b

Universidad Estatal de Milagro. Facultad Ciencias de la Salud, Milagro, Ecuador.

*Corresponding author: galomartinez88@gmail.com

Abstract: Hymenolepidiosis is a zoonosis of worldwide prevalence, especially in children, and it is caused by rodent cestodes called Hymenolepis (H) nana and Hymenolepis diminuta. It is very common in developing countries with hot, temperate and dry climates. The life cycle of H. nana does not require intermediate hosts, and its usual transmission is fecal-oral (by ingestion of infective eggs); and infection of H. diminuta occurs through ingestion of tenebrionid arthropods with the larval form (cysticercoids). The objective of this study was to determine the presence of H. nana and H. diminuta in the “Las Piñas” citadel, in

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the city of Milagro (Ecuador) and to make the public health risk known, through informative talks. For this research, the rodents were captured with the help of Tomahawk and Sherman traps with non-toxic baits (meat, mortadella, fish, bread). A descriptive, prospective cross-sectional study with qualitative approach, carried out between February 1st and July 30th, 2018, analyzed fecal samples using direct methods and flotationcentrifugation with a supersaturated saline solution. Out of 87 captured and processed rodents, 20 cases (22.99 %) were determined for Hymenolepis nana, and 10 cases (11.49 %), for H. diminuta. This was the first report of Hymenolepis nana and diminuta in rodents in the country. It can be concluded that the presence of these parasites at the study site is evident and may become a serious public health problem, due to the risk of transmission to the inhabitants of the sector. Keywords: Trapping, Coproparasitoscopic methods, Parasitism in rodents, Public health.

Received: 05/12/2018 Accepted: 03/09/2019

Introduction Infectious agents transmitted from animals to humans account for the majority of new pathogen outbreaks worldwide(1), with over one billion cases of human disease attributable to zoonotic diseases each year, making the identification of wild reservoirs of zoonotic pathogens a public health priority(2). Rattus norvegicus (brown rat) and Rattus rattus (black rat) are known reservoirs of bacteria, viruses and zoonotic parasites of veterinary importance(3,4). Helminthiasis affects 20 % of the Latin American population and is considered a neglected disease, infecting approximately 3.8 billion people worldwide(5). These infections are common in rural populations and overcrowded areas, especially in countries with tropical and subtropical climates, associated with poverty and marginalization, and do not receive adequate national or international attention(6,7). The social determinants of these diseases include poor housing conditions (without adequate roofs, walls, or floors), lack of access to safe drinking water, basic sanitation and hygiene, low income, poor education, and barriers to accessing health services in general and primary health care in particular(7,8). Hymenolepidiasis is a globally prevalent parasitism caused by cestodes of the genus Hymenolepis and Rodentolepis (Hymenolepipidae: Cyclophyllidea). The life cycles of these parasites involve humans, rats and mice as definitive hosts, and arthropods (Tenebrionids) as intermediate hosts. The presence of tenebrionids in the houses and unhealthy conditions generated by the lack of basic services and environmental and socio-

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economic factors favor the development of the disease in hot and humid areas, causing greater affectations to the child population(9,10). Rats and mice act as definitive hosts of Hymenolepis, and the occurrence of this parasite in humans is usually transmitted by fecal-oral route, by egg ingestion (H. nana); however, occasionally it can also be transmitted by ingestion of an arthropod, which will thus complete its life cycle (H. diminuta)(11,12). It has been estimated that 20 million people in the world are infested with these two parasites(13). However, both parasites share some epidemiological characteristics, such as prevalence in children from marginalized areas with poor hygiene habits, sanitary conditions and overcrowding(12). Ecuador is a country with a great variety of climates with four delimited regions: coast, sierra, east and insular region. The "Las Piñas" citadel is located on the Ecuadorian coast; it is characterized by a tropical climate in addition to high percentages of moisture due to its rivers and plains. The citadel has a marginal urban sector with 2,500 houses, where it is possible to find unoccupied houses; sugarcane, cocoa, and banana plantations, and pastures; in addition, there are ditches and septic wells. On the other hand, it does not have sewage, and few homes have drinking water; therefore, it meets the necessary conditions to have the presence of rodents that represent a health risk(14). In addition, the presence of Hymenolepis nana and diminuta in rodents has not been reported in the country. Rodents must be reported, as they are potential sources of parasites that may cause public health problems as a result of their transmission to humans. On the other hand, the purpose of this study is to inform public health authorities and health professionals about cases of Hymenolepidiasis in rodents, as well as to inform the population and guiding them in prevention efforts.

Material and methods Study area and period The study was conducted in the urban-marginal citadel called "Las Piñas", located in the northern part of the canton Milagro, province of Guayas, on the Ecuadorian coast, at the geographical coordinates 2° 7' 0" S and 79° 36' 0" W. The community "Las Piñas" has 12,000 inhabitants; its climate is tropical-humid, with a marked difference between winter and summer, and with temperatures ranging from 22 to 36 °C(14, 15).

Survey Each one of the selected households was visited, explained about the study and the risk of parasitosis in their environment, and once they gave their consent and signed a consent form for themselves and for the minors, a directed survey was applied, after which a sterile vial was given to each person.

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The survey included the following questions: Do you know about hymenolepidiasis? What is the source of your drinking water? How are excreta handled in your home? Are intra- or peridomestic rodents present? Do you store farinaceous foods (rice, corn, barley, etc.)? Are there tenebrionids (beetles, fleas, weevils, etc.) in the farinaceous food deposits? Do you have a sewer system?

Sampling Before carrying out the study, permission was requested from the authorities of the Faculty of Veterinary Medicine and Zootechnics (FMVZ) of the University of Guayaquil to carry out the study in the Microbiology Laboratory of the FMVZ; permission and collaboration were obtained as well from the “Las Piñas’ New Horizons” Improvement Committee ("Nuevos Horizontes de Las Piñas”). In addition, the research methodology was analyzed and approved by the Research Coordination of the FMVZ. A descriptive, transversal and prospective study was carried out between February 1 and July 31, 2018; 70 homes with a total population of 320 people were researched between winter and summer, at an approximate temperature of 27 to 32 °C. During the research, 87 rodents were sampled from three selected areas: (1) rodents in inhabited houses, vacant lots, ditches, and garbage dumps; 2) ditches and garbage dumps, and 3) Community Police Unit (CPU), ditches, garbage dumps, and vacant lots. It is important to mention that concentrations of garbage were found in all the researched areas. On the other hand, the survey was conducted with 90 people from the sector, divided into 30 for each one of the three abovementioned sectors.

Rodent Capture Rodents were captured with the help of Tomahawk and Sherman traps and non-toxic baits (meat, bologna, fish, bread) taking recommended biosecurity measures. These traps were strategically placed in "Las Piñas" which is an urban-marginal citadel, which has places at risk such as: garbage dumps, ditches, pastures, and in the homes of certain community members who claim to have rodents(16). Those rodents that were captured alive were euthanized with pure chloroform soaked in cotton, according to the method suggested by the CICUAL of the School of Veterinary Medicine and Animal Husbandry of the University of Guayaquil. In addition, fipronil was sprayed at 0.15% before proceeding with the necropsy, as a biosafety rule for the professionals who performed it. Once the animals were captured, they were transported alive to the Microbiology Laboratory of the FMVZ of the University of Guayaquil, where the necropsy was

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performed on them and the sample was extracted to be later analyzed using the techniques reported by Aluja and Constantino(17,18).

Laboratory analysis The samples were analyzed using direct methods and sedimentation with saturated salt solution(17,19), then examined under the optical microscope with 10X and 40X objective lenses. The parameters to be followed during the necropsy for the removal of the gastrointestinal tract from the rodents were those reported by de Aluja and Constantine(18). In addition, the Travassos qualitative technique was applied in order to obtain adult parasites(18,20). The mucous membranes of already washed organs were checked for adult or juvenile helminths attached to, or underneath the mucous membrane.

Results After setting up and baiting the traps at the different study sites, it was waited for a period of 12 h. The traps were subsequently checked, and the rodent species were taxonomically identified(16,17); a total of 87 rodents (40 Rattus rattus and 47 Rattus norvegicus) were thus captured and analyzed using the direct coproparasitic method and sedimentation with saturated salt solution. 20 (22.99 %) of the rodents were positive for H. nana, and 10 (11.49 %) were positive for H. diminuta. Furthermore, it was possible to identify the distribution of the parasite in the rodent species studied: H. nana 12 (100 %) and H. diminuta (0 %) in Rattus rattus, and H. diminuta 10 (55.5 %) and H. nana 8 (44.5 %) in Rattus norvegicus. Cases of Hymenolepis nana and diminuta were found in all the study areas. In area 1, there were a total of 4 cases of H. nana and 2 cases of H. diminuta. In area 2, there were 7 cases of H. nana and 3 cases of H. diminuta. In area 3, a total of 9 cases of H. nana and 5 cases of H. diminuta were determined (Table 1). Table 1: Total number of parasites per area Area 1 (%) Area 2 (%) Area 3 (%)

Total

Hymenolepis nana

4 (4.59)

7 (8.04)

9(10.34)

22.97

Hymenolepis diminuta

2 (2.29)

3 (3.44)

5(5.74)

11.47

In addition, 90 people were surveyed with questions about the risk of transmission of diseases from rodents to humans, all of whom said that they did not know about Hymenolepidiasis; 60 individuals (66.67 %) said they consumed water from drums and

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30 (33.33 %) mentioned buying water from tankers. All respondents reported that they discarded their excreta through septic tanks. In the case of rodent excreta, the respondents indicated that they assumed that they fell to the ground. As for the presence of intra and peridomestic rodents, 32 individuals (35.55 %) reported seeing intradomestic rodents; 48 individuals (53.33 %) reported seeing peridomestic rodents; 10 individuals (11.11 %) reported seeing no rodents inside or outside their home. On the other hand, only 17 individuals (18.88 %) declared to have in their home deposits of farinaceous food. Likewise, 13 individuals (14.44 %) reported having seen tenebrionids in their deposits of farinaceous food located in their homes. Regarding the sanitary situation, 70 surveyed individuals (77.78 %) reported not having drinking water, and all 90 said that they did not have a sewerage system. As for the capture sites, in area 1, cases of intestinal parasites were determined in rodents that roamed areas such as inhabited houses, vacant lots, ditches and garbage dumps. In area 2, intestinal parasites were determined in rodents that prowled in ditches and garbage cans. In area 3, cases of intestinal parasites were detected in rodents around the Community Police Unit (CPU); ditch, garbage dump and vacant lots. In order to estimate the number of houses affected by the close presence of rodents, a radius of action of 40 to 50 m was used for the species Rattus rattus, and of 30 to 45 m for Rattus novergicus (21). The study of impact on the population showed that the perimeter of affectation of area 1 where positive rodents were found comprised an average of 10 to 18 houses, which suggests a direct damage of 10 to 18 families respectively. In area 2, the radius of action of the traps where positive rodents were found included 15 to 18 houses. In Area 3, it comprised 10 to 25 houses.

Discussion The estimated prevalence of Hymenolepis nana, of 22.99 %, is higher than that determined in Costa Rica (0.97 %)(22), in Cuba (2.56 %)(23), in Peru (6.8 %)(17), in Brazil (8.8 %)(24), and in Argentina (8.2 %)(19). Likewise, low prevalences of H. nana have been registered in countries of other continents, such as 2.5 % in Iran(25), 3.3 to 10.3 % in China, and 3.3 to 4.1% in the Netherlands(26); however, higher prevalences have been recorded in Taiwan (21.8 %)(26), Brazil (35.7 %)(27) and Italy (100 %)(26). It should be noted that the coastal region of Ecuador is an area where there is a high, constant environmental temperature throughout the year, due to the presence of two annual seasons (winter/summer), which are characterized by high humidity and the presence of moderate to torrential rains in the former, and low rainfall and high environmental temperatures in the latter.

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The 10.64 % of determined Hymenolepis diminuta is less than the one expressed in Cuba with 11.5 %(23), in Argentina with 12.2 %(19), in Peru between 25.7 and 43.8 %(28) and in Costa Rica with 43.68(22). Also, low prevalences of H. diminuta were determined in countries of other continents, such as Taiwan, with 6.3 %(26), and Iran, with 1.7 %(25), in contrast with prevalences (10.64 %) above that found in countries such as China (27.8 %)(26), Iran (38.8 %)(25), The Netherlands (10.2 to 50 %), Serbia (30.5 %), and India (62.5 %)(26). Notably, in all the studied areas there were cases of H. diminuta and H. nana, zoonotic parasites, regarding which the surveyed individuals said they had no knowledge of the disease they produce. On the other hand, 33.33 % mentioned buying water from tankers. All the individuals said that they discarded their excreta through septic tanks; 35.56 % said they saw rodents in their homes, and 53.33 % said they saw rodents in their homes. Likewise, 18.88 % mentioned having in their homes deposits of farinaceous food, which are the main source of tenebrionids, intermediate hosts of the H. diminuta(29), which may transmit the parasites to humans, especially in the winter season, when the number of vectors increases(25). The 77.78 % of the respondents mentioned that they do not have drinking water, and also all the respondents stated that they do not have sewage, which is a potential risk for the increase of rodents in the face of the present increase in population(21); the latter, in turn, are important indicators of neglected parasitosis in vulnerable populations(7,30). Finally, cases of intestinal parasites were determined in rodents of inhabited houses, ditches, garbage dumps, plots of land and vacant lots, which are places where there is greater proliferation of these mammals(21).

Conclusions and implications This study determined the presence of H. diminuta and H. nana in rodent feces from the "Las PiĂąas" citadel in the Milagro canton, and concluded that the presence of these helminth parasites is evident at the study site, which constitutes a public health problem and involves a risk of new cases. On the other hand, emphasis is placed on promoting an in-depth epidemiological study to identify the most vulnerable members within the family nuclei, for the development of awareness campaigns aimed at age groups at risk of suffering from the parasite.

Acknowledgements and conflict of interest The authors are grateful for the collaboration of the Faculty of Veterinary Medicine and Animal Husbandry of the University of Guayaquil and the Committee for the Improvement of New Horizons of "Las PiĂąasâ&#x20AC;&#x2122;, whose participation and openness to this study made it possible for us to reveal the data presented herein.

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Zonta ML, Navone GT, Oyhenart EE. Parasitosis intestinales en niños de edad preescolar y escolar: situación actual en poblaciones urbanas, periurbanas y rurales en Brandsen, Buenos Aires, Argentina: Parasitol Latinoam 2007;62:54-60.

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20. Leguia G, Casas E. Enfermedades parasitarias y atlas parasitológico de camélidos sudamericanos. Lima: Editorial de Mar; 1999. 21. OPS. Manual para el control integral de roedores. Colombia: Ministerio de Salud y Protección Social y OPS; 2012. 22. Vives N, Zeledón R. Observaciones parasitológicas en ratas de San José, Costa Rica. Rev Biol Trop 1957;5(2):173-194. 23. Companioni-Ibañez A, Atencio-Millán I, Cantillo-Padrón J, Hernández-Contreras N, González-Rizo A, Núñez-Fernández F. Prevalencia de endoparásitos en roedores sinantrópicos (Rodentia: Muridae) en una localidad de La Habana, Cuba. Rev Cubana Med Trop 2016;68:240-247. 24. Simoes RO, Luque JL, Gentile R, Rosa MC, Costa-Neto S, Maldonado A. Biotic and abiotic effects on the intestinal helminth community of the brown rat Rattus norvegicus from Rio de Janeiro, Brazil. J Helminthol 2016;90(1):21-27. 25. Arzamani K, Salehi M, Mobedi I, Adinezade A, Hasanpour H, Alavinia M, et al. Intestinal Helminths in Different Species of Rodents in North Khorasan Province, Northeast of Iran. Iranian J Parasitol 2017;12(2):267-273.

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26. Yang D, Zhao W, Zhang Y, Liu A. Prevalence of Hymenolepis nana and H. diminuta from Brown Rats (Rattus norvegicus) in Heilongjiang Province, China. Korean J Parasitol 2017;55(3):351-355. 27. Chagas CRF, Gonzalez IHL, Favoretto SM, Ramos PL. Parasitological surveillance in a rat (Rattus norvegicus) colony in Sao Paulo Zoo animal house. Ann Parasitol 2017;63(4):291-297. 28. Abad D, Chávez A, Pinedo R, Tantaleán M, González O. Helmintofauna gastrointestinal de importancia zoonótica y sus aspectos patológicos en roedores (Rattus spp) en tres medioambientes. Rev Inv Vet Perú 2016;27(4):376-750. 29. Makki MS, Mowlavi G, Shahbazi F, Abai MR, Najafi F, Hosseini-Farash BR, et al. Identification of Hymenolepis diminuta Cysticercoid Larvae in Tribolium castaneum (Coleoptera: Tenebrionidae) beetles from Iran. J Arthropod-borne Diseases 2017;11(2):338-43. 30. World Health Organization. (WHO). Neglected tropical diseases: hidden successes, emerging opportunities. USA 2009. http://www.who.int/neglected_diseases/resources/9789241598705/en/.

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https://doi.org/10.22319/rmcp.v11i4.5111 Article

Frequency of contamination and serovars of Salmonella enterica and Escherichia coli in an integrated cattle slaughtering and deboning operation

Jorge Alfredo de la Garza-García a María Salud Rubio Lozano a María del Carmen Wacher-Rodarte b Armando Navarro Ocaña c Rigoberto Hernández-Castro d Juan Xicohtencatl-Cortes e Enrique Jesús Delgado Suárez a*

Universidad Nacional Autónoma de México (UNAM). Facultad de Medicina Veterinaria y Zootecnia, Ciudad de México, México. b

UNAM. Facultad de Química. Ciudad de México. México.

UNAM. Facultad de Medicina. Ciudad de México. México.

Hospital General Dr. Manuel Gea González, Departamento de Ecología de Agentes Patógenos. Ciudad de México. México. e

Hospital Infantil de México Dr. Federico Gómez, Laboratorio de Bacteriología Intestinal. Ciudad de México. México.

Corresponding author: enriquedelgado.suarez@gmail.com

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Abstract: This study aimed to determine the frequency of contamination and serovar diversity of Salmonella enterica (SE) and Escherichia coli (EC) in different stages of cattle slaughtering and deboning processes. Fecal, carcass, and primal cut (100 of each type) samples were collected in a Federally Inspected slaughterhouse in Mexicali, Baja California. EC was not analyzed in fecal samples because it is part of the gut microbiota. Strain identity was confirmed by biochemical methods and PCR, using the taxonomic genes invA and gadA for SE and EC, respectively. In EC, the presence of genes associated with the main pathotypes was also investigated. SE had a 34 % frequency in fecal samples, 3% in carcasses, and 2% in cuts, while Montevideo was the predominant serovar (72.5 % of the total strains). EC was detected in carcasses (34 %) and cuts (11 %) at an average concentration of 0.012 and 0.33 log CFU cm-2, respectively. Although several of the identified EC serovars were associated with enterotoxigenic or Shiga toxin-producing strains, none carried the virulence factors typically observed in these pathotypes. In summary, beef carcasses and cuts are not a relevant source of EC pathogenic strains. However, beef is an important reservoir of SE, which represents a public health risk. Genomic studies are required on the virulence profile and genes of SE strains commonly associated with subclinical infections and isolated from apparently healthy animals. Key words: Escherichia coli, Salmonella spp., Cattle, Slaughter, Serovars, Pathotypes.

Received: 16/10/2018 Accepted: 23/09/2019

Introduction Intestinal infections caused by Salmonella enterica and the different pathotypes of E. coli, such as the Shiga toxin-producing E. coli (STEC), constitute a global public health problem(1). Both pathogens are common contaminants of meat from different species, including beef(2,3), which is the second most widely consumed meat type in Mexico(4). Therefore, the characterization of circulating strains of S. enterica and E. coli in the beef cattle production chain is crucial to improve management of the risks associated with both pathogens. In Mexico, most of the studies in this field focus on a single point in the production chain. For example, several authors have observed moderate frequencies (8 to 15 %) of Salmonella spp. in beef carcasses(5-7), although the represented serovars are not reported in all cases.

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More comprehensive studies report higher levels of contamination (25 to 100 %) in hides, feces, lymph nodes, non-refrigerated carcasses, and meat samples(8-10), as well as the predominance of certain serovars in some of the matrices analyzed. However, the comparison between studies is difficult due to variations in sample type, step of the production chain, method of analysis, geographical location, animal production system, and sanitary conditions of the studied process. Regarding E. coli, the situation is similar. Most of the studies focus on a specific fragment of the production chain and deal with enterohemorrhagic STEC strains, such as E. coli O157:H7(6,11,12). Although previous studies have reported a low frequency (1 to 3%) of pathogenic E. coli strains in bovine carcasses and feces(11-13), their distribution has not been thoroughly explored throughout the production chain. This information can contribute to identifying dissemination patterns in different processes and geographic regions, as well as measures to guarantee food safety and protect public health. Therefore, this study aimed to determine the frequency of contamination and serovar diversity of S. enterica and E. coli in a Federally Inspected slaughterhouse with horizontal integration of cattle slaughtering and deboning processes.

Material and methods Study design and sample size determination Samples were collected in three stages of the beef transformation process, from slaughtering to deboning: 1) Rectal contents collected after evisceration, 2) Hot carcasses, and 3) Primal cuts. Each stage was considered as an independent sampling, since it was not possible to determine in advance the destination of the animals, which were sold either as whole carcasses or as primal cuts. The sample size for each evaluated stage was calculated with the statistical equation used to determine the sample size of a population proportion when the number of elements in that population is unknown(14): n=

đ?&#x2018;?đ?&#x203A;ź2 â&#x2C6;&#x2014;đ?&#x2018;?â&#x2C6;&#x2014;đ?&#x2018;&#x17E; đ?&#x2018;&#x2018;2

; n=sample size; ZÎą2= Z value in a normal distribution ZÎą= 1.96 when Îą= 0.05;

p= population proportion with the studied characteristic (if unknown, 0.5 is used, as in this case); q= population proportion without the studied characteristic (1-p); d= desired error or precision, fixed at 10% (0.1). With this formula it was obtained a sample size per stage of 96, which was rounded to 100, for a total of 300 samples in the study. The study was performed in September 2013 in an integrated beef production company in Mexicali, Baja California, comprising feedlots, slaughtering, and deboning operations. The sampled carcasses belonged to crossbred Bos indicus young bulls, with an average age of 24 to 30 months, originating from eight Mexican states and finished during an average of 190 days in the feedlot. The company was selected 973

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due to its level of integration, which allows having a production chain model in a single place. The slaughterhouse is 1 km off the feedlots and can process 300 heads of cattle per 8-h shift.

Sampling Rectal contents Fecal samples were collected from the rectum after evisceration, at 20 min postmortem. Approximately 100 g of fecal material were collected from each rectum. For this, viscera packages were momentarily held in the evisceration ramp. The rectum ligature was cut open and, using new nitrile gloves, it was collected the fecal samples in sterile bags, which were kept inside insulated containers with refrigerated gels (≈4 ºC) until further processing in the laboratory. It was used a new pair of gloves for each sample. As E. coli is part of the gut microbiota of cattle, it was assumed that all fecal samples will test positive and have high concentrations of EC. Therefore, fecal sampling was performed only for S. enterica, not for EC. Hot carcass sampling Carcass sampling was conducted according to the methodology employed by the United States Department of Agriculture for the microbiological baseline studies for cattle(15) with slight modifications. Instead of refrigerated carcasses, were sampled hot carcasses, and was used the peptone water from the same hot carcass to detect E. coli and S. enterica. Carcass swabs were collected from three different areas (leg, skirt, and brisket) of right halves. For that purpose, were used sponges pre-moistened with 10 ml of buffered peptone water and 10 x 10 cm2 sterile disposable frames (Meat/Turkey Carcass Sampling Kit, NASCO®, USA). The total sampling area per carcass was 300 cm2. Cuts Once the primal cuts, were obtained in the cutting room and before packaging, legs, skirts, and briskets were randomly selected for sampling. It was followed the same method previously described for carcasses, except that it was used a single 100 cm2 frame per cut.

Microbiological analysis Once the samples were taken, sponges were sealed in sterile plastic bags and kept inside insulated containers with refrigerant gels (≈4 ºC) for transfer to the plant laboratory. It was inoculated in triplicate 100 μl of the peptone water samples in Salmonella-Shigella (SS) agar plates (MCD Lab®, PRONADISA-CONDA®, Spain). Plates were incubated at 37 °C and 974

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examined for growth at 24, 48, and 72 h. Plates without Salmonella spp. characteristic growth at 72 h were considered negative. For the fecal samples, was used sterile swabs for direct streaking in SS medium and followed the same procedure for plate incubation and reading. Colonies with typical Salmonella spp. morphology (round, convex, regular border colonies, with hydrogen sulfide production) were restreaked in CHROMAgar Salmonella Plus medium (CHROMAgar®, France) for purification and identification. All colonies suggestive of Salmonella spp. (hydrogen sulfide producers or purple in CHROMAgar Salmonella Plus) were recovered. The pure and confirmed isolates of Salmonella spp. in selective and differential media were streaked in trypticase soy agar (TSA, MCD Lab®, PRONADISACONDA®, Spain) for their identification through biochemical methods and PCR. From the remaining volume of peptone water, it was took 1 ml for each rehydratable 3M Petrifilm® E. coli/Coliforms (3M, USA) to estimate the concentration of generic Escherichia coli. Following the instructions provided by the manufacturer, 3M Petrifilm plates were incubated at 37 °C and analyzed at 24 and 48 h. It was used the CHROMAgar ECC medium (CHROMAgar®, France) to isolate the strains identified in the 3M Petrifilm plates.

Biochemical identification Salmonella strains were identified with substrates prepared in the laboratory according to the results of the following tests(16): triple sugar iron (TSI); hydrogen sulfide, indole, and motility (SIM); Simmons citrate; urea; methyl red and Voges-Proskauer; malonate-phenylalanine; gluconate; arginine, ornithine, lysine, and control decarboxylase enzymes. Salmonella enterica subsp. enterica ser. Typhimurium ST19 was used as a positive control. This strain was obtained from the culture collection of the Hospital General Dr. Manuel Gea González, in Mexico City, isolated and characterized by VITEK 2 (bioMerieiux, France)(17). The same tests were used for E. coli, except for amino acid decarboxylation(16), using a strain of E. coli K12 was used as a positive control.

Molecular identification Molecular identification was carried out by end-point PCR, using specific gene sequence primers typical of each species (Table 1). Genomic DNA was extracted from the purified strains previously refreshed in tryptic soy broth (MCD Lab®, PRONADISA-CONDA®, Spain) for 18 to 24 h using the “DNeasy Blood & Tissue Kit” (Qiagen, Inc., USA), following the instructions provided by the manufacturer. For S. enterica, was used the invA gene(18), and for E. coli, the gadA gene(19), which codes for the alpha subunit of the glutamate decarboxylase. Additionally, to identify the different pathotypes, was included six genes associated with enteropathogenic (EPEC), enterotoxigenic (ETEC), and Shiga toxinproducing (STEC) strains. Among these, the eaeA gene codes for an intimin, an important protein for adhesion through the translocated intimin receptor(20). This gene is present in the 975

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genome of EPEC and STEC pathotypes. Moreover, the genes coding for Shiga toxins 1 (stx1) and 2 (stx2) usually occur in STEC strains, which show the same phenotype when they carry one or both of these genes(20). It was also studied the presence of genes coding for the heatstable (estA) and heat-labile (eltA) toxins associated with ETEC(21) strains; as well as the bfp gene (bundle forming pilus), involved in adhesion to the intestinal epithelium, which is found in the genome of EPEC strains(2). The PCR reactions were carried out in a total volume of 25 μl and the reagents from the Top Taq Master Mix Kit (QIAGEN®, USA) were used with the following final concentrations: 1.25 Units of Taq Polymerase, 1.5 mM of MgCl2, 1x PCR Buffer, 200 μM of each dNTP. The conditions used for each reaction were as described in previous publications (Table 1). Table 1: Genes and primers used in the molecular characterization of Salmonella spp. and Escherichia coli Pathogen

Gene

Salmonella spp.

invA

Amplified fragment (bp) 284

gadA

670

eaeA

890

stx1

582

stx2

255

estA

190

eltA

132

bfp

324

E. coli

5´3´ Primer sequence 139 GTGAAATTATCGCCACGTTCGGGCAA 141 TCATCGCACCGTCAAAGGAACC gadA1: ACCTGCGTTGCGTAAATA gadA2: GGGCGGGAGAAGTTGATG EAE1: GTGGCGAATACTGGCGAGACT EAE2: CCCCATTCTTTTTCACCGTCG STX1F: ACACTGGATGATCTCAGTGG STX1R: CTGAATCCCCCTCCATTATG STX2F: GGCACTGTCTGAAACTGCTCC STX2R: TCGCCAGTTATCTGACATTCTG STa-F CTAATGTTGGCAATTTTTATTTCTGTA STa-R AGGATTACAACAAAGTTCACAGCAGTAA LT-1 AGCAGGTTTCCCACCGGATCACCA LT-2 GTGCTCAGATTCTGGGTCTC EP1, CAATGGTGCTTGCGCTTGCT EP2, GCCGCTTTATCCAACCTGGT

Ref.

(19) (20) (21) (21) (21) (22) (22) (2)

Amplified PCR products with high (gadA, eaeA, stx1) and low molecular weights (eltA and estA) were subjected to a 1% and 2% agarose gel electrophoresis (SeaKem® LE Agarose, Lonza, ME, USA), respectively. Gels were run in a tris/borate/EDTA buffer (TBE 1x) at 80 V for 50 min using SYBR Safe DNA Gel Stain (Invitrogen, USA) to reveal the DNA fragments. The visualization and digitization of images were performed in a Gel Logic 2200 imaging system (Kodak, USA) with the Care Stream® software (Carestream Health, Inc., USA). The same strains of both pathogens referred to in the biochemical identification were used as positive controls. Furthermore, to identify the E. coli pathotypes, we included strains of EPEC, ETEC, and STEC as controls. These strains were also obtained from the culture

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collection of the Hospital General Dr. Manuel Gea González and were previously characterized by VITEK 2.

Serotypification

Salmonella spp.

Serotypification of the somatic antigen (O). The serological identification of Salmonella strains was performed using the Kauffmann–White scheme(22,23). The somatic antigen (O) was obtained by boiling the bacterial cultures (≈94 ºC) for 1 h. The O antigen was determined using polyvalent anti-O sera A, B, D, D, E, F, and G (DIFCO, BD) and monovalent (specific) anti-O sera from serogroups A, C, D, E, and F (DIFCO, BD). Serotypification of the flagellar antigen (H). This antigen was obtained by inoculating the strains in a semisolid medium in Cragie's tubes and subculturing them in nutrient broth. Phase I and II H antigen were determined using the H antiserum Spicer-Edwards system (DIFCO) and monovalent sera (specific) from serogroups A, B, C, D, E, and F. Although serovar determination was not carried out in a reference laboratory, the complete genome of the obtained strains was sequenced as part of another investigation(24). This allowed to confirm, through in silico raw sequence analysis, the preliminary serotyping results and to determine the serovar of strains that were untypeable by biochemical methods.

E. coli

E. coli strains were serotyped by microagglutination in a 96-well microplate using 187 somatic antigen (O) antisera, and 53 flagellar antigen (H) antisera from rabbit (SERUNAM), following the method described by Ørskov and Ørskov(25), with minor modifications. Phylogroup classification. As certain E. coli phylogroups are associated with animals or humans, as well as with different bacterial pathotypes, it was decided to perform the classification into phylogenetic groups by PCR, according to the Clermont scheme(26). This technique allows to divide the E. coli isolates into seven species-characteristic phylogenetic groups (A, B1, B2, C, D, E, and F) and one additional group, which corresponds to Cryptic Clade I. The test was performed by a quadruple PCR to detect the arpA, chuA, yjaA, and

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TSPE4.C2 genes. Moreover, when results suggest phylogroups E and C, an additional duplex PCR is performed for an allelic variant of the arpA (specific for group E) or trpA gene (specific for group C), including an internal control directed to the trpBA gene. Reactions were performed directly form fresh colonies grown for 24 h in TSA agar. The PCR reactions were performed in a 25 μl volume, under the same conditions previously described(26). The PCR amplification products were subjected to a 2% agarose gel electrophoresis (SeaKem® LE Agarose, Lonza, USA) at 80 V for 50 min. Visualization and digitization of images were performed as previously described for S. enterica. E. coli K12 and representative strains of each phylogroup were included as positive controls. These strains were obtained from the culture collection of the Hospital General Dr. Manuel Gea González, previously classified according to the Clermont scheme(26).

Data analysis

It was calculated the frequency of contamination for both pathogens in the evaluated samples. Concentration was only determined for E. coli. The Chi-square test and the odds ratio were used to test if there was association between pathogen positivity and sample type.

Results From the analyzed samples (300 for Salmonella spp. and 200 for E. coli), it was obtained 84 isolates. Of these, 39 were identified as Salmonella spp. and 45 as E. coli, with a global frequency of 13.0 and 22.5 %, respectively. Only one carcass sample was positive for both bacteria.

Salmonella spp.

The frequency of contamination with Salmonella spp. was 34 % in fecal samples, and 3 and 2 % in carcasses and cuts, respectively (Figure 1). All these isolates were identified as Salmonella spp. by biochemical assays and PCR. Initially, it was identified two additional strains with positive results based on the biochemical tests and PCR, but they were untypeable by biochemical methods. However, when confirming the serovar by in silico raw sequence analysis, these two strains were identified as Pseudomonas putida, a species also carrying the invA gene(27), and were therefore discarded. 978

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Figure 1: Salmonella spp. in bovine feces, carcasses, and cuts (n=100 for sample type) Number of positive samples

40 35 30 25 20 15

10 5 0

Feces

Carcasses

Cuts

The Chi-square test evidenced a strong association (Ď&#x2021;2=58.5, P<0.0001) between sample type and frequency of contamination with Salmonella spp. (Table 2). This was confirmed by the odds ratio, according to which the probability of finding positive samples for Salmonella spp. in feces was 20.1 times higher compared to the other matrices. Table 2: Association between the frequency of contamination with Salmonella spp. and Escherichia coli and sample type Positivity % Odds ratio 95% C.I.1 Sample type n P2 Ď&#x2021;2 Salmonella spp. Feces 100 34 20.1 7.5-53.5 58.5 <0.0001 Carcasses/cuts 200 5 E. coli Carcasses 100 34 4.2 2.0-8.8 15.2 <0.0001 Cuts 100 11 1

95% confidence interval for the odds ratio. 2 Significance level (probability)

Regarding the serovars (Figure 2), it was possible to typifay 35 of the 39 isolates by serological methods. The remaining four strains were only partially characterized. Since they had a rough O antigen, it was only possible to obtain a partial antigenic formula based on the flagellar antigen. However, the in silico analysis, with raw reads from the fully sequenced genomes reported in another study(24), allowed determining the serovar of 100% of the isolates. In total, it was identified five serovars: Bergen (n= 1), Reading (n= 2), Muenster (n= 3), Newport (n= 4), and Montevideo (n= 29). All Montevideo isolates were monophasic for the H antigen, although the antigenic formula allowed the identification of two subgroups within this serovar, 22 of them coming from feces, carcasses, and cuts, with the formula

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6,7:g,m,s: -, while the seven remaining strains, all from fecal samples, had the formula 6,7:g,m,p,s:-. Figure 2: Distribution of Salmonella enterica subsp. enterica serovars according to isolation source (n=100 per sample type) Montevideo 35

Number of isolates

Newport

Muenster

Reading

Bergen

1 2 2 4

20 15 25

10 5 3

1 1

Carcasses

Cuts

0 Feces

The serovar distribution per sample type showed Salmonella enterica subsp. enterica ser. Montevideo was present in all the analyzed matrices. Conversely, strains of Salmonella enterica subsp. enterica ser. Newport and Reading, detected at a lower frequency than Salmonella Montevideo, were only detected in fecal samples.

E. coli E. coli was detected in 34 % of the carcasses and 11% of cuts. A strong association (Ď&#x2021;2=15.2, P<0.0001) was evidenced between sample type and frequency of contamination with E. coli (Table 2). This was confirmed by the odds ratio, which demonstrated that the probability of finding positive samples in carcasses was higher than in cuts (odds ratio: 4.2, 95% confidence interval: 2.0-8.8, P<0.0001. In cuts, the positive samples were distributed in a relatively uniform way, with five strains from isolated from the brisket, three from the skirt, and three from the leg. The concentration of this bacterium was low, both in carcasses and cuts, with values between 1 and 8 CFU cm-2. Of the 45 isolated and identified strains using 3M Petrifilm plates and CHROMAgar ECC, 41 showed a phenotype characteristic of the species. The four remaining strains, isolated from carcasses, showed atypical results; three were indole negative and slow lactose-

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fermenting, and one was positive for citrate, malonate, and cellobiose. However, all strains were molecularly confirmed by PCR, using the gadA gene as a taxonomic marker. A total of 31 E. coli serovars were totally or partially identified (Table 3). The most frequent serogroups were O8 (29%) and O71 (19.4%), and the most common serovar was O1:H6 (9.7%). Table 3: Frequency of Escherichia coli partially or totally identified serovars by sample type Sample type

Carcass

1 3 1 3 1 1 1 1 1 1 5 1 1 1 1 1 1 2 1 1 2

Leg Brisket

Skirt

Serovar O28ab:-:H30 -:H32 O1:H6 O113:O154:H21 O156:O166:H21 O32:O6:O8:O8:H19 O8:H2 O8:H21 -:H32 O124:O71:O71:H12 O8:H8 O7:H39 O71:H12

The predominant phylogenetic groups were A (60 %) and B1 (26.7 %), group B2 was absent, and groups C and D occurred at low frequencies (2.2 and 6.7 %). There were two strains with inconclusive results; therefore, they were not assigned to a phylogroup. It was interesting to observe how some serogroups were strongly associated with certain phylogenetic groups. In the serogroup O8, 8 out of 9 strains belonged to phylogroup B1. Similarly, 4 out of 5 strains in the serogroup O71 belonged to phylogroup A, and all strains from serogroup O1 belonged to phylogroup D (Figure 3).

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Figure 3: E. coli phylogenetic groups, based on the Clermont scheme(26), represented in each of the identified serogroups (n=43)

Absolute frequency

Phylogenetic groups:

20 18 16 14 12 10 8 6 4 2 0 O?

O71 Serogroups

Others

Discussion In several developed countries, with intensive beef production systems similar to those in developing countries, like Mexico, the contamination frequency of Salmonella spp. tends to be low in carcasses, meat, and feces(28-30). However, in this study, it was observed a moderately high contamination frequency in feces, which coincides with previous reports in other Federally Inspected slaughterhouses in the country(9). This indicates that, in Mexico, beef cattle farms may constitute an important reservoir of this pathogen. This surely represents an important challenge for the interventions applied during slaughter. Although the frequency of contamination in carcasses is drastically reduced compared to feces, a total control of the pathogen is not achieved. Furthermore, Salmonella was also detected in primal cuts, which shows the dissemination potential of this pathogen along the production chain. This is demonstrated by the detection of strains of the same serovar in feces, carcasses, and cuts. Additionally, these results are similar to those of previous studies (2 to 30 % positivity to Salmonella) in meat samples in supermarkets(31,32), which only sell meat from Federally Inspected slaughterhouses. This situation implies a more complicated epidemiological situation in the commercialization chains associated with municipal slaughterhouses, which lack the infrastructure and sanitary conditions of those under federal inspection(33). In fact, the positivity frequency to Salmonella in retail beef samples originated from municipal slaughterhouses generally exceeds 50 %(10,34).

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The above analysis shows the need to reinforce the control measures for Salmonella spp. in live animals, since the interventions applied on farms are limited. Therefore, the evaluation of the prevalence of Salmonella spp. in calves entering feedlots, the monitoring of infected animals and their separate management, and thus the detection of possible reservoirs are just some of the measures that could help to reduce the percentage of Salmonella carrier animals in slaughterhouses. All the isolated serovars have been previously associated with human infections in Mexico(35); therefore, the risk pose by these strains to public health should not be minimized. The clear predominance of Salmonella Montevideo in the evaluated processes is notorious and surprising, considering that the participating company fattens animals from eight Mexican states. The absence of previously common serovars in samples from Mexican beef cattle, such as Typhimurium, Anatum, and Agona, is also interesting(36). Although previous studies have reported a variable distribution of Salmonella spp. serovars through time and between geographical areas and studies, the predominance of Salmonella Montevideo in this study is consistent with the increasing prevalence of this serovar in North America(37,38). Furthermore, recent studies conducted in Mexico reported Montevideo and Reading serovars, but not Typhimurium, in strains isolated from cattle feces, carcasses, and lymph nodes(8,9). In any case, it is difficult to determine the factors associated with the prevalence of specific strains in animal production systems without resorting to molecular studies to evaluate the genetic diversity of populations and the presence of genes associated with virulence, environmental persistence, and subclinical infections. However, these results indicate that apparently healthy cattle can carry Salmonella spp. at moderately high frequencies and that this pathogen can spread beyond the slaughtering process, with consequent risks to food safety. The frequency of contamination with E. coli was similar to that of Salmonella in carcasses, and higher in cut samples; however, this bacterium occurred in low concentrations (<8 UFC cm-2). Although E. coli is part of the normal intestinal microbiota, the interventions applied in the slaughterhouse reduced three times the frequency of this bacterium in cut samples, in which the probability of finding positive samples was lower than in carcasses. Furthermore, in Mexico, the circulation of pathogenic strains in cattle appears to be lower than in other countries, such as the United States of America, where they are considered a public health problem(39). This was further confirmed by the absence of the virulence factors associated with the STEC, EPEC, and ETEC pathotypes in the studied samples. Moreover, these findings coincide with previous studies(13) that reported serovars (O157 and not-O157) associated with STEC strains (n=146), but only two of these carried the characteristic virulence factors. In Mexico, subsequent studies showed the same trend, reporting low rates (<1%) of contamination with pathogenic strains of E. coli in carcasses and ground beef(9,40,41). This behavior could derive from multiple factors. Among these, the photoperiod, longer during the summer in the northernmost countries, has been considered responsible for the 983

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marked seasonal effect on the prevalence of pathogenic E. coli in cattle. Other authors have suggested that the circulation of different enterobacteria, with cross-reaction of somatic (O) antigens, could be a negative selection factor of E. coli pathotypes in Mexican cattle populations(42). This is in line with the high percentage of serum samples, from apparently healthy cattle, with a bactericide response against E. coli O157 (71 %), in herds from central Mexico(43). Regarding the identified E. coli phylogroups, the predominance of A and B1 is similar to what is commonly observed in strains of animal origin(44,45). In line with the absence of virulence genes associated with pathotypes, only one strain was classified in group C, to which other STEC strains of animal origin belong(44,46). However, practically all the identified serovars have been associated with the STEC or ETEC pathotypes, which are important in foodborne diseases(47-49). The potential health risks of non-pathogenic strains should not be overlooked since they could acquire virulence factors by incorporating plasmids or phages(50,51). Hence, further research is needed in this area.

Conclusions and implications This study shows that nearly one third of the cattle approved for slaughter carry different serovars of Salmonella enterica in their feces, despite being apparently healthy animals. Furthermore, the results show the ability of the pathogen to spread to the following segments of the production chain, with the consequent risks to public health. Hence, it is important to conduct further studies on the genetic factors of S. enterica associated with the establishment of subclinical infections in cattle and their persistence in livestock populations. Moreover, the results for E. coli show, as in other regions of the country, a low circulation of pathogenic strains of E. coli in beef carcasses and cuts. However, the analyzed samples were obtained from a single slaughterhouse, and the scope of this study, for E. coli, does not consider hide or feces samples, in which the probability of finding pathogenic strains is higher.

Acknowledgments and conflicts of interest This study was carried out with resources from the SAGARPA-CONACYT sector fund, project 109127. We appreciate the technical support of the professionals in the Hospital General “Dr. Manuel Gea González”, the School of Medicine of the Universidad Nacional Autónoma de México, and the Universidad Autónoma de Baja California, for their assistance

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in conducting the experiments and the laboratory analyses. The authors declare that they have no conflicts of interest regarding this publication.

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19. McDaniels AE, Rice EW, Reyes AL, Johnson CH, Haugland RA, Stelma GNJ. Confirmational identification of Escherichia coli, a comparison of genotypic and phenotypic assays for glutamate decarboxylase and β-d-glucuronidase. Appl Environ Microbiol 1996;62(9):3350-3354. 20. DebRoy C, Roberts E. Screening petting zoo animals for the presence of potentially pathogenic Escherichia coli. J Vet Diagn Invest 2006;18(6):597-600. 21. Bisi-Johnson MA, Obi CL, Vasaikar SD, Baba KA, Hattori T. Molecular basis of virulence in clinical isolates of Escherichia coli and Salmonella species from a tertiary hospital in the Eastern Cape, South Africa. Gut Pathog 2011;3(9). 22. Grimont PAD, Weill F. Antigenic formulae of the Salmonella serovars. WHO Collaborating Centre for Reference and Research on Salmonella, Institut Pasteur. https://www.pasteur.fr/sites/default/files/veng_0.pdf. Accessed Feb 25, 2019. 23. Popoff MY, Le Minor LE. Salmonella. In: Bergey's Manual of Systematics of Archaea and Bacteria. Whitman FR, et al editors. Hoboken, New Jersey: John Wiley & Sons, Inc.; 2015:764-799 24. Delgado-Suárez EJ, Selem-Mojica N, Ortiz-López R, Gebreyes WA, Allard MW, Barona-Gómez F, Rubio-Lozano MS. Whole genome sequencing reveals widespread distribution of typhoidal toxin genes and VirB/D4 plasmids in bovine-associated nontyphoidal Salmonella. Sci Rep 2018;8(1):9864. 25. Ørskov F, Ørskov I. Serotyping of Escherichia coli. Method Microbiol 1984;14:43-112. 26. Clermont O, Christenson JK, Denamur E, Gordon DM. The Clermont Escherichia coli phylo-typing method revisited: improvement of specificity and detection of new phylogroups. Environ Microbiol Rep 2013;5(1):58-65. 27. Duan J, Jiang W, Cheng Z, Heikkila JJ, Glick BR. The complete genome sequence of the plant growth-promoting bacterium Pseudomonas sp. UW4. PLoS One 2013;8(3):e58640. 28. Eguale T, Engidawork E, Gebreyes WA, Asrat D, Alemayehu H, Medhin G, Johnson RP, Gunn JS. Fecal prevalence, serovar distribution and antimicrobial resistance of Salmonellae in dairy cattle in central Ethiopia. BMC Microbiol 2016;16:20. 29. Sandt CH, Fedorka-Cray PJ, Tewari D, Ostroff S, Joyce K, M'Ikanatha NM. A comparison of non-typhoidal Salmonella from humans and food animals using pulsedfield gel electrophoresis and antimicrobial susceptibility patterns. PLoS One 2013;8(10):e77836.

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30. Khen BK, Lynch OA, Carroll J, McDowell DA, Duffy G. Prevalence and characteristics of Salmonella in the beef chain in the Republic of Ireland. Zoonoses Public Health 2014;61(8):534-6. 31. Ballesteros-Nova N, Rubio-Lozano MS, Delgado-Suárez EJ, Méndez-Medina RD, Braña-Varela D, Rodas Suárez O. Perfil de resistencia a antibióticos de serotipos Salmonella spp. aislados de carne de res molida en la Ciudad de México. Salud Pública México 2016;58(3):1-7. 32. Rubio-Lozano MS, Martínez-Bruno JF, Hernández-Castro R, Bonilla-Contreras C, Méndez MRD, Núñez-Espinosa JF, et al. Detección de Listeria monocytogenes, Salmonella y Yersinia enterocolitica en carne de res en puntos de venta en México. Rev Mex Cienc Pecu 2013;4(1):107-115. 33. Signorini M. Evaluación de riesgos de los rastros y mataderos municipales. Nacameh 2007;1(2):118-141. 34. Cabrera-Díaz E, Barbosa-Cárdenas CM, Pérez-Montano JA, González-Aguilar D, Pacheco-Gallardo C, Barba J. Occurrence, serovar diversity, and antimicrobial resistance of Salmonella in ground beef at retail stores in Jalisco state, Mexico. J Food Prot 2013;76(12):2004-2010. 35. Gutiérrez-Cogco L, Montiel-Vázquez E, Aguilera-Pérez P, González-Andrade MC. Serotipos de Salmonella identificados en los servicios de salud de México. Salud Publica Mex 2000;42(6):490-495. 36. Zaidi MB, Calva JJ, Estrada-García MT, León V, Vázquez G, Figueroa G, et al. Integrated food chain surveillance system for Salmonella spp. in Mexico. Emerg Infect Dis 2008;14(3):429-435. 37. Habing GG, Manning S, Bolin C, Cui Y, Rudrik J, Dietrich S, Kaneene JB. Within-Farm Changes in dairy farm-associated Salmonella subtypes and comparison to human clinical isolates in michigan, 2000-2001 and 2009. Appl Environ Microbiol 2015;81(17):5724-35. 38. Webb HE, Brichta-Harhay DM, Brashears MM, Nightingale KK, Arthur TM, Bosilevac JM, et al. Salmonella in peripheral lymph nodes of healthy cattle at slaughter. Front Microbiol 2017;8:2214. 39. Scallan E, Hoekstra RM, Angulo FJ, Tauxe RV, Widdowson M, Roy SL, et al. Foodborne illness acquired in the united states—major pathogens. Emerg Infect Dis 2011;17(1):715.

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https://doi.org/10.22319/rmcp.v11i4.5073 Article

Antimicrobial resistance of Escherichia coli isolated from cattle carcasses and feces in Center of Mexico

Vicente Vega Sánchez a Martín Talavera Rojas b Jeannette Barba León c Andrea Paloma Zepeda Velázquez a Nydia Edith Reyes Rodríguez a*

Universidad Autónoma del Estado de Hidalgo. Instituto de Ciencias Agropecuarias. Área Académica de Medicina Veterinaria y Zootecnia. Tulancingo, Hidalgo, México. b

Universidad Autónoma del Estado de México. Facultad de Medicina Veterinaria y Zootecnia. Centro de Investigación y Estudios Avanzados en Salud Animal. Toluca, México. c

Universidad de Guadalajara. Centro Universitario de Ciencias Biológicas y Agropecuarias. Departamento de Salud Pública, Jalisco, Mexico.

*Corresponding author: nydia_reyes@uaeh.edu.mx

Abstract: Escherichia coli is an important microorganism as an intestinal microbiota of animals and humans, so its presence in food serves as an indicator of possible fecal contamination; some strains can cause disease, mainly due to the consumption of water and animal food contaminated. The objective of this study was to detemine the resistance to antimicrobials and the genetic character of E. coli present in the carcasses and feces of bovines killed in slaugtherhouses, and to know the epidemiological panorama in Mexico. The study was carried out in 32 strains in three municipal slaugtherhouses (A, B and C) obtained from bovines in Central Mexico; their resistance profile and their genetic relationship between the different isolates were analyzed by genotyping with the enzyme XbaI-PFGE; The dendrogram was constructed using the coefficient of similarity of Dice with a tolerance of 1.5 %. It is observed that 75 % (24/32) of the isolates show resistance to some 991

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antibiotic, 84.3 % (27/32) have an intermediate profile and 12.5 % (4/32) are sensitive to all antibiotics, the 28.1 % (9/32), were MDR; 27 PFGE and pulsetypes will be identified; 7 clusters were formed with 2 or more isolates (A-F and I) and two integrated with a strain (G and H). This study shows a diversity antimicrobial resistance present in cattle carcasses and feces in Mexico, which is a risk factor and a public health problem. Key words: Escherichia coli, fecal contamination, Carcass, Food contamination.

Received: 01/10/2018 Accepted: 12/11/2019

Introduction Escherichia coli (E. coli) can colonize the gastrointestinal tract of humans and animals without causing damage, there are pathogenic strains that constitute a heterogeneous group of organisms with different virulence properties, serotypes O:H and epidemiology. Based on their specific virulence factors and phenotypic characteristics, they have been subdivided into six pathogenic groups: enteropathogenic E. coli (EPEC), enteroaggregative E. coli (EAEC), enterotoxigenic E. coli (ETEC), diffuse adhesion E. coli (DAEC), E. coli enteroinvasora (EIEC) and enterohemorrhagic E. coli (EHEC)(1); one of the most important virulence factors in E. coli have been the Shiga toxins (Stx) and the products of the pathogenicity island as the site of the disappearance enterocyte (LEE), these bacteria are called E. coli producer of Shiga Toxin (STEC), a common type of STEC is E. coli O157:H7, but there are other serotypes No-O157:H7(2), these have been detected in different products, such as meat, dairy products, fish, seafood, beverages, ice or legumes(3); however, they have been implicated in outbreaks associated, mainly, with the consumption of bovine meat, they are the main reservoir(4), it is found with prevalences in healthy cattle between 7-30 %; it seems that these strains are not pathogenic for animals, although some researchers find them more frequently in those tha have diarrhea(5), so the meat can be contaminated with fecal matter that contains E. coli by poorly processing the carcass in the slaugtherhouses.

E. coli can exchange genetic material through mobile genetic elements (MGEs) such as plasmids, transposons and integrons, which facilitates their adaptation to new and adverse environments that contributes to intestinal or extraintestinal disease. They differ in their virulence, resistance, incidence and severity; however, they can not only be attributed to these factors, but they are the result of their interaction with the host factors and the environment(4). 992

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In the case of antimicrobials, these are used in animals for three main purposes: growth promotion, prophylactic measures and as therapy when disease occurs(6,7), which contributes to the survival of resistant strains. New resistance mechanisms are appearing, which are spread around the world and endanger our ability to treat common infectious diseases, adding the consequent increase in the prolongation of the disease, disability and deaths(8). Some of these E. coli are present in people, animals and the environment (water, soil and air) and can be transmitted from people to animals and the other way around, including through the consumption of products of animal origin; so an inadequate management in the control of infections, conditions, poor sanitation and inadequate manipulation of food, promote the spread of antimicrobial resistance(8).

The impact of the disease caused by STEC has created the need to increase preventive measures of food handling and surveillance of outbreaks(9). In addition to traditional epidemiological investigations, the main molecular method for surveillance has been pulsed field electrophoresis (PFGE), which is the method recommended by the Center for Disease Control and Prevention (CDC)(10), the use of this technique is crucial in STEC infections(11). In Mexico, there are STEC serotypes in cattle that may be involved in Foodborne Diseases(12,13), so in this study they were characterized and related from different origins to determine the genetic diversity present in Mexico.

Material and methods Isolates

A total of 32 isolates of E. coli obtained from cattle carcasses and feces were used in three municipal slaugtherhouses (A, B and C) in central Mexico. These isolates belonged to 16 serotypes, as previously determined with serotyping agglutination assays, using 96-well microtiter plates and rabbit sera (SERUNAM, Mexico City, Mexico) obtained against 187 somatic antigens and 53 flagellar antigens for E. coli and against 45 somatic antigens for Shigella species. All isolates were retrieved from frozen stock cultures and grown in Mac Conkey Agar and incubated at 37 Â°C for 24 h, a colony with the typical morphology was selected and harvested in trypticase soy broth for further characterization.

Antimicrobial susceptibility testing

The evaluation of bacterial resistance to antimicrobials was carried out using the KirbyBauer technique standardized by the Clinical Laboratory Standars Institute (CLSI). The 993

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control strain was E. coli ATCC 25922 and was used: amikacin (AK 30 μg), ampicillin (AM 10 μg), carbencillin (CB 100 μg), cefalotin (CF 30 μg), cefotaxime (CTX 30 μg), ceftriaxone (CRO 30 μg), chloramphenicol (CL 30 μg), gentamicin (GE 10 μg), netilmicin (NET 30 μg), nitrofurantoin (NF 300 μg) pefloxacin (PEF 5 μg) and trimetropima-sulfamethoxazole (STX 25 μg). (Sensidisks, Gram Negative BIO-RAD Cat # 71080280). The bacterial inoculum was adjusted to a turbidity equivalent to 0.5 of the McFarland scale and plated in Mueller Hinton Agar with a sterile swab and on the inoculum were placed in the sensidiscs. They were incubated for 24 h at 37 °C. All isolates were classified as resistant, intermediate or susceptible as previously described. Isolates were considered as MDR when resistance to three or more kinds of antimicrobial agents were presented(14).

Pulsed fields gel electrophoresis (PFGE)

The clonal relationship of the E. coli isolates was performed using the PFGE technique according to the protocol standardized by the CDC (Center for Diseases and Prevention Atlanta, GA, USA) for the PulseNet Network, as a marker the strain was used of Salmonella Serovar Braenderup H9812(15). The electrophoresis conditions were those established according to the protocol of the PulseNet network suggested for the Cheef Dr-II model (Bio-Rad, Munich, Germany) and are as follows: Initial time: 2.2 sec, final time: 63.8 sec, Voltage: 6v / cm2, run time: 21 h(10). The gel was stained with 200 mL of ethidium bromide for 40 min at 100 rpm and subsequently washed with 200 mL of distilled water for 1 h at 100 rpm.

The band pattern was observed under ultraviolet light (UV) and the digital image of the PFGE patterns was taken using a SmartGeL II photodocument (Sagecreation). The obtained bands were analyzed using the Software BioNumerics Version 7.5 (Applied Maths, Austin, TX), this program allowed to compare the band patterns obtained in different gels and to identify the different restriction profiles. The establishment of the genetic relationship between the strains was carried out by applying the coefficient of similarity of Dice's between the different patterns of bands obtained, and a dendrogram was constructed by means of the UPGMA method (Underweighted Pair-Group Method with Arithmetic Averages) with tolerance values of 1.5 %.

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Results In the antimicrobial resistance 75 % (24 of 32) showed resistance to some antibiotic, of these 28.1 % (9/32) of the isolates was MDR, 84.3 % (27/32) presented intermediate profile in at least one antibiotic and 12.5 % (4/32) was sensitive to all antimicrobials. The resistance frequency of CB is 46.9 % (15/32), CF 50 % (16/32), AK 37.5 % (12/32), GE 28.1 % (9/32), AM 21.9 % (7/32) and CTX 3.1 % (1/32), however several isolates showed intermediate profile as in AM 56.3 % (18/32), PEF 46.9 % (15/32) and only SXT was sensitive in all the isolates (Figures 1 and 2). Fig ure 1: PFGE profile (XbaI) obtained from STEC isolates from a carcasses and feces of cattle in three municipal slaughterhouses of central of Mexico

In the columns: the serotype, identification, origin, slaughterhouse, virulence profile and resistance profile (NF-Nitrofurantoin, CB-Carbencillin, PEF- Pefloxacin, NET-Netilmicin, GE-Gentamicin, CTXCefotaxime, STX-Trimetropim-sulfamethoxazole, AK-Amikacin, AM-Ampicillin, CRO-Ceftriaxone, CL-Chloramphenicol and CF-Cefalotin).

The genetic relationship between the different isolates of E. coli was investigated by Genotyping with the XbaI-PFGE enzyme; of the 32 isolations obtained from carcasses and feces in three slaugtherhouses (A, B and C) of bovine in the Center of Mexico, 27 PFGE pulsotypes were observed; 7 clusters were formed with 2 or more isolates (A-F and I) and two integrated with a strain (G and H) these with percentages of similarity higher than 85 % (Figure 2). Clusters D, E, F and G grouped of the MDR isolates. Five clonal 995

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pulsotypes were identified, in the clonal pulsotypes 1 and 2 are of serotype O22:H8 (cluster B) the first comes from different sources such as carcasses (isolated 48) and feces (isolated 50); the second are feces (isolated 46 and 51); however, the resistance profile is different; the third clonal pulsotypes (Cluster D) of serotype O118:H21 is from feces and carcasses (isolates 44 and 21) but with different resistance profile; the fourth pulsotypes (Cluster F) is from carcasses and feces (isolates 25 and 28) and belong to the serotype O?:H51 however one of them is MDR; the fifth pulsotypes (Cluster I) is feces and carcasses (isolates 3 and 6) and are of serotype O37:H7, both sensitive to all antimicrobials; the MDR isolates are found in different pulsotypes, which shows their diversity (Figure 2). Figure 2: Frequency of antimicrobial resistance from STEC isolates from a carcasses and feces of cattle in three municipal slaughterhouses of central of Mexico

Discussion STEC contributes to 265,000 cases of foodborne illness annually in the United States, the antimicrobial drug resistance among STEC has been reported but is probably underestimated(16). The antimicrobial resistance is one of the most serious problems in medical care(17), representing more than 700,000 deaths per year; this is because of the acceleration of antimicrobial resistance genes (ARGs) into bacteria via de novo mutation. In addition of the horizontal transfer of the mobile genetic elements (MGEs) as plasmids, transposons and integrons(18), which causes an emergency in foodborne bacteria, is a public health issue mainly in meat, as it is considered an important carrier for antimicrobial-resistant E. coli(17).

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In the present study STEC strain is identified in cattle carcasses and feces, where 28.1 % are MDR, so their presence is a risk. However, all isolates are sensitive to STX but its use as a treatment in human infections can induce the production of the shiga toxin(19) leading to more serious disease; several authors indicate that foods of animal origin are often contaminated, and point out the need to improve the meat process and indicate that carcasses contamination occurs due to poor handling of the intestinal content of carrier cattle(2).

A study conducted in Mexico in isolated from beef samples found 92.4 % of strains MDR; unlike our study, they found strains that exhibited multidrug resistance patterns from 7 to 9 antibiotics, simultaneously (44 %, n =77)(20), this could be because the samples were obtained in supermarkets. However, it has been seen that cross contamination is of the utmost importance, the exposure at the sale stage represents a global combination of the entire production chain (from farm to fork) which represents a greater risk to public health(17). In STEC, isolates were outbreak associated from the Michigan Department of Health and Human Services (MDHHS) Reference Laboratory (Lansing, MI, USA), collected during 2010-2014 found resistance to ampicillin, trimethoprim/sulfamethoxazole and ciprofloxacin an indicate that the frequency of resistance vary according to their location and source; although, they do not observe differences in frequency in localities(16), similar to what happens in Mexico.

The permanent surveillance of antibiotics used in livestock is of great importance to determine the presence and prevalence of resistant strains in the carcasses, this puts human health at risk(8); beef contaminated with antimicrobial-resistant bacteria, when not properly handled and cooked, could transfer their resistance genes to other pathogens in addition to its toxins, which could lead to a difficult disease to treat(21). Cooking had the greatest impact on the reduction of the hazards(17); however, there are studies in which E. coli survived to heating at 70Â°C, and also showed that it retains its characteristics and genes that encode the resistance, and it is also possible to transfer by electroporation, so there is a risk of a natural transformation(22). Therefore, a strategy to reduce the risk of STEC infections consists of reducing the prevalence in livestock with adequate ma nufacturing practices as well as improving the handling of slaugtherhouses.

In this study, several STEC elements of importance are observed, however in serotype O157:H7 it can present asymptomatically or it can develop diarrhea, haemorrhagic colitis and/or haemolytic uremic syndrome(2) and resistance of 5 antibiotics: AK, AM, CB, CF, and GE, in the serotype O117:H4 were only resistant to AM, this is an emerging serotype that causes sepsis in humans and it has also been found in cattle with diarrhea(23) epidemiologically, its presence is important. In serotype O22:H8 presents a resistance to AK, CB, CF and GE and it has been associated with disease in humans and it has been found in a serotype in Brazil(24), France(25) and Argentina(26); in the Valley of CuliacĂĄn, 997

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northwest of Mexico, of the STEC O157 and non-O157 strains tested that are considered resistant to antimicrobials belonging to classes such as aminoglycosides, beta-lactams and cephalosporins(19). In Tamaulipas, Mexico, in retail meat samples, 92.4 % of the strains were resistant to cefalotin, ampicillin, cefotaxime and nitrofurantoin(20) with these reports it can be said that in Mexico they are found different but similar resistance profiles in the study area. In another study conducted in Ethiopia, the most frequent resistance isolates were cefoxitin, ampicillin and amoxicillin(27). In China from 2000 to 2012 in E. coli isolates from two intensive poultry farms were highly resistant to SXT, AM and GM; they find resistance in sulfonamides but this is because the poultry production units are subject to problems diarrheal of bacterial and parasitic origin; likewise, non-O157 STECs isolated from humans and animals have shown resistance to multiple antimicrobials, including resistance to trimethoprim-sulfamethoxazole(28). In Egypt, a study of isolates of animals and humans (diarrheal children) showed resistance to one or more antibiotic agents, and they were resistant to cephalothin regardless of their origin (food or human), find closely related isolates (97 %) in feces of Swiss cattle and human disease in Germany(29) so the use of antibiotics in livestock environments can affect and cause the presence of multiresistant bacteria.

The serotype O157:H7 was found only in the slaugtherhouse C, 4 are from carcasses and 1 of feces, this confirms the importance of the handling that there is in the carcasses in slaugtherhouses and the reason why presence in carcasses demonstrates the crossed contamination that exists; also, that these were taken at different times and none of these isolates are clones becasuse they have an 83.3 % similarity. In a study conducted on meat products over the period 2004-2013, they found a similarity of 67 % and found no clonal relationship between the isolates(30), confirming that meat products are an important source of contamination due to inadequate management in the process of obtaining products and by-products of bovine origin(31); isolates have been found in humans with hemolytic uremic syndrome, sauces and cooked and raw beef which have the same origin and mention that 12% of infections in humans in Argentina are of Bovine origin(32).

In another study carried out in 4 rural farms in Culiacรกn, Mexico, it was found that the O157:H7 serotype shows 6 pulsetypes in one of the identified clones from cattle, sheep and birds(12); In another study carried out on a federal inspected slaughterhouses in Mexico in 2009-2010, they found 49 pulsotypes in 97 isolates of O157:H7 grouped into 3 clusters with 80% similarity(13), so this study, despite having fewer isolates, shows greater diversity in Mexico; this may be because one of the studies was conducted in rural farms of Culiacรกn and the other were driven by Federal Inspections on slaugtherhouses, which has more control in the process of obtaining meat. In this study the isolates were from municipal slaugtherhouses, where management is deficient and animals from different production units arrive. They supply one of the main metropolitan areas in Mexico, so the presence of STEC in cattle can be an important factor of contamination in the process of obtaining meat. In serotype O22:H8, the six isolates presented diversity 998

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and were grouped into four pulsetypes: the first is carcasse and feces, the resistance profile is different; in the second profile, the two isolates are feces, so probably these isolates belong to animals that are in the same production unit (91.1 % similarity). These data demonstrate the presence of this serotype in slaugtherhouse C, which indicates a risk; this has been associated with severe disease in humans. The presence of 3 different isolates of serotype O112:H2 was found, two of them are from carcasse and one is from feces, but these were presented in the 3 slaugtherhouses (A, B and C) so in the center of Mexico it is present this serotype as well. Each slaugtherhouse has a variant, they have 88.1 % similarity, and this serotype has been found in beef(33) and hamburgers(34), so its presence in carcasse is to be expected. The clones present of serotype O118:H21 were from carcasse and feces, one of them is MDR; they were taken in the same slaugtherhouse (C), however at different times, so there is the possibility that the animals are of the same pr oduction unit or this serotype is not so diverse.

In a study carried out in north-east of Englad, they drove an epidemiological investigation with the infections produced by STEC and associated with it, the consumption of meat products and livestock production units. By sequencing the isolates they found epidemiological links between the clinical cases, the butchers and the farm that supplied the product, which showed the cross-contamination with ground beef and other products(35).

Conclusions and implications This confirms the importance of the management that occurs in livestock production units, the cattle is considered the main reservoir, and the prudent use of antibiotics is recommended to avoid resistance in addition to maintaining the efficacy of the drug; however, if it is used improperly, it could cause potential adverse effects, clinical implications and the acceleration in the development of resistant bacteria that can colonize the human gastrointestinal tract through the food chain, so that serotypes resistant to antibiotics can be found and with the presence of toxins, which would represent a severe public health problem.

Literature cited: 1. Kaper JB, Nataro JP, Mobley HL. Pathogenic Escherichia coli. Nat Rev Microbiol;2004;2(2):123-40. 2. KrĂźger A, Lucchesi PM. Shiga toxins and stx phages: highly diverse entities. Microbiol 2015;161(3):451-462. 999

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3. Al-Nabulsi AA, Holley RA. Effects on Escherichia coli O157:H7 and meat starter cultures of bovine lactoferrin in broth and microencapsulated lactoferrin in dry sausage batters. Int J Food Microbiol 2007;113(1):84-91. 4. Krüger A, Burgán J, Friedrich AW, Rossen JWA, Lucchesi PMA. ArgO145, a Stx2a prophage of a bovine O145:H- STEC strain, is closely related to phages of virulent human strains. Infect Genet Evol 2018;(60):126-132. 5. Melton-Celsa AR, O’Brien AD. E. coli Methods in Molecular Medicine. First ed. l. Totowa, New Jersey: Humana Press 2003:55-75. 6. Galland JC, Hyatt DR, Crupper SS, Acheson DW. Prevalence, antibiotic susceptibility, and diversity of E coli 0157:H7 isolates from a longitudinal study of beef cattle feedlots. Applied Environ Microbiol 2001;67(4):1619-1627. 7. Schroeder MC, Zhao C, DebRoy C, Torcolini J, Zhao S, White DG, et al. Antimicrobial Resistance of Escherichia coli O157 Isolated from Humans, Cattle, Swine, and Food. Applied Envirol Microbiol 2002;68(2):576-581. 8. World Health Organization. Antimicrobial. Resistance. Global report on Surveillance. WHO, Geneva, Switzerland (2014). http://apps.who.int/iris/bitstream/10665/112642/1/9789241564748_eng.pdf?ua=1. Accessed Feb 12, 2018. 9. Bustamante AV, Sanso AM, Parma AE, Lucchesi PM. Subtyping of STEC by MLVA in Argentina. Front Cell Infect Microbiol 2012;(22):111. 10. Ribot EM, Fair MA, Gautom R, Cameron DN, Hunter SB, Swaminathan B, Barrett TJ. Standardization of pulsed-field gel electrophoresis protocols for the subtyping of Escherichia coli O157:H7, Salmonella, and Shigella for PulseNet. Foodborne Pathog Dis 2006;3(1):59-67. 11. Bustamante AV, Sanso AM, Lucchesi PM, Parma AE. Genetic diversity of O157:H7 and non-O157 verocytotoxigenic Escherichia coli from Argentina inferred from multiple-locus variable-number tandem repeat analysis (MLVA). Int J Med Microbiol 2010;300(4):212-217. 12. Amézquita-López BA, Quiñones B, Cooley MB, León-Félix J, Castro-del Campo N, Mandrell RE, Jiménez M, Chaidez C. Genotypic analyses of shiga toxinproducing Escherichia coli O157 and non-O157 recovered from feces of domestic animals on rural farms in Mexico. PLoS One 2012;7(12):515-565. 13. Narváez-Bravo C, Echeverry A, Miller MF, Rodas-González A, Brashears MT, Aslam M, et al. Virulence characterization and molecular subtyping of typical and atypical Escherichia coli O157:H7 and O157:H(-) isolated from fecal samples and beef carcasses in Mexico. J Food Prot 2015;78(2):264-72. 1000

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14. CLSI (2015) Clinical and Laboratory Standard Institute. Performance standards for antimicrobial susceptibility testing; Twenty-Fifth Informational Supplement. CLSI document M100-S25, Wayne, PA. 15. Jaros P, Cookson AL, Campbell DM, Besser TE, Shringi S, Mackereth GF, et al. A prospective case-control and molecular epidemiological study of human cases of Shiga toxin-producing Escherichia coli in New Zealand. BMC Infect Dis 2013;(30):450. 16. Mukherjee S, Mosci RE, Anderson CM, Snyder BA, Collins J, Rudrik JT, et al. Antimicrobial Drug-Resistant Shiga Toxin-Producing Escherichia coli Infections, Michigan, USA. Emerg Infect Dis 2017;23(9):1609-1611. 17. Nekouei O, Checkley S, Waldner C, Smith BA, Invik J, Carson C, et al. Exposure to antimicrobial-resistant Escherichia coli through the consumption of ground beef in Western Canada. Int J Food Microbiol 2018;(272):41-48. 18. Zhang Y, Gu AZ, Cen T, Li X, Li D, Chen J. Petrol and diesel exhaust particles accelerate the horizontal transfer of plasmid-mediated antimicrobial resistance genes. Environ Int 2018;(114):280-287. 19. Amézquita-López BA, Quiñones B, Soto-Beltrán M, Lee BG, Yambao JC, LugoMelchor OY, et al. Antimicrobial resistance profiles of Shiga toxin-producing Escherichia coli O157 and Non-O157 recovered from domestic farm animals in rural communities in Northwestern Mexico. Antimicrob Resist Infect Control 2016;(5):1. 20. Martínez-Vázquez AV, Rivera-Sánchez G, Lira-Méndez K, Reyes-López MA, Bocanegra-García V. Prevalence, antimicrobial resistance and virulence genes in Escherichia coli isolated from retail meats in Tamaulipas, México. J Glob Antimicrob Resist 2018;(14):266-272. 21. Donkersgoed JV, Graham T, Gannon V. The prevalence of verotoxins, Escherichia coli O157, and Salmonella in the feces and rumen of cattle at processing. Can Vet J 1999;40(5):332-338. 22. Le Devendec L, Jouy E, Kempf I. Evaluation of resistance gene transfer from heattreated Escherichia coli. Int J Food Microbiol 2018;(270):39-43. 23. Mandal PK. Synthesis of the pentasaccharide repeating unit of the O-antigen of E. coli O117:K98:H4. Beilstein J Org Chem 2014;(10):2724-2728. 24. Timm CD, Irino K, Gomes TA, Vieira MM, Guth BE, Vaz TM, et al. Virulence markers and serotypes of Shiga toxin-producing Escherichia coli, isolated from cattle in Rio Grande do Sul, Brazil. Lett Appl Microbiol 2007;44(4):419-425.

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25. Pradel N, Livrelli V, De Champs C, Palcoux JB, Reynaud A, Scheutz F, et al. Prevalence and characterization of Shiga toxin-producing Escherichia coli isolated from cattle, food, and children during a one-year prospective study in France. J Clin Microbiol 2000;38(3):1023-1031. 26. Bentancor A, Rumi MV, Carbonari C, Gerhardt E, Larzábal M, Vilte DA, et al. Profile of Shiga toxin-producing Escherichia coli strains isolated from dogs and cats and genetic relationships with isolates from cattle, meat and humans. Vet Microbiol 2012;156(3-4):336-342. 27. Shecho M, Thomas N, Kemal J, Muktar Y. Cloacael Carriage and Multidrug Resistance Escherichia coli O157:H7 from Poultry Farms, Eastern Ethiopia. J Vet Med 2017;(2017):8264583. 28. Gai W, Wang J, Wang J, Cui Z, Qu Z, Cui J, et al. Molecular classification and drug resistance analysis of Escherichia coli isolated from poultry in China. Int J Clin Exp Med 2015;8(1):836-844. 29. Hamed OM, Sabry MA, Hassanain NA, Hamza E, Hegazi AG, Salman MB. Occurrence of virulent and antibiotic-resistant Shiga toxin-producing Escherichia coli in some food products and human stool in Egypt. Vet World 2017;10(10):12331240. 30. Jure MA, Condorí MS, Pérez Terrazzino G, Catalán MG, López Campo A, Zolezzi G, et al. Isolation and characterization of Escherichia coli O157 in bovine meat products and cattle in the province of Tucuman. Rev Argent Microbiol 2015;47(2):125-131. 31. Bibbal D, Loukiadis E, Kérourédan M, Ferré F, Dilasser F, Peytavin de Garam C, et al. Prevalence of carriage of Shiga toxin-producing Escherichia coli serotypes O157:H7, O26:H11, O103:H2, O111:H8, and O145:H28 among slaughtered adult cattle in France. Appl Environ Microbiol 2015;81(4):1397-1405. 32. D'Astek BA, del Castillo LL, Miliwebsky E, Carbonari C, Palladino PM, Deza N, et al. Subtyping of Escherichia coli O157:H7 strains isolated from human infections and healthy cattle in Argentina. Foodborne Pathog Dis 2012;9(5):457-464. 33. Hussein HS. Prevalence and pathogenicity of Shiga toxin-producing Escherichia coli in beef cattle and their products. J Anim Sci 2007;85(13):63-72. 34. Franci T, Sanso AM, Bustamante AV, Lucchesi PM, Parma AE. Genetic characterization of non-O157 verocytotoxigenic Escherichia coli isolated from raw beef products using multiple-locus variable-number tandem repeat analysis. Foodborne Pathog Dis 2011;8(9):1019-1023.

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35. Wilson D, Dolan G, Aird H, Sorrell S, Dallman TJ, Jenkins C, et al. Farm-to-fork investigation of an outbreak of Shiga toxin-producing Escherichia coli O157. Microb Genom 2018;4(3):1-7.

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https://doi.org/10.22319/rmcp.v11i4.5386 Article

Antimicrobial resistance in Salmonella spp. isolated from pig carcasses in two slaughterhouse types in Jalisco, Mexico

Vicente Vega-Sánchez a Jeannette Barba-León b* Delia Guillermina González-Aguilar b Elisa Cabrera-Díaz b Carlos Pacheco-Gallardo b† Adriana Guadalupe Orozco-García b

Universidad Autónoma del Estado de Hidalgo, Instituto de Ciencias Agropecuarias, Hidalgo, México. b

Universidad de Guadalajara, Centro Universitario de Ciencias Biológicas y Agropecuarias, Departamento de Salud Pública, Camino Ramón Padilla Sánchez No. 2100 Nextipac, Zapopan. 45200, Jalisco México.

* Corresponding author: jeannette.barba@academicos.udg.mx

Abstract: Salmonella is one of the main bacteria causing foodborne illness. Research into antimicrobial resistance in Salmonella is increasingly important as treatment of salmonellosis becomes more difficult. An analysis was done of samples from pig carcasses in two slaughterhouse types (federal-inspected and municipal) in the state of Jalisco, Mexico. Thirty-eight Salmonella strains were isolated, with fewer (P<0.05) strains (n= 1) in the federal-inspected slaughterhouse than in the municipal one (n= 37). This difference is probably due to stricter sanitation measures in the federal-inspected

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slaughterhouse. The main identified Salmonella serotypes were London (44.7 %), Anatum (15.8 %), and Agona, Muenchen and Typhimurium (7.9 %). Resistance was broadest against aminoglycosides (100 %), tetracyclines (73.7 %) and ciprofloxacin (44.7 %). Most (66.6 %) of the strains were resistant to three or four different antimicrobial classes. Presence of the gene coding for integrase 1 was confirmed. In the sampled slaughterhouses Salmonella strains have acquired genetic elements promoting resistance to different antimicrobial classes, potentially complicating treatment of infections caused by them. Implementation of better practices and compliance with existing regulations could contribute to reducing the frequency of Salmonella isolates in the sampled slaughterhouses. Key words: Slaughterhouses, Salmonella, Pig carcasses, Serotypes, Antimicrobial resistance.

Received: 20/05/2019 Accepted: 25/11/2019

Introduction The advent of antimicrobial-resistant pathogenic bacteria drives research into the frequency of these bacteria in food and their resistance(1). In 2018, the Food and Agriculture Organization of the United Nations (FAO) and the World Health Organization (WHO) established five priority areas of surveillance. Primary among them was the presence of resistant bacteria, toxic residues and resistance genes in food production environments. Another area of concern was frequent use of antimicrobials in food production and its relationship with the development of resistant bacteria. And emphasis was placed on the need for integrated monitoring systems to control processes in all stages of food production and distribution(2). The United States of America and the European Union work towards food safety through supervision systems such as the National Antimicrobial Resistance Monitoring System (NARMS) and the European Food Safety Authority (EFSA)(3,4). Pathogenic and resistant bacteria in food is a worldwide problem. The number of resistant pathogenic strains can differ between regions, but is related to implementation and monitoring of control measures in food production processes(4-6).

Pork consumption in Mexico has increased as its price becomes competitive with that of other meats such as poultry or beef. In 2017, pork consumption in Mexico was 19 1005

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kg/person/yr(7), and overall consumption in 2018 was estimated at 1.5 million tons, making Mexico the tenth largest consumer of pork worldwide. Annual pork production in 2018 was predicted to grow by 2.3 % in 2018, with a total production of 113.5 million tons. The states of Jalisco, Sonora, Puebla, Yucatan and Veracruz account for 69.4 % of national production(8). Two types of slaughterhouses operate in Mexico: Federal Inspection Type (Tipo Inspección Federal - TIF) and un-certified municipal (rastros no certificados - RNC)(9). Those certified as TIF process animals to supply meat products manufacturers and are subject to permanent sanitary inspection by the Ministry of Agriculture and Rural Development (Secretaría de Agricultura y Desarrollo Rural - SADER); they guarantee products of optimum sanitary quality(10). The RNC slaughterhouse is municipalityowned, slaughter animals for general use and must comply with federal regulation NOM194-SSA-2004(11).

The present study objective was to isolate Salmonella enterica from pig carcasses processed in TIF or RNC slaughterhouses, identify the strains present and analyze their resistance to ten antimicrobials used in humans and animals.

Materials and methods A total of 159 samples were collected from the surface of pig carcasses. Of these, 79 were from a RNC slaughterhouse in the central highlands of the state of Jalisco, and 80 were from a TIF slaughterhouse in the southern highlands of Jalisco. Between October 2013 and May 2014 eight samples from each slaughterhouse were processed every fifteen days. Samples were collected from the surface of carcasses selected randomly on each visit. A sterile sponge moistened with 10 ml buffered peptone water (APA, DB) was wiped over 100 cm2 each of the belly, the ham and the jowl regions, resulting in a total sampled area of 300 cm2 per carcass. After wiping, each sponge was placed in a sterile bag and placed in a cooler with refrigerants for transport to the Food Safety Laboratory of the University of Guadalajara, where they were analyzed according to the MLG 4.04 technique(12).

Isolation of Salmonella spp. from the collected sponges was done by adding 50 ml APA for a total volume of 60 ml. Each sample was homogenized in a peristaltic homogenizer (BagMixer®) for 1 min and incubated at 35 ± 2 °C for 20 to 24 h. From each homogenized sample a 0.1 ml subsample was transferred into 9.9 ml tetrathionate broth (TTB)(BD), and a 1 ml subsample was transferred into 9 ml modified Rappaport Vassiliadis broth (mRV)(BD). These selective broths were incubated at 42 ± 1 °C for 24 h. Sample 1006

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discrimination was done using polymerase chain reaction (PCR) to amplify the invA and fimA genes following previously described conditions(13). After incubation, 500 µl TTB and 500 µl mRV were taken for DNA purification with the Wizard commercial kit (PROMEGA) following manufacturer instructions. Samples exhibiting invA and fimA amplification were subjected to selective isolation in brilliant green sulfa agar (BGS, BD) and xylose-lysine-tergitol agar (XLT4, DB). The BGS and XLT4 plates were incubated at 35 ± 2 °C for 24 to 48 h. Three colonies with typical Salmonella morphology were selected from each agar and analyzed biochemically on triple sugar and iron agar and on iron and lysine agar (BD) at 35 ± 2 °C for 24 h. Isolates exhibiting a typical Salmonella biochemical profile were confirmed by amplification of invA and fimA with PCR(13). Fifty (50) strains with typical Salmonella morphology and biochemical profile confirmed via PCR were randomly selected assuming the presence of one presumptive positive strain per sample. The strains were sent to the Epidemiological Diagnosis and Reference Institute (Instituto de Diagnóstico y Referencia Epidemiológicos - InDRE) for confirmation and serotyping using the Kauffmann method(14). The number of Salmonella isolates confirmed by InDRE in each slaughterhouse by month was compared with an analysis of variance (ANOVA) applied with the GraphPad Prism8 statistical program(15).

The susceptibility profile of the Salmonella isolates to ten antimicrobial agents was generated following the Kirby-Bauer technique, standardized by the Clinical and Laboratory Standards Institute(16). The ten evaluated antimicrobials were ampicillin (AM, 10 μg); nalidixic acid (NA, 30 μg); cephalothin (CF, 30 μg); ceftriaxone (CRO, 30 μg); ciprofloxacin (CIP, 30 μg); chloramphenicol (C, 30 μg); streptomycin (S, 10 μg); gentamicin (GM, 10 μg); kanamycin (K, 30 μg); tetracycline (TE, 30 μg); and trimethoprim-sulfamethoxazole (SXT, 1.25 and 23.75 μg). Inhibition halo measurement (mm) was interpreted using CLSI tables(17), and Escherichia coli ATCC 25922 was used as a control.

Presence of the tetA and tetB resistance genes(18) was analyzed by PCR in all isolates exhibiting TE resistance. The same was done for integrases 1 (intI1) and 2 (intI2) in strains resistant to three or more antimicrobial classes(17,18).

Results and discussion A Salmonella strain was isolated from 1.3% (1/80) of the TIF samples and 46.8% (37/79) of the RNC samples. The number of strains isolated differed (P<0.05) between the months of October, January and February, but not between March and April (Table 1). The sanitation measures applied in the TIF slaughterhouse apparently control the growth of Salmonella isolates in the carcasses processed there, where only pigs are processed. This 1007

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coincides with EFSA recommendations about that implementation of control measures in the slaughtering of pigs greatly reduces the presence of pathogenic bacteria associated with gastroenteritis in humans(4). The high number of strains isolated from carcasses in the RNC slaughterhouse may be related to failures in the evisceration process. Carcass enterobacteria contamination increases during evisceration of pigs but differs between portions of the carcass(5). Contamination increases in the anterior and ventral portions of the carcass when the tonsils are cut during viscera removal, but can decrease when the tools are sanitized after tonsil removal(5). The contamination documented in the RNC slaughterhouse may also originate in cross contamination between the bacteria present in pigs and cattle, since both are processed in this slaughterhouse(19).

Table 1: Serotype, month isolated and resistance profile of 38 Salmonella enterica strains isolated from pork carcasses in two types of slaughterhouses Type Serotype Month isolated Resistance profile TIF Typhimurium April GM, K, S, C, NA, TE February K, S Agona February K February K January K, NA, STX, TE October K, SXT, TE Anatum January K, NA, TE January K, NA January K, S January K Bovismorbificans October K Bredeney October K, S, C, TE Derby October K October K February K, S, AM, C, TE February K, S, NA, TE October K, NA, TE October K, NA, TE October K, NA, TE RNC January K, NA, TE January K, NA, TE January K, NA, TE London February K, NA, TE February K, NA, TE March K, NA, TE October K, TE January K, TE January K, TE January K, TE February K, TE February K, TE 1008

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Montevideo

October February January January October October October

Muenchen Typhimurium Senftenberg

K K, C, NA, TE K, S, C, TE K, NA, TE GM, K, S, C, NA, TE K, S, C, SXT, TE K, C, TE

TIF= Federal Inspection Type slaughterhouse; RNC= Uncertified municipal slaughterhouse; AM= ampicillin, C= chloramphenicol, GM= gentamicin, K= kanamycin, NA= nalidixic acid, S = streptomycin, TE= tetracycline, SXT= trimethoprim-sulfamethoxazole.

Ten Salmonella serotypes were isolated from carcasses in the RNC slaughterhouse: London (45.9 %, 17/37); Anatum (16.2 %, 6/37); Agona and Muenchen (8.1 % each, 3/37); Derby and Typhimurium (5.4 %, 2/37); and Bredeney, Bovismorficans, Montevideo and Senftenberg (2.7 % each, 1/37). Only one serotype (S. Typhimurium) was isolated from carcasses in the TIF slaughterhouse (Table 1). In previous reports the predominant serotypes of Salmonella isolates are diverse, depending on the year and place if isolation, as well as the type of food sampled. In samples from pig carcasses from the United States collected from 2003 to 2015, the five predominant serotypes were S. Typhimurium monophasic (4,(5),12:i:-), Infantis, Johannesburg, Typhimurium and Derby(20). In Belgium, a study carried out between October 2015 and February 2016 identified S. Typhimurium, Derby, Livingstone, Rissen and Bredeney as the prevalent serotypes(5). In contrast, a study done in Spain between November 2012 and May 2014, found S. Rissen, Typhimurium, Panama and Brandenburg(6). A study done in China using samples collected from pig carcasses from October 2012 to July 2013 identified S. Saintpaul, Agona, Give and Corvallis, as well as Derby and Infantis to a lesser extent(21). The S. Agona, Typhimurium and Derby serotypes isolated in the present study coincide with those recovered from pig carcasses in the aforementioned studies.

Study of Salmonella resistance to antimicrobials has been deemed of worldwide importance(2), making the resistance profiles produced in the present study of interest. The 38 strains isolated here (37 from RNC, and 1 from TIF) were challenged against ten antimicrobial agents. All 38 isolates were resistant to K, but susceptible to CIP and the two evaluated cephalosporins (CF and CRO); this highlights that 100 % were resistant to at least one class of antimicrobials. Resistance was also present in 73.7% (28/38) of the isolates against TE, 44.7 % against NA (17/38), 23.7 % against S (9/38), 21.1 % against C (8/38), 7.9 % against SXT (3/38), 5.3 % against GM (2/38) and 2.6 % against AM (1/38). The S. Derby, Bovismorficans and Montevideo isolates only exhibited resistance against K (Figure 1).

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Figure 1: Percentage of Salmonella enterica strains isolated from pig carcasses exhibiting resistance to different antimicrobial classes

Some of the S. Agona (3), Anatum (4), Bovismorbificans (1), Derby (2), London (6) and Montevideo (1) isolates showed resistance to one and/or two classes of antimicrobials. In contrast, other S. Anatum (3), Bredeney (1), London (11), Muenchen (3), Typhimurium (3) and Senftenberg (1) isolates showed resistance to three and/or four classes (Table 2). Resistance was broadest against the aminoglycosides and tetracyclines classes, as well as ciprofloxacin (Figure 1). The NARMS has reported greater resistance to fluoroquinolones (CIP), third-generation cephalosporins (CF) and macrolides (azithromycin), which are frequently used in treating S. Typhi, Paratyphi and nontyphoidal Salmonella infections(3). Isolates from pork and beef sold in stores in China, however, were resistant to tetracyclines, aminopenicillins (AM), sulfamides (SXT) and aminoglycosides (S)(21). Antimicrobial resistance is difficult to compare between studies because it is influenced by the type of antimicrobials used in growing pigs in a given region(6), as well as by the presence of antimicrobial residues in water, farm soil and slaughterhouses where animals are grown or processed (2).

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Table 2: Number of antimicrobial-resistant isolates within Salmonella enterica serotypes isolated from pig carcasses in two types of slaughterhouses No. Antimicrobial classes Serotype Agona Anatum Bovismorbificans Bredeney Derby London Montevideo Muenchen Typhimurium Senftenberg TOTAL

No. 3 6 1 1 2 17 1 3 3 1 38

% 7.9 15.8 2.6 2.6 5.3 44.7 2.6 7.9 7.9 2.6 100

1 3 2 1

1 2 6

1 3

1 16

A majority (73.7 %, 28/38) of the isolates were resistant to tetracycline, therefore the presence of tetA and tetB, two genes that confer TE resistance, was tested by PCR(6). However, neither gene was amplified from any of the TE-resistant isolates. Many (44.7 %) of the isolates were resistant to NA. Resistance to NA can reduce susceptibility to CIP, which is commonly used in treating salmonellosis(3). No isolates were found to be resistant to AM-C-S-SXT-TE (ACSSuT phenotype) or AM-S-SXT-TE (ASSuT phenotype). Both are commonly sought in non-typhoidal Salmonella because they can cause complications in treatment of salmonellosis(3). Although no Salmonella isolates with these specific phenotypes were identified in the present study, there were strains resistant to four antimicrobial classes, which can pose serious problems when treating salmonellosis in humans (Tables 1 and 2).

Resistance elements are acquired through horizontal transfer of genes or groups of genes and is mediated by mobile genetic elements (integrons, plasmids or transposons)(6,22). Integrons are recombination systems consisting mainly of an integrase, a recombination site, and a strong promoter. Three integron classes are associated with resistance cassette acquisition in the Enterobacteriaceae, but Class 1 is associated with resistance to most of the known Î˛-lactams, as well as aminoglycosides, trimethoprim, rifampicin, chloramphenicol, quinolones, erythromycin, and quaternary ammonium compounds(22). Six of the isolates exhibiting resistance to three or four antimicrobial classes were selected for detection of class 1 and 2 integrons. The intI1 gene (integron class 1) was amplified in four isolates: Anatum (1), London (1) and Typhimurium (2). Integron class 1 is one of the most frequently reported in isolates from resistant pathogens such as Klebsiella, Salmonella, Shigella and Yersinia(22). No amplification band for the intI1 or intI2 genes 1011

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was observed in isolates from the Bredeny and Muenchen serotypes (Table 3), so their resistance may be due to a class 3 integron, or some other mobile element.

Table 3: Amplification of intl1 and intl2 genes in isolates of Salmonella enterica from pig carcasses in two types of slaughterhouses. Antimicrobial Source Serotype intI1 intI2 resistence profile TIF Typhimurium GM, K, S, C, NA, TE + RNC Anatum K, NA, SXT, TE + Bredeney K, S, C, TE London K, S, AM, C, TE + Muenchen K, C, NA, TE Typhimurium K, S, C, SXT, TE + TIF= Federal Inspection Type; RNC= Uncertified municipal slaughterhouse; AM= ampicillin, C= chloramphenicol, GM= gentamicin, K= kanamycin, NA= nalidixic acid, S= streptomycin, TE= tetracycline, SXT= trimethoprim-sulfamethoxazole.

Conclusions and implications Salmonella strains resistant to different antimicrobial classes were identified in samples from pig carcasses. The majority were from samples taken in the RNC slaughterhouse. The presence of these multiresistant strains in pork intended for public consumption is a potential public health problem, because if the pork is not adequately prepared the ensuing Salmonella infections can be challenging to treat. The results also highlight the importance of implementing good practices and control measures during slaughter and processing to reduce the presence of Salmonella and the likelihood of disseminating mobile elements carrying resistance genes to other species of Enterobacteriaceae. Of note is that the present findings are limited in terms of sanitation evaluation and verification at each slaughterhouse. The statement that implementation of good practices reduces the presence of Salmonella is made based on previous studies(23). The results are valid for the sampled slaughterhouses and do not constitute sufficient data to represent RNC and TIF slaughterhouses in the study area or Mexico as a whole.

Acknowledgements The authors thank Carlos Pacheco Gallardo, who passed away on 14 October 2019, for his work on this project. Thanks are also due Diana Arcelia Castro de Sales, MarĂa

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Guadalupe Pilar Soto, José Antonio Vilchis Carmona and Manuel Alejandro Cortez Gómez for technical assistance with sample collection and processing.

Literature cited: 1. Karkey A, Thwaites GE, Baker S. The evolution of antimicrobial resistance in Salmonella Typhi. Curr Opin Gastroenterol 2018;34(1):25-30. 2. FAO/WHO. Food and Agriculture Organization of the United Nations/ World Health Organization. FAO/WHO expert meeting on foodborne antimicrobial resistance: Role of environment, crops and biocides. Rome, Italy. 2018. 3. CDC. National Antimicrobial Resistance Monitoring System for Enteric Bacteria (NARMS). Human Isolates Surveillance Report for 2015. Atlanta, Georgia: U.S. Department of Health and Human Services, Centers for Disease Control, CDC. 2018. 4. EFSA. European Food Safety Authority. Scientific opinion on a quantitative microbiological risk assessment of Salmonella in slaughter and breeder pigs. Parma, Italy. 2010. 5. Biasino W, De Zutter L, Mattheus W, Bertrand S, Uyttendaele M, Van Damme I. Correlation between slaughter practices and the distribution of Salmonella and hygiene indicator bacteria on pig carcasses during slaughter. Food Microbiol 2018;70:192-199. 6. Cameron-Veas K, Fraile L, Napp S, Garrido V, Grillo MJ, Migura-Garcia L. Multidrug resistant Salmonella enterica isolated from conventional pig farms using antimicrobial agents in preventative medicine programs. Vet J 2018;234:36-42. 7.

Consejo Mexicano de la Carne. Compendio Estadístico http://comecarne.org/wp-content/uploads/2018/05/CompendioEstad%C3%ADstico-2017-v7-1-sin-elab.pdf. Consultado 20 Ene, 2019.

2017.

8. CIMA. Centro de Información de Mercados Agroalimentarios & ASERCA. Agencia de Servicios a la Comercialización y Desarrollo de Mercados Agropecuarios. Reporte del Mercado de Carne de Porcino. 2018. https://www.cima.aserca.gob.mx/work/models/cima/pdf/cadena/2018/Reporte_mer cado_porcino_290618.pdf. Consultado 20 Ene, 2020. 9. SADER. Secretaría de Agricultura y Desarrollo Rural. Rastros no certificados. https://www.gob.mx/senasica/acciones-y-programas/rastros-no-certificados. Consultado: 20 Ene, 2020.

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10. SADER. Secretaría de Agricultura y Desarrollo Rural. Rastros Tipo Inspección Federal. https://www.gob.mx/firco/articulos/sabes-que-es-un-rastro-tipoinspeccion-federal?idiom=es. Consultado: 20 Ene, 2020. 11. S.S. Secretaría de Salud. Norma Oficial Mexicana NOM-194-SSA1-2004, Productos y servicios. Especificaciones sanitarias en los establecimientos dedicados al sacrificio y faenado de animales para abasto, almacenamiento, transporte y expendio. Especificaciones sanitarias de productos. Estados Unidos Mexicanos: S.S. 2004. 12. USDA. United State Department of Agriculture. Food Safety and Inspection Service. Isolation and identification of Salmonella from meat, poultry and eggs products, MLG 4.04. Isolation and identification of Salmonella from meat, poultry and eggs products, MLG 4.04. Athens, GA. USA: Food Safety and Inspection Service. Laboratory QA/QC Division; 2008. 13. Perez-Montano JA, Gonzalez-Aguilar D, Barba J, Pacheco-Gallardo C, CamposBravo CA, Garcia S, et al. Frequency and antimicrobial resistance of Salmonella serotypes on beef carcasses at small abattoirs in Jalisco State, Mexico. J Food Prot 2012;75(5):867-73. 14. Kauffmann F. On the history of Salmonella research. Zentralblatt fur Bakteriologie, Parasitenkunde, Infektionskrankheiten und Hygiene 1 Abt Medizinisch-hygienische Bakteriologie, Virusforschung und Parasitologie Originale 1966;201(1):44-8. 15. GraphPad. Computer software GraphPad Prism8. Computer software GraphPad Prism8. San Diego, CA: Digital Millennium; 2018. 16. CLSI. Clinical and Laboratory Standards Institute. Performance standards for antimicrobial disk susceptibility test. M2-A9. Wayne, PA, USA: CLSI; 2006. 17. CLSI. Clinical and Laboratory Standards Institute. Performance standards for antimicrobial susceptibility testing; 27th Informational Suppl. M100-S27. CLSI, Wayne, PA, USA; 2017. 18. Lapierre L, Cornejo J, Borie C, Toro C, San Martin B. Genetic characterization of antibiotic resistance genes linked to class 1 and class 2 integrons in commensal strains of Escherichia coli isolated from poultry and swine. Microb Drug Resist 2008;14(4):265-72. 19. Bersisa A, Tulu D, Negera C. Investigation of bacteriological quality of meat from abattoir and butcher shops in Bishoftu, Central Ethiopia. Int J Microbiol 2019;641683.

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20. Yuan C, Krull A, Wang C, Erdman M, Fedorka-Cray PJ, Logue CM, et al. Changes in the prevalence of Salmonella serovars associated swine production and correlations of avian, bovine and swine-associated serovars with human-associated serovars in the United States (1997-2015). Zoonoses Public Health. 2018. 21. Li Y, Cai Y, Tao J, Kang X, Jiao Y, Guo R, et al. Salmonella isolated from the slaughterhouses and correlation with pork contamination in free market. Food Control 2016;59:591-600. 22. Kaushik M, Kumar S, Kapoor RK, Virdi JS, Gulati P. Integrons in Enterobacteriaceae: diversity, distribution and epidemiology. Int J Antimicrob Agents 2018;51(2):167176. 23. Fajardo-Guerrero M, Rojas-Quintero C, Chamorro-Tobar I, Zambrano C, Sampedro F, Carrascal-Camacho AK. Exposure assessment of Salmonella spp. in fresh pork meat from two abattoirs in Colombia. Food Sci Technol Int 2019:1082013219864746.

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https://doi.org/10.22319/rmcp.v11i4.5255 Article

Influence of milking method, storage conditions and somatic cell counts on the milk quality form tanks

Joadilza da Silva Bezerra a Juliana Paula Felipe de Oliveira b* Danielle Cavalcanti Sales a Yhêlda Maria de Oliveira Silva a Stela Antas Urbano a Luis Henrique Fernandes Borba a Lisandra Murmann a Adriano Henrique do Nascimento Rangel a

Universidade Federal do Rio Grande do Norte, Unidade Acadêmica Especializada em Ciências Agrárias, Macaíba, Brasil. b

Universidade Federal de Campina Grande, Centro de Saúde e Tecnologia Rural, Patos, Brasil.

*Corresponding author: jupaula.oliv@yahoo.com.br

Abstract: The objective of this study was to evaluate the influence of the milking method, the storage conditions and the SCC (Somatic Cell Count) increase on the quality of raw milk. Monthly evaluations were performed out over a year in 21 tanks by monitoring the refrigeration temperature and the storage time of the milk in the tank. The tanks were grouped into three temperature levels. Milk storage time intervals were established in each tank: up to 24 h of storage; between 24 and 48 h; and above 48 h. The effect of SCC on the composition was evaluated in three categories: Low SCC; Medium SCC; High SCC. In the analyzed period, 10.8 % presented low SCC, followed by 46.5 % with medium SCC, while 42.7 % had high SCC. There was a positive correlation between

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SCC and protein, and a negative correlation between SCC and lactose. It is concluded that the milking method does not influence the microbial contamination of the milk; however, longer storage time and increased temperature influenced an increase in microorganism counts in milk. In evaluating the hygienic/sanitary quality of the milk, 42.7 % had high SCC and the total bacterial counts presented values above the values recommended by legislation. Key words: TBC, Chemical composition, Hygiene, Temperature.

Received:12/02/2019 Accepted: 25/11/2019

Introduction One of the current requirements of society is for the productive sector to provide food of high biological value, and that is safe and healthy. This requirement directs legislation, research and technology transfer so that these demands are fulfilled, and is directly linked to the competitiveness and profitability of the sector, being of fundamental importance for being able to enter and maintain products in markets(1).

Milk from all mammalian species is a heterogeneous mixture of milk secretion that contains numerous components and exhibits a wide variety of chemical and functional activities(2). Its characteristics are associated with physical-chemical parameters and adequate hygienic-sanitary milking standards(3). The general health of the herd and in particular that of the mammary gland associated to the milking and milk storage conditions influence milk quality(4).

The raw milk production process with refrigeration and storage in tanks, facilitates collection logistics and reduces economic losses by acidifying activity of mesophilic bacteria(5). Mastitis is a common disease in dairy herds and causes high economic losses due to alterations in the secretory tissue of the mammary gland, causing a reduction in the productive life of the animals and modifications to the main milk components(6).

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SCC is considered to be one of the main parameters for assessing milk quality, having a direct relationship with composition, industrial yield and food safety. At higher levels, SCC and TBC are associated with increased enzyme activity which are potentially harmful to milk constituents, resulting in compromised quality and product characteristics(7).

In view of the relevance of these variables to milk characteristics, the objective of this study was to evaluate the influence of the milking method (manual and mechanical) and the effects of storage conditions (temperature and storage time) and the SCC on the quality of raw milk stored in tanks.

Material and methods Experiment location and sample collection

All procedures have been conducted in accordance with the guidelines set out by the Ethics Research Committee under CEP/UFRN, license number 2.054.761. Animal experiments were not performed in the present study, as described in Law No. 11.794 of October 8, 2008, which regulates item VII of § 1 of art. 225 of the Federal Constitution establishing procedures for the scientific use of animals; revokes Law No. 6.638, of May 8, 1979; and makes other provisions.

Monthly evaluations were carried out over a year in 21 milk storage tanks (15 individual and 6 collective tanks). Cooled raw milk was collected from tanks of producers linked to the Associação de Pequenos Agropecuaristas do Sertão de Angicos (APASA), located in the semi-arid region of Rio Grande do Norte state, at 5° 39’ 56” S, 36° 36’ 04” W and 110 m altitude. Dairy animals were crossbred bred in semi-intensive production system. The climate of the region is BSh (arid) according to the KöppenGeiger classification, presenting temperatures between 25 and 33 °C and average annual rainfall of 753 mm.

The tanks were classified regarding temperature, storage time, milking method (manual and mechanical) and storage form (individual and collective). In order to measure the tank’s milk temperature, a previous homogenization was carried out for 5 min, and then the temperature was measured using a digital thermometer with a Mt-350-Minipa laser marker. To standardize the diagnosed temperature ranges, a three-level scaling was performed: Scale 1: 0 °C to 4 °C (ideal temperature); Scale 2: between 4.01 and 7 °C 1018

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(intermediate temperature); and Scale 3: above 7 °C (high temperature). According to the obtained temperature measurements, the tanks were grouped into the defined temperature scales and represented by values expressed as a percentage of the tank total and tank type.

In order to standardize the milk storage period, the tanks were grouped into three intervals and represented by values expressed as a percentage of the tanks’ total: Interval 1 (up to 24 h of storage); interval 2 (between 24 and 48 h); and interval 3 (over 48 h). The amount of milk collected was checked directly in the tank at the collection time using a stainless steel ruler specifically for this purpose. Information regarding milking method (manual or mechanical) and type of tank (individual or collective) used on the properties were obtained from the database provided by the beneficiary company. The milk collection procedure was performed after homogenization by mechanical stirring. Samples were withdrawn from the tank using a sanitized stainless steel ladle. The milk samples were duly identified, kept at a temperature between 2 ºC and 6 ºC and then sent to the laboratory.

Physico-chemical and microbiological analysis

Aliquots for evaluating SCC and TBC were packed in 40-mL plastic bottles with Bronopol® (2-bromo-2-nitro-1,3-propanediol) and Azidiol® preservatives, respectively. In order to correlate SCC values with chemical composition, a classification was made according to the values found, giving rise to three categories: Low SCC: SCC <200.000 cells L-1; Medium SCC: 201.000<SCC<400.000; High SCC: SCC>400.000 cells mL-1. SCC and TBC analyzes were carried out in a laboratory integrated to the Brazilian Milk Quality Network (RBQL). SCC was determined using the flow cytometry method through a SomaScope® electronic counter [Delta, ISO 13366/International Dairy Federation - IDF - 148-2(8) and the results expressed in one thousand somatic cells per mililiter. TBC was obtained by flow cytometry through a Bactocount® electronic counter [Bentley Instruments Inc., ISO 21187/International Dairy Federation - IDF196], with the results expressed in number of colony forming units per ml. The Fourier Transform Infrared Absorption (FTIR) method through a LactoScope® device [Delta, ISO 9622/International Dairy Federation -IDF- 141C(9) was used in order to determine the fat, protein, lactose, total solids, casein and urea nitrogen contents.

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Statistical analysis

To achieve normal data distribution, SCC was also analyzed using the somatic cell count score (SCS) as a result of log transforming the SCC(10), using the equation SCS= log2(SCC/100) + 3. TBC was transformed into a logarithm (log10 CFU mL), and logarithmic transformation of TBC (x 1000 CFU mL-1) to logTBC (log10 CFU mL) was performed(11).

The general mathematical model used was: yij    t i   ij

Where: Y = dependent variables, composition characteristics or milk quality indicator; µ = general mean; t = independent variable, SCC classes, where i= 1 to 3 (1= SCC lower than 200.000; 2= SCC between 200.001 to 400.000; and 3= SCC higher than 400.000; or the milking method, with i= 1 for manual or 2 = for mechanical milking). ε= random error.

The following analyzes were performed in applying the general mathematical model: analysis of variance and Tukey’s test for comparison of means. In addition, Pearson correlation coefficient was also performed between the milk components. The MEANS, GLM and CORR procedures of the SAS version 9.1 statistical package were used for statistical analysis.

Results and discussion No influence of the milking method was observed on the TBC (P>0.05). The results suggest that both production methods can be used to produce quality milk. However, Franca et al(12) verified greater bacterial contamination in milk obtained by the mechanical milking method when compared to the manual. Even with the results obtained in the present study, in can be inferred that failures in the operation of the milking equipment can cause lesions in the epithelium of the mammary gland, causing mastitis and increased microbial contamination.

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However, regardless of the milking type, the evaluated milk samples presented TBC above the limit recommended by the current legislation(13) for the region and period of the study. This fact may be a reflection of several factors such as the presence of mastitis, inadequate milking hygiene practices and/or inefficient storage/cooling conditions on the farm, as well as the use of poor quality water(14). Some workers adopted good milking practices and sanitary measures to control mastitis in dairy farms, and achieved a reduction of more than 90 % TBC and 74.3 % SCC, highlighting the influence of adequate production and health techniques on milk quality(15). According to the established scale, the results showed that the collective tank suffered a lower temperature oscillation than the individual tank. It was found a positive correlation between temperature and milk bacterial counts and observed that the proliferation of bacteria was higher in tanks with high storage temperature, which shows the importance of maintaining the milk in the tanks at low temperature after milking in order to minimize the microbial growth(16). Keeping the temperature of the milk at the maximum allowable level represents a critical point for the quality and may have future consequences, since the temperature of the milk in the case of an isothermal truck may increase before reaching its destination. Therefore, even if the legislation allows milk to be stored up to 7 Â°C, it is important that it be kept at temperatures below 4Â° C, safeguarding its quality, yield and profitability in the industry. The distribution of the tanks in percentage within the storage time intervals is described in Figure 1.

Figure 1: Distribution of the tanks (%) within the storage time intervals (hours)

Intervals 1: up to 24 h of storage (ideal); 2: between 24 and 48 h (intermediate time) and 3: above 48 h (high). 1021

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The mean TBC in raw milk from tanks at storage time intervals is shown in Figure 2. There was a significant difference (P<0.05) in the TBC of the raw milk according to the storage time. In addition, a positive correlation (r= 0.24; P<0.05) between storage time and milk TBC was also observed, indicating that the longer storage time results in an increase in microorganism counts in milk.

Figure 2: Mean TBC (CFUmL-1) in raw milk from tanks at storage time intervals

TBC= TBC (x105CFU mL-1); Time intervals - 1: up to 24 h of storage (ideal); 2: between 24 and 48 h (intermediate time) and 3: above 48 h (high).

No difference was observed in the TBC between the first two storage intervals, but there was a significant difference (P<0.05) between intervals 1 and 3, demonstrating the importance of a reduced storage period on reduced bacterial multiplication. It is important to note that the TBC values at the three times observed are considered high. The appropriate storage procedure for milk in tanks can minimize the risks of qualitative losses of the raw material that may reflect on the properties and durability of its derivative products(17).

Thus, even when refrigerated at a suitable temperature, the milk storage time in the tank is a determining factor for psychrotrophic microorganism multiplication(4). CEMPIR These may lead to a decrease in the shelf life of pasteurized milk and derivatives due to the production of thermoresistant microbial lipases and proteases, which is an important control point to be verified by the industry(18).

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The chemical composition of milk from tanks is shown in Table 1, which presents descriptive statistical analysis and is in compliance with current legislation.

Table 1: Descriptive statistics of the chemical composition data of tank milk (%) Minimum Maximum Variable N Mean ± SD CV (%) value value Fat 273 3.59±0.55 1.51 7.37 15.24 Protein 269 3.00±0.20 2.21 3.84 6.72 Casein 269 2.33±0.36 1.17 5.25 15.26 Lactose 238 4.61±0.25 3.38 5.05 5.48 Total solids 257 12.11±0.70 9.32 15.63 5.84 DDE 257 8.51±0.40 6.57 9.41 4.64 N= number of observations; SD= standard deviation; CV= coefficient of variation; DDE = defatted dry extract (%).

The hygienic-sanitary quality of tank milk in relation to SCC, SCS and TBC is demonstrated in Table 2, which presents a descriptive statistical analysis of the data showing that the TBC is high and the average SCC is in accordance with the current legislation.

Table 2: Hygienic sanitary quality of tank milk in relation to SCC, SCS and TBC Minimum Maximum Variable N Mean ± SD CV (%) value value SCC (cells mL-1) 267 457.0 ±314.0 30.0 2532.0 69.0 -1 SCS (cells mL ) 267 5.93±0.64 3.40 7.84 10.73 -1) 6 6 4 6 TBC (CFU mL 124 1.35 x10 ±1. 32 x10 6.2x10 6.23x10 97.98 SCC= Somatic cell count; SCS= [log2(SCC/100.000) + 3]; TBC= Total bacterial count. N= number of observations; SD= standard deviation; CV= coefficient of variation.

Data from 44,000 herds throughout Brazil, noted that 62 % had a SCC value of up to 500.000 cells L-1 and approximately 23,760 herds (54 %) had TBC up to 300,000 CFU mL-1(19), with SCC and TBC being in accordance with the legislation in force for the period(20). However, according to the authors, even representing the highest percentage these values indicate that SCC and TBC are high, which leads to losses in industrial yield, losses in the processing and shorter shelf life of the derivatives.

In a similar study(21), they concluded that the tank milk’s composition was within the regulated specifications, but the SCC exceeded the allowed limits. Similarly, in studying the characteristics of tank milk(22) found an average SCC of 750,000 cells/mL. 1023

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These results demonstrated deficiencies in mastitis control in the studied region in relation to the hygiene procedures for milking, the instruments and the equipment used. In bulk tanks, SCC values are routinely used as indicators for milk quality, the herd health and for managing production practices, and are also related to changes in milk and milk products(23). The SCC analysis results exhibited 10.8 % of samples with low SCC (Figure 3). SCC in tank milk is a general indicator of udder health in a herd and is also considered as an indirect method of measuring milk quality(24). An animal is considered infected when SCC in milk is higher than 200,000 cells mL-1. SCC values greater than 200,000 cells. L-1 change the milk components(19). Figure 3: Percentage of milk samples from tanks according to the SCC categories

Low SCC - SCC<200,000 cells mL-1; Medium SCC â&#x20AC;&#x201C; 200,001<SCC<400,000; High SCC SCC> 400,000 cells mL-1.

Among the samples analyzed in this study, more than 42.7 % had high SCC, reflecting inefficiencies in relation to mastitis control and mammary gland hygiene. In addition, producers may not be aware of the extensive damage caused by high SCC, which includes the herd, the raw material, the final product and the consumer. This high count may also be related to a lack of monitoring by the industry, not establishing SCC criteria for receiving milk.

High SCC is related to the type of protein, changes in the composition of fatty acids, lactose, ions, mineral concentration and higher pH of raw milk(25), as well as causing a decrease in milk production(6). These changes in milk components are due to mastitisinduced lesions in the glandular epithelium resulting in reduced synthesis in the breast alveoli and increased influx of blood components into milk such as sodium, chlorine, immunoglobulins, and other serum proteins.

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Some authors studied two values of SCC in raw milk and did not find significant effects on the physico-chemical characteristics (pH, titratable acidity, fat, protein, lactose and total solids). In this study it is noteworthy that SCC categories were high despite the difference in milk types(26).

In the present study, there was no variation (P>0.05) in the chemical composition of the milk among the different SCC categories (Table 3). These data are contrary to others(27), who clarified that there is a decrease in the lactose, fat, casein, calcium and phosphorus contents in milk with high SCC due to the increase of proteolytic and lipolytic enzyme activity which are milk degradation processes. The concentration of milk components may occur, leading to an increase in their percentages. This effect is caused by a significant reduction in the volume of produced milk.

Table 3: Mean and coefficient of variation (CV) for milk quality indicators associated with different somatic cell counts (SCC) classes (%) Variable Fat Protein Casein Lactose Total solids DDE

N 267 267 236 255 255 255

Low SCC

Medium SCC

High SCC

Mean 3.61 2.97 2.31 4.63 12.14 8.53

Mean 3.60 2.97 2.32 4.62 12.09 8.48

Mean 3.57 3.03 2.34 4.59 12.12 8.54

CV (%) 15.34 6.66 15.37 5.50 5.88 4.65

N= number of observations; SCC= Low <200.000 cells mL-1; Medium 200.001<SCC<400.000; High: SCC>400.000 cells mL-1. N= number of observations; DDE= defatted dry extract. P<0.05).

In a study developed by Savic et al(28), the values found for the somatic cell quantities of the tank milk in the interval between the minimum value of 58,000 cells mL-1 and the maximum of 516.000 did not significantly affect the protein and lactose content. Different effects from the present research were revealed in other work(29) in which they observed the influence of the SCC on the lactose and defatted dry extract percentages when analyzing milks with four levels of SCC (less than 400.000 cells L-1 to greater than 1â&#x20AC;&#x2122;000,000 cells L-1).

It was observed an increase in the fat percentage with the increase in SCC in working with tank milk samples collected in the agreste region of Rio Grande do Norte, which

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may be related to a marked decrease in milk production, increasing the total solids concentration with evidence for the fat, according to the authors(7). The results expressed in Table 4 demonstrate that SCC has a positive correlation with protein and negative with lactose. This effect can be explained due to lesions in the alveolar cells, which impaired lactose synthesis and altered the epithelium permeability of the mammary gland, increasing the passage of serum proteins from the blood to the milk, thereby provoking a change in the protein characteristics of milk as reflected by the increase in protein(30).

Table 4: Pearson correlation coefficients (P<0.05) between parameters of fat, protein, casein, lactose, total dry extract, defatted dry extract and somatic cell count (SCC) Variables

Fat

Protein

Casein

Lactose

DDE

SCC

Fat Protein Casein Lactose Total solids DDE SCC

1.00

ns 1.00

ns 0.56 1.00

ns 0.38 0.25 1.00

0.81 0.44 0.28 0.48 1.00

ns ns 0.41 0.84 ns 1.00

ns 0.14 ns -0.13 ns ns 1.00

TS= total solids; DDE = defatted dry extract (%); ns= non-significant.

Conclusions and implications Inadequate milking procedures, refrigeration and the milk storage time in tanks reflect in negative effects on milk characteristics and hygienic sanitary conditions. Adopting efficient production and management practices aimed at controlling mastitis associated with adequate storage can contribute to improving raw milk quality and increased profitability of the industry.

Acknowledgments Funding was provided by CAPES Foundation (Brazilian Government). The authors thanks APASA Dairy Industry for supporting this research.

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Literature cited: 1. Henrichs SC, Macedo REF, Karam LB. Influência de indicadores de qualidade sobre a composição química do leite e influência das estações do ano sobre esses parâmetros. R Acad Cienc Agrar Ambient 2014;12(3):199-208. 2. Park YW, Nam MS. Bioactive peptides in milk and dairy products: a review. Korean J Food Sci Animal Resour 2015;35(6):831-840. 3. Murphy SC, Martin NH, Barbano DM, Wiedmann M. Influence of raw milk quality on processed dairy products: How do raw milk quality test results relate to product quality and yield? J Dairy Sci 2016;99(12):10128-10149. 4. Reis KTMG, Souza CHBS, Santana EHW, Roig SM. Qualidade microbiológica do leite cru e pasteurizado produzido no Brasil: revisão. J Health Sci 2013;15(1):411421. 5. Cempírková R, Mikulová M. Incidence of psychrotrophic lipolytic bacteria in cow’s raw milk. Czech J Anim Sci 2009;54(2):65-73. 6. Montanhini MTM, Moraes DHM, Montanhini-Neto R. Influência da contagem de células somáticas sobre os componentes do leite. Ver Inst Latic Cândido Tostes 2013;68(392):18-22. 7. Silva VN, Rangel AHN, Novaes LP, Borba LHF, Bezerril RF, Lima-Júnior DM. Correlação entre a contagem de células somáticas e composição química no leite cru resfriado em propriedades do Rio Grande do Norte. Ver Inst Latic Cândido Tostes 2014;69(3):165-172. 8. INTERNATIONAL DAIRY FEDERATION (IDF). International Organization for Standardization (ISO) 13366/IDF 148-2: Milk – Enumeration of determination of somatic cell Part 2: Guidance on the operation of fluoro-opto-electronic counters. Brussels: IDF; 2006. 9. INTERNATIONAL DAIRY FEDERATION (IDF). International Organization for Standardization (ISO) 9622/IDF 141C: Determination of milk fat, protein and lactose content – Guidance on the operation of mid-infrared instruments. Brussels: IDF; 2013. 10. Ali AKA, Shook GE. An optimum transformation for somatic cell concentration in milk. J Dairy Sci 1980;63(3):487-490. 11. Lopes-Júnior JEF, Lange CC, Brito MAVP, Santos FR, Silva MAS, Moraes LCD, et al. Relationship between total bacteria counts and somatic cell counts from mammary quarters infected by mastitis pathogens. Ciênc Rural 2012;42(4):691696. 1027

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12. França AIM, Silva MAP, Barros JCB, Silva MR, Neves RBS, Nascimento LEC, et al. Qualidade do leite cru refrigerado granelizado coletado no sudoeste goiano. Rev Inst Latic Cândido Tostes 2015;70(6):316-325. 13. Ministério da Agricultura, Pecuária e Abastecimento. Instrução Normativa nº 7, de 3 de Maio de 2016. Regulamento Técnico de Produção, Identidade e Qualidade do Leite tipo A, o Regulamento Técnico de Identidade e Qualidade de Leite Cru Refrigerado, o Regulamento Técnico da Coleta de Leite Cru Refrigerado e seu Transporte a Granel. Diário Oficial da União, Brasília, Seção 1, 04 de Maio de 2016. 14. Reche NLM, Thaler-Neto A, D`Ovideo L, Felipus NC, Pereira LC, Cardozo LL, et al. Multiplicação microbiana no leite cru armazenado em tanques de expansão direta. Ciênc Rural 2015;45(5):828-834. 15. Bozo GA, Alegro LCA, Silva LC, Santana EHW, Okano W, Silva LCC. Adequação da contagem de células somáticas e da contagem bacteriana total em leite cru refrigerado aos parâmetros da legislação. Arq Bras Med Vet Zootec 2013;65(2):589-594. 16. Oliveira RV, Cunha AF, Castilho NPA, Fernandes EN, Silva SQ, Souza FN, et al. Temperatura do leite mensurada pelo termostato e termômetro em diferentes pontos do tanque de expansão. Ver Bras Tecnol Agroind 2016;10(1):1991-2003. 17. Lee AP, Barbano DM, Drake MA. Short communication: The effect of raw milk cooling on sensory perception and shelf life of high-temperature, short-time (HTST)–pasteurized skim milk. J Dairy Sci 2016;99(12):9659–9667. 18. Ângelo FF, Ribeiro CS, Oliveira L, Araújo TF, Cardarelli HR. Bactérias psicrotróficas em leite cru refrigerado. Rev Cient Med Vet 2014;22(22):1-14. 19. Cassoli LD, Silva J, Machado PF. Mapa da Qualidade: Contagem de Células Somáticas. Piracicaba, SP: Clínica do Leite; 2016. 20. Cassoli LD, Machado PF. Mapa da Qualidade: Contagem Bacteriana Total. Piracicaba, SP: Clínica do Leite; 2016. 21. Campos PPLE, Rangel AHN, Borba LH, Urbano SA, Novaes LP, Galvão-Júnior JGB, et al. Quality indicators of tank milk in different production systems of tropical regions. Semina Ciênc Agrar 2016;37(4):2819-2830. 22. Rangel AHN, Araújo VM, Bezerra KC, Barreto MLJ, Medeiros HR, Lima-Júnior DM et al. Avaliação da qualidade do leite cru com base na contagem de células somáticas em rebanhos bovinos comerciais no estado do Rio Grande do Norte, Brasil. Arch Vet Sci 2013;18(1):40-45.

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23. Boland F, Grady LO, Mais SJ. Investigating a dilution effect between somatic cell count and milk yield and estimating milk production losses in Irish dairy cattle. J Dairy Sci 2013;96(3):477-1484. 24. Schukken YH, Wilson DH, Welcome F, Garrison-Tikofsky L, Gonzalez RN. Monitoring udder health and milk quality using somatic cell counts. Vet Res 2003;34(5):579-596. 25. Umam AAK, Lin M, Radiati LE. Study on the bulk milk somatic cell counts and milk quality in different seasons. Sch J Agri Vet Sci 2017;4(11):498-503. 26. Corassin CH, Rosim RE, Oliveira CAF. Atividade de plasmina e plasminogênio no leite longa vida com alta e baixa contagem de células somáticas durante o armazenamento. Ciênc Rural 2010;40(12):2588-2592. 27. Gargouri A, Hamed H, Elfeki A. Analysis of Raw Milk Quality at Reception and During Cold Storage: Combined Effects of Somatic Cell Counts and Psychrotrophic Bacteria on Lipolysis. J Food Sci 2013;78(9):1405-1411. 28. Savić NR, Mikulec DP, Radovanović RS. Somatic cell counts in bulk milk and their importance for milk processing. In: 59th International Meat Industry Conference Meatcon 2017. IOP Conf. Series: Earth and Environmental Science. 2017:85. 29. Silva VN, Rangel AHN, Galvão-Júnior JGB, Urbano SA, Borba LHF, Novaes LH, et al. Influence of somatic cell count in the composition of girolando cow’s milk in tropical zone. Trop Sub Agro 2016;19(2):101-107. 30. Auldist MJ, Hubble IB. Effects of mastitis on raw milk and dairy products. Aust J Dairy Tech 1998;53(1):28-36.

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https://doi.org/10.22319/rmcp.v11i4.5049 Article

Serotypes and Stx2 subtyping of Shiga toxin producing Escherichia coli isolates from cattle carcasses and feces

Nydia Edith Reyes-Rodríguez a Jeannette Barba-León b Armando Navarro-Ocaña c Vicente Vega-Sánchez a Fabián Ricardo Gómez De Anda a Juan Martín Talavera-González d Martín Talavera-Rojas d*

Universidad Autónoma del Estado de Hidalgo. Instituto de Ciencias Agropecuarias, Área Académica de Medicina Veterinaria y Zootecnia. Tulancingo de Bravo, Estado de Hidalgo, México. b

Universidad de Guadalajara. Centro Universitario de Ciencias Biológicas y Agropecuarias, Departamento de Salud Pública. Zapopan, Jalisco, México. c

Universidad Nacional Autónoma de México. Facultad de Medicina, Departamento de Salud Pública. Ciudad Universitaria, México. d

Universidad Autónoma del Estado de México. Facultad de Medicina Veterinaria y Zootecnia, Centro de Investigación y Estudios Avanzados en Salud Animal. Toluca, Estado de México, México.

*Corresponding author: talaverarojas@gmail.com

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Abstract: Shiga toxin E. coli (STEC) is an important pathogen responsible for foodborne illness, this have been related with epidemic outbreaks in the past, mainly because of consumption of bovine meat. The objective of this study was identify the serotypes and Stx2 subtypes and associate them with their possible epidemiology. There were analyzed a total of 65 isolates from the collection of the Centro de Investigación y Estudios Avanzados en Salud Animal, Facultad de Medicina Veterinaria y Zootecnia of the Universidad Autónoma del Estado de México, from carcasses and feces of bovines at three different Municipal slaughterhouses. The identification of Stx2 gene by PCR at final point, sequencing and analyzed with the help of BLAST software. There were found O157:H7, O70:H16, O91:H10, O112ac:H2, O128ac:H26 serotypes, which have been reported to be present at infectious outbreaks previously by foodborne worldwide; 63.07% (41/65) of the Escherichia coli strains got amplified for Stx2 and after BLAS analysis it was confirmed its presence and a hypothetic protein. The presence of this serotypes in combination with different subtype’s, Stx2a, Stx2c, Stx2d, in carcasses and feces of bovine in must be considered as a potential risk for diseases an important problem of the public health. Key words: Cattle carcasses, Escherichia coli, Serotyping, stx2.

Received: 04/09/2018 Accepted: 09/12/2019

Introduction Some pathogenic serotypes of Escherichia coli (E. coli) are a cause of food borne diseases, and are mainly associated with the consumption of beef, unpasteurized milk, apple juice, yogurt, cheese, fresh water and raw salads(1,2). For E. coli to cause a disease in humans a variety of conditions are required; however, there are some pathogenic strains that are considered as primary agents since they have acquired virulence factors via plasmids, transposons or bacteriophages or transmitted by mobile genetic elements (MGEs)(2). It is important to consider that cattle are the main reservoir and the products and by-products obtained from them are considered as a source of infection of E. coli producing Shiga toxin (STEC)(1-4). Currently in the municipal slaughterhouses there are health risks, the slaughterhouse sampled have a medium health risk and high sanitary risk, according to the

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Guide for carrying out the sanitary diagnosis and detection of operational needs of municipal slaughterhouses of COFEPRIS (Federal Commission for the Protection against Sanitary Risks) since it mentions that approximately 18 % of the annual slaughter of cattle is carried out in establishments with high or very high health risk; this is the concern since annually, in establishments considered as having a high or very high health risk, 112,000 t of beef and if the annual per capita consumption of beef is taken into account, it would be expected that approximately a total of 7â&#x20AC;&#x2122;103,300 people will consume beef produced in establishments of high or very high risk. All this probably due to poor sanitary conditions such as the lack of inadequate toilet facilities and equipment, poor sanitary habits of the workers and deficiency in the cleaning of utensils and work clothing.

An estimated of 265,000 STEC cases occur each year in the United States(5). The main serotype involved in clinical conditions in humans is O157:H7(6), which causes about 36 % of these infections and 64 % is caused by non-O157. Public health expertâ&#x20AC;&#x2122;s opinions are based on estimates and not real data because not all STEC infections are diagnosed and reported, as there are not approved laboratories for the isolation of non-O157(7) or the laboratories vary widely in their stool culture protocols and in their abilities to detect this organism. The Shiga toxin family includes several toxins related to Shiga toxin from Shigella dysenteriae that share a similar structure and biological activity, Shiga toxin nomenclature is a system based on phylogenetic sequence based relatedness of the proteins, the Stx nomenclature is designated Stx1a, Stx1c, Stx1d, Stx2a, Stx2b, Stx2c, Stx2d, Stx2e, Stx2f and Stx2g(8). Stx2a can cause a greater harm than stx1; stx2a was associated with serotype O104:H4 in a foodborne outbreak in Germany and other countries that affected more than 4,000 people in 2011, 23 % of this case evolved to develop hemolytic uremic syndrome (HUS)(9). Other serotypes with this variant are O157:H7(10) and O26:H11(11), it suggests that stx2a is directly associated with HUS(9-11). Stx2b is often present in sheep, goats and deer, meanwhile Stx2c toxin to healthy or sick pigs(6), Stx2d toxin was identified in serotype O26 from a patient with bloody diarrhea and HUS in Germany, also it was found in cheese and cattle(12), meat and pork byproducts and wild pigs(6). The stx2f variant has been reported in pigeons(13), but so far, reports of disease in humans are rare(8). The objective of this study was identify the serotypes and Stx2 subtypes and associate them with their possible epidemiology, there are studies where there is a correlation between the serotype and subtype of Stx2 with the type of virulence besides the risk of associating with infections of importance.

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Material and methods Isolated background

The slaughterhouses where the carcasses and feces were sampled have a medium health risk and high sanitary risk, according to the Guide for carrying out the sanitary diagnosis and detection of operational needs of municipal slaughterhouses of COFEPRIS, during the summer sampling was carried out on slaughterhouses, where pigs, sheep and mainly cattle are slaughtered, the animals that arrive are from livestock production of municipalities adjacent to the slaughterhouses for this studies the number of animals slaughtered weekly in the 3 (A, B and C) was taken, with a sacrifice of 120, 80 and 375 respectively, 575 slaughtered bovines were accounted per week, and the sample size was obtained, considering a 2.7 % prevalence and a 95 % confidence level, by means of the finite population sample size formula; hence, the following sample size was obtained: slaughterhouse A, 8; slaughterhouse B, 5; and slaughterhouse C, 25. Three repetitions were performed in each slaughterhouse taking paired samples (114 carcasses and 114 feces). The carcass sample was taken after its washing and before refrigeration, the fecal matter samples were taken after animal evisceration.

Strains in study

There were analyzed a total of 65 isolates of Escherichia coli. from the collection of the Centro de Investigacion y Estudios Avanzados en Salud Animal, Facultad de Medicina Veterinaria y Zootecnia de la Universidad Autónoma del Estado de México. They were obtained from carcasses and bovine’s feces from three municipal slaughterhouses (A, B and C) in the center of Mexico.

Serotyping

It was performed according to the procedure described by Orskov and Orskov(14). It was used specific serum anti-H and anti-O (SERUNAM, Mexico) for a total of 185 somatic antigens and 56 flagellar.

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DNA extraction It was made from bacterial frozen cells at -70 °C; 20 μl of bacterial culture were first taken and suspended in 200 μl of sterile distilled water, and then incubated at 100 °C for 10 min to be used directly in PCR reactions(15).

Identification of Shiga toxin 2

The following primers were employed 5´CTT CGG TAT CCT ATT CCC GG3´, 5´CTG CTG TGA CAG TGA CAA AAC GC3´(16) y 5´GGC ACT GTC TGA AAC TGC TCC 3´, 5´TCG CCA GTT ATC TGA CAT TCT G3´(17). EDL933 strain of E. coli was used as positive control, the PCR conditions were described according previously mentioned authors. The products from amplification were separated by electrophoresis in agarose gels at 2%, they were visualized and captured with an image photodocumenter (UV Transilluminator UVP Model M-20E and Kodak Digital Science electrophoresis documentation and analysis system 120). Strains that tested positive for stx2 and the PCR reaction were purified using the Wizard® SV Gel kit and PCR Clean -Up System (Promega) and sequenced on an analyzer ABI PRISM 3730XL (Macrogen, Inc., Korea). The nucleotide sequence of the Stx2 toxins tested were analyzed in the Standard Nucleotide BLAST (Basic Local Alignment Search Tool).

Results In this study 36 different serotypes were found; 12.31 % (7/41) O157:H7, 9.23 % (6/65) O22:H8, 6.15 % (4/65) O?:H7, 4.62 % (3/65) O112ac:H2, O128ac:H26, O185:H2 and OR:H7, 3.07 % (2/65) O18ac:H21, O37:H10, O103:H16, O117:H4, O147:H28, O154:H53 and O?:H51, 1.54 % (1/65) O7:H30, O8:H25, O18ac:NM, O18ac:H7, O37:H?, O40:NM, O53:H2, O65:H16, O70:H16, O91:H10, O118ac:H21, O120:H10, O136:H16, O149:H2, O172:H45, O184:H12, O185:H7, OR:H2, OR:H?, O?:H2 y O?:H11 (Figure 1, Table 1 ).

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Figure 1: DNA amplification by PCR

Lane M Molecular weight marker: Lane 1 sample positive stx2 (O157:H7); lane 2-3 sample positive hypothetical protein (O18ac:NM and O102:H40); lane 4 positive control; lane 5 negative control.

From slaughterhouse A were identified 9 serotypes (O37:H10, O40:NM, O65:H16, O70:H16, O112:H2, O157:H7, O172:H45, 0184:H12 and O?:H2) from 10 isolates from which in the 50 % (5/10) the stx2 toxin was identified. In slaughterhouse B from the 10 isolates, were observed 9 serotypes (O18:H7, O112:H2, O120:H10, O128:H26, O185:H7, OR:H2, ORH?, O?:H7 and O?H11) 20 % (2/10) of them tested positive for stx2. At slaughterhouse C, from the 45 isolates were found 23 different serotypes; 64.4 % tested positive for stx2. In this place was found that serotypes got amplified with primers but, after sequencing, only 17.8 % (11/45) was identified to be a hypothetica l protein (Figure 1, Table 1).

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Table 1: Identification of Stx2 gene in shiga toxin producing Escherichia coli and Serotypes isolated from cattle carcasses Sample Accession Serotype Isolate Stx2 Subtypes type* number O8:H25

Carcass (C)

Stx2a

KT356574

O18ac:NM

Carcass (C)

KT356580

O18ac:H7

Carcass (B)

O18ac:H21

Carcass (C)

Stx2a

KT356571

Carcass (C)

Stx2a

KT356576

Carcass (C)

Stx2a

KT356578

Carcass (C)

Stx2d

KT356581

O37:H10

Carcass (A)

O40:NM

Carcass (A)

Stx2a

KT356555

O65:H16

Carcass (A)

Stx2c

KT356546

O91:H10

Carcass (C)

Stx2c

KT356564

O102:H40

Carcass (C)

KT356560

O112ac:H2

Carcass (A)

Stx2a

KT356548

(2)

Carcass (B)

O118ac:H21

Carcass (C)

O120:H10

Carcass (B)

Negative

O128ac:H26

Carcass (B)

Negative

(2)

Carcass (C)

Negative

O22:H8 (3)

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Negative

Negative +

Stx2c

KT356559

KT356573

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O136:H16

Carcass (C)

Stx2c

KT356582

O149:H2

Carcass (C)

KT356567

O154:H53

Carcass (C) Negative

(2)

Carcass (C)

Stx2c

KT356561

Carcass (C)

Stx2c

KT356551

Carcass (C)

Stx2c

KT356552

Carcass (C)

Stx2c

KT356553

Carcass (C)

Stx2c

KT356554

O172:H45

Carcass (A)

Negative

O185:H2

Carcass (C)

Negative

Carcass (C)

Stx2a

KT356547

Carcass (C)

Stx2a

KT356549

Carcass (C)

Stx2a

KT356585

ONT:H2

Carcass (A)

Negative

ONT:H7

Carcass (B)

Negative

(2)

Carcass (C)

ONT:H11

Carcass (B)

ONT:H51

Carcass (C)

O157:H7 (5)

OR:H7 (3)

Negative +

Stx2a

( ) Slaughter plant A, B, or C. HP= hyphotetic protein; NM= no movile; NT= nontypeable; R= rough.

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KT356586

KT356563

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In this study, 63.07 % (41/65) of the strains of Escherichia coli amplified to stx2, after analysis of its sequence in BLAST it was confirmed that 71.5 % (33/41) were Stx2; also strains amplified with primers were found, but after the analysis of the sequence performed in BLAST were identified that 19.5 % (8/41 ) was a hypothetical protein and were submitted to GenBank database whose access numbers are KT356546, KT356547, KT356548, KT356549, KT356550, KT356551, KT356552, KT356553, KT356554, KT356555, KT356556, KT356557, KT356558, KT356559, KT356560, KT356561, KT356562, KT356563, KT356564, KT356565, KT356566, KT356567, KT356568, KT356569, KT356570, KT356571, KT356572, KT356573, KT356574, KT356575, KT356576, KT356577, KT356578, KT356579, KT356580, KT356581, KT356582, KT356583, KT356584, KT356585, KT356586 (Table 1).

Discussion Escherichia coli has been associated with various pathological conditions(4); from 1980 to the present have been recognized over 6,600 entries representing 1,152 STEC serotypes(18) some of these are implicated in outbreaks of sporadic diseases in humans and cattle, and their products act as a primary source of pollution(1,3). In Mexico there are few reports of E. coli serotypes, in this study 36 different serotypes were found. Amézquita-López et al(19) identified 19 serotypes from feces of domestic animals in rural farms in Culiacan Sinaloa, Mexico; the main serotype found was O157:H7 with 43.1 % (28/65) from which 57 % (16/28) were from bovines. Other serotypes found were O8:H19, O15:NT, O73:NT, O75:H8, O168:NT, in this study the serotype O157:H7 was also the most frequent (12.31 %) but none of the serotypes reported by Amézquita-López et al(19) were identified. In Argentina and Germany the most important serotype in cattle and its byproducts is O178:H19(4) and represents an emerging serotype frequently isolated in outbreaks of hemorrhagic colitis and hemolytic uremic syndrome in humans(20). Other studies found serotypes O70:H16, O91:H10, O112ac:H2 and O128ac:H26 as a cause of HUS in Germany(6), in this study these same serotypes were found so they are an important finding for public health in Mexico. Fernández et al(21) identified more frequently O113:H21, O130:H11 and O178:H19 serotypes in bovine’s feces in Argentina. In study in Germany identified O113:H21, O22:H8, O174:H21, O178:H19, O113:H4 and O91:H14 serotypes and they mentioned that these are the most common serotypes in cattle in the world(6), in France reported the serotypes O157:H7, O26:H11, O103:H2, O111:H8 and O145:H28 obtained from bovine feces in slaughterhouses(22). The most frequent was O157:H7 with a percentage up to 1.2 %, which is lowest than the percentage obtained in this study (9.23 %), this difference may arise by the particular conditions of the slaughterhouse or the period in which the study was done. In the serogroup O128, although it is most common isolated from sheep, it has been found in human

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patients, healthy cattle and beef; in this study it was found in carcasses and bovineâ&#x20AC;&#x2122;s feces with the presence of Stx2c(18). Today this serogroup is no longer in a specific species. The distribution and frequency of the serotypes and pathotypes may vary considerably from region to region, in that sense serotypes may vary in their pathogenicity or may become emerging serotypes(23).

Shiga toxins are considered the major virulence factors in the development of HUS(2). Friesema et al(13) mentioned that the presence of subtypes stx2 showed a difference at the point of clinical manifestations in humans, in this study, 63.07 % (41/65) of the strains of Escherichia coli amplified to stx2, after analysis of its sequence in BLAST it was confirmed that 71.5 % (33/41) were Stx2; also strains amplified with primers were found, but after the analysis of the sequence performed in BLAST were identified that 19.5 % (8/41) was a hypothetical protein. In a study from China found Stx2b, Stx2c and Stx2e in beef from supermarkets(3). In the O22 serogroup isolated from beef was found stx2b toxin, these results are significant because the same serotype found could have this same variety; however other study mentioned that this variety it is capable of potential cause of HUS in serogroup O26(11). In report from retail raw meats in China identified the toxin Stx2a in O40 serogroup, also they reported serogroup O91 from pork and lamb with presence of Stx2e and Stx2b respectively(3), and in this study the same serogroup may also have those varieties although this isolate was obtained from bovineâ&#x20AC;&#x2122;s carcasses. In a study from 210 patients with HUS of different regions of Germany reported the serotype O91:H2(24) and compare its virulence with O91:H10 as the main cause of HUS. Bai et al(3) believe that having a serotype with Stx2 toxin present in carcasses from cattle for human consumption could be a major risk also identified serogroup O103 without the presence of Stx2 toxin, in this study two isolates of the same serogroup were obtained but only one had the presence of Stx2, this serogroup is of importance since it has been reported to cause HUS(20). Other study found the serogroup O128 with Stx2b(3), in this study in the same serogroup was found the Stx2c. in Germany investigated for the presence of STEC during routine diagnostic work in the Institute for Hygiene and Microbiology and identified non-O157 Stx2c strains and mentioned that is more likely to find HUS in this variant, while the presence of Stx2d can manifest itself in a milder form(25). In Turkey have associated an increased cytotoxicity in developing HUS from O157:H7 strain with the presence of Stx2c(26); this agrees with another study in Switzerland where they associate the presence of stx2a/stx2d/stx2c in patients with diarrhea, and HUS(27), in this study, in the same serotype was identified the toxin stx2c and it could be a risk factor to cause disease.

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From slaughterhouse A were identified 9 serotypes (O37:H10, O40:NM, O65:H16, O70:H16, O112:H2, O157:H7, O172:H45, 0184:H12 and O?:H2) from 10 isolates from which in the 50 % (5/10) the stx2 toxin was identified. In slaughterhouse B from the 10 isolates, were observed 9 serotypes (O18:H7, O112:H2, O120:H10, O128:H26, O185:H7, OR:H2, ORH?, O?:H7 and O?H11) 20 % (2/10) of them tested positive for Stx2. At Slaughterhouse C, from the 45 isolates were found 23 different serotypes; 64.4 % tested positive for Stx2. In this place was found that serotypes got amplified with primers but, after sequencing, only 17.8 % (11/45) was identified to be a hypothetical protein (Table 1).

Foodborne diseases (FD) are produced by the ingestion of food or water, contaminated with biological agents, which affects the health of the consumer, which can be caused by inadequate handling of food at any stage of its production chain. In Mexico gastrointestinal diseases occupy the second place according to the Ministry of Health with 6â&#x20AC;&#x2122;106,572 cases accumulated in 2017, considering that of these 5â&#x20AC;&#x2122;606,759 have not been associated with an etiological agent, in the Central Mexican High Plateau 94,081 cases have been reported in this area, although in Mexico epidemiological studies do not reveal in all cases the foods associated with the FD, nor the microorganisms involved, much less if the cases of HUS are related to Escherichia coli, only the General Directorate of Epidemiology of the Ministry of Health records a significant number of cases each year, with rates that reached between 4,859 cases per 100 thousand inhabitants. Another estimate indicates that, if there are about 5 million annual cases of diarrheal diseases in Mexico, and a conservative adjustment indicates that only 50 % are caused directly by food, with an underreporting of 1 per 100 episodes, the actual number of cases It would be around 250 million events per year, equivalent to 2.5 episodes per person per year. What leads to a significant economic impact, causing a decrease in the productivity of people due to absenteeism or poor performance at work, economic losses to the country due to an increase in the demand for medicines, medical and hospital services, a negative impact on the tourism and in the development of national and international trade. So a study of the Pan American Institute for Food Protection and Zoonoses (INPPAZ), estimated the economic losses by FD in Mexico, in 1.1 trillion dollars only by reduction of productivity.

This work demonstrates the importance of bovine carriers of E. coli with serotypes of importance in public health, that when it is inadequate management in slaughterhouses can be contaminated the carcasses so there must be the need to improve the process to obtain information on meat, therefore a strategy to reduce the risk of infections by serotypes of this language in humans would be to reduce the prevalence in livestock, as well as improve management in slaughterhouses.

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Conclusions and implications It was showed the presence of a variety of STEC serotypes in bovine’s carcasses and feces from municipal slaughterhouses in Mexico. It was also shown the presence of important serotypes for human and animal health that have present the toxin stx2 which may have the potential to cause HUS, so it is considered as an important risk factor that can trigger human diseases. A slaughterhouse deals with the transformation of one or several kinds of beef cattle for human consumption through a series of basic and determinant stages in the quality of the meat and there is a responsible authority that has the legal power to establish and make comply with the requirements on meat hygiene. There are programs on meat hygiene and its main objective is the protection of public health, in addition to the fact that they base their decisions on the scientific evaluation of the possible risks to human health, where they consider all the food dangers identified in research, monitoring and other relevant activities, so that food hygiene is defined as all the conditions and measures necessary to ensure the safety and suitability of food in all steps of the food production chain. The safety of a production is not guaranteed by the bacteriological examination of the finished product, but through a strict compliance of each one of the stages of the process of obtaining the meat.

Literature cited: 1. Ferens WA, Hovde CJ. Escherichia coli O157:H7: Animal reservoir and sources of human infection. Foodborne Pathog Dis 2011;8(4):465−487. 2. Krüger A, Lucchesi PM. Shiga toxins and stx phages: highly diverse entities. Microbiol 2014;61(3):451−462. 3. Bai X, Wang H, Xin Y, Wei R, Tang X, Zhao A, et al. Prevalence and characteristics of Shiga toxin-producing Escherichia coli isolated from retail raw meats in China. Int J Food Microbiol 2015;4(200):31−38. 4. Miko A, Rivas M, Bentancor A, Delannoy S, Fach P, Beutin L. Emerging types of Shiga toxin-producing E. coli (STEC) O178 present in cattle, deer, and humans from Argentina and Germany. Front Cell Infect Microbiol 2014;17(4):1−14. 5. Scallan E, Hoekstra RM, Angulo FJ, Tauxe RV, Widdowson MA, Roy SL, et al. Foodborne illness acquired in the United States---major pathogens. Emerg Infect Dis 2011;17(1):7−15.

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6. Martin A, Beutin L. Characteristics of Shiga toxin-producing Escherichia coli from meat and milk products of different origins and association with food producing animals as main contamination sources. Int J Food Microbiol 2011;146(1):99−104. 7. Centers for Disease Control and Prevention (CDC). 2012. National shiga toxin-producing Escherichia coli (STEC) surveillance overview. Atlanta, Georgia: US Department of Health and Human Services. Available at: https://www.cdc.gov/ncezid/dfwed/pdfs/national-stec-surveillance-overiew-508c.pdf. Accessed Sep 1, 2017. 8. Smith JL, Fratamico PM, Gunther NW. Shiga toxin-producing Escherichia coli. Adv Appl Microbiol 2014;86:145−197. 9. Beutin, L, Martin A. Outbreak of Shiga toxin-producing Escherichia coli (STEC) O104:H4 infection in Germany causes a paradigm shift with regard to human pathogenicity of STEC strains. J Food Prot 2012;75(2):408−418. 10. Soborg B, Lassen SG, Müller L, Jensen T, Ethelberg S, Mølbak K, et al. A verocytotoxinproducing E. coli outbreak with a surprisingly high risk of haemolytic uraemic syndrome, Denmark, September-October 2012. Euro Surveill 2013;18(2):1−3. 11. Bielaszewska M, Mellmann A, Bletz S, Zhang W, Köck R, Kossow A, et al. Enterohemorrhagic Escherichia coli O26:H11/H-: a new virulent clone emerges in Europe. Clin Infect Dis 2013;56(10):1373−1381. 12. Delannoy S, Mariani-Kurkdjian P, Bonacorsi S, Liguori S, Fach P. Characteristics of emerging human-pathogenic Escherichia coli O26:H11 strains isolated in France between 2010 and 2013 and carrying the Stx2d gene only. J Clin Microbiol 2015;53(2):486−492. 13. Friesema I, Van-der-Zwaluw K, Schuurman T, Kooistra-Smid M, Franz E, et al. Emergence of Escherichia coli encoding Shiga toxin 2f in human Shiga toxin-producing E. coli (STEC) infections in the Netherlands, January 2008 to December. European Surveillance 2014;19(17):26−32. 14. Orskov F, Orskov I. Serotyping of Escherichia coli. Methods Microbiol 1984;41:43−112. 15. Reyes-Rodríguez NE, Soriano-Vargas E, Barba-León J, Navarro A, Talavera-Rojas M, Sanso AM, et al. Genetic characterization of Escherichia coli isolated from cattle carcasses and feces in Mexico State. J Food Prot 2015;78(4):796−801.

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16. Blanco M, Padola NL, Krüger A, Sanz ME, Blanco JE, González EA, et al. Virulence genes and intimin types of Shiga-toxin-producing Escherichia coli isolated from cattle and beef products in Argentina. Int Microbiol 2004;7(4):269−276. 17. Paton JC, Paton AW. Pathogenesis and diagnosis of Shiga toxin-producing Escherichia coli infections. Clin Microbiol Rev 1998;11(3):450−479. 18. Bettelheim K, Goldwater P. Serotypes of Non-O157 Shigatoxigenic Escherichia coli (STEC). Adv Microbiol 2014;4(7):377−389. 19. Amézquita-López BA, Quiñones B, Cooley MB, León-Félix L, Castro-del-Campo N, Mandrell RE, et al. Genotypic analyses of shiga toxin-producing Escherichia coli O157 and non-O157 recovered from feces of domestic animals on rural farms in Mexico. PLoS One 2012;7(12): E51565. 20. Mellmann A, Bielaszewska M, Köck R, Friedrich AW, Fruth A, Middendorf B, et al. Analysis of collection of hemolytic uremic syndrome-associated enterohemorrhagic Escherichia coli. Emerg Infect Dis 2008;14(8):1287−1290. 21. Fernández D, Irino K, Sanz ME, Padola NL, Parma AE. Characterization of Shiga toxinproducing Escherichia coli isolated from dairy cows in Argentina. Lett Appl Microbiol 2010;51(4):377−382. 22. Bibbal D, Loukiadis E, Kérourédan M, Ferré F, Dilasser F, Peytavin-de-Garam C, et al. Prevalence of carriage of shiga toxin-producing Escherichia coli serotypes O157:H7, O26:H11, O103:H2, O111:H8, and O145:H28 among slaughtered adult cattle in France. Appl Environ Microbiol 2015;81(4):1397−1405. 23. Vu-Khac H, Holoda E, Pilipcinec E, Blanco M, Blanco JE, Dahbi G, et al. Serotypes, virulence genes, intimin types and PFGE profiles of Escherichia coli isolated from piglets with diarrhea in Slovakia. Vet J 2007;174(1):176−187. 24. Mellmann A, Fruth A, Friedrich AW, Wieler LH, Harmsen D, Werber D, et al. Phylogeny and disease association of Shiga toxin-producing Escherichia coli O91. Emerg Infect Dis 2009;15(9):1474−1477. 25. Friedrich AW, Bielaszewska M, Zhang WL, Pulz M, Kuczius T, Ammon A, et al. Escherichia coli harboring Shiga toxin 2 gene variants: frequency and association with clinical symptoms. J Inf Dis 2001;185(1):74−84.

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26. Ayaz ND, Gencay YE, Erol I. Prevalence and molecular characterization of sorbitol fermenting and non-fermenting Escherichia coli O157:H7 (+)/H7 (-) isolated from cattle at slaughterhouse and slaughterhouse wastewater. Int J Food Microbiol 2014;17(174):31−38. 27. Nüesch-Inderbinen M, Morach M, Cernela N, Althaus D, Jost M, Mäusezahl M, et al. Serotypes and virulence profiles of Shiga toxin-producing Escherichia coli strains isolated during 2017 from human infections in Switzerland. Int J Med Microbiol 2018;08(7):933-939

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https://doi.org/10.22319/rmcp.v11i4.5379 Article

Impact of the inclusion of foreign information on Mexican genetic evaluation of Holstein sires Gustavo Javier MartĂnez MarĂn a Felipe de JesĂşs Ruiz LĂłpez a Carlos Gustavo VĂĄsquez PelĂĄez b Sergio IvĂĄn RomĂĄn Ponce a Adriana GarcĂa Ruiz a*

Instituto Nacional de Investigaciones Forestales, AgrĂcolas y Pecuarias. Centro Nacional de InvestigaciĂłn Disciplinaria en FisiologĂa y Mejoramiento Animal. Km 1 carretera AjuchitlĂĄn-ColĂłn, AjuchitlĂĄn, QuerĂŠtaro, MĂŠxico. b

Universidad Nacional AutĂłnoma de MĂŠxico, Facultad de Medicina Veterinaria y Zootecnia, Ciudad de MĂŠxico, MĂŠxico.

*Corresponding author: garcia.adriana@inifap.gob.mx

Abstract: This study aimed to evaluate the impact of including foreign information of Holstein sires on their genetic evaluation for the following traits: milk, fat, and protein production in kilograms. This was achieved by comparing breeding values (BV) and reliabilities (R), grouping of sires by number of daughters, ordering of sires, and the genetic superiority expected per year (â&#x2C6;&#x2020;đ??źđ?&#x2018; â &#x201E;đ?&#x2018;Śđ?&#x2018; ), with different scenarios determined by the selection intensity (đ?&#x2018;&#x2013;đ?&#x2018;?đ?&#x2018; ) of sires used in the national genetic evaluation (MEX-GE), those incorporated into the international genetic evaluation with daughters in Mexico (I-GE), and the international genetic evaluation with or without daughters in Mexico (MACE-GE). In total, was analyzed the information of 5,825 sires for MP and 3,914 for FP and PP. The foreign information has a positive impact in the MEX-GE as it improves R and the BV of the sires used in Mexico.

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It also allowed to observe important differences in the BV and R between evaluations, generating an opportunity to improve the Mexican Holstein population. Therefore, it is recommended continuing participating in the Interbull program and consider using the internationally validated information in the selection process of dairy cattle and their components. These actions can significantly contribute to the increase of the productive genetic progress in Mexico. Key words: Genetic value, Reliability, Genetic evaluation, Genetic correlation.

Received: 14/05/2019 Accepted: 22/11/2019

Introduction Genetic evaluation predicts the genetic merit or breeding value (BV) of an animal considering its genealogical and phenotypic information (milk production and its components, somatic cell count, conformation, among others)(1). The first genetic evaluation of Holstein cattle in Mexico was performed in 1974 by the University of Guelph (Ontario, Canada) at the request of the Asociación Holstein de México (AHM), including only conformation traits. During the following decades, other traits were included, such as milk (MP), fat (FP), and protein production (PP) in kilograms, and the scoring system for identifying highly productive and functional animals in Mexico was modified(1,2,3). Nowadays, the AHM and the Centro Nacional de Investigación Disciplinaria en Fisiología y Mejoramiento Animal (CENIDFyMA), belonging to the Instituto Nacional de Investigaciones Forestales, Agrícolas y Pecuarias (INIFAP), are in charge of performing genetic evaluations every four months for milk production and quality, 27 conformation traits, longevity, somatic cells, and selection indices of the Holstein cattle registered before the AHM. The methodology used to predict these BV was the Best Linear Unbiased Prediction (BLUP), which consists of a mixed model that includes simultaneous equations of fixed and random effects to estimate the variance components based on individual performance and representation of all the genetic relationships between individuals(4). The results obtained from these evaluations identify the best specimens, providing various options to the farmers for the selection process. As the exchange of genetic material around the world increases, there is a possibility that the same animal has offspring in different countries, and thus, have more than one genetic evaluation. In Mexico, a high percentage of genetic material is imported as frozen semen;

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thus, when the imported sires have information in Mexico to predict their BV, they already have a genetic evaluation in their country of origin with more information than in Mexico. To incorporate the information generated globally, Schaeffer(5) proposed a genetic evaluation methodology that integrates the information from Multiple Across Country Evaluation (MACE). To achieve this, regression equations are constructed to predict the BV of the imported sires based on the importing country, adjusting according to the origin of the information(6,7). There are numerous advantages of using the MACE system, among which are: 1) the performance of sires adjusted by the production information of their daughters from various evaluations of the participating countries and 2) the identification of existing specimens in other countries that may be good breeders under the conditions of the importing country(5,7). This study aimed to evaluate the impact of Holstein sires foreign information on the genetic evaluation of their MP, FP, and PP traits and to predict the genetic improvement rates expected from these evaluations. By incorporating this information, it is expected to improve the precision of the BV, and therefore, the genetic improvement rates of the evaluated traits.

Material and methods Database

The records for milk production and its components were compiled by the official production control system of the AHM and processed by the CENID-FyMA-INIFAP. To calculate the BV of the Mexican genetic evaluation (MEX-GE), was used the information of 623,207 records of MP corresponding to 355,786 animals from 527 herds and 193,236 records of FP and PP from 103,829 animals from 167 herds. The stables from which the information was obtained are distributed in 17 different Mexican states. The genealogical information corresponded to 403,817 animals, including records from up to five generations for the animals with available production. The records with productive information outside of the biological parameters observed in the production system and breed (MP adjusted to 305 days <3,500 kg or >22,000 kg per lactation, FP and PP <1.5% or >8.0%)(1) were excluded from the study.

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Estimation of genetic breeding values and reliabilities

The BV of the MEX-GE was estimated using a repeatability animal model(1,8) and the BLUPF90 software with the SS-BLUPF90 package(9,10). The model used for each of the three production traits(11) was the following: đ?&#x2018;Ś = đ?&#x2018;&#x2039;đ?&#x203A;˝ + đ?&#x2018;?1 đ?&#x2018;˘ + đ?&#x2018;?2 đ?&#x2018;? + đ?&#x2018;&#x2019; where: đ?&#x2019;&#x161; = vector of the productive records adjusted to 305 days and maturity equivalent (milk, fat, or protein, respectively); đ?&#x153;ˇ = fixed effects vector (herd-year-season, sire-herd interaction, and age at calving); đ?&#x2018;ż = incidence matrix associated with fixed effects; đ?&#x2019; đ?&#x;? = incidence matrix associated with the random effects of đ?&#x2018;˘; đ?&#x2019;&#x2013; = random vector of additive genetic effects; đ?&#x2019; đ?&#x;? = incidence matrix associated with random effects of permanent environment; đ?&#x2019;&#x2018; = vector of permanent environment effects; đ?&#x2019;&#x2020; = vector of residual effects. In addition to estimating the BV of animals, its reliability(8,12) was calculated as: đ?&#x2018;&#x192;đ??¸đ?&#x2018;&#x2030; [1 â&#x2C6;&#x2019; ( 2 ) (đ?&#x153;&#x2020;)] đ?&#x153;&#x17D;đ??¸ 2 where: đ?&#x153;&#x17D;đ??¸ = Environmental variance; đ?&#x153;&#x2020; = proportion of the environmental variance (đ?&#x153;&#x17D;đ??¸2 ) between the additive genetic variance (đ?&#x153;&#x17D;đ??´2 ), which is equal to [(1 â&#x2C6;&#x2019; â&#x201E;&#x17D;2 )â &#x201E;â&#x201E;&#x17D;2 ]; đ?&#x2018;&#x192;đ??¸đ?&#x2018;&#x2030; = Prediction error variance, estimated as: 2 2 đ?&#x2018;&#x192;đ??¸đ?&#x2018;&#x2030; = (1 â&#x2C6;&#x2019; đ?&#x2018;&#x;đ??´đ??´ Ě&#x201A; )đ?&#x153;&#x17D;đ??´ 2 2 where: đ?&#x2018;&#x;đ??´đ??´ Ě&#x201A; = Correlation coefficient between the predicted and true values of the BV; đ?&#x153;&#x17D;đ??´ = additive variance.

During this process, was estimated the BV and R of 7,489 sires for MP and 5,148 for FP and PP. This information was provided to the International Bull Evaluation Service (Interbull) for its integration into the I-GE system.

International genetic evaluation

The BV and R information obtained from the MEX-GE (7,489 sires for MP and 5,148 for FP and PP) was processed following the guidelines established by the Interbull Data Exchange Area (IDEA) for its validation and incorporation into the international genetic evaluation (IGE)(7). The information from each participating country is compiled and adjusted by the 1048

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Interbull with the MACE procedure based on the number, structure of contemporary groups, and international correlations(13,14). Of the total information provided by Mexico, 5,825 sires for MP and 3,914 for FP and PP had productive information on at least one daughter in more than one country; thus, they could be incorporated into the international production and pedigree base. Along the GE-I process, was performed the international genetic correlation (IGC) test, in which was only included, by participating country, the sires that met the minimum test requirements specified by Interbull(7,15). For this test, Mexico provided information from 553 sires for MP, 183 for FP, and 181 for PP.

Comparison of the breeding values and their reliabilities of the national and international genetic evaluations

Genetic trends were analyzed from the average BV obtained in the MEX-GE and the I-GE per sires birth year. To evaluate if the impact of foreign information on the MEX-GE is related to the number of daughters in Mexico, it was assigned each sire to a group (more than 50, between 10 and 50, and less than 10 daughters) and calculated the mean and standard deviation of the BV and R for each group and genetic evaluation (MEX-GE and I-GE). Additionally, to evaluate the similarity between evaluations, was calculated the Pearson correlation coefficient (ď ˛P) for the BV and R of the three traits (MP, FP, and PP). It was also calculated the expected genetic improvement rate (Î˛) of the use of MEX-GE and I-GE in the selection of sires through linear regression analysis. Subsequently, sires were ordered in descending order of their BV for MEX-GE and I-GE and classified into different selection scenarios, considering the percentiles (đ?&#x2018;&#x2013;đ?&#x2018;?đ?&#x2018; = the best 10%, 20%, 30%, 40%, and 50%), to compare the means of the BV and R of the three traits (MP, FP, and PP) per group, the common number of sires in both evaluations during the ordering, their monotonous association using the Spearman correlation (ď ˛S), and the calculation of the expected genetic superiority of sires per year (â&#x2C6;&#x2020;đ??źđ?&#x2018; â &#x201E;đ?&#x2018;Śđ?&#x2018; ) using the method described by Bourdon(16) as: â&#x2C6;&#x2020;đ??źđ?&#x2018; â &#x201E;đ?&#x2018;Śđ?&#x2018; = đ?&#x2018;&#x2013;đ?&#x2018;?đ?&#x2018; đ?&#x2018;&#x;đ?&#x2018; đ?&#x153;&#x17D;đ??´ /đ??żđ?&#x2018; where, đ?&#x2018;&#x2013;đ?&#x2018;?đ?&#x2018; = selection intensity defined by the percentage of genotypically superior sires (10%, 20%, 30%, 40%, and 50%), đ?&#x2018;&#x;đ?&#x2018; = precision of the BV of sires, đ?&#x153;&#x17D;đ??´ =additive genetic deviation of sires, and đ??żđ?&#x2018; =generation interval of six years for sires parents of dams(17). For each of the selection scenarios by percentiles, was calculated the â&#x2C6;&#x2020;đ??źđ?&#x2018; /đ?&#x2018;Śđ?&#x2018; by considering the different đ?&#x2018;&#x2013;đ?&#x2018;?đ?&#x2018; , but with the results for the MEX-GE, I-GE, and complete international genetic evaluation (MACE-GE); the latter refers to all the participating sires in the

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international test (150,300 sires) ordered in descending order, that may or may not have daughters in Mexico.

Results and discussion The BV obtained in the test differ on average 120 kg for MP (from -200 ± 315 kg in MEXGE to -80.1 ± 411.5 kg for I-GE), 4.6 for FP (from -2.4 ± 8.2 kg in MEX-GE to 2.2 ± 12.5 for I-GE), and 5.8 for PP (-3.6 ± 7.9 kg in MEX-GE to 2.2 ± 12.3 kg for I-GE). On average, the R are higher in the I-GE than in the MEX-GE; they are even two times higher (from 73 to 30 for MP and from 74 to 29 for FP and PP) when considering the information from productive daughters in several of the participating countries. In both evaluations (MEX-GE and I-GE), was observed a positive trend in the average genetic improvement per birth year; the BV of the I-GE were higher than those of the MEXGE throughout the study period (Figure 1). It was also observed a decrease in the average R in the last years of study, which coincides with the low number of born sires in recent years (Figure 2). This decrease in R could be explained by the lower number of daughters of young sires evaluated(18,19). A previous study suggests that the decrease in the average of BV could be because initially, the evaluation only includes few offspring; once the best specimens are selected, these are frequently used to increase the average of the genetic trends within the population and the number of daughters per sire(20), which can increase the similarities of the calculated BV between the two evaluations. This is corroborated by observing the differences between the groups created based on the number of daughters they provide to the study (Table 1), where, by increasing this number, the average of BV an R in the MEX-GE and the I-GE approximate to each other, and thus, the P between both evaluations gradually increase. The β and their standard errors, calculated by regression analysis, show a decrease of approximately half for MP and two-thirds for FP and PP of the MEX-GE with the I-GE (from 23 ± 0.6 kg to 42 ± 0.7 kg for MP, from 0.3 ± 0.02 kg to 0.92 ± 0.03 kg for FP, and from 0.48 ± 0.02 kg to 1.1 ± 0.02 kg for PP), but the behavior of both evaluations is similar throughout the years, this may be due to the adjustment in the genetic bases. Another study concludes that the information generated in the international evaluation is a more precise indicator than the results obtained in the daughters evaluated in a single country(21), which was confirmed in this study, where was observed increased R for the I-GE in the three traits.

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25 20 15 10 5 0 -5 -10 -15

1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012

BV of PP

BV of FP

BV of MP

Figure 1: Trend of the breeding values (BV) per birth year of sires included in the Mexican (MEX) and international (MACE) evaluations for milk (MP), fat (FP), and protein (PP) production in kilograms

Birth year MACE

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MEX

Rev Mex Cienc Pecu 2020;11(4): 1045-1058

R for MP

Figure 2: Trend of reliabilities (R) per birth year of sires included in the Mexican (MEX) and international (MACE) evaluations for milk (MP), fat (FP), and protein (PP) production in kilograms 90 80 70 60 50 40 30 20 10 0

90 80 70

50 40 30 20 10 0

90 80

70 60 50 40 30 20 10

Birth year Confiabilidad MACE

1052

Confiabilidad MEX

2012

2011

2010

2009

2008

2007

2006

2005

2004

2003

2002

2001

2000

1999

1998

1997

1996

1995

1994

1993

1992

1991

1990

R for PP

R for FP

Rev Mex Cienc Pecu 2020;11(4): 1045-1058

Table 1: Genetic Breeding Values (BV), reliabilities (R), and Pearson correlations (ď ˛P) for sires classified by the number of daughters in Mexico and participating in the Mexican (MEX-GE) and international (I-GE) genetic evaluations for milk (MP), fat (FP), and protein (PP) production in kilograms (P<0.05)

-2Âą325

BV I-GE 88Âą333

R MEX-GE 45Âą18

R I-GE 86Âą11

ď ˛P BV 0.73

ď ˛P R 0.48

-137Âą302

44Âą364

31Âą9

74Âą6

0.52

0.28

-261Âą295 0.7Âą11.2

-151Âą420 4.7Âą12.5

26Âą7 56Âą18

70Âą5 84Âą9

0.61 0.66

0.19 0.62

-1.1Âą9.4

4.1Âą12.6

35Âą11

76Âą5

0.46

0.24

-3.1Âą7.2 1.5Âą9.1

1.4Âą12.4 5.9Âą11.2

24Âą8 56Âą18

71Âą5 85Âą8

0.45 0.64

0.19 0.62

-1.3Âą8.2

5.8Âą11.1

35Âą11

77Âą5

0.42

0.22

-4.7Âą7.4

0.9Âą12.4

25Âą8

71Âą6

0.53

0.17

BV MEX-GE >50 >10 and â&#x2030;¤50 <10 >50 >10 and â&#x2030;¤50 <10 >50 >10 and â&#x2030;¤50 <10

During the first reordering analysis of sires, based on their BV (MEX-GE and I-GE), it was observed a reordering in sires, confirmed by the low calculated ď ˛S (0.63 for MP, 0.48 for FP, and 0.55 for PP). This correlation decreases its value by increasing the đ?&#x2018;&#x2013;đ?&#x2018;?đ?&#x2018; , being the 10% group superior for the three traits than the other groups, which results in only 35% of sires in common. Table two shows the results of the other groups. Table 2. Genetic Breeding values (BV), reliabilities (R), and Spearman correlations (ď ˛S) for sires grouped by different percentiles (đ?&#x2018;&#x2013;đ?&#x2018;?đ?&#x2018; ) and arranged in descending order by the Mexican (MEX-GE) and international (I-GE) genetic evaluations, with the percentage of sires in common in both arrangements (%Sc) for milk (MP), fat (FP), and protein (PP) production in kilograms (P<0.05)

đ?&#x2019;&#x160;đ?&#x2019;&#x2018;đ?&#x2019;&#x201D;

% Sc

BV MEX-GE

Ordered by the MEX-GE BV R R I-GE MEX-GE I-GE

ď ˛S BV

BV MEX-GE

356Âą148

364Âą278

37Âą16

79Âą11

0.21

149Âą264

654Âą177

28Âą14

73Âą9

249Âą151

312Âą309

34Âą15

77Âą10

0.23

96Âą271

504Âą199

29Âą13

73Âą9

0.17

176Âą162

252Âą330

32Âą14

76Âą10

0.33

52Âą276

402Âą218

29Âą13

74Âą9

0.26

114Âą177

194Âą346

32Âą14

75Âą10

0.39

13Âą279

319Âą238

30Âą13

74Âą9

0.33

58Âą195

137Âą359

31Âą13

74Âą9

0.48

-30Âą285

248Âą256

30Âą13

74Âą9

0.43

11.4Âą5.2

12.6Âą11.1

35Âą17

75Âą8

0.18

4.1Âą8.4

25.5Âą5.7

25Âą14

73Âą7

0.14

8.4Âą5

10.9Âą11.5

32Âą16

74Âą8

0.19

3.3Âą8.1

20.4Âą6.6

26Âą14

74Âą7

0.14

6.8Âą5.1

9.8Âą11.7

31Âą15

73Âą7

0.21

2.3Âą7.9

27.1Âą13.9

17Âą7

74Âą7

0.21

4.7Âą5.3

7.7Âą11.8

30Âą14

73Âą7

0.32

1.4Âą7.9

27.8Âą14.1

14Âą7

74Âą7

0.26

3.4Âą5.5

6.7Âą11.8

30Âą14

73Âą7

0.33

0.8Âą7.8

28.4Âą14.1

12Âą8

74Âą7

0.27

10.1Âą3.5

12.6Âą11.1

34Âą17

76Âą8

3.3Âą6.9

24.6Âą5.4

24Âą13

74Âą7

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Ordered by the I-GE BV R R I-GE MEX-GE I-GE

ď ˛S BV

Rev Mex Cienc Pecu 2020;11(4): 1045-1058

7.2Âą3.8

10.9Âą11.5

32Âą16

75Âą8

0.16

2.4Âą7.3

19.9Âą6.1

26Âą14

74Âą7

0.13

5.8Âą4.1

9.8Âą11.7

31Âą16

75Âą8

0.26

1.9Âą7.4

16.8Âą6.7

27Âą14

74Âą7

0.16

3.7Âą4.7

7.7Âą11.8

31Âą15

74Âą7

0.37

0.8Âą7.6

14.3Âą7.3

28Âą14

74Âą7

0.28

2.5Âą5.1

6.7Âą11.8

30Âą15

74Âą7 0.44 *=(P>0.05).

0.2Âą7.6

12.1Âą7.9

28Âą14

75Âą7

0.32

In the analysis of â&#x2C6;&#x2020;đ??źđ?&#x2018; , when using the highest đ?&#x2018;&#x2013;đ?&#x2018;?đ?&#x2018; (which includes the highest percentile), was observed a more significant increase for the three evaluations (MEX-GE, I-GE, and MACEGE) in the three traits, which suggests the use of these sires for the following generations (Table 3). However, it would be necessary to consider various factors within the population (rates of consanguinity, preferential treatment, adaptation to production systems, etc.) to optimize the use of these specimens. Additionally, was observed a higher â&#x2C6;&#x2020;đ??źđ?&#x2018; when using the MACE-GE results due to the greater availability of animals since it represents an opportunity to increase the â&#x2C6;&#x2020;đ??źđ?&#x2018; , increase the genetic variability, and provide a higher amount of selection material to the producers. However, to recommend its use, it is necessary to consider factors such as environment genotype, germplasm availability, and selection for other population interest traits. Table 3: Expected genetic superiority per year (â&#x2C6;&#x2020;đ??źđ?&#x2018; ) of the sires grouped by different selection intensities (đ?&#x2018;&#x2013;đ?&#x2018;?đ?&#x2018; ) and arranged in descending order for the Mexican genetic evaluation. Under this arrangement, was considered the estimated breeding value in Mexico (MEX), the international with daughters in Mexico (I), and all the sires that participated in the complete international test and that may or may not have daughters in Mexico (MACE)

đ?&#x2019;&#x160;đ?&#x2019;&#x2018;đ?&#x2019;&#x201D; 10 20 30 40 50 10 20 30 40 50 10 20 30 40 50

â&#x2C6;&#x2020;đ?&#x2018;°đ?&#x2019;&#x201D; -MEX 18.5 14.7 11.9 10.2 8.4 0.55 0.44 0.36 0.30 0.25 0.54 0.43 0.35 0.30 0.25

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â&#x2C6;&#x2020;đ?&#x2018;°đ?&#x2019;&#x201D; -I 24.1 19.2 15.6 13.3 10.9 0.84 0.67 0.55 0.46 0.38 0.83 0.66 0.54 0.46 0.38

â&#x2C6;&#x2020;đ?&#x2018;°đ?&#x2019;&#x201D; - MACE 28.5 22.7 18.5 15.7 12.9 0.90 0.72 0.58 0.50 0.41 1.83 1.46 1.19 1.01 0.83

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The IGC provides a different way to evaluate the similarity between the MEX-GE and the IGE countries. The average between the MEX-GE and the 29 participating countries was 0.79 ď&#x201A;ą 0.04 for MP, 0.80 ď&#x201A;ą 0.04 for FP, and 0.81 ď&#x201A;ą 0.04 for PP. The IGC with the countries with which Mexico has a more significant germplasm exchange and that were calculated based on more than 200 sires, such as the United States of America, Canada, the Netherlands, Germany-Austria, Great Britain, Italy, and France, reached medium levels (0.80 approximately). While the highest correlations were observed with Israel, Slovenia, Estonia, Latvia, and Lithuania (reaching IGC of 0.85, 0.86, and 0.87 for MP, FP, and PP, respectively), which could be explained by the low number of sires in common (<50 sires) and their high preference, confirmed by the significant number of daughters (>20 daughters). The lowest IGC were observed in countries with production systems different to those in Mexico, such as: New Zealand, Australia, and Ireland with values of 0.69 for MP and 0.7 for FP and PP). The observed differences between the IGC in the different countries could be because of the sire adaptation to a specific country and not to the differences in the models or preferential treatment of sires(21). Therefore, if this statement is correct, the estimation of the BV of the sires used at a national level will be closer to those obtained in countries with similar production conditions than those in Mexico. The latter could explain the results obtained with countries that contribute significantly to the number of daughters in Mexico, such as the United States of America, Canada, and the Netherlands, but that do not have the highest IGC with Mexico. These countries differ from Mexico in their production conditions, the definition of genetic bases, estimated heritability indices (h2) for each country, and the complexity of the models used to estimate the BV and the effects applied in each model. Therefore, it is advisable to analyze the groups integrated by countries with similar evaluation conditions, thus expanding the expectations of the international market and exchanging information efficiently. An area of opportunity for the countries that are members of Interbull is the use and exchange of genomic information, which can bring important benefits to the systems of genetic evaluation; for example, perform a more effective and faster prediction of genetic interactions(22), reduced commercialization time of the genetic material, referring to characteristics of reproductive and consanguinity importance(23), which reflects in the increased precision of the BV and their R(24).

Conclusions and implications Foreign information has a positive effect on the MEX-GE, as it improves the R of the BV of the sires used in Mexico, mainly those with a limited number of daughters in Mexico; therefore, it is recommended to continue participating in the Interbull program. In this study,

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during the arrangement of sires and the verification of the genetic superiority per year, it was observed significant differences in the BV between the evaluations, generating an opportunity to improve the Mexican Holstein population. The sires not used in Mexico and with a high genetic potential for the evaluated traits represent an opportunity to improve the population. Therefore, it is important to identify, evaluate, and incorporate these sires in the Mexican Holstein population. The results obtained after incorporating the MEX-GE to the I-GE show the differences between the participating countries (explained by their diverse production systems, environmental factors, etc.). Therefore, it is essential to consider the use of the international validated information in the selection process of Mexican dairy Holstein cattle and their components, as it will contribute to the increase of the genetic, productive, and reproductive progress of the population.

Acknowledgments To the INIFAP-CENIDFyMA for the support and financing throughout the project “Estudio de la consanguinidad y su efecto sobre características productivas y reproductivas en ganado Holstein” SIGI: 11513634465 and the School of Natural Sciences of the Universidad Autónoma de Querétaro for the support and partial financing throughout the project “Desarrollo de evaluaciones genéticas internacionales de ganado Holstein en México”.

Conflicts of interest Authors declare no conflicts of interest.

Literature cited: 1. Ruiz-López FJ, García-Ruiz A, Martínez-Marín GJ. ¿Qué Toro? Evaluación genética de toros y vacas Holstein para producción de leche, conformación y longevidad. Centro Nacional de Investigación Disciplinaria en Fisiología y Mejoramiento Animal, INIFAPSAGARPA y Asociación Holstein de México. Libro Técnico No. 58, Colón, Querétaro. 2018. 2. Moro-Méndez J, Ruiz-López FJ. Mejoramiento genético de características de conformación en ganado Holstein. Vet Méx 1998;9(4):385-398.

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3. REDGATRO. Red de Investigación e Innovación Tecnológica para la Ganadería Bovina Tropical. Estado del arte sobre investigación e innovación tecnológica en ganadería bovina tropical. Consejo Nacional de Ciencia y Tecnología. Libro Técnico No. 4, Ciudad de México. 2015. 4. VanRaden PM, Wiggans GR. Derivation, calculation, and use of national animal model information. J Dairy Sci 1991;74(8):2737-2746. 5. Schaeffer LR, Zhang W, Robinson A, Chesnais J, Wilmink H, Wiggans G, et al. Multiple trait across country evaluation of dairy sires. Interbull Bulletin 1993:1-21. 6. Mäntysaari EA, Liu Z, VanRaden PM. Interbull validation test for genomic evaluations. Interbull Bulletin 2010; 41:17-21. 7. INTERBULL. International Bull Evaluation Service. Code of practice for the international genetic/genomic evaluation of dairy bulls at the Interbull Centre. DABG-ICAR. 2017. 8. Cameron ND. Selection indices and prediction of genetic merit in animal breeding. 1st ed. UK: CAB International; 1997. 9. Misztal I, Tsuruta S, Strabel T, Auvray B, Druet T, Lee DH. BLUPF90 and related programs (BGF90). In: CD-ROM communication, Proceedings of the 7th World congress on genetics applied to livestock production. Montpellier, France. 2002:7-28. 10. Misztal I. Computational techniques in animal breeding. 1st ed. USA: University of Georgia; 2000. 11. Lynch M, Walsh B. Genetics and analysis of quantitative traits. 1st ed. USA: Sinauer Associates; 1998. 12. Gilmour AR, Gogel BJ, Cullis BR, Thompson R. ASReml User Guide. Relase 3.0. UK: VSN International Ltd; 2009. 13. Fikse WF, Banos G. Weighting factors in international genetic evaluations: effects on international breeding values and reliability estimates. Interbull Bulletin 1999;(22):1-6. 14. Schaeffer LR. Multiple-country comparison of dairy sires. J Dairy Sci 1994;77(9):26712678. 15. Schaeffer LR. Multiple trait animal models. Technical notes, Univ. Guelph 1999; (15) 113. http://www.aps.ouguelph.ca/~1rs/Animalz/lesson15. Accessed 6 Apr, 2019. 16. Bourdon RM. Understanding animal breeding. 2nd ed. USA: Prentice Hall; 2000. 17. Schaeffer LR. Strategy for applying genome-wide selection in dairy cattle. J Anim Breed Genet 2006;123(4):218-223. 1057

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18. Powell RL, Wiggans GR, VanRaden PM. Factors affecting calculation and use of conversion equations for genetic merit of dairy bulls. J Dairy Sci 1994;77(9):2679-2686. 19. Miglior F, Muir BL, Van Doormaal BJ. Selection indices in Holstein cattle of various countries. J Dairy Sci 2005;88(3):1255-1263. 20. Benhajali H, Jakobsen J, Mattalia S, Ducrocq V. Illustration of an international genetic evaluation robust to inconsistencies of genetic trends in national evaluation. Interbull Bulletin 2013;(47):82-89. 21. Jones LP. A simple approximation to the reliability of Interbull proofs for foreign bulls. Interbull Bulletin 1997;(16):13-15. 22. VanRaden PM, Sullivan PG. International genomic evaluation methods for dairy cattle. Genetics Selection Evolution 2010;42(1):7-15. 23. Hayes BJ, Daetwler HD, Bowman P, Moser G, Tier B, Crump R, et al. Accuracy of genomic selection: Comparing theory and results. Association for the Advancement of Animal Breeding and 30th Anniversary Conference. Proc Assoc Advmt Animal Breed 2009;(17):352-355. 24. Lund MS, de Roos AP, de Vries AG, Druet T, Ducrocq V, Fritz S, et al. A common reference population from four European Holstein populations increases reliability of genomic predictions. Genet Sel Evol 2011;43(1):43-50.

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https://doi.org/10.22319/rmcp.v11i4.5302 Article

Genomic diversity and structure of Lidia breed cattle in Mexico

Paulina G. Eusebi a* Oscar Cortés a Susana Dunner a Javier Cañón a

Universidad Complutense de Madrid. Facultad de Veterinaria, Departamento de Producción Animal. Avenida Puerta de Hierro, s/n, 28040, Madrid, España.

* Corresponding author: paulig01@ucm.es

Abstract: First documented in the 13th Century on the Iberian Peninsula, the Lidia cattle breed has since been the preferred breed for producing bulls for social celebrations known as “bullfighting”, an expression of regional cultural identity in several countries. Specialization of the breed in Mexico began in the late 19th Century when four Mexican families imported a small number of Lidia animals from Spain. Of these original imports, only the lines derived from the Llaguno and González families remain. Different breeding strategies were implemented in the Llaguno family. Antonio Llaguno crossed the recently imported Spanish animals among each other, resulting in what is currently recognized in Mexico as the “Pure” line. Julián Llaguno crossed Creole dams with Spanish sires, creating the line known as “Impure”. In addition, Lidia breed lines such as Domecq, Murube and Santa Coloma were brought to Mexico between 1996 and 1997. The present study objective was to use SNP molecular markers to analyze genomic diversity, population structure, endogamy levels and genetic relationships between Lidia lines in Mexico. Five lines within the Mexican population were studied: Antonio Llaguno, Julián Llaguno, González, Domecq and Santa Coloma. All five lines were found to be genetically distinct, although the Antonio and Julián Llaguno lines are more similar than the others. Genetic isolation between the different lines of the Lidia breed in Mexico has resulted in their being unique. Key words: Lidia breed, population genetics, genetic diversity, genetic structure.

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Received: 23/02/2019 Accepted: 07/10/2019

Introduction First documented in the 13th Century on the Iberian Peninsula, the Lidia cattle breed is distinguished by selection for behavioral characteristics that enhance aggressiveness and for its use in civil and religious events(1). Various social and cultural phenomena involving bulls are currently included in what is known as “bullfighting”(2,3). Several countries consider different bullfighting traditions as practices that reinforce regional cultural identity(2,3); indeed, in Spain and Peru it has been designated an intangible cultural heritage(4). The Lidia breed is characterized for having low genetic and ecological interchangeability(5,6). The first documented bullfighting celebration in Mexico was held in 1523 using aggressive cattle brought mainly from the Navarra region in Spain, which is where the Casta Navarra breed originates(7). It was not until the turn of the 20th Century, however, that specialized breeding of Lidia began in Mexico with importation of a small number of animals from Spain by four breeding families: Llaguno, González, Barbabosa and Madrazo(8,9). Only genetic lines originating with the Llaguno and González families are still extant today(8,10).

The Llaguno family has been located largely in north-central Mexico. Under Antonio Llaguno the reproduction system was closed involving crosses only between Lidia animals directly linked to the original imported animals; in Mexican livestock terminology these are known as “Pure” animals. Julián Llaguno, brother of Antonio, followed a different breeding strategy, crossing Creole dams with Lidia sires of known Spanish origin; these are termed “Impure”(8,9). The González family, located in southcentral Mexico, crosses imported Lidia breed animals with local cattle selected for aggressiveness(8). With the purpose of breeding bulls for bullfights, in 1996 and 1997 a group of Mexican breeders imported animals from Spanish lines such as Domecq, Murube, Santa Coloma and Saltillo, among others; this strategy ended when livestock imports were prohibited for animal health reasons(10).

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The current Lidia breed population in Mexico is approximately 110,000 animals raised on a total of around 135,000 ha(10). This breed is raised under extensive conditions, which favors conservation of endemic flora and fauna. Its central role in many local social traditions supports Mexico’s livestock economy while reinforcing regional cultural identity(7,10,11).

The genetic variability of the Lidia breed population in Mexico versus the original Spanish population has been analyzed using autosomal microsatellite markers, with differentiation between Spanish Lidia lines and the Llaguno and González family lines(12). These results were confirmed using molecular data produced with DNA chips for biallelic single nucleotide polymorphism (SNP) molecular markers. Clear genetic differentiation has also been reported between the Antonio Llaguno and González family lines(13,14), although these analyses did not include samples from the Julián Llaguno line or the lines imported in the late 20th Century (Domecq, Santa Coloma, etc.).

The present study objective was to use SNP molecular markers to analyze the genomic diversity, population structure, endogamy levels and genetic relationships in representative populations of the Lidia breed in Mexico.

Material and methods A total of 306 blood samples were randomly collected from animals belonging to 32 ranches in Mexico affiliated with the Union of Lidia Bull Breeders (Unión de Criadores de Toros de Lidia). The samples were classified into five lines based on the historical origins of each: Antonio Llaguno, Julián Llaguno, González, Domecq and Santa Coloma (Table 1). The samples were collected in tubes containing Magic Buffer® preservative (Biogen Diagnostica, Spain) and kept at 15 °C until DNA extraction. Genomic DNA was extracted using a standard phenol/chloroform protocol(15), and the samples were later genotyped with the 50K medium density SNP bovine chip (http://www.illumina.com).

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Table 1: Number of analyzed animals (N), genetic distance by ranch and averaged by line (FST), endogamy coefficient (FIS), observed heterozy gosity (Ho) and genetic diversity (He) Line

Ranch

FST

FIS

Julian Llaguno

Pozo Hondo Valparaiso El Sauz Caparica Total

21 15 8 11 55

0.05 0.06 0.07 0.05 Avg. = 0.06

0.13 0.19 0.21 0.17

0.27 0.25 0.24 0.26

0.73 0.75 0.76 0.74

Antonio Llaguno

San Mateo Reyes Huerta Fernando de la Mora Los Cues Garfias Antigua Xajay Teófilo Gómez Celia Barbabosa Boquilla del Cármen Fermín Rivera Corlomé Arroyo Zarco Marrón La Punta Total

6 39 6 7 6 6 6 6 6 6 6 6 6 6 19 137

0.09 0.06 0.10 0.07 0.08 0.09 0.05 0.07 0.06 0.06 0.07 0.13 0.05 0.05 0.10 Avg. = 0.07

0.21 0.21 0.04 0.29 0.26 0.27 0.15 0.19 0.15 0.29 0.18 0.00 0.18 0.11 0.11

0.24 0.24 0.30 0.22 0.23 0.23 0.26 0.25 0.26 0.22 0.25 0.31 0.26 0.28 0.27

0.76 0.76 0.70 0.78 0.77 0.77 0.74 0.75 0.74 0.78 0.75 0.69 0.74 0.72 0.73

González

Tenexac Yturbe De Haro Castañeda Zacatepec Rancho Seco Total

8 5 6 6 12 6 43

0.13 0.11 0.08 0.11 0.12 0.14 Avg. = 0.11

0.29 0.14 0.12 0.25 0.03 0.05

0.22 0.27 0.27 0.23 0.30 0.29

0.78 0.73 0.73 0.77 0.70 0.71

Domecq

La Joya Santa Maria de Xalpa Jaral de Peñas Torreon de Cañas Jose Julian Llaguno Total

17 17 17 6 10 106

0.10 0.08 0.06 0.09 0.07 Avg. = 0.08

0.15 0.06 -0.03 -0.02 0.00

0.26 0.29 0.32 0.32 0.31

0.74 0.71 0.68 0.68 0.69

Santa Coloma

Los Encinos San José Total

5 6 11

0.02 0.02 Avg. = 0.02

0.17 0.10

0.26 0.28

0.74 0.72

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Using the PLINK ver. 1.07 software(16), the information was refined by excluding SNPs located on sex chromosomes, those exhibiting a minor (<0.01) allele frequency (MAF), those with <20% missing genotypes and those diverging from Hardy-Weinberg equilibrium (P<0.001). A total of 41,455 SNPs remained for analysis.

Again using PLINK(16), analyses were done of three genetic diversity parameters: observed heterozygosity (Ho), expected heterozygosity (He) and the endogamy coefficient (FIS), estimated as 1-Ho/He. The FST coefficients were calculated using the ARLEQUIN ver. 3.0 software(17). For each individual, the proportion of genetic origins identifiable using the Bayesian grouping algorithm was calculated with the ADMIXTURE software(18,19). Graphs were generated with the POPHELPER ver. 1.0.10 software(20).

A molecular analysis of variance (AMOVA) was run using a linear model to evaluate genetic variation between and within lines(17). The analysis was done in hierarchical mode with three levels (between lines, between ranches in the same line and within ranches). The same software was used to calculate mean distance of the lines in terms of FST.

Individual runs of homozygosity (ROHs) were identified per individual(21). This was done using PLINK with 30 SNP windows, allowing for <100 kb between two consecutive homozygous SNPs, less than two missing genotypes, one heterozygous and a 500 kbp minimum length. The average value per ranch and per line was then calculated.

Results and discussion Genetic diversity

Average endogamy (FIS) values per ranch ranged from -0.03 (Jaral de Peñas) to 0.29 (Los Cues, Boquilla del Cármen and Tenexac) (Table 1). The excess heterozygotes present at the Jaral de Peñas and Torreon de Cañas ranches, both in the Domecq line, explains the negative FIS values as a consequence of the Wallhund effect(22). The average FST distances estimated per line were similar among them (0.06 - 0.11), while average FST distances estimated per ranch ranged from 0.02 (Los Encinos and San José) to 0.14 (Rancho Seco). Ranchers in Mexico are known to exchange sires and dams, a practice more common among ranchers belonging to the same livestock groups and/or working with the same breed. Exchange frequency and the quantity of animals involved undoubtedly depends on

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rancher criteria, but this could explain the minimal genetic distances between ranches in the same line.

Of total genetic variability, 10.8% was due to interline differences and 6.9% to differences between ranches in the same line (Table 2). It is to be expected that the lack of interline exchanges generates greater differences between lines than within them, where exchanges occur more regularly. The average interline FST value observed here (0.18) was similar to the average FST value reported for the Lidia population in Spain (0.15) but higher than found in other cattle breeds (values near 0.07)(6). High FST values result from the characteristic structure of the Lidia breed, in which subdivision into subpopulations or lines produces small effective group sizes.

Table 2: Analysis of molecular variance (AMOVA) between lines, between ranches within lines and between ranches overall Level Variance component Variation (%) Between lines 686.54 10.76 Between ranches within lines 437.74 6.86 Between ranches 5259.13 82.39 Residual 6383.41

Genetic structure and population differentiation

The cross-validation error (CV) used in ADMIXTURE calculates values that decrease as the number of hypothetical ancestral populations (K) increases. When the CV value begins to increase it indicates the most probable hypothetical population prediction. Using the present data the most accurate prediction was identified at K = 5(18,19).

The average proportions of individuals in the ranches coincided with their assignment to each of the five ancestral populations (Figure 1). Each of the five lines largely corresponded to one of the five defined ancestral populations. Discrimination between the Julián Llaguno and Antonio Llaguno lines was less evident between some ranches in these lines, while it was greater between others (e.g., El Sauz and Valparaíso in Julián Llaguno, and Garfias, Los Cués and La Antigua in Antonio Llaguno). This analysis does not explain these differentiations between the Llaguno lines. Perhaps they result from variation in original genetic material since the Julián Llaguno line includes crosses between Creole dams and Spanish Lidia sires. Nonetheless, it is clear that both line (Antonio Llaguno and Julián Llaguno) mostly share common genetic origins. In contrast, the Gonzáles, Domecq and Santa Coloma lines are clearly genetically distinct.

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Figure 1: Cross-validation error analysis of hypothetical ancestral populations (K) using ADMIXTURE

Each vertical line represents an individual animal’s total genome. The proportion of each color (genetic group, K) in the vertical lines is the proportion of each of the five ancestral populations in an individual’s genome (K).

Identification of the ROHs per line produced statistics on the average number of runs or segments and average ROH length in each of the five lines (Figure 2). The number and length of ROHs in the different lines exhibited similar patterns. Greater number of segments and length of the ROH are correlated with recent consanguinity events(23).

ROH´s Average number

Figure 2: Average number of runs of homozygosity (ROHs) and their average size (Mb) in the five studied lines of the Lidia breed in Mexico

Recent importations Recent importations

ROH´sAverage lenght in M b

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In the data for average ROH number and length by ranch (Table 3), length ranged from 5 (Jaral de Peñas) to 7.7 Mb (Boquilla del Carmen and El Sauz). This is consistent with the average FIS values in which the highest values (>0.20) corresponded to Boquilla del Carmen and El Sauz while the lowest was for Jaral de Peñas (Table 1). Table 3: Average number of runs of homozygosity (ROH) per ranch, including number of segments (NSEG) and average length (Mb) Line Julián Llaguno

Antonio Llaguno

González

Domecq

Santa Coloma

Ranch Pozo Hondo Valparaiso El Sauz Caparica Pomedio San Mateo Reyes Huerta Fernando de la Mora Garfias Los Cués La Antigua Xajay Teófilo Gómez Celia Barbabosa Boquilla del Cármen Fermín Rivera Corlomé Arroyo Zarco Marrón La Punta Pomedio Tenexac Gonzalo Yturbe De Haro C.Castañeda Zacatepec Rancho Seco Pomedio La Joya Sta. Maria de Xalpa Jaral de Peñas José Julián Llaguno Torréon de Cañas Pomedio Los Encinos San José Pomedio

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NSEG 108 134 111 114 117 141 124 87 139 131 144 115 135 126 132 131 79 124 105 104 121 135 110 113 134 83 95 109 113 92 64 72 73 85 120 105 112

Average length (Mb) 5.3 6.2 7.7 6.5 6.1 6.5 5.9 5.8 7.1 7.3 6.9 6.6 6.0 5.8 7.7 6.1 5.3 6.3 6.2 6.1 6.2 7.4 6.3 5.6 6.9 5.7 5.2 6.2 6.3 5.7 5.0 5.1 5.2 5.5 6.4 5.8 6.1

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When animals are isolated in relatively small populations the probability is greater that they inherit identical DNA segments that account for ROH(23). In previous studies, the high number and long length of ROHs have been associated with endogamy(23). This coincides with the results observed here for the five studied Lidia breed lines in Mexico, which had values greater than those reported in previous analyses of ROHs in native Spanish and American Creole breeds(13). Both the ROH and FIS values in the studied Mexican Lidia population reflect subdivision into lines and its consequences: reduction of effective sizes and higher consanguinity values(24). This subdivision is effective at preserving intrapopulation genetic variability(25),;however, as each subpopulation experiences genetic drift consanguinity will increase and genetic variability will decrease. Under these circumstances it is advisable to closely monitor degrees of endogamy.

Conclusions and implications The genetic differentiation observed among the Mexican Lidia population, into lines and even between ranches, is due to the different genetic origins of some lines (i.e. Domecq and Santa Coloma) in conjunction with the genetic isolation maintained between the remaining lines (i.e. Antonio Llaguno, Julián Llaguno and González). Both the genetic structure and ROH analyses identified genetic isolation between the lines of the Mexican Lidia population, which has contributed to genomic variations when compared to European Lidia populations. The Mexican Lidia lines are clearly unique from their ancestral populations and quite differentiated amongst themselves.

Acknowledgements The research reported here was financed by the “Macro-proyecto de Caracterización genética de la Raza de Lidia Mexicana” supported by the Consejo Nacional de los Recursos Genéticos Pecuarios (CONARGEN) and the Asociación de Criadores de Toros de Lidia. Thanks are due the Genetics Laboratory of the Animal Production Department of the Veterinary Faculty, Universidad Complutense de Madrid, and the participating Mexican Lidia ranchers.

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Literature cited:

1. Domecq JP. Del toreo a la bravura. España, Alianza Editorial, 2009. 2. Maudet JB. Terres de taureaux: les jeux taurins de L´Europe à L´Amerique. 1ra ed. España, Casa de Velzaquez, 2010. 3. Prieto-Garrido JL. El toro bravo, ganaderías míticas. España Editorial Almuzara, 2012. 4. Cárdenas RVC. Situación del todo de Lidia y de la fiesta en los países de HispanoAmérica. Editado por García AL. Congreso Mundial Taurino de Veterinaria, Consejo General de Colegios Veterinarios de España, 2017. 5. Crandall KA, Bininda-Emonds OR, Mace GM, Wayne RK. Considering evolutionary processes in conservation biology. Trends Ecol Evol 2000;15:290-295. 6. Cañón J, Tupac-Yupanqui I, Garcia-Atance MA, Cortés O, Garcia D, Fernandez J, Dunner S. Genetic variation within the Lidia bovine breed. Anim Genet 2008;39:439-445. 7. Scherrer HL. Historia del toro bravo mexicano. México, Asociación Nacional de Criadores de Toros de Lidia (ANCTL), 1983. 8. Niño de Rivera L. Sangre de Llaguno, la razón de ser del toro bravo mexicano.1ra ed. México: Punto de Lectura; 2004. 9. Villanueva Lagar JA. San Mateo, encaste con historia. México: Aldus; 2005. 10. ANCTL. Asociación Nacional de Criadores de Toros de Lidia. México. 2019. 11. Censo Agrícola, Ganadero y Forestal 2007. INEGI. http://www.inegi.org.mx 12. Eusebi PG, Cortés O, Dunner S, Cañón J. Genetic diversity of the Mexican Lidia bovine breed and its divergence from the Spanish population. J Anim Breed Genet 2017;134(4):332-339.

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13. Eusebi PG, Cortés O, Dunner S, Cañón J. Genomic diversity and population structure of Mexican and Spanish bovine Lidia breed. Anim Genet 2017;8(6):682-685. 14. Eusebi PG, Gardyn OC, Boxberger SD, Ferreras JC. Genetic diversity analysis of the Mexican Lidia bovine breed population and its relation with the Spanish population by using a subset of SNPs under low gametic disequilibrium. Rev Mex Cienc Pecu 2018;9(1):121-134. 15. Sambrook J, Fritsch EF, Maniatis T. Molecular Cloning: A Laboratory Manual, 2nd ed. NY, USA: Cold Spring Harbor Laboratory Press, Cold Spring Harbor, 1989. 16. Purcell SM, Neale B, Todd-Brown K, Thomas L, Ferreira MAR. et al. PLINK: A tool set for whole-genome association and population-based linkage analyses. Am J Hum Genet 2007;81:559-575. 17. Excoffier L, Laval G, Schneider S. Arlequin (version 3.0): an integrated software package for population genetics data analysis. Evol Bioinform Online 2005:1, 47. 18. Alexander DH, Novembre J, Lange K. Fast model-based estimation of ancestry in unrelated individuals. Genome Res 2009;19:55–64. 19. Alexander DH, Lange K. Enhancements to the ADMIXTURE algorithm for individual ancestry estimation. BMC bioinformatics 2011;12:246. 20. Francis RM. Pophelper: an R package and web app to analyze and visualize population structure. Mol Ecol Resour 2016. 21. Purfield DC, Berry DP, McParland S, Bradley DG. Runs of homozygosity and population history in cattle. BMC Genetics 2012;13:70. 22. Crow JF, Kimura M. An Introduction to Population Genetics Theory. New York. USA: Harper & Row, Publ; 1970. 23. Upadhyay MR, Chen W, Lenstra JA. et al. Genetic origin, admixture and population history of aurochs (Bos primigenius) and primitive European cattle. Heredity 2016;118:169–76.

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24. Cortés O, Tupac-Yupanqui I, Dunner S, Fernández J, Cañón J. Y chromosome genetic diversity in the Lidia bovine breed: a highly fragmented population. J Anim Breed Genet 2011:491-498. 25. Wright S. The genetical structure of populations. Ann Eugenics 1951;15:323-354.

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https://doi.org/10.22319/rmcp.v11i4.5457 Article

Pedigree analysis in ten sheep populations in Mexico

Joel Domínguez-Viveros a* Felipe Alonso Rodríguez-Almeida a Adán Medellín-Cázares a Juan Pablo Gutiérrez-García b

Universidad Autónoma de Chihuahua. Facultad de Zootecnia y Ecología. Periférico Francisco R. Almada km 1. 31453, Chihuahua, Chih. México. b

Universidad Complutense de Madrid. Facultad de Veterinaria. Madrid. España.

Corresponding author: joeldguezviveros@yahoo.com.mx; jodominguez@uach.mx

Abstract: Pedigree analysis is vital in designing genetic improvement strategies. Population genetic parameters were analyzed in ten sheep breeds in Mexico: Blackbelly (BBL; n= 19,695); Charollais (CHA; n= 5,033); Dorper (DOR; n= 42,171); White Dorper (DOB; n= 4,213); Dorset (DOS; n= 5,557); Hampshire (HAM; n= 12,210); Katahdin (KAT; n= 77,955); Pelibuey (PEL; n= 42,256); Rambouillet (RAM; n= 11,951); and Suffolk (SUF; n= 14,099). All animals were born between 1992 and 2018. The analyses were run with the ENDOG software. Known parents values ranged from 76.4 % (SUF) to 95.3 % (KAT), with an 86.0 % average; animals with unknown parents corresponded to founders. The consanguineous population (as a percentage of total population) fluctuated from 12.3 % in DOS to 48.7 % in DOB, with a 29.7 % average. Average inbreeding (F) ranged from 3.9% (KAT) to 14.6% (DOB), with an 8.0 % average. The proportion of consanguineous individuals in all populations increased (P<0.05). Genetic relatedness was stable, and F had negative trends (P<0.05). The highest consanguineous population growth rates were present in the KAT, DOB and BBL populations. Inbreeding (F) was highest in DOB and DOS, while genetic relatedness was highest in DOB and CHA. Effective population size (Ne) was greater

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than 50 in six of the populations but less than 37 in the remaining four. These low Ne values highlight the need to monitor the evolution of F and its possible implications. The generational interval (GI) ranged from 3.0 to 4.15, with a 3.45 yearsâ&#x20AC;&#x2122; average. The highest GI values were for RAM and SUF, and the lowest for BBL and DOR. Key words: Inbreeding, Effective size, Population parameters, Generational interval, Founding ancestors.

Received: 17/07/2019 Accepted: 31/03/2020

Introduction Sheep farming occurs throughout Mexico with regional variations in response to natural resources availability and markets(1). The Organism of National Sheep Farmer Unity (Organismo de la Unidad Nacional de Ovinocultores - UNO) encompasses producers of specialized and registered sheep breeds, coordinates the genealogical registry of breed purity, and organizes genetic improvement programs based on genetic evaluations(2). Selection based on the best linear unbiased predictor (BLUP), generated from genetic evaluations, favors selection of related animals, consequently increasing inbreeding(3). Furthermore, levels of inbreeding and kinship are involved in genetic evaluations and BLUP predictions(4,5). Selection schemes can allow a small number of breeder stock or select families to generate changes in population structure, increasing inbreeding levels, reducing genetic variability, and possibly resulting in genetic drift(6,7). Genetic variability determines a populationâ&#x20AC;&#x2122;s capacity to respond to selection and genetic progress. Identifying the factors that affect genetic variability is essential when evaluating breeding strategies and deciding whether to continue with a selection scheme or take corrective actions(8). Pedigree analysis is based on population genetic parameters and describes a populationâ&#x20AC;&#x2122;s genetic dynamics and variability. The genetic structure of a population helps to track gene flow, providing information on the founding ancestors and their contributions to variability in the current population(9,10). The present study objective was to analyze the pedigree and population structure of ten sheep breeds using population genetic parameters such as pedigree integrity, number of

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generations, kinship and inbreeding, ancestors and founders, effective number and generational interval, among others. The results can be applied in developing selection schemes aimed at optimizing population response to selection by limiting the genetic variability loss rate.

Material and methods Analyses were done using the national genealogical registry databases for each of ten sheep breed populations: Blackbelly (BBL); Charollais (CHA); Dorper (DOR); White Dorper (DOB); Dorset (DOS); Hampshire (HAM); Katahdin (KAT); Pelibuey (PEL); Rambouillet (RAM); and Suffolk (SUF). The pedigrees incorporated individuals born between 1992 and 2018, the Table 1 describes the genealogical information analyzed, pedigree analyses were run with the ENDOG ver. 4.0 software(11) to evaluate the following population genetic descriptors. Table 1: Percentage of known parents in the pedigrees of ten sheep breeds in Mexico BBL CHA DOR DOB DOS HAM KAT PEL RAM SUF Parents S 82.0 92.6 89.9 89.5 79.5 82.6 95.4 80.1 90.2 74.3 D 83.2 95.5 90.1 89.5 80.1 81.2 95.1 80.4 91.2 78.5 Grandparents SS 59.6 61.6 63.4 63.9 36.4 56.9 90.7 57.4 67.3 39.1 DS 58.9 68.0 64.6 63.7 42.7 58.2 89.9 57.9 70.4 42.3 SD 59.8 81.9 74.9 76.2 46.8 57.5 89.5 53.8 60.1 48.1 DD 59.2 86.4 75.2 75.3 49.4 57.6 89.1 54.3 60.6 48.1 Great Grandparents SSS 37.2 31.9 37.2 32.8 14.7 27.8 79.7 39.0 43.6 20.5 DSS 38.4 38.6 37.6 32.8 15.6 30.1 78.7 38.1 43.8 19.4 SDS 39.9 49.6 47.9 35.9 18.5 40.7 79.8 38.1 28.1 30.3 DDS 38.2 57.8 46.3 34.9 20.8 41.9 79.6 38.7 27.5 27.7 SSD 38.9 38.9 46.5 42.7 21.4 33.3 79.1 36.4 42.2 26.5 DSD 38.6 46.9 47.5 42.5 24.1 33.3 77.6 36.5 42.4 27.0 SDD 38.7 66.3 55.2 55.3 29.1 35.9 77.9 32.9 31.8 29.9 DDD 39.1 71.1 55.0 54.3 30.0 35.0 77.1 33.8 31.9 28.4 Breeds: Blackbelly (BBL); Charollais (CHA); Dorper (DOR); White Dorper (DOB); Dorset (DOS); Hampshire (HAM); Katahdin (KAT); Pelibuey (PEL); Rambouillet (RAM); and Suffolk (SUF). Parents: S= sire; D= dam.

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Pedigree integrity Integrity was evaluated using four parameters(8,12). First is the proportion of known ancestors to the third generation, that is, parents, grandparents and great-grandparents. Second is the number of complete generations (NCG), which identifies the furthest generation with two known ancestors. Third is the number of traced generations (NTG), an indicator of the number of generations separating an individual from its furthest ancestor. Finally, the number of complete equivalent generations (NEG) expresses the sum of all known ancestors based on the number of generations (n) separating an individual from each ancestor (NEG=  (1/2)n).

Reproductive management Reproductive management was quantified using four parameters: average number of progeny per sire (PS); average number of progeny per dam (PD); total number of sires and dams as a proportion of a pedigree’s total population (SD%); and ratio of number of dams to number of sires (D/S).

Inbreeding (F) Inbreeding was estimated for each individual (Fi) and its mother (Fm) using the MTDFNRM program in the MTDFREML package(13). Trends over time were generated using the birth year of consanguineous individuals from 2010 to 2018. The percentage of consanguineous animals (P) and average inbreeding (F) were calculated with a linear regression analysis for the period 2010 to 2018, based on the model ŷ = 0 + x; where ŷ is the variable analyzed in year x, 0 is the intercept, and  is the slope or rate of change. The analysis was run with the SAS statistical software package(14).

Generational interval (GI) This parameter was calculated using the mean age of a reproducing animal and replacing it with that of a descendent(15). Average age of parents was calculated at the birth of their descendants using four selection routes: father-son, father-daughter , mother-son and mother -daughter(16,17).

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Average additive genetic relationship coefficient (ARC) This parameter was generated using the matrix of additive genetic relationships between all the individuals in a pedigree by calculating the average value of the coefficients of each individual with the rest of the pedigree; that is, the average additive genetic relationship coefficient (ARC)(9,18).

Effective number of founders (fe) Individual founders are animals with unknown parents. The effective number of founders (fe) was defined as the number of founders that, when contributing equally, would produce the genetic diversity in the existing population(10,19).

Effective number of ancestors (fa) An ancestor is every individual, founder or not, that has contributed to the population’s genetic variability. The effective number of ancestors (fa) was defined as the number of ancestors required to explain a population’s total genetic variability, considering the genetic variability contributed by an individual that cannot be explained by its offspring’s contribution(19,20).

Effective population size (Ne) Realized Ne was estimated based on the formula 1 / 2ΔF; where ΔF is the average change in inbreeding as calculated from the number (t) of complete equivalent generations (ΔF = 1 – (1-Fi)1/(t-1)). It considers the amount of a pedigree’s genealogical information and generational overlap(21,22). Effective population size (Ne) is defined as the number of breeding animals that could generate the calculated inbreeding and/or rate of change in genetic variance in an ideal population(10,23).

Results and discussion The precision of a population structure analysis depends on pedigree integrity and genealogical information content over generations. Incomplete information can lead to only approximate assignment of individuals to generations and inaccurate calculations of F and Ne. The present results for percentage of ancestors reflect more complete, deep genealogical information for the maternal route (Table 2). At the parents level, values ranged from 76.4 % (SUF) to 95.3 % (KAT), with an overall average of 86.0 %. The percentages of animals with unknown parents corresponded to the group of founding animals. Similar integrity levels and genealogical information content have been reported for pedigrees of the 1075

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Nilagiri and Sandyno(24), Santa InĂŠs(25) and Malpura(17) breeds. In contrast, analyzed pedigrees for the Mehraban(12), Guilan(26) and Morada Nova(8) had percentages of less than 60% for known parents, less than 40 % for grandparents and less than 30 % for greatgrandparents. Of particular note is that, in all these reports the genealogical information was more extensive and complete for the maternal route, as occurred in the present study. Table 2: Number of generations, founding ancestors and effective size in ten sheep breeds in Mexico Anc Found Breeds NCG NTG NEG Anc% Ne (fa) (fe) BBL 5.0 11.0 7.19 2,110 39 3,425 36.8 (1.66) (3.23) (2.24) (105.0) (3.3) (182.3) CHA 4.0 11.0 5.89 235 13 299 22.1 (1.67) (4.73) (2.67) (35.0) (7.1) (44.3) DOR 5.0 12.0 6.75 2,836 74 4,219 53.8 (1.65) (4.66) (2.55) (173.0) (3.0) (226.1) DOB 4.0 10.0 5.61 271 7 441 12.2 (1.67) (3.58) (2.31) (14.0) (22.3) (16.9) DOS 4.0 8.0 5.0 735 32 1,104 50.0 (1.06) (2.4) (1.60) (86.0) (4.2) (143.4) HAM 4.0 10.0 5.28 1,380 28 2,090 56.8 (1.29) (3.33) (1.97) (74.0) (4.7) (124.4) KAT 6.0 13.0 8.03 2,578 48 3,295 73.5 (2.70) (6.12) (4.02) (109.0) (3.9) (227.6) PEL 6.0 11.0 6.99 5,296 94 8,348 51.5 (1.50) (3.12) (2.10) (196) (3.6) (349.3) RAM 5.0 8.0 5.78 1,073 38 1,111 53.2 (1.60) (2.98) (2.12) (93.0) (5.6) (147.7) SUF 4.0 9.0 5.39 1,746 44 3,332 34.7 (1.09) (2.60) (1.65) (82.0) (5.1) (159.1) NCG = Maximum values (average values) for number of complete generations (NGC); NGT = number of traced generations; NEG = number of equivalent complete generations; Anc = total ancestors (fa = effective number of ancestors); Anc% = number of ancestors required to explain 50% of pedigree variability (maximum percentage that one ancestor explains pedigree variability); Found = total number of founders (fe = effective number of founders); Ne = realized effective population size. Breeds: Blackbelly (BBL); Charollais (CHA); Dorper (DOR); White Dorper (DOB); Dorset (DOS); Hampshire (HAM); Katahdin (KAT); Pelibuey (PEL); Rambouillet (RAM); and Suffolk (SUF).

Pedigree integrity is linked to estimates of NCG, NTG and NEG. In the present results the maximum values were similar across the ten breeds (Table 3). However, the interbreed averages differed noticeably, with the highest values for KAT and the lowest for DOS. Population structure is the result of the selection and reproductive management strategies 1076

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applied by producers. The differences observed between the analyzed populations may be attributed to sire-based reproductive management which could have implications in Ne and GI. The PS and PD values (Table 3) show to what extent breeder stock were used across generations, and the SD% and D/S estimates are related to selection intensity and pressure. Table 3: Pedigree structure, inbreeding and average relatedness coefficient levels in ten sheep breed populations in Mexico P Sires Dams SD% Fi Fm Breed Pedigree ARC (PS) (PD) (D/S) (AFi) (AFm) F 544 5,847 32.4 26.8 16.9 5.6x * BBL 19,695 0.88 (29.7) (2.8) (10.7) (8.4) (9.1) -0.29x ns 266 1,433 33.8 45.8 36.5 5.6x * CHA 5,033 3.06 (17.5) (3.4) (5.4) (8.0) (10.1) -0.56x * 1,571 12,818 34.1 26.9 17.2 6.5x * DOR 42,171 0.66 (24.1) (2.9) (8.2) (6.1) (6.9) -0.77x * 166 1,287 34.4 48.7 32.3 5.6x * DOB 4,213 7.78 (22.7) (2.9) (7.7) (14.6) (14.9) -0.59x * 173 1,601 31.9 12.3 8.6 5.5x * DOS 5,557 1.00 (25.5) (2.8) (9.3) (9.8) (9.9) -0.42x ns 467 3,687 33.4 21.3 12.6 4.9x * HAM 12,210 1.13 (22.9) (2.7) (8.5) (5.9) (6.3) -0.54x * 2,927 23,844 34.3 47.8 33.5 6.5x * KAT 77,955 1.28 (27.3) (3.3) (8.2) (3.9) (4.1) -0.01x ns 1,285 13,293 34.5 22.8 13.8 7.7x * PEL 42,256 0.47 (26.3) (2.6) (10.3) (6.8) (7.7) -0.24x * 291 3,534 32.1 24.9 15.4 7.4x * RAM 11,951 1.21 (37.1) (3.1) (12.1) (7.4) (7.5) -0.17x * 347 4,006 30.1 19.2 14.2 1.4x ns SUF 14,099 0.86 (30.2) (2.8) (11.5) (9.2) (9.6) -0.53x * Breeds: Blackbelly (BBL); Charollais (CHA); Dorper (DOR); White Dorper (DOB); Dorset (DOS); Hampshire (HAM); Katahdin (KAT); Pelibuey (PEL); Rambouillet (RAM); and Suffolk (SUF). Pedigree = total individuals in the pedigree; Sires = total sires in pedigree (PS, average number of progeny per sire); Dams = total dams in pedigree (PD, average number of progeny per dam); Fi = percentage of consanguineous animals (AFi, average inbreeding); Fm = percentage of consanguineous mothers (AFm, average inbreeding of mothers); Slope of percentage of consanguineous animals (P) and level of inbreeding (F); ARC = average additive genetic relatedness coefficient; ns = not significant (P>0.05); * = significant (P<0.05).

In the evaluated pedigrees the consanguineous population fluctuated from 12.3 % in DOS to 48.7 % in DOB, with an overall average of 29.7 % (Table 3). Inbreeding (F) levels ranged from 3.9 % in KAT to 14.6 % in DOB, with an 8.0 % average. The levels and trends of F and

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its components (ACR, Ne, fe and fa) help in evaluating the evolution of genetic variability over time. Consanguineous animals are directly affected by the effects of inbreeding depression and all the consequences that an increase in F brings with it. Given the importance of maternal effects in sheep(27,28), the possible effects of inbreeding depression also need to be evaluated through maternal inbreeding levels, using parameters such as percentage of consanguineous mothers and average inbreeding (Table 3). In the present results F exhibited three overall trends in its evolution. First, in all the studied pedigree populations the percentage of consanguineous animals increased over time (Table 1; Figure 1), with ď ˘P values ranging from 1.4 to 7.7 %. Second, inbreeding levels exhibited negative trends (Table 1; Figure 2), with an average ď ˘F value of -0.412 across the ten pedigrees. Third, ARC levels have remained stable over time and within each pedigree (Figure 3). Genetic improvement strategies need to consider an adequate balance between selection intensity, inbreeding and genetic variability. The scenarios commonly observed in the evolution of F can be attributed to three general factors: use of related breeders within a numerically large population with low ARC levels; selection based on BLUP, which raises the probability of selection of related animals; and advances in reproductive technologies, which can reduce the number of parents needed to produce the next generation of breeders(3,4,10). Figure 1: Trends of percentage of inbreeding individuals. Breeds: Blackbelly (BBL), Charollais (CHA), Dorper (DOR), White Dorper (DOB), Dorset (DOS), Hampshire (HAM), Katahdin (KAT), Pelibuey (PEL), Rambouillet (RAM) and Suffolk (SUF)

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Figure 2: Trends in average inbreeding in the consanguineous population; inbreeding is defined as homozygosis levels in individuals caused by related progenitors. Breeds: Blackbelly (BBL), Charollais (CHA), Dorper (DOR), White Dorper (DOB), Dorset (DOS), Hampshire (HAM), Katahdin (KAT), Pelibuey (PEL), Rambouillet (RAM) and Suffolk (SUF).

Figure 3: Trends of average additive genetic relatedness coefficient. Breeds: Blackbelly (BBL), Charollais (CHA), Dorper (DOR), White Dorper (DOB), Dorset (DOS), Hampshire (HAM), Katahdin (KAT), Pelibuey (PEL), Rambouillet (RAM) and Suffolk (SUF)

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The KAT, PEL and RAM pedigree populations had the highest growth rates in consanguineous population (Table 3; Figure 1). The DOB and DOR pedigrees had the largest negative trends in F while DOB and CHA had the highest ARC values. The variation in the present results coincides with a diversity of F values reported elsewhere. For example, F values were near zero in a study of seven sheep populations in France(29), but positive in a study of six breeds from Canada(10). Positive trends in F and ARC have also been reported for the breeds Finnsheep(30), Merino(17) and Malpura(18).

The concept of realized effective population size (Ne) was developed based on ideal population guidelines and is a basic concept in the design of genetic conservation and/or improvement programs. It reflects the accumulation of genetic relationships between individuals, making it possible to predict changes in F levels. In addition, it quantifies changes in genetic variance through genetic drift and changes in gene frequencies(31). The breeding structure and reproductive demographics of the evaluated sheep populations differed from ideal population approaches, but, when applied, realized Ne tends to adjust for some of these differences(32). Low Ne levels are associated with decreased genetic variability, increased crossing between related individuals, allele fixation and the greatest reduction in selection response(33). A Ne value <50 is cause for concern; when developing pedigree populations, Ne values ≥50 are preferable since these imply the presence of more F levels ≤1%(34). Even higher Ne values are recommended for populations subject to genetic improvement because these optimize selection response but with a minimal increase in F(35). Six of the evaluated pedigree populations had Ne values between 50 and 73.5 (Table 3), indicating that any increases in F will be ≤1%. However, four populations had Ne values between 12.2 and 36.8, highlighting the need for close monitoring of F and ARC values, and their possible consequences in genetic improvement. The highest six Ne values in the present results are within previously reported ranges. In a report on forty sheep breeds evaluating Ne estimation methods the value range was 38 to 675, with a 191 average(32). A series of studies evaluating the pedigree of a total of fifteen sheep populations found Ne estimates ranging from 55 to 276(29,30,36,37).

The genetic relationships between founders and fe represent initial genetic variability, since the founders’ contribution to pedigree variability is the set of genes which has remained intact through generations(38). The number of individuals explaining 50% of pedigree variability was 7 in DOB, 13 in CHA and 44 in SUF (Table 3). Low ancestor numbers explaining pedigree variability is associated with higher F and ARC values. The effective number of ancestors (fa) includes the possible causes of losses of genetic variability. In general, fe>fa; a wider discrepancy between them indicates that fewer founders are participating in the pedigree over the generations. The fe/fa ratio represents differential breeding management, considering any bottlenecks a population may have experienced. Higher ratio values indicate 1080

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that most of the ancestors were founders, without bottlenecks(39). In the present results the fe/fa ratio ranged from 1.2 to 2.0, a range which coincides with those reported for the breeds Baluchi(16), Afshari(40), Kermani(36), Moghani(37) and Morada Nova(8).

The ARC can be seen as a summary of a populationâ&#x20AC;&#x2122;s breeding management, while F represents the crossing of related animals but does not explain why these crosses occurred. In the relationship between fe and F, a founderâ&#x20AC;&#x2122;s ARC indicates the percentage of a population originating in her or him(11). Use of the ARC allows design of crosses by maintaining certain levels of F in the progeny. In the present results ARC levels remained unchanged and F levels did not increase (Figures 2 and 3). However, over time the breed stock came from a small number of families, tended to be genetically related and was selected from within herds, with minimal interherd genetic flow (Figure 1). Generational interval (GI) is vital in validating losses of genetic variability and genetic progress over time. Selection intensity, which is associated with SD% and D/S, tends to reduce the GI but produces losses in genetic variability given the minimal contribution of this breed stock to the population(9,38). Average estimated GI in the present study was 3.45 yr with a 3.0 to 4.15 yr range, and no substantial differences between the four pairings (Table 4). The highest GI estimates were for the RAM and SUF pedigrees and the lowest for BBL and DOR. A study of seven sheep breeds in France reported an estimated average GI of 3.5 years and a range of 1.9 to 5.0(29); lower average GI values have been reported for Xalda sheep (2.9 yr)(19) and Somali sheep (2.1 yr)(41). Table 4: Generational interval (GI) estimates (years) in ten sheep pedigrees in Mexico Father Father Mother Mother Breed Mean son daughter son daughter BBL 3.15 3.12 3.06 3.02 3.09 CHA 3.77 3.64 3.55 3.29 3.56 DOR 3.04 3.13 3.02 3.08 3.07 DOB 3.70 3.55 3.00 3.30 3.39 DOS 3.28 3.69 3.97 3.79 3.68 HAM 3.23 3.33 3.31 3.64 3.37 KAT 3.47 3.25 3.53 3.29 3.38 PEL 3.31 3.09 3.46 3.36 3.30 RAM 3.55 4.15 3.89 4.06 3.91 SUF 3.86 3.59 3.84 3.58 3.71 Mean

3.44

3.45

3.46

3.44

Breeds: Blackbelly (BBL), Charollais (CHA), Dorper (DOR), White Dorper (DOB), Dorset (DOS), Hampshire (HAM), Katahdin (KAT), Pelibuey (PEL), Rambouillet (RAM) and Suffolk (SUF).

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Conclusions and implications

The present pedigree evaluation represents a summary of the results of producers’ genetic and breeding management strategies. It is useful in designing genetic selection programs because it contemplates the relationship between selection response and increases in inbreeding, including their consequences. The trends did not differ greatly between the ten evaluated pedigree populations: inbreeding levels tended to decrease, with negative slopes (P<0.05); genetic relationships were stable over time; and the consanguineous population increased, with positive slopes (P<0.05). The KAT, PEL and RAM populations had high consanguineous population growth rates. Inbreeding was highest in the DOB and DOS populations, and genetic relationships were highest in DOB and CHA. Effective population size estimates were lowest in the BBL, CHA, DOB, and SUF populations, highlighting the need to monitor the evolution of inbreeding and its possible implications in these pedigrees.

Literature cited: 1. Partida de la PJA, Braña VD, Jiménez SH, Ríos RFG, Buendía RG. Producción de carne de ovina. Instituto Nacional de Investigaciones Forestales, Agrícolas y Pecuarias. Libro técnico No. 5. México. 2013. 2. Domínguez-Viveros J, Rodríguez-Almeida FA. Resumen de evaluaciones genéticas en ovinos. Catálogo de sementales de alto valor genético de doce razas. Organismo de la unidad nacional de ovinocultores. Universidad Autónoma de Chihuahua. Chihuahua, México. 2017. 3. Verrier E, Colleau J, Foulley JL. Long-term effects of selection based on the animal model BLUP in a finite population. Theo Applied Genet 1993;87:446-454. 4. Wu L, Schaeffer R. Reducing the effect of parent averages from animal solution in mixed model equations. J Anim Breed Genet 2000;117:361-374.

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5. Ruiz-Flores A, García-Munguia CA, Núñez-Domínguez R, Ramírez-Valverde R, LópezOrdaz R, García-Muñiz JG. Inclusión del coeficiente de consanguinidad en los modelos de evaluación genética de bovinos Jersey y Suizo Americano en México. Rev Mex Cienc Pecu 2011;2:381-391. 6. Selvaggi M, Dario C, Peretti V, Ciotola F, Carnicella D, Dario M. Inbreeding depression in Leccese sheep. Small Ruminant Res 2010;89:42-46. 7. Vostry L, Milerski M, Schmidova J, Vostra-Vydrova H. Genetic diversity and effects of inbreeding on litter size of the Romanov. Small Ruminant Res 2018;168:25-31. 8. McManus C, Facó O, Shiotsuki L, Jivago de PRJL, Peripolli V. Pedigree analysis of Brazilian Morada Nova hair sheep. Small Ruminant Res 2019;120:37-42. 9. Gutiérrez JP, Altarriba J, Diaz C, Quintanilla R, Cañón J, Piedrafita J. Pedigree analysis of eight Spanish beef cattle breeds. Genet Sel Evol 2003;35:43-64. 10. Stachowicz K, Brito LF, Oliveira HR, Miller SP, Schenkel FS. Assessing genetic diversity of various Canadian sheep breeds through pedigree analysis. Can J Anim Sci 2018;98:741-749. 11. Gutiérrez JP, Goyache F. A note on ENDOG: a computer program for analysis pedigri information. J Anim Breed Genet 2005;122:172-176. 12. Yavarifard R, Hossein-Zadeh NG, Shadparvar AA. Population genetic structure analysis and effect of inbreeding on body weights at different ages in Iranian Mehraban sheep. J Anim Sci Tech 2014;56:31-39. 13. Boldman KG, Kriese LA, Van Vleck DL, Van Tassell CP, Kachman SD. A Manual for use of MTDFREML. A set of programs to obtain estimates of variances and covariances (Draft). USDA. ARS. 1995. 14. SAS. SAS/STAT User's Guide (Release 9.0). Cary, NC, USA. SAS Inst. Inc. 2005. 15. James JW. A note on selection differentials and generation length when generations overlap. Animal Prod 1977;24:109-112. 16. Tahmoorespur M, Sheikhloo M. Pedigree analysis of the closed nucleus of Iranian Baluchi sheep. Small Ruminant Res 2011;99:1-6.

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17. Gowane GR, Ashish C, Misra S, Prince LL. Genetic diversity of a nucleus flock of Malpura sheep through pedigree analyses. Small Ruminant Res 2014;120:35-41. 18. Gowane GR, Prakash V, Ashish C, Prince LL. Population structure and effect of inbreeding on lamb growth in Bharat Merino sheep. Small Ruminant Res 2013;114:7279. 19. Goyache E, Gutiérrez JP, Fernández L, Gómez E, Álvarez I, Diez J, Royo LR. Using pedigree information to monitor genetic variability of endangered populations: the Xalda sheep of Asturias as an example. J Anim Breed Genet 2003;120:95-105. 20. Sheikhlou M, Abbasi MA. Genetic diversity of Iranian Lori-Bakhtiari sheep assessed by pedigree analysis. Small Ruminant Res 2016;141:99-105. 21. Gutiérrez JP, Cervantes I, Molina A, Varela M, Goyache F. Individual increase in inbreeding allows estimating realized effective sizes from pedigrees. Genet Sel Evol 2008;40:359-378. 22. Gutiérrez JP, Cervantes I, Goyache F. Improving the estimation of realized effective population sizes in farm animals. J Anim Breed Genet 2009;126:327-332. 23. Falconer DS, Mackay. TFC Introducción a la genética cuantitativa. Editorial Acribia. Zaragoza, España. 1996. 24. Venkataramanan R, Subramanian A, Sivaselvam SN, Sivakumar T, Sreekumar C, Iyue M. Effect of inbreeding and individual increase in inbreeding on growth in Nilagiri and Sandyno breeds of sheep. Animal Genetic Res 2016;58:63-71. 25. Teixeira NMR, Ferreira CJ, Souza CPL, Mendes MCH, Neves FHH. Parâmetros populacionais da raça ovina Santa Inês no Brasil. Pesq Agrop Bras 2013;48:1589-1595. 26. Eteqadi B, Hossein-Zadela NG, Ahad SA. Population structure and inbreeding effects on body weight traits of Guilan sheep in Iran. Small Ruminant Res 2014;119:45-51. 27. Bradford GE. The role of maternal effects in animal breeding. VII. Maternal effects in sheep. J Anim Sci 1972;35:1324-1334.

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28. Gowane GR, Ashish C, Prakash V, Prince LL. The role of maternal effects in sheep breeding: a review. Indian J Small Rumin 2014;20:1-11. 29. Danchin-Burge C, Palhiere I, Francois D, Bibé B, Leroy G, Verrier E. Pedigree analysis of seven small French sheep populations and implications for the management of rare breeds. J Anim Sci 2010;88:505-516. 30. Li MH, Strandén I, Kantanen J. Genetic diversity and pedigree analysis of the Finnsheep breed. J Anim Sci 2009;87:1598-1605. 31. Crow JF, Kimura M. An introduction to population genetic theory. Haper & Row, New York, USA. 1970. 32. Leroy G, Mary-Huard T, Verrier E, Danvy S, Charvolin E, Danchin-Burge C. Methods to estimate effective population size using pedigree data: examples in dog, sheep, cattle and horse. Genet Sel Evol 2013;45:1-10. 33. Breda FC, Euclydes RF, Silva PC, Robledo de AT, Souza CPL, Rocha SJL, de Almeida TFR, França MAK. Endogamia e Limite de Seleção em Populações Selecionadas Obtidas por Simulação. Rev Brasil Zoot 2004;33:2017-2025. 34. FAO. Secondary guidelines for development of national farm animal genetic resources management plans: management of small populations at risk. Rome, Italy. 1998. 35. Meuwissen THE, Sonesson AK. Maximizing the response of selection with a predefined rate of inbreeding: overlapping generations. J Anim Sci 1998;76:2575-2583. 36. Mokhtari MS, Moradi SM, Esmailizadeh AK, Abdollahi-Arpanahi R, Gutiérrez JP. Genetic diversity in Kermani sheep assessed from pedigree analysis. Small Ruminant Res 2013;114:202-205. 37. Mokhtari MS, Miraei-Ashtiani SR, Jafaroghli M, Gutiérrez JP. Studying genetic diversity in Moghani sheep using pedigree analysis. J Agric Sci Tech 2015;17:1151-1160. 38. Biochard D, Maignel L, Verrier E. The value of using probabilities of gene origin to measure genetic variability in a population. Genet Select Evol 1997;29:5-23. 39. Barros EA, de A Brasil LH, Tejero JP, Delgado-Bermejo JV, Ribeiro MN. Population structure and genetic variability of the Segureña Sheep breed through pedigree analysis and inbreeding effects on growth traits. Small Rumianant Res 2017;149:128-133.

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40. Ghafouri-Kesbi F. Using pedigree information to study genetic diversity and reevaluating a selection program in an experimental flock of Afshari sheep. Arch Tierz 2012;55:375-384. 41. Paiva SR, Olivardo F, Faria DA, Lacerda T, Baretto GB, Carneiro PLS, Lobo RNB, McManus C. Molecular and pedigree analysis applied to conservation of animal genetic resources: the case of Brazilian Somali hair sheep. Trop Animal Health Prod 2011;43:1449-1457.

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https://doi.org/10.22319/rmcp.v11i4.4950 Article

Forage accumulation in Lotus corniculatus L. as a function of harvest strategy

Perpetuo Álvarez Vázquez a Juan de Dios Guerrero Rodríguez b* Gabino García De Los Santos c María Esther Ortega Cerrilla d Sergio Iban Mendoza Pedroza d Santiago Joaquín Cancino e

Universidad Autónoma Agraria Antonio Narro. Departamento de Recursos Naturales Renovables. México. b

Colegio de Postgraduados. Campus puebla, Desarrollo Agrícola Regional. México.

Colegio de Postgraduados. Campus Montecillo, Recursos Genéticos y Productividad Producción de Semillas. México. d

Colegio de Postgraduados. Campus Montecillo, Recursos Genéticos y ProductividadGanadería. Estado de México, México. e

Universidad Autónoma de Tamaulipas. Facultad de Ingeniería y Ciencias. México.

*Corresponding author: grjuan2000mx@yahoo.com

Abstract: Different harvest strategies can substantially affect yield in forages. A study was carried out to determine the optimum harvest strategy for Lotus corniculatus, genotype 255301, during two production periods among four tested strategies: three determined by the percentage of light intercepted by the canopy (90, 95 and 100 % IL), and one season-defined fixed cut (FC). 1087

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The treatments (IL and FC) were distributed in a randomized block design with three repetitions. Forage yield in the FC was 27 % lower than the 95 % IL in the first period (19,915 vs 28,417 kg DM ha-1), and 29 % lower than the average of all three IL treatments in the second period (19,100 vs 26,952 kg DM ha-1). Average seasonal yield in both periods was higher in spring (9,447 kg DM ha-1) than in autumn (3,120 kg DM ha-1). The leaf was the component that contributed most (56 %) to yield, particularly in spring in the 95 % IL treatments. Plant height was greatest in the 90, 95 and 100 % IL treatments (average= 21.5 cm) and lowest in the FC treatments (average= 17 cm). By season, plant height was greatest (average= 24 cm) in spring and lowest in winter (average= 17 cm). In both periods, average leaf:stem ratio was highest in the FC treatment (2.3) followed by the 90, 95 and 100 % IL treatments. Lotus corniculatus genotype 255301 yield was optimum when harvested using intercepted light percentages as an indicator; leaf production was highest in the 95 % intercepted light treatments. Key words: Lotus corniculatus L., Forage production, Harvest strategy, Intercepted light.

Received: 15/06/2018 Accepted: 23/10/2019

Introduction Lotus corniculatus L., commonly known as birdâ&#x20AC;&#x2122;s-foot trefoil, is the most important forage species of its genus. This includes about 200 species, both annuals and perennials(1), which occupy approximately 90 % of the planetâ&#x20AC;&#x2122;s crop surface(2). Its yield and nutritional quality (between 18.9 to 21.8 % of crude protein, dry basis) are similar or superior to alfalfa (Medicago sativa L.) and white clover (Trifolium repens L.)(3). It also contains less cellulose and more non-structural carbohydrates than these species(4), additionally, due to its concentration of condensed tannins does not produce bloat in grazing ruminants(5). As with all other forage species, productivity and persistence in L. corniculatus are a function of forage accumulation, and both are influenced by harvest strategy and efficiency(6,7). Plant growth and management practices are variables which interact with soil and climate(8). In forages, competition between individuals occurs as growth progresses, particularly during regrowth periods when pasture light quantity and quality are reduced(9). For example, the point when 95 % intercepted light is reached in a pasture is optimum for harvest since it is when optimum productivity is obtained(10). In other words, proper management of intercepted 1088

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light in a pasture ensures the best forage productivity(11). High correlations have been found between forage accumulation and intercepted light in temperate climate legumes(12). Only limited research has been done on L. corniculatus in this regard, therefore, the present study objective was to identify optimum harvest strategy for Lotus corniculatus (genotype 255301) in four harvest interval scenarios: three light interception-dependent scenarios and one seasonally-defined fixed cutoff.

Material and methods Two experiments were carried out under field conditions at the College of Postgraduates (Colegio de Posgraduados), Texcoco, Mexico (19°29’N, 98°54’W; 2,250 m asl): one in autumn-summer 2014-2015 (POV1); and a second in autumn-summer 2015-2016 (POV2). Soil texture at the experimental field is sandy loam and slightly alkaline, with 7.8 pH(13). Regional climate is temperate subhumid with summer rains, average annual precipitation 645 mm and average annual temperature of 15 °C(14). During the study periods, air temperature (minimum and maximum) and precipitation data were collected at the meteorological station of the Autonomous University of Chapingo (Universidad Autónoma de Chapingo) (Figure 1), located 2 km from the experimental field. During period POV1 accumulated rainfall was 1,043 mm, while during POV2 it was 877 mm. Maximum temperatures occurred in the spring-summer in both periods. Figure 1: Maximum and minimum mean monthly temperature, and monthly accumulated rainfall

POV1= Autumn 2014-Summer 2015; POV2 = Autumn 2015-Summer 2016. Data from Autonomous University of Chapingo meteorological station.

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The experimental field consisted of Lotus corniculatus L. genotype 255301 established by transplanting in March 2014 at a planting interval of 33 cm. Plants were from greenhouse material. No fertilizer was applied. In seasons with little or no rainfall the field was irrigated to field capacity every two weeks. At the beginning of the study (5 September 2014), a manual cut was made 7 cm above ground level to standardize forage height. Experimental units were 4 m2 plots. The treatments consisted of four manual harvest strategies: cutting intervals when intercepted light percentages reached 90, 95 and 100 %; and fixed cut intervals implemented by season (i.e. autumn= 35-d interval, winter = 42-d interval, spring-summer = 28-d interval). Residual forage height was 7 cm in all treatments(3). Intercepted light percentages were monitored prior to cutting, by taking six readings in each plot at 1200 h with a ceptometer (Accupar LP-80, Decagon Devices, USA). The four treatments were randomly assigned to four 4 m2 plots in a completely randomized block design with three replicates, and four plots per block, creating twelve experimental plots. Forage yield (kg DM ha-1) was measured using the biomass harvested in two fixed 0.25 m2 quadrants per replicate, established at the beginning of the experimental period. Harvested material was placed in labeled bags and dehydrated at 60 Â°C to constant weight in a forced air oven (Felisa, Mod. FE-243A). The botanical and morphological composition (BMC) of the harvested forage was quantified by taking an approximately 10 % subsample and separating it into leaves, stems, dead (senescent) material and weeds. Each fractionâ&#x20AC;&#x2122;s contribution to yield was calculated in kg DM ha-1. Leaf and stem data from the BMC were used to calculate the leaf:stem ratio by dividing the weight of the leaf fraction by the weight of the stem fraction. Estimation of average plant height was done by taking twelve measurements one day before cutting at random within each replicate using a 50 cm long graduated ruler. The values of the cuts from each season were averaged and these averages used to calculate the equivalence in forage yield per centimeter of plant height, dividing yield by plant height by the number of cuts(15). The effect of the treatments (harvest strategy) on the response variables was analyzed by grouping the data seasonally and by study period. The data were analyzed using a randomized block experimental design with four treatments and three replicates. Comparison of means was done with a Tukey test (Pâ&#x2030;¤0.05). All statistical analyses were run with the PROC GLM procedure in the SAS statistical package(16).

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Results and discussion Forage yield Average seasonal yield was highest in spring (9,447 kg DM ha-1), although this did not differ with that of summer in POV1. This variable was lowest in autumn (3,120 kg), which did not differ from winter in the same period (Table 1). Yield behavior was directly related to optimal temperatures (22 Â°C) for growth in L. corniculatus(3). Temperatures were favorable in spring (Figure 1), which benefited growth and production. This agrees with yield behavior reported in a study of five populations of L. corniculatus using grazing intervals of 20 and 40 d(17). Seasonal changes in forage species growth performance can therefore be attributed to seasonal environmental conditions(18). For instance, in L. corniculatus seasonal distributions in forage production have been reported of 32 % in spring, 30 % in summer, 23 % in winter and 15 % in autumn; 62 % of production occurred in spring-summer(19). Slightly lower average yields (7,700 kg DM ha-1) have been reported for L. corniculatus at a 45-d cutting interval in Texcoco, which were influenced by climate, management and genotype growth habit(3). Table 1: Forage yield (kg DM h-1) of L. corniculatus, genotype 255301, as a function of intercepted light (IL) and seasonally-defined fixed cut IL (%) POV1 90 95 100 FC Average SEM POV2 90 95 100 FC Average SEM

Autumn Winter

Spring

Summer Accumulated

SEM

4527 Ab 4956 Ab 4235 Ab 3300 Ab 4255 b 683

2736 Bb 4422 Ab 2716 Bb 2431 Bb 3076 b 522

10326 Aa 10346 Aa 11002 Aa 8147 Aa 9956 a 1432

9746 Aa 9942 Aa 9178 Aa 6851 Ba 8929 a 637

27336 AB 28417 A 27132 AB 20730 B 25904 2461

1271 481 802 529 564

4749 Ac 4676 Ab 5501 Ac 4603 Ab 4882 c 628

3247 Ad 3835 Ab 3749 Ad 1826 Bc 3164 d 329

9953 Aa 9087 ABa 9732 Aa 6982 Ba 8938 a 940

8565 Ab 9515 Aa 8246 Ab 5689 Bb 8004 b 668

26515 A 27113 A 27227 A 19100 B 24989 2397

355 505 477 402 306

FC = Fixed cut (autumn = 35-day interval, winter = 42-day interval, spring-summer = 28-day interval). POV1= Autumn 2014-Winter 2015; POV2= Autumn 2015-summer 2016. SEM = standard error of the mean. Significant difference (Pď&#x201A;Ł0.05) is indicated by different uppercase letters between columns and different lowercase letters between rows.

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Accumulated forage production differed between treatments (Pâ&#x2030;¤0.05) (Table 1). In POV1, yield was lowest in the FC treatment, with 27 % less production than in the 95 % IL treatment (28,417 vs 20,730 kg DM ha-1). In POV2 the FC treatment had a cumulative yield 29 % less than the average of the three IL treatments (26,952 vs 19,100 kg DM ha-1). This may be related to the shorter harvest interval (< 33 d) in the FC treatments versus the longer average harvest interval (70 d) in the IL treatments (Figure 2). This coincides with a study in which L. corniculatus (cultivar 202700) grown in Texcoco exhibited a lesser adaptation to a seasondefined fixed cut than cuts based on IL percentages; the fixed cut yielded 29 % less forage than the IL treatments(20). In some forage species, frequent cuts decrease yield and foliar area, leading to greater presence of undesirable species(21), and consequent greater competition with the desired species and depletion of their carbohydrate reserves(22). Figure 2: Average cut intervals for L. corniculatus, genotype 255301

90 = 90 % IL; 95 = 95 % IL; 100 = 100 % IL; CF = season-defined fixed cut (autumn = 35-d interval, winter = 42-d interval, spring-summer = 28-d interval).

Botanical and morphological composition

The morphological component contributing most to yield was the leaf (average= 14,273 kg DM ha-1; 56 %), followed by the stem (30.5 %), dead material (8.5 %) and weeds (4.5 %). Of the IL treatments, the 95 % IL produced the highest average leaf yield (16,526 kg DM ha-1), which was higher (Pâ&#x2030;¤0.05) than the other IL treatments during POV2. The FC treatments in both study periods produced the lowest average yields of leaves (12,276 kg DM 1092

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ha-1) and stems (4,710 kg DM ha-1). Dead material and weed percentages did not differ between treatments (P â&#x2030;Ľ 0.05) in either period (Table 2).

Table 2: Accumulated seasonal forage yield (kg DM h-1) by botanical and morphological component in L. corniculatus, genotype 255301, as a function of intercepted light (IL) and season-defined fixed cut IL (%) POV1 90 95 100 FC Average SME POV2 90 95 100 FC Average SEM

Leaf

Stem

Dead Material

Weeds

SEM

13829 Aa 15979 Aa 14540 Aa 12715 Aa 14266 a 1332

8426 Ab 9450 Ab 7964 ABb 5364 Bb 7801 b 1048

3337 Ac 2051 Ac 4622 Ac 2616 Ab 3156 c 971

459 Ad 937 Ac 1291 Ac 1329 Ab 1004 d 662

961 836 754 1692 753

14315 ABa 17074 Aa 13893 ABa 11838 Ba 14280 a 1102

7996 Bb 9412 ABb 10403 Ab 4056 Cb 7967 b 757

1608 Ac 969 Ac 2326 Ac 1718 Ac 1655 c 570

970 Ac 719 Ac 605 Ad 2053 Abc 1087 c 750

1022 877 533 748 565

FC = fixed cut (autumn = 35-day interval, winter = 42-day interval, spring-summer = 28-day interval). POV1= Autumn 2014-Summer 2015; POV2= Autumn 2015-Summer 2016. SEM = standard error of the mean. Significant difference (Pď&#x201A;Ł 0.05) is indicated by different uppercase letters between columns and different lowercase letters between rows.

The higher leaf and stem production observed in the 95% IL treatment may be related to greater crop growth age(23). In addition, the higher leaf production in the 95% IL versus the FC treatment may have resulted from a compensation for greater stem biomass caused by a longer growth period, which implies more time producing photosynthates(24). The leaf component also contributed most to forage yield in different seasons (Figure 3). Average yield in both periods was highest in the spring (5,141 kg DM ha-1) and lowest in the winter (1,580 kg DM ha-1), with the 95% IL treatment having the highest yield (5,852 kg DM ha-1) and the FC the lowest (1,163 kg DM ha-1). Stem yield was also highest in the spring (3,347 kg DM ha-1) and lowest in the winter (772 kg DM ha-1). These variations in component yields are probably responses to changes in environmental conditions between different seasons (Figure 1). For example, changes in stem production are known to be a function of seasonal variations in pasture light quantity and quality, precipitation and temperature(18). 1093

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Dead material (873 kg DM ha-1) and weed (268 kg DM ha-1) production was highest in summer, although these yields did not differ (Pâ&#x2030;Ľ0.05) from the corresponding spring yields during POV1. These yields were lowest in autumn (237 and 133 kg DM ha-1, respectively). This may result from self-shading of the basal area by the plant since growth is greater in seasons with favorable development conditions(25).

Figure 3: Botanical and morphological composition of L. corniculatus, genotype 255301, as a function of intercepted light (90, 95 and 100 %) and season-defined fixed cut (FC) (autumn = 35-d interval, winter = 42-d interval, spring-summer = 28-d interval)

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Plant height

Plant height differed between treatments and seasons (P0.05). Height was greatest in the 90, 95 and 100 % IL treatments, which averaged 21.5 cm. This is slightly taller (P≥0.05) than in the 90 % IL treatment in POV2 and noticeably taller than the 17 cm average for the FC treatments in both periods (Table 3). These results are related to pasture age because in the IL treatments the plants had 70 d for regrowth compared to the FC treatments with an average of 33 d in both periods (Figure 2). In the FC treatments, plants were younger and cut more frequently, resulting in lower forage yield(17). Greater height is reported to correspond to higher forage yield(15), and for L. corniculatus yield and height have also been linked to erect and prostrate growth habits(3).

Table 3: Plant height (cm) in L. corniculatus, genotype 255301, as a function of intercepted light (%) and season-defined fixed cut IL (%) Autumn Winter Spring Summer Average POV1 90 21 ABa 18 Ab 23 Aa 24 Aa 22 A 95 23 Aa 17 Aa 23 Aba 24 Aa 22 A 100 18 Bc 20 Abc 24 Aa 23 Aab 21 A FC 24 Aa 12 Bc 21 Bab 18 Bb 19 B 21 a 17 b 23 a 22 a 21 Average SEM 1.6 1.1 0.6 0.7 0.8 POV2 90 19 Ab 13 Bc 24 Ba 24 ABa 20 B 95 17 ABc 22 Ab 30 Aa 24 Ab 23 A 100 19 Ac 26 Ab 31 Aa 22 ABbc 25 A FC 15 Bb 8 Cc 16 Cb 22 Ba 15 C Average 18 c 17 c 25 a 23 b 21 SME 1.2 1.3 0.9 0.9 0.5

SEM 0.9 2.4 1.6 1.5 1.5

1.0 1.1 1.2 0.8 0.4

FC = fixed cut (autumn = 35-d interval, winter = 42-d interval, spring-summer = 28-d interval). POV1= Autumn 2014-Winter 2015; POV2= Autumn 2015-summer 2016. SEM = standard error of the mean. Significant difference (P0.05) is indicated by different uppercase letters between columns and different lowercase letters between rows.

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In both study periods the highest average height (24 cm) was recorded in spring, although this did not differ (P≥0.05) from average height in the summer and autumn in POV1. Average height was lowest in winter (17 cm), which, in POV2, did not differ (P≥0.05) from autumn. Plant height was greatest during the seasons with optimal humidity and temperature conditions for growth and yield in L. corniculatus (Figure 1). These results coincide with those in a study of twelve L. corniculatus genotypes in the State of Mexico, Mexico(3). Adequate photoperiod, temperature and humidity can allow forage plants to accelerate growth and exhibit changes in height between seasons(26). This directly affects forage yield(12), which, in L. corniculatus genotype 255301, is associated with its prostrate growth habit(3). Pasture height is therefore predictive of forage production(8). Based on this assumption and using the present results, calculations were done of the equivalence (per centimeter of plant height) of the highest yields by season and treatment. In the spring each centimeter of height corresponded to a yield of 167 kg DM ha-1, as an average of both study periods. In POV1, height in the 95 % IL treatment corresponded to a 144 kg DM ha-1 yield, while in POV2 the height in the 90, 95 and 100 % IL treatments corresponded to an average yield of 192 kg DM ha-1.

Leaf:stem ratio

Average leaf:stem ratio values in both periods were highest in the fixed cut treatment (P≤0.05), followed by the 90, 95 and 100 % IL treatments (Table 4). In POV1, this ratio in the FC treatment was 36 % higher than the average of the 90, 95 and 100 % IL treatments (2.8 vs 1.8), while in POV2 the ratio in the FC was 44 % higher than in the 100% IL treatment (3.2 vs 1.4). The larger leaf:stem ratio in the FC treatments was caused by more frequent harvests (average= 33-d interval)(Figure 2). Lotus corniculatus is in the accelerated growth phase at this interval, which is not optimal for harvest, since it exhibits the highest percentage of young leaves and fewer stems(27). In addition, post-cut incident light quality and quantity in a pasture are altered by cutting interval, leading to variations in leaf and stem production and consequent changes in the leaf:stem ratio(10).

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Table 4: Leaf:stem ratio in L. corniculatus, genotype 255301, as a function of intercepted light (%) and season-defined fixed cut SEM IL (%) Autumn Winter Spring Summer Average POV1 90 2.5 Aa 2.0 Bab 1.7 Bb 1.5 Ab 1.9 B 0.2 95 1.9 Aa 2.3 Ba 1.5 Ba 1.4 Aa 1.8 B 0.3 100 2.1 Aa 2.2 Ba 1.8 Ba 1.8 Aa 1.8 B 0.2 FC 2.5 Ab 4.0 Aa 2.6 Ab 2.1 Ab 2.8 A 0.4 Average 2.3 ab 2.6 a 1.9 b 1.7 b 2.1 0.2 SEM 0.2 0.3 0.2 0.4 0.2 POV2 90 2.1 Ab 2.9 Ba 1.7 Bb 1.7 Bb 2.1 B 0.1 95 3.0 Aa 1.8 Cb 1.0 Cc 1.7 Bb 1.9 B 0.1 100 2.3 Aa 1.3 Cb 1.0 Cb 1.2 Cb 1.4 C 0.2 2.9 Aa 3.7 Aa 3.4 Aa 2.7 Aa 3.2 A 0.3 FC 2.6 a 2.4 a 1.8 b 1.8 b 2.2 0.1 Average 0.3 0.2 0.2 0.1 0.1 SEM FC= fixed cut (autumn = 35-d interval, winter = 42-d interval, spring-summer= 28-d interval). POV1= Autumn 2014-Winter 2015; POV2= Autumn 2015-summer 2016. SEM= standard error of the mean. Significant difference (P 005) is indicated by different uppercase letters between columns and different lowercase letters between rows.

The leaf:stem ratio was higher in winter (P≤0.05): 2.6 in winter POV1; 2.5 average in autumn and winter POV2. In some forage species leaf:stem ratio values are lower in seasons when plant growth is lower (e.g. autumn and winter) due to higher stem density coupled with lower weight(27). The lower ratio values (P≤0.05) observed here during the spring and summer in both periods resulted from higher individual stem weight(26), which is a possible response to greater translocation of assimilates from the leaves to the stems during these seasons(7).

Conclusions and implications The harvest strategies dependent on the percentage of intercepted light exhibited similar forage yields and plant heights, both markedly superior to the seasonally-defined fixed cut strategy. However, the latter had a higher leaf:stem ratio. The leaf was the morphological component that the made largest contribution to forage yield in all treatments, but particularly in the 95 % intercepted light harvest strategy.

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Literature cited: 1. Lagler JC. Lotus: Un género que no acaba en dos especies. Rev Forrajes y Granos 2003;(62):72-76. 2. Escaray FJ, Menendez AB, Gárriz A, Pieckenstain FL, Estrella MJ, Castagno LN, et al. Ecological and agronomic importance of the plant genus Lotus. Its application ingrassland sustainability and the amelioration of constrained and contaminated soils. Plant Sci 2012;(182):121-133. 3. García BDV, Guerrero RJD, García DSG. Lagunes RSA. Rendimiento y calidad de forraje de genotipos de Lotus corniculatus L., en el Estado de México. Nova Scientia 2014;7(13):170-189. 4. Grant FW. Lotus corniculatus. Sci Topics. 2009. http://www.scitopics.com/ Lotus_corniculatus.html. Accessed Sep 30, 2017. 5. MacAdam JW, Bruner J, Islam A, Shewmaker G. The benefits of tannin-containing forages. Plants, Soils and Climate, Utah State University AG/Forages 2013;03. 6. Da Silva NIM, Lima DSA, De Moura ZA, Gonçalves DAJ, De Jesus FD, Buranelo TFL, et al. Morphogenetic and structure characteristics of marandu grass subjected to grazing management strategies. Biolog Rhythm Res 2017;929-1016. 7. Giacomini AA, Da-Silva CS, Sarmento DLDO, Zeferino VC, Souza JS, Da-Trindade KJ, et al. Growth of marandu palisadegrass subjected to strategies of intermittent stocking. Sci Agric Piracicaba 2009;66(6):733-741. 8. Parsons A, Rowarth J, Thornley J, Newton P. Primary production of grassalands, herbage accumulation and use, and impacts of climate change. In: Lemaire G, et al. editors. Grassland productivity and ecosystems services. CABI, 2011:1-18. 9. Da Silva SC, Hernández GA. Manejo del pastoreo en praderas tropicales. En: Velazco ZME et al. editores. Los forrajes y su impacto en el trópico. 1ra. ed. Chiapas, México: Universidad Autónoma de Chiapas; 2010:63-95. 10. Montagner DB, Nascimento-Jr D, Vilela HH, Sousa MLB, Euclides BVP, Da-Silva CS, et al. Tillering dynamics in pastures of guinea grass subjected to grazing severities under intermittent stocking. Rev Bras Zootec 2012;41(3):544-549. 11. Difante GS, Nascimento-Jr D, Da-Silva SC, Bautista EVP, De-Moura ZA, Adese B. Dinâmica do perfilhamento do capim-marandu cultivado em duas alturas e três intervalos de corte. Rev Bras Zootec 2008;37(2):89-196.

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12. Rojas GAR, Hernández GA, Joaquín CS, Maldonado PMA, Mendoza PSI, Álvarez VP, et al. Comportamiento productivo de cinco variedades de alfalfa. Rev Mex Cienc Agríc 2016;7(8):1855-1866. 13. Delgado R, Escalante J, Díaz R, Trinidad SA, Morales E, Sosa E. Defoliación en maíz y su efecto sobre el rendimiento de frijol-maíz en asociación. Rev Mex Cienc Agríc 2014;5(6):1015-1027. 14. García E. Modificación al sistema de clasificación climática de Köppen. 4ta ed. Instituto de Geografía, Universidad Autónoma de México; 2004. 15. Hakl J, Hrevusˇova´ Z, Hejcman M, Fuksa P. The use of a rising plate meter to evaluate lucerne (Medicago sativa L.) height as an important agronomic trait enabling yield estimation. Grass Forage Sci 2012;67:589-596. 16. SAS Institute. SAS/STAT® 9.2. User Guide Release. Cary, NC: SAS Institute Inc. USA. 2009. 17. Scheffer BMS, Brustolin R, Dall AM. Performance of Lotus corniculatus L. genotypes submitted to cutting interval: subsidies to a breeding program. Rev Bras Zootec 2011;40(8):1645-1650. 18. Sbrissia AF, Da-Silva CS, Sarmento DOL, Molan LK, Andrade MF, Goncalves CA, et al. Tillering dynamics in palisadegrass swards continuously stocked by cattle. Plant Eco 2010;(206):349-359. 19. Álvarez VP, Hernández GA, García DSG, Guerrero RJD, Mendoza PSI, Ortega CME, et al. Potencial forrajero de Lotus corniculatus L. con diferentes estrategias de manejo. Rev Agroproductividad 2018;11(5):24-28. 20. Álvarez VP, García DSG, Guerrero RJD, Mendoza PSI, Ortega CME, Hernández GA. Comportamiento productivo de Lotus corniculatus L. dependiente de la estrategia de cosecha. Rev Agrociencia 2018;52(8):1081-1093. 21. Mendoza PSI, Hernández GA, Pérez PJ, Quero CAR, Escalante EJAS, Zaragoza RJL, et al. Respuesta productiva de la alfalfa a diferentes frecuencias de corte. Rev Mex Cienc Pecu 2010;1(3):287-296. 22. Teixeira EI, Derrick JM, Hamish BB, Fletcher LA. The dynamics of lucerne (Medicago sativa L.) yield components in response to defoliation frequency. Eur J Agron 2007;(26):394-400. 23. Gomide AMC, Chavesb SC, Ribeiroc KG, Sollenbergerd EL, Paciulloa SCD, Pereirae TP, et al. Structural traits of elephant grass (Pennisetum purpureum Schum.) genotypes under rotational stocking strategies. Afr J Range Forage Sci 2014:1-7. 1099

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24. Pereira JC, Gomes FK, Oliveira DBLM, Lara ASM, Bernardes TF, Casagrande DR. Defoliation management affects morphogenetic and structural characteristics of mixed pastures of brachiaria grass and forage peanut. Afr J Range Forage Sci 2017;34(1):1319. 25. Baguet HA y Bavera GA. Fisiología de la planta pastoreada. Facultad de Agronomía y Veterinaria. Universidad Nacional del Río Cuarto. Provincia de Córdoba,Argentina2001.http://www.produccionovina.com.ar/produccioymanejopastra s/pastoreosistemas/04fisiologia_de_la_planta_pastoreada.htm. Accessed Sep 10, 2017. 26. Villegas AY, Hernández GA, Pérez PJ, López CC, Herrera HJG, Enríquez QJF, et al. Patrones estacionales de crecimiento de dos variedades de alfalfa (Medicago sativa L.). Téc Pecu Méx 2004;42(2):145-158. 27. Barbosa RA, Nacimiento-Jr D, Vilela HH, Da-Silva CS, Batista-Euclides PV, Sbrissia FA, Da-Lana SB. Morphogenic and structural characteristics of guinea grass pastures submitted to three frequencies and two defoliation severities. Rev Bras Zootec 2011;(40):947-954.

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https://doi.org/10.22319/rmcp.v11i4.5301 Article

Presence of the yeast Kodamaea ohmeri associated with Aethina tumida (Coleoptera: Nitidulidae) collected in Africanized honey bee colonies from two apiaries of Yucatan, Mexico

Azucena Canto a* Luis A. Medina-Medina b Elisa Chan a Rosalina Rodríguez a

Centro de Investigación Científica de Yucatán AC. Unidad de Recursos Naturales, Calle 43, Num. 130, Colonia Chuburná de Hidalgo, Mérida, 97200, Yucatán, México. b

Universidad Autónoma de Yucatán, Facultad de Medicina Veterinaria y Zootecnia, Mérida, Yucatán, México.

* Corresponding author: azucanto@cicy.mx

Abstract: Aethina tumida (Coleoptera: Nitidulidae), commonly known as the Small Hive Beetle (SHB), is becoming a significant pest in the beekeeping industry outside of its natural distribution range. In Mexico, recent reports indicate that the SHB is distributed throughout the Yucatan peninsula. The invasion of honey bee colonies by SHB it is mainly chemically mediated by volatiles produced by the yeast Kodamaea ohmeri which is regarded as a secondary symbiont of the SHB. It was analyzed the presence of this yeast in honey bee colonies of Yucatan based on the premise that symbionts are often conjointly distributed with their hosts, therefore the presence of K. ohmeri in hives will be closely associated with the presence of SHB. In managed Africanized honey bee (AHB) colonies, yeasts associated with adult beetles were isolated and identified and the results show that the SHB together with their associated yeast, K. ohmeri, have invaded AHB colonies in Yucatan.

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It was also reported the presence of yeasts other than K. ohmeri associated with SHB that for the first time are recorded in a geographical region where they had not been recorded before. Key words: Aethina tumida, Apis mellifera, Beetle-yeast association, Secondary symbiont, Kodamaea ohmeri, Small Hive Beetle, rDNA, Tropical beekeeping.

Received: 26/03/2019 Accepted: 23/09/2019

Introduction Aethina tumida Murray 1867 (Coleoptera: Nitidulidae), commonly known as the Small Hive Beetle (SHB), is an opportunistic scavenger which invades the nests of the honey bee Apis mellifera, becoming a significant pest in the beekeeping industry outside of its natural distribution range. SHB females proliferate within honey bee colonies, and their larvae consume the pollen, honey and bee brood present in the combs causing honey fermentation and collapse of the colony(1,2). SHB was first reported in Mexico in 2007 in the state of Coahuila(3) and since then, it has been reported in other states, including Campeche, MichoacĂĄn, Jalisco, Quintana Roo, San Luis PotosĂ and Yucatan, which are the main honey producing states(4). SHB was first reported in 2012 in apiaries located at northeast of the state of Yucatan(5), and recent reports indicate its presence throughout the Yucatan peninsula(6). The invasion of the SHB adults into honey bee colonies is thought to be chemically mediated by volatiles that are produced by microbial fermentation of food reserves. One of the predisposing factors that allows SHB to become a pest in bee colonies is the association with the fermentative yeast Kodamaea ohmeri(7,8). This yeast is a facultative or secondary symbiont of SHB and has been isolated from the digestive tracts of SHB adults, and their eggs and larvae(7,9-11) and is considered the primary factor responsible for fermenting the food stored in the colonies and producing the attractants sensed by other adult beetles(7,12,13). In the interaction between symbionts and hosts, symbionts assist their hosts in colonizing new habitats and expanding to new geographical zones, mainly because they play a key role in the nutrition of host insects(14-17). Insect-associated yeasts provide essential nutrients as sterols and produce allelochemicals to attract the insect dispersers for targeted dispersal to a new environment(17). It is feasible to expect that K. ohmeri as symbiont of the SHB is being carried by the adults as they colonize new hives and apiaries. The purpose of this study was to analyze the

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presence of yeasts, specifically K. ohmeri, associated with the SHB adults that invade managed Africanized honey bee (AHB) colonies in Yucatan. It is assumed that K. ohmeri is highly frequent in association with SHBs in this region, based on the premise that symbionts are often conjointly distributed with their hosts(15,16). The results will help to elucidate the relationship between the SHB and its associated yeasts and to understand the beetle-yeasts impact on honey bee colonies, as well as to design adequate strategies to control this pest using SHB’s symbiotic yeasts in neotropical environments.

Material and methods The present study was conducted from February to July 2016 in AHB colonies from apiaries located in two different sites in the state of Yucatan, Mexico. One group of colonies was located in the experimental apiary of the Campus of Biological and Agricultural Sciences of the Autonomous University of Yucatan in the municipality of Merida (20° 51' 51.62" N; 89° 36' 45.35" W) and the other group was located in a private apiary of the municipality of Motul (21° 08′ 03″ N 89° 19′ 03″ W). The apiaries contained approximately 30 AHB colonies, from which a total of six colonies were randomly selected to sample. All combs, as well as the hive box, cover, bottom board, and artificial feeders (inside feeders) were removed from each colony and checked to detect and collect adult of the SHB. Adult beetles were collected from the bottom board, brood combs and artificial feeders. In all these places was observed a great number of SHB individuals. Each beetle was collected using tweezers that were sterilized in 99 % alcohol to avoid contamination among beetles. Subsequently, each beetle was placed in a sterile bottle with a screw cap and was labeled according to the colony of origin, place inside the hive and number of specimen. Adult beetles were identified morphologically according to the standard methods for identification at the level of species(18). In total, 27 live adult individuals (1 to 6 beetles per colony) were collected. To obtain the yeasts, each individual beetle was placed inside the YPD (yeast extract-peptonedextrose) agar plates and allowed to walk freely over the entire surface of agar plates during 40 min. There were not externally sterilize the beetles with 70 % alcohol and there were not rinsed with sterile water, with the purpose of keeping them alive and allowing the yeasts to be obtained by imitating the natural form of yeast dispersal inside the honey bee colonies. Each beetle was allowed to nibble the agar surface and move freely over the plate mimicking the way in which the beetles disseminate the yeast when they move through the honey bee combs. This method increases the probability of obtaining yeasts associated with the beetles’ mouthparts and digestive system(15).

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After the time of walk on the agar surface, each beetle was sacrificed and stored in 70 % alcohol. The agar plates were incubated at 25 °C and checked every 24 h to detect the growth of yeasts. It was monitored the growth of microbial colonies by observing each plate under a stereoscopic microscope at 50x magnification. To isolate and purify the yeasts from the agar plates, colonies of each morphotype were selected and were individually placed in Eppendorf tubes with 600 µL of sterile distilled water. The contents were re-suspended by shaking and seeded onto a new agar plate. The resulting plate was incubated at 25 °C for 5 d or until microbial growth was observed. This procedure was performed twice for each morphotype observed on the original agar plates. All yeast morphotypes were stored in the Yucatan Scientific Research Center (CICY) yeast collection. Identification of species was performed through the symmetric sequencing of the D1/D2 domain (nucleotides 63-642 of Saccharomyces cerevisiae) of the large subunit (LSU) rRNA gene, following the standard DNA extraction protocols(19,20). For DNA extraction, cells were grown for approximately 48 hours in YPD broth (3 g of yeast extract, 3 g of malt extract, 5 g of peptone, and 10 g of glucose per liter of distilled water) in a rotary shaker at 100 rpm at 27 ºC and harvested by centrifugation. The packed cells were immersed in liquid nitrogen for 10 min and then crushed in a mortar and placed in a sterile tube with 800 µL of buffer (50 mM Tris-HCl, 250 mM NaCl, 50 mM EDTA, 0.3 % sodium dodecyl sulfate). Twenty microliters of RNase were added to clean the samples, which were then heated in a thermoblock at 65 °C for 30 min and gently shaken every 10 min. They were cooled to 23 °C, and then 500 µL of chloroform was added to extract the DNA. The resulting volume was centrifuged at 1,300 rpm for 10 min. The supernatant was recovered, and 700 µL of isopropanol was added. Samples were gently mixed and centrifuged again for 5 min to precipitate DNA. The pellet was recovered and washed with 500 µL of 70 % ethanol and then centrifuged for 5 min. The supernatant was discarded, and the pellet was allowed to dry for 24 h at room temperature. Subsequently, the pellet was re-suspended in 70 μL of TE buffer (10 mM TrisHCl, 1 mM EDTA [pH 7.4]). DNA samples were prepared for PCR using 5 µL (120-500 ng) of the diluted samples with 1 mL of TE buffer at 0.5X. To verify the extraction, 5 μL (120-500 ng) of DNA was used in a 1 % agarose gel electrophoresis using TBE (Tris-Borate-EDTA) 0.5X and a current of 100 V for 15 min. Subsequently, the DNA was quantified by spectrophotometry with a Nano Drop 2000 (ThermoFisher Scientific) and diluted to 20 ng/μL for PCR. Amplification of the D1/D2 sequence was performed using the primers NL-1 (5´GCATATCAATAAGCGGAGGAAAAG-3´) and NL-4 (5´-GGTCCGTGTTTCAAGACGG3´)(20) and the following PCR protocol: 95 °C for 12 min followed by 40 cycles of denaturation at 94 °C for 15 sec annealing at 55 °C for 10 sec, extension at 72 °C for 20 sec, and a final extension of 5 min at 72 °C . The amplified DNA was prepared for sequencing using a BigDye® Terminator v3.1 Cycle Sequencing Kit (Applied Biosystems) following the manufacturer’s instructions with the primers NL-1 and NL-4. The samples were individually injected for electrophoresis into an ABI 3730xl DNA Analyzer (Applied Biosystems). Sequences were aligned and assembled, and a

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consensus sequence was obtained for each yeast isolate using Geneious Pro 8.1.7 bioinformatics software (Biomatters Ltd, Auckland, New Zealand). The GenBank nucleotide database was queried using the Basic Local Alignment Search Tool (BLAST)(21) to look for named yeast species with DNA sequences that matched the isolates of this study. All sequences yielded significant correlations with named yeast accessions in GenBank, with 98.8-100 % of sequence coverage and identity. The degree of divergence in the D1/D2 portion between the study sequences and concordant sequences found in the GenBank database did not exceed 1 %; therefore, they were considered co-specific sequences(22). The sequences obtained in this study were deposited in GenBank under the accession numbers shown in Table 1. Table 1: Yeast isolates resulted from SHB adults collected in colonies of Africanized honey bee Apis mellifera in apiaries of Yucatan, Mexico Strain GenBank Beetle AHB Site of Species ND* designation accession specimen colony collection CICY number 1 1 1 2 2 2 2 2 2 3 3 3 3 4 5 6 7 7 8 9 9 10 10 11 12 13

1 1 1 2 2 2 2 2 2 2 2 2 2 2 2 3 3 3 3 4 4 4 4 4 4 4

Kodamaea ohmeri Kodamaea ohmeri Meira argovae Kodamaea ohmeri Kodamaea ohmeri Citeromyces siamensis Kodamaea ohmeri Kodamaea ohmeri Citeromyces siamensis Kodamaea ohmeri Kodamaea ohmeri Citeromyces siamensis Kodamaea ohmeri Citeromyces siamensis Lachancea fermentati Kodamaea ohmeri Kodamaea ohmeri Kodamaea ohmeri Kodamaea ohmeri Kodamaea ohmeri Kodamaea ohmeri Kodamaea ohmeri Kodamaea ohmeri Kodamaea ohmeri Kodamaea ohmeri Kodamaea ohmeri

Brood comb Brood comb Brood comb Artificial feeder Artificial feeder Artificial feeder Artificial feeder Artificial feeder Artificial feeder Artificial feeder Artificial feeder Artificial feeder Artificial feeder Artificial feeder Artificial feeder Bottom board Bottom board Bottom board Bottom board Bottom board Bottom board Bottom board Bottom board Bottom board Bottom board Bottom board

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0 0 1 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0

CICYRN1044 CICYRN1045 CICYRN1047 CICYRN1048 CICYRN1049 CICYRN1050 CICYRN1051 CICYRN1052 CICYRN1053 CICYRN1054 CICYRN1055 CICYRN1056 CICYRN1058 CICYRN1059 CICYRN1060 CICYRN1062 CICYRN1063 CICYRN1064 CICYRN1066 CICYRN1069 CICYRN1070 CICYRN1072 CICYRN1073 CICYRN1091 CICYRN1074 CICYRN1075

MF431846 MF431847 MF431848 MF431849 MF431850 MF431851 MF431852 MF431853 MF431854 MF431855 MF431856 MF431857 MF431858 MF431859 MF431860 MF431861 MF431862 MF431863 MF431864 MF431865 MF431866 MF431867 MF431868 MF431869 MF431870 MF431871

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13 14 14 15 16 17 18 19 20 20 20

4 Kodamaea ohmeri Bottom board 0 CICYRN1076 MF431872 4 Kodamaea ohmeri Bottom board 0 CICYRN1089 MF431873 4 Kodamaea ohmeri Bottom board 0 CICYRN1090 MF431874 5 Kodamaea ohmeri Bottom board 0 CICYRN1077 MF431875 5 Kodamaea ohmeri Bottom board 0 CICYRN1078 MF431876 6 Kodamaea ohmeri Bottom board 0 CICYRN1081 MF431877 6 Kodamaea ohmeri Bottom board 0 CICYRN1082 MF431878 6 Kodamaea ohmeri Bottom board 0 CICYRN1084 MF431879 6 Kodamaea ohmeri Bottom board 0 CICYRN1086 MF431880 6 Kodamaea ohmeri Bottom board 0 CICYRN1087 MF431881 6 Kodamaea ohmeri Bottom board 0 CICYRN1088 MF431882 * rDNA nucleotide differences between type strain from GenBank and conspecific isolate from this study.

Results Adult beetles of A. tumida were found in all the AHB colonies examined and, individuals were found inhabiting mainly in the small cracks of the bottom board. The presence of yeasts was detected in the agar plates (each one depicting an individual beetle sample) after 5 d of incubation. Thirty-seven strains were obtained from 20 of the 27 collected beetles, of which four different yeast species were identified; three of them are reported for the first time associated to A. tumida. The most frequent yeast identified was K. ohmeri with 31 isolates, followed by Citeromyces siamensis with four isolates, Lachancea fermentati and Meira argovae with one isolate respectively (Table 1). Concerning the place where the individuals were collected inside the hive, SHB adults from the bottom board have associated only with K. ohmeri, while the beetles collected from the brood combs have associated with two, K. ohmeri and M. argovae. Beetles collected in the artificial feeders have associated with three species of yeasts, K. ohmeri, C. siamensis and L. fermentati.

Discussion Of the four yeast species that were identified, only one can be considered as close-associated with SHB(11), K. ohmeri. This yeast was isolated from most of the beetles collected in the hive, and it is considered as secondary symbiont of A. tumina(7). Results give evidence that symbionts are conjointly distributed with their hosts(15,16) and point out that the invasion of SHB in apiaries of this Mexican region have resulted in the expansion and distribution of K. ohmeri into new areas not been previously registered before.

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In contrast, the other yeasts isolated from the SHBs cannot be defined as symbionts and probably were externally acquired by SHB adults during theirs movements through brood combs, feeders, and other structures inside the hive. Citeromyces siamensis, which belongs to Saccharomycetales order, is a fermentative yeast associated with high osmotic foods, such as salted squid and fermented soybeans(23). In this study, it was isolated it from beetles that were feeding on sucrose syrup within the artificial feeders and probably this yeast was passively acquired by beetles feeding on the syrup. Unlike C. siamensis, another fermentative yeast, L. fermentati (Saccharomycetales) is associated with the gut of insects such as fruit flies and neuropters(24) and has been isolated from a variety of liquid substrates such as fruit juice and olive and tequila ferments(25). In this study, L. fermentati was isolated from a beetle that was feeding on the artificial feeder, which is consider a circumstantial acquisition. Meira argovae is a basidiomycetous anamorphic yeast-like fungus belonging to class Ustilaginomycetes and has been reported to be associated with phytophagous mites(26) in bamboo shoots(27). Meira argovae may have potential for controlling these mites in important crops because it secretes antagonistic substances(28). M. argovae was isolated from a beetle collected in a bee brood comb of a colony, therefore, the acquisition of this yeast by the SHB adult may have been occurred when the beetle walked through brood combs. Kod amaea ohmeri (Saccharomycetales, family Metschnikowiaceae) was the most frequently isolated species in this study and it is also the only species that has been repeatedly isolated from fermented material found in A. mellifera colonies infested with SHB, as well as from within the body of beetles(2,7,8,29). The importance of the relationship between SHBs and K. ohmeri yeast is not well understood, although the presence of K. ohmeri has been shown to increase the beetlesâ&#x20AC;&#x2122; ability to invade and reproduce in A. mellifera colonies(7,30) because this yeast is responsible for producing volatile components in the food, which act as a strong attractant to other beetles(31).

The results showed that the SHBs together with their associated yeast, K. ohmeri, have invaded A. mellifera colonies in Yucatan, suggesting that the impact of the beetles on AHB colonies in this region may increase due to the presence of K. ohmeri. However, experimental studies are required to test the hypothesis that the presence of K. ohmeri isolated in this study increase the capacity of SHB to infest AHB apiaries. In Yucatan, AHB have similar behavior to their African ancestors, which entrap, encapsulate and confine SHB adults within the cracks and crevices of the hive and also remove the beetlesâ&#x20AC;&#x2122; eggs and larvae, avoiding honeycomb fermentation, the production of chemicals and reducing the attraction of more beetles inside the colonies.

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It has been proposed to use K. ohmeri to ferment pollen substitutes or the pollen itself, as bait for beetle traps to control SHB in the honey bee colonies(32). In addition to the use of K. ohmeri in baits, experimental data using C. siamensis, L. fermentati and M. argovae are also needed to explore the role of these yeasts as attractants of beetles. Although K. ohmeri is not exclusive of SHBs as this yeast has been also isolated from nests of bumblebees such as Bombus impatiens and Bombus pensylvanicus that do not have SHBs in their colonies(12), it is plausible to assume that SHB are active disperser of K. ohmeri and other yeasts to new resources and hosts(8,16).

Conclusions and implications The results pointed out that SHB is present in Africanized apiaries in the Yucatan area and that this colonization has also resulted in the presence of their facultative symbiont K. ohmeri and of other food- and invertebrate-associated yeasts, C. siamensis, L. fermentati and M. argovae, in substrates not previously recorded for these yeasts, and for the first time, in a region where they had not been observed.

Acknowledgements To Matilde Margarita Ortiz GarcĂa for assistant with PCR methods.

Funding This research was supported by the Consejo Nacional de Ciencia y Tecnologia under Grant number 219922.

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Disclosure statement No potential conflict of interest was reported by the authors.

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Neumann P, Pettis JS, Schäfer MO. Quo vadis Aethina tumida? Biology and control of small hive beetles. Apidologie 2016;47(3):427-466.

Del Valle-Molina J. Informe de notificación inmediata. Small hive beetle infestation (Aethina tumida) in Mexico: Immediate notification report (Report No. OIE: 6397) OIE World Organization for Animal Health. 2007.

SAGARPA. Secretaría de Agricultura, Ganadería, Desarrollo Rural, Pesca y Alimentación. Servicio de Información Agroalimentaria y Pesquera (SIAP). Avance de la producción pecuaria por producto, México. http://www.gob.mx/siap/acciones-y-programas/produccionpecuaria. Consultado 1 Dic, 2016.

Galván-Hernández G. Nota informativa: Hallazgo de presencia de Aethina tumida (Pequeño Escarabajo de la Colmena: PEC) en flora silvestre en el estado de Yucatán en la periferia de un apiario negativo a PEC. Servicio de Sanidad, Inocuidad y Calidad Agroalimentaria. SAGARPA Secretaría de Agricultura, Ganadería, Desarrollo Rural, Pesca y Alimentación. Mexico. 2013.

Reyes-Escobar O, Dosal-Alonso E, Lara-Álvarez C, Lara-Álvarez LG, Dorantes-Ugalde JA, Saldaña-Loza LM. Lethal effect of boric acid and attractants against the small hive beetle, Aethina tumida Murray (Coleoptera: Nitidulidae). J Apicult Res 2016;(54):1-7.

Benda ND, Boucias D, Torto B, Teal P. Detection and characterization of Kodamaea ohmeri associated with small hive beetle Aethina tumida infesting honey bee hives. J Apicult Res 2008;47(3):194-201.

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10. Leemon D. In-hive fungal biocontrol of small hive beetle. Brisbane Queensland, Australia: Rural Industries Research and Development Corporation; 2012. 11. Amos BA, Leemon D, Hayes RA, Cribb BW, Furlong MJ. Associations between the small hive beetle and the yeast Kodamaea ohmeri throughout the host life cycle. J Econom Entomol 2018;111(4):1501-1508. 12. Graham JR, Ellis JD, Carroll MJ, Teal P. Aethina tumida (Coleoptera: Nitidulidae) attraction to volatiles produced by Apis mellifera (Hymenoptera: Apidae) and Bombus impatiens (Hymenoptera: Apidae) colonies. Apidologie 2011;(42):326-336. 13. Neumann P, Hoffmann D, Duncan M, Spooner-Hart R, Pettis JS. Long-range dispersal of small hive beetles. J Apicult Res 2012;(51):214-215. 14. Ishikawa H. Insect Symbiosis: An Introduction. In: Bourtzis K, Miller TA, editors. Insect Symbiosis. Boca Raton, FL, USA: CRC Press; 2003:1-22. 15. Vega FE, Dowd PF. The role of yeasts as insect endosymbionts. In: Vega FE, Blackwell M, editors, Insect-fungal associations: ecology and evolution. Oxford, USA: Oxford University Press; 2005:211-243. 16. Henry LM, Peccoud J, Simon JC, Hadfield JD, Maiden MJC, Ferrari J, et al. Horizontally transmitted symbionts and host colonization of ecological niches. Curr Biol 2013;23(17):1713-1717. 17. Blackwell M. Made for each other: Ascomycete yeasts and insects. Microbiol Spectr 2017;5(3): FUNK-0081-2016. doi:10.1128/microbiolspec.FUNK-0081-2016. 18. Neumann P, Evans JD, Pettis JS, Pirk C. SchĂ¤fer MO, Tanner G, Ellis JD. Standard methods for small hive beetle research. J Apicult Res 2013;(52):1-32.

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19. Tapia-Tussell R, Lappe P, Ulloa M, Quijano-Ramayo A, Cáceres-Farfán M, Larqué-Saavedra A, Perez-Brito D. A rapid and simple method for DNA extraction from yeasts and fungi isolated from Agave fourcroydes. Mol Biotechnol 2006;(33):67-70. 20. Kurtzman CP, Robnett CJ. Identification and phylogeny of ascomycetous yeasts from analysis of nuclear large subunit (26S) ribosomal DNA partial sequences. Antonie van Leeuwenhoek, 1998;(73):331-371. 21. Altschul SF, Madden TL, Schäffer AA, Zhang J, Zhang Z, Miller W, et al. Gapped BLAST and PSI-BLAST: a new generation of protein database search programs. Nucleic Acids Res 1997;25(17):3389-3402. 22. Peterson SW, Kurtzman CP. Ribosomal RNA sequence divergence among sibling species of yeasts. Syst Appl Microbiol 1991;(14):124-129. 23. Nagatsuka Y, Kawasaki H, Limtong S, Mikata K, Seki T. Citeromyces siamensis sp. nov., a novel halotolerant yeast isolated in Thailand. Int J Syst Evol Microbiol 2002;(52):2315-2319. 24. Nguyen NN, Suh S, Blackwell M. Five novel Candida species in insect-associated yeast clades isolated from Neuroptera and other insects. Mycologia 2007;(99):842-858. 25. Lachance MA, Kurtzman CP. Lachancea Kurtzman (2003). In: Kurtzman CP, Fell JW, Boekhout T. editors. The yeasts, a taxonomic study. London: Elsevier; 2011:511-520. 26. Boekhout T, Theelen B, Houbraken J, Robert V, Scorzetti G, Gafni A, et al. Novel anamorphic mite-associated fungi belonging to the Ustilaginomycetes: Meira geulakonigii gen. nov., sp. nov., Meira argovae sp. nov. and Acaromyces ingoldii gen. nov., sp. nov. Int J Syst Evol Microbiol 2003;(53):1655-1664. 27. Tanaka E, Shimizu K, Imanishi Y, Yasuda F, Tanaka C. Isolation of basidiomycetous anamorphic yeast-like fungus Meira argovae found on Japanese bamboo. Mycoscience 2008;(49):329-333. 28. Paz Z, Bilkis I, Gerson U, Kerem Z, Sztejnberg A. Argovin, a novel natural product secreted by the fungus Meira argovae, is antagonistic to mites. Entomol Exp Appl 2011;(140):247253. 29. Torto B, Fonbong A, Mutyabai DM, Muli E, Arbogast RT, Teal P. Aethina tumida (Coleoptera: Nitidulidae) and Oplostomus haroldi (Coleoptera: Scarabaeidae): occurrence in Kenya, distribution within honey bee colonies, and responses to host odors. Ann Entomol Soc Am 2010;(103):389-396.

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30. Arbogast RT, Torto B, Willms S, Fombong AT, Duehl A, Teal P. Estimating reproductive success of Aethina tumida (Coleoptera: Nitidulidae) in honey bee colonies by trapping emigrating larvae. Environ Entomol 2012;41(1):152-158. 31. Torto B, Arbogast RT, Alborn H, Suazo A, Engelsdorp D, Boucias D, et al. Composition of volatiles from fermenting pollen dough and attractiveness to the small hive beetle Aethina tumida, a parasite of the honeybee Apis mellifera. Apidologie 2007;(38):380-389. 32. Torto B, Arbogast RT, Engelsdorp DV, Willms S, Purcell D, Boucias D, et al. Trapping of Aethina tumida Murray (Coleoptera: Nitidulidae) from Apis mellifera L. (Hymenoptera: Apidae) colonies with an in-hive baited trap. Environ Entomol 2007;(36):1018-1024.

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https://doi.org/10.22319/rmcp.v11i4.5292 Article

Determining factors for the use of sorghum as fodder for bovines in Northwestern Mexico

Venancio Cuevas-Reyes a Blanca Isabel Sánchez Toledano b* Roselia Servín Juárez c Juan Esteban Reyes Jiménez d Alfredo Loaiza Meza d Tomas Moreno Gallegos d

Instituto Nacional de Investigaciones Forestales Agrícolas y Pecuarias (INIFAP). Campo Experimental Valle de México. Estado de México, México. b

INIFAP. Campo Experimental Zacatecas, Zacatecas, México.

Colegio de Postgraduados Campus Córdoba, Córdoba, Veracruz, México.

INIFAP. Campo Experimental Valle de Culiacán, Sinaloa, México.

*Corresponding author: sugammx@hotmail.com

Abstract: The objective of the study was to analyze the factors that determine the use of free pollination varieties of sorghum in the north of the state of Sinaloa, in order to characterize the type of producers that use this type of seeds. A discrete choice model was utilized to identify the factors that influence the adoption of sorghum by 199 farmers. Later, adopters (n= 11) and non-adopters (n= 188) of the technology were characterized based on nonparametric tests. The results show that 5.5 % of the producers have adopted sorghum varieties. The number of years with technical assistance and milk production were

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significant (P<0.05) for the adoption. Also, the characterization of the farmers showed that those who have more resources—infrastructure, machinery, livestock, land, wages and technical assistance, —were the ones who adopted the varieties of sorghum. It is concluded that the adoption of seeds is low and requires public goods, such as agricultural outreach programs, for the dissemination of its benefits to allow greater appropriation by farmers in the region of study. Key words: Fodder, Free pollination, Adoption, Technological innovation, Dual purpose, Probit.

Received: 14/03/2019 Accepted: 06/09/2019

Introduction

Sorghum is one of the basic foods consumed by the world's poorest people. From a genetic point of view, this crop adapts well to hot and dry agro-ecological areas where it is difficult to grow other cereals. In many of these areas, sorghum, both as grain and as fodder, is given a high use value(1). Mexico is the second largest producer of sorghum in the world, with 10% of the world production(2). In 2017, the area planted with sorghum in Mexico was 1'456,330 ha, with a yield of 4’853,109 t. Within the main producing states is Sinaloa, which occupies the third place nationally in area planted with 109,382.59 ha(3). In 2017, 1’149,320 hectares were cultivated in Sinaloa, of which the production of vegetables occupied 6.18 % ; grains, 67.52 %; oilseeds, 13.14 %; sugar cane, 0.30 %; fruits, 3.63 %, and other crops, 9.24 %. The production of sorghum amounted to 14.10% of the planted area in the state(3). The most widely used sorghum varieties in Sinaloa are "free pollination" materials— Costeño-201, Fortuna and Gavatero 203—from the National Institute for Research on Forestry, Agriculture and Livestock (INIFAP), which have been shown acceptance by producers and which, when harvested, can be used for planting without affecting the genetic quality of the grain produced(4). Despite the fact that the sorghum grain is an important fodder for animals, it is ascribed a low "digestibility" compared to other cereals, due to the presence of condensed tannins. Tannins are generally found in brown sorghum grains, but not in white 1114

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sorghum grains, and rarely in red sorghum grains. In Sinaloa, most of the sorghum varieties developed by INIFAP are white or creamy grains(4). Among the various factors that explain the low adoption of improved seed varieties, the studies identify the lack of adaptation of the materials offered, the high perception of the risk involved in their use due to lack of knowledge of its advantages compared with that of the materials utilized by the producer, and deficient distribution(5). Some of the varieties generated by INIFAP and currently in demand by producers are Gavatero 203, Costeño 201, Sinaloa, and, to a lesser extent, Perla 101(6,7,8,9). Gavatero 203 sorghum is the variety most widely accepted by producers; in 2016, seeds of this variety were distributed for the sowing of 70 thousand hectares of rainfed crops in Sinaloa(10). Gavatero sorghum has an intermediate cycle (61 d at flowering and 110 d at harvest). Its grain is reddish-orange, has a grain yield of 2,849 kg ha-1 and a green fodder yield of 35,367 kg ha-1; also, it has 66.4 % digestibility and a 7.3 % protein content(6). The success achieved by this variety lies in its consistency and good behavior under storm conditions (350-600 mm) and in the acceptance of the fodder by the cattle. In addition, under adverse conditions (drought), the variety is vigorous at the early stages of its development, and its seed can be produced at any time of year. In the case of Sinaloa, the adoption of sorghum-free pollination varieties for silage or fodder directly used by bovine cattle has been partial, and progress in their use has been made only in the central and southern zone of the state, where evaluations and demonstrations of this crop have been carried out(6). The present study aims to analyze the factors that determine the use of free pollination varieties of sorghum in the north of the state of Sinaloa, in order to characterize the type of producers who use this type of seeds.

Material and methods Location of the study area

Sinaloa is located within the northwestern coastal plain, which directly borders the Sierra Madre Occidental. Geographically, it is located in northwestern Mexico, bordering the states of Sonora and Chihuahua to the north, Durango to the east, Nayarit to the south, and the Pacific Ocean and Gulf of California to the west (Figure 1), bounded by the extreme coordinates 22°31' and 26°56' N and 105°24' and 109°27' W of the Greenwich Meridian(11).

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Figure 1: Location of the study area

Data collection

The analyzed information comes from a personal and face-to-face survey, conducted in 2015, on a sample of 214 producers of the dual-purpose cattle system (PSBDP, Spanish acronym) in the dry tropics of Sinaloa. The surveys were conducted in northern Sinaloa, a region that has three municipalities with favorable climatic and geographic conditions for sorghum production: Ahome, El Fuerte and Guasave. The selection of producers was made by non-probability sampling(12). The following inclusion criteria were used: (a) ownership of livestock, (b) no previous participation in agricultural extension programs, and (c) agreement to respond to the initial diagnostic survey of their livestock production unit. The survey consisted in collecting data from each producer through a questionnaire with information structured in ten sections, with closed and open questions, which was tested before its final application. The variables included in the questionnaire were divided into the following sections: 1) General aspects of the productive unit, 2) Social and economic characteristics of the producer, 3) Type of ownership of the agricultural area, 4) Level of available resources (number of animals in possession, agricultural land, pasture, water sources), 5) Available facilities (infrastructure, machinery and equipment), 6) Aspects of animal reproduction, 7) Type of feeding and supplementation of livestock, 8) Aspects related to the livestock health of the herd, 9) Aspects related to the management of milking and finally, 10) Issues related to the market for livestock products. The analysis of the information identified 15 surveys with atypical data (e.g. repeated surveys and out-of-average data), so 1116

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only 199 surveys with reliable information were considered for the study. This survey was part of a project that aimed to characterize livestock production systems in northern Sinaloa, the results of the characterization, as well as the description and calculation of the index of infrastructure, machinery and equipment are described in Cuevas-Reyes and RosalesNieto(13).

Probit model

The Probit model is a discrete choice model, which is characterized by the fact that the dependent variable takes only two values; 0 and 1(14), which correspond to each of the two possible alternatives (in this study: 1 adopts and 0 does not adopt sorghum free pollination varieties). The model uses a normal Cumulative Distribution Function (CDF), according to Gujarati(15) the decision to choose an alternative depends on an unobservable convenience index Ii, determined by one or several explanatory variables (Xi), the index Ii can be expressed as follows: đ??źđ?&#x2018;&#x2013; = đ?&#x203A;˝0 + đ?&#x203A;˝đ?&#x2018;&#x2DC; đ?&#x2018;&#x2039;đ?&#x2018;&#x2013; . With the assumption of normality, the probability (Pi) that đ??źđ?&#x2018;&#x2013;â&#x2C6;&#x2014; is less than or equal to Ii is calculated from the standard normal FDA as: Pi = P[Y = 1|X] = P(đ??źđ?&#x2018;&#x2013;â&#x2C6;&#x2014; â&#x2030;¤ đ??źđ?&#x2018;&#x2013; ) = P(đ?&#x2018;?đ?&#x2018;&#x2013; â&#x2030;¤ đ?&#x203A;˝0 + đ?&#x203A;˝đ?&#x2018;&#x2DC; đ?&#x2018;&#x2039;đ?&#x2018;&#x2013; ) = Ď&#x2022;(đ?&#x203A;˝0 + đ?&#x203A;˝đ?&#x2018;&#x2DC; đ?&#x2018;&#x2039;đ?&#x2018;&#x2013; )

(1)

Where P[Y = 1|X] signifies the probability of an event occurring, given the value of Xi, Zi is the standardized normal variable; i.e., đ?&#x2018;?đ?&#x2018;&#x2013; ~ N(0, đ?&#x153;&#x17D; 2 ) and Ď&#x2022;(Xi Î˛) represent the cumulative distribution (CDF) of a standard normal variable (15). Information regarding I, as well as đ?&#x203A;˝0 and đ?&#x203A;˝đ?&#x2018;&#x2DC; , is obtained using the equation (1): đ??źđ?&#x2018;&#x2013; = đ?&#x153;&#x2122; â&#x2C6;&#x2019;1 (đ??źđ?&#x2018;&#x2013; ) = đ?&#x153;&#x2122; â&#x2C6;&#x2019;1 (đ?&#x2018;&#x192;đ?&#x2018;&#x2013; ) = đ?&#x203A;˝0 + đ?&#x203A;˝đ?&#x2018;&#x2DC; đ?&#x2018;&#x2039;đ?&#x2018;&#x2DC;đ?&#x2018;&#x2013;

(2)

Where: Ii= dichotomous dependent variable reflecting the difference between the use and non-use of a technology (1 if the sorghum variety is adopted, 0 if not). đ?&#x153;&#x2122; â&#x2C6;&#x2019;1 is the reverse of the normal CDF. đ?&#x203A;˝0 = is a constant. đ?&#x203A;˝đ?&#x2018;&#x2DC; , k = 1, 2 ... n are the coefficients of the independent variables to be estimated. Xki = vector of exogenous variables that explain the adoption of sorghum varieties.

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Once the model (1) has been estimated, the following equation will result from the partial derivative (14): â&#x2C6;&#x201A;Ď&#x2022; â&#x2C6;&#x201A;đ?&#x2018;&#x2039;đ?&#x2018;&#x2DC;

= đ?&#x153;&#x2122;(đ?&#x2018;Ľđ?&#x2018;&#x2DC;đ?&#x2018;&#x2013; đ?&#x203A;˝)đ?&#x203A;˝đ?&#x2018;&#x2DC;

(3)

Thus, the equation (3) represents the effect of the change (marginal probability) of a Xki unit on the likelihood that I = 1. The model estimation was done by the maximum likelihood method, which provides consistent, asymptotically efficient estimators. The individual significance of the parameters was contrasted using Wald's test, whose statistic Z follows a standardized normal distribution. The overall goodness of fit was evaluated using McFadden's R2, and the LR statistic or likelihood ratio. The results of the econometric model were obtained with the Data Analysis and Statistical Software (Stata), version 12. Once the factors that determine the use of the sorghum varieties were identified, the producers who use (or adopt) the sorghum varieties were characterized in comparison with those who decided not to adopt them. Descriptive statistics were used to analyze the identified groups, and the Mann-Whitney U test was applied to the ordinal variables, as when the (Kolmogorov-Smirnov) normality test was performed on the quantitative variables, they failed to meet the requirements of the test for establishing the normality of the data (P<0.05). The information was processed with SPSSÂŽ, version 21 (IBM Corp).

Results and discussion Factors that determine the use of sorghum varieties Table 1 shows the results obtained from the binary probabilistic model. The value of ď &#x192;2 was used to contrast the global significance of the model; its null hypothesis is that all the coefficients of the equation, except the constant, are null. 96.98 % of the cases were correctly classified, the LR Ji2(9) statistic was 40.04, and the associated probability was less than 0.05; therefore, the null hypothesis was rejected, and the global model was statistically significant. The z statistic applied to the model's coefficients shows that two variables (technical assistance and milk production) were significant (P<0.05) to explain the probability of adopting sorghum varieties in the study region.

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Table 1: Variables that influence the probability of adopting sorghum varieties Variable Coefficient z P>z dy/dx Index of machinery and equipment, % Infrastructure Index, % Technical assistance, years Age, years Schooling, years Distance to the municipality, km Number of adult cows Agricultural area, ha Milk production, L Constant

0.0057 0.0346 0.2149 -0.0167 -0.1470 -0.0073 0.0379 -0.0224 -0.0306 -1.6908

0.47 1.65 2.47 -0.91 -1.19 -0.28 1.42 -1.07 -2.46 -1.50

0.639 0.100 0.013* 0.361 0.234 0.779 0.155 0.238 0.014* 0.134

0.00008 0.0004 0.0030 -0.0002 -0.0020 -0.0001 0.0005 -0.0003 0.0004

dy/dx is the marginal effect of variable x on the dependent variable, and Level of significance dy/dx: P<0.05*. Number of observations (n): 199. LR Ji2(9) =40.04; Prob >Ji2=0.0000; Pseudo R2=0.4706, Correctly classified = 96.98 %.

The marginal effects of the significant variables were small; the PSBDP who have technical assistance have a 0.3 % probability of adopting new sorghum varieties, while the probability of adopting this technology by producers who wish to engage in milk production is as low as 0.04 %. The results obtained in the technical assistance variable in this study are consistent with those found by other authors(16-19), who point to the existence of a positive relationship between technical assistance and the adoption or use of technology. A relevant factor in the adoption of new varieties is the price of the seeds; price is an extrinsic attribute that affects the farmer's decision to buy(20). In the case of Gavatero 203 sorghum, the purchase price during 2018 was $12.00 per kg, which was the same as that of the commercial "milon" sorghum variety (generation F2 or F3 of fodder hybrids Silo Miel, Cow Vittles, and others) used by producers. However, when comparing both types of sorghum, it was observed that Gavatero sorghum exhibits a better quality, with 90 % of complete and viable seeds, compared to 75 % of the "milon". It also has a higher germination viability (85 to 90 %) in contrast to the 75-80 percent of other sorghum varieties(4). An additional advantage of the open-pollinated varieties is their low price compared to sorghum hybrids. In this regard, in the study region there are seeds of hybrid sorghum for silage with a price of sale to the producer of approximately $63.00 pesos per kilo; this price is 425 % higher than the price ($12.00 per kg) of the recommended free pollination varieties in the study area.

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Studies on the adoption of corn varieties generated by INIFAP found that in the state of Veracruz, the variables associated with the producer's decision to use a seed were: proximity to the place of purchase, knowledge of INIFAP seeds and, to a lesser extent, preference for planting improved seeds to replace the native variety(21). On the other hand, in the Yucatan Peninsula, in spite of knowing about INIFAP's improved seeds, the adoption process is interrupted by factors such as the scarce availability of these seeds in the market, or the lack of economic resources on the part of the producer to acquire the seeds(22). As in Yucatan, one of the factors limiting the adoption of sorghum in Sinaloa is the limited availability and knowledge of these materials by the users. Therefore, in order to improve adoption, the available resources and the decisions of producers must be considered.(23) In addition, the reproduction of these seeds must be promoted through producers' organizations that grow or offer more than one type of seed and receive constant training on production technology, processing, distribution, packaging and marketing of open-pollinated varieties(24). The production of sorghum for fodder in the region of study constitutes an important alternative as, since it is a variety of free pollination, the same producer can store seeds for the following cycles. However, in order to maintain its genetic quality, controlled production of this type of seed is required. In the region of study, the Produce Sinaloa Foundation is the only institution that has developed a production scheme for some of the free pollinated sorghum varieties released by INIFAP. However, the greatest effort is currently being made by individual producers who, on their own initiative and at their own risk, are engaging in this activity to meet the demand of the PSBDP across the state.

Characterization of adopters and non-adopters of sorghum varieties

The results obtained are consistent with previous studies that indicate that the adoption of free pollination varieties by producers is low(5,21,22). In this regard, only 5.5 % of the producers in this study adopted new varieties of sorghum. The following is a description of the adopting and non-adopting producers in terms of social and productive aspects and types of products obtained.

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Social aspects The social variables analyzed among the group of adopters and non-adopters were the same in both groups (P>0.05), so from the social point of view they are families with similar characteristics (Table 2). These results differ from those found by other authors(25), who point out that social characteristics are variables that differentiate the users of technological innovations. Table 2: Social variables between adopters and non-adopters (median) Variable Adopters Non-adopters Age, yr 55.0 47.5 Schooling, yr 3.0 3.0 Dependents aged over 18 yr (#) 1.0 1.0

P* 0.497 0.389 0.224

* Mann-Whitney U.

Availability of productive resources Significant differences (P<0.05) between adopters and non-adopters were identified for six productive variants: infrastructure index, machinery and equipment index, type of employment (casual or hired), number of animal units, total agricultural area and number of years that technical assistance has been provided. In other words, producers who adopt sorghum seeds have more productive resources (agricultural area, number of animal units), more infrastructure and equipment, and also salaried labor and technical assistance (Table 3). Adopters held a larger number of hectares (median of 15 ha) than non-adopters (median of 8 ha); it seems that the availability of land together with other productive resources makes the producers less adverse to risk and, therefore, they may venture to plant new varieties. Table 3: Productive variables between adopters and non-adopters (median) Variable Infrastructure index, % Index of machinery and equipment, % Daily wage (0= casual; 1=hired) Total number of adult cows, # Stocking rate, # Agricultural area, ha Technical assistance, years

Adopters 36.36 40.00 1.00 20.00 34.30 15.00 4.00

* Mann-Whitney U.

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Non- adopters 18.18 20.00 0.00 13.50 21.30 8.00 0.00

P* 0.000 0.006 0.010 0.102 0.027 0.029 0.000

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Technology adoption is positively associated with larger plot sizes and higher incomes(26). However, farmers usually tend to face any innovation with uncertainty and preconceptions regarding how it will affect them(27). The results obtained with respect to the variables of farm size and contact with technical assistance services coincide with various studies conducted on the use of rice and wheat varieties by small producers(28,29).

Types of products obtained: milk and meat

The explanatory milk production variables were significant at 1% (P= 0.000) for nonadopters and 5% (P=0.005) for calf production in the group of adopters. This result indicates that the group of adopters of new sorghum varieties are inclined to produce calves. Adopters had a median of 0 and non-adopters had a median of 30 L of milk. In contrast, adopters' calf production had a median of 2 and non-adopters had a median of 0. Seemingly, then, the use of new varieties of sorghum has to do with the possibility of having more fodder to improve the production of calves for fattening. The use of sorghum free pollination varieties in the study area has several advantages over other materials and hybrids, including: 1) Better yield in grain and fodder, 2) Low price compared to commercial sorghum, 3) Produces even under drought conditions, 4) The producer can "harvest" its seed for the next sowing cycle. However, the varieties are little known by the producers. In this regard, Kaliba et al(30) mention that greater adoption of seeds of sorghum varieties requires linking research, extension services, and decision makers in order to promote appropriate agricultural technology that is up-to-date and easily accessible by producers in the face of production, the market, and information access constraints.

Conclusions and implications In the study region, only 5.5 % of the producers decided to use free pollinated varieties of sorghum as fodder for cattle. Technical assistance and milk production were the significant variables that determined the adoption of new sorghum varieties. By characterizing the analyzed groups, it was possible to identify that producers who have technical assistance and more resources, such as land and livestock, are more willing to innovate with new technological alternatives. The results of the research allow us to conclude that free pollinated varieties are not so rapidly adopted due to the low availability of new sorghum materials and to the lack of dissemination actions and technology transfer to the producers of the dualpurpose cattle system. Schemes must be designed for the reproduction and dissemination of 1122

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freely pollinated sorghum seeds. Other public goods, such as agricultural extension, are also required to enable the dissemination and transfer of these technological innovations as well as a greater adoption by livestock producers in northern Sinaloa and in regions with similar characteristics and issues.

Acknowledgements The authors wish to thank INIFAP for the funding of this research within the framework of the project "Evaluation of the agricultural training process and the use of technology promoted in the 2015-2018 comprehensive training programs ", with the SIGI number 14462132918.

Literature cited: 1. FAO. Organización de las Naciones Unidas para la Alimentación y la Agricultura. La economía del sorgo y del mijo en el mundo: hechos, tendencias y perspectivas. Italia, 1997. http://www.fao.org/3/w1808s/w1808s00.htm#Contents. Consultado 28 Feb, 2019. 2. FAOSTAT. Organización de las Naciones Unidas Para la Alimentación y la AgriculturaEstadísticas. 2019. http://faostat.fao.org. Consultado 5 Mar, 2019. 3. SIAP. Servicio de Información Agroalimentaria y Pesquera. Avances de Siembras y Cosechas por Estado y Año Agrícola. México. 2019. http:// www.gob.mx/siap. Consultado 6 Mar, 2019. 4. Palacios VO, Moreno GT, Loaiza MA, Reyes JJE, Medina ChS. Gavatero-203, Nueva variedad de sorgo forrajero para Sinaloa. INIFAP-CIRNO. Campo Experimental Valle de Culiacán. Folleto técnico No. 31. Culiacán, Sinaloa. 2009. 5. López PMA, García JC. Las industrias de la semilla de maíz en Brasil y México: desempeño anterior, problemas actuales y perspectivas para el futuro. Documento de Trabajo de Economía del CIMMYT 97-02. CIMYT. México. 1997. 6. Hernández ELA, Moreno GT, Loaiza MA, Reyes JJE. Gavatero-203, nueva variedad de sorgo forrajero para el estado de Sinaloa. Rev Mex Cienc Agric 2010;1(5):727-731. 7. Hernández ELA, Moreno GT, Loaiza MA, Reyes JJE. Sinaloense-202, nueva variedad de sorgo para el estado de Sinaloa. Rev Mex Cienc Agric 2010;1(5):733-737.

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8. Hernández ELA, Moreno GT, Loaiza MA, Reyes JJE. Perla-101: nueva variedad de sorgo para el estado de Sinaloa. Rev Mex Cienc Agric 2011;2(5):779-784. 9. Hernández ELA, Moreno GT, Loaiza MA, Reyes JJE. Costeño-201: nueva variedad de sorgo de temporal de doble propósito para Sinaloa. Rev Mex Cienc Agric 2011;2(5):785-790. 10. SENASICA. Servicio Nacional de Sanidad, Inocuidad y Calidad Agroalimentaria. Reporta el Cesavesin rendimientos de 10 toneladas por hectárea de maíz durante las primeras trillas en el estado. México. 2016. http://sinavef.senasica.gob.mx/ALERTAS/inicio/pages/single.php?noticia=1531. Consultado 5 Mar, 2019. 11. INEGI. Instituto Nacional de Estadística y Geografía. Anuario estadístico y geográfico de Sinaloa 2017. México 2017. https://www.datatur.sectur.gob.mx/ITxEF_Docs/SIN_ANUARIO_PDF.pdf. Consultado 10 Ene, 2019. 12. Abascal FE, Grande El. Análisis de encuestas. España: Edit. ESIC; 2005. 13. Cuevas-Reyes V, Rosales-Nieto C. Caracterización del sistema bovino doble propósito en el noroeste de México: productores, recursos y problemática. Rev MVZ 2018;23(1):6448-6460. 14. Aldrich JH, Nelson FD. Linear Probability, Logit, and Probit Models. Newbury Park, California, USA: Sage Publications; 1984. 15. Gujarati DN, Porter DC. Econometría. México. McGraw Hill; 2010. 16. Galindo GG. Uso de Innovaciones en el grupo de ganaderos para la validación y transferencia de tecnología "Joachin", Veracruz, México. Terra 2001;(19):385-392. 17. McGinty MM, Swisher EM. Agroforestry adoption and maintenance: self-efficacy attitudes and socio-economic factors. Agrof Syst 2008;(73):99-108. 18. Solís D, Bravo-Ureta BE, Quiroga RE. Technical efficiency among peasant farmers participating in natural resource management programs in Central America. J Agric Econ 2009;60(1):202-219. 19. Cuevas RV, Baca MJ, Cervantes EF, Espinosa GJA, Aguilar AJ, Loaiza MA. Factores que determinan el uso de innovaciones tecnológicas en la ganadería de doble propósito en Sinaloa, México. Rev Mex Cienc Pecu 2013;4(1):31-46.

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20. Lockshin L, Jarvis W, D'Hauteville F, Perrouty J. Using simulations from discrete choice experiments to measure consumer sensitivity to brand, region, price, and awards in wine choice. Food Qual Prefer 2006;7(3-4):166-178. 21. Del Angel-Pérez A, Villagómez-Cortés J, Larqué-Saavedra B, Adame-García J, TapiaNaranjo C, Sangerman-Jarquin D, et al. Preferencias y percepciones asociadas con semilla mejorada de maíz según productores de Veracruz Central, México. Rev Mex Cienc Agric 2018;9(1):163-173. 22. Uzcanga PNG, Larqué SB, Del Ángel PAL, Rangel FMA, Cano GAJ. Preferencias de los agricultores por semillas mejoradas y nativas de maíz en la Península de Yucatán, México. Rev Mex Cienc Agric 2017;(5):1021-1033. 23. Sánchez-Toledano BI, Kallas Z, Gil JM. Importancia de los objetivos sociales, ambientales y económicos de los agricultores en la adopción de maíz mejorado en Chiapas, México. Rev Fac Cienc Agrar 2017;49(2):269-287. 24. Trejo HL, Gil MA, Sánchez HM, Carballo CA, López PA. Producción de semilla mejorada por organizaciones de agricultores caso "Productora de maíz teocintle". Rev Fitotec Mex 2004;27(1):93-100. 25. Sánchez-Toledano BI, Zegbe JA, Espinoza-Arellano JJ, Rumayor-Rodríguez AF. Adopción tecnológica de surcos-doble hilera con pileteo en cebada maltera. Trop Subtrop Agroecosyt 2017;20(1):25-33. 26. Chirwa, E. Adoption of fertilizer and hybrid seeds by smallholder maize farmers in Southern Malawi. Dev South Afr 2005;22(1):1-12. 27. Allub, L. Aversión al riesgo y adopción de innovaciones tecnológicas en pequeños productores rurales de zonas áridas: un enfoque causal. Est Soc 2001;(2):467-493. 28. Hagos H, Ndemo E, Yosuf, J. Factors affecting adoption of upland rice in Tselemti district, northern Ethiopia. Agric & Food Secur 2018;7-59. 29. Chandio AA, Yuansheng J. Factors influencing the adoption of improved wheat varieties by rural households in Sindh, Pakistan. AIMS Agri Food 2018;3(3): 216-228. 30. Kaliba AR, Mazvimavi K, Gregory TL, Mgonja FM, Mgonja, M. Factors affecting adoption of improved sorghum varieties in Tanzania under information and capital constraints. Agric Econ 2018;6-18.

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https://doi.org/10.22319/rmcp.v11i4.5344 Article

Genetic improvement of aerial alfalfa biomass and its components: half-sib family selection

Milton Javier Luna-Guerrero a Cándido López-Castañeda a* Alfonso Hernández-Garay a†

Colegio de Postgraduados. Postgrado en Recursos Genéticos y Productividad. Carretera México-Texcoco km. 36.5, Montecillo, Texcoco, Estado de México, México.

*Corresponding author: clc@colpos.mx

Abstract: In this study, it was evaluated the genetic variation in aerial biomass (BM) or dry matter yield (DMY) and its components in 400 alfalfa half-sib families (HSF), derived from direct (DC, San Miguel x Oaxaca) and reciprocal crosses (RC, Oaxaca x San Miguel), and the original varieties (SM, San Miguel; O, Oaxaca). The experiment was performed in pots under outdoor conditions in Montecillo, Texcoco, Estado de México, Mexico. Complete plants were cut at a 5 cm height, every five weeks in the fall-winter of 20142015, and every four weeks in the spring-summer of 2015. The DMY, AGR (absolute growth rate), RUE (radiation use efficiency), NT (number of tillers per plant), and PH (plant height) were 32, 31, 32, 6, and 36 % higher in DC. The DMY, AGR, RUE, and PH were 30, 28, 30, and 34 % higher in RC than the mean of SM and O varieties. The selection allowed the identification of 13 and 17 % of HSF outstanding in DMY and its components in DC and RC. The DMY of the outstanding HSF of DC was 11% higher than the DMY of the outstanding HSF of RC, indicating maternal genetic effects. Key words: Maternal genetic effects, Heritability, Selection, Dry matter.

Received: 16/04/2019 Accepted: 27/09/2019

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Introduction Alfalfa (Medicago sativa L.) is a polymorphic species with wide genetic variability that adapts to various soil and climatic conditions. Alfalfa heritability is complex, mostly because of its autotetraploid meiosis. This species produces a diploid gamete (2n=32), which profoundly affects its phenotypic behavior. Due to its allogamy, alfalfa depends on insects for its pollination and produces some autosterile or autoincompatible plants, and at a lower proportion, plants that produce sterile pollen and ovules(1). The primary aim of selecting new alfalfa varieties is to maximize forage yield with optimal nutritional value, without quality-detrimental compounds, and with high field persistence and minimum use of fertilizers, pesticides, or herbicides(2). Forage quality has been rarely included in selection programs; however, given its importance in livestock production, there is interest in obtaining cultivars with high forage quality(3). The leaf:stem ratio is an indicator of forage quality due to its positive association with digestibility and forage consumption(4), which results from a greater digestibility of leaves in relation to stems(5). The selection of high-yield and high-quality forage, using the accumulation of dry matter in the aerial organs and the leaf:stem ratio, could facilitate the identification of genotypes with higher yield and quality in a phenotyping platform under controlled conditions. The complexity of the hereditary mechanisms of alfalfa and its autotetraploid nature make it difficult to choose the best selection method. However, identifying alleles of individual genetic traits in the phenotype is easier using individual selection methods than inter- or intra-population methods, where the gene flow from one population to another is open and less controlled; also, these methods require a significant number of plants and lower frequencies of favorable genes(6). Family selection allows to evaluate the genotype of each plant; the seed of the selected plants is harvested in an open pollinated or polycross population; the seed of each selected plant is kept separately and sown in replicated progeny tests; the worst families are eliminated, and the best families are crossed with each other to allow recombination and production of subsequent generations(6). Due to its polymorphic nature, the wide variability in alfalfa yield and its components offers excellent selection opportunities. However, although alfalfa's genetic variability favors selection by forage yield, in some cases, the phenotypic expression can be inhibited by a low heritability due to non-additive genetic effects(3). In alfalfa, the high impact of non-additive genetic variation in forage yield is attributed to the high intra-locus interaction caused by its autotetraploid nature (which also includes tri- and tetra-allelic interactions) and the interactions of complementary genes that involve favorable alleles with additive effects in linkage blocks(7). By eliminating non-additive genetic effects, the evaluation is based on additive effects and, therefore, on heritable genetic effects, which can manifest in highly productive genotypes with superior additive alleles(2,8,9). However,

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the complexity of genetic inheritance, selection could be performed with different strategies, such as paternity testing in diverse germplasm, introgression of quantitative traits, and genomic selection(10). In Mexico, the germplasm in commercial use includes traditional low productivity varieties and new, generally introduced, varieties with low field adaptation and persistence. The new challenges posed by climate change and the increasing demand for high-quality forage require new germplasm with greater adaptation to stressful environments, productivity, forage quality, and durability under field conditions. This study aimed to evaluate the variability of aerial biomass or dry matter yield and its components in 400 half-sib families derived from the segregating populations of San Miguel x Oaxaca and Oaxaca x San Miguel, and the original populations of San Miguel and Oaxaca, to perform the first familiar selection cycle in pots under outdoor conditions.

Material and methods Localization

The experiment was performed in plastic pots during the fall-winter of 2014-2015 and the spring-summer of 2015 at Colegio de Postgraduados, Texcoco, Estado de México (19° 29’ N, 98° 54’ W, 2,250 masl). The climate is humid subtropical (Cb(wo)(w)(i´)g), with rains during summer, annual precipitation of 637 mm, and temperature of 15 °C(11).

Plant material

It was used a total of 200 half-sib families (HSF) from the commercial varieties of San Miguel and Oaxaca (certified seeds from Casa Cobos S.A. de C.V., Central de Abastos, Ciudad de México, Mexico) and 200 HSF from the segregating populations of San Miguel (female progenitor) x Oaxaca (male progenitor) (direct cross) and Oaxaca (female progenitor) x San Miguel (male progenitor) (reciprocal cross). The seeds from segregating populations were obtained by the genetic cross between the San Miguel and Oaxaca varieties (direct cross) and the Oaxaca and San Miguel varieties (reciprocal cross), using pollinator insects (bees) under field conditions (Figure 1). The San Miguel and Oaxaca varieties were chosen for the crossings due to their notorious durability under field conditions in an experiment designed to study the productive behavior of five commercial varieties of alfalfa (San Miguel, Oaxaca, Moapa, Valenciana, and Cuf-101) during March 2000(12), this experiment was conducted until the winter-spring cycle of 2006 at Colegio de Postgraduados, Montecillo, Texcoco, Estado de México.

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Figure 1: Genetic crossing system between two alfalfa varieties using pollinator insects under field conditions. Montecillo, Municipio de Texcoco, Estado de MĂŠxico

Seed production took place in the period from January to May 2006. Seeds were harvested from all the plants in each cross's population and used to establish the first family selection cycle(6). A styrofoam seed tray with 100 cells was used for each population. Five seeds of the same size were sown I n each cell. The soil used had a sandy loam texture with 41.6 % of field capacity (FC), 28.2 % of permanent wilting point (PWP), 9.3 % of organic matter , 0.019 % of nitrogen, 4.8 ppm of phosphorus, 4 mmol L-1 of potassium, 1.5 dS m-1 of electric conductivity, and pH of 6.9. Seeds were sown on June 6, 2014; when the seedlings presented the first trifoliate leaf (15 days after sowing, das), the most vigorous seedling was chosen from each cell, and it was transplanted into a plastic pot with a soil capacity of 3 kg. The HSF of each population were randomly assigned to the plastic pots in a completely randomized experimental design. It was fertilized with the 60-140-00 dose at 15 and 240 das, with urea as a nitrogen source and calcium triple superphosphate as a phosphorus source. At 98 das, a uniformization cut was made in the plants. During the experiment, soil humidity was maintained close to FC by applying water every other day.

Variables

Cuts were made every five weeks in the fall-winter 2014-2015 period and every 4 wk in the spring-summer 2015 period from day 98; the cuts were made 5 cm above ground level. The plant's morphological composition was evaluated in a subsample of four complete secondary tillers from each plant and cut in all the HSF, separating the leaves

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(leaflets and petioles) from the tillers. The subsample and the rest of the secondary tillers were dried at 65 °C until constant weight and were then weighted. Plant height (PH, cm) was measured in all the HSF before the cutting with a 1 m long wooden ruler (graduated in cm) from the soil surface to the stem apex. The number of secondary tillers (NT) per plant was determined in each cut in all the HSF. The dry matter yield (DMY, g of DM plant-1) or aerial biomass was obtained by adding the dry weight of the subsample of the four stems and the dry weight of the rest of the plant. The leaf:stem ratio (L:S) was calculated with the dry weight data from the leaves (LDW) and secondary tillers (TDW) (L:S = LDW / TDW). The absolute growth rate (AGR, g of DM plant-1 d-1) was calculated by dividing the DMY by the number of days (t) elapsed between one cut and the next (AGR = DMY/t). The radiation use efficiency (RUE, g of DM MJ-1) was calculated by dividing the DMY by the amount of photosynthetically active radiation (PAR, MJ MJ m-2 d-1) accumulated (PAR= incident global radiation (cal cm-2 d-1) x 0.5 x 0.04148) between one cut and the next (RUE= DMY / PAR)(13). The incident global radiation data were obtained from the meteorological station of the Universidad Autónoma Chapingo, Chapingo, Estado de México, located 4 km away from the experiment location.

Air temperature and rain

The maximum (TM) and minimum (Tm) air temperature data were recorded daily at 0800 h with a Six's thermometer (Taylor, model 5458P) placed 2 m above ground level. The TM ranged from 23.5 to 29.9 °C and the Tm from 4.0 to 12.6 °C during the experiment. Daily rainfall data were determined with a weekly accumulation rain gauge placed next to the plants. The total rain precipitation was 1,042 mm; the lowest levels occurred from November 2014 to January 2015; this did not affect plant growth as plants were irrigated as necessary.

Statistical analysis

The analysis of variance for all variables was performed using the Windows software SAS, Version 9.1(14) and the statistical model: Yijk = µ + Popi + Fam(Pop)ik + Cutoffj + (Pop*Cutoff)ij + Cutoff*Fam(Pop)ijk + Eijk; Where: Yijk represents the value of the response variable in the population i of the cutoff level j and k of the HSF; µ is the general mean, Pobi is the Population effect at level i = 1, 2, 3, and 4; Fam(Pop)ik is the effect of population nested half-sib families at level i and k;

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Cutoffj is the effect of the cutoff date at level j = 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, and 11; (Pop*Cutoff)ij is the effect of the Population x Cutoff interaction at level i and j; Cutoff*Fam(Pop)ijk is the effect of the Cutoff x population nested HSF interaction at level i, j, k; Eijk is the experimental error. The Cutoff*Fam(Pob)ijk component could not be separated from the error because there was no source of variation for replications in the analysis of variance.

Genetic and environmental effects Genetic and environmental effects were calculated through phenotypic variance (đ?&#x153;&#x17D;đ?&#x2018;&#x201C;2 ) and its components, genetic (đ?&#x153;&#x17D;đ?&#x2018;&#x201D;2 ) and environmental variance (đ?&#x153;&#x17D;đ?&#x2018;&#x2019;2 ), where the total or phenotypic variance is the sum of đ?&#x153;&#x17D;đ?&#x2018;&#x201D;2 and đ?&#x153;&#x17D;đ?&#x2018;&#x2019;2 (đ?&#x153;&#x17D;đ?&#x2018;&#x201C;2 = đ?&#x153;&#x17D;đ?&#x2018;&#x201D;2 + đ?&#x153;&#x17D;đ?&#x2018;&#x2019;2 )(15). Variances were estimated with the mean squares of each population based on the analysis of variance obtained with the statistical model Yijk = Âľ + Fam(Pop)ij + Eijk, where Yijk represents the value of the response variable in the population i at level j of the HSF; Âľ is the general mean; Fam(Pop)ij is the effect of the population nested HSF at level i, j; and Eijk is the experimental error. Broad-sense heritability was also estimated (h2b = đ?&#x153;&#x17D;đ?&#x2018;&#x201D;2 / đ?&#x153;&#x17D;đ?&#x2018;&#x201C;2 = đ?&#x153;&#x17D;đ?&#x2018;&#x201D;2 /(đ?&#x153;&#x17D;đ?&#x2018;&#x201D;2 + đ?&#x153;&#x17D;đ?&#x2018;&#x2019;2 ))(15,16). The statistically superior HSF were selected in the segregating populations using the selection intensity value (i= D / ď łf), calculated by dividing the standardized selection differential (D) and the phenotypic standard deviation (ď łf)(15), and the value of the selected proportion or selection pressure (p)(15,17).

Results and discussion Selection parameters

Family selection increased 32, 31, 32, 6, and 36 % the DMY, AGR, RUE, NT, and PH in the San Miguel x Oaxaca population; and 30, 28, 30, and 34 % the DMY, AGR, RUE, and PH in Oaxaca x San Miguel compared to the progenitorsâ&#x20AC;&#x2122; mean. Meanwhile, the L:S ratio decreased 48 % in San Miguel x Oaxaca and 51 % in Oaxaca x San Miguel (Table 1). The gain in DMY observed in this study was higher than that reported in progeny testing; the HSF and CSF (full-sib families) derived from open pollination (OP1) of a diallel cross (F1) produced only 8 and 9 % higher yield of green matter and 5 and 12 % higher number of tillers per plant than the original varieties(18). The results of familial

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selection versus mass selection in alfalfa have indicated similar percentages for DMY (16 and 14 %) and raw fiber (-1.6 vs -1.5 %)(19). Table 1. Selection parameters and determination of the selection pressure (p) for dry matter yield (DMY), absolute growth rate (AGR), radiation use efficiency (RUE), leaf:stem ratio (L:S), number of tillers (NT), and plant height (PH) in two segregating populations. 2014 -2015 cycle Variables Populations µ1 µ2 D i p f San Miguel x Oaxaca 8.03 5.45 2.58 1.58 1.63 13% DMY (g of DM plant-1) Oaxaca x San Miguel 7.78 5.45 2.33 1.58 1.47 17% San Miguel x Oaxaca 0.26 0.18 0.08 0.05 1.6 13% AGR (g of DM plant-1 d-1) Oaxaca x San Miguel 0.25 0.18 0.07 0.05 1.4 17% San Miguel x Oaxaca 0.84 0.57 0.27 0.17 1.59 13% RUE (g of DM MJ-1) Oaxaca x San Miguel 0.81 0.57 0.24 0.17 1.41 17% San Miguel x Oaxaca 0.97 1.44 -0.47 0.21 -2.24 L:S Oaxaca x San Miguel 0.95 1.44 -0.49 0.21 -2.33 San Miguel x Oaxaca 18 17 1 5.05 0.2 NT Oaxaca x San Miguel 17 17 0 5.05 0 San Miguel x Oaxaca 58 37 21 5.08 4.13 PH (cm) Oaxaca x San Miguel 56 37 19 5.08 3.74 µ1= mean of the 100 HSF derived from San Miguel x Oaxaca and Oaxaca x San Miguel; µ2 = mean of the 200 HSF derived from the original varieties San Miguel and Oaxaca; D = selection differential; f = phenotypic standard deviation, i = selection intensity; p = selection pressure.

The values obtained for the selection parameters (D, f, i, and p) indicated that 13 % of the HSF derived from San Miguel x Oaxaca and 17 % of the HSF derived from Oaxaca x San Miguel had higher DMY, AGR, and RUE per plant than the rest of the HSF (Table 1). The mean (µ1) of DMY, AGR, and RUE for the 13 San Miguel x Oaxaca families and the 17 Oaxaca x San Miguel families was higher than the mean (µ2) for the HSF derived from the original varieties of San Miguel and Oaxaca. However, the L:S ratio, number of tillers, and plant height behaved differently than the DMY. The mean L:S ratio of San Miguel x Oaxaca and Oaxaca x San Miguel HSF was lower than the mean of San Miguel and Oaxaca. The mean NT of the HSF derived from San Miguel x Oaxaca was greater than the mean of San Miguel and Oaxaca. The mean NT of Oaxaca x San Miguel HSF was similar to the mean of San Miguel and Oaxaca. The mean PH of the HSF derived from the two segregating populations was much higher than the mean in San Miguel x Oaxaca (Table 1). The intrapopulation individual selection is effective in identifying superior genotypes using progeny testing(6). The high average values of the L:S ratio, NT, and PH observed in the segregating populations did not allow the detection of outstanding HSF for these plant characteristics (Table 1).

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Dry matter yield and its components

The analysis of variance detected 13 outstanding HSF, based on their DMY, AGR, and RUE, in the San Miguel x Oaxaca population (Table 2) and 17 HSF in the Oaxaca x San Miguel population (Table 3); this research also present data for the five families with the lowest DMY in these populations (Table 2 and 3). The 13 outstanding HSF of the San Miguel x Oaxaca population produced 53 % higher DMY than the average on the original varieties and 82 % higher DMY than the mean of the HSF with the lowest yield (Table 2). The outstanding HSF derived from the Oaxaca x San Miguel population produced 47 and 68 % higher DMY than the mean of the original varieties and the families with the lowest yield, respectively (Table 3).

Table 2: Dry matter yield (DMY), absolute growth rate (AGR), and radiation use efficiency (RUE) of the statistically superior HSF and the five HSF with the lowest yield derived from San Miguel x Oaxaca, 11 successive cutoffs on average. 2014 -2015 cycle Family number Genealogy DMY AGR RUE Statistically superior HSF 282

Family-82

13.8

0.45

1.45

264

Family-64

12.1

0.39

1.26

268

Family-68

11.9

0.39

1.22

260

Family-60

11.7

0.38

1.22

212

Family-12

11.7

0.38

1.21

201

Family-1

11.6

0.38

1.21

253

Family-53

11.2

0.36

1.17

300

Family-100

11.1

0.36

1.16

248

Family-48

10.9

0.36

1.14

252

Family-52

10.9

0.35

1.15

219

Family-19

10.9

0.36

1.14

250

Family-50

10.9

0.36

1.13

237

Family-37

10.8

0.36

1.13

11.5

0.38

1.20

Mean Lowest yield HSF

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276

Family-76

2.5

0.08

0.26

266

Family-66

2.5

0.08

0.26

257

Family-57

2.2

0.07

0.24

220

Family-20

2.1

0.07

0.22

272

Family-72

2.1

0.07

0.21

2.3

0.07

0.24

Standard deviation (ď łf) of the 200 half-sib families derived from Oaxaca 1.6 and San Miguel

0.05

0.17

Mean (Âľ2) of the 200 half-sib families 5.4 derived from San Miguel and Oaxaca

0.18

0.57

Mean

DMY (g DM plant-1); AGR (g DM d-1); RUE (g DM MJ-1).

Table 3: Dry matter yield (DMY), absolute growth rate (AGR), and radiation use efficiency (RUE) of the statistically superior HSF and the five HSF with the lowest yield derived from Oaxaca x San Miguel, 11 successive cutoffs on average. 2014 -2015 cycle Family number Genealogy DMY AGR RUE Statistically superior HSF 646

Family-46

11.4

0.37

1.21

619

Family-19

11.1

0.37

1.16

694

Family-94

11.0

0.35

1.15

661

Family-61

10.7

0.35

1.11

631

Family-31

10.4

0.34

1.09

639

Family-39

10.3

0.33

1.08

697

Family-97

10.3

0.34

1.08

605

Family-5

10.2

0.33

1.07

690

Family-90

10.2

0.33

1.07

630

Family-30

10.1

0.33

1.06

652

Family-52

10.1

0.33

1.06

649

Family-49

9.9

0.33

1.02

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685

Family-85

9.8

0.32

1.02

673

Family-73

9.7

0.32

1.01

609

Family-9

9.7

0.31

1.01

623

Family-23

9.6

0.31

1.00

682

Family-82

9.5

0.31

1.00

10.2

0.33

1.07

Mean Lowest yield HSF 687

Family-87

4.2

0.13

0.43

688

Family-88

4.2

0.14

0.42

683

Family-83

2.8

0.09

0.29

678

Family-78

2.7

0.09

0.27

641

Family-41

2.6

0.09

0.26

3.3

0.11

0.26

Standard deviation (ď łf) of the 200 half-sib families derived from Oaxaca 1.6 and San Miguel

0.05

0.17

Mean (Âľ2) of the 200 half-sib families 5.4 derived from San Miguel and Oaxaca

0.18

0.57

Mean

DMY (g DM plant-1); AGR (g DM d-1); RUE (g DM MJ-1).

The DMY of the outstanding HSF derived from the San Miguel x Oaxaca (direct cross) population (Table 2) was 11 % higher than the DMY of the outstanding HSF derived from the Oaxaca x San Miguel (reciprocal cross) population (Table 3). However, the San Miguel x Oaxaca HSF with the lowest yield produced 30 % lower DMY (Table 2) than the Oaxaca x San Miguel HSF with the lowest yield (Table 3). The different genetic behavior, regarding DMY and its components, between the HSF derived from direct (San Miguel x Oaxaca) (Table 2) and reciprocal (Oaxaca x San Miguel) (Table 3) crosses could indicate the presence of DNA molecules in cytoplasmic organelles (chloroplasts and mitochondria), also known as cytoplasmic inheritance or maternal genetic effects, that express in reciprocal crosses by showing different results with respect to the direct cross(16). The selection of the outstanding progeny derived from parental populations with cytoplasmic genetic effects on DMY inheritance is important to maximize genetic gains in productivity(20).

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The outstanding San Miguel x Oaxaca HSF exhibited a higher AGR= 0.2 g of DM d-1 than the progenitors’ mean and 0.3 g of DM d-1 than the HSF with the lowest DMY (Table 2). The outstanding Oaxaca x San Miguel HSF showed a higher AGR= 0.15 g of DM d-1 than the mean of San Miguel and Oaxaca and an AGR= 0.22 g of DM d-1, higher than the mean of the HSF with the lowest DMY (Table 3). The AGR differences between the outstanding HSF and those with the lowest DMY were similar to the ones reported in other studies(12). The AGR is an important component of the DMY. In a previous study, the San Miguel, Oaxaca, and Moapa varieties produced a higher seasonal AGR and DMY than the Cuf-101 and Valenciana varieties in Montecillo, Texcoco, Edo. de México(12). Additionally, AGR is significantly related to the accumulation of dry matter and the leaf area index under field conditions; this is not the case for the number of leaves per stem(21). The RUE of the outstanding San Miguel x Oaxaca HSF was 52 % higher than the average of the original varieties and 82% higher than the mean of the HSF with the lowest DMY (Table 2). Similarly, the RUE of the outstanding Oaxaca x San Miguel HSF was 47 % higher than the progenitors’ mean and 78 % higher than the mean of the HSF with the lowest DMY (Table 3). Radiation use efficiency (RUE) determines the plant's capacity to capture solar radiation and transform it into biomass. Several studies have determined that the RUE of alfalfa is approximately 1.13 g of DM MJ-1 in biomass production(22). The average RUE increased linearly from 0.60 to 1.60 g of DM MJ-1 as the air temperature increased from 6 to 18 °C(23). Regarding PH, NT, and L:S ratio, it is important to consider their importance for DMY and forage quality in the selection of new varieties since, in this study, the selection by high DMY resulted in tall plants with a low L:S ratio and NT. However, it was identified some HSF with high DMY, L:S ratio (HSF-201), and NT (HSF-219, HSF-250, HSF-260, and HSF-282) in the San Miguel x Oaxaca population (Table 2) and high DMY and L:S ratio (HSF-652, HSF-673, and HSF-685), and NT (HSF-605, HSF-623, HSF-631, HSF639, HSF-652, HSF-673, HSF-685, and HSF-690) in the Oaxaca x San Miguel population (Table 3). The selection by DMY and other plant morphological traits depends on the number of genes that control the trait of interest (additive, dominant, or epistatic genes); qualitative traits (controlled by one or few genes) are easier to select than quantitative traits (controlled by numerous genes). It is also important to consider the heritability level and negative genetic interactions that by improving one genetic trait inhibit the expression of another, as well as the appropriate general steps such as the aims, variability creation/collection, selection, evaluation, and release of cultivars, use of methods and techniques based on the species reproduction (autogamy, allogamy, or clonal propagation)(24).

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Genetic, environmental, and heritability effects

The genetic variance, for DMY and its components, was higher in the direct cross (San Miguel x Oaxaca) than in the reciprocal cross (Oaxaca x San Miguel); the values of genetic variance for the San Miguel and Oaxaca varieties were intermediate between both crosses (Table 4). Genetic variance derives from the contribution of segregating genes and their interactions with other plant genes; therefore, the effective selection of genetically superior individuals requires that: 1) the phenotypic variation is appropriate in the original population, and 2) the heritability is sufficiently high for an effective selection. Overall, an increase in heritability and phenotypic variance is considered to increase genetic gain through selection(25).

The DMY, NT, and PH showed greater genetic variance than the other yield components, both in the crosses and in the progeny varieties. One of the major aspects of genetic improvement programs is the heritability (h2) of useful plant traits present in the available genetic variability. In the direct cross, heritability ranged from moderate to high in DMY and its components and from low to moderate in the reciprocal cross. PH was the exception; this trait showed a greater heritability than the other traits (Table 4). Heritability measures the genotype contribution to the total phenotypic variance, which can theoretically vary from zero, when there is no genetic variation, to one, when the total variation is genetic in origin. However, the previously described broad-sense heritability (h2b) must be distinguished from narrow-sense heritability (h2n), which represents the quotient between the additive variance, instead of the total genetic variance, and the phenotypic variance(26).

Table 4: Genetic (đ?&#x153;&#x17D;đ?&#x2018;&#x201D;2 ) and environmental (đ?&#x153;&#x17D;đ?&#x2018;&#x2019;2 ) variance, and broad-sense heritability (h2b) for dry matter yield (DMY), absolute growth rate (AGR), radiation use efficiency (RUE), leaf:stem ratio (L:S), number of tillers (NT), and plant height (PH) in four alfalfa segregating populations. 2014 -2015 cycle San Miguel x Oaxaca x San San Miguel Oaxaca Oaxaca Miguel Variables 2 2 2 2 2 2 đ?&#x153;&#x17D;đ?&#x2018;&#x201D; đ?&#x153;&#x17D;đ?&#x2018;&#x201D; đ?&#x153;&#x17D;đ?&#x2018;&#x201D; đ?&#x153;&#x17D;đ?&#x2018;&#x201D;2 đ?&#x153;&#x17D;đ?&#x2018;&#x2019; h2b đ?&#x153;&#x17D;đ?&#x2018;&#x2019; h2b đ?&#x153;&#x17D;đ?&#x2018;&#x2019; h2b đ?&#x153;&#x17D;đ?&#x2018;&#x2019;2 h2b DMY 2.9 4.4 0.40 5.8 7.1 0.45 1.5 4.0 0.27 2.9 7.8 0.27 AGR 0.003 0.01 0.34 0.006 0.01 0.42 0.001 0.01 0.21 0.003 0.01 0.23 RUE 0.03 0.05 0.41 0.06 0.08 0.43 0.02 0.04 0.28 0.03 0.09 0.27 L:S 0.04 0.06 0.41 0.02 0.02 0.51 0.04 0.07 0.35 0.01 0.02 0.36 NT 18.2 37.4 0.33 31.9 29.0 0.52 11.6 37.5 0.24 10.5 33.1 0.24 PH 20.1 55.2 0.27 45.1 40.0 0.53 21.8 49.0 0.31 30.9 29.5 0.51

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The DMY and forage quality, represented by the L:S ratio, are primary objectives for alfalfa improvement. The heritability for DMY, L:S ratio, and the rest of the growth components was higher in San Miguel x Oaxaca than in Oaxaca x San Miguel. However, the data confirm genotypes with high DMY, L:S, and remaining growth components in both populations (Tables 2 and 3). These results differ from those obtained for DMY and L:S ratio in alfalfa HSF and lines (S1), in which forage yield and L:S ratio were independent due to the lack of L:S variability in the materials selected because of their high DMY(9). However, these results indicate that the selection allowed the identification of HSF with high values for DMY and other desirable traits, such as L:S ratio, NT, AGR, and RUE (Tables 2 and 3), which may favor a continuous response to selection(26). The combination of the nuclear gene inherited traits that result from the additive genetic variance and the cytoplasmic inheritance or maternal genetic effects, due to the presence of DNA molecules in mitochondria and chloroplasts, can contribute to maximize the genetic gain in DMY and increase the selection efficiency in alfalfa and other cultivated species with maternal genetic effects expressed in the progenies phenotype. The breeder hardly needs to deal with genetic traits carried by one or the other of said organelles; however, sometimes, these organelles can be carriers of exceptional important traits for genetic improvements, such as the cytoplasmic male sterility that resides in the mitochondria(27).

Conclusions and implications The selection allowed the identification of some outstanding HSF with high DMY and L:S ratio in both populations; these HSF could be used to form a new synthetic variety or broad genetic base population to continue with subsequent selection cycles. The direct cross (San Miguel x Oaxaca) showed higher genetic variance and heritability for DMY and its components than the reciprocal cross (Oaxaca x San Miguel). The outstanding HSF derived from the direct cross produced higher DMY than the HSF derived from the reciprocal cross, which indicates that the San Miguel variety used as a female progenitor has a better genetic behavior than the Oaxaca variety progenies. In the future, obtaining new and improved varieties of alfalfa could be more practical by making direct and reciprocal crosses in breeding programs to identify the progenitor with the best behavior as female cytoplasm, that allows maximizing dry matter yield through selection.

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Literature cited: 1. Busbice TH, Hill RR (Jr.), Carnahan HL. Genetics and breeding procedures. In: Hanson CH editor. Alfalfa science and technology. American Society of Agronomy. Number 15, Series Agronomy. Madison, WI, USA;1972:283-318. 2. Brummer EC, Bouton JH, Casler MD, McCaslin MH, Wadron BL. Grasses and legumes: Genetics and plant breeding. In: Wedin WF, Fales SL editors. Grassland: Quietness and strength for a new American agriculture. Am Soc Agron. Madison WI, USA;2009:157-171. 3. Annicchiarico P, Barrett B, Brummer EC, Julier B, Marshall AH. Achievements and challenges in improving temperate perennial forage legumes. Crit Rev Plant Sci 2015;34(1-3):327–380. 4. Lemaire G, Allirand JM. Relation entre croissance et qualité de la luzerne: interaction génotype-mode d’ exploitation. Fourr 1993;134:183-198. 5. Marten GC, Buxton DR, Barnes RF. Feeding value (Forage quality). In: Hanson AA, et al editors. Alfalfa and alfalfa improvement. Agronomy Monograph 29, ASA, Madison, WI. USA;1988:463-491. Doi:10.2134/agronmonogr29.c14. 6. Rumbaugh MD, Caddel JL, Rowe DE. Breeding and quantitative genetics. In: Hanson AA, et al editors. Alfalfa and alfalfa improvement. Agronomy Monograph 29, ASA, Madison, WI. USA;1988:777-808. Doi:10.2134/agronmonogr29.c25. 7. Bingham ET, Groose RW, Woodfield DR, Kidwell KK. Complementary gene interactions in alfalfa are greater in autotetraploids than diploids. Crop Sci 1994;34(4):823-829. 8. Katepa-Mutondwa FM, Christie BR, Michaels TE. An improved breeding strategy for autotetraploid alfalfa (Medicago sativa L.). Euphytica 2002;123(1):139–146. 9. Annicchiarico P. Alfalfa forage yield and leaf/stem ratio: narrow-sense heritability, genetic correlation and parent selection procedures. Euphytica 2015;205(2):409– 420. Doi:10.1007/s10681-015-1399-y. 10. Li X, Brummer EC. Applied genetics and genomics in alfalfa breeding. Agron 2012;2:40–61. Doi:10.3390/agronomy2010040. 11. García E. Modificaciones al sistema climático de Köppen. 5a. ed, Instituto de Geografía. Serie de libros No. 6. Universidad Nacional Autónoma de México. México, DF. México;2004. 12. Rivas-Jacobo MA, López-Castañeda C, Hernández-Garay A, Pérez-Pérez J. Efecto de tres regímenes de cosecha en el comportamiento productivo de cinco variedades comerciales de alfalfa (Medicago sativa L.). Téc Pecu Méx 2005;43(1):79-92.

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13. The international system of units (SI) – Conversion factors for general use. In: Butcher K, Crown L, Gentry EJ, Hockert C editors. Weights and Measures Division, Technology Services, NIST, Special Publication 1038. Department of Commerce, USA;2006. 14. SAS. The SAS System release 9.1 for windows. Institute Inc., Cary, North Carolina, United States. 2009. 15. Márquez SF. Genotecnia vegetal. Tomo I. México, DF: AGT Editor, SA; 1992. 16. Hartl DL, Freifelder D, Snyder LA. Cytoplasmic inheritance. In: Basic genetics. Jones and Bartlett Publishers, Inc. Portola Valley, CA, USA;1988:179-193. 17. Falconer DS. Introducción a la genética cuantitativa. Traducción de Fidel Márquez Sánchez, PhD. C.E.C.S.A. México, DF; 1984. 18. Milić D, Katić S, Boćanski J, Karagić Đ, Mikić A, Vasiljević S. Importance of progeny testing in alfalfa breeding (Medicago sativa L.). Genetika 2010;42(3):485492. 19. Bakheit BR, Ali MA, Helmy AA. Effect of selection for crown diameter of forage yield and quality components in alfalfa (Medicago sativa L.). Asian J Crop Sci 2011;3(2):68-76. Doi:10.3923/ajcs.2011.68.76. 20. Rebetzke GJ, Richards RA, López-Castañeda C. Nuclear and maternal gene action affect selection for early vigour in wheat. In: Langridge P, et al editors. Proc. Aust Plant Breed Conf. Adelaide, South Australia; 1999:146-147. 21. Villegas-Aparicio Y, Hernández-Garay A, Pérez-Pérez J, López-Castañeda C, Herrera-Haro JG, Enríquez-Quiroz JF, et al. Patrones estacionales de crecimiento de dos variedades de alfalfa (Medicago sativa L.). Téc Pecu Méx 2004;42(2):145-158. 22. Khaiti M, Lemaire G. Dynamics of shoot and root growth of lucerne after seeding and after cutting. Europ J Agron 1992;1(4):241-247. 23. Brown HE, Moot DJ, Teixeira EI. Radiation use efficiency and biomass partitioning of lucerne (Medicago sativa) in a temperate climate. Europ J Agron 2006;25(4):319327. 24. Aquaah G. Conventional plant breeding principles and techniques. In: Al-Khayri J, et al editors. Advances in plant breeding strategies: Breeding, biotechnology and molecular tools 2015:115-158. Doi:10.1007/978-3-319-22521-0_5. 25. Brewbaker JL. Agricultural genetics. New Jersey, United States of America: PrenticeHall, Inc.; 1964.

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26. Hill J, HC Becker, PMA Tigerstedt. Quantitative and ecological aspects of plant breeding. London, England: Chapman and Hall; 1998. 27. Cubero JI. Introducción a la mejora genética vegetal. 3.a ed. España: Ediciones Mundi-Prensa; 2013.

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https://doi.org/10.22319/rmcp.v11i4.5437 Review

Post vitrification pregnancy rate of in vivo produced embryos derived from equids. Review Christian Urías-Castro a* Ana Myriam Boeta b

Universidad Autónoma de Sinaloa. Facultad de Medicina Veterinaria y Zootecnia, Boulevard San Ángel S/N, Fraccionamiento San Benito, Predio Las Coloradas, Culiacán Sinaloa, México. b

Universidad Nacional Autónoma de México. Facultad de Veterinaria y Zootecnia, Departamento de Reproducción . Ciudad de México, México.

Corresponding author: cesar.urias@uas.edu.mx

Abstract: Vitrification is a cryo-preservation method often used in embryos obtained from mares or jennies. It consists in the dramatic reduction of temperature to levels close to -196 °C, that allows the cryopreserving solution containing the embryo to pass from liquid to vitreous state. Several improvements to vitrification protocols have made possible to cryo-preserve embryos with different sizes; since during the first decade after the year 2000, only small embryos were successfully vitrified. Embryos collected at the sixth day post ovulation (PO) are usually smaller or equal to 300 micrometers in diameter (≤ 300 µmØ) and can be routinely vitrified following simple protocols; they have a higher post vitrification pregnancy rate (PVPR) when compared to large embryos which have more than 300 micrometers in diameter (˃ 300 µmØ). The high PVPR of embryos ≤ 300 µmØ is due to an embryo capsule (EC) that is not fully developed yet and has a high permeability to cryo-preserving solutions. At present time, embryos collected either the seventh or eighth day PO are ˃ 300 µmØ and are characterized to have a low post vitrification survival; in order to increase their PVPR their EC might be punctured to make it permeable to cryopreserving solutions. Additionally, there are at least two factors that can be manipulated to increase the PVPR of embryos ˃ 300 1142

Rev Mex Cienc Pecu 2020;11(4): 1142-1149

µmØ; one is to reduce their size by aspiring their blastocoelic liquid (BL), and the other is to induce a high temperature transfer index (TTI) to rapidly reach -196 °C. Key words: Mares, Jennies, Embryo, Cryopreservation, Vitrification, Pregnancy rate.

Received: 02/07/2019 Accepted: 21/10/2019

Introduction FAO estimates(1), show that the number of donkeys has been reduced from the 2007-2017 period in Brazil (1’163,316 to 841,307), China (7’306,000 to 4’568,500), Ecuador (102,058 to 49,729), India (438,000 to 247,000) and Italy (24,000 to 20,928). In the African continent, donkey’s number is also being reduced due to the demand from the chinese market of some products like their skin(2). However in contries like Italy(3) and Brazil(4) donkeys are an important part of the equine industry due to the production of milk(5,6), cheese(7) and meat(4). Donkey’s population recovery is important due to the roles they play in the economy of emerging contries(8) and the production of hybrid animals like mules(9). The estrous cycle of jennies and mares differs mainly in the duration of the heat period reflected in a longer estrous length in jennies(10); however the time lapse between the ovulation-fertilization relative to embryo entrance into the uterus seem to be similar between jennies(11,12,13) and mares(14,15). The size of the embryo recovered either during the sixth, seventh, eighth or ninth day post ovulation (PO) varies dramatically; both, in jennies(12,13,16,17) and mares(15,16,18); however, it follows a similar trend in size increase. In equids, the time in which the embryo reach the uterus has been established, and is known that it enters the uterus by the sixth day PO(18). The PO day selected to proceed with the intrauterine infusion of solutions to collect embryos in jennies or mares, determines their development stage (morula, blastocyst or expanded blastocyst), and will dictate the protocol suitable to optimize their PVPR(12,13,15,19-22). By infusing the uterus on the sixth day PO, the recovered embryo will generally be smaller than 300 micrometers(12,16,21); on the other hand, embryos collected the eighth or ninth day PO will be equal or larger than 300 micrometers in size(12,13,20,23,24). The optimization of embryo vitrification protocols requires a precise determination of the ovulation’s day (day zero) to accurately determine the age/stage of embryo, since the ones collected during the sixth day

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PO do not require size reduction; while embryoss collected the seventh, eighth, or ninth need to be reduced in size previous to vitrification(12,21,25,26,27). Due to the dramatic reduction in the number of donkeys, studies leading to improvement of biotechinologies like embryo vitrification could facilitate their reproduction, making possible their gradual recovery. The aim of the present study is to provide information leading to protocol improvements; that could result in higher PVPR of embryos ˃ 300 µmØ derived from both jennies and mares.

Relevance of embryo size over the post vitrification pregnancy rate During the first decade past the year 2000 the size of the embryo vitrified, either small (≤ 300 µmØ) or large (˃ 300 µmØ) resulted in high and low pregnancy rates respectively(15). Pregnancy rates close to 62 % were obtained when vitrifying embryos ≤ 300 µmØ in mares(28); while cryopreserving embryos ˃ 300 µmØ resulted in pregnancy rates near the 10 % range(29). After the year 2010, adjustments to cryopreservation protocols designed for embryos ˃ 300 µmØ have been performed; between these adjustments are the punction of the embryo capsule (EC) and the reduction of its size through aspiration of blastocoelic liquid (BL)(23). These adaptations allowed the successful vitrification of embryos ˃ 300 µmØ giving as result post vitrification embryo survival in donkeys(30) and pregnancy rates higher than 40 % in mares(31). However, research assessing the effect of embryo size in post vitrification pregnancy rate (PVPR) have not been conducted using embryos derived from jennies(17,30).

Embryo capsule permeability, its association with the post vitrification pregnancy rate

Embryos derived from equine species develop a glycoproteic structure denominated embryo capsule (EC) that is required for the adequate progression of pregnancy(32). Vitrification of embryos derived from mares have proven, that the presence of the EC lowers their permeability to cryo-preserving solutions, both; in vitro(33) and in vivo(34,35). In mares, one of the first successful pregnancies obtained after the vitrification of embryos ˃ 300 µmØ was achieved thanks to the treatment of the EC with trypsin, previous to the vitrification process, which increased their permeability to cryo-preservatives(36). However; the increase in EC permeability by the use of trypsin or microfilament inhibitors (cytochalasin-B), have not shown positive results in a constant manner(37). In equine embryos ˃ 300 µmØ; the degree if integrity of the EC seems to keep a close relationship with their PVPR, since the EC is much more developed in this category of embryos, any dramatic alteration in their EC will result

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in adverse effects over their PVPR(23). Interestingly, there is a lack of studies analyzing EC permeability in donkey or mule embryos and whether their permeability is associated with the PVPR.

Embryo capsule punction and blastocoelic liquid extraction before the vitrification process In equine embryos ˃ 300 µmØ, it is important to puncture their EC(38) and extract their BL to promote a reduction in size before the vitrification process(39). The relevance of EC punction and size reduction in embryos ˃ 300 µmØ has been corroborated in mares; since the application of these procedures resulted in an increased PVPR(23,24,31,38,40,41). Data also obtained in horses; reported before the year 2010, showed that PVPRs from embryos ˃ 300 µmØ were lower than 40 %(15,29). However after year 2010, by applying the EC punction and size reduction; a dramatic increase in post vitrification embryo survival was achieved, giving pregnancies rates between the 60 and 80 % in mares(23,24,40). A recent study in embryos derived from jennies, demonstrated that size reduction by blastocoelic fluid aspiration results in a 83 % of post vitrification embryo survival rate(30); however, the embryos from this study were not transferred into the uterus of the jenny during the post vitrification process, and hence the in vivo embryo development and pregnancy rate were not documented. Whether the EC punction is needed in embryos ˃ 300 µmØ derived from jennies is an area that requires further investigation(21,30), since no studies have documented yet the PVPR of embryos derived from jennies after EC puncture and size reduction(16,17,21,30). Interestingly; when comparing horse and donkey embryos, the last ones seem to tolerate vitrification better(16) and thereby, it should be possible that EC punction might not be needed as suggested by the study of Bottrel(17); however, no studies directly testing this hypothesis has been conducted in donkeys. Historically, studies assessing the PVPR have been limited in jennies(21) and more work is needed in this area(16,17,21).

The influence of increased temperature transfer index (TTI) over the post vitrification pregnancy rates (PVPR) of equine embryos

In equids, a high TTI during the vitrification and devitrification process improves the PVPR of embryos ˃ 300 µmØ. The TTI is related with the velocity and amount of temperature transferred from liquid nitrogen, (around -196 °C) to the cryo preserving solution containing the embryo which oscillates the 25 °C. If the TTI is incresed, the speed at which the temperature of the cryo preserving solution containing the embryo reaches - 196 °C is

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increased. One way of promoting an increase in the TTI is to directly put into contact the cryopreserving solution containing the embryo with liquid nitrogen, another one is to reduce the volume of the solution containing the embryo that will be put into contact with liquid nitrogen. An increase in the post vitrification survival to levels higher than 60 % have been achieved recently; by promoting an increased TTI, using volumes of cryo preserving solutions lower than one micro liter, when immersing the embryo into liquid nitrogen, both in mares(23,40) and jennies(17). In addition to EC punction, an increased TTI has resulted in higher pregnancy rates in mares when compared to lowered TTI, using small (less than one micro liter) and large (about one or more micro liters) volume crarriers respectively(40). The use of small volume carriers which in theory increase the TTI, is a topic recently addressed in embryos derived from jennies(17). The use of less than one microliter in the final vitrification solution containing the embryo resulted in a post vitrification embryo survival between the 40 and 50 % range(17); however, in this study neither EC punction, nor embryo transfer into the jennyâ&#x20AC;&#x2122;s uterus were performed and hence the PVPR was not established.

Acknowledgements The authors want to thank the finantial aid received through the Project: PROFAPI2015/287 that made possible the publication of this article.

Literature cited: 1. FAO, 2018 Statistical Database Website, Food and Agriculture Organization. Rome Italy (Home page: http://www.fao.org/faostat/en/#data/QA). 2. Cox L. Dramatic decline in donkey populations blamed on consumer demand for 'ejiao'. Vet Rec 2017;(181):610. 3. Camillo F, Rota A, Biagini L, Tesi M, Fanelli D, Panzani D. The current situation and trend of donkey industry in Europe. J Equine Vet Sci 2018;(65):44-49. 4. Carneiro GF, Lucena JE, de Oliveira Barros L. The current situation and trend of the donkey industry in South America. J Equine Vet Sci 2018;(65):106-110. 5. Monti G, Bertino E, Muratore MC, Coscia A, Cresi F, Silvestro L, et al. Efficacy of donkey's milk in treating highly problematic cow's milk allergic children: an in vivo and in vitro study. Pediat Allergy Immunol 2007;(3):258-264. 6. Licitra R, Li J, Liang X, Altomonte I, Salari F, Yan J, et al. Profile and content of sialylated oligosaccharides in donkey milk at early lactation. LWT 2019;(115):108437.

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7. D'Alessandro AG, Martemucci G, Loizzo P, Faccia M. Production of cheese from donkey milk as influenced by addition of transglutaminase. J Dairy Sci 2019;16615. 8. Getachew AM, Trawford AF, Feseha G, Reid SWJ. Gastrointestinal parasites of working donkeys in Ethiopia. Trop Anim Health Prod 2010;(42):27–33. 9. Vidament M, Vincent P, Martin FX, Magistrini M, Blesbois E. Differences in ability of jennies and mares to conceive with cooled and frozen semen containing glycerol or not. Anim Reprod Sci 2009;(112):22-35. 10. Hagstrom D. Donkeys are different: an overview of reproductive variations from horses. 2004. http://livestocktrail.illinois.edu/horsenet/paperDisplay.cfm?ContentID=7449 Accessed March 15, 2019. 11. Allen WR, Kydd J, Boyle MS, Antczak DF. Between‐species transfer of horse and donkey embryos: A valuable research tool. Equine Vet J 1985;(17):53-62. 12. Vendramini OM, Bruyas JF, Fieni F, Battut I & Tainturier D. Embryo transfer in Poitou donkeys, preliminary results. Theriogenology 1997;(47):409. 13. Pérez-Marín CC, Vizuete G, Galisteo JJ. Embryo recovery results in Hispano-Arabe horse and Spanish donkey breeds. Livestock Sci 2017;(206):76-81. 14. Oguri N, Tsutsumi Y. Non-surgical egg transfer in mares. Reproduction 1974;(41):313320. 15 Eldridge-Panuska WD, di Brienza VC, Seidel Jr GE, Squires EL, Carnevale EM. Establishment of pregnancies after serial dilution or direct transfer by vitrified equine embryos. Theriogenology 2005;(63):1308-1319. 16. Pérez‐Marín CC, Vizuete G, Vazquez‐Martinez R, Galisteo JJ. Comparison of different cryopreservation methods for horse and donkey embryos. Equine Vet J 2018;(50):398404. 17. Bottrel M, Ortiz I, Pereira B, Díaz-Jiménez M, Hidalgo M, Consuegra R, et al. Cryopreservation of donkey embryos by the cryotop method: Effect of developmental stage, embryo quality, diameter and age of embryos. Theriogenology 2018;(125):242248. 18. Freeman DA, Weber JF, Geary RT, Woods GL. Time of embryo transport through the mare oviduct. Theriogenology 1991;(36):823-830. 19. McKinnon AO, Squires EL. Equine embryo transfer. Vet Clin North Am Equine Prac 1988;(2):305-333.

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20. Silva PC, Oliveira JP, Dutra GA, Paiva SO, Caram DF, Junqueira RG, et al. Taxa de recuperação e características morfológicas de embriões muares (Equus caballus x Equus asinus). Pesqui Vet Bras 2018;(7):1453-1457. 21. Panzani D, Rota A, Romano C, Pratelli G, Sabatini C, Camillo F. Birth of the first donkey foals after transfer of vitrified embryos. J Equine Vet Sci 2012;(32):419. 22. Bottrel M, Fortes T, Ortiz I, Hidalgo M, Dorado J. Establishment and maintenance of donkey-in-mule pregnancy after embryo transfer in a non-cycling mule treated with oestradiol benzoate and long-acting progesterone. Span J Agric Res 2017;(4):10. 23. Diaz F, Bondiolli K, Paccamonti D, Gentry GT. Cryopreservation of day 8 equine embryos after blastocyst micromanipulation and vitrification. Theriogenology 2016;(85):894-903. 24. Wilsher S, Rigali F, Couto G, Camargo S, Allen WR. Vitrification of equine expanded blastocysts following puncture with or without aspiration of the blastocoele fluid. Equine Vet J 2018;(0):1-6. 25. Camillo F, Panzani D, Scollo C, Rota A, Crisci A, Vannozzi I, et al. Embryo recovery rate and recipients' pregnancy rate after nonsurgical embryo transfer in donkeys. Theriogenology 2010;(73):959-965. 26. Betteridge KJ, Eaglesome MD, Mitchell D, Flood PF, Beriault R. Development of horse embryos up to twenty two days after ovulation: observations on fresh specimens. J Anat 1982;(135):191-209. 27. Iuliano MF, Squires EL, Cook VM. Effect of age of equine embryos and method of transfer on pregnancy rate. J Anim Sci 1985;(60):258-263. 28. Campos-Chillon LF, Suh TK, Barcelo-Fimbres M, Seidel GE Jr, Carnevale EM. Vitrification of early-stage bovine and equine embryos. Theriogenology 2009;(79):349354. 29. Barfield JP, McCue PM, Squires EL, Seidel Jr GE. Effect of dehydration prior to cryopreservation of large equine embryos. Cryobiology 2009;(59):36-41. 30. Ottmann L, Reigner F, Allamellou JM, Magistrini M, Guignot F. Preimplantation genetic diagnosis in donkey embryos after biopsy. J Equine Vet Sci 2018;(66):195. 31. Ferris RA, McCue PM, Trundell DA, Morrissey JK, Barfield JP. Vitrification of large equine embryos following manual or micromanipulator-assisted blastocoele collapse. J Equine Vet Sci 2016;(41):64-65.

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32. Stout TAE, Meadows S, Allen WR. Stage-specific formation of the equine blastocyst capsule is instrumental to hatching and to embryonic survival in vivo. Anim Reprod Sci 2005;(87):269-281. 33. Gillard Kingma SE, Thibault ME, Betteridge KJ, Schlaf M, Gartley CJ, Chenier TS. Permeability of the equine embryonic capsule to ethylene glycol and glycerol in vitro. Theriogenology 2011;(76):1540-1551. 34. Legrand E, Krawiecki JM, Tainturier D, Corniere P, Delajarraud H, Bruyas JF. Does the embryonic capsule impede the freezing of equine embryos? Proceedings of the 5th International Symposium on Equine Embryo Transfer, Havemeyer Foundation Monograph 2000;(3):62-65. 35. Scott BR, Carwell DB, Hill RA, Bondioli KR, Godke RA, Gentry GT. Evaluation of capsule permeability in the equine blastocyst. J Equine Vet Sci 2012;(32):795-798. 36. Legrand E, Bencharif D, Barrier-Battut I, Delajarraud H, Corniere P, Fieni F, et al. Comparison of pregnancy rates for days 7-8 equine embryos frozen in glycerol with or without previous enzymatic treatment of their capsule. Theriogenology 2002;(58):721723. 37. Maclellan LJ, Carnevale EM, Coutinho da Silva MA, McCue PM, Seidel Jr GE, Squires EL. Cryopreservation of small and large equine embryos pre-treated with cytochalasinB and/or trypsin. Theriogenology 2002;(58):717-720. 38. Guignot F, Blard T, Barrière P, Gasgogne T, Gaude Y, Yvon JM, et al. Easy, quick and cheap technique to cryopreserve Welsh B pony blastocyst. J Equine Vet Sci 2016;(41):53-60. 39. Choi YH, Velez IC, Riera FL, Roldán JE, Hartman DL, Bliss SB, et al. Successful cryopreservation of expanded equine blastocysts. Theriogenology 2011;( 76):143-152. 40. Sánchez R, Blanco M, Weiss J, Rosati I, Herrera C, Bollwein H, et al. Influence of embryonic size and manipulation on pregnancy rates of mares after transfer of cryopreserved equine embryos. J Equine Vet Sci 2017;(49):54-59. 41. Guignot F, Reigner F, Perreau C, Tartarin P, Babilliot JM, Bed'hom B, et al. Preimplantation genetic diagnosis in Welsh pony embryos after biopsy and cryopreservation. J Anim Sci 2015;(93):5222-5231.

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https://doi.org/10.22319/rmcp.v11i4.5202 Review

Molecular tools used for metagenomic analysis. Review

Nohemí Gabriela Cortés-López a Perla Lucía Ordóñez-Baquera a* Joel Domínguez-Viveros a

Universidad Autónoma de Chihuahua, Facultad de Zootecnia y Ecología. Chihuahua, México.

*Corresponding author: plordonez@uach.mx

Abstract: Metagenomics uses molecular biology techniques to analyze the diversity of microbial genomes (metagenomes). Metagenome diversity has been analyzed using molecular markers to classify bacteria and archaea into taxonomic groups at the genus level. Among the most widely used molecular markers are ribosomal genes, genes encoding subunits of cytochrome C, and certain constitutive genes (gyrB, rpoB, rpoD, recA, atpD, infB, groEL, pmoA, sodA). The most widely used marker for classifying bacteria and metagenomic samples is the 16S rRNA gene, although it does not allow certain sequences to be properly classified. However, all the sequences of the hypervariable regions can be identified with the sequencing of the complete 16S rRNA gene, and, therefore, this molecular marker has made it possible to classify them at the species taxonomic level. Next generation sequencing, also called mass sequencing or high throughput sequencing, has helped to describe complex metagenomes such as those of environmental samples, which have an ecological importance, as well as metagenomes growing in extreme environments. They have also proved helpful in studies related to animal and human health, and in the agro-food field. Specifically, both the 16S rRNA molecular marker and high throughput sequencing combined with bioinformatic tools for metagenomic analysis have been used to describe the ruminal metagenome, a microbial community of great importance because it is involved in animal production of meat and milk.

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Despite the many studies that have been conducted in this field, some microorganisms still remain to be discovered and characterized. Key words: Molecular Marker, 16S rRNA Gene, Metagenomics, Microbial diversity, High throughput sequencing.

Received: 17/12/2018 Accepted: 23/10/2019

Introduction Metagenomics is based on the use of molecular biology techniques to analyze the diversity of microbial genomes, also called metagenomes, from environmental samples. The microbial diversity of metagenomes has been analyzed using the 16S rRNA gene, which encodes for the ribosomal RNA that forms the small subunit of the ribosomes. This gene comprises preserved and variable regions in bacteria and archaea. The 16S rRNA gene has been used as a molecular marker, since it allows the classification of bacteria and archaea into taxonomic groups according to families or genera. The first studies of microbial diversity in environmental samples were carried out using culture-dependent methods, where only those microorganisms that could be isolated in the laboratory were studied. Through the advance of molecular biology techniques, it has been possible to analyze microbial diversity through the use of independent culture methods, obtaining more precise information about bacterial genomes. One of the most widely used methods is PCR amplification of 16S rRNA gene fragments, in some cases followed by denaturing gradient gel electrophoresis (DGGE). These techniques have been used to analyze ruminal bacterial diversity, changes in the microbial community, and gene expression after changes in the ruminantsâ&#x20AC;&#x2122; diet(1,2). Another advance that has allowed a broader analysis of microbial diversity in the rumen is the targeted sequencing of the variable regions of the 16S gene in order to differentiate microorganisms that are phylogenetically very close, analyze the genes and genomes that degrade the biomass in the rumen, characterize the rumen microbiota, and study the effects of yeasts on bacterial diversity in the rumen(3,4,5). The recent development of metagenomics has allowed the study of microbial diversity in environmental samples by isolating and analyzing the total genetic material present in an environmental sample(6,7). At the beginning, this strategy was used to search for new enzymes

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with biotechnological potential, extracting the total DNA contained in an environmental sample, fragmenting it and cloning genes of different size in vectors such as plasmids (15 kb), phages (up to 20 kb), phosmids and cosmids (up to 40 kb), as well as Artificial Bacterial Chromosomes (for larger fragments). These vectors were inserted into different host strains, and fluorogenic substrates were used as expression indicators. However, in the functional search for genes through clones, protein expression and enzymatic activity were of a small magnitude(8,9,10). A crucial part in the construction of metagenomic libraries is the extraction of the nucleic acids from the sample. There are two main strategies for metagenomic DNA extraction: chemical treatment and direct lysis with mechanical methods. Both methods have advantages and disadvantages. DNA of greater microbiological diversity is recovered with mechanical lysis than with chemical treatment; however, chemical treatment allows obtaining DNA of greater molecular weight. Regarding RNA extraction, the same extraction methods are used for any expression analysis in which RNAsase inhibitors are included, and it is recommended to freeze the samples at -80 ยบC immediately after collection to avoid RNA degradation(9). To select the ideal extraction method, the type of sample, the nucleic acid to be purified and the type of analysis to be performed must be taken into account. Different strategies have been used for metagenomic analysis. Within the mechanical methods, magnetic beads have been used for oral, dermal or fecal samples, as well as samples of soil and water, from which high-quality sequences have been obtained(11). For the analysis of ruminal microbiomes, methods combining magnetic bead extraction (mechanical lysis) with extraction columns (chemical treatment) have been used to purify ruminal microbial DNA(11,12). This combination increased extraction performance over the use of separate magnetic beads and extraction columns. Other identification methods use Stable Isotope Probing (SIP), which identify the microorganisms that incorporate these isotopes through the use of marked substrates. In particular, the nucleic acid stable isotope probe technique (Nucleic acids-SIP) uses substrates with 13C and/or 15N isotopes, which are incorporated into bacterial genomes and can thus be traced(13). Other substrates with stable isotope probes are 13CH3-OH, 13Cphenol and 5-bromo-2-deoxyuridine. However, limitations of the use of substrates marked with stable isotopes include the crosslinking and recycling of the isotopes within the microbial community, resulting in the loss of specific enrichment of the analyzed microorganisms(13). Techniques have also been developed to identify genes that change their expression levels during various biological processes. For example, Suppression Subtractive Hybridization (SSH) has been used to identify variations between complex DNA samples such as those in the ruminal environment(9,13). Differential expression analysis allows to compare the gene expression profile of a microbial community before and after being exposed to a specific condition and/or substrate and, thus, to identify important genes that exhibit changes in gene 1152

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expression profiles due to the effect of such condition and/or substrate(13). Another technique that is widely used in gene expression studies is microarrays, which offer the advantage of rapidly identifying and characterizing a large number of clones. Although microarrays can be used to identify a large number of conserved genes, they depend on known sequences previously reported in databases, thus eliminating the possibility of identifying new genes(8,10). More recently, mass sequencing has been used to obtain as much information as possible about the metagenome present in a sample. One of the first works with massive sequencing was the identification of the metagenome of the Sargasso Sea, where 1,045 trillion base pairs of non-redundant sequences were generated, noted and analyzed in order to identify the genetic content, diversity and relative abundance of the microorganisms. It was estimated that the data obtained came from at least 1,800 genomic species that included 148 phylotypes of unknown bacteria and more than 782 genes never before described that code for rhodopsin-like photoreceptors(10,14). The massive sequencing of the metagenome by "shotgun" has the characteristic of sequencing all the DNA present in the sample so that the microorganisms can be classified taxonomically up to the species level. Furthermore, with the sequences obtained by this type of sequencing, genes with functions never before described can be discovered, and even sequences belonging to the 16S rRNA gene can be selected for taxonomic annotation. These classifications are made with the use of bioinformatic tools that search for homology with the sequences analyzed in different existing databases(15). Specifically in ruminal environments, metagenomic libraries have been analyzed in order to evaluate the effects of diets on ruminal microbiome by means of metagenomic profiles, and the 16S rRNA gene marker has been used to determine and classify the microbial diversity of the sequences(3,5). However, some of the sequences of these samples have not been adequately classified; therefore, using at least one molecular phylogenetic marker other than the 16S rRNA gene may improve taxonomic classification(15). In the present work it was reviewed the tools used for metagenome analysis, ranging from classical molecular markers to those used with data obtained from massive sequencing, with an emphasis on metagenomes from ruminal environments.

Molecular markers for metagenomic analysis A molecular marker is a segment of DNA that corresponds to a non-coding gene or regions of the genome, these segments of DNA allow different variants (alleles) to be identified and are located at a particular site on the chromosomes (locus). The differences obtained in these DNA fragments are known as polymorphisms and can be detected by hybridization of nucleic

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acid sequences, nucleotide sequencing, comparison of the length of the fragments produced by the polymerase chain reaction (PCR) and through sites recognized by restriction enzymes. Molecular markers can be used to classify taxonomic groups, populations, families or individuals in both eukaryotes and prokaryotes(16,17). Various molecular markers have been used in genetic studies in domestic animals, in wildlife, in endangered species, and in forensic and paternity tests. The best known are RFLPs, mini-satellites, AFLPs, RAPDs, microsatellites and SNPs (Table 1). The most relevant characteristics that molecular markers must have in order to optimize metagenomic studies include (1) that they are single copy genes (genes that have only one or two copies in the entire genome), as they provide less uncertainty than markers for genes with multiple copies (genes with repeated copies in the genome); (2) that the sequence of the marker gene is easily aligned to facilitate phylogenetic analysis; (3) that the proportion of the gene replacement region is sufficient to provide information needed for classification; (4) that primers are selective to amplify the marker gene, but not universal, in order to avoid false positives; (5) that there is no excessive variation in the marker sequence that limits the determination of ancestry. The genes that are used as molecular markers to classify microorganisms are described below.

Ribosomal genes

Ribosomal RNA genes are considered the ideal tool for taxonomic classification since they are highly conserved and evolutionarily stable genes, but they contain hypervariable regions. The sequencing of these regions has generated large databases that assist in the taxonomic classification(18). Ribosomes of bacteria and archaea consist of two subunits: a small subunit containing a single type of RNA (16S) and a large subunit containing two types of RNA (5S and 23S)(17). 16S rRNA. This gene is also designated 16S rRNA, but the American Society for Microbiology (ASM) has decided to use the term "16S rRNA" in order to standardize the information. It has an approximate sequence length of 1,550 bp and contains variable and preserved regions with unique oligonucleotide sequences for each phylogenetic group(18,19). The comparison of 16S rRNA gene sequences of unknown bacteria with known sequences in databases is of great help in classifying bacteria at the genus level and has even identified species in some cases(20,21). 5S rDNA. It is a gene of approximately 120 nucleotides in length and is found in virtually all ribosomes except mitochondria, some fungi, higher animals and most protists. Although

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the sequence of this gene is highly conserved, the reliability of this gene as a marker is questioned because its length is very small and therefore does not offer sufficient resolution to contribute significantly to the understanding of phylogenetic relationships between taxa(17). 23S rDNA. It is a gene of approximately 3,000 nucleotides in length that is located in the large subunit of the ribosomes in prokaryotes. This gene has larger insertions and deletions than the 16S rRNA gene. Stable insertions and deletions of some bases in the 23S rDNA gene are common characteristics in some classes and subclasses of bacteria. These changes complicate the analyses, since the different positions cannot be considered for correct phylogenetic classifications(22). The 23S rDNA gene has been used in conjunction with the 16S rRNA gene for the taxonomic classification of non-cultivable bacteria. The intergenic spacer (IGS) located in the 16S-23S region, which is very variable, has also been used to differentiate between two strains belonging to the same subspecies(22,23).

Genes encoding subunits of cytochrome C

Cytochrome Oxidase I/II (COI/II). The cytochrome C oxidase enzyme is an electron transport chain protein found both in bacteria and in the mitochondria of eukaryotic organisms. The COI and COII genes encode for two of the seven polypeptide subunits of the cytochrome C oxidase complex. The COI gene evolves more slowly compared to other mitochondrial genes and is widely used in phylogenetic studies(17).

Genes encoding proteins with preserved functions

In studies that have found a greater diversity of microorganisms, molecular community analysis techniques based on the 16S rRNA gene have been used, supported by multilocus sequence analysis (MLSA) studies, which involve the sequencing of several genes encoding proteins with conserved functions (housekeeping genes) to evaluate the diversity in collections of isolated strains(24). In these studies, the partial sequences of genes that encode for proteins with conserved functions are used to generate phylogenetic trees and, subsequently, to solve phylogenies. The main disadvantage of using the 16S rRNA gene as a phylogenetic marker is its insufficient resolution at the species level. However, the use of a complementary phylogenetic analysis based on protein coding genes(25) allows to increase the resolution of phylogenies at an infra-generic level and to determine new strains. Over 50

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individual MLSA schemes are available, and MLSA databases (http://www.mlst.net/ and http://www.pubmlst.org) can also be used to identify microbial sequences not known at the species level(24,26). The genes that have been used in MLSA are those that encode ubiquitous enzyme subunits, such as the of DNA gyrase subunit β (gyrB), the RNA polymerase subunit β (rpoB), the sigma 70 factor (sigma D) of RNA polymerase (RpoD) , the recombinase A (recA), the β subunit of ATP synthase F0F1 (atpD), the translation initiation factor IF-2 (infB), the tRNA modification GTPase ThdF or TrmE (thdF) and the chaperonin GroEL (groEL)(24,26). The particulate methane-monooxygenase subunit β (pmoA) has been used as a functional marker for the detection of aerobic methanotrophs. Methane-monooxygenase is the enzyme responsible for the initial conversion stage from methane to methanol. Two forms of this enzyme are known, soluble methane-monoxygenase (sMMO) and a membrane-bound enzyme, particulate methane-monoxygenase (pmoA). The pmoA gene is the most frequently used marker, as it is present in most methanotrophic aerobic bacteria. It is also present in anaerobic denitrifying bacteria(27). Another marker that can be used for the detection of methanotrophs is the mxaF gene that encodes the major subunit of methanol dehydrogenase(27,28). As an example of this approach, can be cited the work of Sánchez-Herrera et al(26), who have used the 16S rRNA gene as a molecular reference marker to identify and classify strains of the genus Nocardia at the genus level. However, being a gene with multiple copies generates problems in the identification of isolated strains of clinical cases. After testing other genes through PCR amplification of their segments: sodA (gene encoding the enzyme superoxide dismutase), hsp65 (heat shock protein), secA1 (preprotein translocase subunit secA), gyrB (DNA gyrase subunit β), rpoB (RNA polymerase subunit β) and the 16S-23S intergenic spacer, the authors were able to discriminate only between closely related species of Nocardia using the sodA gene. The 386 bp fragment of the sodA gene includes variable regions, with 4 and 5 bp segments, and has the potential to be used as a molecular marker. In conclusion, although there is a great diversity of molecular markers to analyze microbial communities, so far, the gold standard for the classification of sequences obtained from samples remains the 16S rRNA gene.

The use of mass sequencing in metagenomics Although metagenomic analysis started with the use of different molecular markers such as AFLP, RAPDs, 16S rRNA etc. (Table 1), some of these markers have been observed to

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improve their efficiency when the technique used to identify them includes their sequencing instead of characterizing them by means of reactions with restriction enzymes and/or amplification by PCR. From its inception, DNA sequencing with Sanger's technology has had a major impact on virtually every branch of the biological sciences, including microbial community studies. Currently, the use of Sanger sequencing can generate up to 96 sequences per run with an average length of 650 bp, which may be sufficient for phylogenetic marker analysis(15). This type of study is known as first generation sequencing and results in high quality sequences of a length between 500 and 1,000 bp. However, its disadvantage is that the proportion of molecular markers that can be sequenced in a run, compared to the total number of microorganisms present in a metagenomic sample, is very low(11). Table 1: Molecular methods used in genetic studies Molecular Marker

Characteristics

Reference

It is based on nucleotide changes in a genome that Khlestkina(16) occur at a restriction enzyme recognition site. In (50) (restriction forensic science it has been used to prove whether Wakchaure et al fragment length tissues from crime scenes (blood, skin, sperm, etc.) polymorphism) belong to a suspect. In the management of animal breeds, it is used to track progeny, as well as for paternity testing and disease diagnosis. RFLP

Minisatellites or They are short sequences of 10 to 60 bp, repeated in Kumar et al(51) VNTR variable number at one or more sites of the genome. (52) They have been used to identify paternal lineages in Lang et al (variations in individuals and to assess genetic diversity in the number of domestic animal, wildlife and grass populations. tandem repeats)

It is the amplification of digested genomic fragments Khlestkina(16) with restriction enzymes that recognize sequences (51) (amplified dispersed throughout the genome. It has been used Kumar et al fragment length for "fingerprinting" DNA studies, to clone and map polymorphism) specific DNA sequences and to make genetic maps. AFLP

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RAPD (randomly amplified polymorphic DNA)

Microsatellites or SSR (simple sequence repeats)

SNP (single nucleotide polymorphism)

They use short, arbitrarily sequenced primers to Beuzen et al(53) direct an amplification reaction in discrete regions of (54) the genome, resulting in fragments of various sizes. Vignal et al They have been used for fingerprinting DNA Wakchaure et al(50) studies, to relate close species, in genetic mapping, in population genetics, in molecular evolutionary genetics, and in genetic breed studies in animals and plants. They are sequences of 2 to 6 bp repeated in tandem throughout the genome and have a high polymorphism depending on the number of repetitions found in non-coding gene regions. They have been used in animal identification studies, genetic resource evaluation, paternity testing, disease research, determination of genetic variation within and between races, population genetics, gene and genome mapping migration, and the detection polymorphisms even in silico studies.

Khlestkina(16)

These are regions of DNA in which the substitution of one nucleotide by another, or the addition or removal of one or a few nucleotides, is observed. It has been used in the analysis of biparental inheritance genes and in the analysis of genetic differences, to make genetic maps and to detect genetic variations within species.

Khlestkina(16)

Beuzen et al(53) Kumar et al(51) Duran et al(55)

Yu et al(56) Beuzen et al(53) Kumar et al(51)

With the emergence of mass sequencing technologies, known as "Next Generation Sequencing technologies (NGS)" millions of DNA molecules can be sequenced simultaneously, which greatly facilitates the study of microbial diversity(15). One of the first high-throughput sequencing technologies was 454 pyrosequencing, which was used for targeted sequencing of ribosomal RNA gene amplicons(29). This technique had the advantage of allowing the obtainment of sequences of up to 1,200 bp, albeit with a significantly higher error than other sequencing platforms (1%) and at a higher cost(15). Second generation sequencing, also known as short reading sequencing (50 to 400 bp) uses mainly the Illumina platform(11). Among its advantages, it is worth mentioning that it allows a greater number of readings, with an approximate error rate of 0.1% and at a comparatively low cost(15). It is currently the most popular technology, but it requires a more complex bioinformatic analysis phase than other platforms. Traditionally, when these two platforms (454 pyrosequencing and Illumina) are used for metagenomic analysis with the 16S rRNA marker, a previous amplification step by PCR is 1158

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performed, limiting the identified species to bacteria and archaea only, since the primers will always be used for amplifying fragments of the 16S rRNA gene. If the population also includes eukaryotic microorganisms such as yeasts and protozoa, they cannot be detected. On the other hand, this step of amplification by PCR entails an enrichment of the DNA which produces a bias towards the species that are found in greater proportion causing that the species that are found in smaller percentage to become hard to detect. Finally, this type of analysis identifies microorganisms down to the gender level(29).

An alternative for increasing resolution at the taxonomic level lies in the metagenomic study with the mass sequencing techniques called "Whole-Genome Shotgun sequencing" (WGS) and "Shotgun metagenomics sequencing (SMS)", in which the total metagenomic DNA is sequenced(30,31). The major advantage of these methods is that microorganisms can be classified down to the species level and that not only prokaryotes but also eukaryotes can be identified; also, it does not require the previous amplification step by PCR, and therefore the bias is eliminated. Another advantage of these sequences is that by having sequences of all the DNA present in the sample, those corresponding to the 16S rRNA gene can be selected for use as taxonomic molecular markers; sequences of genes of other constituent polymorphic markers (MLSA) can also be sought in order to achieve a better classification of the microorganisms. The main disadvantages are that it has a higher cost than targeted sequencing of the 16S rRNA gene and requires more complex bioinformatic data analysis(32). Several studies have been conducted to identify metagenomes in a wide range of population environments, using both 16S rRNA gene targeted sequencing and full metagenome sequencing with WGS and/or SMS.

Bioinformatic tools for metagenomic analysis It is important to point out that bioinformatic tools must be used to analyze data obtained from massive sequencing. The greater the amount of data generated, the greater the need for bioinformatics resources(15), both for applications implementing analysis algorithms and for databases with information on microbial genomes (Table 2).

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Table 2: Bioinformatic software for the analysis of metagenomic sequences Bioinformatic application

Method of analysis

Reference Meyer et al(33)

MG-RAST

Assigns structural and functional annotations according to nucleotide and protein databases by homology.

MOTHUR

Analyzes 16S rRNA gene sequences, quantifies ecological Schloss et al(34) parameters to measure Îą and Î˛ diversity; visualizes the analysis using Venn diagrams, heat maps and dendrograms; selects sequence collections based on their quality, and calculates the sequence distance in pairs.

QUIIME

Analyzes microbial sequences of the 16S rRNA gene, performs taxonomic and phylogenetic profiles, and compares between samples.

PhaME

Performs SNP-based comparisons of entire genomes, Ahmed et al(36) assembled sequences, and processed sequences for phylogenetic and molecular evolutionary analysis.

VITCOMIC1

Analyzes the 16S rRNA gene and high throughput sequences to visualize the phylogenetic composition of metagenomic samples.

Mori et al(37)

16SPIP

Rapidly detects pathogenic microorganisms in clinical samples based on metagenomic sequences of the 16S rRNA gene.

Miao et al(38)

PICRUSt

Algorithm with a predictive metagenomics approach based on 16S rRNA gene data and a reference genome database.

Langille et al(39)

CowPI

Uses PICRUSt to Analyze 16S rRNA Gene Data from Rumen Microbiome.

Wilkinson et al(57)

Kraken

Assigns taxonomic tags on metagenomic DNA sequences Wood et al(58) using k-mers alignment achieving more accurate classification compared to BLAST.

Kaiju

Metagenome classifier that finds maximum matches at the Menzel et al(59) protein level using the Burrows-Wheeler transformation; classifies readings with similar sensitivity and accuracy compared to k-mers based classifiers, especially in genera that are underrepresented in reference databases.

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One of the most used applications since its launch is the MG-RAST(33) server, which assigns functional annotations to the analyzed sequences by comparing them with protein and nucleotide homology databases, in addition to allowing phylogenetic analysis. This tool is free and easily accessible, and it is fed with information provided by researchers; therefore, it helps to end the main bottleneck in metagenome sequence analysis, which lies in the availability of information to assign genomic annotations(33). Two other widely used bioinformatic tools in metagenomics are MOTHUR(34), which is also freely accessible and which feeds on metagenomic information that users add to a database with monthly updates, and QUIIME(35), which is used for the analysis of microbial communities from bacterial and archaeal data. Another software widely used for metagenome analysis is PhaME(36) (Phylogenetic and Molecular Evolutionary), which uses whole genome SNPs to measure interspecific diversity by phylogenetic analysis. PhaME(36) can be used to measure inter-species and inter-strain divergence and minimize errors in sequencing and assembly. Comparative genomics, including phylogenetic analysis based on ortho genes and SNPs, requires assembled or finished genomes. PhaME uses the SNP-based approach of complete genomes available in the databases, assembled sequences (contigs) and raw sequences to perform phylogenetic and molecular evolutionary analysis. This software combines algorithms for genome-wide alignment, reading mapping, and phylogenetic construction; it uses internal commands to infer the main genome and SNP, infer trees, and perform other molecular evolution analysis. PhaME is especially useful for the analysis and detection of organisms that are not very abundant in metagenome samples and has been used in data on bacterial samples, viruses, such as Ebola in Zaire, and yeasts, among others(36). Other tools focus on the analysis of the hypervariable regions of the 16S rRNA gene, such as VITCOMIC1(37), which combines the information obtained from the targeted sequencing of the 16S rRNA gene as well as from the massive WGS or SMS sequencing to better visualize the phylogenetic composition of metagenomic samples, in addition to generating a more accurate record of the microbial community. Similarly, the 16SPIP(38) application has also been used for rapid detection of pathogenic microorganisms in clinical samples based on 16S rRNA metagenomic sequence data. As for "predictive metagenomics" approaches, the PICRUSt(39) algorithm, which uses evolutionary models to predict metagenomes from 16S rRNA gene data and a reference genome database, should be highlighted. This tool has been used with data from soil microbiome samples, mammalian intestines, microbial mats, and humans(39), such as the human oral microbiota study which analyzed 6,431 samples of the 16S rRNA gene from the Human Microbiome Project(39,40).

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Examples of metagenomic characterization with high throughput methodologies Several metagenomic characterization works have been carried out to identify microorganisms living in environments of interest due to their great variability and ecological importance (Table 3). The following are a few examples of these works, without being exhaustive. For example, a massive sequencing of 29 metagenomes from samples from three marine stations that are part of the global Tara expedition was performed(29). The taxonomic analysis carried out with the sequence data corresponding to the 16S rRNA gene made it possible to identify all the variable regions of the gene (V1 to V9). Targeted sequencing of the 16S rRNA gene was also performed for comparative purposes. The results obtained indicated that the efficiency in taxonomic classification with the use of ribosomal database RDP (Ribosomal Database Project) is similar for both types of sequencing. However, massive sequencing offers two major advantages: it reduces the error caused in amplicon PCR and it generates a large amount of functional data that can be analyzed along with the taxonomic analysis. Table 3: Examples of metagenomic characterization Sample

Type of analysis

Reference

Marine Plankton from Tara Taxonomic profiles and structure of Logares et al(29) Oceans Expedition marine prokaryotic communities through massive stations 16S rRNA directed sequencing Sundarban Sediments

Mangrove Analysis of bacterial diversity and Basak et al(41) distribution through targeted sequencing of 16S rRNA

Sediments from the Arabian Analysis of bacterial structure and diversity Nair et al(42) Sea based on the sequencing of a 16S rRNA library Malaysia Sungai Klah Hot Diversity analysis through 16S rRNA V3- Chan et al(43) Springs V4 region targeted sequencing Mushroom Spring in Microbial diversity based on 16S rRNA Thiel et al(44) Yellowstone National Park gene targeted sequencing and metagenomic sequencing.

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Basal ice of Glacier, Alaska

Matanuska 16S rRNA gene directed sequencing Kayani et al(45) microbial diversity analysis and metagenomic sequencing PaĂŻsse et al(46)

Blood from healthy donors

Analysis of the microbiome by PCR amplification and directed sequencing of 16S rRNA

Human Fecal Microbiome

Comparative study of the entire genome by Ranjan et al(32) massive and targeted sequencing of 16S rRNA

Pasteurized and un- Diversity analysis through targeted Salazar et al(47) pasteurized Gouda cheese sequencing of the 16S rRNA gene Ileal and cecal microbiota Diversity analysis by amplification of the Mohd-Shaufi et from broilers V3 region of the 16S rRNA gene al(48) Microbiota attached to fiber Characterization of genes and genomes of in bovine rumen metagenomic DNA

Hess et al(3)

Rumen of dairy and beef Taxonomic analysis of the rumen cattle microbiome through directed pyrosequencing of the 16S rRNA gene

Wu et al(20)

Rumen microbiota in cattle supplemented with yeast

Analysis of rumen microbial diversity through pyrosequencing

Pinloche et al(5)

Rumen microbiota in cattle supplemented with thiamine

Analysis of bacterial diversity through Pan et al(49) targeted sequencing of the 16S rRNA gene

Microbiome of healthy skin Microbial characterization and functional Zinicola et al(30) and with digital bovine gene composition of healthy skin or skin in dermatitis active and inactive lesion stages by massive sequencing of the entire genome and annotation of the samples by MG-RAST Rumen fluid from three Metagenomic profiling of the rumen by Ross et al(31) fractions of the bovine rumen non-directed parallel mass sequencing in metagenomic DNA

Another metagenomic work in the field of mass sequencing focused on analyzing the diversity and bacterial distribution present in sediments of the tropical mangrove of Sundarban(41). For this identification, it was used the 16S rRNA directed sequencing through 454 pyrosequencing, obtaining a total of 153,926 sequences. The analysis with MG-RAST software made possible the identification of 56,547 species belonging to 44 different

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phylotypes, being the most dominant the phylotype Proteobacteria. On the other hand, metagenomic analysis of sediments from the Arabian Sea(42) with Sanger 16S rRNA sequencing classified the sequences obtained into seven different phylotypes where the phylotype Proteobacteria also predominated. A large number of papers have focused on the characterization of metagenomes from extreme environments. For example, sequencing of 16S rRNA and complete genomes has been used to identify the diversity of thermophilic bacteria present in thermal waters in Malaysia whose temperature varies between 50 and 110 ยบC(43). An analysis of the 16S rRNA data identified approximately 35 phylotypes, of which Firmicutes and Proteobacteria represented 57 % of the microbiome. As for thermophiles, 70 % of those detected were strictly anaerobic; however, Hydrogenobacter spp. (forced chemolithotrophic thermophilotypes) constituted one of the most frequent taxa, and a large number of thermophilic photosynthetic microorganisms were found as well. Most of the identified phylotypes coincided with the findings of the sequencing of complete genomes. Thanks to this type of analysis, it was possible to identify and classify extreme microorganisms, such as thermophilotypes, anaerobes and chemolithophytes, that would have been difficult to characterize with classic microbiological methods(43).

Another study for identifying microbiota from extreme environments was conducted from samples of microorganisms that grow in the fungi that inhabit Yellowstone Park through the directed sequencing of 16S rRNA(44). Over the years, the study of microorganisms in this habitat has focused on chlorphototrophic bacteria belonging to the Cyanobacteria and Chloroflexi. However, the results of the study revealed that microbial variation is dominated by a single taxon: Roseiflexus spp. which belongs to the group of anoxigenic phototrophic microorganisms(44). Targeted 16S rRNA sequencing, along with full genome sequencing, has equally been used in glaciers, for which microbial information is also very limited. The first reported metagenomic study of glaciers(45) identified nine different genomes, including Anaerolinea, Synthrophus and Thiobacillus, and metabolic pathways involved in sulfur oxidation and nitrification were identified. There are examples of the use of mass sequencing in metagenomic populations within the health and agro-food sectors. As an example within the field of human health, studies of directed sequencing of 16S rRNA to describe the microbiota present in the blood of healthy individuals have shown that this body fluid is not a sterile tissue(46). At the phylotype level, more than 80% of the microorganisms present in the blood belonged to Proteobacteria, although phylotypes of Actinobacteria, Firmicutes and Bacteriodetes were also found. Ranjan et al.(32) used different strategies to characterize the human fecal microbiome. From a single sample they obtained 194.1 x106 readings from different sequencing strategies (16S rRNA directed sequencing, Illumina HiSeq, Illumina MiSeq). When comparing these,

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especially the 16S rRNA gene directed sequencing with the WGS sequencing, they concluded that the latter has more advantages, as it increases the ability to identify bacterial species and the detection of diversity and gene prediction, and it also improves the accuracy of species detection by increasing the length of the sequences. In the agro-food field, directed sequencing of 16S rRNA has also been used to identify microorganisms present in Gouda cheese(47) whether prepared with pasteurized or unpasteurized milk, and to evaluate changes due to the effect of aging. This study identified 120 genera in unpasteurized cheese and 92 in pasteurized cheese. In addition, depending on the aging time, it had a significant influence on the presence of microbiota. The most abundant genera in all samples were Bacillaceae, Lactococcus, Lactobacillus, Streptococcus and Staphylococcus. In the case of growing broilers, the variation of ileal and cecal microbiota through time has been studied(48). In order to do this, the hypervariable V3 region of the 16S rRNA gene was amplified and sequenced. The results showed that the cecal microbial communities were more diverse than the ileal ones. In addition, the presence of (potentially pathogenic) Clostridium bacteria was observed to increase as the animals grew and that the population of beneficial microorganisms such as Lactobacillus was low in all intervals(48). In the case of ruminal metagenomes, it should be noted that one of the first sequencing studies was conducted to search for cellulolytic enzymes never before described(3). In this study, 454 pyrosequencing was performed, obtaining 268 gigabases of metagenomic DNA information. From this information, 27,755 supposed genes of carbohydrate-active enzymes were identified, of which 90 codified for possible proteins, and 57% of them were enzymatically activated by cellulosic substrates. Another study focusing on ruminal metagenome in dairy calves and beef cattle steers(20) used 16S rRNA targeted pyrosequencing to assess population variation according to the type of livestock. This study found 8 phylotypes, 11 classes, 15 families and 17 different genera, and differences in the abundance of phylotypes found between dairy and beef cattle. The most abundant phylotypes were Bacteriodetes, Firmicutes, Proteobacteria, Fibrobacteres and Spirochaetes in both types of cattle, but with a lower abundance of Bacteriodetes and Proteobacteria in beef cattle. The use of yeast as a nutritional additive in cattle is known to improve milk production and weight gain. However, there is no knowledge of whether the effect caused by yeasts is a general stimulus to all microbial species or only affects some of the ruminal environment. Due to the above, a study was conducted to evaluate the changes in the rumen microbiota when the animals were fed with a yeast additive compared to when they consumed only the basal diet(5). In this work, 454 pyrosequencing of the V1 region of the 16S rRNA gene was used to identify the population of ruminal microorganisms. The results showed that a change was observed in the main fibrolytic bacteria (Fibrobacter and Ruminococcus) and in lactate-using bacteria (Megasphaera and Selenomonas) when the yeast additive was added. Targeted sequencing 1165

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of the 16S rRNA gene in the adult dairy cattle ruminal microorganism population when combining thiamine with high grain diets has been used to evaluate its effect as an additive in animal nutrition(49). The results confirmed that thiamine supplementation can improve ruminal function, as the number of cellulolytic bacteria increased when this amino acid was administered. In the field of animal health, the sequencing of complete metagenomes has also been used. For example, skin metagenome with active and receding bovine digital dermatitis has been compared with the skin of healthy cattle to see if pathogens involved in the pathogenesis of the disease were detected(30). The sequences obtained were analyzed with MG-RAST and six main phylotypes were identified, among which Firmicutes and Actinobacteria predominated in the microbiome of healthy patients, while Spirochetes, Bacteroidetes and Proteobacteria were the most abundant in active and recession patients; this confirms that the presence of the disease changes the population of the metagenome. Rumen metagenomic profiles have been obtained by sequencing complete metagenomes from samples of ruminal fluid from three different cattle and between different locations in the rumen(31). In addition to comparing with the metagenome from feces of the same animals, the results indicated that the variation in metagenomic profiles was less among samples taken from the same animal, even if they were taken from different regions of the rumen. Contrary to expectations, no relationship was found with the metagenomic profile of faeces and ruminal fluid from the same animal.

Conclusions Traditionally, metagenomic analysis used laborious methodologies, such as denaturing gradient gel electrophoresis, the digestion of genomes with restriction enzymes, and their visualization by means of agarose and/or acrylamide gels. The development of nucleic acid sequencing methodologies, especially new mass sequencing technologies, has helped to reduce this problem. The 16S rRNA gene has traditionally been considered the gold standard for classifying prokaryotic microorganisms (bacteria and archaea), as it meets all the characteristics required to be a molecular marker. However, despite the large number of works that have used the sequencing of the hypervariable regions of this marker, it has the disadvantage of not being able to determine taxa at an infra-generic level. A strategy used to improve taxonomic classification has been the combination of the 16S rRNA marker with some other constitutive expression genes such as the genes sodA, hps65, gyrB, among others, and even genes

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encoding for subunits of the cytochrome c enzyme complex have been used to classify microorganisms into species. In the last decade, mass sequencing technologies have made it possible for microbial populations to be analyzed in greater depth, either by sequencing the entire 16S rRNA gene, thus increasing the resolution of that marker, or by combining the information of that gene with the sequencing of complete metagenomes. In this last type of analysis, sequences of all the genomic material present in the sample are obtained, which offers the great advantage that in addition to making the taxonomic classification it is also possible to obtain functional information of the detected genes. Thus, despite the limitations of the required bioinformatic analysis, the use of these methodologies allows for more complete analyses. However, despite the development of high-performance sequencing techniques, the targeted sequencing of 16S rRNA on the Sanger platform is not entirely obsolete, and the selection of the analysis strategy will depend on the objectives of the study, the degree of precision desired, the sample size and the financial resources that can be allocated by the research team. For example, if you are looking for the presence and/or absence of a single bacterial genus, Sanger sequencing would be ideal because it has the ability to sequence relatively large fragments with greater precision than any mass sequencing platform. If what is wanted is to discriminate between species of a single bacterial genus, two strategies can be utilized: the sequencing of some hypervariable region of the 16S rRNA together with some other constitutive gene (MLSA), or the sequencing of the whole gene in order to obtain the information of all the hypervariable regions. Today, metagenomics faces numerous challenges arising from the large amount of information generated, its storage and the way in which it must be treated. Although many tools and applications have been designed for bioinformatic analysis of metagenomes, there is no single "protocol" of analysis; therefore, each study must be adapted to the nature of the samples and the objectives of the experiment. In conclusion, microbial diversity studies will always use the 16S rRNA molecular marker to make taxonomic classifications, either through the sequencing of one or two of its hypervariable regions or through that of the whole gene, and it can even be combined with the use of another constitutive gene as a molecular marker to achieve a better taxonomic classification. On the other hand, mass sequencing technologies have greatly improved the study capacity and speed of metagenome analysis. This has occurred particularly in environmental samples with ecological importance, in both human and animal health, in studies on symbiosis of plants with endophytic fungi, and in the evaluation of ruminal metagenomes, to mention a few.

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https://doi.org/10.22319/rmcp.v11i4.5171 Review

Metabolic origin and bioactive properties of odd and branched-chain fatty acids in ruminants’ milk. Review

Pilar Gómez-Cortés a* Miguel Ángel de la Fuente a

Instituto de Investigación en Ciencias de la Alimentación (CSIC-UAM), Nicolás Cabrera, 9. Universidad Autónoma de Madrid, 28049 Madrid, España.

* Corresponding author: p.g.cortes@csic.es

Abstract: Milk odd and branched-chain fatty acids (OBCFA) are a group of lipids that represents less than 5 % of the total fatty acids (FA) and that includes a group of molecules, among which the most abundant are the isomers of the pentadecanoic (15:0, iso-15:0 and anteiso15:0), hexadecanoic (iso-16:0), and heptadecanoic (17:0, iso-17:0 and anteiso-17:0) FA. OBCFA are synthesized by rumen microorganisms from the molecules produced during feed fermentation processes. Recent research indicates the possibility of endogenous synthesis of some odd (15:0 and 17:0) and branched-chain (iso-1:0 and anteiso-17:0) FA. The presence of these FA in milk is influenced by dietary factors, mainly the starch vs fiber proportion, forage to concentrate ratio, and the supplementation with fat sources that change the lipid metabolism, which modifies the OBCFA profile of milk. Milk and dairy products are the main and almost only source of OBCFA in the human diet. Despite their low concentration, OBCFA possess bioactive properties that have been shown in different investigations. This article reviews the metabolic origin, bioactive properties, and most recent nutritional strategies directed to manipulate the contents and profiles of OBCFA in milk fat. Key words: Ruminant, Fatty acids, Milk, Dairy products, Rumen, Lipids.

Received: 30/11/2018 Accepted: 23/10/2019

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Introduction The lipids in milk are physically in the form of globules, which form an emulsion with the aqueous phase of milk. Inside these globules reside the triglycerides (TG), which are molecules of esterified glycerol with three FA. TG are more abundant (more than 95 %), and thus, they are mainly responsible for the properties of most milk lipids" for "TG (more than 95 % of total lipids) are mainly responsible for the properties of milk lipids, and their characteristics vary in function of the FA composition. Although milk fat has more than 400 different FA(1), only 30 or 40 are present at concentrations higher than 0.1 %. The FA profile of milk and dairy products is mainly related to dietary factors, followed by ruminant species, and, to a lesser extent, genetic factors, milk yield, and lactation status.

Based on their structure, FA are classified as saturated or unsaturated FA. Most saturated FA have an even number of C atoms, ranging from 4 up to 20 C. Although the most abundant are those with a chain length of 10 to 20 C atoms, the ruminant milk fat is characterized by significant amounts of short-chain FA, especially 4:0 and caproic acid (6:0). Among the unsaturated FA, which can have one to four bonds, the most abundant (15 to 20 %) is oleic acid (cis-9 18:1). The presence of small amounts of linoleic (2 %) and Îą-linolenic (0.5 %) acids in milk derives from the diet, and since both are not synthesized in tissues, they are considered essential FA.

Ruminant milk also contains odd and branched-chain FA (OBCFA). Those with an odd number of C atoms represent 2 % of the total FA; 15:0 and 17:0 are the most abundant and representative (Table 1). Branched-chain FA represent a similar proportion and include a higher number of molecules, classified as iso and anteiso, with variable concentrations in dairy products. Although the concentration of OBCFA in fat milk is lower than 5 %, their presence is of great relevance because they work as indicators of ruminal function and, in humans, as indicators of the intake of dairy products. Branchedchain FA, especially anteiso, have lower melting points than their unbranched counterparts; this allows them to contribute to the fluidity of milk fat.

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Table 1: Content of odd and branched-chain fatty acids (g/100 of total fatty acids) in dairy products Fatty Milk Butter Yogurt Cream Cheese (7) (58) (59) (43) acid 0.04 iso 13:0 0.05-0.13 0.09 0.17 0.12-0.13 0.00-0.05 0.00-0.22 iso 14:0 0.14-0.22 0.22 0.10 0.14-0.15 0.00-0.11 0.02-0.42 iso 15:0 0.21 0.34 0.29-0.30 0.24 0.00-1.18 iso 16:0 0.27 0.31 0.16-0.25 0.27-0.30 0.05-0.30 iso 17:0 <0.01 0.00-0.04 <0.01 0.00-0.09 iso 18:0 0.08 anteiso 13:0 0.46 0.63 0.62-0.63 0.46-0.49 0.38-0.88 anteiso 0.32-0.45 15:0 0.50 0.38 0.56-0.59 0.36-0.37 0.29-0.61 anteiso 17:0 15:0 0.84-1.31 0.89 17:0 0.45-0.66 0.52 0.55-0.90 Fievez et al(7); OÂ´Donnell-Megaro et al(58); Shingfield et al(59); Ran-Ressler et al(43).

Although a great proportion of the OBCFA in milk fat is synthesized during the fermentative processes in the rumen, recent studies have suggested that a small amount could be endogenously synthesized (e.g., mammary gland). Moreover, in the last decade, increasing evidence suggests that OBCFA could have an important role in human health. Therefore, their presence in dairy products should be viewed positively, as these products are almost the only source of these components in the diet. This review aimed to update the information about the origin and synthesis of these FA in ruminants, reviewing the influence of the type of feed on their milk content, and compile evidence on the nutritional benefits of OBCFA in humans.

Origin of odd and branched-chain fatty acids Ruminal synthesis of OBCFA The fat in ruminant milk has higher concentrations of OBCFA than the milk of other mammals. Vlaeminck et al(2) compiled data from numerous studies about the composition of OBCFA in milk and showed that the main OBCFA are isomers of the tetradecanoic 1176

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(iso-14:0), pentadecanoic (15:0, iso-15:0 and anteiso-15:0), hexadecanoic (iso-16:0), and heptadecanoic (17:0, iso-17:0 and anteiso-17:0) FA. The OBCFA are mainly synthesized during the microbial fermentation processes in the rumen. Rumen bacteria contain between 50 and 90 g/kg of lipids in their dry matter, and approximately 5 % of these lipids are OBCFA, which are preferentially located in the membranes(3). Protozoa have less total OBCFA than bacteria (110 vs 160 g/kg of total FA), although they possess a higher proportion of iso 16:0 and anteiso 17:0(4). The precursors of the microbial synthesis of branched-chain FA in the rumen are leucine, isoleucine, and valine, branched-chain amino acids obtained from the diet (Figure 1). Initially, the rumen microbiota transforms these amino acids into short branched-chain carboxylic acids; isovaleric, 2-methylbutyric, and isobutyric, respectively; linked to Coenzyme A. Subsequently, the microbial FA synthase (FAS) elongates the FA chains. The even-numbered iso FA originate from the isobutyric acid; the odd-chain iso and anteiso FA originate from the isovaleric and 2-methylbutyric acids, respectively. The precursor of medium odd-chain FA (13:0, 15:0, and 17:0) in the rumen is propionic acid, which results from the fermentation of specific ration components, although the 15:0 and 17:0 FA can also originate by Îą-oxidation from the 16:0 and 18:0 FA in the lipids in the diet. Figure 1: Synthesis of odd and branched-chain fatty acids by the rumen microbiota

Adapted from Vlaeminck et al(2)

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After ruminal digestion, the OBCFA profile is strongly associated with the activity of the microorganisms in this digestive cavity(2). Thus, the OBCFA profile variation reflects the relative abundance of the different microbial species in the rumen ecosystem(5,6). Cellulolytic bacteria, those with enzymes that hydrolyze cellulose, such as Ruminococcus flavefaciens, Ruminococcus albus, or Butyrivibrio fibrisolvens, possess significant contents of iso OBCFA(7). Higher proportions of anteiso-15:0 would indicate the presence of bacteria specialized in the fermentation of pectin and sugars(8), such as Prevotella spp., Lachnospira multiparus, and Succinovibrio dextrinosolvens. Amylolytic bacteria, such as Succinivibrio dextrinosolvens, Succinimonas amylolytica, Ruminobacter amylophilus, Selenomonas ruminantium, and Streptococcus bovis, have lower proportions of branchedchain FA, but higher proportions of odd-chain FA.

Transfer of OBCFA from the intestinal tract to the mammary gland

The preponderant role of rumen microorganisms in the presence of OBCFA in dairy products is well known(9). However, recent reports(2,10) have questioned the strictness of the correlation between the content of OBCFA in the intestinal fluid and the milk fat. Theoretically, disarrangements could occur during the transfer of these FA from the intestinal tract to the internal tissues, particularly in the mammary gland. These disarrangements could occur during the intestinal absorption process or during the transport through the bloodstream. Like other FA that reach the small intestine, OBCFA are absorbed in the jejunum. Apparently, the intestinal absorption of microbial FA would be higher(11), but the few available data are not enough to render a definitive conclusion.

After being absorbed, the OBCFA and remaining FA are esterified in the glycerol by the intestinal epithelial cells to form TG and phospholipids (PL), and transported, first to the lymphatic system and then to the bloodstream, where they form part of macromolecular complexes, such as chylomicrons and very low density lipoproteins (VLDL). Chylomicrons and VLDL contain different types of lipids (TG, PL, cholesterol esters (CE), and free fatty acids), but each one differs in composition since each type of FA selectively binds to the different fractions. The transfer of the FA from the bloodstream to the cytoplasm of mammary gland cells occurs after their release from these macromolecules by the lipoprotein lipase enzyme (Figure 2).

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Figure 2: Metabolic pathways of fatty acid synthesis in the mammary gland cells of ruminants

ACC: acetyl-CoA carboxylase; FAS: fatty acid synthase; FA: fatty acid; TG: triglycerides; VLDL: very low-density lipoproteins.

The main targets of the lipoprotein lipase are the FA of the TG. On the contrary, the characteristic FA of the CE and PL fractions are more poorly transferred to milk fat because this enzyme has a low affinity for these FA. Fievez et al(7) reported that the branched-chain FA are more abundant in the CE and TG than in the PL or free fatty acids. However, these last two fractions are richer in odd-chain FA. Nevertheless, the available literature about the distribution of OBCFA between the different types of plasma lipids is still too scarce to predict trends or forecast consolidated metabolic behaviors. Therefore, it would be worth exploring the metabolic processes in the mammary gland cells in detail to find the mechanisms responsible for the differences in the OBCFA profiles between rumen fluid and milk.

Endogenous synthesis of OBCFA

Most of the saturated FA with an even number of C atoms in the milk fat are synthesized de novo in the epithelial cells of the mammary gland(12). Their synthesis occurs from the blood-circulating acetate and Î˛-hydroxybutyrate molecules generated in the rumen during the fermentation of carbohydrates in the diet. Acetyl-CoA carboxylase (ACC) and FAS are the two enzymes responsible for this de novo synthesis in the mammary gland (Figure 2). The first step in the synthesis consists of the activation of acetate to acetyl-CoA, 1179

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followed by the condensation of two acetyl-CoA molecules to form malonyl-CoA. This step is catalyzed by the ACC. Subsequently, FAS regulates the chain elongation of the FA synthesized de novo. If the initial substrate instead of acetate was propionate, methylmalonate, or volatile branched-chain FA (isovaleric, isobutyric, and 2methylbutyric), then the final products of the de novo synthesis would be odd-chained FA, non-terminal methyl-substituted FA, or iso and anteiso, respectively, as it occurs in the rumen (Figure 1). The first studies in this field demonstrated that 15:0 and 17:0 could be synthesized de novo in the mammary gland of ruminants using propionyl-CoA instead of acetyl-CoA as the primer molecule(13). The elongation of this molecule, catalyzed by FAS, would explain the presence in milk of 5:0, 7:0, 9:0, and 11:0, as well as the increase in the amounts of 13:0, 15:0, and 17:0 compared to those already generated in the rumen and transferred from the duodenum. The importance of this endogenous synthesis was confirmed in subsequent studies(10,14,15). Theoretically, these odd-chained FA (13:0, 15:0, and 17:0) could also be metabolized to cis-monounsaturated by the delta-9 desaturase enzyme, However, only the conversion from 17:0 to cis-9 17:1 seems to be of quantitative importance(16) (Figure 2). In contrast to odd-chained FA, the mammary synthesis of iso and anteiso FA did not respond to the increase in the availability of its biological precursors, the isovaleric, 2methylbutyric, and isobutyric FA(13,14). This observation would indicate that the FAS might not be active in the elongation process, and thus, the de novo synthesis would not occur in extraruminal tissues. However, these results would contradict the increased content of iso 17:0 and anteiso 17:0 in milk fat, compared to the intestinal fluid, reported by other researchers(2,10,17). Fievez et al(7) postulated that the lowest values of the iso 15:0/iso 17:0 and anteiso 15:0/anteiso 17:0 ratios in milk, compared to those in the duodenal fluid, could be explained if the chain elongation of the iso 15:0 and anteiso 15:0 molecules was demonstrated to be viable after being absorbed into the bloodstream. In this sense, it seemed striking that the secretion in the milk of iso 15:0 + iso 17:0 and anteiso 15:0 + anteiso 17:0 was very similar to the sum of these FA in the duodenum(7). These data corroborated the hypothesis about the existence of an extraruminal elongase activity on the iso and anteiso FA with 15 C atoms; it also supported the idea of an almost complete transfer of total branched FA from the duodenum to the milk. In a subsequent study, Vlaeminck et al(15) observed higher levels of iso 17:0 and anteiso 17:0 in milk fat than in the duodenal fluid (Table 2). This fact, along with lower iso-15:0/iso-17:0 and anteiso 15:0/anteiso 17:0 ratios in milk, would rule out the postruminal de novo synthesis of these FA and confirm the predominant role of postabsorption elongases, which would exert their activity on the iso 15:0 and anteiso 15:0 FA. The lowest value of the iso 15:0/iso 17:0 and anteiso 15:0/anteiso 17:0 ratios in the plasma TG, regarding the duodenal fluid samples, would also indicate that the elongation could be taking place in tissues other than the mammary gland. 1180

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Table 2. Fatty acid proportion in cow blood plasma Blood plasma Duodenum Milk TG FFA Assay g/100 g of total odd and branched-chain fatty acids 1 12.87d 7.20b 9.13c 6.22ª c b 2 10.45 5.42ª 6.80 7.54b 1 26.98d 14.54b 19.07c 12.47ª c ab 2 33.57 13.22ª 15.00 19.06b 1 5.76ª 8.85b 10.02c 13.45d 2 5.79ª 6.64ª 9.54b 10.09b 1 7.32ª 13.24b 14.21bc 15.50c 2 9.90ª 16.18bc 14.41b 17.63c

Fatty acid

iso-15:0 anteiso 15:0 iso 17:0 anteiso 17:0

C15/C17 ratios iso 15:0/iso 1 17:0 2 ant 15:0/ant 1 17:0 2 a-d

Pvalue <0.001 <0.001 <0.001 <0.001 <0.001 <0.001 <0.001 <0.001

2.28c

0.83b

0.92b

0.48ª

<0.001

1.82b 3.98b

1.18ab 1.10ª

0.78ª 1.37ª

0.77ª 0.82ª

<0.001 <0.001

3.73ª

0.84b

1.10b

1.13b

<0.001

TG= triglycerides; FFA= free fatty acids. Values in a row with different superscripts are different (P<0.05). Source: Vlaeminck et al(15).

Overexpression of the gene that codifies the ELOVL6 elongase in ruminants is described in mammary epithelial cells(18), and, more recently, an in vitro study evaluated for the first time the role of this enzyme in the regulation of FA elongation(19). Upregulation of ELOVL6 increases the elongation indices of 16:0 and 18:0, which suggests an important role of this enzyme in controlling the chain length of FA in the mammary gland. However, the effects on branched FA are yet to be investigated.

Influence of the cattle diet on the OBCFA contents in milk The chemical composition of the ration, the proportion of starch and fiber, the forage to concentrate ratio (F/C), and the lipid profile in the diet exert a significant influence on the type of ruminal microbial populations and the microbial synthesis of FA; therefore, the proportion of OBCFA that reaches the small intestine reflects the composition and quantity of rumen microbiota(2,3,20).

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Effects of the basal diet

Among the different diet components, the starch to fiber ratio has an important role in the production of OBCFA through its influence on the microbial ecosystem, particularly on the proliferation of cellulolytic bacterial strains(21,22). An increase in starch in the rations limits the growth of cellulolytic microorganisms, promoting the proliferation of amylolytic bacteria. As previously described, cellulolytic bacteria possess mainly branched iso FA in their membranes(7) and are sensitive to low ruminal pH values, which are characteristic of feeds with a high starch content(23). Vlaeminck et al(2) observed that starch-rich diets, characterized by a higher proliferation of amylolytic bacteria, decreased the levels of iso 14:0, iso 15:0, and iso 16:0 (Table 3). Table 3: Mean content (g/100 of total fatty acids) of odd and branched-chain fatty acids in the milk of ruminants fed with different ingredients Fatty Shingfield et Vlaeminck et Patel et al(24) Li et al(25) CĂvico et (60) (2) acid al al al(27) GS CS GS CS HGS LGS HF LF HF HS iso 13:0 iso 14:0 iso 15:0 iso 16:0 iso 17:0 iso 18:0 anteiso 13:0 anteiso 15:0 anteiso 17:0 11:0 13:0 15:0 17:0

0.03 0.08 0.21 0.21 0.74 0.03 0.05

0.04 0.06 0.18 0.23 0.91 0.01 0.07

0.39

0.37

1.22 0.63

0.78 0.54

0.02 0.07 0.17 0.19 0.27 0.05

0.01 0.03 0.14 0.15 0.23 0.04

0.09 0.24 0.18 0.19

0.05 0.17 0.16 0.23

0.08 0.21 0.26 0.47

0.07 0.18 0.26 0.33

0.14 0.32

0.13 0.23

0.38

0.49

0.46

0.42

0.39

0.49

0.45

0.30

0.22

0.46

0.55

0.89

0.76

0.29

0.26

1.21 0.55

0.20 0.09 1.00 0.73

0.22 0.09 0.98 0.68

0.82

0.62

0.95 0.48

1.06 0.67

0.94 0.53

GS= grass silage; CS= corn silage; HGS= high grass silage; LGS= low grass silage; HF= high-fiber; LF= low-fiber; HS= high-starch.

Subsequent studies have confirmed the idea that the fiber and starch ratios influence the content of milk OBCFA (Table 3). Patel et al(24) reported that an increase in fiber resulting from the presence of grass silage in the rations increased the milk contents of iso 15:0, iso 17:0, 15:0, and 17:0; while substituting fiber to the detriment of starch in the diet increased the content of iso 15:0 in the milk(25) and rumen(26). These responses were associated with a higher abundance of cellulolytic versus amylolytic bacteria. Moreover, 1182

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CĂvico et al(27) measured higher levels of iso 14:0, iso 17:0, and 15:0 in milk fat when the diet was enriched with fiber and low on starch (Table 3). The F/C ratio in the rations could modify the contents of OBCFA in dairy products. Vlaeminck et al(2) concluded that a greater proportion of forage in the basal diet contributed to a selective increase of specific OBCFA, such as iso 14:0 and iso 15:0. However, the levels of anteiso 15:0 were less affected. These results are explained by changes in the ruminal ecosystem induced by the variation in the F/C ratio of the diets. An increase in the concentrate would favor the proliferation of amylolytic bacteria. could increase anteisos and odd-chain FA. In this line, researchers(10) observed lower levels of 15:0 and 17:0 in the milk of cows fed diets with an elevated F/C ratio. The analysis of the digestive fluids extracted from goats with duodenal cannulation confirmed that increasing the F/C ratio in the ration increases all the OBCFA synthesized de novo by the bacteria(5). A similar experiment in cows(28) had similar results. More recently, Zhang et al(29) confirmed that the OBCFA profiles in the digestive fluids of bovines are drastically affected by the F/C ratio in the basal diet. The concentrations of 11:0, 13:0, iso 15:0, iso 16:0, iso 17:0, and 17:0 were higher when the proportion of forage in the ration was higher. They also observed that only the anteiso 15:0 and 15:0 increased with higher proportions of the concentrate.

Effects of lipid supplementation

The levels of OBCFA in dairy products show a significant decrease when they come from animals whose diet has been supplemented with lipid sources. This pattern, observed in the milk of bovines(6,30) and small ruminants(31,32), is characteristic of supplementation with oilseeds rich in unsaturated FA. These results could be explained by the inhibitory effect of the polyunsaturated fatty acids (PUFA) on the gut microbiota. The severity of the effect of the FA incorporated into the diet on the viability of rumen bacteria is greater as the number of unsaturations increases. The effects would be more pronounced if the geometric configuration of the double bonds is of the cis type(2,3). Furthermore, not all microorganisms would be affected in the same way by the lipid supplementation of the diet. Previous studies have observed that cellulolytic and Gram-positive bacteria are more sensitive to the lipids in the diet than amylolytic and Gram-negative bacteria(20,33,34).

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Branched-chain fatty acids as bioactive components Neonatal gut microbiota

Recent studies have highlighted the role of branched-chain FA as health-protective bioactive components. The presence of branched-chain FA is very low in adult human tissues; however, they are essential bioactive components in the digestive tract at the final stages of fetal development and after delivery(35). Approximately 30 % of the total FA in the vernix caseosa are branched-chain FA, with a great variety of molecular structures, among which iso 14:0 and iso 16:0 stand out(35). The vernix is a waxy material with a cheese-like texture that coves the skin of the fetus and newborn. It consists of a mixture of fatty secretions originated from the 18th week of gestation from the sebaceous glands. The vernix avoids water loss, protecting the skin of the fetus from dehydration, preventing its hardening, and reducing friction and cracking. Moreover, it helps regulate the temperature of the fetus by acting as an insulating layer. There is no other land mammal that produces vernix-covered neonates; however, the fetuses of aquatic mammals present this same fatty film composed of branched FA(36). A complementary hypothesis postulates that the vernix may have antibacterial activity. This idea is based on the fact that vernix particles are detached from the skin during the last months of pregnancy and pass into the amniotic fluid, increasing its turbidity. In the last trimester, the fetus ingests a significant part of the amniotic fluid, and thus, its intestine impregnates with the branched-chain FA in the vernix(35). Moreover, the significant amount of branched-chain FA in the meconium (the first feces of the newborn) constitutes a sufficiently relevant indication of the type of microorganisms that begin to colonize the intestinal tract of the newborn, and that would be favored by the presence of these non-fermentable prebiotics(35). As previously described, branched-chain FA are among the most important molecules in the membrane of several microorganisms, particularly of most species of the genus Bacilli(37). A previous report indicated that the substitution of the dietary fat with branched-chain FA in newborn rat pups modifies their microbiome. These changes translate into an increase of the microorganisms that can incorporate branched-chain FA into their membranes, and a simultaneous reduction of the incidence of necrotizing enterocolitis(38), one of the major causes of mortality in preterm infants. Furthermore, in vitro studies have demonstrated that branched-chain FA reduce mortality and virulence of pathogens such as Pseudomonas aeruginosa(39).

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High concentrations of branched-chain FA, as a consequence of the presence of vernix in the intestinal lumen of the fetus, may have an important role in the growth and metabolism of enterocytes, as well as in intestinal health and regulation. Recent studies have observed that branched-chain FA can be incorporated into the membrane PL of enterocytes, conferring them an anti-inflammatory activity(40,41). Liu et al(42) postulated that this incorporation of branched-chain FA to the PL would contribute to the modulation of the biophysical properties of membranes. Branched-chain FA are assigned biophysical functions comparable to monounsaturated FA with cis configuration, but they have the advantage of presenting greater oxidative stability due to the absence of double bonds in their structure. Moreover, the lower melting points of branched-chain FA compared to their linear homologous would be associated with the fluidity of cell membranes(43).

Other bioactive properties of branched-chain fatty acids

Besides their positive effects on the composition of gut microbiota, branched-chain FA in the diet could help prevent different diseases. The first study that attributes anti-cancer activity to branched-chain FA was published at the beginning of this century(44). This study describes the inhibitory effects of iso 15:0 on cell proliferation and apoptosis induction in prostate cancer, leukemia, and adenocarcinoma cell lines. More recently, Cai et al(45) reported that iso 15:0 could contribute to human lymphomas inhibition. Other studies(46,47) determined that branched-chain FA could also induce apoptosis in breast cancer cells and inhibit tumor development in cell cultures and animal models.

Moreover, a recent study in overweight humans(48) reported for the first time the possibility that the abundance of iso branched-chain FA in blood serum could be inversely correlated with the presence of TG and negatively associated with other characteristic indicators of inflammatory processes. In any case, the beneficial effects of this group of FA require more research to help clarify the mechanisms underlying the prevention of these pathologies.

Odd-chain fatty acids as bioactive components Different recent studies have demonstrated that 15:0 and 17:0, the most abundant oddchain FA in dairy products, could benefit human health(49,50). For example, there is an inverse association between the concentration of these FA in plasma and the risk of developing type 2 diabetes(51-53). This result has also been observed in European populations subjected to different diets(54). Even several prospective studies on cardiovascular diseases have shown that the plasma concentration of these FA would be 1185

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associated with a lower risk of developing cardiovascular diseases(55-57). However, more detailed research is needed to help elucidate the metabolic pathways involved in these health effects.

Conclusions Milk and dairy products are the most significant sources of OBCFA in the human diet. Despite their low concentrations, recent investigations have demonstrated their potential as bioactive components and their nutritional importance. Although they derive mainly from the microbial activity in the rumen, there is recent evidence that their formation is not limited to the biochemical processes that occur in the digestive tract of ruminants. The ability of other tissues to endogenously synthesize specific OBCFA must be carefully considered and may encourage very promising lines of research in the future.

Conflicts of interest Authors declare no conflicts of interest.

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https://doi.org/10.22319/rmcp.v11i4.5295 Technical note

QTL analysis associated to single nucleotide polymorphisms (SNP) involved in the dairy phenotype of Holstein cattle

Jose Manuel Valdez-Torres a Juan Alberto Grado Ahuir a Beatriz Elena Castro-Valenzuela a M. Eduviges Burrola-Barraza a*

Universidad Autónoma de Chihuahua. Facultad de Zootecnia y Ecología. Periférico Francisco R. Almada Km 1, C.P. 31453. Cd. Chihuahua, Chih. México.

Corresponding author: mburrola1@uach.mx

Abstract: The aim was to identify QTLs associated with single nucleotide polymorphisms (SNPs) whose action contributes to the productive, reproductive and health phenotypic development of Holstein dairy cattle. 341 QTLs located in 120 genes of the Bos taurus UMD_3.1.1 genome and associated with 189 SNPs with effects on productive (FY, NM, MY, MTCAS, MBLF and PL), reproductive (CONCRATE, DPR, EMBSUR, DAYOPEN and CONCEPT) and health traits (SCC, BTBS and RESRATE) were identified. SNPs were verified in the dbSNP-NCBI database, according to which 42 % were located in introns. The Jvenn platform revealed that the SNPs rs135744058, rs110828053 and rs109503725 were common in all three traits. The network of correlations between traits and genes generated by MetScape (Cytoscape 3.4), showed a positive correlation between PL, DPR, DAYOPEN, CONCRATE and CONCEPT, and a negative correlation of FY with PL, NM, DPR and CONCRATE. The functionality of each gene was validated in the Gene-NCBI and UniProt databases, and ClueGo (Cytoscape 3.4) was used to select functional pathways with a significance value less than 0.05, which rendered an intertwining between the development of the mammary gland, the activation of the immune system and the response to steroid hormones evident, the GH

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gene being the one that directs this functionality. Although the genetic panorama shows that there is an antagonism between productive and reproductive traits, the functional genetic activity due to the 189 SNPs analyzed exhibits an interwoven action in ontological pathways that influence the production processes, as well as in reproductive and health pathways. Key words: QTL, SNP, Holstein

Received: 19/03/2019 Accepted: 17/09/2019

In Holstein cattle, several studies have identified quantitative trait loci (QTL) associated with productive, reproductive and health traits(1-3). Thanks to the improvement of statistical methods and the development of molecular tools, it has been possible to carry out whole genome association studies (GWAS)(4,5) to identify QTLs associated with a locus, whose influence on the phenotype can vary between individuals of the same species through the change of a single base in the genome; this is what is known as single nucleotide polymorphism (SNP). This type of QTL which correlates with a single SNP has an effect on the functionality of a specific gene and, therefore, an immediate action in the development of a phenotypic trait of interest(6). Thus, identification of the biological pathways and genes that are associated with significant SNPs may provide a deeper biological understanding of the expression mechanism of a particular phenotypic trait(7). The objective of this study was to detect, with the aid of bioinformatics tools, those QTLs associated with a SNP with a potential effect on the phenotypic traits of production, reproduction and health of Holstein dairy cattle. 15 phenotypical characters were used for the QTL search: duration of productive life (PL), net merit (NM), milk yield (MY), milk protein yield (PY), milk fat yield (FY) , casein content (MTCAS), ď ˘-lactoglobulin content (MBLG), susceptibility to bovine tuberculosis (BTBS), respiratory rate (RESRATE), somatic cell content (SCC), conception rate (CONCRATE), daughtersâ&#x20AC;&#x2122; pregnancy rate (DPR), early embryo survival (EMBSUR), parturition-conception interval (DAYOPEN) and services by conception (CONCEPT). The selection was made Out of the 114,685 QTL reported in the QTLdb database (Animal QTL Database)(2), those QTL associated with one of the productive and health traits and whose peak was related to a single SNP with a significance value of less than 0.05 were selected, while the QTL selected for the reproductive SNPs were those reported by Cochran et al(8) that did not have a negative effect on any productive trait. Based on the Bos taurus UMD_3.1.1 genome, each SNP was verified in the dbSNP database of the NCBI (National Center for Biotechnology Information, www.ncbi.nlm.nih.gov/snp/), where its chromosomal location was verified, and the affected gene was classified according to its 1193

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location, as: intron (if it was in an intronic region), change of direction (if it caused a change in the amino acid sequence), synonym (if the change of base did not imply a change in the amino acid sequence), promoter (if it was located in the promoter of the affected gene), deletion / insertion (if the SNP resulted in a deletion or a base insertion), or UTR3 'and UTR5' (if the SNP was located in the 5'or 3 'untranslated region of mRNA). The function of each of the genes involved in this study was validated in both the NCBI Gene database (www.ncbi.nlm.nih.gov/gene/) and UniProt (The Universal Protein Resource, www. uniprot.org). Common SNPs among phenotypic traits were identified through a Venn diagram using the Jvenn platform(9). The Pearson correlation coefficients were calculated using the MetScape algorithm(10), in order to establish associations between phenotypic traits with the presence of a gene. These values were visualized as a colorimetric matrix (heat map) composed of a color spectrum that went from green to red with correlation values from -1 to 1, respectively. These data were also analyzed as a hierarchical grouping and, with them, a network of correlations was generated using the MetScape application of the Cytoscape 3.4 software(11). The ontological functional network of the genes was carried out with the ClueGo application of the Cytoscape 3.4 software, under the following criteria: the ontological data of each gene was taken from the â&#x20AC;&#x153;GO Biological Process-GOA; with a "GO Tree" range from 3 to 8; the selection terms for each pathway included at least 1 gene per cluster with a kappa score of 0.3; in addition, it was tested with a bilateral hypergeometric statistical test with Bonferroni correction, only those pathways with a significance value less than 0.05 were taken into account. The results published to date on GWAS in dairy cattle have provided information on the influence of SNP on the expression of a QTL(4,5,12). In the genome, SNP are the most abundant forms of DNA and due to their low mutation rate; they are excellent selection markers(13,14), in addition to being easy and inexpensive to perform genotyping(15). Taking the information included in the QTLdb database(2), 341 QTLs associated with a SNP were found, of which 70 % were production QTL, 17 % were involved in health and 13 % in reproductive traits (Figure 1). Within the production phenotype, the most favored trait was PY, followed by MY, then FY, NM, PL, MBLG and lastly MTCAS. In health characters, it was BTBS followed by SCC and RESRATE. In reproductive traits, more QTL were associated with CONCRATE, followed by DPR, CONCEPT, and EMBSUR, which was the least favored. There were QTL that had the same SNP, as well as SNPs that were present in different regions of the same gene. Therefore, in the end, 341 QTL associated with 189 SNP located in 120 genes of the Bos taurus genome were identified. Each of the 189 SNP were verified in the NCBI dbSNP database, along with their location and the gene that they affected.

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Figure 1: Distribution of 341 QTLs associated with a single SNP, according to the phenotype they favor

According to the information collected, 42 % of the SNP were intronic; 27 % SNP were located in coding regions, of which 17 % had an effect on the amino acid change of the protein and 5 % were synonymous mutations; 6 % were localized at the untranslated 3 'end (UTR3'), and 1 %, in UTR5 '. 6.7 % of the SNPs were located within the SAA2 gene, 4.1 % were located in the ATF3 gene, 3.8 % in the HSD17B7 gene, and 3.0 % were located in each of the AP3B1 and CARD15 genes. The BCHE and PDE9A genes had 2.6 %, while the PCC8, CDKN1A and CSN2 genes had 2 % each. 1.2 to 1.8 % of the SNP were located in the regions of the APP, GNAS, NRPL48, SERPINA5, SLC8A1, CACNA1D, COQ9, DGAT1, DSC2, FASN, IGF1R, LEP, PRLR, CSNK1E, BRINP3 and HSD17B13 genes (Figure two). With the exception of the Bta-9 and Bta-12 chromosomes, the 189 SNP were distributed in all autosomes and on the X chromosome of the Bos taurus UMD 3.1.1 genome (Figure 3). However, the distribution was uneven; there were chromosomes that exhibited more SNP than others, and in the same way, there were chromosomes that included few SNP, but these were associated with many QTL.

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Figure 2: Percentage distribution of genes with a SNP associated with a QTL

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Figure 3: Distribution of productive, reproductive and health traits in the Bos taurus genome

PY,

FY,

RESRATE,

MY, CONCEPT,

NM,

PL,

MBLG,

CONCRATE,

MTCAS,

DAYOPEN,

DPR,

BTBS,

SCC,

EMBSUR

The QTLs most often identified were mainly productive, which supports the fact that the quantity and quality of milk produced go hand in hand with economic gains(16). In Bta-14, the SNP rs109421300 located in the DGAT1 gene has more influence on the MY, FY and PY traits(14,15,17-19). Bta-5 associates with MY, while FY and PY associate with the SNP rs41591907, rs41256890, rs41592943, rs41592948, rs137408198, and rs133449166(7,17,20), located in the BCATI, MGP, GUCY2C, CDEABAR1, and CDEABAR1 genes. Bta-18 has

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been associated with both MY and PY, due to the influence of the SNP rs41581694(14,17), which is located in the FOXA3 gene. In Bta-20, the SNP rs385640152 causes a change in the GHR protein that affects MY and PY (7). In Bta-21, the SNP rs41644615 located in the SERPINE5 gene is associated with MY and PY(19). Finally, the SNP rs110475419 is associated with PY(14), which is in the ADAM12 gene in Bta-26. Milk is made up of the proteins -lactalbumin, -lactoglobulin, and the ,  and  caseins(21). Bta-11 has been associated with -lactoglobulin (MBLG) concentrations in milk, due to the effect of the PAEP gene, which has the SNP rs41255679(16,21,22), rs110066229(21), and rs110180463(21). On the other hand, Bta-6 has been related to MTCAS through the SNP rs109299401(23), which, when expressed in the CSN2 gene, causes a functional change in casein. MTCAS has also been associated with Bta-14 and Bta-19 due to the SNPs rs110757796 and rs41923484, respectively(23). In dairy cattle, the most common disease and the one that causes the most economic losses is mastitis, a disease with which the somatic cell count (SCC) in milk is associated as a predictor(12). The chromosomes that have the greatest influence on SCC are Bta-6, Bta-13, Bta-14, Bta-19 and Bta-20(24). There are 6 SNPs in Bta-6, of which rs43703013, rs43703011and rs109299401 are in the coding region of the CSN2 gene, where each one of them causes an amino acid change in the -casein protein, while rs110239379 and rs110118210 are located in the same intron of the KIT gene, and rs109757609 is located in the promoter of the CSNIS1 gene. The association with SCC in Bta-13 was related to two SNPs in the SRC gene, rs41703851 and rs41602996. Bta-14 contributed to SCC due to SNPs rs109162116 and rs109234250 in the DGAT1 gene. Other SNPs associated with SCC are rs109149276 and rs109149276, of the FASN gene of Bta-19 and of the promoter of the PRLR gene in Bta-20, respectively(24), as well as rs43315150, located in Bta-2, in an intron of the CYP27A1 gene(20). Tuberculosis is another disease that causes economic losses in the livestock industry; bovines are not only one of the animal species that most coexists with humans but have also become one of the main sources of spread of this disease worldwide(25). Richardson et al(1) found BTBS associated to Bta-1 with 9 SNP (rs42294486, rs29020933, rs29020933, rs42294431, rs42294441, rs110098599, rs132953892, rs41665131 and rs43741780 gene, located in the BCHE region), and with 2 SNPs (rs109186526 and rs110679397) located in the same intron of the KALRN gene. In the chromosomes Bta-3, Bta-8, Bta-10 and Bta-23, the same research identified the SNP rs110622046, rs135916795, rs109277058 and rs41642913, located in intronic regions of the MAEL, PTPRD, AP3B1 and FKBP5 genes, respectively. On the other hand, Finlay et al(26) reported that in Bta-22 the SNP rs42286978, rs42287005 and rs42724727, which are located in the promoter region of the SLC6A6 gene, are also associated with susceptibility to tuberculosis. Wang et al(27) reported that in Bta-18 the CARD15 gene had 3 SNPs within the coding region of exon 4, as well as one SNP located in the intron prior to exon 4.

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As for reproductive traits, these were located very close to productive traits in chromosomes Bta-1, Bta-3, Bta-5, Bta-10, Bta-15, Bta-16, Bta-18, Bta-22, Bta-23 and Bta-24 (Figure 3). Notably, although these SNPs represent only a small portion of the genes involved in the reproductive process, they were selected from the works of Cochran et al(8) and Ortega et al(28), who proved that these SNP have a positive effect on the reproductive traits associated with fertility and, at the same time, they exert no negative influence on productive traits. Of the total SNPs only rs135744058, rs110828053 and rs109503725 were common to productive, health and reproductive traits alike. The SNP rs135744058 is associated with the MY(8), CONCRATE(8,28), DPR(8), and RESRATE characters(29). In the genetic language, it is composed of the A/G variants (8) and is located in the exonic region of the CACNAID gene where it brings about the change in the ď Ą1D subunit of the calcium voltage channel(30). In cattle, its presence has been detected at the level of the hypothalamus during the development of the central nervous system(31). The SNP rs110828053 has been reported to be associated with NM(8), DPR(28), CONCRATE(28), CONCEPT(28) and RESTATE(29) and it generates an A/G swap in the HSD17B7 gene that causes a change that substitutes alanine for threonine at position 308 of the protein. The HSD17B7 gene encodes for dehydrogenase 7 hydrosteroid 17-ď ˘, which participates in the biosynthesis of sex steroids and cholesterol(28); in bovines, it has been located in the oviduct epithelial cells(32), being essential for ovarian function and regulation of fertility(33). The SNP rs109503725, composed of the variables T/C in the DSC2 gene, is associated with the PL(8), DPR(8,28), CONCRATE(28) and SCC(8) characters. This SNP generates a change in amino acid 535, substituting arginine for tryptophan in the protein Desmocholine 2, which is a protein involved in cell-to-cell junctions, forming desmosomes in epithelial cells(34). During the last two decades in dairy cattle, selection strategies have focused on developing highly dairy producing animals, which has resulted in a decline in reproductive capacity. This has generated a negative genetic association, i.e. an antagonist, between the productive and reproductive traits(20,35-37). Now, in this study, reproductive SNP were previously reported as non-antagonists with productive traits(8,28); in fact, these SNP are located in the same regions as SNPs associated with productive traits (Figure 3). In order to locate those traits that are positively related, a correlation analysis of phenotypical characters was performed, based on the genes that they shared (Figure 4A). This analysis managed to classify the characters in two groups, grouped into two clades. Clade 1 comprised all the reproductive traits together with the PL and NM characters of the productive traits. The rest of the productive traits (MY, PY, FY, MTCAS and MBLG) were grouped together with the health traits in clade 2 (Figure 4B).

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Figure 4: Correlation of phenotypic traits based on a SNP associated with a QTL

(A) Pearson's correlation matrix (Heat map), the color spectrum goes from green to red, with values from -1 to 1, respectively. (B) Hierarchica grouping of A. DPR daughtersâ&#x20AC;&#x2122; = pregnancy rates; CONCRATE = conception rate; PL = duration of reproductive life; NM = Net merit; DAYOPEN = birth-conception interval; CONCEPT = conception inseminations; RESRATE = respiratory rate; EMSUR = early embryo survival; MY = milk yield; PY = protein yield in milk; FY = fat yield in milk; SCC = somatic cell count in milk; MTCAS = casein content in milk; MBLG = content of Î˛-lactoglobuin in milk; BTBS = susceptibility to bovine tuberculosis.

Likewise, when analyzing the correlation network of genes based on the trait, two groups of genetic networks could be observed (Figure 5): Group A, corresponding to those genes present in the characters FY, MY and PY (productive traits), and Group B, with genes present in BTBS, SCC, CONCEPT, CONCRATE, DAYOPEN, DPR, EMBSUR, NM, PL, RESRATE, encompassing the health, reproduction and production phenotypes. These results indicate that at the genetic level there is an antagonistic scenario between the production and reproduction QTL.

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Figure 5: Pearson's correlation network of genes associated with a phenotypic trait

The nodes represent the gene, and the black lines represent the correlations between the nodes. The analysis was carried out with the MetScape application of the Cytoscape software, considering the positive ones from 0.5 to +1. The thicker the line, the higher the correlation. The nodes in yellow, purple, blue, green and pink; represent nodes with high correlation generating groups, and the nodes in red represent the genes with the most SNP involved in the panel. The legends within the black ovals represent the phenotypical character associated with the genes in each group of nodes.

In order to encompass the functional information of the genes involved in this study, an ontological network was designed in which those genes whose functions were related to each other were located. Figure 6 shows that, starting from the development of the mammary gland where the genes CSN2, FASN, GH, GPAT4, SRC and TPH1 participate, the GH gene is linked to the pathways that correspond to lipid development, where the DGAT1, GPAT4 and THRSP genes are found. Hence, GH and GPAT4 are linked to cell growth pathways such as the

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development of adipose tissue, in which the APP, ARID5B, FTO, GNAS, GPAT4, LEP, PPARGC1A genes participate; of these, ARID5B is the one that leads to the differentiation of fat cells, while FTO, LEP and PPARBC1A participate in the differentiation into brown fat cells. Likewise, GH, LEP and PPARGC1A are linked, together with CSN1S1, with the steroid hormone response pathways and are related to the regulation of the immune system. Figure 6: Genetic ontology (GO) of the genes that make up the Panel, depicted within a network of biological functions generated with the ClueGo application of the Cytoscape 3.4 software

Each GO is represented as a circular node, whose size is related to the significance (P <0.05) and the number of genes associated with a particular biological process; they are grouped by color, each color representing one of these processes. The genes involved in each GO are indicated in bold letters.

The genes involved in the response to steroid hormones are CSN1S1, CYPP7B1, GH, HSD17B12, HSD17B7, and LEP; thus, again, GH and CSN1S1 lead to the hormone signaling pathways in which CNOT1, NRH13, SRC, UBR, CTBP2, CYPB7B1, NOD2 and UBR5 participate. GH is also linked to the regulation of the immune system, specifically to the production of the tumor necrosis factor involving the genes APP, BAIAP2, GH, IGF1R, NOD2, SRC, STAT1 and TLR4. Hence, NO2 and TLR4 are the bridge to other pathways of the immune system, such as the activation of cytosines and interleukins and that of the innate

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immune system. Interestingly, at this point we find LEP once more, which leads back to the lipid metabolic process. When it is looked at this functional ontological pathway from a global perspective, it seems that GH acts as a master gene that directs, like an orchestra conductor, the functional activity of the other genes involved in the development of dairy cattle. The GH gene is located in Bta-19 and codes for the protein somatotropin. This gene is associated with PY and MTCAS due to the effect of the SNP rs41923484, which causes a change in amino acid 153 by substituting valine for leucine in somatotropin(23). Somatotropin, also known as growth hormone, participates in multiple activities, which range from its effect on cell growth to the differentiation of various tissues, such as the development of follicles(38) and of the mammary gland(39); therefore, it is not surprising that its action is crucial for the entire body of Holstein cattle to function properly. The functional activity of the 120 genes associated with 189 SNPs within 341 QTL is intertwined within the ontological pathways that influence both the production processes, such as the development of the udders, and the reproductive and health pathways. Awareness of this fact opens the door to improving selection in order to generate animals that will gradually acquire both productive and reproductive traits.

Acknowledgements The authors thank CONACYT for the support granted through project No. 216179.

Literature cited: 1. Richardson IW, Berry DP, Wiencko HL, Higgins IM, More SJ, McClure J, et al. A genome-wide association study for genetic susceptibility to Mycobacterium bovis infection in dairy cattle identifies a susceptibility QTL on chromosome 23. Genet Sel Evol 2016;48:19-31. 2. Hu ZL, Park CA, Reecy JM. Developmental progress and current status of the Animal QTLdb. Nucleic Acids Res 2016;44(D1):D827-D833. 3. Yudin NS, Voevoda MI. Molecular genetic markers of economically important traits in dairy cattle. Russian J Genetics 2015;51(5):506-517. 4. Abo-Ismail MK, Brito LF, Miller SP, Sargolzaei M, Grossi DA, Moore SS, et al. Genomewide association studies and genomic prediction of breeding values for calving performance and body conformation traits in Holstein cattle. Genet Sel Evol 2017;49(1):82-110.

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5. Pegolo S, Mach N, Ramayo-Caldas Y, Schiavon S, Bittante G, Cecchinato A. Integration of GWAS, pathway and network analyses reveals novel mechanistic insights into the synthesis of milk proteins in dairy cows. Sci Rep 2018;8(1):566-580. 6. Wang M, Hancock TP, Chamberlain AJ, Vander Jagt CJ, Pryce JE, Cocks BG, et al. Putative bovine topological association domains and CTCF binding motifs can reduce the search space for causative regulatory variants of complex traits. BMC Genomics 2018;19(1):395-411. 7. Nayeri S, Sargolzaei M, Abo-Ismail MK, May N, Miller SP, Schenkel F, et al. Genomewide association for milk production and female fertility traits in Canadian dairy Holstein cattle. BMC Genet 2016;17(1):75-85. 8. Cochran SD, Cole JB, Null DJ, Hansen PJ. Discovery of single nucleotide polymorphisms in candidate genes associated with fertility and production traits in Holstein cattle. BMC genetics 2013;14(1):49-71. 9. Bardou P, Mariette J, EscudiĂŠ F, Djemiel C, Klopp C. jvenn: an interactive Venn diagram viewer. BMC bioinformatics 2014;15(1):293-299. 10. Basu S, Duren W, Evans CR, Burant CF, Michailidis G, Karnovsky A. Sparse network modeling and metscape-based visualization methods for the analysis of large-scale metabolomics data. Bioinformatics 2017;33(10):1545-1553. 11. Shannon P, Markiel A, Ozier O, Baliga NS, Wang JT, Ramage D, et al. Cytoscape: a software environment for integrated models of biomolecular interaction networks. Genome research 2003;13(11):2498-2504. 12. Duran Aguilar M, Roman Ponce SI, Ruiz Lopez FJ, Gonzalez Padilla E, Vasquez Pelaez CG, Bagnato A, et al. Genome-wide association study for milk somatic cell score in Holstein cattle using copy number variation as markers. J Anim Breed Genet 2017;134(1):49-59. 13. Karim L, Takeda H, Lin L, Druet T, Arias JA, Baurain D, et al. Variants modulating the expression of a chromosome domain encompassing PLAG1 influence bovine stature. Nat Genet 2011;43(5):405-413. 14. Kolbehdari D, Wang Z, Grant JR, Murdoch B, Prasad A, Xiu Z, et al. A whole genome scan to map QTL for milk production traits and somatic cell score in Canadian Holstein bulls. J Anim Breed Genet 2009;126(3):216-227. 15. Fang M, Fu W, Jiang D, Zhang Q, Sun D, Ding X, et al. A multiple-SNP approach for genome-wide association study of milk production traits in Chinese Holstein cattle. PLoS One 2014;9(8):e99544-e99551.

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16. Gambra R, Penagaricano F, Kropp J, Khateeb K, Weigel KA, Lucey J, et al. Genomic architecture of bovine kappa-casein and beta-lactoglobulin. J Dairy Sci 2013;96(8):5333-5343. 17. Gervais O, Pong-Wong R, Navarro P, Haley CS, Nagamine Y. Antagonistic genetic correlations for milking traits within the genome of dairy cattle. PLoS One 2017;12(4):e0175105-e0175117. 18. Cole JB, Wiggans GR, Ma L, Sonstegard TS, Lawlor TJ, Crooker BA, et al. Genomewide association analysis of thirty one production, health, reproduction and body conformation traits in contemporary US Holstein cows. BMC genomics 2011;12(1):408. 19. Pesek P, Pribyl J, Vostry L. Genetic variances of SNP loci for milk yield in dairy cattle. J Appl Genet 2015;56(3):339-347. 20. Pimentel EC, Bauersachs S, Tietze M, Simianer H, Tetens J, Thaller G, et al. Exploration of relationships between production and fertility traits in dairy cattle via association studies of SNPs within candidate genes derived by expression profiling. Anim Genet 2011;42(3):251-262. 21. Huang W, Penagaricano F, Ahmad KR, Lucey JA, Weigel KA, Khatib H. Association between milk protein gene variants and protein composition traits in dairy cattle. J Dairy Sci 2012;95(1):440-449. 22. Schopen GC, Visker MH, Koks PD, Mullaart E, van Arendonk JA, Bovenhuis H. Wholegenome association study for milk protein composition in dairy cattle. J Dairy Sci 2011;94(6):3148-158. 23. Viale E, Tiezzi F, Maretto F, De Marchi M, Penasa M, Cassandro M. Association of candidate gene polymorphisms with milk technological traits, yield, composition, and somatic cell score in Italian Holstein-Friesian sires. J Dairy Sci 2017;100(9):7271-7281. 24. Fontanesi L, Calo DG, Galimberti G, Negrini R, Marino R, Nardone A, et al. A candidate gene association study for nine economically important traits in Italian Holstein cattle. Anim Genet 2014;45(4):576-580. 25. Wang W, Cheng L, Yi J, Gan J, Tang H, Fu MZ, et al. Health and production traits in bovine are associated with single nucleotide polymorphisms in the NOD2 gene. Genet Mol Res 2015;14(2):3570-3578. 26. Finlay EK, Berry DP, Wickham B, Gormley EP, Bradley DG. A genome wide association scan of bovine tuberculosis susceptibility in Holstein-Friesian dairy cattle. PLoS One 2012;7(2):e30545-e135094.

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27. Wang Y, Wang S, Liu T, Tu W, Li W, Dong G, et al. CARD15 Gene Polymorphisms Are Associated with Tuberculosis Susceptibility in Chinese Holstein Cows. PLoS One 2015;10(8):e0135085. 28. Ortega MS, Denicol AC, Cole JB, Null DJ, Hansen PJ. Use of single nucleotide polymorphisms in candidate genes associated with daughter pregnancy rate for prediction of genetic merit for reproduction in Holstein cows. Anim Genetics 2016;47(3):288-297. 29. Dikmen S, Wang XZ, Ortega MS, Cole JB, Null DJ, Hansen PJ. Single nucleotide polymorphisms associated with thermoregulation in lactating dairy cows exposed to heat stress. J Anim Breed Genet 2015;132(6):409-419. 30. Srivastava U, Aromolaran AS, Fabris F, Lazaro D, Kassotis J, Qu Y, et al. Novel function of alpha1D L-type calcium channel in the atria. Biochem Biophys Res Commun 2017;482(4):771-776. 31. Peruffo A, Giacomello M, Montelli S, Panin M, Cozzi B. Expression profile of the poreforming subunits alpha1A and alpha1D in the foetal bovine hypothalamus: a mammal with a long gestation. Neurosci Lett 2013;556:124-128. 32. Cerny KL, Garrett E, Walton AJ, Anderson LH, Bridges PJ. A transcriptomal analysis of bovine oviductal epithelial cells collected during the follicular phase versus the luteal phase of the estrous cycle. Reprod Biol Endocrinol 2015;13:84-96. 33. Kemilainen H, Adam M, Maki-Jouppila J, Damdimopoulou P, Damdimopoulos AE, Kere J, et al. The hydroxysteroid (17beta) dehydrogenase family gene HSD17B12 is involved in the prostaglandin synthesis pathway, the ovarian function, and regulation of fertility. Endocrinology 2016;157(10):3719-3730. 34. Gehmlich K, Lambiase PD, Asimaki A, Ciaccio EJ, Ehler E, Syrris P, et al. A novel desmocollin-2 mutation reveals insights into the molecular link between desmosomes and gap junctions. Heart Rhythm 2011;8(5):711-718. 35. Pryce J, Royal M, Garnsworthy P, Mao IL. Fertility in the high-producing dairy cow. Livestock Prod Sci 2004;86(1-3):125-135. 36. Pryce J, Veerkamp R. The incorporation of fertility indices in genetic improvement programmes. BSAP Occasional Publication 2001;26(1):237-249. 37. PeĂąagaricano F, Khatib H. Association of milk protein genes with fertilization rate and early embryonic development in Holstein dairy cattle. J Dairy Res 2011;79(01):47-52.

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38. Cushman R, DeSouza J, Hedgpeth V, Britt J. Effect of long-term treatment with recombinant bovine somatotropin and estradiol on hormone concentrations and ovulatory response of superovulated cattle. Theriogenology 2001;55(7):1533-1547. 39. Akers RM. A 100-Year Review: Mammary development and lactation. J Dairy Sci 2017;100(12):10332-10352.

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https://doi.org/10.22319/rmcp.v11i4.5192 Technical note

Evaluation of the biochemical and hematological profiles of feedlot hair sheep after the supplementation with generic zilpaterol hydrochloride

Arnulfo Vicente Pérez a† Leonel Avendaño Reyes b Ulises Macías Cruz b Antonio Aguilar Quiñones a Ricardo Vicente Pérez c Miguel Mellado Bosque d Miguel Ángel Gastélum Delgado e Abelardo Correa Calderón b G. López-Rincón f Juan Eulogio Guerra Liera e*

Universidad Autónoma de Sinaloa. Facultad de Medicina Veterinaria y Zootecnia, Culiacán, Sinaloa, México. b

Universidad Autónoma de Baja California. Instituto de Ciencias Agrícolas. Ejido Nuevo León, Valle de Mexicali, Baja California, México. c

Universidad de Guadalajara. Centro Universitario de la Costa Sur. Autlán de Navarro, Jalisco, México. d

Universidad Autónoma Agraria Antonio Narro. Departamento de Nutrición Animal. Saltillo, Coahuila, México. e

Universidad Autónoma de Sinaloa. Facultad de Ciencias Agrícolas. Culiacán, Sinaloa, México. f

Laboratorios Virbac México, S.A. de C.V., Zapopan, Jalisco, México.

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*Corresponding author: dr.juanguerral@gmail.com

†

Doctorate student in Agricultural Sciences, FMVZ-UAS.

Abstract: This study aimed to evaluate zilpaterol hydrochloride (ZH, generic) supplementation on fattening hair sheep, using hematological and biochemical variables as health status indicators. A total of 32 hair lambs (Dorper x Pelibuey) were grouped by initial weight and randomly assigned into four treatments: T1= basal diet (control group), T2= basal diet supplemented with ZH at 0.10 mg·kg-1 of LW d-1 (Grofactor®, Virbac México, Guadalajara, Mexico), T3= basal diet supplemented with ZH at 0.20 mg·kg-1 of LW d-1, and T4= basal diet supplemented with ZH at 0.30 mg·kg-1 of LW d-1. Blood samples were collected on days 1, 15, and 30 of the study. The hematological profile was determined in fresh blood samples; metabolites, electrolytes, and hormones were determined in serum samples. The study followed a randomized complete block experimental design, using an orthogonal polynomial analysis to determine the trend of the responses at the different concentrations of ZH. Cholesterol and urea levels were higher (P<0.05) in T3 than in T2. Furthermore, the mean corpuscular hemoglobin concentration was higher (P<0.05) in T1 than in T3; the red blood cell distribution width was higher (P<0.05) in T2 and T3 than in T4. The Na levels and the number of platelets showed a linear trend (P <0.05) to decrease and increase, respectively, as ZH levels increased. A quadratic trend was observed (P<0.05) in mean corpuscular hemoglobin concentration and red blood cell distribution width with increasing dose of ZH (generic). The remaining variables did not show significant trends at ZH levels (generic). The values of the biochemical and hematological profiles were within the reference range, which suggests that the addition of ZH did not alter the health status of fattening lambs. Key words: Hemoglobin, Lambs, Metabolites, Electrolytes, Zilpaterol hydrochloride.

Received: 23/12/2018 Accepted: 25/09/2019

In Mexico, sheep farming focused on meat production is growing considerably, but animal demand for supply is greater than what is produced, translating into imports of live cattle and carcasses from the United States, New Zealand, and Australia, mainly(1). Hair sheep have been an alternative to cover these meat demands since they require less care than wool breeds. Additionally, hair sheep can adapt, reproduce, and produce under any production system. This species maintains lamb production throughout the year;

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however, it has the disadvantage of limited weight gain(2). Therefore, farmers use growth promoters to obtain more efficient animals for meat production(3). The beta-adrenergic agonists (β-AA) are an attractive strategy that has positively impacted the production of fattening sheep(4).

A study that compared different β-AAs (zilpaterol hydrochloride, ractopamine hydrochloride, terbutaline, isoproterenol, etc.) concluded that zilpaterol hydrochloride (ZH) is a good option for lamb fattening(5). However, results are contradictory in terms of performance(4,6) and carcass characteristics(6,7). Since ZH supplementation promotes physiological, metabolic, hormonal, and hematological changes, the health of the animal can be compromised(8). Additionally, the use of β-AA in ruminants and its negative effects on animal welfare have been one of the most important concerns in the livestock sector(9). ZH supplementation has focused on animal performance and carcass characteristics; meanwhile, its effects on health and welfare are cared for trivially(10). Although cattle supplemented with β-AA showed higher morbidity rates at the end of fattening(11), few studies have evaluated the effects of β-AA on animal health and included the analysis of biochemical and hematological components. This study aimed to evaluate the effect of different doses of a generic ZH on hematological and biochemical variables in feedlotfinished hair sheep.

The study was performed during the fall-winter season of 2015 in the Sheep Experimental Unit of the Instituto de Ciencias Agrícolas of the Universidad Autónoma de Baja California, in Valle de Mexicali, Baja California, México (32.8o N, 114.6o W). The climatic conditions of this region are similar to those found in the Sonoran Desert, defined by an extremely dry and warm climate, with a maximum temperature during summer ≥ 42 °C and a minimum temperature during winter ≤ 0 °C; the annual mean precipitation is 85 mm(12).

A total of 32 (Dorper x Pelibuey) F1 male hair lambs were used with an average weight of 29.3 ± 0.22 kg and age between 5 and 6 months. Groups of four lambs were formed according to their initial weight; these groups were randomly assigned to one of four treatments: T1= basal diet (control group); T2= basal diet supplemented with ZH at 0.10 mg·kg-1 of LW d-1 (Grofactor®, Virbac México, Guadalajara, Mexico); T3= basal diet supplemented with ZH at 0.20 mg·kg-1 of LW d-1; and T4= basal diet supplemented with ZH at 0.30 mg·kg-1 of LW d-1. Lambs were housed in individual pens (1.0 x 1.5 m) provided with drinking and feeding troughs, and shade. Feed was offered twice a day (0700 and 1500 h) in a 40:60 ratio. The diet consisted of ground wheat grain (60%), alfalfa hay (17.5%), wheat straw (11%), soybean flour (7%), soybean oil (2%), limestone (1%), dicalcium phosphate (1%), and common table salt (0.5%). This formulation provides 15% of PC and 2.9 Mcal of EM kg-1 of MS(13). To ensure ZH consumption, the daily dose of the product was mixed in 30 g of ground wheat grain and offered during the morning 1210

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before providing the basal diet. On d 30, ZH was withdrawn from the diet following the instructions provided by the manufacturer. The experiment lasted 47 d (15 for adaptation, 30 for fattening, and 2 for withdrawal).

To analyze metabolites, electrolytes, and hematological components, we collected blood samples in 10 and 4 ml Vacutainer tubes by jugular venipuncture. Blood was collected in the morning (0600 h), under fasting conditions, at the initial, intermediate, and final phases of fattening (days 1, 15, and 30). Fresh blood samples were used for the hematological analysis using an automated equipment (Auto Hematology Analyzer, MINDRAY, BC-2800 Vet; Shenzhen, China). The blood collected in 10 ml tubes was centrifuged at 3,500 rpm at 10 °C for 15 min. Then, serum was separated in duplicate in 2 ml vials and stored at -20 °C for subsequent glucose (Glu), cholesterol (Cho), urea (Ur), triglycerides (Trig), total protein (TP), electrolytes (Na, K, and Cl), thyroxine (T4) and triiodothyronine (T3) hormone analysis. Metabolites were measured with a blood chemistry analyzer (Model DT-60, Johnson Co.; High Wycombe, UK); electrolytes were determined using an automated equipment (Electrolyte Analyzer LW E60A; Landwind Medical; Shenzhen, China). Hormone determination was performed using a Thunderbolt® Analyzer (Davis, CA, USA) for ELISA and chemiluminescence (CLIA) assays.

Responses were analyzed following a randomized complete block design. An orthogonal polynomial analysis was performed to determine the response trend through β-AA levels. Significance was declared at a probability of P≤0.05 using the PROC MIXED of SAS. Since the cubic trend was not significant for any of the analyzed variables, it was omitted from the result tables. All data were processed using the Statistical Analysis System software(14).

Table 1 shows the results of ZH supplementation on the blood metabolites of fattening lambs. The levels of Cho and Ur were higher (P<0.05) in T3 than in T2. The levels of Trig, Glu, and TP were not affected (P>0.05) by ZH supplementation. These values fall within the reference interval for sheep(15,16). Similar results were reported by LópezCarlos et al(6); they found no differences in TP, Glu, or Trig when supplementing with similar ZH doses using Dorper x Katahdin sheep. Other authors(17) reported that ZH supplementation at 0.20 mg·kg-1 of LW d-1 in wool sheep did not modify blood levels of Glu, Trig, TP, or Cho, finding only a decrease in Ur levels. It has also been reported that ZH supplementation in cattle does not modify blood metabolites(8,18). Hatefi et al(19) found that ZH supplementation decreases Glu and Cho plasma levels in male goats without affecting Trig levels.

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β-AA administration immediately increases gluconeogenesis, which in turn increases plasma Glu levels(8). However, it is estimated that due to a decrease in tissue sensitivity, the initial increase in Glu concentration returns to normal as the administration time increases(20). This effect could have been present in this study, where metabolite levels returned to their normal concentrations. This is confirmed by the fact that the metabolites associated with the energy and protein status were not affected by the supplementation with different doses of ZH, indicating that the lambs did not modify or compromise their homeostasis or metabolism. Therefore, the use of Grofactor® β-AA may have no adverse health effects on sheep. However, to date, few studies have been performed on the effect of β-AA on blood metabolites, and the results obtained are still inconsistent(19).

Table 1: Serum metabolite concentrations in hair sheep in response to different doses of zilpaterol hydrochloride ZH dose (mg·kg-1 of LW d-1) SEM Effects Cho, mg/dL Trig, mg/dL Glu, mg/dL TP, mg/dL Urea, mg/dL ab

0 49.4ab 26.0 64.3 6.80 38.3ab

0.10 46.7 a 26.7 62.6 6.85 35.4a

0.20 55.4b 25.8 65.7 6.82 40.9b

0.30 50.5ab 26.4 61.8 7.03 38.5ab

3.02 1.40 4.12 0.108 1.69

L2 0.36 0.95 0.83 0.17 0.40

Q3 0.71 0.98 0.82 0.47 0.91

Averages with different letters in the same row indicate statistical difference (P<0.05). SEM= standard error of the mean; L= linear; Q= quadratic. Cho= cholesterol; Trig= triglycerides; Glu= glucose; TP= total protein.

Table 2 shows the electrolyte and thyroid hormone levels after supplementation with different doses of ZH. ZH administration significantly reduced (P<0.01) Na serum levels, showing a linear trend with increased ZH levels in the diet. Serum levels of Cl, K, T3, and T4 were not affected (P>0.05) by the different treatments. β-AAs are considered excellent nutrient redistributors for skeletal muscle formation and fat deposit reduction in the carcass(21). However, β-AAs may affect the concentration of various biochemical components involved in muscle and adipose tissue development(22). The quadratic effect observed in Na concentrations depended on the increasing levels of ZH in the diet. The addition of 0.10 mg of ZH decreased Na in serum, while the 0.20 mg dose of ZH maintained serum Na levels. Moreover, the addition of 0.30 mg of ZH considerably decreased Na levels. Although there was a significant difference in the serum Na levels between doses, all values are within the normal reference interval for sheep(15,16), which suggests that the treated animals maintained their osmotic pressure and acid-base equilibrium without any major stress symptoms.

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K plays an important role in the regulation of water inside and outside the cell. Therefore, maintaining optimal plasma K levels is essential for proper body function. However, Buntyn et al(23) found that ZH supplementation decreases blood K levels, resulting in an increase in lean muscle deposition during ZH supplementation. This study obtained similar results; the lowest K level occurred at the highest ZH level. We also observed that control sheep had higher K concentrations compared to those treated with 0.30 mg·kg-1. Frese et al(8) reported that supplementation with ZH and ractopamine hydrochloride did not modify K levels compared to the control in finishing steers. The decrease of serum Na levels after β-AA ingestion has not been reported in sheep. However, equines have been reported to decrease blood Na levels after β-AA ingestion in very extreme cases, either due to loss during profuse sweating or by impaired renal Na transport; hypochloremia may even be associated with endogenous glucocorticoid release or renal failure(24), an effect that possibly did not occur in this study. Moreover, other studies(8,23) reported that finishing steers and heifers had similar Na concentrations after ZH and ractopamine hydrochloride supplementation; these results were attributed to the availability and consumption of free access to water.

Table 2: Serum electrolyte and thyroid hormones concentrations in hair sheep in response to different doses of zilpaterol hydrochloride ZH dose (mg·kg-1 of LW d-1) SEM Trend Na, mmol Cl, mmol K, mmol T3, ng/ml T4, ng/ml

0 139.7 a 113.3 6.29 a 1.40 2.92

0.10 138.2ab 114.3 5.85ab 1.19 3.06

0.20 138.5ab 113.3 6.31 a 1.32 2.40

0.30 137.1b 113.9 5.57b 1.25 2.29

0.584 0.489 0.181 0.728 0.343

L <0.01 0.95 0.10 0.33 0.10

Q 0.94 0.84 0.51 0.38 0.74

SEM= standard error of the mean; L= linear; Q= quadratic. K= potassium; Na, sodium; Cl= chlorine; T3= triiodothyronine; T4= thyroxine. ab Means with different letters in the same row indicate statistical difference (P<0.05).

Previous studies have observed that T4 and T3 hormones are not affected during acute or chronic treatments with β-AA(25); in this study, was also found no effects after ZH supplementation for 30 days. However, Hatefi et al(19) reported that T3 and T4 levels increased after chronic administration of different β-AA in goats, which could be explained by the increase in lipolysis. Thyroid hormones are widely related to carbohydrate metabolism, which includes glucose absorption and mobilization to the adipose and muscle tissues, increasing glycolysis, gluconeogenesis, and insulin levels(26), favoring the increase in lipolysis. However, studies regarding the effects of the administration of β-AA on blood biochemical components and hormones remain inconsistent and scarce. The concentration of circulating blood components indicates the physiological equilibrium and health status of the animal. However, external agents can modify the optimal concentration of these components and, consequently, result in a

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physiological disequilibrium, affecting animal well-being. Therefore, these components are important indicators of the physiologic and pathologic status of organisms. Table 3 shows the concentration of blood variables after supplementation with different doses of ZH.

Table 3: Concentration of blood components in hair sheep in response to different doses of zilpaterol hydrochloride SEM= standard error of the mean; L= linear; Q= quadratic.

ZH dose (mg·kg-1 of LW d-1)

Variables RBC, x 10 L

0 11.90

0.10 12.21

0.20 11.32

0.30 11.27

Hgb, g/dL

10.68

10.67

10.19

Hct, %

35.60

36.30

MCV, x 1015 L MCH, Pg MCHC, g/dL

30.21 9.10 29.96 a

RDW, %

17.96ab 18.70 a

Plt, x 10⁹ L

445.3

SEM

Trend

0.457

L 0.16

Q 0.70

10.25

0.374

0.26

0.91

35.00

34.8

1.15

0.48

0.68

30.27

31.30

31.90

0.870

0.12

0.78

8.79 29.3ab

8.98 28.8b

9.24 29.2ab

0.205 0.303

0.51 0.03

0.17 <0.05

18.62 a

17.58b

0.375

0.46

<0.05

549.7

575.5

46.07

<0.05

0.65

517.0

RBC= red blood cells; Hgb= hemoglobin; Hct= hematocrit; MCV= mean corpuscular volume; MCH= mean corpuscular hemoglobin; MCHC= mean corpuscular hemoglobin concentration; RDW= red blood cell distribution width; Plt= platelets. ab Averages with different letters in the same row indicate statistical difference (P<0.05).

The mean corpuscular hemoglobin concentration was higher (P<0.05) in T1 than in T3; the red blood cell distribution width was higher (P<0.05) in T2 and T3 than in T4. The mean corpuscular hemoglobin concentration (MCHC) and red blood cell distribution width (RDW) values showed a quadratic trend (P<0.05), with higher concentrations of MCHC in control lambs compared to those supplemented with Grofactor (Table 3); RDW was also higher in T2 and T3 compared to the other treatments. It was observed a linear trend (P<0.05) in platelet levels, in such a way that by increasing ZH levels, platelet concentrations increased. There were no significant differences in the blood components between treatments (P>0.05). One possible explanation for the decrease in MCHC is that the use of growth promoters markedly reduces the amount of adipose tissue through lipolysis(18), promoting peripheral vasodilation by adding fatty acids and glycerol to the bloodstream during this process, which causes an increase in blood components over plasma volume(27). Some studies have shown that the addition of β-AA affects some hematologic components in ruminants(19,28) by modifying physiologic processes, such as vasodilation and cardiovascular action(28). The cardiovascular effects that occur after βAA supplementation include the increase of the animal respiratory and heart rates. On the contrary, it has been described that the increase of the respiratory rate leads to an increase

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in the levels of red blood cell components in the bloodstream(29) due to a high oxygen demand to activate heat dissipation mechanisms by the increase in metabolic heat. Boyd et al.(30) reported that rumen temperature was lower in steers treated with ZH than those in the control group. The results obtained were attributed to an increase in the respiratory rate of treated animals as a mechanism to reduce the heat accumulated by the increase in ruminal fermentation. Therefore, these results suggest that the addition of 0.10 mg of ZH increased lipolysis; however, adding higher doses would increase metabolic heat production, increasing the animal respiratory rate as a mechanism to maintain a constant body temperature. However, this would result in vasodilation, causing an imminent decrease in red blood cells. Similar results were reported by Hateffi et al(19), who found that the supplementation with 20 mg·kg-1 of ZH decreased the hematocrit and hemoglobin values; these results were attributed to the increase of the respiratory rate. However, the effects of β-AA on hematological components are still unclear since few studies have investigated this topic.

The increase in the average number of platelets due to ZH supplementation could be explained by an effect on blood thrombocytosis, increasing platelet concentration as a response to lesions that may increase blood vessels, and the homeostatic imbalance of the organism(16). Similar results were obtained by Wagner et al(24); they found higher levels of platelets in equines supplemented with 0.17 mg·kg-1 of ZH. However, the number of platelets has not been previously evaluated in ruminants after the administration of β-AA. Nevertheless, the hematological values of all the variables obtained in this study are within the reference interval(31). Frese et al(8) evaluated the effect of adding zilpaterol and ractopamine to the diet of finishing cattle on cardiovascular variables; they concluded that supplementation with these β-AAs did not affect the arrhythmia rate, although there was a slight increase in heart rate, which finally returned to the normal reference interval mentioned in the literature for this type of cattle. However, it is important to continue these studies to confirm if these products represent a risk, both for animal welfare and human health.

In summary, it was observed minimal differences in Cho, Ur, Na, number of platelets, mean corpuscular hemoglobin concentration, and red blood cell distribution width. Nonetheless, all hematological and biochemical variables were within their reference interval after increasing doses of zilpaterol hydrochloride. These results suggest that the experimental animals did not modify their acid-base status or cellular homeostasis, and that their metabolic and physiologic status were not compromised after consuming ZH in their diet. Furthermore, the results demonstrate that no pathologic state was generated in hair sheep after zilpaterol hydrochloride (generic) supplementation.

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Acknowledgments and conflicts of interest We acknowledge the Secretaría de Educación Pública (Proyecto Cuerpos Académicos PRODEP, program No. 10427) and the Fondo de Educación Superior Empresa A.C. (program No. 10994) for the support received during this study. Authors declare no conflicts of interest.

Literature cited: 1. Arteaga CJD. La industria ovina en México: Memoria 1er Simposium Internacional de Ovinos de Carne. Desafíos y oportunidades para la ovinocultura en México ante los nuevos esquemas de mercado abierto. Pachuca de Soto, Hidalgo. 2003. 2. Gastélum-Delgado MA, Avendaño-Reyes L, Álvarez-Valenzuela FD, CorreaCalderón A, Meza-Herrera CA, Mellado M, Macías-Cruz U. Circannual estrous behavior in Pelibuey ewes under arid conditions of Northwestern of Mexico. Rev Mex Cienc Pecu 2015;6(1):109-118. 3. Beermann DH. ASAS Centennial paper: A century of pioneers and progress in meat science in the United States leads to new frontiers. J Anim Sci 2009;87:1192-1198. 4. Avendaño-Reyes L, Macías-Cruz U, Álvarez-Valenzuela FD, Águila-Tepato E, Torrentera-Olivera NG, Soto-Navarro SA. Effects of zilpaterol hydrochloride on growth performance, carcass characteristics, and wholesale cut yield of hair-breed ewe lambs consuming feedlot diets under moderate environmental conditions. J Anim Sci 2011;89:4188-4194. 5. Partida-de-la-Peña JA, Casaya-Rodríguez TA, Rubio-Lozano MS, Méndez-Medina RD. Effect of zilpaterol hydrochloride on the carcass characteristics of Katahdin lamb terminal crosses. Vet Méx 2015;2(2):2448-6760. 6. López-Carlos MA, Ramírez RG, Aguilera SJ, Aréchiga CF, Méndez LF, Rodríguez H, Silva JM. Effect of ractopamine hydrochloride and zilpaterol hydrochloride on growth, diet digestibility, intake and carcass characteristics of feedlot lambs. Livest Sci 2010;131(1):23-30. 7. Estrada-Angulo A, Barreras-Serrano A, Contreras G, Obregón JF, Robles-Estrada JC, Plascencia A, Zinn RA. Influence of level of zilpaterol hydrochloride supplementation on growth performance and carcass characteristics of feedlot lambs. Small Ruminant Res 2008;80(1-3):107-110.

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8. Frese DA, Reinhardt CD, Bartle SJ, Rethorst DN, Bawa B, Thomason JD, Loneragan GH, Thomson DU. Effect of ractopamine hydrochloride and zilpaterol hydrochloride on cardiac electrophysiologic and hematologic variables in finishing steers. J Am Vet Med Assoc 2016;249:668-677. 9. FDA. New Animal Drug Application (NADA) 141-258. ZILMAX (Zilpaterol Hydrochloride) type A medicated article for cattle fed in confinement for slaughter. Approval date: 10 August, 2006. 10. Arcella D, Baert K, Binaglia M, Gervelmeyer A, Innocenti ML, Ribo O, Steinkellner H, Verhagen H. Review of proposed MRLs, safety evaluation of products obtained from animals treated with zilpaterol and evaluation of the effects of zilpaterol on animal health and welfare. EFSA 2016;14(9):4579. 11. Loneragan GH, Thomson DU, Scott HM. Increased mortality in groups of cattle administered the β-Adrenergic agonists ractopamine hydrochloride and zilpaterol hydrochloride. PLoS ONE 2014;9(3):e91177. 12. García, E. Modificaciones al sistema de clasificación climática de Koeppen. 3ra. ed, México, DF. Instituto de Geografía. Universidad Nacional Autónoma de México, 1985. 13. NRC. Nutrient requirements of sheep. National Academy Press, Washington, DC, 1985. 14. SAS Institute Inc.2004. SAS/STAT® User’s Guide. Cary, NC: SAS Institute Inc. 15. Radostits OM, Gay CC, Blood DC, Hinchcliff KW. Clínica Veterinária: um tratado de doenças dos bovinos, ovinos, suínos, caprinos e eqüinos. 9th ed. Guanabara Koogan, Rio de Janeiro, 2002. 16. Kaneko JJ, Harvey JW, Bruss ML. Clinical biochemistry of domestic animals. 6th ed. San Diego (USA): Academic Press; 2008. 17. Vahedi V, Towhidi A, Zare –Shahneh A, Sadeghia M, Zamanic F, Dunshea FR. Effects of β-agonist zilpaterol hydrochloride feeding and supplementation period on growth and carcass characteristics of Lori-Bakhtiari lambs. Small Ruminant Res 2014;4672:4677. 18. Bibber-Krueger CLV, Miller KA, Parsons GL, Thompson LK, Drouillard JS. Effects of zilpaterol hydrochloride on growth performance, blood metabolites, and fatty acid profiles of plasma and adipose tissue in finishing steers. J Anim Sci 2015;93:24192427. 19. Hatefi A, Towhidi A, Zali A, Zeinoaldini S, Ganjkhanlou M, Plascencia A. Effects of dietary zilpaterol hydrochloride (β2-agonist) supplementation on finishing castrated male goats: metabolic endocrine, blood constituents, plasma volume, respiratory rate and cardiac changes. J Appl Anim Res 2017;45(1):447-453.

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20. Zimmerli U, Blum J. Acute and longterm metabolic, endocrine, respiratory, cardiac and skeletal muscle activity changes in response to perorally administered β‐ adrenoceptor agonists in calves. J Anim Physiol Anim Nutr 1990;63:157-172. 21. Nourozi M, Abazari M, Raisianzadeh M, Mohammadi M, Zare-Shahne A. Effect of terbutaline and metaproterenol (two beta-adrenergic agonists) on performance and carcass composition of culled Moghani ewes. Small Ruminant Res 2008;74:72-77. 22. Chikhou FH, Moloney AP, Austin FH, Roche JF, Enright WJ. Effects of cimaterol administration on plasma concentrations of various hormones and metabolites in Friesian steers. Domest Anim Endocrinol 1991;8:471-480. 23. Buntyn JO, Steffen D, Burdick-Sanchez NC, Sieren SE, Jones SJ, Erickson GE, Carroll JA, Schmidt TB. Serum blood metabolite response and evaluation of select organ weight, histology, and cardiac morphology of beef heifers exposed to a dual corticotropin-releasing hormone and vasopressin challenge following supplementation of zilpaterol hydrochloride. J Anim Sci 2017;95:5327-5338. 24. Wagner A, Mostrom S, Hammer C, Thorson JF, Smith DJ. Adverse effects of zilpaterol administration in horses: three cases. J Equine Vet Sci 2008;28:238-243. 25. O’Connor RM, Butler WR, Finnerty KD, Hogue DE, Beermann DH. Acute and chronic hormone and metabolite changes in lambs fed the beta-agonist, cimaterol. Domest Anim Endocrinol 1991;8:537-548. 26. Dickson WM, Feldman EC, Hedge GA, Martin R, McDonald LE. Fisiología Veterinaria Cunningham. Cunningham JG editor. 3ra ed. Madrid, España: Elsevier; 2003. 27. Byrem TM, Beermann DH, Robinson TF. The beta-agonist cimaterol directly enhances chronic protein accretion in skeletal muscle. J Anim Sci 1998;76:988–998. 28. Shirato K, Tanihata J, Motohashi N, Tachiyashiki K, Tomoda A, Imaizumi K. β2agonist clenbuterol induced changes in the distribution of white blood cells in rats. J Pharmacol Sci 2007;104:146-152. 29. Vicente-Pérez A, Avendaño-Reyes L, Barajas-Cruz R, Macías-Cruz U, CorreaCalderón A, Vicente-Pérez R, Corrales-Navarro JL, Guerra-Liera JE. Parámetros bioquímicos y hematológicos en ovinos de pelo con y sin sombra bajo condiciones desérticas. Ecosist Recur Agropec 2018;5(14):259-269. 30. Boyd BM, Shackelford SD, Hales KE, Brown-Brandl TM, Bremer TM, Spangler ML, et al. Effects of shade and feeding zilpaterol hydrochloride to finishing steers on performance, carcass quality, heat stress, mobility, and body temperature. J Anim Sci 2015;93:5801-5811.

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31. Ahmadi-Hamedani M, Ghazvinian K, Atyabi N, Khanalizadeh P, Masoum MA, Ghodrati MS. Hematological reference values of healthy adult Sangsari sheep (Iranian fat-tailed sheep) estimated by Reference Value Advisor. J Appl Anim Res 2016;25:459-464.

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https://doi.org/10.22319/rmcp.v11i4.5460 Technical note

Dietary supplementation effects with Ruta graveolens on performance, carcass traits and meat quality on rabbits

Maricela Ayala Martínez a Armando Zepeda-Bastida a Sergio Soto-Simental a*

Universidad Autónoma del Estado de Hidalgo. Instituto de Ciencias Agropecuarias. Ave Universidad s/n km 1. Ex Hacienda de Aquetzalpa. 43600, Tulancingo Hidalgo, México.

*Corresponding author: sotos@uaeh.edu.mx

Abstract: Ruta graveolens is a weed that can be used to feed rabbits. The aim of this study was to determine growth performance, carcass and meat quality of rabbits after their dietary supplementation with Ruta graveolens. Sixty (60) weaned rabbits were randomly assigned to five treatments; control diet (C) or diets supplemented either with leaves (25RL or 50RL) or complete plant of Ruta graveolens (25CP or 50CP). The use of Ruta graveolens has a similar (P>0.05) growth performance to the control group and feed conversion rate. Carcass quality was different (P<0.05) among treatments in empty body weight, empty gastrointestinal tract and fat. The pH decreased when Ruta graveolens was used to feed growing rabbits, but meat produced better texture parameters than control group. The results obtained in the present study suggest that Ruta graveolens can be considered as an alternative feed source in the diets of rabbits. Key words: Aromatic plant, Meat quality, Growth efficiency, Rabbit.

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Received:24/07/2019 Accepted: 25/09/2019

In recent years, there has been a growing interest in the use of plants in animal production due to their bioactive compounds that improve productive performance, carcass traits and meat quality(1). Several medicinal plants are used in diets of fattening rabbits as they are a source of phytochemicals that possess properties such as antioxidants or antimicrobials(1,2). Bilberry pomace dietary supplementation was used as a feeding strategy to produce favourable nutritional performance and changes in fatty acids in rabbit meat content(3). Moreover, the supplementation with oregano and rosemary had positive effects on growth performance and carcass traits of fattening rabbits(4). But, dietary supplementation with onion, cranberry, strawberry and their extracts did not produced differences on productive performance and meat quality and oxidative stability in weaned rabbits(5). Ruta graveolens is a plant used in traditional medicine around the world and is known by different names, such as rue, herb of grace, and others in several languages(6). This plant is recognised due its antimicrobial(7,8,9) and antioxidant(1,2) properties. Ruta graveolens is a plant with a high content of secondary metabolites, such as coumarins, alkaloids, volatile oils, flavonoids and phenolic acids, which are responsible for several biological effects(6). It seems that, this is the first time that Ruta graveolens is been used in diets of growing rabbits. However, the extracts or leaves of this herb were previously used to investigate in vitro antibacterial activity(7). Ruta graveolens and its flavonoids can be used as antimicrobial(8) or antioxidant agent(8,10). Rabbit production faces a problem during rabbit growth. Weaning is a crucial period for rabbits, since increased digestive disturbances are observed at this age, possibly due to increased susceptibility to several pathogens caused by enhanced stress rates. One disease that is characterised by diarrhoea, abdominal bloating and distention of intestinal cavity is the epizootic rabbit enteropathy; this disease has high morbidity and mortality rates(2). As Ruta graveolens has antimicrobial and antioxidant properties could be used as a feed additive to increase productive parameters and obtain better carcass and meat quality. Based on the above considerations, this study was conducted to determine effects of feeding Ruta graveolens using leaves or complete plant at two different dietary levels on productive performance, carcass traits and meat quality of growing rabbits. Trial was conducted in the experimental rabbitry of the Instituto de Ciencias Agropecuarias (Tulancingo, Hidalgo, MĂŠxico) and was approved by the Animal Care Committee of the Universidad AutĂłnoma del Estado de Hidalgo. Sixty (60) weaned rabbits (35 d of age, 25 males and 35 females) were assigned randomly to five treatments (n= 12 by treatment), each with three replications; rabbits were housed in cages (90 cm x 60 cm) equipped with manual feeders and

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automatic drinkers. Rabbits were reared in controlled conditions with average temperature of 20 °C. Rabbits were hybrids of New Zealand, California and English Spot breeds with an average weight of 756.79 ± 97.69 g. The feed used was pelletized using a pellet machine (SKJ120 model, Shandong, China). Animals were fed ad libitum using isoproteic (17 % of crude protein) and isoenergetic (2.4 Mcal/kg of digestible energy) experimental diets, according to De Blas and Mateos(11). Animals were divided in the following treatments: control, supplemented with 25 and 50 g of rue leaves/kg of food, or supplemented with 25 and 50 g of complete plant/kg of food (C, 25RL, 50RL, 25CP, 50CP diets, respectively) as indicated in Table 1. Table 1: Experimental diets ingredients Treatments Ingredient

25RL

50RL

25CP

50CP

1.79 1.61 1.07 0.78 1.54 0.77 1.90 0.25 0.30 0

1.81 1.37 0.99 0.78 1.60 0.77 1.90 0.25 0.30 0.23

1.82 1.14 0.99 0.78 1.60 0.77 1.90 0.25 0.30 0.46

1.81 1.37 0.99 0.78 1.60 0.77 1.90 0.25 0.30 0.23

1.82 1.14 0.99 0.78 1.60 0.77 1.90 0.25 0.30 0.46

16.6 16 8.7 2.5 0.8 0.5

16.7 16 8.7 2.5 0.8 0.5

16.6 16 8.7 2.5 0.8 0.5

16.7 16 8.7 2.5 0.8 0.5

Kg Corn Oat straw Bran wheat Soybean husk Soybean meal Canola meal Sorghum Molasses Vit. and minerals premix Ruta graveolens (rue) Calculated composition Crude protein, % NDF, % ADF, % ED, Mcal/kg Ca, % P, %

C=Control, 25RL= 25 g.kg-1 of rue leaves; 50RL= 50 g.kg-1 of rue leaves; 25CP= 25 g.kg-1 of complete plant; 50CP= 50 g.kg-1 of complete plant.

The plant was obtained in Tulancingo, Hidalgo State in the centre of México. After transporting to the lab, plants were separated into leaves (RL) and complete plant (CP) and dried at room temperature for 5 d in shadow. All parts were grounded into a miller (Mexicana de Suministros Agropecuarios SA de CV, Tulancingo, Hidalgo, México) using a sieve with a 5 mm diameter. Once

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grounded, the plant was stored in a dark plastic container until use. During the experiment, rabbits were weighed individually each week, and feed intake was daily measured during the fattening period. From this data, daily average weight gain (DAWG), total weight gain (TWG), and feed conversion ratio (FCR) were calculated. Rabbits were slaughtered at 63 d old in the meat lab belonging to the Instituto de Ciencias Agropecuarias in Tulancingo, Hidalgo, Mexico. Before rabbits were slaughtered there was no fasting. Animal length was determined in live animals by measuring distance from the atlas to last ischion vertebra while animal was in a dorsal position. Pelvis and lumbar circumference on live animal and carcass were measured using a measuring tape. Animals were weighed, mechanical stunned and processed according to the national legislation(12). Hot carcasses, liver, kidneys, gastrointestinal tract full and empty, bladder full and empty, scapular fat, perirenal fat, and skin were weighed, and then carcasses were stored under refrigeration at 4 °C for 24 h. Empty body weight was calculated by measuring weight differences between full and empty gastrointestinal tract and bladder. Carcasses were divided after 24 h of refrigerated storage(13). Head was cut at the level of atlas, forequarter was obtained by cutting between 6th and 7th ribs, thoracic cage was determined by cutting in last rib, and loin was obtained between 6th and 7th lumbar vertebra cutting transversally to vertebral column to finally obtain hind leg. Hind legs were detached in fat, bone and meat. All these parts were weighed separately. Meat colour was measured on loin surface between last rib and 6th lumbar vertebra at room temperature (22 °C) using a portable colorimeter i-Lab S560 (Microptix, Wilton, Maine, USA). Values were recorded in terms of CIE L*a*b* colour space using a standard illuminant D65 and observer of 2° as indicated in American Meat Science Association meat colour measurement guidelines(14), pH was determined using a pH meter suitable for meat samples (HI99163 model, Hanna instruments, Cluj-Napoca, Romania). Water holding capacity (WHC) was expressed as percentage of water loss(15). Cooking losses were measured in loins. Samples were put in a plastic bag and cooked at 80 °C until meat internal temperature reaches 68 °C using a check temp digital thermometer (Hanna Instruments, Portugal). Cooked samples were cooled at room temperature and weighed, cooked losses was calculated by weight differences before and after cooking and expressed as a percentage. Cold samples were used to determine texture profile analysis (TPA) by cutting cubes (1 cm each side) and then compressing to 50 % perpendicular to the muscle fibre direction using 1 mm/s of crosshead speed(16). After that hardness, cohesiveness, springiness and chewiness were calculated using Texture Pro CT software on a Brookfield CT3 texture analyser (Brookfield, Middleboro, MA, USA).

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An analysis of variance was carried out with obtained data following the general linear model procedure, with feeding treatment being the fixed factor, using the SAS Institute software(17). When statistical differences were found (P<0.05), a Tukey comparison test was used. Daily gain weight, total weight gains and feed conversion rate during the fattening period in rabbits are shown in Table 2. Daily gain during first and fourth weeks of growing period showed statistical differences among treatments (P<0.05). Highest total weight gains in fattening rabbits (P<0.05) was found in treatments C and 25CP (1,175 ± 131 and 1,190 ± 186, respectively), moreover, control group was different (P<0.05) to 25RL group. Feed conversion rate values during the fattening period ranged from 2.21 and 2.78 for 25CP and 50CP treatments, respectively. However, 50CP group had a greater feed conversion rate to control group (2.78 ± 0.58 and 2.22 ± 0.24 for 50CP and Control group, respectively). The lowest daily gain during first week (39.15) was found in 25RL group and highest (52.22) in control treatment, but in fourth week highest daily gain (39.76) was observed in 25CP group and the lowest was in 50RL treatment. Finally, the highest total daily gain weight (41.98) was detected in control group and lowest was (33.35) in 25RL group. The highest weight gain (1,175 g) was observed in control group and the lowest (934 g) in the 25 RL group. Ruta graveolens use as a complete plant at 25 g.kg-1 in fattening rabbits was similar to control group in daily weight gain and feed conversion rate every week during fattening period.

Table 2: Effects of Ruta graveolens dietary supplementation on performance of fattening rabbits (Mean ± SD) Treatments C

25RL

50RL

25CP

50CP

LW35d

629.54±15.06e

686.68±20.03d

765.62±95.4c

821.63±22.14b

889.54±19.80a

LW63d

1763.18±150.0b

1632.50±95.4ab

1803.75±169.49ab

1928.48±255.59a

1816.98±222.19a

DGW1 (g) DGW2 (g) DGW3 (g) DGW4 (g) DGWT (g) TWG (g) FCR

52.22±8.42a 40.63±6.51 39.44±6.88 35.63±5.69a 41.98±4.70a 1175.55±131.68a 2.22±0.24b

39.15±7.92b 33.31±11.09 29.92±9.45 33.05±12.28ab 33.35±3.39b 934.09±95.17b 2.48±0.28ab

44.71±9.89ab 44.47±6.55 34.49±8.09 17.96±17.93b 35.40±4.34ab 991.42±121.74ab 2.48±0.30ab

50.00±11.54ab 41.74±9.07 38.49±11.64 39.76±7.44a 42.49±6.65a 1190.00±186.41a 2.21±0.32b

45.31±7.80ab 37.06±12.65 36.82±8.72 25.87±17.42ab 36.27±7.42ab 1015.55±207.85ab 2.78±0.58a

C= Control, 25RL= 25 g.kg-1 of rue leaves; 50RL= 50 g kg-1 of rue leaves; 25CP= 25 g kg-1 of complete plant; 50CP= 50 g kg-1 of complete plant; LW35d= Live weight at 35 d of age. LW63d= Live weight at 63 d of age. DGW1-4= Daily gain weight during 14 fattening weeks. DGWT= Daily gain weight during all fattening period. TWG= Total weight gained. FCR= Feed conversion ratio. abc Different superscripts in the same row indicate significant differences (P<0.05).

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So far, there is few information about rue used as a dietary supplementation in rabbits. The use of 2.5 g.kg-1 of rue in fattening rabbits increase carcass weight and meat proportion(18). Some researchers, did not find a significant difference in productive parameters of rabbits fed diets with Trametes maxima(19). Moreover, the supplementation with Silybum marianum at the levels of 5 or 10 g kg-1 into the diet of rabbits, found a similar performance productivity(20). At the same time, Lythrum salicaria supplemented in diets to growing rabbits did not find an increase in growth performance(21). Ruta graveolens has been reported to have antioxidant, antibacterial and anticancer activity in vitro(8,9). A similar growth performance to control group was shown in this study, especially when animals ingest the complete plant. The stems, leaves, and flower of rue are used in human traditional medicine as anthelmintic, antiparasitic, antidiarrheic and antimicrobial properties(7).

Effects of dietary supplementation with Ruta graveolens on rabbit’s carcass quality are shown in Table 3. Empty body weight was significantly different among groups (P<0.05). Rabbits supplemented with 25CP (1,780.33 ± 216.84 g) and 50CP (1,821.78 ± 265.10 g) were heavier than those supplemented with 25RL (1,496.33 ± 74.36 g); however, there were no significant differences (P>0.05) between control group (1,645.00 ± 13.36 g) and rabbits supplemented with rue complete plant (25CP=1,780.33 ± 216.8 and 50CP=1,821.78 ± 265.1 g) producing lighter carcasses (P<0.05) than control group (control=1.94 vs 50CP=0.56). These findings are supported by the increase of live weight and weight gained in treatment of 50CP. The use of rue to supplement diets to grow rabbits could be used to promote carcass quality. However, previous researchers did not find differences in carcass traits when diets were supplemented with other plants. Carcass traits of rabbits fed with Silybum marianum were similar among treatments(20). Likewise, another research group that replaced conventional ingredients with Amaranthus dubius at levels up to 32 % did not observe a negative impact in carcass traits, suggesting that this plant should be a potential substitute of conventional ingredients in rabbit diet formulations(22). By other hand, the supplementation with Lythrum salicaria to growing rabbits, did not find differences in carcass traits, suggesting that this plant could be used in rabbit diets during the fattening period(21).

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Table 3: Ruta graveolens dietary supplementation effects on carcass traits of fattening rabbits (Mean ± SD) Treatments Variable C

25RL

50RL

Carcass hot weight

976.11±46.69ab

897.77±58.90b

961.87±118.62ab

EBW, g/kg

1645.00±13.36ab 1496.33±74.36b

Hot dressing, %

59.44±2.29

60.10±4.59

59.40±1.92

59.66±2.02

60.01±2.13

CCY, %

57.48±2.01

58.05±3.92

56.95±2.162

57.14±1.872

56.25±2.74

Viscera

250.40±2.11

278.20±3.46

288.40±5.55

287.70±2.44

274.4±7.13

Heart

3.40±0.10

3.00±0.10

3.30±0.10

0.31±0.69

0.39±0.94

Lungs

7.60±1.90

8.40± 3.20

7.80±2.16

8.60±1.99

7.30±1.03

Spleen

0.60±0.10

0.70±0.20

0.60±0.09

0.60±0.06

0.70±0.30

Liver

44.7±10.83

43.50±10.40

47.00±7.84

49.30±6.76

38.80±11.08

Kidneys

6.70±0.90

6.30±0.74

6.40±1.18

6.90±1.16

7.20±0.86

EGTW

258.28±46.94b

260.95±36.12b

294.93±34.50ab

310.85±49.04ab 317.30±30.13a

Bladder

2.10±0.50

2.70±1.11

1.70±0.47

2.20±1.08

2.80±1.19

Kidney fat

8.00±1.60

7.60±2.40

6.80±3.24

8.30±4.22

9.60±2.47

Scapular fat weight

2.40±0.70

2.10±0.80

1.90±0.69

2.20±1.13

3.20±1.79

Head

57.9±0.41

63.40±7.90

61.30±5.53

59.40±5.43

57.6±8.79

Forepart weight

139.30±6.90

137.60±11.00

138.00±6.16

140.80±7.04

139.90±6.32

IPW

53.10±4.70

57.10±9.50

54.70±8.07

52.80±4.80

54.00±7.61

Hind part weight

111.80±7.80

107.20±12.60

106.50±10.37

110.70±11.58

112.10±9.70

Legs

203.70±8.70

204.80±12.80

199.90±7.83

196.10±11.26

196.60±9.08

Meat

152.80±10.10

153.40±11.10

147.40±7.58

150.00±8.49

147.20±14.56

Bone

45.40±7.30

46.50±7.00

48.70±9.46

41.10±6.55

44.60±6.84

0.56±0.59b

Dissectible fat

1.94±0.12

1.44±0.97

25CP

1617.88±187.04a b

0.81±0.62

50CP

1061.11±125.0 4a 1780.33±216.8 4a

0.75±0.68

1097.22±182.62a 1821.78±265.10a

C= Control, 25RL= 25 g kg-1 of rue leaves; 50RL= 50 g kg-1 of rue leaves; 25CP= 25 g kg-1 of complete plant; 50CP= 50 g kg-1 of complete plant. EBW= empty body weight; CCY= chilled carcass yield; EGTW= empty gastrointestinal tract weight; IPW= intermedia part weight. abc Different superscripts in the same row indicate differences (P<0.05).

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Colour, pH, WHC and other variables of meat from rabbits fed with Ruta graveolens supplemented diets are presented in Table 4. Significant differences (P<0.05) were found in all measured traits, apart from WHC and hardness. Ruta graveolens leaves affected (P<0.05) the colour of rabbit meat, providing lower L* (25RL= 55.3) and b* (50RL= 8.22) values, and complete plant resulted in lower a* values (50CP= 0.61) compared to control group (1.23). Likewise, pH decreased when rabbits were fed diets supplemented with 25 g kg-1 of rue complete plant. In the results of texture profile analysis, hardness did not shown differences (P>0.05) among groups, but resilience, cohesiveness, springiness and chewiness were different between control and Ruta graveolens groups. Resilience and cohesiveness were higher in meat of rabbits fed with Ruta graveolens leaves, while springiness was higher in 25RL (3.05), 50RL (4.00) and 25CP (2.35) to control group (0.60).

Table 4: Ruta graveolens dietary supplementation effects on meat quality of fattening rabbits (Mean ± SD) Treatments Variable C 25RL 50RL 25CP 50CP L*

57.90±3.21a

55.30±3.60b

58.07±3.91a

58.21±3.05a

56.72±2.84ab

1.23±1.75ab

1.65±1.43a

1.18±1.61ab

1.29±1.17ab

0.61±1.40b

b* pH

9.74±2.04a 5.85±0.11a

9.36±2.00ab 5.81±0.04ab

8.22±2.45b 5.77±0.03ab

8.96±2.23ab 5.75±0.05b

9.42±2.19ab 5.80±0.19ab

WHC, %

21.12±5.19

20.02±4.86

18.85±6.19

18.06±5.69

19.48±5.26

Hardness, N Resilience Cohesiveness Springiness Chewiness

8.31±2.17 3.05±0.17b 0.24±0.02c 0.60±0.13c 12.81±0.76b

9.76±3.13 5.08±0.77a 0.48±0.08ab 3.05±1.17b 19.47±5.91a

9.36±3.30 4.94±0.42a 0.54±0.01a 4.00±0.52a 22.42±4.59a

10.18±4.37 2.63±0.46b 0.44±0.08b 2.35±0.62b 8.75±1.97b

10.59±3.62 2.75±0.04b 0.22±0.01c 0.57±0.12c 13.56±2.79b

C=Control, 25RL= 25 g kg-1 of rue leaves; 50RL= 50 g kg-1 of rue leaves; 25CP= 25 g kg-1 of complete plant; 50CP= 50 g kg-1 of complete plant. abc Different superscripts in the same row indicate differences (P<0.05).

Other research did not find differences in meat quality with dietary supplementation of plants fed to growing rabbits. Meat quality did not affect fed rabbits with Silybum marianum(20). Replace conventional ingredients for Amaranthus dubius at levels up to 32 %, there was not negative impact in meat quality, suggesting this plant could be a potential substitute of conventional ingredient in rabbit diets formulation(22). Similar results were obtained using Lythrum salicaria to growing rabbits, did not find differences in carcass traits and suggested that this plant could be used in rabbit feeding during the fattening period(19).

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The pH has been associated with the colour. Low pH values (4.49) were found in the Longissimus dorsi muscle in rabbits fed with Amaranthus dubius(21). While L* values around to 54 and pH around to 5.6 were reported when rabbits were fed with purple loosestrife(20). Colour and pH of rabbit meat can be affected by age, breed, muscle type, sex, feeding, ante mortem and post mortem conditions and others factors (23,24). Ruta graveolens inclusion in diets of fattening rabbits can be considered as a viable feeding alternative to maintain high production parameters and obtain similar carcass traits and meat quality characteristics to conventionally fed rabbits.

Acknowledgements The authors would like to thank the PRODEP program for financial support of this work. Project number DSA/103.5/16/10281. SEP-PFCE 2018.

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7. França-Orlanda JF, Nascimento AR. Chemical composition and antibacterial activity of Ruta graveolens L. (Rutaceae) volatile oils, from São Luís, Maranhão, Brazil. S Afr J Bot 2015;(99):103-106. 8. Pushpa H, Ramya-Shree N, Shetty SP, Ramesh DH. (2015). Screening of antimicrobial, antioxidant and anticancer activity of Ruta graveolens. Adv Biol Res 2015;(9):257-264. 9. Chaftar N, Girardot M, Labanowski J, Ghrairi T, Hani K, Frere J, Imbert C. Comparative evaluation of the antimicrobial activity of 19 essential oils. Adv Exp Med Biol–Adv Microbiol Inf Dis Pub Health 2016;(901):1–15. 10. Mohammadi-Motamed S, Shahidi-Motlagh S, Bagherzadeh H, Azad-Forouz S, Tafazoli H. Evaluation of antioxidant activity of Ruta graveolens L. extract on inhibition of lipid peroxidation and DPPH radicals and the effects of some external factors on plant extract's potency. Res J Pharmacogn 2014;(1):45-50. 11. De Blas C, Mateos GG. Feed formulation. In: de Blas C, Wiseman J, editors. Nutrition of the rabbit. 2nd ed. Wallingford, UK: CAB International; 2010:222-232. 12. NOM-033- SAG/ZOO. Norma Oficial Mexicana. Métodos para dar muerte a los animales domésticos y silvestres. 2014. 13. Blasco A, Ouhayoun J, Masoero, G. Harmonization of criteria and terminology in rabbit meat research. World Rabbit Sci 1993;(1):3-10. 14. AMSA. Meat colour measurement guidelines. American Meat Science Association Champaign. Il. USA. 2012. 15. Honikel KO. How to measure the water-holding capacity of meat? Recommendation of standardized methods. In: Tarrant PV, et al editors. Evaluation and control of meat quality in pigs. Current topics in veterinary medicine and animal science. Volume 38. Dordrecht. Germany: Springer; 1987:129-142. 16. Bourne MC. Texture profile analysis. Food Technol 1978;(35):62-66. 17. SAS. SAS User’s guide: Statistics (version 5 ed.). Cary, NC, USA: SAS Inst Inc. 1999. 18. Herrera-Soto IA, García-Flores M, Soto-Simental S, Zepeda-Bastida A, Ayala-Martinez M. Plantas aromáticas en la alimentación de conejos y su efecto en la carne. Abanico Veterinario 2018;(8):81-87.

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19. Hernández-Martínez CA, Maldonado-Herrera JA, Méndez-Zamora G, Hernández-Luna CE, Gutiérrez-Soto G. Enzymatic extract of Trametes maxima CU1 on productive parameters and carcass yield of rabbits. Can J Anim Sci 2017;(97):683-688. 20. Cullere M, Dalle-Zotte A, Celia C, Renteria-Monterrubio AL, Gerencsér Zs, Szendró Zs, Kovács M, Kachlek ML, Matics Zs. Effect of Silybum marianum herb on the productive performance, carcass traits and meat quality of growing rabbits. Lives Sci 2016;(194):31-36. 21. Kovitvadhi A, Gasco L, Ferrocino I, Rotolo L, Dabbou S, Malfatto V, et al. Effect of purple loosestrife (Lythrum salicaria) diet supplementation in rabbit nutrition on performance, digestibility, health and meat quality. Animal 2016;(10):10–18. 22. Molina E, González-Redondo P, Moreno-Rojas R, Montero-Quintero K, Sánchez-Urdaneta A. Effect of the inclusion of Amaranthus dubius in diets on carcass characteristics and meat quality of fattening rabbits. J Appl Anim Res 2018;(46):218-223. 23. Hulot F, Ouhayoun J. Muscular pH and related traits in rabbits: A review. World Rabbit Sci 1999;(7):15-36. 24. Dalle-Zotte A. Perception of rabbit meat quality and major factors influencing the rabbit carcass and meat quality. Livest Prod Sci 2002;(75):11-32.

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Revista Mexicana de Ciencias Pecuarias

Edición Bilingüe Bilingual Edition

Rev. Mex. Cienc. Pecu. Vol. 11 Núm 4, pp. 933-1230, OCTUBRE-DICIEMBRE-2020

ISSN: 2448-6698

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Revista Mexicana de Ciencias Pecuarias Rev. Mex. Cienc. Pecu. Vol. 11 Núm 4, pp. 933-1230, OCTUBRE-DICIEMBRE-2020

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Rev. Mex. Cienc. Pecu. Vol. 11 Núm. 4, pp. 933-1230, OCTUBRE-DICIEMBRE-2020