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Byeng R. Min
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Leluo Guan
Melanie R. Mormile
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Shanghai Institute for Biol. Sciences, P.R. China
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TABLE OF CONTENTS ARTICLES 102
A Comparison Of the Microbiomes Of Litopenaeus Vannamei From Disparate Geographical Regions E. Kawaler, A. Seetharam, Z.-Q. Du, A. Severin, M. F. Rothschild
111 Tracking Enterococcus faecium Antibiotic Resistance, Dissemination and Risk Assessment Modeling
A. Limayem
117 Risk Assessment on the Use of Genetically Modified Organisms (GMOs) for Biofuel Production
A. Limayem
135 Influence of Market Setting and Time of Purchase on Counts of Aerobic Bacteria, Escherichia coli, and Coliform and Prevalence of Salmonella and Listeria in Beef in Vietnam A. K. McCain, P. T. T. Vu, N. T. Mai, M. V. V. Le, D. H. Nguyen, P. R. Broadway, L. M. Guillen, M. M. Brashears, J. R. Donaldson, M. W. Schilling, and T. T. N. Dinh
153 Effects of feeding two different tannin-containing diets on ruminal fermentation profiles and microbial community changes in meat goats
B. R. Min, D. Perkins, C. Wright, A. Dawod, B. J. Min, T. H. Terrill, J.-S. Eun, R. Shange, S. Y. Yang, and N. Gurung
166 Influence of Market Setting and Time of Purchase on Bacterial Counts and Prevalence of Salmonella and Listeria in Pork in Vietnam
A. K. McCain, P. T. T. Vu, N, T. T. M. Tran, M. V. V. Le, D. H. Nguyen, P. R. Broadway, L. M. Guillen, M. M. Brashears, J. R. Donaldson, M. W. Schilling, and T. T. N. Dinh
Introduction to Authors 187 Instructions for Authors
The publishers do not warrant the accuracy of the articles in this journal, nor any views or opinions by their authors. Agric. Food Anal. Bacteriol. • AFABjournal.com • Vol. 5, Issue 3 - 2015
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www.afabjournal.com Copyright © 2015 Agriculture, Food and Analytical Bacteriology
A Comparison Of The Microbiomes Of Litopenaeus Vannamei From Disparate Geographical Regions E. Kawaler1, A. Seetharam2, Z.-Q. Du3, A. Severin2, M. F. Rothschild1
Department of Animal Science, Iowa State University, Ames, Iowa, USA, 50011 2 Biotechnology Center, Iowa State University, Ames, Iowa, USA, 50011 3 College of Animal Science and Technology, Northeast Agricultural University, Harbin, China, 150030 1
ABSTRACT With the advent of high-throughput sequencing technologies, interest has developed in the sequencing of large non-model genomes as well as the microbiomes associated with them. Sequencing results have applications of biological interest that include previous natural and artificial selection patterns, ancestry determination and diversity analysis, as well as implications for food safety. The Pacific White Shrimp (L. vannamei) is one of the most commonly eaten seafood products worldwide, but genetic resource development for this species lags severely behind when compared to other farmed animals. In this study, the microbiota of shrimp sourced from five countries across South America and Southeast Asia were determined and compared. The sequences collected from the samples were assembled de novo and comparative analysis was performed. The assembled sequences for all the lines were classified based on their similarity to the genomes in the public database. The results provide evidence for a great deal of similarity between the microbiota of shrimp from disparate regions, as well as the presence of some DNA from bacteria known to cause food poisoning in humans. Keywords: Shrimp, Litopenaeus vannamei, microbiome, next-generation sequencing, de novo assembly, geographical diversity, Bowtie2, aquaculture, bacteria, high-throughput sequencing Agric. Food Anal. Bacteriol. 5: 102-110, 2015
INTRODUCTION The Pacific White Shrimp (Litopenaeus vannamei), sometimes known as the whiteleg shrimp, is one of the most commonly eaten shrimp species worldwide Correspondence: Max F. Rothschild,mfrothsc@iastate.edu Tel: +1-515-294-6202
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(Globefish, 2014). Methods of obtaining L. vannamei vary widely, from capture of wild-caught shrimp to farming of shrimp in ponds. Farming methods themselves – population density, feed composition and mechanics, pond aeration, and more – tend to differ across geographical regions of the world (Rosenberry, 2004). Aquaculture of L. vannamei continues to expand and shrimp farming, especially that using
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high-density culturing techniques, is proliferating rapidly to meet the demand and increase profits. However, this has also led to an increase in shrimp disease risk (Kautsky, 2000), and post-harvest techniques have led to concerns over food safety. Many research groups have been working to develop genomic resources for this economically and agriculturally important livestock, among them groups at the Chinese Academy of Sciences (Yu et al., 2014; Zhao et al., 2012) and Iowa State University (Du et al., 2009; Gorbach et al., 2008). Unfortunately, genomic resource development for L. vannamei has been hindered by the large size of the genome (2,500 megabases (Chow et al., 1990)) and its com-
The objective of this study was to determine how the microbiota of commercial L. vannamei differ across world regions and to provide material for future methods for screening shrimp for foodborne pathogens.
plexity, with large numbers of repeats and high rates of polymorphisms (Meehan et al., 2003). The microbiome of an organism is the set of organisms that share the body space with a host (Lederberg and McKay, 2001). Together with that host, it forms an ecological system, often benefiting the host and providing immunity against pathogens. Studying the microbiome for cultivated domesticated animals can provide valuable insights into those animals such as the nature of their diet, growth and sanitary conditions, biotic and abiotic stresses during cultivation, and suitability for human consumption – the microbiome can contain pathogens that are harmful to humans. Studying the microbiota can also promote improved performance by increasing the metabolism of these farm animals (Deusch et al., 2015; Medini et al., 2008). Although there have been several recent studies focusing on the gut microbiota of L. vannamei across different stages of development (Huang et al., 2014; Pangastuti et al., 2010), components of the digestive system (Tzuc et al., 2014), and different diets (Li et al., 2007, Zhang et al., 2014), none have explored the microbiota found on the market product or attempted to characterize the microbiota based on geographical origin. In order to ensure the samples taken here would
quencing. The raw shrimp were peeled, and a tissue sample derived from the last abdominal portion (part of the pleuron, just above the tail) was used to isolate genomic DNA using the DNEasy Blood and Tissue Kit (Qiagen, Hilden, Germany). A flowchart of the process is provided in Figure 1 and detailed below. Single-end libraries were constructed using the Illumina TruSeq DNA Sample Preparation Kit (Illumina, Inc., San Diego, CA, USA), as per the instructions. Each library was ligated to a different index-tag adapter and sequencing was performed using the TruSeq Unique and Universal Adapters on a HiSeq 2000 sequencer, which produced 100bp single-end reads. The data can be found in the NCBI Bioproject Database with ID PRJNA282154. The raw reads obtained were quality tested using FastQC version 0.10 (Andrews, 2010). Digital normalization using Khmer version 1.01 (Brown et al., 2012; Crusoe et al., 2014) was then performed on the reads to reduce the complexity as well as to improve overall quality. Khmer, using k-mer frequencies, estimates per-read coverage and either accepts each as novel or rejects as redundant. The default 10-kmer count was used as the cutoff for rejection of reads. De novo assembly was performed using the Ray assembler (version 2.3.1) (Boisvert et al., 2010). Reads
be taken from actual products, frozen raw shrimp were purchased from a local supermarket and muscle samples were extracted from the last abdomen section – an easily accessed portion of the anatomy with which consumers are likely to come in contact.
from all of the samples were pooled together and assembly was performed with a k-mer size of 33. Standard assembly statistics were calculated using the Assemblathon statistics script (Bradnam et al., 2013) and are presented in Table 1. In order to retain only non-L.
MATERIALS AND METHODS Frozen L. vannamei samples were purchased at the local grocery. Each bag had been packaged and imported from different countries: Indonesia, Vietnam, Thailand, Venezuela, and Honduras. From each population, 6-10 shrimp were individually selected for se-
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Figure 1. Workflow for this experiment. Shrimp samples were extracted, sequenced and normalized separately, then pooled for assembly and BLAST. The normalized sample reads were then compared to the prokaryote sequences identified by BLAST to determine which prokaryotes were contained in each sample, and correlation analysis was performed on this output.
Samples Extracted
Sequencing Performed
NormalizaAon with Khmer
De Novo Assembly with Ray BLAST for Prokaryote IdenAficaAon BowAe for Presence/ Absence DeterminaAon
CorrelaAon Analysis
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Table 1. Assembly statistics for the full set of scaffolds Metric
All Scaffolds
Number of Scaffolds
2,153,627
Total Size of Scaffolds
688,183,728
Longest Scaffold
80,363
Shortest Scaffold
131
Scaffolds > 500 nt
302,243 (14.0%)
Scaffolds > 1,000 nt
92,359 (4.3%)
Mean/Median Scaffold Size
320/195
N50
396
L50
419,259
Non-Eukaryotic Scaffolds
5,185 (5.61%)
vannamei scaffolds, the blastx tool from NCBI-BLAST version 2.2.30 (Altschul et al., 1990) was used to query the assembled scaffolds against the NCBI NR (nonredundant) protein database, which contains known protein sequences aggregated from multiple sources over a wide variety of organisms, both prokaryotic and eukaryotic. All scaffolds of 500 nt or more finding matches to prokaryotic taxa with e-value less than 0.001 were used for this study. Presence/absence of these scaffolds on each of the L. vannamei lines were then determined using the Bowtie2 aligner (Langmead and Salzberg, 2012). Reads from each line were separately aligned to the indexed shrimp assembly and, based on hits to the scaffolds, they were either classified as present or absent. Diversity analysis of the microbiome was performed using simple correlations. For each shrimp line, the proportion of isolates containing reads from each prokaryotic genus was calculated, and the re-
RESULTS AND DISCUSSION
sulting dataset was used to build a correlation matrix summarizing microbiome relatedness between lines.
sequences in the database, and the rest of the sequences were non-eukaryotic. The mapped fraction of the shrimp metagenome had good quality scaffolds, with over 70% being greater than 5,000 nt in length. This non-eukaryotic portion of the assembly
The average number of reads per shrimp varied greatly, with 5.5 million for the six Honduran shrimp, 8.0 million for the eight Indonesian shrimp, 10.6 million for the eight Thai shrimp, 12.1 million for the ten Venezuelan shrimp, and 11.7 million for the eight Vietnamese shrimp. Read quality varied, with Phred scores routinely dipping below 30 for the last 20 bases of each read and below 20 at the ends; therefore, the total number of reads per shrimp was reduced by anywhere from 30 to 50% after application of Khmer. The assembly produced 92,359 scaffolds of 1,000 nucleotides or more. Only 10.69% of these scaffolds mapped successfully to the NCBI-NR database, which is likely due either to lack of open reading frames in the scaffolds or to the heterogeneity of the genomes causing misassembly of scaffolds. Nearly 48% of the mapped scaffolds matched other shrimp
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Table 2. Proportion of shrimp from each country that contained each type of organism Kingdom
Honduras
Venezuela
Indonesia
Thailand
Vietnam
Thermoprotei
1.00
1.00
1.00
1.00
1.00
Halobacteria
0.83
1.00
1.00
1.00
0.38
Methanomicrobia
0.33
0.90
1.00
0.75
0.13
Thermococci
0.50
1.00
1.00
0.88
0.13
Actinobacteria
1.00
1.00
1.00
1.00
1.00
Coriobacteriia
0.17
1.00
0.75
1.00
0.00
Thermoleophilia
0.67
0.90
0.50
0.63
0.00
Bacteroidia
1.00
1.00
1.00
1.00
0.75
Cytophagia
1.00
1.00
1.00
1.00
0.50
Flavobacteriia
1.00
1.00
1.00
1.00
0.63
Sphingobacteriia
0.00
0.70
0.00
0.00
0.00
Cyanobacteria
Cyanophyceae
1.00
1.00
1.00
1.00
1.00
DeinococcusThermus
Deinococci
0.83
1.00
0.88
0.75
0.13
Bacilli1
1.00
1.00
1.00
1.00
1.00
Clostridia
1.00
1.00
1.00
1.00
0.88
Fusobacteria
Fusobacteriia
0.83
0.90
0.88
1.00
0.88
Lentisphaerae
Lentisphaeria
0.50
0.90
0.75
0.88
0.50
Alphaproteobacteria
1.00
1.00
1.00
1.00
1.00
Betaproteobacteria
1.00
1.00
1.00
1.00
1.00
Deltaproteobacteria
1.00
1.00
1.00
1.00
1.00
Epsilonproteobacteria
0.67
1.00
1.00
1.00
0.13
Gammaproteobacteria2,3
1.00
1.00
1.00
1.00
1.00
Spirochaetes
Spirochaetia
1.00
1.00
1.00
1.00
0.63
Tenericutes
Mollicutes
0.67
1.00
1.00
1.00
0.25
Verrucomicrobia Verrucomicrobiae
0.50
1.00
0.75
1.00
0.00
dsDNA viruses, no RNA stage
N/A
1.00
1.00
1.00
1.00
1.00
ssDNA viruses
N/A
1.00
0.90
0.88
1.00
0.00
Phylum/Group Crenarchaeota
Archaea
Euryarchaeota
Actinobacteria
Bacteroidetes
Bacteria
Firmicutes
Proteobacteria
Viruses
Class
Contains genus Bacillus with proportions: Honduras 1.00, Venezuela 1.00, Indonesia 1.00, Thailand 1.00, Vietnam 0.75 2 Contains genus Escherichia with proportions: Honduras 1.00, Venezuela 1.00, Indonesia 1.00, Thailand 1.00, Vietnam 1.00 3 Contains genus Salmonella with proportions: Honduras 1.00, Venezuela 1.00, Indonesia 0.88, Thailand 1.00, Vietnam 0.00 1
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provided an excellent resource to test the relative occurrences of various bacterial and viral clades in different shrimp isolates. Yuan et al. (2013) posit that parts of the shrimp genome may be horizontally transferred from bacteria, but the scaffolds observed in this study were not among the fourteen predicted HGT genes from the Yuan paper. With the limited quality of these reads it cannot be ruled out that some of the scaffolds identified as microbiota might actually be part of the shrimp genome, though it is unlikely. A wide range of prokaryotic organisms was observed in these data. Among the major clades were two phyla of Archaea, consisting of four classes;
shrimp are farmed and grown in similar environmental (pond) conditions, there were high levels of association among the samples from different locations. Fewer non-eukaryotic sequences were detected in the Vietnam shrimp, which had the lowest read coverage. As a result, microbiota composition of this line appeared to differ considerably from other isolates. This was reflected in the correlation values (Table 3). The correlation values between the isolates ranged from 0.14 to 0.83, with all of the lowest correlations belonging to the Vietnamese shrimp. Thailand, Indonesia and Venezuela were the three most similar. Honduras, with fewer isolates as compared to other locations and a below-average number of
eleven phyla of bacteria, comprised of 21 classes; and two groups of viruses. Phyla and classes found in each of the 40 isolates, along with their abundance estimates, are presented in in Table 2. In particular, Proteobacteria, Tenericutes and Actinobacteria were found, which is consistent with the results seen by Zhang et al. (2014). Huang et al. (2014) and Tzuk et al. (2014) both found an abundance of Vibrionaceae, which was seen in only a few individuals here (the Gammaproteobacteria appearing in Table 2 are of other orders); the samples in those papers, however, were taken from the digestive system of the shrimp, implying that the gut microbiome and microbiome of the section we sampled may be of different compositions. The order Enterobacteriales contains strains that are known to cause gastrointestinal disorders in humans and are a common cause of food poisoning (Al-Mutairi, 2011). In particular, as seen in the footnote to Table 2, the genus Escherichia was found in all isolates, and Salmonella was found in all except for the Vietnamese shrimp and one Indonesian shrimp. Bacillus, a genus of bacteria containing some strains that are also known to cause food poisoning (Logan, 2012), appears in all but one isolate. Due to the limitations of BLAST and incompleteness of the NR database, genus-level, not species-level,
reads, was slightly less so, but still had no correlations lower than 0.58. From these results, it appears that many of the differences between the shrimp can be explained by lack of genome coverage. One-third of the organisms in Table 2 are found in all shrimp samples analyzed. If one looks at organisms that are found in at least half of the shrimp from each country, that number jumps to 59%, and if Vietnam is then removed, it becomes 70%. From the data in these two tables, it is apparent that the shrimp sampled have remarkably consistent microbiota. Though they were raised in different parts of the world, under likely similar grow-out conditions, the abundances of the organisms in their microbiomes are quite highly correlated. One caveat to this conclusion may be the fact that the coverage was low. Therefore, the organisms found may be only the most common microbiota – at a higher coverage level, organisms that are rarer but more unique to their particular regions may have been detected, meaning that the similarity could be at least partially artificial. It is also possible that, as this section of the shrimp is more external than the gut and therefore more susceptible to contamination, some of the similar microbiota may have come from outside sources between harvesting and purchase rather than being
information was assigned for these scaffolds. As there were over one hundred distinct genera, the list is not included here, but is available upon request. The correlations among the microbiota of various shrimp isolates are given in Table 3. As most of these
part of the native microbiome. However, overall, the data does appear to suggest that there is an underlying native microbiome to the Pacific White shrimp that is independent of the environment in which it is raised.
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Table 3. Correlation coefficients of organism presence between shrimp from different countries (Correlations were calculated at the genus level)
Honduras Venezuela
Honduras
Venezuela
Indonesia
Thailand
Vietnam
1.0
0.58
0.67
0.66
0.30
1.0
0.77
0.83
0.14
1.0
0.82
0.26
1.0
0.24
Indonesia Thailand Vietnam
To confirm these findings, an experimental study is needed – this was an observational study with the limitations of same, so an experimental study would be able to elucidate the results. The shrimp samples used should come from known facilities, spanning multiple types of grow-out conditions, as well as wild-caught shrimp from several different geographical areas. The sequencing should be done with higher coverage and quality if at all possible. However, as the shrimp samples were all taken from the last abdominal section rather than a more variable region like the gut, the expectation is that the results would remain consistent.
1.0
ACKNOWLEDGEMENTS This project was supported by funding from the USDA NIFA, NRSP8 Aquaculture Genome Coordination program and from the College of Agriculture and Life Sciences, the State of Iowa and Hatch funds. The advice and assistance provided by Dr. James Dickson is appreciated.
REFERENCES
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omics-technologies to study the microbiota in the gastrointestinal tract of farm animals. Comput. Struct. Biotechnol. J. 13:55-63. Du, Z., D. C. Ciobanu, S. K. Onteru, D. Gorbach, A. J. Mileham, G. Jaramillo, and M. F. Rothschild. 2009.
Patnaik, F. L. Castille, and A. L. Lawrence. 2007. Dietary supplementation of short-chain fructooligosaccharides influences gastrointestinal microbiota composition and immunity characteristics of Pacific White Shrimp, Litopenaeus vannamei, cultured
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in a recirculating system. J. Nutr. 137:2763-2768. Rosenberry, B. 2004. About shrimp farming. Shrimpnews.com. http://www.shrimpnews.com/ FreeReportsFolder/GeneralInformationFolder/ About.html Accessed May, 2015. Tzuc, J. T., D. R. Escalante, R. R. Herrera, G.G. Cortés, and M. L. A. Ortiz. 2014. Microbiota from Litopenaeus vannamei: digestive tract microbial community of Pacific white shrimp (Litopenaeus vannamei). SpringerPlus. 3:280. Yu, Y., J. Wei, X. Zhang, J. Liu, C. Liu, F. Li, and J. Xiang. 2014. SNP discovery in the transcriptome of White Pacific Shrimp Litopenaeus vannamei by next generation sequencing. PloS one. 9:e87218. Yuan, J., X. Zhang, C. Liu, J. Wei, F. Li, and J. Xiang. 2013. Horizontally transferred genes in the genome of Pacific white shrimp, Litopenaeus vannamei. BMC Evol. Biol. 13:165. Zhang, M., Y. Sun, K. Chen, N. Yu, Z. Zhou, and L. Chen. 2014. Characterization of the intestinal microbiota in Pacific white shrimp, Litopenaeus vannamei, fed diets with different lipid sources. Aquaculture. 434:449–455. Zhao, C., X. Zhang, C. Liu, P. Huan, F. Li, J. Xiang, and C. Huang. 2012. BAC end sequencing of Pacific White Shrimp Litopenaeus vannamei: a glimpse into the genome of Penaeid shrimp. Chin. J. Oceanol. Limnol. 30:456-470.
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www.afabjournal.com Copyright © 2015 Agriculture, Food and Analytical Bacteriology
Tracking Enterococcus faecium antibiotic resistance, dissemination and risk assessment modeling A. Limayem1 1
Department of Cell Biology, Microbiology and Molecular Biology, University of South Florida, 4202 East Fowler Avenue, Tampa, FL 33620, USA
ABSTRACT While fecal bacteria including Enterococcus faecium strains are part of the normal flora found in human and animal guts, the latter became a serious issue in hospitals and animal food through their high proficiency to simultaneously acquire multi-antibiotic resistance. Vat (E) and vat (D) are among the most common genes encoding to streptogramin A in E. faecium identified frequently in farm animals, particularly in poultry where virginiamycin, the homologous of quinupristin/dalfopristin (QD), human therapeutics was added to promote animal growth in agricultural systems. Consequently, transfer of drug resistance genes from food animals’ broad continuum to humans and surrounding environment is a matter of great concern that urges effective intervention strategies to trace contaminants from the source and mitigate the risk. This review summarizes the most current knowledge on antibiotic resistance E. faecium profile, dissemination, and risk level. Future directions and mitigation strategies through system approaches namely, risk assessment modeling are thoroughly reviewed. Keywords: Multidrug resistant Enterococcus faecium, gastrointestinal tract, antibiotic resistance, VRE, poultry, food, nosocomial, hospitals, risk assessment, exposure modeling, dissemination
Agric. Food Anal. Bacteriol. 5: 111-116, 2015
INTRODUCTION Enterococci have always been a part of the gastrointestinal tract (Arias and Murray, 2012; CDC, 2013). While some strains of E. faecium are classified as a probiotics and beneficial for human and animal Correspondence: Alya Limayem, alimayem@usf.edu Tel: +1 -813-974-7404
health, the rising of the resistant strains remains of prime concerns. The nosocomial multidrug resistant E. faecium (MEF) strains are growing at a rapid pace and constitute serious threat to the surrounding environment (Arias and Murray, 2012; CDC, 2013). Recent studies have reported that the erm (B) genes in enterococci have already been found widely disseminated in the environment (De Leener et al., 2005; Hayes et al., 2005; Werner et al., 2000). Such serious
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evidence correlated with 60% virginiamycin resistance in ground turkey and beef has urged the FDA to prioritize the dissemination issue and explore effective control methods to protect food and public health from antibiotic resistance spread. A recent study on the systemic approach for fecal indicators and surrogates that lives in conjunction with E. faecium in human and animal gut revealed a 50% chance of infection associated with 20% of the human populations exposed to drug resistance from contaminated recreational waters (Limayem and Martin, 2014). Postulating on these statistics, the level of infected populations could climb by ten fold in situations where an agricultural field is exposed to inaccurate use of antibiotics. The main focus of this review is to thoroughly explore the quantitative risk assessment for potentially Multi-Drug Resistant Enterococcus faecium in the food chain and surrounding environment and provide a mitigation strategy for antimicrobial resistance control.
1) (Haas et al., 1999; Jaykus, 1996; Lammerding and Paoli, 1997; McKone, 1996; Vose, 1996). This approach involves a qualitative exposure estimation that aims to elucidate major steps for determining the risk. It thus provides ways to optimize operations and mitigate the risk to a more tolerant level for the ecosystem and human population (Limayem and Martin, 2014).
A HYPOTHETICAL RISK ASSESSMENT MEF exposure assessment model from producers-to-consumers
Risk analysis has received considerable attention for its value in providing transparent data and a comprehensive model. Microbial risk assessment (MRA) is an emerging systematic tool derived from the risk analysis concept (NRC, 1983; U.S. EPA, 1986; Haas, 1983). It provides a quantitative estimation of the probability and severity of infections that could result from exposure of susceptible human populations to, for example, pathogens (Haas, 1983; Miller, 1998; FAO/WHO, 2003) . Several MRA frameworks have been modified and improved to fit the continued economic evolution over the years. Therefore, adding risk management and communication to the MRA structure (NRC, 1983; Haas, 1983; U.S.EPA,
While the hazard identification has been described by CDC (2013) and reviewed by some publications (Arias and Murray, 2012; Limayem, 2015), there is a need to illustrate the potential exposure assessment. A Schematic diagram illustrating the systemic model from production-to-consumption dynamic flow and transmission route of MEF in the food chain and surrounding environment including hospitals is shown in Figure 1. The major steps that are stated for cooked products are primarily the harvesting and the transport correlated to temperature and bacterial growth. Farm animals are exposed to antibiotic use for therapeutic and growth enhancing purposes. Likewise, during food processing, bacterial contaminants are subjected to antibiotic and chemical treatment to control their load during slaughter. Food processing is followed by preparation and cooking, also dependent on temperature. The cooking temperature can be definitive of the prevalence and level of contamination consumed. It is quite possible that bacterial load undergo either growth or inactivation during the whole process in addition to cross-contamination prior to cooking. Under-cooked products would enable the bacterial hazard to survive in the human gut
1986; CAC, 1999). The basic risk assessment components remain the same, including primarily, hazard identification, hazard characterization, along with dose-response assessment and exposure assessment, followed by the risk characterization (Figure
and form a niche of a resistant reservoir detrimental to the health of the immunocompromised population subjected to a long hospital stay. In the hospital, the patient’s density of vancomycin resistant enterococci (VRE) can be exacerbated by the consump-
THE UPSURGE OF MICROBIAL RISK ASSESSMENT TREND
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Figure 1. Systemic representation of the MEF exposure assessment model from production-toconsumption and surrounding environment
tion of several antimicrobials forming a niche in the gut, which facilitate the dissemination of VRE via fecal route. VRE would thus contaminate the medical equipment and inanimate objects including primarily the indwelling catheter designed for patient with urinary tract infection. However, the main vectors of VRE transfer in clinical settings are the hands of staff along with visitors and infected patients (Arias and Murray, 2012). A dynamic cycle, including the hazard from farm animals to food chain and the surrounding environment, is determined to elucidate the hazard
based on the available data. Each scenario involves a specific change and potential scenarios are presented in Table 1. An efficient exposure assessment model estimates all steps from farm-to-consumers that determine the risk at various points of the food chain. The exposure assessment will first state the unit and the size of the sample that is of concern. It will place the emphasis on the route of contamination (e.g., inhalation or ingestion) associated with the uncertain variable that concerns the bacterial level of contamination and prevalence. During these
route throughout the entire system. A baseline model would be explored thoroughly to determine the relations between the different uncertain variables. Nine alternative scenarios would be explored and other scenarios can be added
steps microbial contaminants might undergo either growth or inactivation (i.e. cell death) (Jaykus, 1996; ICMSF, 1998). The correlation of all these factors is estimated via the probabilistic model, as it includes all available data. It considers population uncertainty
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Table 1. Potential Scenarios
Mitigation Scenarios
•
Inadequate freezing temperature
•
Cross-contamination at the factory (e.g., defeathering would increase crosscontamination)
•
Herd size/prevalence
•
Antibiotic addition during Slaughter
•
Cross-contamination at home
•
Chemical treatment during Slaughter
•
Undercooking
•
Cooking
•
Niche formation in the gut
•
Antibiotic use in hospital-selective pressure (cycle formation)
and population diversity involving probability distribution modeling from which a set of random inputs are selected and evaluated several times via MontCarlo Simulations Method (MCM) (Vose, 1998). MCM could be performed via emergent software, such as Crystal Ball® and @ Risk (Oracle Crystal, Fusion Edition, 2011; Palisade Corporation).
Monte-Carlo simulation: Computational technique The computational technique of MRA requires the establishment of a deterministic model. Once the stochastic model implemented, the Monte Carlo Method (MCM) simulation is carried out in an effort to analyze the inputs called the uncertain variables. The computational software (i.e., Crystal Ball ® tool) is used to select a set of random data from numerous probability distributions (Hass et al., 1999). It thus enables to evaluate the deterministic models and elucidate the uncertain variables through a wide number of iterations (i.e., 103 trials). The probability density function f(x) equation is expressed as follows:
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Alternate assumption scenarios
(Ribeiro, 2004) The horizontal and the vertical axes illustrate the uncertain variable and the probability of uncertain values, respectively.
POTENTIAL PITFALLS-LIMITATION PROPOSED PROCEDURES
TO
The exploratory software tools, @Risk and Crystal Ball, would yield a comprehensive model including primarily the infective dose. However, there are substantial gaps to fulfill in an effort to reach a complete risk estimate. The determination of the biological activity of antimicrobials in cooked food would provide a greater addition to the risk estimation. Hence, a chemical risk assessment is recommended to evaluate the minimal drug dose causing resistance in vivo (Limayem and Martin, 2014). Aside from the antibiotic dose, the biological epidemiology is lacking methods to estimate the extent of drug resistance
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transfer. From the microbiological standpoint, there has been an extensive range of data associated with pathogens and their prevalence in different food matrices and water sources. Although an extensive range of dose-response models have been provided for pathogens, there is a limited consideration to the raising pathogens. Some strains from human and animal gastrointestinal tracts namely, E. faecium are emerging as drug resistant pathogens and are being the leading nosocomial pathogens. The resistant E. faecium are primarily noxious to the immunocompromised human population. As such, there is a need to establish a new dose-response model that estimate the sensitive community including the
CDC (Centers for Disease Control and Prevention), 2013. Antibiotic Resistance Threats in the United States, 2013. De Leener, E., A. Martel, E.M. De Graef, J. Top, P. Butaye, F. Haesebrouck, R. Willems, A. Decostere. 2005. Molecular analysis of human, porcine, and poultry Enterococcus faecium isolates and their erm(B) genes. Appl. Environ. Microbiol. 71:27662770. FAO/WHO. Codex Principles for Risk Analysis of Food Derivedfrom Modern Biotechnology. Codes Alimentarius Commission. 2003. Haas, C.N. 1983. Estimation of risk due to low doses of microorganisms: a comparison of alternative
young and the immune-deficient population.
Arias, C.A., and B.E. Murray. 2012. The rise of the Enterococcus: beyond vancomycin resistance. Nat. Rev. Microbiol. 10:266-278.
methodologies. Am. J. Epidemiol. 118:573-582. Haas, C.N., J.B. Rose, C.P. Gerba. 1999. Quantitative microbial assessment. John Wiley & Sons, New York. Hayes, J.R., D.D. Wagner, L.L. English, L.E. Carr, S.W. Joseph. 2005. Distribution of streptogramin resistance determinants among Enterococcus faecium from a poultryproduction environment of the USA. J. Antimicrob. Chemother. 55:123-126. ICMSF. 1998. Potential application of risk assessment techniques to microbiological issues related to international trade in food and food products. J. Food. Prot. 61:1075-1086. Jaykus, L. 1996. The application of quantitative risk assessment to microbial food safety risks. CRC Crit.Rev. Microbiol. 22:279–293. Lammerding, A.N., and G.M. Paoli. 1997. Quantitative risk assessment: An emerging tool for emerging foodborne pathogens. Emerg. Inf. Diseases 3:383-487. Limayem, A. 2015. The mutating gastrointestinal flora, multidrug resistant Enterococcus faecium, Agric. Food Anal. Bacteriol. 2:56-64. Limayem, A., and E.M. Martin. 2014. Quantitative risk analysis for potentially resistant E. coli in surface waters caused by antibiotic use in agricultural sys-
CAC. 1999. Codex principles and guidelines for the conduct of microbiological risk assessment. Document CAC/GL-30 (1999). http://www.fao.org/docrep/005/y1579e/y1579e05.htm Accessed January, 2015.
tems. J. Environ. Sci. Health B 49:124-133. McKone, T.E. 1996. Overview of the risk analysis approach and terminology: the merging of science, judgement and values. Food Control 7:69-76. Miller, H.I. 1998. Biotechnology and risk. Thomas,
CONCLUSIONS While a substantial studies on nosocomial E. faecium strains have been conducted (Arias and Murray, 2012; Pesavento, 2014; Ryan, 2004; Limayem, 2015), there is an unmet need to track MEF pathway and distribution from the source to animals and humans broad continuum. As such, a complete characterization of the MEF strain revealing rapid detection and quantification within a comprehensive risk model will enable the development of effective mitigation strategies for the emerging drug resistance in food and hospital settings. It thus, offers a clear insight to managers to track the contamination pathways and set preventive actions to ensure food safety and public health.
REFERENCES
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J.A. 9th ed. Biotechnology and safety assessment. Braun-brumfield inc; p. 219-240. NRC. 1983. Risk Assessment in the Federal Government: Managing the Process, National Research Council. National Academy of Sciences Press, Washington D.C. Pesavento, G., C. Calonico, B. Ducci, A. Magnanini, A. Lo Nostro. 2014. Prevalence and antibiotic resistance of Enterococcus spp. isolated from retail cheese, ready-to-eat salads, ham, and raw meat. Food Microbiol. 41:1-7. Ribeiro MI. Gaussian probability density functions: Properties and error characterization. Institute for Systems and Robotics, Lisboa, Portugal. 2004. Ryan, K.J. 2004. Streprococci and Enterococci, p 294-296. In Ryan KJ, Ray CG (Ed), Sherris Medical Microbiology: An Introduction to Infectious Disease, 4th ed. McGraw-Hill, New York. U.S.EPA. Guidelines for health risk assessment of chemical mixtures. 51 FR 34014.1986. Vose, D. 1996. Quantitative Risk Analysis: A guide to Monte Carlo Simulation Modelling. John Wiley & Sons, Chichester. Vose, D.J. 1998. The application of quantitative risk assessment to microbial food safety. J. Food Prot. 61:640–648. Werner, G., I. Klare, H. Heier, K.H. Hinz, G. Bohme, M. Wendt, W. Witte. 2000. Quinupristin/dalfopristinresistant enterococci of the satA (vatD) and satG (vatE) genotypes from different ecological origins in Germany. Microb. Drug Resist. 6:37-47.
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www.afabjournal.com Copyright © 2015 Agriculture, Food and Analytical Bacteriology
Risk Assessment on the Use of Genetically Modified Organisms (GMOs) for Biofuel Production A. Limayem1
1
Department of Cell Biology, Microbiology and Molecular Biology, University of South Florida, 4202 East Fowler Avenue, Tampa, FL 33620, USA
ABSTRACT In recent years concerns about energy security and climate change have sparked government interest in biofuels from crops. However, water and land availability for biofuel production could become major obstacles, if effective conservation practices are not implemented. In an effort to increase crop productivity with minimal use of natural resources, genetic manipulations of corn plants are conducted in the U.S. and worldwide to convey pest and herb resistance. Moreover, genetically engineered microorganisms have been developed to render biomass conversion to fuels cost competitive. This review summarizes the evolution of biotechnology in agricultural systems and its most current use in biofuel production. This includes the review of the recent genetically engineered microorganisms (GMOs) as well as the nanotechnology used to biofuel yield optimization. Potential bottlenecks pertaining to GMOs dispersal from biofuel production are thoroughly addressed. Novel point-of-care approaches exclusively adopted by the federal agencies and arising from systemic core modeling such as biotechnology risk assessment are discussed. Optimizing these tools by revealing a proficient model engineering practices toward achieving greater GMO traceability, biosafety and operational performance remains the option of choice to intervention. Keywords: Biotechnology, Biofuel, Biomass, Genetically modified organisms (GMOs), Risk assessment modeling Agric. Food Anal. Bacteriol. 5: 117-134, 2015
INTRODUCTION With rising concerns about energy security, climate change, and sustainable development, agriculture-based biofuels have gained considerable atCorrespondence: Alya Limayem, alimayem@usf.edu Tel: +1 -813-974-7404
tention from governments, investors, and scientists in the U.S. and worldwide (Youngquist, 1999). The passage in the U.S. of the Energy Policy Act of 2005 and the Energy Independence and Security Act of 2007 (Bothast and Schlicher, 2005; Brookes 2009) has spurred growth in biofuel production from 1.6 billion gallons per year (6 billion liters) in 2000 to 13.3 billion gallons (50 billion liters) in 2014 (RFA, 2014).
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The continuous expansion of the biofuels industry at such a fast pace has prompted substantial growth in modern biotechnology (James, 2011; Sanchez and Cardona, 2008). To date, agro-biotechnology involves mainly the use of genetically modified organisms (GMOs), including both transgenic crops and genetically transformed microorganisms (Holmes, 2010). GMO manipulations offer economic advantages to investors by maximizing product yield from limited land and water use (Ramasamy et al., 2007). In the last decade, the U.S. has become the world leader in the cultivation of genetically modified (Ladisch et al., 2010) crops, involving primarily corn and soybeans. In 2010, Brazil achieved the world’s sec-
provide a qualitative and quantitative risk estimate to biotechnology investors and policy makers. These approaches assess the probability of occurrence and the severity of effects from exposure to GM living organisms. While a temperature of 500°F would be sufficient to inactivate the DNA from biofuel downstream operations (Gryson, 2010; Krohn et al., 2011), there is an imperative need to generate a comprehensive insight of all the operation steps that determine the risk. The biotechnology risk assessment is a comprehensive approach that should emerge as the method of choice to provide greater predictability of GMO dispersal during bioprocessing operations (Flory et al., 2012).
ond largest increase in soybean cultivation, reaching a total area of approximately 23 million hectares (Cerdeira et al., 2010). In contrast, in Europe mandatory labeling and public concerns have so far substantially limited GM investments (Carter and Gruère, 2003). Currently, China is emerging in biotechnology, particularly in the cultivation of GM rice and cotton, followed by India in development of GM fiber, primarily cotton (Huang et al. 2002). Aside from transgenic plants, biofuel production from corn and lignocellulosic feedstocks also involves the use of GM fermentative microorganisms in various parts of the technologies (Phillips, 2008). However, as investment in agricultural biotechnology and in the biofuels industry expands, concerns about the risks of adverse health effects from GMOs have also increased. Within the last decade there have been a number of different groups calling for protection of the ecosystem and of biodiversity from GMO effects. Researchers indicate that adverse outcomes of GMOs could present a real risk to the environment from irreversible and unforeseen dissemination (Ho et al., 1999). Although clear evidence of adverse effects from GMO applications is currently lacking, the adoption of a scientifically sound method, such as risk assessment, would help develop
This review encompasses the advancement of agricultural biotechnology in the U.S. and worldwide. It provides a summary on the most current GMOs used in biofuel production in the U.S. This review also examines potential obstacles related to GMOs dissemination. Future directions describing systemic core modeling such as Biotechnology Risk Assessment approach are suggested to maintain biosafety and bioprocessing operational performance.
biosafety measures through a comprehensive model that could subside public concern and ensure environmental safety (Brookes, 2009). Systemic methods, including Biotechnology Risk Assessment, are gaining attention as potential statistical approaches that
isms. From this perspective, the molecular explanation of life arose at the Rockefeller Institute of New York in the late 1930s as a novel discipline named “molecular biology” by Warren Weaver (Sarkar, 1991). Between 1926 and 1960 there were consid-
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HISTORICAL AND EMERGING TRENDS Background Although the public has become aware of genetic alterations rather recently, modifying genomes of plants via breeding methods has been carried out over a long period of time (Phillips, 2008). At the end of the nineteenth century physiological genetics started to emerge over the classical theory of chromosome heredity, along with the segregation and inheritance law of Mendel (Burian and Gayon, 1999). At that time, several disciplines, such as physics, biology and virology, started to interact in an effort to achieve a scientific understanding of living organ-
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erable advances in physiological genetics that were devoted to explaining the gene and protein relationship from the Drosophila fly and Neurospora fungus (Beadle and Tatum, 1941) up to the new model organism, a bacteriophage (T1 through T7), described by Delbrück and his group at the beginning of 1940 (Delbrück, 1949; Machine, 1984). More modern genetic engineering approaches that involve precise manipulation of genomic vectors (plasmid constructs and recombinant DNA) in bacteria and mice began in 1972 and 1974, respectively ( Arnold, 2009; Cohen et al., 1972; Jaenisch and Mintz, 1974). In the early 1980s the development of insulin-producing microorganisms (Crea et al., 1978; Whitman
along with the employed major GMO techniques and gene sources used, are summarized in Table 1. Currently, both herbicide tolerance and insect resistance are among the most prevalent GMO traits that are used in agronomical biotechnology (Cerdeira et al. 2010; Edmeades, 2013; James, 2011). In 2006 insect- and herbicide-resistant GM plant cultivations, mainly soybean and corn, reached 101 million hectares across 22 countries (James, 2011). The most common herbicide tolerance is achieved by the insertion of glyphosate and glufosinate resistance genes, such as the 5-enolpyruvylshikimate-3-phosphate synthetase (EPSPS) gene, into the target plant (Powles and Yu, 2010). On the other hand, insect re-
et al., 1996) was commercialized in the medical field. Then, the rapid evolution of agricultural biotechnology by the end of the twentieth century gave rise to genetically modified crops first for food and later for biofuel production (James, 2011). Agricultural biotechnology approaches have been widely used to ensure biological and economic benefits from the extensive cultivation of GM crops (Brookes, 2007). Increasing yields associated with less land erosion and water use are among the most desirable benefits of transgenic plants (Dunn et al., 2013; Mumm et al., 2014; Ramasamy et al., 2007). Genetically engineered corn has been grown in the U.S. since 1997 (James, 2011). It has been reported that 36% of corn planted around the world is genetically modified with 86% of it been planted in the U.S. (Boryan et al., 2011; Edmeades, 2013).
sistance is conferred by genes that originate from the well-known Bacillus thuringiensis strain, whose toxic crystal protein causes host cell death (Shen et al., 2013). As a result, plants transformed by B. thuringiensis genes for insect resistance are called Bt crops (Entwistle et al., 1993). The Bt gene can be incorporated into the plant cell via various transformation techniques.
ADVANCEMENTS IN AGRO-BIOTECHNOLOGY
Currently, there are almost 150 million hectares of GM crops planted in 25 countries around the world. The U.S. alone grows almost 50% of the world’s transgenic plants (soybean and corn) with approximately 67 million hectares, followed by Brazil and Argentina that account for approximately 25.5 and 23
Although research and development in gene transfer technology has led to enhancements in cell transformation, genetic trait isolation (Feltus and Vandenbrink, 2012) has also achieved considerable progress, leading to greater insect resistance and herbicide-tolerance in biofuel and food crop. The Bt protein is variably pathogenic, meaning that it impacts specific species via a specific toxin receptor interaction, but not others lacking the receptor (Shen et al., 2013). During the last decade, B. thuringiensis strains including the Cry1A and Cry1B delta toxins were known for their effects on specific type of strains such as, Lepidoptera and Diptera insects
million hectares, respectively (Cerdeira et al., 2010). Transgenic crops have reportedly helped U.S. farmers increase their product yield by 30% over the last decade (Erickson and Winters, 2012). The most prevalent transgenic plants in the world,
(Carpenter et al., 2002). Currently, the pilot studies have demonstrated that genetically modified E. coli vectors are engineered so as to contain a wide range of Bt toxins in the same strain, thus optimizing Bt utility to convey greater stability, delivery, and ver-
Current trends
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Table 1. Most prevalent transgenic plants and GMO techniques used for biofuel production. (Brookes, 2007; Phillips 2008; Piekarowicz, 1978; Schell et al., 2007; Uchtmann and Nelson, 2000) Plants
Corn
Major components of the delivery system Desired traits and sources
Transciption/translation genes and sources
• Pest resistance: Cry genes
• Promoter from rice
from Bacillus thuringiensis (i.e., Cry 1A.105, Cry 2Ab2, Cry 1F for aerial pests and Cry 3Bb1, Cry 34Ab1 and Cry 35 Ab1 for subsoil pest resistance)
• Terminator from Agrobacterium tumefaciens
• CTP peptide (EPSPS transporter) from
Selective markers • Antibiotic
resistance marker (ARM), beta-lactamase (bla)
corn itself and sunflower
• Resistance to herbicides:
EPSPS genes isolated from A. tumefaciens CP4 (resistance to glyphosate)
• Promoter from cauliflower mosaic virus,
Soy
CamV 35S
beans
• Terminator from Arabidopsis plant
ARM, neomycin phosphotranspherase II
• CTP peptide (EPSPS transporter) from • Resistance to herbicides: Sugar beet
EPSPS genes isolated from A. tumefaciens CP4 for herbicide resistance
Canola • Herbicide resistance: EPSPS from A. tumefaciens CP4
• Increased content of laurate:
petunia plant
• Promoter from cauliflower mosaic virus,
Marker genes NPTII (neomycin/kanamycin • 3’nos terminator from A. tumefaciens phosphotrans• Tn 5 terminator from bar from Streptomy- ferase) from ces hygroscopicus along with 3’ocs and microbial trans3’g7 controlled by bidirectional TR1/2 poson promoter from A. tumefaciens CamV 35S
• Promoter from figwort mosaic virus • Terminator from pea
ARM, streptomyin
GOX from Ochrobactrum anthorpi strain LBAA; ACP thiosterase genes from California tree
Sugar cane
• Resistance to some insecticides: Cry genes from B. thuringiensis
• Increased sugar content in the
• Promoter from cauliflower mosaic virus, CamV 35S
• Terminator, nopaline synthase (nos) from A. tumafaciens
Selective markers, kanamycin (Kan) or hygromycin (Hyg)
plant: Gene BetA from Escherichia coli (EcBetA) or Rhizobium meliloti (RmBetaA)
Rice
• Drought tolerant • Resistance to insects: CryIA (b) and CryIA (c) from B. thuringiens
• Promoter from cauliflower mosaic virus, CamV 35S
• Nopaline synthase promoter (Pnos) • NT, 3’ terminator
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ARM, neomycin phosphotranspherase II (NPTII, hygromycin (Hyg)
Figure 1. Generalized method for creating transgenic plants using the A. tumefaciens delivery system: (1) Sources of genes; (2) Delivery system (e.g. E. coli plasmid); (3) Mediated transformation of plant with A. tumefaciens.
(1) Trait, promoter, and terminator genes from soil or other microorganisms and plants
(2)
Desired traits 1
Promoter 5
Sel. Marker 6 TT 7
(3) Agrobacterium tumefaciens
plasmid OriT 3 Oriv 4
Transgenic plants Amp 2 1
Desired “traits”: (e.g. EPSPS gene isolated from A. tumefaciens; Cry1Ab gene from Bacillus thuringiensis)
2
Amp: Ampicillin marker gene for selecting transformation in A. tumefaciens
3
OriT: Transfer origin for conjugal transfer of the plasmid to recipient cell
4
OriV: Origin of replication
5
Promoter: Promoter gene (e.g. gene from cauliflower mosaic virus, such as CamV 35S, used for soybean vectors)
6
Sel. Marker: Selection marker (e.g kanamycin resistance or bla for beta lactamase or NPTII for neomycin/ kanamycin phosphotransferase) for selecting transformation in plants
7
TT: Termination of transcription (e.g. nopaline synthase, nos, from Arabidopsis plant)
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satility to cope with different hosts and to control a wide range of insects (Feltus and Vandenbrink, 2012; Shen et al., 2013). Biolistic injection of constructs containing Bt, as well as the EPSPS gene, is now the most common technique to protect plants from insects and herbicides (FAO, 2009). In addition to its herbicide tolerance ability, the EPSPS gene is also used as a selection marker in the plant, thus conferring both resistance and selection to the target plant. It should be noted that along with insect- and herbicide-resistance traits that are used to increase plant yield performance, there is an increasing interest in enhancing the nutritional value of GM plants, as well as in developing cold- and drought-tolerance
sect resistance Bt genes (e.g. Cry1A), are most commonly isolated from soil microorganisms (Agrobacterium tumefaciens and B. thuringiensis) or plants. E. coli plasmids constitute an ideal platform and cloning vector for gene transcription and translation to proteins, with most such genes originating from plants and microorganisms (Abbott, et al., 1998). They include promoters that enable target gene transcription (e.g. cauliflower mosaic virus (CamV 35S) and nopaline synthase (nos) from Arabidopsis plant) and provide termination signals (James, 2011; Nida et al., 1996). In addition, a selective gene marker is often incorporated to aid in the detection or tracking of the DNA delivery package within the
crop variants via genetic modification. Perhaps the best-known example thus far is the drought-resistant gene BetA from E. coli (EcBetA) and Rhizobium meliloti (RmBetA), which have been demonstrated to be beneficial to plants without adverse effects (Kempken and Jung, 2009).
There are a substantial number of transformation techniques that have been used till date, ranging from the indirect delivery-system-based Agrobacterium method to the direct transfer Agrobacterium microprojectile bombardment (biolistic) method ( FAO, 2009; Klein et al., 1987; Koziel et al., 1993). Typically, the delivery-system-based Agrobacterium method has been widely used to form a transgenic plant. This technique, also called binary-vector Agrobacterium, is summarized in Figure 1. It requires first the isolation of the gene of interest for its “desired traits” before its insertion into a delivery vector (plasmid) to form recombinant DNA (rDNA) (Berg and Mertz, 2010; Kiermer, 2007). The most common transfer and cloning vectors used in medical and agricultural biotechnology are bacterial plasmids
transformed cells. This way only the transformed cells carrying the selective marker will be regenerated and transferred to the mediated transformation strain. The causative agent of crown gall disease A. tumefaciens has been used for its ability to infect plants and transfer genes into a callus (embryonic plant tissue) via insertion of its tumor-inducing Ti plasmid (Gelvin, 2003; Nester, 2014). Initially, the desired gene is transferred into the Ti plasmid through DNA recombination. This mechanism is enabled by the cleavage of the plasmid at specific sites for gene insertion, which is carried out with the use of endonuclease restriction enzymes (Piekarowicz et al., 1978). The same enzymes are also used to cleave the host cell DNA before ligation via a DNA-joining enzyme, ligase (Zimmerman et al., 1967). Finally, the transformed Ti plasmid (with its tumor-inducing mechanism deactivated) is injected into plant embryos (callus). Successful transfer of the desired trait gene to the plant chromosome is detected via the selective marker that was originally incorporated in the plasmid vector. Thus, transgenic plant regeneration is ensured by successfully injecting trait genes into the plant chromosome, which subsequently propagates as the plant grows. Molecular techniques including Polymerase Chain Reaction
from Escherichia coli, selected primarily for their ability to generate numerous copies of the desired gene (James, 2011). The desired trait genes, such as the herbicide tolerance gene EPSPS (Marketed as Roundup Ready®) (Powles and Yu, 2010) or the in-
(PCR), Southern hybridization, and DNA sequencing confirm gene transfer and its inheritance by the target plant (Lupien, 2000). The PCR technique traces the inserted gene in the plant through DNA amplification, confirming gene inheritance by the plant.
Gene transfer methods and mechanisms in crops
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Figure 2. Ethanol and major co-product manufacturing from GM corn using the wet-milling and dry-milling processes. Existing and potential GMO flows are depicted with black arrows. Potential gene dissemination through co-products is marked with an asterisk (*).
Desired trait genes
E. coli plasmids
Agrobacterium
GM corn Wet-milling Process
Dry-milling Process
Cleaning Steeping (Degermination)
*
* *
Enzymes
Milling Cyclone Separation
Corn Oil
*
Cleaning Grinding Cooking
Liquefaction/Saccharification
Yeast Corn Gluten Feed
Grinding Starch/Gluten Separation
Corn Gluten Meal
Centrifugation
Dextrin
Washing/Filtering Acid/Cooking of Starch
Fermentation
CO2
Distillation/Dehydration
Ethanol
Enzymes Centrifugation
Liquefaction/Saccharification
Yeast Fermentation
CO2
Distillation/Dehydration
Ethanol
Residue
*
Drying
Evaporation
Distillers Dry Grains
Distillers Solubles
Distillers Dry Grains with Solubles (DDGS) (animal feed)
*
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While the binary-vector Agrobacterium technique requires a vector (plasmid) to transfer the desired trait, direct gene transfer methods do not require a delivery system to enable gene transfer to the recipient organism. Currently, there are several direct gene transfer methods that include primarily Agrobacterium microprojectile bombardment in addition to chemical mediation and electroporation, as well as microinjection (Kempken and Jung, 2009; Klein et al., 1987). However, Agrobacterium-mediated recombination is often preferred over direct methods because it causes less damage to the plant tissue (Koziel et al., 1993). Direct techniques have been extensively described by the Food and Agriculture
cesses and the generated co-products are illustrated in Figure 2. Wet milling separates corn into several components, such as oil, gluten meal, starch, and fiber, producing livestock co-products and corn oil in addition to ethanol from starch, but also increasing the chances of GMO release into the environment. The more popular dry milling process requires less capital per gallon of ethanol produced and involves fewer steps (Bothast and Schlicher, 2005). The solid residue from the ethanol distillation column is centrifuged and dried to form distiller’s dry grain with solubles (DDGS), a major source of animal feed that is vulnerable to GMO concerns. Although the corn ethanol industry has expe-
Organization (FAO, 2009). Aside from modification of crops for higher plant productivity and resilience, considerable research has also been directed towards engineering enzymeproducing and fermentative microorganisms for wider sugar utilization and increased biofuel yield.
rienced significant growth in the last 15 years, increasing societal and economic concerns regarding food-based biofuels have shifted researchers’ focus towards non-food biomass sources, such as cellulosic materials. Such biofuels, termed advanced or second-generation, could help significantly the U.S. reach its Renewable Fuel Standard (RFS) annual target of 36 billion gallons of biofuels by 2022 (Corredor et al., 2007).
CROP-BASED BIOFUEL PRODUCTION IN THE U.S.
Biomass-derived biofuels Corn-based biofuels Biofuels have gained significant attention in the last decade thanks to benefits they bring to energy security, lower carbon emissions, and cleaner air (Sticklen, 2008). Among several countries around the world that have invested in the biofuel sector, the U.S. is the leader in ethanol production with approximately 70% of the total world production, followed by Brazil (RFA, 2015). In the U.S. corn is the primary feedstock for ethanol production, which reached 13.3 billion gallons (50 billion liters) in 2014 (RFA, 2015). Among the 29 ethanol-producing States, the top ones are located in the U.S. Midwest “corn belt” (James, 2011).
Lignocellulosic feedstocks are the most prevalent biofuel resources worldwide in the form of agricultural residues, forestry residues, energy crops (e.g. Miscanthus and switchgrass), and municipal solid waste (MSW) (Pettersen, 1984). The U.S. is believed to generate over one billion tons of cellulosic biomass annually that, if converted to ethanol, could replace 30% of petroleum-derived gasoline by 2030 (Perlack et al., 2005). Another study estimated that the same ethanol target could be reached if energy crops were cultivated on available federal land, in addition to agricultural and forestry residues, to reduce feedstock cost and make ethanol cost competitive (Khanna et al., 2011). Feedstocks for biofuel
According to the Renewable Fuels Association (RFA) there are 204 operational corn-ethanol plants in the U.S. using the wet milling (33%) or the dry milling (67%) technology (RFA, 2014). The major steps involved in these two corn ethanol manufacturing pro-
production have already been extensively reviewed (Sticklen, 2008). The major steps involved in cellulosic ethanol production are illustrated in Figure 3. Pretreatment, which intended to render recalcitrant biomass more
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Figure 3. Ethanol and major co-product manufacturing from lignocellulosic biomass. Potential GMO inputs and outputs are depicted with black arrows, whereas potential gene dissemination through co-products is marked with an asterisk (*).
Lignocellulosic biomass
Pretreatment - Mechanical and/or - Thermo-chemical and/or - Biological
Potential GMO inputs Cellulolytic enzymes
Liquefaction/Saccharification CBP microorganisms CO2
Fermentation SSCF microorganisms
Ethanol
Lignin combustion
Distillation/Dehydration
Residue
* White-rot fungi (lignin degradation)
Power Generation
Biological Decomposition
Fungal proteins (animal feed)
*
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Figure 4. Hypothetical modeling of GMOs risk exposure in lignocellulosic-based biofuel (CBP: Consolidated Bioprocessing, SSCombF: Simultaneous Enzymatic Saccharification and Combined Fermentation).
GMOs use for CBP or SSCombF
Cellulosic feedstocks
Bioprocessing:
Distillation/ Dehydration (500°F)
GMOs Concentrations
GMOs Prevalence
-Pretreatment -Hydrolysis -Fermentation
Potential GMOs spread
Biofuel and Co-products
Risk Characterization
amenable to further processing, accounts for almost one third of the total operating cost (Mosier et al., 2005). The high level of non-conventional pentose sugars in cellulosic materials constitutes another challenge for ethanol productivity (Wyman et al., 2005). Furthermore, the use of high-cost enzymes in the hydrolysis step is of particular financial concern (Wyman et al., 2005; 2009). Residual lignin can serve
would be potential sources of GMOs that could affect the environment.
as a fuel for power generation (Ladisch et al., 2010) or can be converted by fungal microorganisms, such as the white rot fungus Basidiomycete, to fungal proteins for animal consumption (Zadrazel, 1976). The distillation residue and any animal feed byproducts
Fermentative microorganisms can be engineered with several important characteristics: wider sugar substrate range, elimination of toxicity by cellulose hydrolysates and fermentation products, and improvement of regulatory functions (Lee et al., 2008).
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CURRENT GENETICALLY ENGINEERED MICROORGANISMS AND NANOTECHNOLOGY FOR BIOFUEL
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• Inactivation of gfo (ZMO0689) gene by
Zymomonas mobilis
• -ncreasing in ethanol yield
• Deletion of lactate dehydrogenase gene
• Functional heterologous expression of an • The expression of a critical C. thermocellum cel-
• Isolation of endogenous GAP promoter
Thermoanaerobacterium saccharolyticum
Candida lignohabitans and GAP terminator
engineered full length CipA from Clostridium thermocellum
(ldh)
(Currie et al., 2013)
(Bellasio et aconitate decarboxylase resulted in stable and re- al., 2015) producible production of lactic acid and itaconic acid, respectively
• Expression of lactate dehydrogenase and cis-
lulosomal component in T. saccharolyticum as a step toward creating a thermophilic bacterium capable of consolidated bioprocessing
(Biswas et al., 2014)
2014)
Clostridium thermocellum
dehydrogenase are overexpressed, resulting in high ethanol production
• The enzymes pyruvate decarboxylase and alcohol (Yang et al.,
mobilis
• Insertion of pdc and adhB genes from Z.
Escherichia coli
(Yuan et al., 2013)
• Increasing ethanol production from Jerusalem artichoke tubers by CBP
(Sootsuwan et al., 2013, Wang et al., 2013)
• Overexpression of Inulinase Gene (INU)
under osmotic, heat and ethanol stresses
• Reduction of cell growth and ethanol production
formation of sorbitol as a by-product in sucrose medium
• Improves growth and ethanol production without
Kluyveromyces marxianus
homologous recombination (fusion-PCRbased construction technique)
• Inactivation of gfo (ZMO0689) gene by
site-specific FLP recombinase
• Increasing tolerance to inhibitors such as ethanol
rates and partial cofermentation in various ligno- al., 2013 cellulose hydrolysates with very high ethanol yield
• Use glucose and D-xylose with high consumption Demeke et
XylA, encoding D-xylose isomerase (XI), and enzymes of the pentose phosphate pathway was inserted
• The gene Clostridium phytofermentans
Saccharomyces cerevisiae
References
Expected Outcomes
Genetic Modification
Species
Table 2. The most promising GMOs for biofuel production in the U.S
Genetically engineered fermentative microorganisms, such as Saccharomyces cerevisiae (Almaida et al., 2008; Toon et al., 1997) and Zymomonas mobilis (He et al., 2014), along with Phanerochaete chrysosporium, Kluyveromyces marxianus and Clostridium cellulolyticum, have been developed over the last 20 years with a potential to advance the commercialization of advanced biofuels. The promising microorganisms for genetic manipulation in lignocellulosebased biofuel systems are extensively reviewed by Limayem and Ricke (Limayem and Ricke, 2012) and the expected outcomes are summarized in Table 2. Recent development in nanotechnology has been also made to advance the biofuel system productivity through gene transformation (Tzfira and Citovsky, 2006; Ziemienowicz, 2001; Ziemienowicz et al. 2012). A novel nano-complex method derived from Agrobacterium T-DNA has been developed by Pitzschke and Hirt (Pitzschke and Hirt, 2010) and optimized by Gelvin ( 2012) to add substantial value to agricultural biotechnology (Tzfira and Citovsky, 2006). The nanocomplex is composed of Agrobacterium T-DNA, single stranded DNA binding protein RecA and virulence protein VirD2. It is delivered to triticale microspores by the assistance of a Tat2 cell- penetrating peptide (CPP) (Chugh et al., 2009). This evidence will protect the integration of single transgene copy and inhibit the degradation of the target DNA (Ziemienowicz et al., 2012).
tions have been associated with new allergens, toxins, and antibiotic resistance. Transgenic crops could develop tolerance to abiotic hurdles and conditions (Mei et al., 2005). The case of the Bt Cry9C protein used in corn that was alleged to have caused allergic reactions has raised concerns among epidemiologists and regulators (EPA, 2003). However, the Centers for Diseases Control (CDC) suggest otherwise by concluding that the risk of allergic reactions to Cry9C is very low (EPA, 2003). Concerns also exist about altered genes resisting abiotic stressors that could be transferred to microorganisms via animal digestion and hence could transform bacteria into altered-gene vectors. In a biofuels system altered genes could be transmitted to organisms like Lactobacillus strains, which are common contaminants in ethanol fermentations. The main issue would be the dispersal of these strains in the surrounding environment (Curragh and Collins, 1992).
BIOTECHNOLOGY RISK ASSESSMENT MODELING
According to an extensive study conducted by the Council for Agricultural Science and Energy (Carpenter et al., 2002), GMOs could impact considerably the environment by reducing biodiversity via cross-
The Biotechnology Risk Assessment models have emerged as potential means for enhancing biosafety in agricultural systems (Wolt, 2009). This statistical approach is a leading scientific method that estimates the biological and physical risks of release to the environment of genetically engineered microorganisms, plants, and animals. It integrates the distribution of exposure to GMOs with data on probability of occurrence and the severity of the effect (dose response). Currently, the systemic approach is emerging as the most effective point-of-care method adopted by federal regulatory agencies attempting to create a set of practical management tools pertaining to GMO dispersal in the environment (Flory et al., 2012). It provides proper accounting of inputs and outputs and hence helps elucidate all potential
pollination or by destroying beneficial organisms, in addition to other unforeseen outcomes. The study placed emphasis on the potential adverse effects of GMOs on human health through the risk assessment approach. Potential hazards from GMO manipula-
process steps that carry the risk of exposure to irreversible potentially harmful gene mutations (Krimsky and Golding, 1992). It also provides a qualitative and quantitative risk estimation that can assist users in setting up preventive action to ensure ecosystem
POTENTIAL BOTTLENECKS GMOs dispersal
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biosafety. The risk analysis relies primarily on hazard identification, collection of data, and subsequent estimation of the likelihood of occurrence and severity of effects in all steps of the production process. This includes the exposure assessment modeling through the GMO dynamic flow (Flory et al. 2012; Krimsky and Golding, 1992; Schierow, 2008).
GMO dynamic flows in biofuel system Unlike biomass-derived ethanol that usually requires genetic modifications to enable microorganisms to efficiently co-ferment pentose sugars, corn-based biofuel production includes transgenic
(Ribeiro, 2004) Where the horizontal axe shows the interdependent variables and vertical axe conveys the probability of uncertain numbers. Thus, the algorithm analyzes uncertainty variables, such as size of GMO-containing stream (solid, liquid or gaseous) released to the environment and GMO concentration in the released stream. This holistic integration estimates the overall risk posed by
feedstocks but not necessarily GM microorganisms (Tomás-Pejó et al., 2009; Yanase et al., 2010). From this perspective, aside from GM corn by-products, it is quite possible that inadvertent GMO dissemination could also occur during various processing steps, such as starch hydrolysis and sugar fermentation (Kádár et al., 2004). Moreover, a number of modified microorganisms in lignocellulosic biofuels have been identified to render the promising CBP and SSCF processes cost-effective (Limayem and Ricke, 2012; Yamada et al., 2010,). Although a high temperature of 500°F in downstream operations would suppress GMOs residues, such GMO applications need to be traced from the source to the final product. A hypothetical modeling to GMOs risk exposure from feedstocks to biofuel production and end-products is depicted in Figure 4. Computationally, the assessment uses a probability density function in conjunction with Monte Carlo simulations that select randomly a set of data from numerous probability distributions. The certified software (i.e., @ Risk tool) enables the random inputs selection from extensive number of probability distributions (Haas et al., 1999). As such, it determines the interdependent variables through a high selected number of repetitions (i.e.,
GMOs. Hence, a holistic view of the GMO dynamic flow constitutes a comprehensive first step for GMO users in the biofuel industry through Biotechnology Risk Assessment analysis and development of proper management practices (Haas et al., 1999).
104 trials). The Gaussian probability density function p(x) equation is described as follows:
can serve as a decision-making tool to help institute comprehensive protective standards as the sector grows. The same risk assessment approach could also serve the industry well when GMOs are introduced on a large scale biofuel production annually
CONCLUSIONS The development of agricultural biotechnology in the 1980s has given rise to a genetic revolution in crops. More recently, the biofuel industry has opted for GMO practices to increase product yields and minimize land and water use via transgenic plants and engineered microorganisms. Corn-derived biofuels have benefitted from government incentives and extensive cultivation of transgenic plants. Biomass-derived ethanol and other biofuels hold great promise for energy security owing to the development of novel GM microorganisms that allow process-step integration and higher efficiencies with minimal capital cost. Although to date there is no clear evidence or direct proof of GMO side effects on the environment, preventive measures should be undertaken by the industry to drastically ensure the environmental safety. The risk assessment approach
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as dictated by the federal government’s renewable fuel standard. Elucidating a practical engineered core modeling to optimization of GMOs traceability and biosafety in biofuel production holds promises as the future method of interest to sustain biotechnology advancement.
ABBREVIATIONS GMOs: Genetically Modified Organisms GM: genetically modified RA: Biotechnology Risk Assessment RFA: Renewable Fuels Association DDGS: distiller’s dry grain with solubles RFS: Renewable Fuel Standard MSW: municipal solid waste EPSPS: 5-enolpyruvylshikimate-3-phosphate synthetase Bt: Bacillus thuringiensis DNA: recombinant DNA CR: Polymerase Chain Reaction CBP: consolidated bioprocessing SSCF: simultaneous saccharification and co fermentation CDC: Centers for Disease Control and Prevention FIB: fecal indicator bacteria HGT: Horizontal Gene Transfer CTP: Cytoplasmic Transduction Peptide GOX: Glyphosate Oxidase ACP: Palmitoyl-Acyl Carrier Protein
ACKNOWLEDGEMENTS This research was partially supported by grants from the South Central Sun Grant Program (U.S. Department of Transportation) and Novozyme North America, Inc. (Franklinton, NC).
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www.afabjournal.com Copyright © 2015 Agriculture, Food and Analytical Bacteriology
Influence of Market Setting and Time of Purchase on Counts of Aerobic Bacteria, Escherichia coli, and Coliform and Prevalence of Salmonella and Listeria in Beef in Vietnam A. K. McCain1, P. T. T. Vu2, N. T. Mai2, M. V. V. Le2, D. H. Nguyen2, P. R. Broadway3, L. M. Guillen4, M. M. Brashears4, J. R. Donaldson5, M. W. Schilling6, and T. T. N. Dinh1 Department of Animal and Dairy Sciences, Mississippi State University, Mississippi State, MS Department of Food Technology, Ho Chi Minh City University of Technology, Ho Chi Minh City, Vietnam 3 USDA, Agricultural Research Services, Livestock Issues Research Unit, Lubbock, TX 4 Department of Animal and Food Sciences, Texas Tech University, Lubbock, TX 5 Department of Biological Sciences, Mississippi State University, Mississippi State, MS 6 Department of Food Science, Nutrition, and Health Promotion, Mississippi State University, Mississippi State, MS 1
2
ABSTRACT The objective of this study was to determine the influence of market type and sampling time on Salmonella and Listeria prevalence and microbiological quality of 180 beef samples collected in 6 supermarkets (SM), 6 indoor markets (IM), and 6 open markets (OM) at the opening of the market (T0) and 4 h after the opening (T4) in Vietnam. Salmonella prevalence was greater than 50% and was influenced by both market type (P = 0.082) and sampling time (P = 0.019). Listeria prevalence was greater than 90% and did not differ among markets and sampling times (P > 0.773). Beef samples had more than 11.6, 7.0, and 9.1 logs of aerobic bacteria, E. coli, and coliforms, respectively. In SM, E. coli was greater at T0, whereas it was greater at T4 in IM (Pmarket type × sampling time = 0.029). Covered meat display case was used by 63.3, 33.0, and 0.0% of SM, IM, and OM vendors at T0 and by 100.0, 0.0, and 13.0% of SM, IM, and OM vendors at T4, respectively. Only at T4 was refrigeration used by 100.0% of SM vendors. Gloves and hairnets were used only by SM vendors at T4. Hot water was used only by 16.7% SM vendors at T4. In addition, only 29.2, 2.5, and 8.3% of SM, IM, and OM vendors, respectively, used cold water for cleaning purposes. These results highlighted the high levels of bacterial contamination in beef at retail in Vietnam, which requires immediate intervention and education so that the public health can be protected. Keywords: Beef, Salmonella, Listeria, Escherichia coli, coliforms, retail, developing countries, safety, quality, Vietnam Agric. Food Anal. Bacteriol. 5: 135-152, 2015
Correspondence: Thu T. N. Dinh, thu.dinh@msstate.edu Tel: +1 -662-325-7554
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INTRODUCTION Despite global efforts to combat foodborne pathogens, data on the societal consequences of foodborne illnesses is only available to industrialized countries (Chaves et al., 2015). In developing countries, this information gap has hindered epidemiological investigations and limited approaches towards public health interventions that could minimize the number of cases of foodborne illness (Käferstein, 2003; Chaves et al., 2015). In addition, poor hygienic conditions of vendors, lack of clean water, and poorly designed and regulated packing plants in developing countries may subject meats to
a confirmed pathogen in beef carcasses (Korsak et al., 1998; Rivera-Betancourt et al., 2006). Listeria monocytogenes has been identified as a foodborne pathogen with a greater lethality in raw beef products ( Rivera-Betancourt et al., 2006). These pathogens are associated with the hide, the intestinal tract of healthy animals, and the environment (Galland, 1997; Brown et al., 2000; Elder et al., 2000; Bell, 2002; Rivera-Betancourt et al., 2006; Sofos, 2008). Salmonella and Listeria cause 35 and 19% of foodborne illnesses in the U.S., respectively (Scallan et al., 2011). In addition, Salmonella caused over one million illnesses with 19,000 hospitalizations and 380 deaths (CDC, 2015a) and Listeria was associated with ap-
a high risk of contamination. Many markets and vendors in developing countries do not use refrigeration and expose fresh meat and poultry products to pathogenic contamination by practicing unsafe food processing, packaging, handling, and cooking (Sun et al., 2012). All of these factors pose serious challenges to food security (Kinsey, 2005). Beef is very nutritious because it has a balanced composition of essential nutrients (Maharjan et al., 2006; McNeill, 2007; USDA, 2014). The Nutrition Collaborative Research Support Program (NCRSP) reported positive associations between meat intake and physical growth, cognitive function, school performance, physical activity, and social behaviors (McNeill, 2007). Because of its nutritional composition, beef is a suitable medium for the growth of various microorganisms and a reservoir through which foodborne illnesses may spread ( Milios et al., 2014). Although the interior of beef carcasses is considered to be free of bacteria, hide removal, evisceration, contact with equipment and humans and among carcasses, and exposure to the environment may cause cross-contamination on the carcass surface (Huffman, 2002; Maharjan et al., 2006; RiveraBetancourt et al., 2006). Moreover, whenever beef is further processed, more surfaces are created and
proximately 1,600 illnesses with 260 deaths in 2014 (CDC, 2015a; CDC, 2015b). In developed countries, many studies have focused on the prevalence of Salmonella, Listeria, and E. coli at the beef production stage (Capita et al., 2004; Hussein and Sakuma, 2005; Arthur et al., 2010; Meyer et al., 2011; Schneider et al., 2011; MartínezChávez et al., 2015). The focus of Listeria contamination has been associated with ready-to-eat meat products because Listeria monocytogenes is a zerotolerance adulterant in these products (FSIS, 2014). However, evidence indicates that it is possible for Listeria contamination to occur in fresh beef, although the risk is relatively low at feedlots (Mohammed et al., 2010). The meat industry in developed countries minimizes the amount of processing at retail stores because most retail subprimals and cuts are provided by the packing plants or large purveyors (Sofos, 2008). Therefore, there have been fewer studies pertaining to bacterial pathogens in retail setting (Vipham et al., 2012; Martínez-Chávez et al., 2015). In addition, many studies have explored the use of indicator organisms such as E. coli to predict potential presence of a pathogen on carcasses (Brown et al., 2000; Brown et al., 2002; Milios et al., 2014). Meat is among the most nutritious foods in devel-
beef products become more susceptible to contamination (Maharjan et al., 2006). Animals are major sources of foodborne bacterial pathogens, e.g., E. coli and Salmonella (RiveraBetancourt et al., 2006). In addition, Listeria is also
oping countries, especially for young children (Godfray et al., 2010). Meat consumption increases with improved standards of living (Sofos, 2008); therefore, meat safety is increasingly important in developing countries. Foodborne illnesses mostly occur during
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Table 1. Characteristics used to classify supermarkets (SM), indoor markets (IM), and open markets (OM) across three regions of Vietnam. Market Type Market Characteristics SM Multiple vendors
IM
OM
√
√
Air-conditioning
√
Refrigeration
√
Walls
√
√
Roof
√
√
Clean water availability
√
√
√
√ Existing characteristics
processing and retail fabrication or because of inadequate cooking (McMeekin, 2007). Consumers in developing countries are accustomed to traditional fresh meat markets, similar to indoor and open markets described in the current study, because of their loyalty to familiar vendors, perceived availability of fresher meat, and competitive prices through bargaining (Chamhuri and Batt, 2013). Traditional markets pose serious safety risks to consumers because of the lack of refrigeration and exposure of meats to the open atmosphere (Trappey and Lai, 1997). Supermarkets store meat products in refrigerated display cases but still face safety challenges because they primarily sell meats from similar sources (Chamhuri and Batt, 2013). In developing countries such as Nepal, Vietnam, and China, most studies have focused on the contamination of one specific species of microorganism on meat products (Maharjan
important that a comprehensive retail study be conducted to establish a baseline of contamination so that further mapping and risk mitigation strategies can be elucidated. In developing countries, Vietnam in particular, and even in developed countries, the influence of market setting, time of purchase, and meat merchandising practices has not been evaluated. Therefore, it was the objective of this study to investigate the prevalence of Salmonella and Listeria, microbiological quality, and vendors’ practices in various beef markets at two sampling times in three regions of Vietnam.
et al., 2006; Van et al., 2007; Yang et al., 2010). Similar to developed countries, multi-pathogen data in the retail setting are lacking because it is difficult to account for many sampling variations and biases and to pinpoint the sources of contamination. However, it is
Ho Chi Minh City, Da Nang, Ha Noi, and their surrounding areas were selected to achieve adequate representation of regional variation in meat merchandising in Vietnam. The three types of markets included supermarkets (SM), indoor markets (IM),
MATERIALS AND METHODS Sample Collection
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and open markets (OM) and were classified by their infrastructure (Table 1). Within each market type, two of the most popular grocery markets were selected in each region, resulting in six markets per region. Samples collection was conducted from January to May, 2015. Domestically produced beef were purchased at two sampling times at each market after careful exploration of the distribution and purchase patterns of each market type. The opening time (T0) was the opening of individual markets, which varied from 5 A.M. (most open markets) to 8 A.M. (most supermarkets), and the closing time (T4) was 4 h after opening. Five 200-g beef Longissimus muscle samples
(Greiner Bio-One, Monroe, NC) of BPW rinsate were collected and stored on ice for transportation to Ho Chi Minh City University of Technology for further analyses.
were purchased aseptically and separately from various vendors in each market at each sampling time, resulting in 180 samples. Vendors were randomly selected for sampling. If a market had less than five vendors, at least one vendor was sampled repeatedly in a rotating order so that samples from the same vendors were purchased separately and from different beef strip loins. There was no vendor randomization in the SM because each SM was the sole meat vendor. However, beef samples in the SM were purchased individually from different beef strip loins and by different purchasers. The randomization at T4 was performed in the same manner as at T0. The samples were placed separately in sterile Whirl-Pak® bags (Nasco, Fort Atkinson, WI) and the bags were sealed immediately after the meat surface temperature was recorded by a Fisher Scientific™ Traceable™ Infrared Thermometer Gun (Fisher Scientific, Waltham, MA). Samples were stored in an Igloo Super Tough Sportsman ice chest (Igloo, Katy, TX) with frozen ice packs.
Meat samples were transported in the ice chests back to a local university in each region. Samples
al, 2014) with modifications for the 3M™ Petrifilm™ Salmonella Express System (3M, St. Paul, MN). The previously collected BPW rinsate was shaken for 60 s and 2.5 mL of the rinsate was combined with 22.5 mL of Salmonella Enrichment Broth (3M, St. Paul, MN) in a sterile Whirl-Pak® bag (Nasco, Fort Atkinson, WI). The solution was incubated at 45°C for 24 h. After incubation, 1 mL of the solution was transferred into a 15-mL sterile polypropylene tube (Greiner Bio-One, Monroe, NC) containing 10 mL of Rappaport-Vassiliadis R10 Broth (RVR10; 3M, St. Paul, MN), which was then incubated at 41.5°C for 24 h. A single streak of 10 µL of RVR10 solution was made onto a hydrated 3M™ Petrifilm™ of the Salmonella Express System. The Petrifilm™ was incubated at 41.5°C for 24 h. Salmonella colonies were identified by a red color with yellow halo (3M, 2015a). Presumptive positive colonies were isolated, inoculated in Tryptic Soy Agar (3M, St. Paul, MN) slants, and stored under refrigeration. Listeria spp. were detected according to the Official Method of Analysis 911.02 (AOAC International, 2002) using ALOA® medium (BioMerieux, St. Louis, MO) with modifications to the enrichment process. After being shaken for 60 s, 2.5 mL of BPW rinsate was combined with 22.5 mL of Demi-Fraser Listeria Enrichment Broth (3M, St. Paul, MN) in a sterile
were weighed (approx. 200 g) and shaken for 60 s in 90 mL of Buffered Peptone Water broth (BPW; 25.5 g/L; 3M, St. Paul, MN; Vipham et al., 2012), which was added to the Whirl-Pak® bags (Nasco, Fort Atkinson, WI) . Two sterile 15-mL polypropylene tubes
Whirl-Pak® bag (Nasco, Fort Atkinson, WI). The solution was incubated at 30°C for 24 h. A volume of 0.1 mL of the solution was subsequently spread onto an ALOA® agar petri dish. The dish was inverted and incubated at 37°C for 24 h. Listeria colonies were
Sample Preparation
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Microbiological Analysis Except for sterile sampling bags, all apparatuses and solutions were autoclaved before microbiological analyses. Blank enrichment, isolation, and incubation of all solutions including sterile water were performed for all microbiological analyses. Salmonella spp. were identified by using the Official Method of Analysis 2014.01 (AOAC Internation-
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identified by a blue to green color with or without halo. Presumptive positive colonies were isolated, inoculated in Tryptic Soy Agar (3M, St. Paul, MN) slants, and stored under refrigeration. Aerobic Plate Count (APC), E. coli , and coliforms analyses were performed according to the Official Method of Analysis 990.12 (APC; AOAC International, 2012) and 998.08 (E. coli and coliforms; AOAC International, 2008) with 3M™ Petrifilm™ Aerobic Count Plates and 3M™ Petrifilm™ E. coli/coliforms Plates instructions, respectively (3M, 2015b; 3M, 2015c). Original BPW rinsate (15 µL) was serially diluted (1:100) to a volume of 1.5 mL with sterile BPW broth in two 2-mL sterile polypropylene microcentri-
with N, V, DF, V0, and m being number of colony forming units on a Petrifilm™, volume of a dilution spread onto a Petrifilm™ (1 mL), dilution factor, original volume of BPW rinsate (90 mL), and sample weight (g), respectively. Market characteristic data were recorded for each sample and reported as crude percentage without statistical analysis. The prevalence of Salmonella and Listeria were analyzed as a 3 × 2 factorial arrangement in a randomized complete block design with region as block, market type (SM, IM, and OM) and sampling time (T0 and T4) as two factors, and a specific market at a specific sampling time as experimental unit (n = 6 per factorial combination). For APC, E. coli, and co-
fuge tubes for either APC or E. coli/coliforms. One milliliter of each dilution was spread onto an APC Petrifilm™ or an E. coli/Coliform Petrifilm™. The Petrifilms™ were incubated with clear side up in a stack of 10 at 35°C for 24 h. Colony forming units (CFU) were counted according to the 3M interpretation guides (3M, 2015b; 3M, 2015c).
liforms, the experimental unit was beef sample (n = 30 per factorial combination). The effects of market type and sampling time on pathogenic prevalence (%) and bacterial count (log CFU/g) were statistically analyzed by SAS version 9.4 (SAS Institute, Inc., Cary, NC, USA). Analysis of variance for binomially distributed data (prevalence) was performed through logistic regression, whereas that for normally distributed data (log CFU/g) was conducted through linear regression. A generalized linear mixed model was used for both analyses in the GLIMMIX procedure of SAS, with market type, sampling time, and their interaction being the fixed effects and region being the random effect. Means were separated by the protected t-test, using the LSMEANS statement with the PDIFF option in the GLIMMIX procedure. Statistical significance was determined at P ≤ 0.10.
Market Characteristics An observational data form was developed to collect data that were considered relevant to microbiological safety of fresh meat products. Outdoor temperature (ºC), relative humidity (%), meat surface temperature (ºC), type of retail display (display case, suspended by hook, or open counter), use of refrigeration, gloves and hairnets, cleaning of knife before cutting meat, and use of water for cleaning purposes (hot water or fresh cold water) were recorded for individual samples.
Calculation and Statistical Analysis
RESULTS AND DISCUSSION Microbiological Quality
The prevalence of Salmonella and Listeria was reported as percentage of positive samples estimated by the statistical model. Aerobic Plate Count (APC),
Beef in all markets had more than 11 log CFU/g of APC (Table 2). Many of the APC Petrifilm™ were too numerous to count (TNTC) at the 10-6 dilution,
E. coli, and coliforms were reported as log CFU/g, calculated from CFU as follows:
because they contained a pink color in the entire growth area (3M, 2015b). These TNTC Petrifilms™ were estimated at 108 CFU. However, there were differences between the OM and SM (P = 0.030; Figure 1) and the two sampling times (P = 0.054; Figure 2).
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89.6 ± 6.1ax
Salmonella, detected using 3M™ Petrifilm™ Salmonella Express System (3M, St. Paul, MN)
Listeria, detected using ALOA® media (BioMerieux, St. Louis, MI)
Within market type, means without common letters differ, P ≤ 0.1.
Within sampling time, means without common letters differ, P ≤ 0.1.
5
xy
ab
Values were reported as estimated least squares means ± standard error of the means
Coliform, enumerated using 3M™ Petrifilm™ E. coli/Coliform Count Plates (3M, St. Paul, MN)
4
*
Escherichia coli, enumerated using 3M™ Petrifilm™ E. coli/Coliform Count Plates (3M, St. Paul, MN)
3
93.6 ± 4.6ax
43.2 ± 10.3ax
100.0 ± 0.0ax
83.8 ± 7.2ax
Aerobic Plate Count, enumerated using 3M™ Petrifilm™ Aerobic Plate Count (3M, St. Paul, MN)
93.6 ± 4.6ax
53.4 ± 10.3ay
2
1
90.4 ± 5.7ax
70.4 ± 9.3ax
type
Pmarket
6.99 ± 0.70ax 0.380
11.65 ± 0.01ax 0.060
T4
93.6 ± 4.6ax
0.773
56.8 ± 10.3abx 0.082
10.43 ± 0.34ax 10.30 ± 0.35abx 10.46 ± 0.50ax 0.005
7.81 ± 0.72ax 7.30 ± 0.88abx
Listeria5 prevalence, %
6.58 ± 0.76by
9.71 ± 0.50bx 10.16 ± 0.36ax
7.14 ± 0.70ay
61.2 ± 10.3ax
T0
Salmonella4 prevalence, %
9.12 ± 0.53bx
T4
Coliform3, l og CFU/g
T0
OM
8.28 ± 0.61ax
E. coli2, log CFU/g
T4
IM
11.60 ± 0.01by 11.63 ± 0.01bx 11.62 ± 0.01abx 11.62 ± 0.01bx 11.62 ± 0.01ax
T0
APC1, log CFU/g
Measurement*
Microbiological
SM
0.975
0.019
0.196
0.837
0.034
Ptime
0.998
0.380
0.790
0.029
0.398
type*time
Pmarket
Table 2. Bacterial counts and the prevalence of Salmonella and Listeria in beef (N = 180) procured from supermarkets (SM), indoor markets (IM), open markets (OM) at the market opening (T0) and 4 h after the opening (T4) across three regions of Vietnam (Ho Chi Minh City, Da Nang, and Ha Noi).
Figure 1. Aerobic Plate Count (APC) and coliform counts (log CFU/g) of beef (N = 180) purchased from supermarkets (SM), indoor markets (IM), and open markets (OM) in Ho Chi Minh City, Da Nang, and Ha Noi of Vietnam, averaged across two sampling times. Within a category of bacterial count, means without common letters differ, (Pmarket type = 0.060 and 0.005, respectively). APC
14 12
ab
b b
10 log CFU/g
a
Coliforms a
a
8 6 4 2 0 SM
IM
OM
Market type Both differences were small and might not be biologically meaningful. E. coli counts were greater than 7 log CFU/g and there was no market type or sampling time effect (P = 0.380 and 0.837, respectively; Table 2). However, the market type × sampling time interaction was significant (P = 0.029). The IM had a 1.2-log increase (P = 0.052; Figure 3), whereas the SM had a 1.1-log decrease in E. coli from T0 to T4 (P = 0.074; Figure 3). Coliforms, excluding E. coli, was greater in the IM (10.29 log CFU/g) and OM (10.38 log CFU/g) than in the SM (9.43 log CFU/g; Figure 1; P = 0.016 and 0.009, respectively). Similarly, many E. coli/coliforms Petrifilms™ were TNTC for either E. coli or coliforms and were indicated by a purple (E. coli) or pink (coliforms) color in the entire growth area at the 10-6 dilution (3M, 2015c). These bacterial counts were much greater than those reported in most studies in the U.S. However, beef samples
rial counts were caused by poor hygienic conditions throughout the production and distribution systems; however, the occurrence was not long enough for spoilage to progress. Arthur et al. (2004) reported 7.8 log of APC and 6.2 log of Enterobacteriaceae on the hide and only 1.4 log of APC and 0.4 log of Enterobacteriaceae on chilled beef carcasses. Jones et al. (2014) reported that vacuum-packaged beef in Canada had 1.1 to 2.5 log CFU/100 cm2 of E. coli and that beef from retail establishments had a maximum of 3.1 log CFU/100 cm2 of coliforms. It is important to note that most beef packing plants in the U.S. and other developed countries employed various interventions (Pohlman et al., 2002) during lairage and carcass dressing (Buncic and Sofos, 2012). Lactic acid (Castillo et al., 2001), acetic acid, and chlorine sprays have been used as carcass decontamination treatments to de-
did not show any sign of spoilage. Therefore, it was suspected that these levels had occur in beef products for a short period before they were purchased. It is common that most beef is consumed within 24 h post-mortem and it was possible that great bacte-
crease Salmonella counts by 1.3 to 5.1, 2.0 to 4.8, and 0.6 to 1.3 log CFU/cm2, respectively (Buncic and Sofos, 2012). Various studies have indicated that up to a 4-log reduction can be achieved through carcass chilling (Buncic and Sofos, 2012). Although car-
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Figure 2. Aerobic Plate Count (APC) and coliform counts of beef (N = 180) purchased at two sampling times (opening - T0 and 4 h after opening - T4) in Ho Chi Minh City, Da Nang, and Ha Noi of, averaged across supermarkets, indoor markets, and open markets. Within a category of bacterial count, means without common letters differ, (Psampling time = 0.034 and 0.196, respectively).
APC 14 a
12
Coliforms
b
a
a
log CFU/g
10 8 6 4 2 0 0
Sampling time
4
Figure 3. E. coli counts of beef (N = 180) purchased at the opening (T0) and 4 h after the opening (T4) in supermarkets (SM; P = 0.074), indoor markets (IM; P = 0.052), and open markets (OM; P = 0.623), varied by market type × sampling time interaction (Pmarket type × sampling time = 0.029).
14 IM
12
SM OM
log CFU/g
10 8 6 4 2 0 T0
T4 Sampling time
142
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Figure 4. Salmonella and Listeria prevalence in beef purchased from supermarkets (SM), indoor markets (IM), and open markets (OM) in Ho Chi Minh City, Da Nang, and Ha Noi of Vietnam, averaged across two sampling times. Within a pathogen category, means without common letters differ, (Pmarket type = 0.082 and 0.773, respectively).
a
100
Prevalence, %
80
a
Salmonella Listeria
a
a
a
b
60 40 20 0 SM
IM
OM
Market type cass decontamination interventions are important for microbiological safety and quality of beef (Huffman, 2002), these interventions, with the exception of washing, are unavailable in Vietnam. Moreover, most domestically produced beef in Vietnam is processed in small to very small processing facilities, where interventions are unavailable and microbiological evaluation is neither required nor regulated. Indicator bacteria are widely used as a measure of hygienic conditions and microbiological quality of foods (Jordan et al., 2007). Indicator organisms such as aerobic bacteria, E. coli, and coliforms can be enumerated and quantified more inexpensively and easily than other bacterial pathogens (Jordan et al., 2007). E. coli and total coliform counts have been used in packing plants as indicator organisms (Milios et al., 2014). Arthur et al. (2004) reported significant correla-
tor organisms are commonly indicative of specific pathogenic species. For example, Ghafir et al., (2008) reported both that E. coli and APC counts on beef carcasses were significantly correlated and that E. coli counts were greater on beef carcasses that were the origin of Salmonella contaminated beef samples. These authors suggested that E. coli count was a reliable index of Salmonella incidence in beef. E. coli, coliforms and Enterobacteriaceae, and APC are indicators of fecal contamination, environmental contamination, and overall hygienic conditions (Arthur et al., 2004). Although the measures may be correlated, each can be indicative of different bacterial pathogens, which infers that multiple indicators should be used (Milios et al., 2014). A decrease in the population of indicators is generally assumed to correspond to a similar decrease in the population of pathogens
tions between APC, Enterobacteriaceae, and E. coli O157 loads on pre- and post-evisceration carcasses. Therefore, there are benefits of monitoring indicator organisms to evaluate the effectiveness of interventions or risk mitigation strategies. Moreover, indica-
(Brown et al., 2000), although there are no clear correlation between indicator organisms and the contamination of specific pathogens. It is generally accepted that pathogens occur less frequently and with lower counts than indicators (Milios et al., 2014).
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Figure 5. Salmonella and Listeria prevalence in beef purchased at opening (T0) and 4 h after opening (T4) in Ho Chi Minh City, Da Nang, and Ha Noi of Vietnam, averaged across supermarkets, indoor markets, and open markets. Within a pathogen category, means without common letters differ, (Psampling time = 0.019 and 0.975, respectively).
100
a
Salmonella
a
Listeria b
Prevalence, %
80
60
a
40
20
0 0
4 Sampling time
Prevalence of Salmonella The average prevalence of Salmonella in SM, IM, and OM was 66.0, 71.0, and 50.0%, respectively (Figure 4). Across two sampling times, SM and IM were similar in prevalence of Salmonella, and both markets had greater incidence than OM (P = 0.098, P = 0.037; Table 2; Figure 4). Across the three market types, the Salmonella prevalence in beef was greater at T4 than at T0 (71.7 and 52.6%, respectively; P = 0.019; Figure 5). In general, Salmonella can be transferred to carcass during hide removal (Galland, 1997). During the slaughter process, pathogens can be directly translocated onto carcasses, thereby affecting the safety of beef products (Dong et al., 2014). Salmonella prevalence in beef could be attributed to tropical climate with significantly higher temperature and humidity (greater than 26.3°C and 68.5%, respectively; Table 3) than most regions of the 144
U.S., which might allow more growth of Salmonella on carcasses and increase the likelihood of crosscontamination onto the final retail products (Van et al., 2007). A similar study screened retail meat products collected from various regions in China and observed greater Salmonella prevalence (44%) than the U.S. (6 to 35%; Yang et al., 2010). However, several factors must be considered when comparing Salmonella prevalence among countries (Yang et al., 2010), including origin, type of meat samples (ground or whole muscle), sampling seasons, plant sanitation, and collection methods. Baseline studies revealed that Salmonella prevalence in retail whole muscle beef products in the U.S. was at 0.7% (Vipham et al., 2012), which is much less than that in Vietnam (Van et al., 2007). Ground beef in the U.S. is made by grinding and mixing trimmings from various sources, however, has similar Salmonella incidence levels to that of whole muscle products (Vipham et al., 2012).
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The FSIS tested 2,983 raw ground beef samples under the MT43 project (Risk-based Sampling for Raw Ground Beef) during the first quarter of 2015 and only 0.9% (27 samples) were positive for Salmonella (USDA, 2015a). Although the Salmonella prevalence in beef in Vietnam was high in the current study, similar incidence levels (62%) were previously reported for raw beef in Ho Chi Minh City of Vietnam (Van et al., 2007). It is important to note that the current study confirmed Salmonella prevalence in beef on a much larger scale throughout Vietnam in various market settings, including supermarkets. Gill and Baker (1998) reported correlation among bacterial counts on sheep carcasses from various
genes prevalence in pre-evisceration beef carcasses at two geographically distant commercial beef packing plants in the U.S., which was decreased to 0.0 to 1.1% post-intervention. Approximately 3.5% (18 of 512 samples) incidence of L. monocytogenes was reported for retail raw ground beef in the state of Washington (Samadpour et al., 2006). Guerini et al. (2007) reported a consistently high prevalence (up to 77 to 92%) of Listeria on the hide of cows and bulls, but also reported that post-intervention contamination was almost undetectable, with the exception of a 19% incidence at one packing plant. Rahmat et al. (1991) conducted a similar study in Malaysian wet markets and reported a Listeria incidence level of 25
primals. Similar results showed a correlation between E. coli, Enterobacteriaceae, and aerobic colony counts for cattle and pig carcasses (Ghafir et al., 2008). These authors also suggested that a correlation existed between bacterial counts, specifically those of E. coli, and Salmonella presence on carcasses. However, it is more difficult to elucidate such a correlation in retail samples because bacteria in retail meats come from various sources, including random cross-contamination. A Spearman rank correlation between E. coli count and Salmonella prevalence in this study was not significant (P = 0.628).
Market type and sampling time did not affect Listeria prevalence in beef across all three regions of Vietnam (P > 0.773; Figure 4 and 5). The average prevalence of Listeria was determined at 90.0, 100.0, and 93.6% for SM, IM, and OM, respectively (Figure 4) and at 92.5 and 99.9% for T0 and T4, respectively (Figure 5). Listeria, especially L. monocytogenes, is predominantly a safety concern for ready-to-eat meat products. The latest incidence prompted Shirk’s Meat in New York to recall approximately 2,478 lbs. of ready-to-eat pork and beef
to 50%. Such a high level of incidence is not unusual because even in Canada, a developed country, L. monocytogenes was found in 52% of raw ground beef (Bohaychuk et al., 2006). Similarly, Yücel et al. (2005) and Buncic (1991) observed 86.4 and 69.0% Listeria contamination in raw minced meat collected from supermarkets and local butcher shops in Turkey and Yugoslavia, respectively. The great incidence rate of Listeria in raw meat could be attributed to fecal contamination during slaughter, vendor’s hygienic conditions, or unsafe food processing, packaging, and handling (Rahimi et al., 2012; Ismaiel et al., 2014; Stea et al., 2015). In raw whole muscle meat, contamination occurs on meat surface until further processing such as size reduction creates additional surface area that are susceptible to cross-contamination (Milios et al., 2014). In raw muscle meat purchased at retail stores, relatively high levels of Listeria contamination were observed in Japan (56.6%; Ryu et al., 1992) and Australia (24.0%; Ibrahim and Mac Rae, 1991) although they are less than the incidence level in this study. However, more recently, L. monocytogenes were undetectable in raw beef in South Korea (Park et al., 2015). In general, Listeria is capable of surviving on meat surfaces regardless of extrinsic factors
products that might have been contaminated with L. monocytogenes (USDA, 2015b). The data on Listeria in beef and beef packing plants are minimal (Guerini et al., 2007). Rivera-Betancourt et al. (2004) reported a maximum of 14.6% Listeria monocyto-
(Thévenot et al., 2006). Freezing, surface dehydration, and simulated spray chilling do not appear to affect to the survival of Listeria (Farber and Peterkin, 1991). Growth of Listeria, however, appears highly dependent on the temperature and the pH
Prevalence of Listeria
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0.0 ± 0.0 16.7 ± 0.1
Fresh water, %
33.3 ± 21.1
Hairnet, %
Hot water, %
16.7 ± 22.4
Gloves, %
0.0 ± 0.0
50.0 ± 16.9
Refrigeration, %
Cleaned knife before cutting, %
33.3 ± 16.9
63.3 ± 18.2
Covered display, %
Open display, %
20.2 ± 2.0
Meat surface temperature, °C
3.3 ± 3.3
69.3 ± 6.0
Humidity, %
Hang display, %
26.7 ± 1.1
T0
Outdoor temperature, °C
Market Characteristics
18.3 ± 1.4
67.2 ± 6.7
29.3 ± 1.7
T4
41.7 ± 8.3
16.7 ± 16.7
0.0 ± 0.0
83.0 ± 16.7
50.0 ± 22.4
100.0 ± 0.0
0.0 ± 0.0
0.0 ± 0.0
100.0 ± 0.0
SM
16.7 ± 10.5
0.0 ± 0.0
0.0 ± 0.0
33.3 ± 21.1
16.7 ± 16.7
36.7 ± 20.3
36.7 ± 20.3
30.0 ± 19.2
33.0 ± 21.1
25.8 ± 0.7
82.7 ± 4.1
25.2 ± 0.5
T0
IM
0.0 ± 0.0
0.0 ± 0.0
10.0 ± 0.1
0.0 ± 0.0
0.0 ± 0.0
0.0 ± 0.0
66.7 ± 21.1
16.7 ± 16.7
0.0 ± 0.0
25.3 ± 0.9
72.9 ± 5.5
27.5 ± 1.6
T4
0.0 ± 0.0
0.0 ± 0.0
0.0 ± 0.0
0.0 ± 0.0
0.0 ± 0.0
0.0 ± 0.0
76.7 ± 16.7
23.3 ± 16.7
0.0 ± 0.0
25.8 ± 1.0
73.5 ± 5.8
27.2 ± 1.6
T0
OM
1.7 ± 1.7
0.0 ± 0.0
0.0 ± 0.0
0.0 ± 0.0
0.0 ± 0.0
0.0 ± 0.0
70.0 ± 13.4
16.7 ± 10.9
13.0 ± 0.1
26.2 ± 0.8
63.5 ± 5.6
30.8 ± 1.8
T4
Table 3. Observational and environmental data collected during the purchase of beef from supermarkets (SM), indoor markets (IM), and open markets (OM) at the market opening (T0) and 4 h after the opening (T4) across three regions of Vietnam (Ho Chi Minh City, Da Nang, and Ha Noi).
of the meat, the muscle tissue type, and the type and amount of background microflora (Farber and Peterkin, 1991). Listeria grows between -0.4 to 45°C with 37°C being the optimum temperature (Low and Donachie, 1997). Surface temperature of beef samples in this study were 19.2, 25.9, and 25.5°C in the SM, IM, and OM, respectively. The environmental temperature was 26.3 to 29.0°C. Guerini et al. (2007) reported that Listeria prevalence was greater on the hide during cooler weather in their investigation into cull cows and bulls; however, temperature-dependent phenomenon could not be evaluated in this study, because temperature variation was minimal.
Market Characteristics
for Disease Control estimates that 20% of foodborne illnesses are the result of cross-contamination from workers to food products (Michaels et al., 2004). The author also reported that bare hand contact with meat surfaces in the U.S. resulted in 182 of 308 foodborne illness outbreaks (59%) because bare hand contact directly caused contamination. Proper hand washing significantly decreased the possibility of pathogens being transmitted to foods (Guzewich and Ross, 1999; Montville et al., 2002; Michaels et al., 2004). The authors reported a 30 to 40% decrease in foodborne illnesses when hand washing programs were implemented (Michaels, 2002). Hot water was only used by 16.7% of the SM vendors for cleaning
Characteristics of markets and vendors as they related to the safety of beef in Vietnam were summarized in Table 3. Covered meat display case a physical barrier between consumer and non-refrigerated meat, was used at T0 and T4 by 63.3 and 100.0% of the SM across all three regions of Vietnam, respectively. Similarly, refrigeration was used at T0 by 50.0% of the SM for storage to replenish the displayed products throughout the day. At T4, 100.0% of the SM used refrigeration for storage of products to be sold the next day. In comparison, only 33.3 and 16.7% of IM vendors used cover displays at T0 and T4, respectively. In addition, 36.7% of the IM vendors used refrigeration at T0 and no vendor used refrigeration at T4. No OM vendor used refrigeration at either of the sampling times. Use of refrigeration was recorded if temperature of display or storage case was at or below 4°C. If a vendor utilized refrigeration, the temperature was measured to ensure 4°C was met. At T0 and T4, 76.7 and 70.0% of the OM vendors, respectively, used open meat displays without any physical barrier between consumer and products. Appropriate use of gloves and hairnets were lacking in IM, at both T0 (16.7 and 33.3%, respec-
purposes at T4. No vendors used hot water at T0. In addition, 16.7% of the SM vendors used fresh water at T0 and 41.7% used fresh water at T4. Furthermore, only 16.7% of IM vendors used fresh water at T0 to clean the retail area and 1.7% of OM vendors indicated that cold water was used for cleaning purposes at T4. Although water was available in all markets, at the time of surveying, no SM, IM, or OM vendor indicated that knives were cleaned before cutting meat. These practices could be related to the high level of bacterial contamination found in the current study. Salmonella and E. coli counts can be reduced significantly if beef carcasses are treated by hot water washing, lactic acid spray, and carcass trimming (Castillo et al., 1998). Developing countries with limited resources can apply these physical interventions to reduce bacterial contamination levels. Vendors in the IM and OM provide reasonably priced and conveniently available meat products for the lower income population. However, most foods sold in these markets create major food safety and quality concerns because meat products are being prepared and distributed under poor hygienic conditions, with limited access to safe water and sanitary services (WHO, 2002). There is an increased health risk to consumers because of the lack of knowledge
tively) and T4 (0.0 and 0.0%, respectively). Similarly, OM vendors used neither at both sampling times. However, in the SM, gloves and hairnets were used predominately at T4 (50.0 and 83.0%, respectively), compared with 16.7% and 33.3% at T0. The Centers
about food safety measures and incentives for vendors to comply with food safety guidelines and regulations (Choudhury et al., 2011). Chamhuri and Bratt (2013) reported that consumers in Malaysia still preferred to shop at traditional markets, i.e., open and
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street vendor markets, when compared to supermarkets even though they were informed that meat from supermarkets were safer (Chamhuri and Batt, 2013). Some reports claim that traditional markets will soon be displaced and lose their customers to more modern retailers that offer higher quality and safer products (Trappey and Lai, 1997; Goldman et al., 1999; Giovannucci and Reardon, 2000). However, consumers in developing countries have not abandoned traditional markets when purchasing fresh meat because of the loyalty to a vendor, the perception of the availability of “fresher” meat, and competitive prices through bargaining (Chamhuri and Batt, 2013). Bacterial count in beef in Vietnam is 7 to
most important pathogens, in beef products. The occurrence of Salmonella and Listeria on beef products was much more frequent than reported in the literature. In addition, there were greater than 7 logs of indicator organisms such as APC, E. coli, and coliforms. Both results indicated great risk for beef consumers in Vietnam. The high incidence and bacterial loads could be partially attributed to the improper practices at the markets and various sources at the production level, such as lack of refrigeration, cleanliness, water usage, and proper attire. Therefore, more research is needed in this area to map the prevalence of pathogens from live animals to retail display so that risk mitigation strategies can be
8 logs greater than that in the U.S. Fresh meat having 7 to 8 logs of total aerobic bacteria is considered spoiled. However, in most developed countries, counts greater than 7 logs in fresh meat are developed through storage and retail display for at least 5 to 7 days under refrigeration, whereas great bacterial counts in meat in most developing countries are indicative of poor initial hygienic conditions. Bacterial growth over a long period of storage and display such as that in the developed countries causes spoilage, whereas most beef in Vietnam is consumed within 24 h post-mortem, which can explain the lack of signs of spoilage and the perceived freshness of beef in traditional grocery markets. Even though traditional markets do not provide a clean and hygienic environment, they do provide a personal relationship that is lacking at other more modern market types. Emphasis on the importance of hygiene and food safety is needed in all markets because unsafe behaviors were not limited to traditional market types. Furthermore, it is important to intensify the efforts in educating food-handlers and consumers in food safety principles, proper cooking of foods of animal origins, personal hygiene, and sanitation of processing equipment (Sofos, 2008).
devised. Moreover, regulations and the control of hazards of beef processing in Vietnam are lacking. These data justify the establishment of food safety regulations and training in Vietnam.
CONCLUSIONS This study documented the levels of contamination of Salmonella, Listeria, and E. coli, three of the 148
ACKNOWLEDGEMENTS This study was funded in part by the U.S. Borlaug Fellows in Global Food Security Program Graduate Research Grant (Grant #00000861). Work in Dr. Janet R. Donaldson’s laboratory was supported by NIH #P20GM103646. The data are also based upon work that is supported by the National Institute of Food and Agriculture, U.S. Department of Agriculture, Multi-state Hatch project #1005775.
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www.afabjournal.com Copyright © 2015 Agriculture, Food and Analytical Bacteriology
Effects of feeding two different tannin-containing diets on ruminal fermentation profiles and microbial community changes in meat goats B. R. Min1, D. Perkins1, C. Wright1, A. Dawod2, B. J. Min1, T. H. Terrill3, J.-S. Eun4, R. Shange1, S. Y. Yang4, and N. Gurung1 Department of Food and Nutritional Sciences, Tuskegee University, Tuskegee, AL, USA Department of Husbandry and Animal Wealth Development, Faculty of Veterinary Medicine, University of Sadat City, Sadat City, Menofia, Egypt 3 Department Agricultural Sciences, Fort Valley State University, Fort Valley, GA, USA 4 Department of Animal, Dairy, and Veterinary Sciences, Utah State University, Logan, UT, USA 1
2
ABSTRACT Phytochemical plant tannins (condensed and hydrolysable tannins) are one of the most abundantly available plant secondary metabolites. These plant tannins have potential to significantly impact rumen fermentation, rumen microbiota population changes, nutrient digestibility and animal production. The objective of this study was to determine whether the phytochemical tannin-containing sericea lespedeza leaf pellet, ground pine bark, or its combination would have effects on rumen fermentation and microbial diversity in meat goats. Twenty four Kiko-crossbreed intact male goats (Capra hircus; BW= 38.6 ± 2.7 kg) were randomly assigned to four treatments (n = 6): 1) 30% bermudagrass hay and 70% grain mix, 2) 30% pine bark (PB) and 70% grain mix, 3) 30% sericea lespedeza (SLP) and 70% grain mix, and 4) 15% PB, 15% SL pellet, and 70% grain mix. Goats supplemented with mixed diets had decreased (P < 0.05) concentrations of acetate, while goats received PB diet exhibited reduced concentrations of isobutyrate (P < 0.001), isovalerate (P < 0.01), and valerate (P < 0.01) acids compared to those in the control and SLP diets. In this study, Bacteroides (30 to 55%) and Firmicutes (30 to 47%) were the major bacterial phyla, while Prevotella spp. was the most predominant rumen bacterial species in the percentage of 22.1, 42.2, 28.9, and 23.9 for control, PB, SLP, and mixed diets, respectively. The community of rumen bacterial species in PB-supplemented group was greater for Marinifilum spp. (P < 0.04), Bacteroides spp. (P < 0.02), and Oribacterium spp. (P < 0.03) compared with other treatment groups. Supplementing tannins in goat diets such as PB and SLP diets has a potential to positively modify rumen bacterial community, but there were no synergistic effects on rumen fermentation. Keywords: Goats, Microbiota, Condensed Tannins, Pine Bark, Lespedeza Agric. Food Anal. Bacteriol. 5: 153-165, 2015
Correspondence: Byeng ryel Min, minb@mytu.tuskegee.edu
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INTRODUCTION The rumen hosts a diverse, dynamics and varied microbial community, dominated mainly by bacteria, protozoa, archaea, fungi and viruses. This microbiome plays a significant role in modulating host’s immune system, body growth, and rumen biomass degradation (Russell and Rychlik, 2001). Therefore, altered rumen metabolism is directly concomitant to the rumen microbial community (Min et al., 2003; 2014a). However, there have been limited studies concerning molecular technology sequence data of rumen bacteria with fermentation patterns in response to feeding diets containing different sources
reason to use a combination of two different CT-rich diets, instead of single diet, was to examine the possibility that some phenolic metabolites derived from mixed diets, may have synergistic effects on ruminal fermentation and microbial diversity, resulting in optimization of beneficial effects of CT on microbial physiology in the rumen. Thus, our objective was to investigate concurrent changes in rumen microbiota and ruminal fermentation patterns in goat due to plant CT using a modern pyrosequencing approach.
MATERIALS AND METHODS
of tannins or its mixture. Plant tannins [condensed (CT) and hydrolysable tannins (HT)] are one of the most abundantly available plant secondary metabolites and have positive or adverse effects on rumen microbiota, nutrient digestibility and animal production (Min et al., 2003). A few studies have provided evidence for the relationship between the tannin-containing diets and rumen bacterial community. Recently, Min et al. (2014b) have shown that supplementation of chestnut plant tannin extract in grazing goats noticeably inhibited growth of rumen Firmicutes, whereas growth of Bacteroides members of the group was greatly enhanced compared with a control alfalfa hay. Moreover, a grazing study in goats with a CTrich pine bark powder diet [11% CT on dry matter (DM)] showed that Proteobacteria were the most dominant phylum, and this gut microorganism was linearly decreased with increasing CT-rich diet supplementation (Min et al., 2014b). It has been shown that the gastrointestinal microbial population was dominated by Prevotella (18.2% of total population) in the rumen and Clostridium (19.7% of total community) in the feces of cattle (Callaway et al., 2010). The rumen microbial community is dynamic and reflects the effect of tannin in diet with an in-
Care and handling of all experimental animals were conducted under protocols approved by the Tuskegee University Institutional Animal Care and Use Committee.
crease and decrease in selective microbial community. However, there is a need for detailed study involving effect of CT-containing diets on rumen microbiota of goat in response to ingestion of different sources of CT-containing diets. In our study, the
drical strainer, before the morning feeding, into 50 ml serum vials that were filled to capacity, capped immediately and stored at -20°C until analysis later that day.
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Experimental animals and diets Twenty four Kiko-crossbreed intact male goats (Capra hircus; initial body weight = 38.6 ± 2.7 kg) were randomly assigned to one of four treatments (n = 6): 1) 30% bermudagrass hay and 70% grain mix, 2) 30% pine bark (PB; Pinus taeda L.) and 70% grain mix, 3) 30% sericea lespedeza (Lespedeza cuneate; SLP) leaf pellet and 70% grain mix, and 4) 15% PB, 15% SL pellet and 70% grain mix. The grain mix consisted of 70% commercial concentrate premix (Noble goats, Purina) and 30% alfalfa hay pellet. Animals were confined indoors for a period of 40 days. Feed intake and body weight were monitored every 2 weeks for 42 days. Diet samples were taken (20 g per treatment) every week, composited and analyzed for chemical composition of diets, while ruminal fluid samples were taken at day 0, 20 and 42 for microbial DNA analysis. Ruminal fluid was collected via stomach tube, fitted with a small cylin-
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Chemical analysis Feed samples were collected daily during the collection period, dried at 60°C for 48 h, ground to pass a 1 mm screen (standard model 4; Arthur H. Thomas Co., Swedesboro, NJ) and stored for subsequent analyses. Daily portions of ground samples were composited for each animal and analyzed for DM, crude protein (CP), acid detergent lignin, ether extract and crude ash according to the methods described by AOAC (1998). Nitrogen content of the diet samples was determined using a Kjeldahl N, and CP was calculated by multiplying N by 6.25. Concentration of neutral detergent fiber (NDF) and acid detergent fiber were sequentially determined using an ANKOM200/220 Fiber Analyzer (ANKOM Technology, Macedon, NY). Sodium sulfate and heat stable amylase (Type XI-A from Bacillus subtilis; Sigma-Aldrich Corporation, St. Louis, MO) were used in the procedure for NDF determination. Acetone (70%) extractable CT in diet samples were determined using a butanol-HCl colorimetric procedure (Min et al., 2012). Tannin composition of whole PB, aqueous acetone extracts and PB residue after extraction were also analyzed by thiolytic degradation as previously described by Min et al. (2015). For volatile fatty acid (VFA) analysis, 5 ml of ruminal fluid was diluted with 1 ml of 3 M meta-phosphoric acids and quantified using a GLC (model 5890 series II; Hewlet Packard Co, Palo Alto, CA.) with a capillary column (30 m × 0.32 mm i.d., 1 μm phase thickness, Zebron ZB-FAAP, Phenomenex, Torrance, CA) and flame-ionization detection. The oven temperature was 170°C held for 4 min, which was then increased by 5°C/min to 185°C, and then by 3°C/min to 220°C, and held at this temperature for 1 min. The injector temperature was 225°C, the detector temperature was 250°C, and the carrier gas was helium (Eun and Beauchemin 2007).
DNA extraction Genomic bacterial DNA was isolated from 1 ml of rumen samples according to the method described
in the QIAamp DNA Mini Kit (QIAGEN Inc., Valencia, CA). Extracted DNA (2 μl) was quantified using a Nanodrop ND-1000 spectrophotometer (Nyxor Biotech, Paris, France) and run on 0.8% agarose gel with 0.5 M Tris-borate-EDTA buffer. The samples were then transported to the Research and Testing Laboratory (Lubbock, TX) for PCR optimization and pyrosequencing analysis. Bacterial tag-encoded FLX amplicon pyrosequencing (bTEFAP) PCR was carried out according to procedure previously described by Min et al. (2014b).
bTEFAP sequencing PCR The bTEFAP and data processing were performed as described previously (Dowd et al., 2008). All DNA samples were adjusted to 100 ng/μl. A 1000 ng (1 μl) aliquot of each sample’s DNA was used for a 50 μl PCR reaction. The 16S universal eubacterial primers 530F (5’-GTG CCA GCM GCN GCG G) and 1100R (5’-GGG TTN CGN TCG TTG) were used for amplifying the 600 bp region of 16S rRNA genes. HotStar Taq Plus Master Mix Kit (QIAGEN Inc.) was used for PCR under the following conditions: 94°C for 3 min followed by 32 cycles of 94°C for 30 sec; 60°C for 40 sec and 72°C for 1 min; and a final elongation step at 72°C for 5 min. A secondary PCR was performed for FLX (Roche, Nutley, NJ) amplicon sequencing under the same condition by using designed special fusion primers with different tag sequences: LinkerA-Tags-530F and LinkerB1100R. The resultant individual sample after parsing the tags into individual FASTA files was assembled using CAP3. The ace files generated by CAP3 were then processed to generate a secondary FASTA file containing the tentative consensus (TC) sequences of the assembly along with the number of reads integrated into each consensus. The TC was required to have at least a 3-fold coverage. The resulting TC FASTA for each sample was then evaluated using BLASTn (Altschul et al., 1990) against a custom database derived from the RDP-II database (Cole et al., 2005) and GenBank website (http://www.ncbi.nlm. nih.gov/). The sequences contained within the cu-
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Table 1. Chemical compositions (g kg -1 dry matter) of experimental diets fed to meat goats
Experimental diet Item
*
Control
PB
SLP
PB+SLP
Dry matter (g kg -1)
903
902
903
898
Crude protein
148
130
160
147
Neutral detergent fiber
486
516
429
456
Ether extract
31.1
33.5
41.3
36.5
Crude ash
91.0
99.7
102
100
NFC
287
230
268
258
Total condensed tannins
4.92
49.0
40.1
45.0
mDP
-
10.5
30.0
-
PC (% in condensed tannins)
-
87.6
0.84
-
PD (% in condensed tannins)
-
12.4
99.2
-
Control, diet without condensed tannins (CT) supplementation; PB, diet with 30% pine bark (PB) and 70% grain mix; SLP, diet with 30% sericea lespedeza (SLP) leaf pellet and 70% grain mix; PB+SLP, diet with 15% PB, 15% SL pellet and 70% grain mix. *NFC: non-fibre carbohydrates (1000 – crude protein – neutral detergent fibre – ether extract – crude ash, g kg -1 dry matter); mDP = mean degree of polymerization; PC = procyanidins; PD = prodelphinidins.
rated 16S database were both >1200 bp and considered as a high quality based upon RDP-II standards.
Data processing and statistical analysis Statistical analyses were performed using the SPSS package (SPSS Inc., v 17.0, Chicago, IL). Relative abundance data are presented as percentages/ 156
proportions, but prior to subjection to GLM, they were transformed using the arcsine function for normal distribution prior to analysis. Package of NCSS (NCSS, 2007, v 7.1.2, Kaysville, UT) was used for cluster analysis through which double dendrograms were generated through the use of the Manhattan distance method with no scaling and the unweighted pair technique. Raw sequences were submitted to the NCBI Sequence Read Archive. In addition,
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Table 2. Ruminal volatile fatty acid (mol 100 mol-1) profiles in the rumen of meat goats fed diets without or with a source of condensed tannins Dietary treatment Item
SEM
P-value
Control
PB
SL
PB+SLP
Acetate (A)
61.8a
50.1b
65.3a
46.1b
4.80
0.05
Propionate (P)
15.2
12.4
16.2
13.4
1.35
0.29
Butyrate
12.5
10.2
13.7
8.91
1.65
0.23
Isobutyrate
1.31ab
0.90b
1.83a
1.48ab
0.118
<0.01
Isovalerate
2.23ab
1.46b
3.24a
2.52ab
0.192
0.01
Valerate
1.35b
1.04b
1.87a
1.53ab
0.154
0.01
Caprorate
0.21ab
0.18b
0.31a
0.04b
0.045
0.01
A:P
4.17a
3.51b
4.08a
4.15a
0.136
0.05
Control, diet without condensed tannins (CT) supplementation; PB, diet with 30% pine bark (PB) and 70% grain mix; SLP, diet with 30% sericea lespedeza leaf pellet (SLP) and 70% grain mix; PB+SLP, diet with 15% PB, 15% SLP and 70% grain mix. *Means with different superscripts in the same rows are different (P < 0.05).
quantification of major rumen bacterial phylum, classes and species populations was analyzed using the mixed model procedure in SAS (SAS Inst., Cary, NC) in a completely randomized design with a model that included the fixed effect of dietary treatments and the random effect of animal. Tags which did not have 100% homology to the original sample tag designation were not included in data analysis. Sequences which were less than 250 bp after quality trimming were also not considered. Means were compared using a protected (P < 0.05) Least Significant Differences (LSD) test. Unless otherwise stated, significance was declared at P ≤ 0.05, and tendency towards significance at 0.05 < P ≤ 0.10. All results are reported as Least Squares Mean (LSM).
RESULTS Characteristics of experimental diets Ingredients and chemical composition of experimental diets are presented in Table 1. Goats were provided diets that met all animals’ requirements for growth and gain according to National Research Council (2007). All the experimental diets provided similar nutrient profiles, except CT. Tannin analysis (Table 1) revealed that PB CT was mostly procyanidins: total CT consisted of 87.6% procyanidins (PC) and 12.4% prodelphinidins (PD), and total CT of 49.0% with mean degree of polymerization (mDP)values of 10.5. However, SLP was mostly PD with mDP values of 30.0.
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Table 3. Predominant bacterial phyla in the rumen of meat goats fed diets without or with a source of condensed tannins
Item
Dietary treatment
SEM
P-value
3.61
1.882
0.40
0.64
1.13
0.943
0.61
2.29
1.81
29.8
14.50
0.56
3.71
1.45
7.27
3.46
2.240
0.42
Firmicutes
47.7a
30.1b
43.7a
28.5b
7.82
0.05
Bacteroidetes
33.9b
55.7a
36.0ab
30.1b
7.15
0.05
Spirochaetes
0.86
2.28
2.42
1.57
0.662
0.49
Lentisphaerae
3.28
2.64
0.14
0.58
1.313
0.36
Control
PB
SLP
PB+SLP
Fibrobacteres
5.44
4.51
6.72
Actinobacteria
2.23
0.60
Proteobacteria
1.35
Tenericutes
Control, diet without condensed tannins (CT) supplementation; PB, diet with 30% pine bark (PB) and 70% grain mix; SLP, diet with 30% sericea lespedeza leaf pellet (SLP) and 70% grain mix; PB+SLP, diet with 15% PB, 15% SLP and 70% grain mix. *Means with different superscripts in the same rows are different (P < 0.05).
Ruminal VFA profiles Goats fed PB and PB+SLP (Table 2) decreased molar proportion of acetate (P < 0.05), while goats received PB reduced proportions of isobutyrate (P < 0.001), isovalerate (P < 0.01), and valerate (P < 0.01) compared to those in control and SLP (Table 2). Goats supplemented with CT-containing PB and SLP diets decreased (P < 0.01) caproic acid concentration compared to the control group.
Relative Abundance of Bacterial Phyla
position of the rumen fluids were examined at descending levels of biological classification to determine the effect of CT-containing diets on community membership. In this study, Bacteroides (30 to 55%) and Firmicutes (30 to 47%) were the major bacterial phyla. Goats received tannin-containing PB or PB with SLP mixed diets had significant (P < 0.05) decreased in Firmicutes bacterial phylum community, while Bacteroidetes population in the rumen of goats fed the PB diet was significantly increased. The bacterial distribution showed that Firmicutes was the most dominant phyla with mean relative abundance values ranging from 48% in control to 28 % in PB diets (Table 3).
In this study, bacterial (Table 3) community com158
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Table 4. Bacterial species community diversity (%) in the rumen of meat goats fed diets without or with a source of condensed tannins Item
Dietary treatment
SEM
P-value
0.49
1.312
0.38
5.73
3.09
2.223
0.57
2.17
5.81
4.60
2.159
0.45
0.76
2.17
2.36
1.52
0.651
0.41
Solobacterium spp.
0.16
0.03
1.00
0.07
0.469
0.49
Pseudobutyrivibrio spp.
3.96
0.13
1.25
4.67
2.184
0.49
Faecalibacterium spp.
1.18
1.37
2.98
0.71
1.290
0.49
Streptococcus spp.
3.22
1.82
2.66
0.93
0.819
0.34
Dermatophilus spp.
2.04
0.48
0.51
0.32
0.889
0.54
Bacteroidales spp.
2.64
0.30
0.22
0.19
0.577
0.09
Anaerosporobacter mobilis
0.56
0.10
0.16
0.10
0.251
0.47
Prevotella spp.
22.2
42.2
28.9
23.9
6.88
0.30
Fibrobacter spp.
5.32
4.04
5.82
3.15
1.891
0.75
Sphingobacterium spp.
1.37
0.10
0.10
0.10
0.685
0.49
Marinifilum spp.
0.14b
2.86a
0.10b
0.59b
0.476
0.04
Catenibacterium spp.
1.34
0.98
1.90
0.71
0.308
0.17
Paludibacter spp.
2.47
0.26
0.10
0.37
0.977
0.38
Blautia spp.
4.19
4.59
7.33
1.93
1.113
0.11
Asteroleplasma spp.
0.36
0.10
0.66
0.25
0.289
0.61
Bacteroides spp.
0.82b
3.24a
0.95b
1.00b
0.350
0.02
Fibrobacter succinogenes
0.07
0.43
0.90
0.35
0.014
0.05
Clostridium spp.
7.24
2.88
6.66
2.94
1.478
0.19
Xylanibacter spp.
1.19
2.54
2.96
1.48
0.794
0.43
Barnesiella spp.
2.06
2.58
1.56
0.83
0.484
0.21
Succiniclasticum spp.
2.14
5.70
4.71
3.92
0.917
0.18
Ruminococcus spp.
4.69
2.70
2.78
3.41
0.606
0.21
Coprococcus spp.
0.91
0.41
0.22
0.11
0.191
0.12
Oribacterium spp.
0.39
1.18
0.69
0.52
0.119
0.03
Robinsoniella spp.
2.71
1.19
0.24
0.30
1.259
0.54
Control
PB
SLP
PB+SLP
Victivallis spp.
3.16
2.61
0.10
Anaeroplasma spp.
2.71
1.00
Saccharofermentans spp.
7.59
Treponema spp.
b
a
b
b
Control, diet without condensed tannins (CT) supplementation; PB, diet with 30% pine bark (PB) and 70% grain mix; SLP, diet with 30% sericea lespedeza leaf pellet (SLP) and 70% grain mix; PB+SLP, diet with 15% PB, 15% SLP and 70% grain mix. *Means with different superscripts in the same rows are different (P < 0.05). Agric. Food Anal. Bacteriol. â&#x20AC;˘ AFABjournal.com â&#x20AC;˘ Vol. 5, Issue 3 - 2015
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Diversity and Abundance of Rumen Bacterial Species More than 390 bacterial species (including unknown) were classified (not in the text) from the ruminal fluid of the goats in this study. However, the relative abundances of the 29 most abundant species (>0.1%) are presented in Table 4. As shown in Table 4, Prevotella spp. was the most predominant rumen bacterial species in the percentage of 22.1, 42.2, 28.9, and 23.9 for the control, PB, SLP, and mixed diets, respectively. The community of rumen bacterial species in PB-supplemented group was greater for Marinifilum spp. (P < 0.04), Bacteroides spp. (P < 0.02), and Oribacterium spp. (P < 0.03) compared with other treatment groups. However, the community of Fibrobacter succinogenes was greater (P < 0.05) for SLP than for other treatments. Supplementing tannins in goat diets such as PB and SLP diets has a potential to positively modify rumen bacterial population, but there were no synergistic effects on rumen fermentation and rumen bacterial species. For ease of presentation and interpretation, we present prevalent bacterial genera (Figure 1) observed in the community based on a cutoff value of 0.1% of relative abundance for inclusion in a hierarchal cluster analysis of individual animal microbial diversity within and among diets in Figure 1. Overall, animals clustered relatively well within diet and animals. However, one of the PB powder supplemented goats had more dissimilarity between treatments. Control diet of goats (sample # 1 and 2) clustered more closely compared to the PB (# 3 and 4) and SLP (#5 and 6) supplementation. Goats that received SLP supplementation (# 5 and 6) had greater relative community abundance of Bacteroidia population compared to the control diet, and the opposite was observed for the Clostridia population (Figure 1).
DISCUSSION Regardless of numerous studies (Callaway et al., 160
2010; Pitta et al., 2010; Hristov et al., 2012) demonstrating the role of the gut microbial diversity in ruminants associated with different sources of forages or dried distillers grains, responses of the bacterial diversity to feeding various sources of phytochemical tannins-containing diets remain largely unknown. The most significant findings in the present study were when goats fed tannin-containing PB or PB with SL mixed diets, there was a decreased in the Firmicutes bacterial phylum populations (P < 0.05), while Bacteroidetes populations were increased. The bacterial distribution showed that Firmicutes was the most dominant phyla with mean relative abundance values ranging from 48% in control to 28 % in PB diets. This suggests that phytochemical tannins supplementation sizably affected microbiota diversity on goats grazing fresh forage diets.
Relative Abundance of Bacterial Phyla In this study, Bacteroides (30 to 55%) and Firmicutes (30 to 47%) were the major bacterial phyla. Goats received tannin-containing PB or PB with SLP mixed diets had significant (P < 0.05) decreases in Firmicutes bacterial phylum community, while the Bacteroidetes population in PB diet was significantly increased. The bacterial distribution showed that Firmicutes was the most dominant phyla with mean relative abundance values ranging from 48% in control to 28 % in PB diets (Table 3). Interestingly, the gastrointestinal tracts of humans and many other vertebrae are mostly dominated by two groups of bacteria, Bacteroidetes and Firmicutes (Backhed et al., 2004), which is similar to the result obtained current study. This finding agrees with the results within the current goat study showing that Firmicutes and Bacteroidetes were the dominant bacterial phyla in the goat rumen fluid. This has been confirmed by the findings that Firmicutes, Bacteroidetes, Actinobacteria, and Proteobacteria were reported to be dominant bacterial phyla in the goat intestine (Min et al., 2014a) and human gut (Schloss et al., 2009). Levels of the two types of beneficial microbes or bacteria in the gastrointestinal tract that help to
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Figure 1. Thermal double dendrogram of the 60 most abundant bacterial genera in the rumen of various sources of tannin-containing supplementation from a common cohort of 24 meat goats. Clustering in the Y-direction is indicative of abundance, not phylogenetic similarity. RA = relative abundance; Control (0%) = tag no. 1 and 2; 30% pine bark (PB) = tag no. 3 and 4; 30% sericea lespedeza pellets (SLP) = tag no. 5 and 6, and 15% PB + 15% SLP = tag no. 7 and 8 on an as-fed basis. Rumen fluid samples from six animals per treatment were pooled to two samples sizes within treatment for bacterial analysis. 140
Relative Abundance (%) 61.6
120
Dissimilarity
100 80 60 40
0
20 0
liliopsida phycisphaerae methanobacteria brc1 (candidate division) erysipelotrichi synergistia thermoleophilia dothideomycetes opitutae thermomicrobia methanomicrobia ws3 (candidate division) bacilli nitriliruptoria lentisphaeria thermoplasmata sordariomycetes planctomycetacia spirochaetes elusimicrobia fusobacteria (class) archaeoglobi betaproteobacteria bacteroidia verrucomicrobiae alphaproteobacteria chloroflexi sphingobacteria gammaproteobacteria epsilonproteobacteria clostridia aquificae mollicutes actinobacteria (class) streptophyta spartobacteria nitrospira (class) coriobacteria acidimicrobiia tm6 (candidate division) actinobacteria charophyceae acidobacteria tm7 (candidate division) planctomycea ksb1 (candidate division) gemmatimonadetes flavobacteria op11 (candidate division) chlamydiae (class) candidatus_methylacidiphilum anaerolineae ws6 (candidate division) fibrobacteria cytophagia deltaproteobacteria fibrobacteres (class) pezizomycetes subsectionii
3
5
1
2
7
6
4
8
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break down foods are different in obese and lean people and mice (Ferrer et al., 2013). There are trillions of bacteria in the gastrointestinal tract, but the two groups called the Bacteroidetes and the Firmicutes are the most dominant and their proportion varies in lean and obese mice and humans (Turnbaugh et al., 2006). Ferrer et al. (2013) reported that in the obese gut, the total microbiota was more abundant on the phylum Firmicutes (94.6%) as compared with Bacteroidetes (3.2%), but the lean gut showed a remarkable shift towards Bacteroidetes. The proportion of Bacteroidetes bacteria is lower in obese mice and people than in lean people (Turnbaugh et al. 2006). Gut microorganisms benefit the
due to microbiota, such that all are affected by alterations in nutritional state. McNabb et al. (1997) reported on the ruminal digestion of plant proteins in relation to different types of CT from Lotus corniculatus (Birdsfoot trefoil) and Lotus pedunculatus (big trefoil). L. corniculatus CT consisted largely of procyanidins, while L. pedunculatus CT contained largely prodelphinidins. Although CT from both species were able to reduce in vitro ruminal degradation of plant proteins, CT from L. pedunculatus were more effective than CT from L. corniculatus at reducing protein degradation. Data from the current study revealed that PB CT were mostly procyanidins, while SLP contained
host by assembling the energy from the fermentation of undigested carbohydrates and the subsequent absorption of short-chain fatty acids (SCFA). Although exact mechanisms are not yet known, it has been observed that obesity due to a high fat or high polysaccharide diet correlates with a decrease in the amount of Bacteroidetes and a proportional increase in Firmicutes (Dibaise et al., 2012). There may be a microbial component to obesity. Turnbaugh et al. (2006) observed that the microbiota of obese individuals are more heavily enriched with bacteria of the phylum Firmicutes and less with Bacteroidetes, and they surmise that this bacterial mix may be more efficient at extracting energy from a given diet than the microbiota of lean individuals (which have the opposite proportions). However, it is unclear what factors in the setting of average daily gain tip the scales in favor of the Firmicutes over Bacteroidetes in ruminants. The current study exhibited similar trends to human studies particularly for the PB and PB+SLP mixed supplemented groups, except SLP had lower rumen acetate concentrations, and had lower Firmicutes populations compared to control group. Perhaps the Bacteroides may possess may more tannins-resistant mechanisms or more diverse enzymatic capabilities (Od-
mostly prodelpinidins that may be important for determining the ruminal microbial community in livestock. Recently, Han et al. (2015) reported that phyla Firmicutes and Synergistetes were predominant in samples taken from 80 to 100-day-old goats, but Bacteroidetes and Firmicutes became the most abundant phyla in samples from 110-day-old goats. A study by Henderson et al. (2013) demonstrated an increase in the abundance of the phylum Firmicutes correlated with a decrease in the abundance of Bacteroidetes in cow (r= -0.805) and sheep (r= -0.976), which also shows similarity to the results obtained in the current study. Data in the current study has shown that the number of Bacteroidetes were notably greater than the number of Firmicutes in PB fed animals compared to alfalfa supplemented animals, and vice versa for the SLP diet compared to control group in meat goats. The mechanism of action of tannin-resistant bacteria in animals exposed to condensed tannins is not known between two different dietary supplementations.
enyo and Osuji,1998; Smith et al., 2003) that more efficiently extract energy when a variety of complex organic substrates are available in goat rumens. This hypothesizes that the metabolic and energy extraction functions in ruminants may be fundamentally 162
Diversity and Abundance of Rumen Bacterial Species As shown in Table 4, Prevotella spp. was the most predominant rumen bacterial species in the percentage of 22.1, 42.2, 28.9, and 23.9 for control, PB, SLP, and mixed diets, respectively. This has been
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confirmed by the findings that Prevotella spp. (21 to 40%) and Ruminococcaceae spp. (12 to15%) were reported to be dominant bacterial species in the goat intestine (Min et al., 2014a). However, in the present study, Prevotella spp. was not changed (P = 0.30) with or without CT-containing diets. It has been shown that the gut microbial community is dominated by Prevotella (18.2% of total population) in the rumen and Clostridium (19.7% of total population) in the feces of cattle (Callaway et al., 2010). However, these findings did not always reveal similar trends of fecal bacterial species between cattle and goats based on the results of similar molecular sequencing study of goats. Min et al. (2014a) report-
CONCLUSIONS
ed that Stenotrophomonas koreensis was the most dominant fecal bacterial species (23.9% of total fecal bacterial population) in meat goats. The community of rumen bacterial species in PB-supplemented group was greater for Marinifilum spp. (P < 0.04), Bacteroides spp. (P < 0.02), and Oribacterium spp. (P < 0.03) compared with other treatment groups. However, the population of Fibrobacter succinogenes was greater (P < 0.05) for SLP than for other treatments. Supplementing tannins in goat diets such as PB and SLP diets has the potential to positively modify rumen bacterial population, but there were no synergistic effects on rumen fermentation and rumen bacterial species. In this study, bacterial groups from goats fed the control diet (sample # 1 and 2) clustered more close compared to the PB (# 3 and 4) and SLP (#5 and 6) supplementation. Goats that received SLP supplementation (# 5 and 6) had greater relative community abundance of Bacteroidia population compared to the control diet, and the opposite was observed for Clostridia population (Figure 1). Lower abundance of Clostridia in CT-containing diet groups compared to control diet, indicated that CT-containing diets supplementation may have decreased the abundance of Clostridia population in the rumen
and decreases in selected microbial population. There is also possible adaptation of the ruminal microbiota to tannin and a beneficial effect of tannin on some classes of rumen microorganisms has been observed. However, there is a need for detailed studies involving effects of varying concentrations of tannins on rumen bacteria, archaea and fungal diversity of goats in response to ingestion of different sources of tannin-containing diets.
community of goats. Likewise, analysis of human microbiota-associated rat feces using molecular approach has revealed that the Bacteroides/Prevotella and Faecalibacterium species are dominant in both humans and rats post-transfection (Licht et al., 2007).
MD, USA, 16th edition. Backhed, F., H. Ding, T. Wang, L.V. Hooper, G.Y. Koh, A.A. Nagy, C.F. Semenkovich, and J.I. Gordon. 2004. The gut microbiota as an environmental factor that regulates fat storage. Proc. Natl. Acad.
In conclusion, our study provides a potentially specific explanation of goat rumen fermentation responses associated with alterations in the goat rumen bacterial microbiome. The current results demonstrate that dietary tannin can employ a positive/ negative effect both on rumen fermentation and on the rumen microbiome, and it is possible that this effect is dependent on dietary sources of tannins or tannin-containing diets. Rumen microbial population can be considered to be relatively dynamic and consequently reflective of the impact of tannin intake from the diet by the corresponding increases
ACKNOWLEDGEMENTS The authors thank the George Washington Carver Agricultural Experiment Station, Tuskegee University and USDA Southern Region Sustainable Agriculture Research and Education grant.
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Influence of Market Setting and Time of Purchase on Bacterial Counts and Prevalence of Salmonella and Listeria in Pork in Vietnam A. K. McCain1, P. T. T. Vu2, T. T. M. Tran2, M. V. V. Le2, D. H. Nguyen2, P. R. Broadway3, L. M. Guillen4, M. M. Brashears4, J. R. Donaldson5, M. W. Schilling6, and T. T. N. Dinh1 Department of Animal and Dairy Sciences, Mississippi State University, Mississippi State, MS Department of Food Technology, Ho Chi Minh City University of Technology, Ho Chi Minh City, Vietnam 3 USDA, Agricultural Research Services, Livestock Issues Research Unit, Lubbock, TX 4 Department of Animal and Food Sciences, Texas Tech University, Lubbock, TX 5 Department of Biological Sciences, Mississippi State University, Mississippi State, MS 6 Department of Food Science, Nutrition, and Health Promotion, Mississippi State University, Mississippi State, MS 1
2
ABSTRACT The objective of this study was to determine the influence of market type and sampling time on Salmonella and Listeria prevalence and bacterial population levels from 180 pork samples collected in 6 supermarkets (SM), 6 indoor markets (IM), and 6 open markets (OM) at the opening of the markets (T0) and 4 h after the opening (T4) in Vietnam. Salmonella and Listeria prevalence was greater than 42.9 and 64.0%, respectively. Salmonella prevalence was influenced by market type (P = 0.049), but not by sampling time (P = 0.700). On average, pork from these markets exhibited more than 11.5, 7.4, and 10.4 logs of aerobic bacteria, Escherichia coli, and coliforms, respectively. Escherichia coli counts of pork in IM and OM were greater at T4 by 2.9 and 1.5 logs (P < 0.001 and P = 0.045, respectively), whereas they were similar in SM at both sampling times (P = 0.925). Covered meat display case was used by 50.0, 33.3, and 0.0% of SM, IM, and OM vendors at T0 and by 83.3, 0.0, and 0.0% of SM, IM, and OM vendors at T4, respectively. Refrigeration was used by 50.0 and 100.0% of SM vendors at T0 and T4, respectively and only by 53.3% of IM at T0 for storage. No OM pork vendor used refrigeration, gloves, or hairnets. No SM, IM, or OM pork vendor employed hot water. Cold water was used at T0 by 16.7, 25.5, and 0.0% of SM, IM, and OM vendors and by 45.0, 8.3, and 1.7% of SM, IM, and OM vendors at T4. Pork at retail establishments in Vietnam had great bacterial population levels and incidence rate of Salmonella and Listeria in addition to widespread improper handling practices, which highlights a need for mandatory interventions and educational programs that can improve food safety and protect public health. Keywords: Pork, Salmonella, Listeria, Escherichia coli, coliforms, retail, developing countries, safety, quality, Vietnam Agric. Food Anal. Bacteriol. 5: 166-182, 2015
Correspondence: Thu T. N. Dinh, thu.dinh@msstate.edu Tel: +1 -662-325-7554
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INTRODUCTION Pork is the most highly consumed meat in the world (FAO, 2014). In the U.S., pork consumption has remained constant over the past 20 years (Baer et al., 2013). However, in Asian countries, pork has always been a major source of animal proteins, and consumption continues to increase with economic development (USDA/ERS, 2013). Because pork products are very popular in developing countries and pork can be a source of foodborne diseases (Baer et al., 2013), microbiological attributes of pork supply are important for public health. Small-scale operations with less than 20 pigs con-
countries lack information and capabilities to develop systematic approaches towards processing interventions and epidemiological investigations to minimize the impact of foodborne illnesses (Kaferstein, 2003; Chaves et al., 2015). In addition, improper practices of meat vendors and poorly designed and regulated packing plants in developing countries increase the risk of contamination. Many meat vendors in developing countries do not refrigerate fresh meat and poultry products, allowing pathogenic bacteria such as Salmonella and Listeria to grow, which causes serious food security challenges (Kinsey, 2005). Meat is among the most nutritious foods in developing countries, especially for young children (God-
stitute 70% of pig production in Vietnam (Huynh et al., 2007). A few large-scale swine farms can accommodate 18,000 pigs, accounting for 15 to 20% of pig production (Huynh et al., 2007; Lemke et al., 2008). Most swine farms in Vietnam serve multiple purposes because Vietnamese producers use integrated systems, combining animal species with crops and fish, in which manure production may become more important and more profitable than pork (Huynh et al., 2007). Because of small-scale production, a major challenge for commercial pork production in Vietnam is the lack of knowledge of zoonotic disease control (Foley et al., 2008). Zoonotic diseases, such as salmonellosis, can be spread by poor hygienic practices and improper waste disposal (Huynh et al., 2007). Salmonella resides in the intestinal tract of pigs and shedding of bacteria is the major route for Salmonella infection (Baer et al., 2013). Similarly, Listeria monocytogenes can also persist in wet feeds and moist areas of farms (Baer et al., 2013). When pigs are slaughtered, carcass contamination can occur through infected live animals or cross-contamination from the environment (Li et al., 2016), processing equipment, and other carcasses (Van Damme et al., 2015). However, prevalence of Salmonella and Listeria can be reduced by physical interventions such
fray et al., 2010). Moreover, pork is the most important source of animal proteins in Vietnamese households (Tisdell, 2009). Per capita consumption of all meats has been increasing with increased incomes; however, pork remains the most highly consumed meat in Vietnam (USDA/ERS, 2013). The government of Vietnam believes that large-scale pig production can improve the safety and quality of pork supply (Tisdell, 2009). However, traditional markets, e.g., indoor and open markets described in the current study, and small-scale pig processing plants are valuable to consumers in developing countries because they are loyal to familiar vendors and perceive meat and poultry at these vendors as fresher and cheaper. Traditional meat markets expose products to open atmosphere without refrigeration. Supermarkets have access to refrigeration and covered display cases, but still face safety challenges because they primarily sell meats from similar sources (Chamhuri and Batt, 2013). Studies in developing countries such as Nepal, Vietnam, and China, have focused on the contamination of one microorganism in meat products (Maharjan et al., 2006; Van et al., 2007; Yang et al., 2010). Multi-pathogen data in retail settings are lacking. Therefore, prevalence of important pathogenic bacteria such as Salmonella and Listeria and total
as removal of lymph nodes, hot water wash, acid spray, carcass rinse, and carcass chilling (Schmidt et al., 2012). These interventions are commonly used in developed countries (Pearson et al., 2004; Schmidt et al., 2012; Baer et al., 2013). However, developing
aerobic bacterial loads in meat and poultry products, specifically pork, are important to establish a baseline of contamination so that further investigations into contamination sources and interventions can be devised. A previous study indicated that market
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Table 1. Characteristics used to classify supermarkets (SM), indoor markets (IM), and open markets (OM) across three regions of Vietnam.
Market Type Market Characteristics SM Multiple vendors
IM
OM
√
√
Air-conditioning
√
Refrigeration
√
Walls
√
√
Roof
√
√
Clean water availability
√
√
√
√ Existing characteristics.
setting and time of purchase are important merchandising factors for microbiological attributes of retail beef (McCain et al., 2015); therefore, the objective of this study was to investigate the effects of these two factors on bacterial counts and prevalence of Salmonella and Listeria in retail pork in Vietnam.
Sample collection occurred between January and May of 2015 and followed the similar procedure described by McCain et al. (2015). Ho Chi Minh City, Da Nang, Ha Noi, and their surrounding areas were selected to represent regional variation
of individual markets (T0) and 4 h after the opening (T4). Five 200-g pork Longissimus muscle samples were collected separately and aseptically from various vendors at each sampling time, resulting in 180 samples. Vendors were randomized as described by McCain et al. (2015). Although no vendor randomization was performed in the SM, pork samples in the SM were purchased individually from different pork loins by different purchasers. Samples were placed separately in sterile Whirl-Pak bags® (Nasco, Fort Atkinson, WI) and the bags were sealed immediately after the meat surface temperature was recorded by a Fisher Scientific™ Traceable™ Infrared Thermometer Gun (Fisher Scientific, Waltham, MA). Samples were stored in an Igloo Super Tough Sportsman ice chest (Igloo, Katy, TX) with frozen ice packs and transported to a local university in each region. An
in meat merchandising in Vietnam. Supermarkets (SM), indoor markets (IM), and open markets (OM) were described in Table 1. Two markets per market type in each region were geographically selected to procure domestically produced pork at the opening
entire sample (approx. 200 g) was weighed and shaken for 60 s in 90 mL of Buffered Peptone Water broth (BPW; 25.5 g/L; 3M, St. Paul, MN; Vipham et al., 2012), which was added to Whirl-Pak® bags (Nasco, Fort Atkinson, WI). Two sterile 15-mL polypropyl-
MATERIALS AND METHODS Sample Collection and Preparation
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ene tubes (Greiner Bio-One, Monroe, NC) of BPW rinsate were transported on ice to Ho Chi Minh City University of Technology for further analyses.
Microbiological Analysis Salmonella was analyzed as described by McCain et al. (2015). Briefly, 2.5 mL of BPW rinsate was combined with 22.5 mL of Salmonella Enrichment Broth (3M, St. Paul, MN) in a sterile Whirl-Pak® bag (Nasco, Fort Atkinson, WI) and incubated at 45°C for 24 h. One milliliter (1 mL) of the incubated solution was combined with 10 mL of Rappaport-Vassiliadis R10
Market Characteristics Market and environmental data were collected by using a form to record outdoor temperature (ºC), relative humidity (%), meat surface temperature (ºC), type of retail display, availability of refrigeration, use of gloves and hairnets, knife cleaning, and water availability. Data were recorded for individual samples.
Calculation and Statistical Analysis Salmonella and Listeria prevalence was reported as percentage of positive samples estimated by the
Broth (RVR10; 3M, St. Paul, MN) in a 15-mL polypropylene tube (Greiner Bio-One, Monroe, NC) and incubated at 41.5°C for 24 h. Ten microliters (10 µL) of the incubated RVR10 solution was streaked onto a hydrated 3M™ Petrifilm™ of the Salmonella Express System. The Petrifilm™ was incubated at 41.5°C for 24 h. Presumptive positive Salmonella spp. colonies were identified by a red color with yellow halo (3M, 2015a). Listeria was also detected as described by McCain et al. (2015). Similarly, a volume of 2.5 mL of BPW rinsate was combined with 22.5 mL of Demi-Fraser Listeria Enrichment Broth (3M, St. Paul, MN) in a sterile Whirl-Pak® bag (Nasco, Fort Atkinson, WI) and incubated at 30°C for 24 h. A volume of 0.1 mL of the incubated solution was spread onto an ALOA® agar petri dish and incubated at 37°C for 24 h. Presumptive positive Listeria spp. colonies were identified by a blue to green color with or without halo. Analyses of aerobic bacteria (Aerobic Plate Count, APC), E. coli, and coliforms were performed as described by McCain et al. (2015). Fifteen microliters (15 µL) of BPW rinsate was serially diluted (1:100) by combining with 1,485 µL of sterile BPW broth. One milliliter (1 mL) of each dilution was spread onto an APC Petrifilm™ or an E. coli/Coliform Petrifilm™.
statistical model. Counts of aerobic bacteria, E. coli, and coliforms were reported as log CFU/g, calculated from CFU as follows:
The Petrifilm™ was incubated at 35°C for 24 h. Colony forming units (CFU) were counted according to the 3M interpretation guides (3M, 2015b; 3M, 2015c).
ket type, sampling time, and their interaction were the fixed effects, whereas region was the random effect. Means were separated by the protected t-test in the PDIFF option of the LSMEANS statement. Statistical significance was determined at P ≤ 0.10.
with N, V, DF, V0, and m being number of colony forming units on a Petrifilm™, volume of a dilution spread onto a Petrifilm™ (1 mL), dilution factor, original volume of BPW rinsate (90 mL), and sample weight (g), respectively. Market characteristic data were reported as crude percentage without statistical analysis. Prevalence of Salmonella and Listeria were analyzed as a 3 × 2 factorial arrangement in a randomized complete block design with region as block, market type (SM, IM, and OM) and sampling time (T0 and T4) as two factors, and a specific market at a specific sampling time (n = 6) as the experimental unit, by using logistic regression. Bacterial counts were analyzed as the same design using linear regression; however, the experimental unit was pork sample (n = 30). Statistical analyses were performed by using a generalized linear mixed model of SAS version 9.4 (SAS Institute, Inc., Cary, NC, USA) in the GLIMMIX procedure. Mar-
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RESULTS AND DISCUSSION
There was no overall market type effect on bacterial counts (Figure 1; P > 0.191). Pork purchased in these markets had greater than 11.4, 7.4, and 10.4 log CFU/g of aerobic bacteria, E. coli, and coliforms, respectively. Similar to a previous study on beef (McCain et al., 2015), many of the APC Petrifilms™ were too numerous to count (TNTC) at the 10-6 dilution (3M, 2015d) and estimated at 108 CFU. Although no sampling time effect was found for APC (P = 0.277; Figure 2), coliform counts were greater at T4 (11.4
especially those from bacterial metabolism of amino acids (Nychas et al., 2008). Some pork products have been classified as either spoiled or having unacceptable quality by having 4.5 to 6.0 logs of total bacteria counts (Zhao et al., 2015; Ma et al., 2014). However, in developing countries, these excessive bacterial counts are common and indicative of poor hygienic conditions across the production systems. Most pork in Vietnam is consumed within 24 h postmortem because it is the most popular food protein at the markets. In a separate study on beef off-flavor using the same experimental design as in the current study, there was no significant off-flavor development (data in review) even though bacterial counts
logs) than T0 (10.9 logs; P = 0.083). There was a market type × sampling time interaction (P = 0.016; Figure 3) influencing E. coli counts. They were 7.4 and 8.6 logs at T0 for IM and OM, respectively and were increased to 10.3 and 10.1 logs at T4 (P < 0.001 and P = 0.045, respectively; Table 2). However, E. coli counts remained the same on pork purchased from SM (P = 0.925). Bacterial genera on post-slaughter meat surface primarily are Pseudomonas spp., Acinetobacter spp., Aeromonas spp., Brochothrix thermosphacta, lactic acid bacteria such as Lactobacillus, and Enterobacteriaceae (Duffy et al., 2009), causing meat spoilage. It is generally understood that meat is an excellent environment for microbial growth; however, the bacterial counts determined in retail pork in Vietnam were much greater than those normally observed in the U.S. and other developed countries. Total plate counts on pork in Vietnam were 11.5 to 11.6 log CFU/g (200-g samples), compared with approximately 3.8 log CFU/cm2 of aerobic mesophilic bacteria (50-cm2 swabbed area) on chilled pork carcasses in the EU (Pearce et al., 2004) and 2.0 log CFU/cm2 of total viable count (300-cm2 swabbed area) on post-chill pork carcasses in the U.S. (USDA/ FSIS, 2011). A total viable count of 7 to 8 logs in fresh
(McCain et al., 2015) were as excessive as those in the current study and were much greater than conventionally acceptable counts of fresh meats. Bacterial composition, i.e., species and counts, depends on the initial contamination and environmental conditions (Duffy et al., 2009). Therefore, composition of the bacterial flora of retail pork is the end result of these factors and the colonization occurring during slaughter, processing, and distribution (Van Damme et al., 2015). Traditional markets do not provide a clean and hygienic environment that helps prevent bacterial contamination and growth. In Nigeria, a developing country with similar meat merchandising venues to those in Vietnam, 5.6 log CFU/g of E. coli was documented in pork retail establishments (Adesiji et al., 2011). These authors attributed the greater bacterial population levels to contamination during slaughter processing and water contamination because the markets were close to a stream where fecal materials were to be disposed. Similar E. coli counts for minced pork were reported in Greece, a developed country (UN, 2012), at 6.7 and 7.2 log CFU/g in both butcher’s shops and supermarkets, respectively (Andritsos et al., 2012). In the current study, it was observed that contamination during processing, transportation of whole carcasses
meat in most developed markets is a strong indicator of meat spoilage (Duffy et al., 2009) because the growth of bacteria from 2 logs to 7 logs requires prolonged storage or display under refrigeration, which ultimately produces numerous unpleasant off-odors,
in open air, and non-hygienic conditions at the markets could contribute greatly to the bacterial counts.
Microbiological Quality
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Within market type, means without common letters differ (P ≤ 0.10).
Within sampling time, means without common letters differ (P ≤ 0.10).
ab
Values were reported as estimated least squares means ± standard error of the means.
Listeria, detected using ALOA® media (BioMerieux, St. Louis, MI).
xy
*
Salmonella, detected using 3M™ Petrifilm™ Salmonella Express System (3M, St. Paul, MN).
64.0 ± 11.0 ax
42.9 ± 11.6ax
5
84.4 ± 7.6 ax
T4
10.11 ± 0.42 ay
81.1 ± 8.4 ax
53.4 ± 11.7ax
10.85 ± 0.45 ax 11.23 ± 0.39 ax
8.63 ± 0.80 ax
Coliform, enumerated using 3M™ Petrifilm™ E. coli/Coliform Count Plates (3M, St. Paul, MN).
90.8 ± 5.7 ax
60.5 ± 11.4bx
T0
OM
11.61 ± 0.01 ax 11.62 ± 0.01 ax
4
77.7 ± 9.0 ax
71.0 ± 10.3bx
11.49 ± 0.12ax
Escherichia coli, enumerated using 3M™ Petrifilm™ E. coli/Coliform Count Plates (3M, St. Paul, MN).
77.7 ± 9.0 ax
Listeria5 prevalence, %
67.5 ± 10.8bx
10.41 ± 0.56ax
10.33 ± 0.44 ay
11.62 ± 0.02 ax
T4
3
74.4 ± 9.8bx
Salmonella4 prevalence, %
11.47 ± 0.12 ax
7.38 ± 0.74 ax
11.46 ± 0.12 ax
T0
Aerobic Plate Count, enumerated using 3M™ Petrifilm™ Aerobic Plate Count (3M, St. Paul, MN).
11.49 ± 0.13ax
Coliform3, log CFU/g
9.07 ± 0.45 ax
IM
2
9.14 ± 0.44 ax
E. coli2, log CFU/g
11.59 ± 0.01ax
T4
1
11.62 ± 0.01ax
T0
APC1, log CFU/g
Measurement*
Microbiological
SM
0.162
0.049
0.245
0.613
0.313
type
Pmarket
0.817
0.700
0.083
< 0.001
0.277
Ptime
0.319
0.469
0.216
0.016
0.163
type*time
Pmarket
Table 2. Bacterial counts and the prevalence of Salmonella and Listeria in pork (N = 180) procured from supermarkets (SM), indoor markets (IM), open markets (OM) at the market opening (T0) and 4 h after the opening (T4) across three regions of Vietnam (Ho Chi Minh City, Da Nang, and Ha Noi).
Figure 1. Aerobic bacteria and coliforms counts (log CFU/g) of pork (N = 180) purchased at the supermarket (SM), indoor market (IM), and open market (OM), in Ho Chi Minh City, Da Nang, and Ha Noi in Vietnam, averaged across the two sampling times. Within a category of bacterial count, means without common letters differ (Pmarket type = 0.313 and 0.245, respectively).
APC
14 12
a
a
a
a
Coliforms a
a
log CFU/g
10 8 6 4 2 0 SM
IM
OM
Market type
Prevalence of Salmonella Salmonella prevalence was 71.1, 65.9, and 48.1% in SM, IM, and OM, respectively (Figure 4). Market type influenced Salmonella prevalence in pork (P = 0.049) with OM exhibiting a smaller positive rate than both IM and SM (P = 0.069 and P = 0.021, respectively), whereas IM and SM Salmonella prevalence was similar (P = 0.559). There was no effect of sampling time on Salmonella incidence rate (P = 0.700; Figure 5). Vendors in the IM and OM received pork carcasses that might be different in microbial profile and production settings. These carcasses were cut at the markets, thereby creating opportunities for cross-contamination of pathogenic microorganisms,
can be carried over during display (Lo Fo Wong et al., 2002). A study conducted in Ha Noi in Vietnam discovered that more than 50% of pigs brought to packing plants carried Salmonella spp. (Le Bas et al., 2006). These authors concluded that farm practices, including transportation and lairage conditions were favorable for Salmonella shedding among pigs (Le Bas et al., 2006). They also revealed that water was greatly contaminated with Salmonella (62%), and was used for carcass rinsing after evisceration. Similar studies on pork carcasses, environmental surfaces in slaughter facilities, and retail markets conducted in Hue, Bac Ninh, Ha Noi, and Ha Tay in Vietnam revealed that more than 30% of retail pork (Thai et al., 2012), 15.5% of carcasses, and 16.7% of tank water
such as Salmonella. Researchers agree that retail display is possibly the weakest link in a commercial cold chain (James and Bailey, 1990). Therefore, if meat products are not refrigerated, Salmonella may proliferate to a dangerous number of cells that
were contaminated with Salmonella (Takeshi et al., 2009). Although these authors reported similar results, Salmonella incidence rate in their studies was smaller than that in the current study. In addition, the current study had a more comprehensive sam-
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Figure 2. Aerobic bacteria and coliform counts of pork (N = 180) purchased at two sampling times (opening - T0 and 4 h after opening - T4) in Ho Chi Minh City, Da Nang, and Ha Noi of Vietnam, averaged across supermarkets, indoor markets, and open markets. Within a category of bacterial count, means without common letters differ (Psampling time = 0.277 and 0.083, respectively).
APC 14 a
12
a
b
a
Coliforms
log CFU/g
10 8 6 4 2 0 T0
T4
Sampling time
Figure 3. E. coli counts of pork (N = 180) at opening (T0) and 4 h after opening (T4) in supermarkets (SM; P = 0.925), indoor markets (IM; P < 0.001), and open markets (OM; P = 0.045), varied by market type × sampling time interaction (Pmarket type × sampling time = 0.016).
14 IM
12
SM OM
log CFU/g
10 8 6 4 2 0 T0
T4 Sampling time
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Figure 4. Salmonella and Listeria prevalence in pork (180 samples) purchased from supermarkets (SM), indoor markets (IM), and open markets (OM) in Ho Chi Minh City, Da Nang, and Ha Noi of Vietnam, averaged across the two sampling times. Within a pathogen category, means without common letters differ, (Pmarket type = 0.049 and 0.162, respectively).
100
Prevalence, %
80
a
Salmonella Listeria
a
a
a
a b
60 40 20 0 SM
IM
OM
Market type
pling plan across the three regions of Vietnam. In narrower studies, Phan et al. (2005) and Van et al. (2007) also reported 69.9 and 64.0% prevalence of Salmonella in pork in the Mekong Delta region and Ho Chi Minh City, respectively, which was comparable to the occurrence rate in the current study. Developed countries such as Austria, Ireland, the U.K., and the U.S. have observed considerably less prevalence of Salmonella in retail markets, at 1.8, 9.9, 1.9, and 2.6%, respectively (Mayrhofer et al., 2004). A study in commercial pork slaughter facilities in the U.S. indicated that, 91% of pre-scald, 19.1% of preevisceration, and 3.7% of post-chill carcasses were contaminated with Salmonella (Schmidt et al., 2012). The decrease in Salmonella prevalence as carcasses moved through processing stages indicated that
nation decreased to 7% after evisceration and 1% after scalding. This finding was confirmed by an earlier nationwide microbiological baseline of market hogs, reported by the USDA/FSIS (2011), documenting a prevalence of 69.6% in pre-evisceration carcasses and 2.7% in post-chill carcasses. Duggan et al. (2010) reported that a Salmonella incidence rate of up to 69% on pork carcasses was the result of a contaminated slaughter environment. Differences between developing countries such as Vietnam and developed countries could be the contamination at various critical control points in the pork production chain. When pork products are contaminated, crosscontamination can occur unless carcasses or cuts are decontaminated (Berends et al., 1998), possibly through interventions such as carcass sprays of or-
appropriate critical control points during slaughter would decrease Salmonella incidence rate (Schmidt et al., 2012; Baer et al., 2013). Pearson et al. (2004) investigated critical control points in pork slaughter and reported that 31% prevalence during exsangui-
ganic acids (Castillo et al., 1998), which decreases pH to suppress bacterial growth (Baer et al., 2013). Hot water wash is as effective as organic acid spray (Baer et al., 2013), which is an applicable method in developing countries. According to data from Pearson et
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Figure 5. Salmonella and Listeria prevalence in pork (180 samples) purchased at opening (T0) and 4 h after opening (T4) in Ho Chi Minh City, Da Nang, and Ha Noi of Vietnam, averaged across supermarkets, indoor markets, and open markets. Within a pathogen category, means without common letters differ (Psampling time = 0.700 and 0.817, respectively).
Salmonella
100 a
Prevalence, %
80
a
Listeria
a
a
60 40 20 0 T0
T4 Sampling time
al. (2004) and FSIS report (USDA/FSIS, 2011), a reduction of Salmonella prevalence was accompanied by a decrease in total plate counts and coliforms. Ghafir et al. (2008) suggested a correlation between counts of E. coli, Enterobacteriaceae, and aerobic bacteria on cattle and pig carcasses. However, a Spearman rank correlation between aerobic bacteria, E. coli, and coliform counts and Salmonella prevalence in the current study was not significant (P > 0.388). Even with postharvest interventions, developed countries still face challenges in minimizing Salmonella prevalence in retail establishments. Sixty-four attendees in Hamilton County, Ohio were determined to suffer salmonellosis during a private event after consuming pulled pork (CDC, 2010). Most recently in 2015, the FSIS issued a public health alert for pork from Kapowin Meats of Graham, WA, because of possible Salmonella contamination, which was associated with a whole pig that was used for
pig roast (Johnston, 2015). Retail pork in Demark was reported to have a Salmonella incidence rate of 3 to 8%, with butcher shops being positive twice as often as supermarkets (Hansen et al., 2010). The authors indicated that this difference could be from hygienic conditions and cross-contamination caused by variation in handling procedures among retail venues (Hansen et al., 2010). However, in the current study, the prevalence of Salmonella in the IM and OM, which are similar to the butcher shop setting, was either similar to or smaller than that in the SM. It was observed that SM vendors in Vietnam behaved similarly to vendors of other market types, who did not follow good manufacturing practices such as cleaning knives, using hot water, or wearing gloves and hairnets.
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Prevalence of Listeria Market type did not affect Listeria prevalence in pork across the three regions of Vietnam (P = 0.162; Figure 4) with an average of 77.7, 87.9, and 73.4% in SM, IM, and OM, respectively. Moreover, similar to the case of Salmonella, Listeria prevalence was not affected by sampling time (P = 0.817, Figure 5), with an average of 79.7 and 81.2% for T0 and T4, respectively. Although there was much less prevalence of Listeria compared with that of Salmonella, it was clear that these rates of occurrence in retail venues in Vietnam were much greater than what has been reported in other studies. Columbian research-
thermore, chilling and cutting significantly increased the contamination of Listeria in pork (Nesbakken et al., 1996), and van den Elzen and Snijders (1993) reported that Listeria prevalence in the cutting areas was 71 to 100%. These findings suggest that postslaughter processing can increase bacterial contamination in meat, and that refrigeration may not be sufficient to suppress Listeria growth. The current study only assessed contamination at retail establishments. However, with the current greater rate of Listeria incidence, it was suspected that processing facilities, transportation, and water could be potential sources of contamination. Postharvest interventions combined with antimicrobials have been re-
ers observed a 33.9% prevalence in pork carcasses (Gamboa-Marín et al., 2012), which agreed with a study conducted in Tokyo with 35.7% positive pork carcasses (Ochiai et al., 2010). In contrast, research in the U.S., Finland, Bulgaria, Greece, and Canada revealed much lower Listeria incidence rates in pork products, ranging from 0.15 to 24% (Wesley and Ashton, 1991; Samelis and Metaxopoulos, 1999; Bohaychuk et al., 2006; Karkolev, 2009; Hellstrom et al., 2010). The lower prevalence in these countries was likely the result of implementing HACCP throughout the supply chain (Gamboa-Marín et al., 2012). Without proper practices at critical control points, a Listeria incidence rate of 69.8%, comparable to 77% in the SM in the current study, was reported in supermarkets in Ethiopia (Molla et al., 2004). Boerlin and Piffaretti (1991) observed less Listeria monocytogenes on live pigs than in pork after slaughter and fabrication. In addition, van den Elzen and Snijders (1993) indicated that chilling and a cold environment in the cutting room could facilitate Listeria contamination because Listeria is psychrotropic. Moreover, delicacies such as lungs, heart, diaphragm, kidneys, and liver are frequently consumed in Asian culinary cultures. In the current study, all markets in Vietnam had viscera on display and subse-
ported to decrease Listeria in pork products (Chen, 2005). Chlorine as well as thermal treatment can remove biofilm on processing equipment to reduce cross-contamination (Sánchez-Escalante et al., 2001) because Listeria, although more thermotolerant than other pathogens, is inactivated when heated above 70°C (Thévenot et al., 2006). These technologies can be applied in a multi-hurdle approach to eliminate Listeria contamination in Vietnam’s meat markets. However, it is important to recognize that Listeria has unique characteristics that help the bacteria adapt to environmental stress and become greatly resistant to pre- and post-harvest interventions (Thévenot et al., 2006).
quently in contact with pork whole muscle products. Autio et al. (2000) hypothesized that Listeria spread through contact with the viscera during processing. This can partially explain the great incidence rate of Listeria in pork at Vietnam’s meat markets. Fur-
covered pork during sampling time. As opposed to the previously reported retail beef study (McCain et al., 2015), pork loins were suspended from hooks in numerous markets in Vietnam. The hook suspension method was used to attract customers in IM and OM.
176
Market Characteristics Characteristics of markets and pork vendors are summarized in Table 3. Physical barriers among meat products and consumers were used only in SM and IM. At T0, 50 and 33.3% of SM and IM vendors, respectively, used covered display cases; however, 83.3% of SM vendors but no IM vendor used covered display cases at T4. This variation in meat display was observed across various supermarkets and indoor markets in the current study. No OM vendor
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16.7 ± 0.1
Fresh water, %
16.7 ± 0.2
0.0 ± 0.0
83.3 ± 0.2
22.0 ± 4.2
66.0 ± 6.5
29.3 ± 1.7
T4
45.0 ± 0.1
0.0 ± 0.0
0.0 ± 0.0
83.3 ± 0.2
50.0 ± 0.2
100.0 ± 0.0
SM
*Values were reported as means ± standard error of the means.
0.0 ± 0.0
33.3 ± 0.2
Hairnet, %
Hot water, %
16.7 ± 0.2
Gloves, %
0.0 ± 0.0
50.0 ± 0.2
Refrigeration, %
Cleaned knife before cutting, %
50.0 ± 0.2
50.0 ± 0.2
Covered display, %
Open display, %
21.7 ± 1.8
Meat surface temperature, °C
0.0 ± 0.0
68.7 ± 5.8
Humidity, %
Hang display, %
26.7 ± 1.1
T0
Outdoor temperature, °C
Market Characteristics
25.0 ± 0.1
0.0 ± 0.0
0.0 ± 0.0
33.3 ± 0.2
6.7 ± 0.1
53.3 ± 0.2
63.3 ± 0.2
3.3 ± 0.0
33.3 ± 0.2
27.3 ± 0.8
82.7 ± 4.1
25.4 ± 0.4
T0
IM
8.3 ± 0.1
0.0 ± 0.0
0.0 ± 0.0
0.0 ± 0.0
16.7 ± 0.1
0.0 ± 0.0
76.7 ± 0.2
23.3 ± 0.2
0.0 ± 0.0
25.9 ± 1.1
70.7 ± 5.8
27.8 ± 1.5
T4
0.0 ± 0.0
0.0 ± 0.0
0.0 ± 0.0
0.0 ± 0.0
0.0 ± 0.0
0.0 ± 0.0
83.3 ± 0.2
16.7 ± 0.2
0.0 ± 0.0
26.5 ± 1.0
73.5 ± 5.6
27.1 ± 1.7
T0
OM
1.7 ± 0.0
0.0 ± 0.0
0.0 ± 0.0
0.0 ± 0.0
0.0 ± 0.0
0.0 ± 0.0
100.0 ± 0.0
0.0 ± 0.0
0.0 ± 0.0
26.7 ± 1.0
63.6 ± 5.5
31.2 ± 1.7
T4
Table 3. Observational and environmental data collected during the purchase of pork (N = 180) from supermarkets (SM), indoor markets (IM), and open markets (OM) at the market opening (T0) and 4 h after the opening (T4) across three regions of Vietnam (Ho Chi Minh City, Da Nang, and Ha Noi).
Vendors in SM and IM used refrigeration, whereas OM vendors did not. The SM and IM vendors stored pork products under refrigeration at T0 to restock their meat displays. Unlike SM pork vendors, who always used refrigeration (100.0%), IM vendors did not use the refrigeration at T4 (0.0%) because refrigeration was only used for restocking purposes. Supermarkets stored pork products that were not purchased in the refrigerator, usually at 4°C, to be sold the next day. Gloves and hairnets were not frequently used either at T0 by SM and IM vendors, at 16.7 and 6.7%, or at T4, at 50.0 and 16.7%, respectively. Gloves and hairnets were not worn by any OM pork vendors across the three regions of Vietnam. Nei-
ria, and E. coli in pork, which contributes to the baseline of bacterial counts and prevalence in retail establishments in Vietnam. The percentage of Salmonella and Listeria positive pork products and population levels of indicator organisms such as aerobic bacteria, E. coli, and coliforms were much greater than previously reported data. Listeria prevalence is of particular concern because of consistently large percentage of positives across all markets instead of the sporadic presence observed in developed countries. Listeria is difficult to eliminate in processing environments and must be an important factor to be considered when developing interventions in Vietnam. The greater incidence rate and bacterial loads could
ther did pork vendors in SM, IM, or OM clean their knives before cutting meat nor did they have access to hot water. However, clean water was available to 16.7, 25.0, and 0.0% of SM, IM, and OM vendors at T0, respectively. At T4, 45.0, 8.3, and 1.7% of SM, IM, and OM vendors had access to clean water, respectively. Vendors did not use large quantities of water for cleaning because pork is such a popular meat protein in Vietnam (Tisdell, 2009) that it was sold rapidly across all markets. Vendors in IM and OM provided more reasonably priced pork products to the Vietnamese population than the SM. It was initially thought that SM vendors would provide safer pork products. However, all market types still lead to the same food safety concerns for pork because of the poor hygienic conditions and the lack of adherence to good manufacturing practices by the majority of the vendors. Particularly in IM and OM, limited access to safe water and sanitary services increases the safety risks of meat products (WHO, 2002). Although Salmonella and Listeria risks can be eliminated with proper cooking temperature, increased food safety knowledge and incentives for both consumers and vendors are needed to ensure compliance with food safety guidelines and regulations (Choudhury et al., 2011).
be partially attributed to the lack of good manufacturing practices at these respective markets, although contamination during production must also be considered. The current study, together with a previous report of microbiological baseline of retail beef (McCain et al., 2015), emphasizes the need of regulations, control of hazards, and education to improve the safety of meat products in Vietnam.
CONCLUSIONS The current study investigated Salmonella, Liste178
ACKNOWLEDGEMENTS This study was funded in part by the U.S. Borlaug Fellows in Global Food Security Program Graduate Research Grant (Grant #00000861). Work in Dr. Janet R. Donaldson’s laboratory was supported by NIH #P20GM103646. Microbiological training of researchers was provided by the International Center for Food Industry Excellence at Texas Tech University. The data are also based upon work that is supported by the National Institute of Food and Agriculture, U.S. Department of Agriculture, Multi-state Hatch project #1005775.
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Hops (Humulus lupulus) ß-Acid as an Inhibitor of Caprine Rumen Hyper-Ammonia-Producing Bacteria In Vitro M. D. Flythe, G. E. Aiken1, G. L. Gellin, J. L. Klotz, B. M. Goff, K. M. Andries
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ing author. Author proofs should be read carefully and checked against the typed manuscript, because the responsibility for proofreading is with the author(s). Corrections must be returned by e-mail. Changes sent by e-mail to the technical editor must indicate page, column, and line numbers for each correction to be made on the proof. Corrections can also be marked using “track changes” in Microsoft Word or using e-annotation tools for electronic proof correction in Adobe Acrobat to indicate necessary changes. Author alterations to proofs exceeding 5% of the original proof content will be charged to the author. All correspondence of proofs must be agreed to by the editorial office and the author within 48 hours or proof will be published as is and AFAB will assume no responsibility for errors that result in the final publication.
PUBLICATION CHARGES AFAB has two publication charge options: conventional page charges and rapid communication. The current charge for conventional publication is $25 per printed page in the journal. There is no additional charge for the publication of pages containing color images, micrographs or pictures. For authors who wish to have their papers processed as a rapid communication, authors will pay the rapid communication fee when proofs are returned to the editorial office in addition to twice the conventional page charges. Charges for rapid communications are $1000 per manuscript for guaranteed peer review within one week and $100 per journal page.
HARD COPY OFFPRINTS If you are wishing to obtain a physical hard copy of the AFAB journal, offprints are available in any quantity at an additional charge: $100/page for black-white and $150/page for color prints. You may order your offprints at any time after publication on our website. Scientific conference organizers may be expected to agree to a set number of offprints as a part of their agreement with AFAB.
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MANUSCRIPT CONTENT REQUIREMENTS Preparing the Manuscript File Manuscripts must be written in grammatically correct English. AFAB offers a fee based language service upon request (language@afabjournal.com). Manuscripts should be typed double-spaced, with lines and pages numbered consecutively. All documents must be submitted in Microsoft Word (.doc or .docx, PC or Mac). All special characters (e.g., Greek, math, symbols) should be inserted using the symbols palette available in this font. Tables and figures should be placed in separate sections at the end of the manuscript (not placed in the text). Failure to follow these instructions will cause delays of the processing and review of the manuscript.
Title Page At the very top of the title page, include a title of not more than 100 characters. Format the title with the first letter of each word capitalized. No abbreviations should be used. Under the title, the authors names are listed. Use the author’s initials for both first and middle names with a period (full-stop) between initials (e.g., W. A. Afab). Underneath the authors, a list affiliations must be listed. Please use numerical superscripts after the author’s names to designate affiliation. If an authors address has changed since the research was completed, this new information must be designated as “Current address:”. The corresponding author should be indicated with an asterisk e.g., * Corresponding author. The title page shall include the name and full address of the corresponding author. Telephone and e-mail address must also be provided for the corresponding author, and emailaddresses must be provided for all authors.
at the beginning of the manuscript. In vivo, in vitro and bacterial names must be italicized (obligatory). Authors must avoid single sentence paragraphs and merge such paragraphs appropriately. Authors must not begin sentences with “Figure or Table shows…” as these are inanimate objects and cannot “show” anything. When number are reported in text or in tables, always put a zero in front of decimal numbers: “0.10” instead of “.10”.
MANUSCRIPT SECTIONS Abstract The abstract provides an abridged version of the manuscript. Please submit your abstract on a separate page after the title page. The abstract should provide a justification of your work, objectives, methods, results, discussion and implications of study or review findings . Your abstract must consist of complete sentences without references to other work or footnotes and must not exceed 250 words. On the same page as your abstract, please provide at least ten (10) keywords to be used for linking and indexing. Ideally, these keywords should include significant words from the title.
Introduction The introduction should clearly present the foundation of the manuscript topic and what makes the research or the review unique. The introduction should validate why this topic is important based on previously published literature, and the relevance of the current research. Overall goals and project objectives must be clearly stated in the final sentence of the last paragraphs of the introduction.
Materials and Methods Editing Author-derived abbreviations should be defined at first use in the abstract and again in the body of the manuscript. If abbreviations are extensive authors may need to provide a list of abbreviations
Information on equipment and chemicals used must include the full company name, city, and state (country if outside the United States or Province if in Canada) [i.e., (Model 123, ACME Inc., Afab, AR)].
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Variability, Replication, and Statistical Analysis To properly assess biological systems independent replication of experiments and quantification of variation among replicates is required by AFAB. Reviewers and/or editors may request additional statistical analysis depending on the nature of the data and it will be the responsibility of the authors to respond appropriately. Statistical methods commonly used in the bacteriology do not need to be described in detail, but an adequate description and/or appropriate references should be provided. The statistical model and experimental unit must be designated when appropriate. The experimental unit is the smallest unit to which an individual treatment is imposed. For bacterial growth studies, the average of replicate tubes per single study per treatment is the experimental unit; therefore, individual studies must be replicated. Repeated time analyses of the same sample usually do not constitute independent experimental units. Measurements on the same experimental unit over time are also not independent and must not be considered as independent experimental units. For analysis of time effects, assess as a rate of change over time. Standard deviation refers to the variability in the biological response being measured and is presented as standard deviation or standard error according to the definitions described in statistical references or textbooks.
Results Results represent the presentation of data in words and all data should be described in same fashion. No discussion of literature is included in the results section.
Discussion The discussion section involves comparing the current data outcomes with previously published work in this area without repeating the text in the results section. Critical and in-depth dialogue is encouraged.
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Results and Discussion Results and discussion can be under combined or separate headings.
Conclusions State conclusions (not a summary) briefly in one paragraph.
Acknowledgments Acknowledgments of individuals should include institution, city, and state; city and country if not U.S.; and City or Province if in Canada. Copies being reviewed shall have authors’ institutions omitted to retain anonymity.
References a) Citing References In Text Authors of cited papers in the text are to be presented as follows: Adams and Harry (1992) or Smith and Jones (1990, 1992). If more than two authors of one article, the first author’s name is followed by the abbreviation et al. in italics. If the sentence structure requires that the authors’ names be included in parentheses, the proper format is (Adams and Harry, 1982; Harry, 1988a,b; Harry et al., 1993). Citations to a group of references should be listed first alphabetically then chronologically. Work that has not been submitted or accepted for publication shall be listed in the text as: “G.C. Jay (institution, city, and state, personal communication).” The author’s own unpublished work should be listed in the text as “(J. Adams, unpublished data).” Personal communications and unsubmitted unpublished data must not be included in the References section. Two or more publications by the same authors in the same year must be made distinct with lowercase letters after the year (2010a,b). Likewise when multiple author citations designated by et al. in the text have the same first author, then even if the other authors are different these references in the text and the references section must be identified by a letter. For example
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“(James et al., 2010a,b)” in text, refers to “James, Smith, and Elliot. 2010a” and “James, West, and Adams. 2010b” in the reference section.
Book Chapter: Examples:
Author(s) of the chapter. Year. Title of the chapter. In: author(s) or editor(s). Title of the book. Edition or volume, if relevant. Publisher name, Place of publication.
b) Citing References In Reference Section In the References section, references are listed in alphabetical order by authors’ last names, and then chronologically. List only those references cited in the text. Manuscripts submitted for publication, accepted for publication or in press can be given in the reference section followed by the designation: “(submitted)”, “(accepted)’, or “(In Press), respectively. If the DOI number of unpublished references is available, you must give the number. The year of publication follows the authors’ names. All authors’ names must be included in the citation in the Reference section. Journals must be abbreviated. First and last page numbers must be provided. Sample references are given below. Consult recent issues of AFAB for examples not included in the following section. Journal manuscript: Author(s). Year. Article title. Journal title [abbreviated]. Volume number:inclusive pages.
Inclusive pages of chapter.
O’Bryan, C. A., P. G. Crandall, and C. Bruhn. 2010. Assessing consumer concerns and perceptions of food safety risks and practices: Methodologies and outcomes. In: S. C. Ricke and F. T. Jones. Eds. Perspectives on Food Safety Issues of Food Animal Derived Foods. Univ. Arkansas Press, Fayetteville, AR. p 273-288. Dissertation and thesis:
Author. Date of degree. Title. Type of publication, such as Ph.D. Diss or M.S. thesis. Institution, Place of institution. Total number of pages.
Maciorowski, K. G. 2000. Rapid detection of Salmonella spp. and indicators of fecal contamination in animal feed. Ph.D. Diss. Texas A&M University, College Station, TX.
Examples: Chase, G., and L. Erlandsen. 1976. Evidence for a complex life cycle and endospore formation in the attached, filamentous, segmented bacterium from murine ileum. J. Bacteriol. 127:572-583.
Donalson, L. M. 2005. The in vivo and in vitro effect of a fructooligosacharide prebiotic combined with alfalfa molt diets on egg production and Salmonella in laying hens. M.S. thesis. Texas A&M University, College Station, TX.
Jiang, B., A.-M. Henstra, L. Paulo, M. Balk, W. van Doesburg, and A. J. M. Stams. 2009. A typical one-carbon metabolism of an acetogenic and hydrogenogenic Moorella thermioacetica strain. Arch. Microbiol. 191:123-131.
Van Loo, E. 2009. Consumer perception of ready-toeat deli foods and organic meat. M.S. thesis. University of Arkansas, Fayetteville, AR. 202 p.
Book: Author(s) [or editor(s)]. Year. Title. Edition or volume (if relevant). Publisher name, Place of publication. Number of pages.
Examples: Hungate, R. E. 1966. The rumen and its microbes Academic Press, Inc., New York, NY. 533 p.
Web sites, patents: Examples: Davis, C. 2010. Salmonella. Medicinenet.com. http://www.medicinenet.com/salmonella /article. htm. Accessed July, 2010. Afab, F. 2010, Development of a novel process. U.S. Patent #_____
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Abstracts and Symposia Proceedings: Fischer, J. R. 2007. Building a prosperous future in which agriculture uses and produces energy efficiently and effectively. NABC report 19, Agricultural Biofuels: Tech., Sustainability, and Profitability. p.27 Musgrove, M. T., and M. E. Berrang. 2008. Presence of aerobic microorganisms, Enterobacteriaceae and Salmonella in the shell egg processing environment. IAFP 95th Annual Meeting. p. 47 (Abstr. #T6-10) Vianna, M. E., H. P. Horz, and G. Conrads. 2006. Options and risks by using diagnostic gene chips. Program and abstracts book , The 8th Biennieal Congress of the Anaerobe Society of the Americas. p. 86 (Abstr.)
Data Presentation in Tables and Figures Figures and tables to be published in AFAB must be constructed in such a fashion that they are able to “stand alone” in the published manuscript. This
means that the reader should be able to look at the figure or table independently of the rest of the manuscript and be able to comprehend the experimental approach sufficiently to interpret the data. Consequently, all statistical analyses should be very carefully presented along with variation estimates and what constitutes an independent replication and the number of replicates used to calculate the averages presented in the table or figure. Each table and figure must be on a separate page in the submitted paper. In addition, you will need to submit all data for charts, tables and figures in native format when possible (e.g., Microsoft Excel, Powerpoint). Photographs should be submitted as high-resolution (600 dpi) .jpg or tif. files. All figures should be clearly presented with well defined axis and units of measurement. Symbols, lines, and bars must be made distinct as “stand alone” black and white presentations. Stippling, dashed lines etc. are encouraged for multiple comparison but shades of gray are discouraged. Color images, micrographs, pictures are recommended and there is no additional fee for their submission.
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