Scientia 2019 by baylor_scientia

SPRING 2019

SCIENTIA THE BAYLOR UNDERGRADUATE RESEARCH JOURNAL OF SCIENCE AND TECHNOLOGY

College of Arts & Sciences

Editors in Chief Jianna Lin and Sean Ngo Student Editorial Board Michael Munson, Ryan Rahman, Samuel Shenoi, Shubhneet Warar Advisory Board Dean Brian Raines, D. Phil. Professor Review Board Tamarah Adair, Ph.D., Patrick Farmer, Ph.D., Linda Olafsen, Ph.D., and Dennis Johnston, Ph.D. Publishing Advisor Rizalia Klausmeyer, Ph.D., Baylor Office of Undergraduate Research Design Team Shubhneet Warar, Michael Munson, and Sean Ngo Funding and Support Rizalia Klausmeyer, Ph.D., Baylor Office of Undergraduate Research Photography Kyle Dinh, Photographer Kathrine Do, Photo Editor Back Cover; Michael Valencia, Alyssa Alaniz Inside Front Cover (Top); Mary Elizabeth Overcash, Sarah Antrich Inside Front Cover (Bottom); Michael Valencia, Alyssa Alaniz

IN THIS ISSUE

A Letter From the Scientia Editorial Board

ORIGINAL RESEARCH

Credit Hours and Undergraduate Student Depression

Jianna Lin, Sean McKay, Sue Zhu, Maddie Larson, Shawn Latendresse, Ph.D.

Light Variance Changes Chlorophyll b Production in Chlamydomonas sp. Jana Heady, Kathleen Klinzing, Kevin Thompson, Marty Harvill, Ph.D.

A Comparative Survey of Tail Fiber Proteins from Phages Isolated on Three Arthrobacter Hosts Emily Schultz, Emily Dunn, Grip Gilbert, Tamarah Adair, Ph.D.

Comparison of Caffeine Dependence between Education Settings Jay Jackson, Ellie Jeung

The Relationship Between AttP Sites and tRNA in Cluster FF Arthrobacter Phages

The Influence of Imipramine on the Egg-Laying Behavior of Caenorhabditis elegans Michael Valencia, Emily Feese, Neha Hussain, Annie Luksch, Victoria Mancillas, Alyssa Alaniz, Myeongwoo Lee, Ph.D.

Tape Measure Protein PCR Successfully Classifies Arthrobacter Phages Stu Mair, Tamarah Adair, Ph.D.

URSA ABSTRACTS 2018 Engineering

The Effect of Al Doping on the Electronic Properties of Amorphous InZnO Thin Films Chandon Stone, Austin Reed, Seung Kim, Ph.D.

Using HPC to Model Quantum-dot Cellular Automata Gabriel Hahn, Enrique Blair, Ph.D.

Mary Elizabeth Overcash, Sarah Antrich, Long Pham, Lathan Lucas, Ashley Young, Tamarah Adair, Ph.D.

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Graphene Transfer and Spectrum Analysis Benjamin Jones, Linda Olafsen, Ph.D.

Mary High, Taylor Luster, Daniel Zeter, Chenlu Gao, Stacy Nguyen, Michael Scullin, Ph.D.

Physics

Epsilon-Near-Zero Perfect Absorber in Ultra-Thin Films Catherine Arndt, Sudip Gurung, Long Tao, Aleksei Anopchenko, Ph.D., Ho Wai Howard Lee, Ph.D.

Geology

Molecular Analysis of Flood Deposits in the Tennessee River Valley: Implications for Understanding Carbon Cycling in Fluvial Environments Emily Blackaby, Owen Craven, William Hockaday, Steve Forman, Gary Stinchcomb, Lance Stewart, William Hockaday, Ph.D.

Psychology and Neuroscience

Examination of cytokine expression and sickness behavior in a mouse model of Fragile X syndrome Lindsay Tomac, Samantha Hodges, Suzanne Nolan, Ilyasah Muhammad, Joaquin Lugo, Ph.D.

Does Mozart Make Memories? An Experimental Test of Targeted Memory Reactivation During Slow Wave Sleep

Environmental Science

Preliminary Results of Residential Curbside Residual Lead in West Dallas, TX, Liana DeNino, Grace Hutchinson, Julia Frandesen-DeLoach, Jonah Salazar, Clark Coneby, Trey Brown, Ph.D.

The Ketamine Epidemic and its Ecotoxicity in Aquatic Systems Kyle Wolfe, George Cobb, Ph.D.

Biology

Cancer, Bacteria, and Inflammation: Outer Membrane Vesicles from Enterotoxigenic Bacteroides fragilis and Non-enterotoxigenic Bacteroides fragilis Emily Lin, Aadil Sheikh, Joe Taube, Ph.D., Leigh Greathouse, Ph.D.

Identification of mutant C. elegans resistant to valproic acid Quynh-An Phan, Hailey Beattie, Sihan Hu, Chi-Hung Lee, Kavya Munnangi, Sean Tran, Myeongwoo Lee, Ph.D.

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Oncomodulin modulates intracellular calcium level Taronish Madeka, Kelsey Chaykowski, Dwayne Simmons, Ph.D.

Chemistry

Release Kinetics of Dyes Covalently Attached to Various Tissues Allie Stinchcomb, Jerry Quintana, Kayla Murphy, Robert Kane, Ph.D.

STUDENT RESEARCH SPOTLIGHT

Kathleen Klinzing Sarah Lathrop Angelo Wong Emily Ziperman

FOR PROSPECTIVE AUTHORS

54 55

About Scientia Submitting to Scientia

About BURST

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A Letter From the Editors

Jianna Lin, Sean Ngo, Michael Munson, Shubhneet Warar, Samuel Shenoi, Ryan Rahman An Introduction to Scientia 2019! It is with great enthusiasm that we introduce to you Scientia 2019, Baylor's Undergraduate Research Journal in Science and Technology. Scientia is an interdisciplinary publication that aims to increase awareness of the wide-reaching nature of undergraduate research at Baylor and to provide a platform for undergraduate students at Baylor University to showcase their original research. To do this, Scientia publishes a collection of selected original research articles and abstracts that span the interdisciplinary sciences chosen by Scientia's editorial board. It also features some of the winning abstracts from the previous year's URSA Scholars Week. As advocates of maximizing our education, we believe that the promotion of research here at Baylor provides students with quintessential and formative experiences that develop valuable characteristics such as intellectual curiosity, resilience, and simply, strong interest in the pursuit of knowledge. Our hope is that this edition of Scientia fosters a curiosity for scientific discovery and encourages undergraduates to get involved with research through our many Baylor Faculty Mentors. To us, research is a hallmark of education, because it involves applying the information learned in classes to unanswered questions in hopes of advancing our knowledge of the world around us. For these reasons, we commit ourselves to reviewing, editing, and publishing the work initiated and collaborated on by Baylor undergraduates. Scientia is created through the student organization Baylor Undergraduate Research in Science and Technology (BURST), whose mission statement is to increase awareness of undergraduate research within the Baylor campus, providing opportunities for undergraduates to optimize their research experiences, and educating them in the proper habits and techniques of research in scientific fields. In conjunction with BURST, we hope this array of student discovery will inspire an increasing number of students to partake in the many diverse research opportunities present at Baylor. The process of crafting Scientia includes a methodical review of submissions and collaboration between student researchers and editors of Scientia's Editorial Board. Student editors are closely involved in the selection of submissions for acceptance through careful review and meeting with authors to improve the professionality of the work. As undergraduate editors, we understand that undergraduate researchers are in the process of developing the skills required of professional research, and we strive to enhance these qualities in those who submit. Each author is provided personalized guidance and an opportunity to improve their ability to compose scientific literature. Following student editor review of each accepted submission, the articles are reviewed by Scientia's Professor Review Board, which includes Tamarah Adair, Ph.D., Patrick Farmer, Ph.D., Linda Olafsen, Ph.D., and Dennis Johnston, Ph.D. Further revisions are then made by the authors based on comments from these professors. This overall process is modulated and funded by the Deans of the College of Arts & Sciences, while the Office of Undergraduate Research assists in the journal's printing. We are grateful to Dean Brian Raines for his continuous support and thoughtful guidance. This year's edition of Scientia features a wide variety of submissions from the different research-based courses offered at Baylor University. This includes three full-length research articles from students in Dr. Tamarah Adair's BIO 1406 class, a two-semester course involving the discovery and bioinformatic analysis of bacteriophage genomes. It also features articles from Dr. Marty Harvill's BIO 1406 Wetlands course and Dr. Myeongwoo Lee's Cell and Developmental Biology course. These types of classes offer undergraduates the unique opportunity to design their own scientific research studies as a part of their curriculum. They are the perfect platform for undergraduates to take their first steps into academia, and we highly recommend them to any students with an interest in basic research. We hope that our efforts in creating Scientia 2019 excites the scientific community at Baylor to pursue further research experiences at Baylor and beyond, and that this journal effectively conveys the magnitude of undergraduate research here at Baylor so that you will be inspired to partake in this community. Sincerely, The Scientia Editorial Board

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Original Research

Credit Hours and Undergraduate Student Depression Jianna Lin, Sean McKay, Sue Zhu, Maddie Larson, Shawn Latendresse, Ph.D. Baylor University, Waco, TX

Abstract The high prevalence of depression among the undergraduate student population has raised concern across the nation. Literature that indicates academic stress as a leading contributor to undergraduate student depression warrants investigation of whether students enrolled in more demanding course loads experience more symptoms of depression than students enrolled in less demanding course loads. This study examined whether undergraduate students enrolled in equal to or more than 15 credit hours report more symptoms of depression than undergraduate students enrolled in fewer than 15 credit hours. Participants were 91 undergraduate students at Baylor University and were recruited through a text message. Data were collected through an online survey in which participants reported the number of credit hours they were enrolled in and completed the Patient Health Questionnaire Version 8 (PHQ-8), a measure of depression. An independent samples t-test assuming equal population variances was conducted to compare the mean PHQ-8 scores of participants enrolled in equal to or more than 15 credit hours and participants enrolled in fewer than 15 credit hours. There was no significant difference in the PHQ-8 scores between the two groups. Results suggest that undergraduate students enrolled in equal to or more than 15 credit hours and undergraduate students enrolled in fewer than 15 credit hours experience similar levels of depression.

Introduction

There has been increasing national concern regarding the prevalence of depression among young adults (World Health Organization, 2012). Depression is a mental disorder characterized by feelings of sadness, hopelessness, and loss of enjoyment in regularly pleasurable activities (Merrill & Joiner, 2007). Researchers have reported that depression is one of the most common health problems among undergraduate students (Bayram & Bilgel, 2008). Depression is also known to have devastating consequences for undergraduate students (Nagi et al., 2016). In 2012, the World Health Organization (WHO) reported that among people 15-29 years old, suicide caused by depression was the second leading cause of death (World Health Organization, 2012). Depression has been known to have a wide range of negative effects on undergraduate students. Researchers have reported that depression contributes to poorer academic achievement, lower social well-being, and poorer overall health outcomes (Herman, Reinke, Parkin, Traylor, & Agarwal, 2009). Depression may also

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contribute to the development of maladaptive behaviors among undergraduate students (Capron, Bauer, Madson, & Schmidt, 2018). Drinking alcohol and illicit drug use are common maladaptive coping strategies that undergraduate students adopt when experiencing symptoms of depression (Capron et al., 2018; Dawson, Grant, Stinson, & Chou, 2005). The many negative consequences of depression warrant concern for how often undergraduate students with depression do not receive treatment for their symptoms. Auerbach et al. (2016) reported that depression is one of the most commonly untreated disorders among the undergraduate population. The high incidence of untreated depression among undergraduate students and the extent of depressionâ&#x20AC;&#x2122;s impact on individuals in this population have motivated strong research efforts aimed at identifying factors that contribute to depression among undergraduate students (Dawood, Mits, Ghadeer, & Alrabodh, 2017). Low social support, unoptimistic attitudes, and decreased gratitude have all been found to contribute to depression

in undergraduate populations (Hakim, Tak, Nagar, & Bhansali, 2017; Jiang, Yue, Lsu, Yu, & Zhu, 2016; Lee, 2016). Among the many factors that have been found to contribute to depression among undergraduate students, stress related to academic responsibilities and academic coursework is a leading contributor to depression (Iorga, Dondas, & Zugun-Eloae, 2018; Jayanthi, Thirunavukarasu, & Rajkumar, 2015). Literature suggests that students with a more demanding course load may experience more symptoms of depression than students enrolled in fewer credit hours. A greater course load is associated with more academic stress (Owens, Stevenson, Hadwin, & Norgate, 2012) and academic stress is associated with depression (Saleh, Camart, & Romo, 2017). Although these findings suggest that taking a demanding course load may contribute to depression, whether students enrolled in more demanding course loads experience more symptoms of depression than students enrolled in less demanding course loads has not yet been studied. The most widely used measure of educational credit by undergraduate institutions in the United States is the credit hour (U.S. Department of Education, 2008). Credit hours are typically assigned to a course by an undergraduate institution based on the number of hours per week the course meets in class. According to federal guidelines by the U.S. Department of Education (2008), one credit hour must entail at least one hour of class time and two hours of out-of-class work per week. While the minimum credit hour enrollment for a full-time student in most universities is 12 credit hours per semester, universities typically recommend full-time students to take 15 credit hours per semester (U.S. Department of Education, 2008). These federal guidelines suggest that students enrolled in more credit hours experience more symptoms of depression than students enrolled in fewer credit hours. In particular, the federal guideline that one credit hour must entail at least one hour of class time and two hours of out-of-class work per week (U.S. Department of Education, 2008) suggests that students enrolled in a higher number of credit hours are taking more courses and expected to spend more time on academic work. This indicates that students enrolled in a higher number of credit hours have a more demanding academic course load. Examining differences in depression levels among students enrolled in equal to or more than 15 credit hours and students enrolled in fewer than 15 credit hours is particularly meaningful because many undergraduate institutions recommend students to enroll

in 15 credit hours each semester (U.S. Department of Education, 2008). Do undergraduate students enrolled in equal to or more than 15 credit hours report more symptoms of depression than undergraduate students enrolled in fewer than 15 credit hours? It is hypothesized that undergraduate students enrolled in equal to or more than 15 credit hours will report higher symptoms of depression than students enrolled in fewer than 15 credit hours.

Methods Participants Participants in both groups, those enrolled in equal to or more than 15 credit hours (N = 66) and those enrolled in fewer than 15 credit hours (N = 25) were undergraduate students at Baylor University (43 women, 48 men, Mage = 20.42 years, age range: 18 – 26 years). Of the 105 participants who began the study, 14 did not complete the study, and their responses were not used in analyses. The 91 participants whose responses were used in data analysis were 72% Caucasian, 16% East Asian, and 11% other. Among the participants were 4 freshmen, 26 sophomores, 47 juniors, and 14 seniors. Measures Patient Health Questionnaire. Symptoms of depression were measured using the Patient Health Questionnaire version eight (PHQ-8). This self-administered measure has been used to assess the severity of depression symptoms (Kroenke et al., 2009). The PHQ-8 is a four-point Likert scale. The scale ranges from 0 (not at all) to 3 (nearly every day). On eight items, participants are asked to respond to the question, “Over the last 2 weeks, how often have you been bothered by any of the following problems?” Items include “Feeling down, depressed, or hopeless” and “Feeling bad about yourself or that you are a failure or have let yourself or your family down.” In a study by Ory et al. (2013), the PHQ-8 had good reliability with Cronbach’s α = .86. Evidence of the validity of the PHQ-8 include a study by Schantz et al. (2017), who reported that the PHQ-8 was associated with the Center for Epidemiologic Studies-Depression scale (CES-D, r = .49). Participants’ responses to each item of the PHQ-8 were summed to determine their total PHQ-8 score. Credit Hours. Participants were asked, “How many credit hours are you taking this semester at Baylor University?” Response options on this two-point scale include 1 (Less than 15 credit hours) and 2 (Equal to OR more than 15 credit hours).

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Procedure All study materials and measures were presented through an online Qualtrics survey. Participants were recruited through a text message that included a link to the online survey. The text message informed the receiver that the study was for a course project. No other information regarding the topic or objective of the study was included in the text message. After providing consent, participants completed the PHQ-8. Participants were then asked to complete the credit hour item. Next, participants completed a demographics questionnaire. Finally, participants were presented a debriefing page that detailed the studyâ&#x20AC;&#x2122;s objectives, hypothesis, and relevant background information. Participants were not compensated for their participation.

Results Means and standard deviations of PHQ-8 scores for both groups are reported in Table 1. Leveneâ&#x20AC;&#x2122;s test was not significant, indicating homogeneity of variances, F(1, 89) = 3.26, p = .074. An independent samples t-test assuming equal population variances was conducted to compare depression scores in the participants enrolled in equal to or more than 15 credit hours and the participants enrolled in less than 15 credit hours. The difference in mean PHQ-8 scores between participants enrolled in equal to or more than 15 credit hours and participants enrolled in fewer than 15 credit hours was not statistically significant, t(89) = -0.57, p = .571, d = 0.13.

Table 1 Summary of Means and Standard Deviations of PHQ-8 Scores n M SD t(89) d

Less than 15 Credit Hours 25 7.92 5.61 -0.57 0.13 Equal to or More than 15 Credit Hours 66 7.27 4.53 Note. The PHQ-8 score is a measure of the depression symptoms in each group. *p < .05

Discussion

Results do not support the hypothesis that undergraduate students enrolled in equal to or more than 15 credit hours will report higher symptoms of depression than students enrolled in fewer than 15 credit hours. There was no significant difference between the mean PHQ-8 scores of students enrolled in 15 or more credit hours and students enrolled in fewer than 15 credit hours. These results suggest that students enrolled in equal to or more than 15 credit hours and students enrolled in fewer than 15 credit hours experience similar levels of depression. The results also indicate that the

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number of credit hours that a student is enrolled in should not be utilized as an indication of the level of depression that a student is experiencing. A potential explanation for the discrepancy between the results and the hypothesis is that credit hours may be a poor measure of academic stress. While many researchers have documented a strong association between academic stress and depression (Ang & Huan, 2006; Saleh et al., 2017), other researchers have also noted that the number of credit hours an undergraduate student is enrolled in is not the only factor of academic stress (Saleh et al., 2017). Researchers have indicated the difficulty of the course exams, the number of projects and assignments assigned, and the studentâ&#x20AC;&#x2122;s performance in the course, as major contributors to academic stress (Abouserie, 1994; Owens et al., 2012). To accurately measure the academic stress that an undergraduate student is experiencing, researchers may need to assess a variety of factors rather than credit hours alone. These findings may be helpful to faculty at undergraduate institutions. In particular, they may help faculty in their efforts to identify students with depression. Faculty may believe that students enrolled in few credit hours are not as likely to suffer from depression as students who are enrolled in more credit hours. Despite federal guidelines that suggest that a higher number of credit hours is an indication of a more demanding course load (U.S. Department of Education, 2008), faculty should not assume that students enrolled in equal to or more than 15 credit hours experience more symptoms of depression than students enrolled in fewer than 15 credit hours. Although a student may be enrolled in fewer than 15 credit hours, the student may still be experiencing academic stress and symptoms of depression. Faculty at undergraduate institutions should therefore not utilize credit hours as an indication of the number of symptoms of depression that a student may be experiencing. The finding that students enrolled in equal to or more than 15 credit hours do not experience significantly more symptoms of depression than students enrolled in fewer than 15 credit hours can also be helpful knowledge for undergraduate students. Undergraduate students may hold the inaccurate belief that students enrolled in fewer than 15 credit hours do not experience as much academic stress or symptoms of depression as students enrolled in equal to or more than 15 credit hours. This inaccurate belief may lead students to believe that if they enrolled in fewer than 15 credit hours, they would not experience as many symptoms of depression as if they enrolled in equal to or more than 15 credit hours. Knowing that students who are enrolled in fewer than 15 credit hours do not report significantly fewer symptoms

of depression than students enrolled in equal to or more than 15 credit hours may prevent undergraduate students from attempting to decrease the number of depression symptoms they experience by simply enrolling in fewer than 15 credit hours. Potential limitations to the results in this study include limits in generalizability. All participants were undergraduate students attending the same undergraduate institution, Baylor University. It is possible that at other undergraduate institutions, students who are enrolled in equal to or more than 15 credit hours do indeed experience significantly different levels of depression symptoms than students enrolled in fewer than 15 credit hours. The self-report nature of the measure of depression used may have also affected the studyâ&#x20AC;&#x2122;s results. Participants may have not been comfortable in sharing that they suffer from depression. Therefore, while completing the PHQ-8, participants may have attempted to conceal the symptoms of depression they were experiencing or the severity of their symptoms by reporting fewer symptoms of depression than they experience in reality. Participants may also have been in denial of their symptoms of depression. The accuracy of the measured levels of depression may have therefore been compromised because of the self-report nature of the PHQ-8. Future researchers should utilize a measure of course load that is more comprehensive than credit hours alone. Such measures may include course difficulty and the amount of time a student spends on academic coursework. Future researchers should also avoid utilizing self-report measures of depression when possible. Utilizing measures of depression that are not self-report may decrease the effects of participant denial of depression. In the future, researchers should also examine whether there are differences in depression among undergraduate students who are enrolled in different numbers of credit hours at other undergraduate institutions to determine whether the findings in this study are unique to students at Baylor University. For instance, researchers may investigate whether there are differences in depression among undergraduate students at public universities and students at private universities. Researchers may also examine whether there are regional differences in depression levels among the undergraduate student population. Such studies would elucidate whether this studyâ&#x20AC;&#x2122;s findings are representative of undergraduate students across the nation. The finding that there is no significant difference in depression symptoms between undergraduate students enrolled in equal to or more than 15 credit hours and

undergraduate students enrolled in fewer than 15 credit hours is applicable to both faculty and students and undergraduate institutions. Further research regarding differences in the experiences of depression among undergraduate students may aid in the identification of aspects of an undergraduate education that contribute to the high prevalence of depression among undergraduate students.

References [1] Abouserie, R. (1994). Sources and levels of stress in relation to locus of control and self-esteem in university students. Educational Psychology, 14, 323-331. doi:10.1080/01443419401 40306 [2] Ang, R. P., & Huan, V. S. (2006). Relationship between academic stress and suicidal ideation: Testing for depression as a mediator using multiple regression. Child Psychiatry and Human Development, 37, 133-143. doi:10.1017/S0033291716001665 [3] Auerbach, R. P., Alonso, J., Axinn, W. G., Cuijpers, P., Ebert, D. D., Green, J. G., . . . Bruffaerts, R. (2016). Mental disorders among college students in the World Health Organization world mental health surveys. Psychological Medicine, 46(14). [4] Bayram, N., & Bilgel, N. (2008). The prevalence and socio-demographic correlations of depression, anxiety and stress among a group of university students. Social Psychiatry and Psychiatric Epidemiology, 43, 667â&#x20AC;&#x201C;672. doi:10.1007/s00127-008-0345-x [5] Capron, D., Bauer, B., Madson, M., & Schmidt, N. (2018). Treatment seeking among college students with comorbid hazardous drinking and elevated mood/anxiety symptoms. Substance Use & Misuse, 53(6), 1041-1050. [6] Dawood, E., Mits, R., Ghadeer, H. A., & Alrabodh, F. (2017). Assessment of depression and its contributing factors among undergraduate nursing students. International Journal of Nursing, 4. doi:10.15640/ijn.v4n2a6 [7] Dawson, D. A., Grant, B. F., Stinson, F. S., & Chou, P. S. (2005). Psychopathology associated with drinking and alcohol use disorders in the college and general adult populations. Drug and Alcohol Dependence, 77, 139-150. doi:10.1016/j.drugalcdep.2004.07.012 [8] Hakim, A., Tak, H., Nagar, S., & Bhansali, S. (2017). Assessment of prevalence of depression and anxiety and factors associated with them in undergraduate medical students of Dr. S. N. medical college, Jodhpur. International Journal of Community Medicine and Public Health, 4, 3267. doi:10.18203/2394-6040. ijcmph20173826

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[9] Herman, K. C., Reinke, W. M., Parkin, J., Traylor, K. B., & Agarwal, G. (2009). Childhood depression: Rethinking the role of the school. Psychology in the Schools, 46, 433-446. doi:10.1002/pits.20388 [10] Iorga, M., Dondas, C., & Zugun-Eloae, C. (2018). Depressed as freshmen, stressed as seniors: The relationship between depression, perceived stress and academic results among medical students. Behavioral Sciences, 8, 70. doi:10.3390/bs8080070 [11] Jayanthi, P., Thirunavukarasu, M., & Rajkumar, R. (2015). Academic stress and depression among adolescents: A cross-sectional study. Indian Pediatrics, 52, 217-219. doi:10.1007/s13312-015-0609-y [12] Jiang, F., Yue, X., Lu, S., Yu, G., & Zhu, F. (2016). How belief in a just world benefits mental health: The effects of optimism and gratitude. Social Indicators Research, 126, 411-423. doi:10.1007/s11205-015-0877-x [13] Kroenke, K., Strine, T. W., Spitzer, R. L., Williams, J. B. W., Berry, J. T., & Mokdad, A. H. (2009). The PHQ-8 as a measure of current depression in the general population. Journal of Affective Disorders, 114, 163-173. doi:10.1016/j.jad.2008.06.026 [14] Lee, H. (2016). The influencing factors of optimism and emotional intelligence on depression among undergraduate students. Journal of the Korea Academia-Industrial Cooperation Society, 17, 177-185. doi:10.5762/KAIS.2016.17.11.177 [15] Merrill, K., & Joiner, T. (2007). Depression. In R. Baumeister & K. Vohs (Eds.), Encyclopedia of social psychology. Retrieved from http://ezproxy.baylor.edu/ login? url=https://search.credoreference.com/content/ topic/depression?institutionId=720 [16] Nagi, F., Muhammad, L., Kazmi, H., Tehseen, S., Qureshi, S., Wasiq, D. K., â&#x20AC;Ś Butt, D. K. (2016). Depression; Private undergraduate medical students. The Professional Medical Journal, 23, 858-863. doi:10.17957/ TPMJ /16.3379 [17] Ory, M. G., Ahn, S., Jiang, L., Lorig, K., Ritter, P., Laurent, D. D., . . . Smith, M. L. (2013). National study of chronic disease self-management: Six-month outcome findings. Journal of Aging and Health, 25, 1258-1274. doi:10.1177/0898264313502531 [18] Owens, M., Stevenson, J., Hadwin, J. A., & Norgate, R. (2012). Anxiety and depression in academic performance: An exploration of the mediating factors of worry and working memory. School Psychology International, 33(4), 433-449. [19] Saleh, D., Camart, N., & Romo, L. (2017). Predictors of stress in college students. Frontiers in Psychology, 8, doi:10.3389/fpsyg.2017.00019 [20] Schantz, K., Reighard, C., Aikens, J. E., Aruquipa, A., Pinto, B., Valverde, H., & Piette, J. D. (2017). Screening for depression in Andean Latin America:

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Factor structure and reliability of the CES-D short form and the PHQ-8 among Bolivian public hospital patients. The International Journal of Psychiatry in Medicine, 52, 315-327. doi:10.1177/0091217417738934 [21] U.S. Department of Education (2008). Structure of the U.S education system: Credit systems. Retrieved from https://www2.ed.gov/policy/ highered/reg/hearulemaking/2009/credit.html [22] World Health Organization (2012). Depression a global public health concern. (WHO publication). Retrieved from http://www.who.int/mental_health/management/depression/ who_paper_depression_wfmh_2012. pdf.

Original Research

Light Variance Changes Chlorophyll b Production in Chlamydomonas sp. Jana Heady, Kathleen Klinzing, Kevin Thompson, Marty Harvill, Ph.D. Baylor University, Waco, TX

Abstract The algae Chlamydomonas sp. is a multicellular, photosynthetic organism that lives in stagnant ponds. Chlamydomonas sp. has a circadian rhythm that uses environmental cues to regulate certain internal processes. Tubs of Chlamydomonas were put under varying cycles of light and darkness to test its circadian rhythm adaptability. Readings of chlorophyll b concentration in each sample were taken. The results suggest that a significantly greater amount of chlorophyll b was produced by algae under the randomized light cycle than by algae under any other light cycle. This result contradicts the hypothesis, which predicted that the 12-hour light/12-hour dark cycle would result in the highest chlorophyll b density. This hypothesis was derived from the approximate 12-hour light/ 12-hour dark cycle of natural summer sunlight. However, the findings of this experiment may be supported by the findings of Cross and Umen (1), who found that Chlamydomonas sp. divides at the end of a light period or beginning of a dark period when provided light for enough time to grow. The results of this experiment provide significant evidence that suggests Chlamydomonas sp. cell division does not follow a circadian rhythm.

Introduction A circadian rhythm is an internal “clock” that regulates various processes in an organism based on environmental cues such as light and temperature (11). The effect these environmental cues have on a plant differ from species to species, but a predictable rhythm seems to be established under specific conditions for each plant species (3). Circadian rhythms allow some plants to measure time to the extent that they can biologically prepare themselves for sunrise or sunset before it occurs (8). This internal regulator has been shown to increase plant fitness by maximizing cellular work to coincide with the day/night cycle (12). Typically, a plant cell will have its cycling genes prepared to express themselves during times of dusk and dawn to support various cellular processes such as photosynthesis (5). In 1998, it was found that the genes encoding chloroplasts are directed by the circadian rhythm of the organism (4). Additionally, many connections have been made between plant immune response and exposure to light (9). Recent research studies have also remarked on the need for more experiments investigating circadian rhythm response to “variable photoperiods” (2). However, there has been little research done on the importance of circadian

rhythms on plant immune response. An understanding of the role of light on the circadian rhythms of plants would provide insight into the point in the cell cycle at which the immune system is strongest. This information could be crucial to the new field of chrono therapeutics, which investigates the most effective times to prescribe drugs to humans (9). Furthermore, algae can be converted to biodiesel fuel, so understanding the conditions that encourage the rapid growth of algae could help in the development of alternative energy sources (10). The purpose of this experiment was to understand the relationship between the photosynthetic rate of the algae Chlamydomonas sp. and its circadian rhythms. This relationship was tested by evaluating the effects of different periods of light on the photosynthetic rate. Previous experiments carried out by Li and colleagues showed that algal cell density was lower in shaded areas than unshaded under controlled conditions. They also noted that removal of shading allowed for swift rebound of algal cell density levels (7). Our experiment further explored the connection between cell density rebound and variable artificial light

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conditions as a measurement of circadian rhythm. The experiment combined the connection between chloroplasts and circadian rhythms with the light-dependent growth in Chlamydomonas sp. It was hypothesized that algae under light conditions distinct from natural sun cycles would have comparatively lower photosynthetic rates than those exposed to light cycles close to that of the sun.

Materials The materials used in this experiment include 16 plastic containers, the algae Chlamydomonas sp., pipettes, Aqua Fluor Handheld Fluorometer and cuvettes, a thermometer, LED light bulbs and automatic light timers.

Methods

Figure 1: Cabinet Layout

Preparation of Algae Stock Solution: Algae was harvested from a cultured solution and mixed with DI water.

Initial Set Up: Plastic tubs (15cmX11cmX9cm) were thoroughly rinsed with DI water before use. Grated shelves of a large cabinet were covered in cardboard and gray duct tape and lightbulbs were hung at the center of each cabinet hanging 2cm down from the bottom of the above shelf (see Figure 1). Light leakage between the shelves was tested by taking a video of the shelves with the cabinet closed and plugging holes with foam as necessary. One light was hung from the grating above each shelf (a total of 4 shelves were illuminated). Lights were operated on a 12 hours light/12 hours dark cycle for the first week of the experiment. Four plastic tubs (intended for the algal solution) were placed on each shelf in the same orientation and spacing on each shelf. The placement of the plastic tubs was then outlined to ensure correct placement in the future (in case the tubs were moved for any reason). After the initial calibration period of 1 week, the experimental conditions were imposed upon the samples.

12 | SCIENTIA | VOL 6 | Spring 2019

27.4 cm

26.67 cm

26.67 cm 36 cm

Algae solution: Sixteen plastic containers were thoroughly cleaned with DI water and left to air dry. Once dry, 500mL of DI water was measured into each container. 10 mL of algae solution was added to the 500 mL of DI water using a glass pipet. The containers were then transferred to their corresponding spots in the cabinet.

72 cm

Light Bulb

C 6.35 cm 6.35 cm

Figure 2: Shelf Layout Setting the timers: For one week, all lights were plugged into the same power strip that was set to a daily 12 hours light/12 hours dark cycle. The lights turned on automatically at noon every day and off at midnight for that week. All light groups experienced 12 hours of light and 12 hours of dark each day, with only the timing of the light cycles differing between the groups/ shelves. For every week following the first, each shelf was given a different time period of light. Timers were initially set following the one-week calibration period:

12/12: The top shelf light bulb remained on the original calibration cycle of 12 hours of light (noon to midnight) and 12 hours of dark (midnight to noon) every day.

cuvettes were then thoroughly rinsed with DI water and dried.

6/6: The second shelf (from the top) lightbulb was set to 6 hours of dark followed by 6 hours of light repeated twice each day. The first 6 hours of dark began at 3 PM.

Data collection: Data was collected twice weekly for 4 weeks. A plastic cuvette was filled ¾ of the way with the sample and was inserted into the Aqua Fluor reader to measure in vivo chlorophyll b concentration. Algae solution was returned to its respective tub immediately after readings in cuvettes. Cuvettes were then rinsed out via pipetting of DI water. Three readings were completed for each tub (A, B, C, D) on each shelf, and an additional reading was taken if there was an outlying data point in the first three readings. A new pipette was also used for each new shelf. A thermometer was placed in a random tub for each shelf (A, B, C, D) each day during collection and temperature was recorded to ensure no major temperature fluctuations occurred. Microsoft Excel was used to record and analyze the collected data, and JMP was used for further data analysis.

1/1: The third shelf (from the top) light bulb alternated between being on for one hour and off for one hour daily. Random: The bottom shelf lightbulb was set to random durations of light and dark. Each Wednesday and Saturday, the durations of light and dark were determined by the rolling of a twelve-sided dice. The conditions were then kept the same between each randomization. Sterilization of cuvettes: Cuvettes were soaked for 5 minutes in a solution of 450 mL DI of water and 50 mL of 90% alcohol. The

Results

650

Least Squared Means of Individual Shelves

Chlorophyl b Means

600

Figure 1: Error bars represent standard error. n=96 measurements of chlorophyll b for each shelf. Chlorophyll b density measured in μg/L. Algae on the random shelf generally had the highest chlorophyll b density throughout the experiment.

550 500 450 400 350 300

Shelf

Least Squared Means of Date/Shelf Relationship 1200

Chlorophyl b Means

1000 800 600 400 200 0 -200 1 6 12 R 1 6 12 R 1 6 12 R 1 6 12 R 1 6 12 R 1 6 12 R 1 6 12 R 1 6 12 R 03/17/2018

03/21/2018

03/24/2018

03/28/2018 03/31/2018 Date/Shelf

04/04/2018

04/07/2018

Figure 2: Least Squared means over time for individual shelves indicate chlorophyll b densities. Error bars represent standard error. Chlorophyll b density measured in μg/L. n=12 chlorophyll b measurements per shelf each data collection day. A general downward trend in chlorophyll density is apparent until the algae were fed nutrient Miracle Grow on 04/04/2018.

04/11/2018

Spring 2019 | VOL 6 | SCIENTIA | 13

Figure 3: Relationships between pairs of shelves. Error bars represent standard error. Graph values represent confidence interval ranges. The center line represents a value of zero within the confidence interval. Chlorophyll b density measured in Îźg/L. Significance is statistically relevant for most pairings.

Conclusion The results suggest that the random light cycle led to the production of the greatest concentration of chlorophyll b. The 12 hour and 1 hour light cycles produced close to the same amount of chlorophyll b, and the 6-hour light cycle produced the least amount of chlorophyll b. A Two-way ANOVA test gave a p-value less than 0.0001 for date, shelf, and date-shelf (paired). Due to this significant p-value, a Tukey multiple comparisons test was run for shelves, dates, and shelf-date (paired). P-values from the Tukey multiple comparisons test were less than 0.0001 for shelf comparisons 1-6, 1-R (random), 12-6, 12-R, 12-6. The 1-12 shelf comparison yielded a p-value of 0.913. A pairwise comparisons scatter chart revealed that the 6-R pair had the most statistical difference from one another. The Tukey least-means squared test suggests that R had the highest mean concentration of chlorophyll b concentration by a statistically significant amount (df=352). The data also suggests that the concentration of chlorophyll b is connected to the cellular reproductive cycle. The more frequently the algae can divide, the more algae there will be that contain chlorophyll b. One explanation for the results is that the random cycle provided enough light often enough that the algae was able to grow to the proper size before dividing. This

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division likely took place during the end of the light period or beginning of the dark period. As a result of frequently changing the time off and on in a 24-hour period (depending on the randomization of the time), there were constant periods with sufficient light for the algae to prepare for cell division. While it seems that the algae were also able to divide after the 12-hour light/dark cycle, there were only two periods of division (change from light to dark) each day. Reduced divisions cause lower chlorophyll b levels due to a low number of cells. The 1-hour light/dark cycle maintained about the same chlorophyll b density as the 12-hour light/dark cycle, suggesting that this specific time was not ideal for the cycle of growth and division of Chlamydomonas sp. However, the light cycle was efficient enough for some cell division to take place over time. The 6-hour light/ dark cycle had the lowest chlorophyll b concentration of all the groups, suggesting it is likely that six hours is not the ideal growth period for Chlamydomonas sp. In future experiments, the algae should be mixed before every reading to ensure that the density value represents the whole sample (6). Furthermore, the algae should be fed earlier than halfway through the experiment. All the algae showed a decrease in chlorophyll b density, as it was evident that they did not have enough nutrients for efficient survival. At the end of the experiment, the 6-hour light/dark cycle algae were not producing much chlorophyll b, which suggests that they were dying. Therefore, if the experiment were to be redone, the algae should be fed more frequently. The experiment should be repeated with the aforementioned criteria, along with a greater sample size, in order to increase the statistical significance of the results. Further experiments should also pay special attention to why the 6-hour light/dark cycle had a significantly lower chlorophyll b density than the other light/dark cycles. By studying the growth of algae under various light cycles, inferences towards the ideal growing conditions for algae can be made. As seen, random cycles of light fostered significantly more growth than the 12-hour cycles of light. Therefore, Chlamydomonas sp. has grown to be more capable of surviving in cycles of sporadic light. This could be due to random shading given by plants around the shore. On the other hand, it shows that long periods of light and long periods of darkness may ruin the cultures for algae. In the case of construction, building bridges across lakes that rely on algae could cause disruptions to the fragile ecosystem. Drastic effects on the ecosystem can have harmful effects on algae, which are the main producers in lakes. Knowledge of algal cell division cycles and circadian rhythms could also provide valuable

information in the pharmaceutical and health industries. An understanding of these cycles could help scientists investigate when cells are most vulnerable and should be targeted with drugs. Such a discovery would increase the efficiency and effectiveness of modern medicine.

Acknowledgements We would like to thank the Baylor Biology Department for funding this project and use of equipment. We are also grateful to Margaret Klausmeyer for her experimental advice and editing; Joshua Patrick, Ph.D. for his statistical support; and Amber Ludtke-Smith for her logistical contributions.

[10] Singh, S. P., & Singh, P. (2015). Effect of temperature and light on the growth of algae species: a review. Renewable and Sustainable Energy Reviews, 50, 431-444. [11] Sorek, M., Yacobi, Y. Z., Roopin, M., Berman-Frank, I., & Levy, O. (2013). Photosynthetic circadian rhythmicity patterns of Symbiodium, the coral endosymbiotic algae. Proc. R. Soc. B, 280(1759), 20122942. [12] Suzuki, L., & Johnson, C. H. (2001). Algae know the time of day: circadian and photoperiodic programs. Journal of Phycology, 37(6), 933-942.

Literature Cited [1] Cross, F. R., & Umen, J. G. (2015). The Chlamydomonas cell cycle. The Plant Journal, 82(3), 370-392. [2] Goncalves, E. C., Koh, J., Zhu, N., Yoo, M. J., Chen, S., Matsuo, T., ... & Rathinasabapathi, B. (2016). Nitrogen starvationâ&#x20AC;?induced accumulation of triacylglycerol in the green algae: evidence for a role for ROC 40, a transcription factor involved in circadian rhythm. The Plant Journal, 85(6), 743-757. [3] Hennessey, T. L., & Field, C. B. (1991). Circadian rhythms in photosynthesis: oscillations in carbon assimilation and stomatal conductance under constant conditions. Plant Physiology, 96(3), 831-836. [4] Hwang, S., Kawazoe, R., & Herrin, D. L. (1996). Transcription of tufA and other chloroplast-encoded genes is controlled by a circadian clock in Chlamydomonas. Proceedings of the National Academy of Sciences, 93(3), 996-1000. [5] Khan, S., Rowe, S. C., & Harmon, F. G. (2010). Coordination of the maize transcriptome by a conserved circadian clock. BMC Plant Biology, 10(1), 126. doi:10.1186/1471-2229-10-126 [6] Lee, J. W., & Kim, G. H. (2017). Two-track Control of Cellular Machinery for Photomovement in spirogyra varians (streptophyta, Zygnematales). Plant and Cell Physiology, 58(10), 1812-1822. [7] Li, W., Guo, Y., & Fu, K. (2011). Enclosure experiment for influence on algae growth by shading light. Procedia Environmental Sciences, 10, 1823-1828. [8] McClung, C. R. (2006). Plant circadian rhythms. The Plant Cell, 18(4), 792-803. [9] Roden, L. C., & Ingle, R. A. (2009). Lights, rhythms, infection: the role of light and the circadian clock in determining the outcome of plantâ&#x20AC;&#x201C;pathogen interactions. The Plant Cell, 21(9), 2546-2552.

Spring 2019 | VOL 6 | SCIENTIA | 15

Original Research

A Comparative Survey of Tail Fiber Proteins from Phages Isolated on Three Arthrobacter Hosts Emily Schultz, Emily Dunn, Grip Gilbert, Tamarah Adair, Ph.D. Department of Biology, Baylor University, Waco, TX

Abstract The rise in the number of antibiotic resistant pathogens has increased the need for alternative therapies to treat bacterial infections. One alternative therapy to antibiotics is the use of phages; however, the effectiveness of phage therapy is limited by the high host specificity of bacteriophages. Tail fiber proteins play an important role in attachment and infection of bacteria. This study was conducted to determine whether the tail fiber proteins of Arthrobacter phages isolated on different hosts contain similar sequences and protein folding patterns which both play a determining role in specific host wall binding. Using bioinformatic tools such as PhagesDB BLASTp, Clustal Alignment, RaptorX, and DichroCalc, a comparative analysis of tail fiber proteins in Arthrobacter phages isolated on different hosts was completed. By utilizing RaptorX’s protein structure prediction tool, predicted tail fiber protein structures were generated for phages ArV1 (host Arthrobacter sp. 68b), Colucci (host Arthrobacter sp. ATTC 21022), Ingrid (host Arthrobacter globiformis B-2979), Loretta (host Arthrobacter globiformis B-2979), and Beagle (host Arthrobacter globiformis B-2979). These structures were compared qualitatively and quantitatively for regions of similarity or variability. Current data supports that variable regions of tail fiber proteins are unique to each host and are potentially the determining factor of host specificity; however, this can only be determined experimentally. Further bacteriophage genomes should be analyzed to identify more of these tail fiber regions. Our cross-host comparative analysis of tail fiber proteins within the species Arthrobacter provides a broader understanding of phage-host interactions which may lead to more effective phage therapies through the ability to alter host specificity.

Introduction

Bacteriophages, viruses that infect bacteria, are considered the most abundant biological agents on the planet and have a significant impact on the balance of microbial life (Moineau & Tremblay, 2017). In addition, there are many different biotechnology applications with bacteriophages, or phages, in areas such as immunotherapy, vaccine delivery, and phage therapy (Czapar & Steinmetz, 2017; Mahichi, F., Synnott, A.J., Yamamichi, K., Osada, T. & Tanji, Y., 2009). Phage therapy demonstrates the successful use of phages’ natural ability to eradicate bacteria, especially in cases where bacteria have become resistant to antibiotics, a growing issue in healthcare worldwide (Schooley, Biswas, Gill, Hernandez-Morales, Lancaster, Lessor, … Hamilton, 2017). However, because the high host specificity among

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phages limits the range of bacterial infections in which they can be used treat, many researchers have turned away from phage therapy. With this understanding, this study explores the genes responsible for host range of Arthrobacter phages isolated in the SEA-PHAGES program. Arthrobacter phages are specific to the host Arthrobacteria and have not been studied as extensively as other phages (Cross, Schoff, Chudoff, Graves, Broomell, Terry, … Dunbar, 2015). Arthrobacteria is a gram positive bacteria commonly found in soil and contains the ability to degrade a variety of chemicals such as herbicides and agricultural pesticides (Turnbull, Ousley, Walker, Shaw, & Morgan, 2001). Although there are many different species of Arthrobacter, this research specifically analyzed

bacteriophages that were isolated on the hosts Arthrobacter sp. 68b, Arthrobacter sp. ATCC, and Arthrobacter globiformis. Most phages are known to have a specific host range of only one or a few species of a bacteria, meaning a bacteriophage that infects Arthrobacter sp. ATCC won’t necessarily infect other species of Arthrobacter, such as Arthrobacter globiformis (Ross, Ward & Hyman, 2016). Phages infect bacteria through a process of recognition and attachment to a specific host cell surface receptor. Each phage has a specific receptor binding protein (RBP) that corresponds to their host’s cell surface receptors; these RBP’s have been identified as phage tail fiber proteins in some phages, meaning the tail fiber proteins ultimately enable the phage to attach to receptors on a bacteria’s surface (Schwarzer, Buettner, Browning, Nazarov, Rabsch, Bethe, … Gerardy-Schahn, 2012). For example, it has been determined in the extensively studied Lambda phage that the C-terminal portion of the tail fiber is responsible for the phage’s ability to interact with cell surface receptors of its bacterial host, Escherichia coli, and thus allows for the phage’s genetic material to enter the cell (Wang, Hofnung & Charbit, 2000). For a phage to be of use in phage therapy, it must be able to bind to the cell wall of its specific bacterial host via the RBP. Additionally, it must successfully lyse the bacterial cell after replication to avoid the spread of infection and the risk of a phage-resistant bacteria emerging (Lin, Koskella, & Lin, 2017). Important to overcoming the narrow host range of most phages, it has been demonstrated that replacing even a single tail fiber protein in a phage has the ability to alter its host specificity (Le, He, Tan, Huang, Zhang, Lux, … Hu, 2013). Similarly, it has been shown that the replacement of the long tail fiber proteins of phage T2, with those of phage IP008, resulted in a broader host range while retaining lytic activity, therefore optimizing its usefulness in phage therapy (Mahichi, Synnott, Yamamichi, Osada, & Tanji, 2009). The phage ArV1 was chosen for comparison specifically due to its fully annotated genome and isolation on the host bacteria Arthrobacter sp. 68b. Four other Arthrobacter phages were found in the PhagesDB database to have high similarity with the tail fiber protein of ArV1, and were chosen for analysis. This study aims to compare the putative tail fibers of five bacteriophages isolated on three different species of Arthrobacter through bioinformatic means. This comparative analysis found that the tail fiber proteins each contained a region of high similarity, as well as regions of lower similarity when cross compared. This finding provides more insight into the similarities and differences in phage tail fiber

proteins, which can be used in further studies exploring the host specificity of bacteriophages.

Methods Phage Selection In order to find specific phages to be used in this investigation of tail fiber proteins, the Actinobacteriophage Database (PhagesDB) was searched for sequenced Arthrobacter phages (Russell & Hatfull, 2017). A completely annotated phage, ArV1, was identified within the database. This phage has been extensively studied, specifically because of its intermediate classification as Siphoviridae with a contractile tail typical of Myoviridae (Kaliniene, Šimoliūnas, Truncaitė, Zajančkauskaitė, Nainys, Kaupinis, …Meškys, 2017). Considering this, the tail proteins of this phage are of particular interest. Its gene product twenty-two (ArV1_22) was annotated as the tail fiber protein for phage ArV1 and designated as the RBP for this phage (Kaliniene, Šimoliūnas, Truncaitė, Zajančkauskaitė, Nainys, Kaupinis, …Meškys, 2017). ArV1_22 was then analyzed through the protein Basic Local Alignment Search Tool (BLASTp) found within PhagesDB. Through analyzing the 64 different phages resulting from the BLASTp of ArV1_22 against the Actinobacteriophage Database, four phages, Colucci, Ingrid, Loretta, and Beagle, in addition to ArV1, were chosen to have their tail fiber proteins compared. Three of the selected phages, Ingrid, Loretta, and Beagle were isolated on the same host, Arthrobacter globiformis, while ArV1 and Colucci were isolated on different hosts, Arthrobacter sp. 68b and Arthrobacter sp. ATCC respectively (Table 1). Primary Protein Structure Analysis The amino acid sequences of the five phage tail fiber proteins selected were compared using Clustal Omega, a software that aligns multiple amino acid sequences based upon regions of high similarity (Sievers, Wilm, Dineen, Gibson, Karplus, Li, … Higgins, 2011). The software analyzed the protein sequences by first aligning the sequences with the highest similarity, and best alignment scores, then aligning the sequences with less similarity. Through this method of alignment, Clustal Omega differentiated regions of similarity that might be associated with specific features. These similarity regions consist of consecutive amino acids that are either identical or contain similar properties within the proteins being compared. This allowed for in-depth comparisons to be made between the entire protein sequences of each phage.

Spring 2019 | VOL 6 | SCIENTIA | 17

Tertiary Protein Structure Analysis Qualitative. A 3D structure prediction was generated for each of the five tail fiber proteins using the program RaptorX (Källberg, Wang, Wang, Peng, Wang, Lu & Xu, 2012). Given the inputted amino acid sequence, the RaptorX software predicted protein folding based upon amino acid interactions. Once the predicted tertiary structures were obtained for each of the tail fibers, the bioinformatic tool SWISS PDB viewer was used to visualize the folding patterns, alpha helices and beta pleated sheets, of each tail fiber protein (Guex & Peitsch, 1997). The software was also used to differentiate the 131 amino acid similarity region by color from the rest of the protein for each individual protein. This visualization and differentiation of each protein allowed for a qualitative analysis by enabling areas of similarity and dissimilarity to be compared. Quantitative. The predicted tertiary structures of the tail fiber proteins were then used in a quantitative analysis completed through the use of predictive circular dichroism spectroscopy. Circular dichroism (CD) spectroscopy is a method of analysis that channels left circularly polarized light (LCP) and right circularly polarized light (RCP) through an optically active biological molecule, such as the proteins examined in this study, in order to calculate the differences in light absorption. This analysis allows for the types and concentrations of secondary structures found within the proteins, such as alpha helices and beta pleated sheets, to be determined. The bioinformatic tools DichroCalc, which calculates protein circular dichroism, and CAPITO, which analyzes and plots CD data, were utilized to predict the outcome of the circular dichroism spectroscopy (Bulheller & Hirst, 2009; Wiedemann, Bellstedt & Görlach, 2013). The generated CD curves were then compared and analyzed based on the peaks and troughs of the wavelength (nm) versus molar ellipticity (θ) curves for each tail fiber protein. The difference in light absorbance, which is a function of the wavelength, is corrected for concentration by conversion to molar ellipticity since various external factors affect CD data (concentration, pH, temperature, etc.). This method allowed for the similarities to be compared quantitatively through analyzing the various wavelengths and molar ellipticities of each phage.

Results Phage Selection Upon the input of ArV1_22, a determined RBP, into the BLASTp within PhagesDB, a list of similar phage

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gene products was generated by the program. Four similar phage gene products, found on hosts other than Arthrobacter sp. 68b, the host of ArV1, were selected for cross host comparison (Table 1). Colucci_25 was chosen for comparison because it was the top hit, or had the closest similarity to ArV1_22, with an E-value of 0.0, and because it was isolated on Arthrobacter ATCC. Ingrid, Loretta, and Beagle, were chosen because they were all isolated on the same host Arthrobacter globiformis, and even though they had lower E-values, they were still considered significant BLAST hits to ArV1_22 because they had an E-value >1x10-5 (Table 2). These were the only other phages isolated on an Arthrobacter host species within the generated BLASTp results. Primary Protein Structure Analysis After a Clustal Omega multiple sequence alignment of the five selected phages was conducted, regions of similarity and dissimilarity were able to be identified. As demonstrated in Figure 1, despite the amino acid sequences of the five selected proteins being different sizes, a 131 amino acid region of high similarity across all of these proteins was identified by Host Arthrobacter sp. 68b Arthrobacter sp. ATCC Arthrobacter globiformis

Phage Name Gene Product ArV1 22 Colucci 25 Ingrid 21 Loretta 21 Beagle 37

Size of Gene (aa) 654 aa 655 aa 295 aa 295 aa 295 aa

Table 1. Phages Selected for Analysis. The color blue represents tail fiber proteins identified on the host Arthrobacter sp. 68b; green represents Arthrobacter sp. ATCC; yellow represents Arthrobacter globiformis. ArV1_22 Colucci_25 ArV1_22 0 0 Colucci_25 0 0 Ingrid_21 2.00E-45 2.00E-45 Loretta_21 2.00E-45 2.00E-45 Beagle_37 1.00E-45 9.00E-44

Ingrid_21 Loretta_21 Beagle_37 6.00E-46 6.00E-46 6.00E-46 8.00E-46 8.00E-46 4.44E-44 1.00E-164 1.00E-164 1.00E-104 1.00E-164 1.00E-164 1.00E-104 1.00E-113 1.00E-113 0

Table 2. Comparison of E-values of the Selected Phage Tail Fiber Proteins. Phages listed at the top of columns are the particular proteins inputted into PhagesDB BLASTp. The rows are the resulting phage hits and their corresponding E-values. An E-value of 0 indicates there is a 0% chance the proteins match coincidentally, and any hit with an E-value less than 0.00001 is considered a significant match.

ArV1_22

N-terminus

131 amino acid similarity

Colucci_25 Ingrid_21 Arthrobacter sp. 68b Arthrobacter sp. ATCC Arthrobacter globiformis

Loretta_21 Beagle_37

C-terminus

aa353

aa483

aa654

aa354

aa484

aa655

aa98

aa229 aa295

aa98

aa229 aa295

aa99

aa230

Clustal Omega. Although not all the phage proteins completely aligned in regard to starting position, a region of 131 consecutive amino acids aligned due to the sequences having either the same amino acids or amino acids with similar molecular properties (the blue region in Figure 1). Additionally, as seen in Figure 1, the determined similar region in all the tail fiber proteins was between two variable regions (the red regions in Figure 1). These regions were considered variable because when the entire tail fibers were aligned, a large consecutive region of greater 131 amino acids of similarity was not identified. When looking at the percent identity matrices computed by Clustal Omega of the different regions, as seen in Table 3, it can be observed that while there is similarity across the entirety of the tail fiber proteins, there is a higher percentage of similarity between all the phages when only the 131 amino acid region of similarity is being aligned. The percent identity between phages isolated on different hosts also increases when only the 131 amino acid region is being compared. For example, when the entire tail protein of Ingrid_21 is compared to the entire tail fiber protein of ArV1_22, they are only 45.42% similar. Yet when the determined 131 amino acid region is compared between the two, the similarity increases significantly between these two phages to 66.15%. Tertiary Protein Structure Analysis Qualitative. Using the software SWISS, the alpha helices and the beta pleated sheets of the predicted tail fiber protein foldings were manipulated to be visually displayed as ribbon diagrams with the 131 amino acid similarity region coded as blue and the remainder of the protein as red (Figure 2). With each tail fiber protein having these distinctions, they were compared against one another to reveal similarities and differences in tail fiber protein folding. The blue similarity region in each of the phage proteins appeared to be very similar in its folding pattern, with each containing a region of beta

Figure 1. Clustal Omega Multiple Sequence Alignment of the Tail Fiber Proteins of the Selected Phages. The color red denotes the variable regions found with little similarity across all phages. Similarly, blue represents the region of 131 amino acid similarity identified across all phages.

aa356

pleated sheets in the middle of the protein. Furthermore, in the phages isolated on Arthrobacter globiformis, this blue similarity region also consisted of a single alpha helix. When the non-similarity regions of phages isolated on different hosts were compared, it could be seen that there were regions of variability (red) that surround the common blue region in all 5 phages. Furthermore, these red regions of variability seem to be host specific, which is demonstrated in proteins from

75%-100% 50%-75% 25%-50% 0%-25%

Table 3. Percent Similarity Found Within Proteins. The matrices above show the percent identity similarities between (a) the entire tail fiber protein for each phage (b) the 131aa region of similarity within each tail fiber protein of each phage. Dark red denotes 75% to 100%, red, 50% to 75%, pink, 25% to 50%, white, 0% to 25%.

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a. Arv1_22

c. Ingrid_21

b. Colucci_25

d. Loretta_21

e. Beagle_37

Figure 2. SWISS Protein Predictions of the Tail Fiber Protein- Tertiary Structures. Blue region is the 131 amino acid similarity region, red is the remainder of the protein. (a.) ArV1_22 (b.) Colucci_24 (c.) Ingrid_21 (d.) Loretta_21(e.) Beagle_37 phages isolated on different hosts, such as ArV1_22 (Figure 2a) and Ingrid_21 (Figure 2c). The regions of variability between these two proteins, when visually comparing the two, appeared to contain little similarity, as Ingrid_21 contained more alpha helices than ArV1_22 in the red variable region. In contrast, when the phage tail fiber proteins of phages isolated on the same host (Arthrobacter globiformis) were analyzed, it was found that their entire tail fiber proteins, both red and blue regions, folded more similarly than when visually compared with different hosts. This can be seen in Figure 2c, 2d, and 2e, where the tail fiber proteins of Ingrid_21, Loreta_21, and Beagle_37 appeared to closely overlap in protein folding patterns of the variable regions, having a region of beta pleated sheets separated from a region of alpha helices. In comparing the similarity region between all 5 tail fiber proteins, it was found to have the same folding pattern of beta pleated sheets between the proteins from phages isolated on the same host and phages isolated on different hosts. However, there is one difference within the similarity region between the phages isolated on Arthrobacter globiformis and phages isolated on other hosts. The tail proteins of phages isolated on Arthrobacter globiformis appear to have a alpha helix in the blue region whereas phages isolated on the other hosts lack this alpha helix. For example, when Colucci_25 (Figure 2b), isolated on Arthrobacter ATTC, was compared to Ingrid_21 (Figure 2c), isolated on Arthrobacter globiformis, it was seen that both blue regions appeared to have high similarity with a region of beta pleated sheets, yet Colucci_25 lacked an alpha helix that Ingrid_21 contained. Quantitative. Combined with the predicted proteins foldings, the circular dichroism curves assisted

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in quantitatively describing similarities and differences in these tail fiber proteins based on their secondary structures. For instance, similarity between Loretta_21 and Ingrid_21 (both isolated from the same host) is confirmed in Figure 3d, where the CD curves of both proteins peak at a wavelength of ~195 nm. As for tail fiber proteins from phages isolated on different hosts, such as ArV1_22 versus Ingrid_21, the curve shows less similarity in their alignments and peaks. However, the CD curves of ArV1_22 and Colucci_25 show high similarity in the wavelengths of their peaks regardless of the fact that they were isolated on different hosts. Additionally, it is important to note that the singleton phage, Beagle_37, showed less similarity with all other proteins in the CD analysis, regardless of its host bacteria (Figure 3c, 3e, 3f).

Discussion This study aimed to compare different genes in Arthrobacter phages that may play a role in bacterial adsorption and subsequent lysis. Specifically, we compared putative tail fiber proteins in phages that infect both identical and non-identical hosts, with hopes to learn more about similarities and differences between tail fiber proteins, some of which have been determined in previous research to be RBPs. This area of research is of great interest because phages have shown potential as an alternative therapy for treating bacterial infections, which may become a necessary route of treatment as multidrug-resistant bacteria become more prevalent. It became clear early in our research that tail fiber proteins are highly diverse, even within the genus Arthrobacter. However, we did discover a 131 amino acid region of high similarity across all five proteins selected.

220

240

wavelength (nm)

260

280

30 20 10 0 -10 -20 -30 180

280

ArV1_22 vs. ING_21 ArV1_22 ING_21

200

20 15 10 5 0 -5 -10 -15 180

[θ](103 deg cm2 dmol-1)

30 20 10 0 -10 -20 -30 180

ArV1_22 COL_25

200

220

240

wavelength (nm)

260

ArV1_22 vs. BEG_37 ArV1_22 BEG_37

200

220

240

wavelength (nm)

260

30 20 10 0 -10 -20 -30 180

[θ](103 deg cm2 dmol-1)

ArV1_22 vs. COL_25

[θ](103 deg cm2 dmol-1)

[θ](103 deg cm2 dmol-1) [θ](103 deg cm2 dmol-1) [θ](103 deg cm2 dmol-1)

25 20 15 10 5 0 -5 -10 -15 -20 180

ING_21 vs. LOR_21 ING_21 LOR_21

200

220

240

wavelength (nm)

260

280

LOR_21 vs. BEG_37 LOR_21 BEG_37

200

220

240

wavelength (nm)

260

280

ING_21 vs. BEG_27 ING_21 BEG_37

200

220

240

wavelength (nm)

260

280

Figure 3. CD Curves of Tail Fiber Proteins from the Selected Phages. Analyzed in regard to wavelength (nm) versus molar ellipticity (θ) (a) ArV1_22 (black) compared with Colucci_25 (green). (b) ArV1_22 (black) compared with Ingrid_21 (green). (c) ArV1_22 (black) compared with Beagle_37 (green). (d) Ingrid_21 (black) compared with Loretta_21 (green). (e) Loretta_21 (black) compared with Beagle_37 (green). (f) Ingrid_21 (black) compared with Beagle_37 (green).

We determined this to be a conserved region with a domain of unknown function. Furthermore, the similarity was qualitatively confirmed in the conserved region by using bioinformatic tools such as Clustal and SWISS PDB viewer. Clustal determined this conserved region to have a high percent identity in regards to the amino acid sequence, and SWISS determined that the conserved region contained the similar folding pattern of beta pleated sheets among the analyzed proteins within all of the five phages. Since this 131 amino acid region showed both sequence and folding conservation, it can then be inferred that the function of this region is also conserved. The CD analysis allowed for a more thorough comparison of the proteins, and when combined with other data from this study, conclusions were able to be drawn regarding the tail protein similarities. When analyzing Beagle_37, it was found that despite the confirmed similarity between Beagle_37 and the tail fiber proteins from phages isolated from the same host with software such as SWISS and Clustal Omega, the CD data seemed to show otherwise. This was demonstrated by the CD curves of Beagle_37 compared with Ingrid_21 (Figure 3f) and Beagle_37 compared with Arv1_22 (Figure 3c) showing great variability, despite the proteins being on the same host or different hosts.

The discrepancy in the CD data may have resulted from the difference between Beagle_37 and the other proteins (Table 3) due to its gene position being farther down in the genome (Table 1). Despite the CD data of Beagle_37 showing no similarities to other proteins, it was still of interest to see that there were similarities of the CD data between the proteins of phages isolated on different hosts. For instance, the similarity between the CD curves of ArV1_22 and Colucci_25 is likely attributed to Colucci_25 being the top BLASTp result for ArV1_22, having an E-Value of 0, and their high amino acid sequence similarity. However, the proteins from phages Loretta_21 and Ingrid_21, which were isolated on the same host, were not only seen to have nearly identical CD curves, but also high similarity in the SWISS protein foldings and amino acid sequence. Limitations to this study include the sparse collection of sequenced and annotated Arthobacter phages for selection and the use of predictive software in the secondary and tertiary protein analyses. Currently there are only 45 sequenced phages on host Arthrobacter globiformis and six sequenced phages on Arthrobacter sp. 68b, with only a few of these having been annotated. This small population of annotated phages within the desired host limited the sample size of what could be compared across isolation hosts, highlighting the importance of the

Spring 2019 | VOL 6 | SCIENTIA | 21

SEA-PHAGES program’s effort to publish annotations of this group of phages. Furthermore, the secondary and tertiary comparisons, analyzed by Dicrocalc and RaptorX respectively, were made using predictive software, not crystal structures and true laboratory spectroscopy. This may have been the reason as to why few conclusions could be made regarding host specificity based on the CD analysis. This may further prove that additional properties, other than secondary structures, could affect phage tail fiber specificity, and need to be explored. The protein foldings generated by RaptorX and SWISS were also predictions, further increasing the possibility of error by using multiple predictive softwares in conjunction with one another. If host specificity was to be altered in these phages, genetic manipulation would likely need to occur outside the conserved region we found. It had previously been discovered with phages PaP1 and JG004 that tail fiber proteins could be recombined to alter host range (Le, He, Tan, Huang, Zhang, Lux, … Hu, 2013). In this study, phages isolated on different hosts contained the conserved region, implying the variable region is host specific. This proposition could best be tested by creating recombinant phages in a laboratory to determine whether altering a region within the tail fiber gene truly gives the phage an altered host specificity. However, cross host infectability would need to be determined in the selected phages first, since it is not currently known if four of the five phages used in this study can normally infect other hosts. Since there is relatively little known about Arthrobacter phages, it may be wise to consider a more complex adsorption interaction in which the phages possess multiple RBP, as seen in the “nanosized Swiss army knife” of tail fiber proteins in phage phi92 (Schwarzer, Buettner, Browning, Nazarov, Rabsch, Bethe, … Gerardy-Schahn, 2012). This theory emphasizes how a comprehensive study of both phage RBPs and host receptors would lead a better understanding of the use of phages against bacteria in areas such as phage therapy. Although Arthrobacteria does not pose a risk to humans, further research identifying and comparing phage RBPs may lead to a better understanding of expanding the host range of phages, therefore advancing the field of phage therapy. By further exploring the genome of phages and the potential to alter their genome, the application of phages could significantly broaden. There is clearly much more to learn about the interactions between Arthrobacteria and Arthrobacter phages, and the discoveries made while studying them could be applied in other areas of research, the medical field, and beyond.

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References [1] Bulheller, B.M. & Hirst, J.D. (2009). DichroCalc—circular and linear dichroism online. Bioinformatics, 25(4), 539–540. doi:10.1093/ bioinformatics/btp016 [2] Cross, T., Schoff, C., Chudoff, D., Graves, L., Broomell, H., Terry, K., … Dunbar, D. (2015). An Optimized Enrichment Technique for the Isolation of Arthrobacter Bacteriophage Species from Soil Sample Isolates. Journal of Visualized Experiments: JoVE, (98), e52781. doi:10.3791/52781 [3] Czapar, A. E., & Steinmetz, N. F. (2017). Plant viruses and bacteriophages for drug delivery in medicine and biotechnology. Current Opinion in Chemical Biology, 38(Supplement C), 108–116. doi:10.1016/ j.cbpa.2017.03.013 [4] Guex, N. & Peitsch, M.C. (1997). SWISS-MODEL and the Swiss-PdbViewer: An environment for comparative protein modeling. Electrophoresis. 18, 27142723. [5] Kaliniene, L., Šimoliūnas, E., Truncaitė, L., Zajančkauskaitė, A., Nainys, J., Kaupinis, A., … Meškys, R. (2017). Molecular Analysis of Arthrobacter Myovirus VB_ArtM-ArV1: We Blame It on the Tail. Journal of Virology, 91(8), e00023-17. doi:10.1128/JVI.00023-17. [6] Källberg, M., Wang, H., Wang, S., Peng, J., Wang, Z., Lu, H. & Xu, J. (2012). Template-based protein structure modeling using the RaptorX web server. Nature Protocols, 7, 1511–1522. [7] Le, S., He, X., Tan, Y., Huang, G., Zhang, L., Lux, R., … Hu, F. (2013). Mapping the Tail Fiber as the Receptor Binding Protein Responsible for Differential Host Specificity of Pseudomonas aeruginosa Bacteriophages PaP1 and JG004. PLoS One, 8(7), e68562. doi:10.1371/journal.pone.0068562 [8] Lin, D. M., Koskella, B., & Lin, H. C. (2017). Phage therapy: An alternative to antibiotics in the age of multi-drug resistance. World Journal of Gastrointestinal Pharmacology and Therapeutics, 8(3), 162–173. doi:10.4292/wjgpt.v8.i3.162 [9] Mahichi, F., Synnott, A.J., Yamamichi, K., Osada, T. & Tanji, Y. (2009). Site-specific recombination of T2 phage using IP008 long tail fiber genes provides a targeted method for expanding host range while retaining lytic activity. FEMS Microbiology Letters, 295(2), 211–217. doi:10.1111/j.1574-6968.2009.01588.x [10] Moineau, S. & Tremblay, D. M. (2017). Bacteriophage☆ Reference Module in Life Sciences. Elsevier. doi: 10.1016/B978-0-12-809633-8.06122-7 [11] Ross, A., Ward, S., & Hyman, P. (2016). More Is Better: Selecting for Broad Host Range Bacteriophages. Frontiers in Microbiology, 7.

doi:10.3389/fmicb.2016.01352 [12] Russell, D.A. & Hatfull, G.F. (2017). PhagesDB: the actinobacteriophage database. Bioinformatics, 33(5), 784–786. doi:10.1093/bioinformatics/btw711 [13] Schooley, R. T., Biswas, B., Gill, J. J., Hernandez-Morales, A., Lancaster, J., Lessor, L., … Hamilton, T. (2017). Development and Use of Personalized Bacteriophage-Based Therapeutic Cocktails To Treat a Patient with a Disseminated Resistant Acinetobacter baumannii Infection. Antimicrobial Agents and Chemotherapy, 61(10), e00954–17. doi:10.1128/ AAC.00954-17 [14] Schwarzer, D., Buettner, F. F. R., Browning, C., Nazarov, S., Rabsch, W., Bethe, A., … Gerardy-Schahn, R. (2012). A Multivalent Adsorption Apparatus Explains the Broad Host Range of Phage phi92: a Comprehensive Genomic and Structural Analysis. Journal of Virology, 86(19), 10384–10398. doi:10.1128/JVI.00801-12 [15] Sievers, F., Wilm, A., Dineen, D., Gibson, T.J, Karplus, K., Li, W., … Higgins, D.G. (2011). Fast, scalable generation of high-quality protein multiple sequence alignments using Clustal Omega. Molecular Systems Biology, 7, 539. doi:10.1038/msb.2011.75 [16] Turnbull, G. A., Ousley, M., Walker, A., Shaw, E., & Morgan, J. A. W. (2001). Degradation of Substituted Phenylurea Herbicides by Arthrobacter globiformis Strain D47 and Characterization of a Plasmid-Associated Hydrolase Gene, puhA. Applied and Environmental Microbiology, 67(5), 2270–2275. doi:10.1128/AEM.67.5.2270-2275.2001 [17] Wang, J., Hofnung, M., & Charbit, A. (2000). The C-Terminal Portion of the Tail Fiber Protein of Bacteriophage Lambda Is Responsible for Binding to LamB, Its Receptor at the Surface of Escherichia coli K-12. Journal of Bacteriology. 182(2), 508–512. [18] Wiedemann, C., Bellstedt, P. & Görlach, M. (2013). CAPITO- A web server based analysis and plotting tool for circular dichroism data. Bioinformatics, 29(14), 17501757.

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Original Research

Comparison of Caffeine Dependence between Education Settings Jay Jackson, Ellie Jeung

Department of Psychology, Baylor University, Waco, TX

Abstract This study investigated discrepancies of caffeine expectancy and possible caffeine dependence across university demographics, specifically comparing a private university to a community college. Our investigation was prompted from the observation of unhealthy caffeine consumption around the campus of the private university we investigated. For measurement we used the Caffeine Expectancy Questionnaire (CaffEQ) which is a 47-item additive psychometric that can be used to associate perceived effects of: caffeine dependence, caffeine withdrawal, caffeineâ&#x20AC;&#x2122;s effects on sleep, mood elevation, and appetite suppression. After a month of data collection, we had a total of N = 116 participants (nBU = 27 & nMCC = 89), each of who participated for an average of 10 min. We did not find a significant difference between levels of caffeine expectancy (t(70.3) = .439, p = .337). Despite this we were able to observe a significant difference between perceived levels of course rigor between a community college and a private university. Sampling was found to be extremely difficult, as one of the campuses under investigation experienced a large nonresponse bias. Following our study, we concluded that a further investigation may reveal an actual difference between the two college campuses.

Introduction Privatized education has advantages and disadvantages compared to government institution education. In contrast to the typical equipotential nature of government standards for education, a proposed difference between the two would be accounted in variation among privatized education. Generalizations of the aforementioned are tenanted with frail stability, while those of the comparison can be said to house a level of uniform influence at one and another. Quickly reviewing collegiate institutions that commonly dwell in the realm of prestige, are all of private ownership and enterprise. This information is to guide the premise of private universities being of higher expectations and rigor, which is where our study begins (Lucas et al., 2014). The reason why we chose caffeine expectancy and the relation to dependence for comparison is the correlates to caffeine dependence that should give insight to ways the student bodies of a private university or community college differ. Another reason being that most of the research done with caffeine dependence and caffeineâ&#x20AC;&#x2122;s psychoactive effects have been conducted on college age populations accessible to researchers (Ozsungur, Brenner, & El-Sohemy, 2016). These

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correlates include: taking risks from a dare, seatbelt omission, fighting, sexual risk-taking, marijuana, tobacco, and alcohol. Correlations have also been found to constructs of behavior such as: self-reported violence, conduct disorders, stress level, educational attainment, personality, and depressive symptomatology (Richards, & Smith, 2015; Penolazzi, Natale, Leone, & Russo, 2012). Aside from assumptions, not all findings in caffeine research are negative, such as Lucas et al., who provided evidence that populations with higher levels of caffeine intake, have lower levels of completed suicide (2014). In a continuation to caffeine as a positive, caffeine has shown to improve workplace productivity, long term memory, vigilance, exertion, and self-perception (McLellan, Caldwell, & Lieberman, 2016; Hameleers et al., 2000). The advantages of caffeine are consistent in effects on long term memory and its ability to maintain optimal higher functioning cognition on with onset of sleep deprivation. These effects that caffeine has on long term memory were discussed by McLellan, Caldwell, & Lieberman in detail with there being larger effect size in the difference of habitual caffeine users in long-term memory than the difference in short-term

memory (2008). This research on caffeine’s effect on long-term memory is important when considering the anticipated effects of caffeine that college students typically have during exam cycles. Hameleer's finding of caffeine levels being negatively correlated with short-term memory, provides irony that most students find humor in (2000). The second positive effect of caffeine’s use in collegiate populations is the extension of functional wakeful hours. This extension of functioning wakeful hours has been shown to be elicited by caffeine in a study by Kamimori et al. in college aged military personnel (2015). Aside from these aspects of caffeine, caffeine dependence is said to be the most common form of dependence in the United States, being reported that 87% of adolescents and adults consume caffeine on a regular basis (Ferré, 2016). Although caffeine dependence is typically not addressed as an addiction, the withdrawal effects and the pharmacological operates of caffeine resemble that of several highly addictive psychostimulants (Turgeon, Townsend, Dixon, Hickman, & Lee, 2016). When considering drug effects in aspect of the psychostimulant theory of addiction, caffeine is similar to drugs such as amphetamine and cocaine, caused by caffeine at lower spectrum levels contributing to positive subjective effects, while at upper spectrum levels, caffeine tenets anxiety and nervous impulsions (Ferré, 2016). These ‘upper-spectrum’ levels of caffeine are typically those in the 500mg and above range, where daily consumption of such is informally termed ‘caffeinism’ by Richards & Smith (2015). Nevertheless, caffeine dependence has been characterized by subjective behaviors, along with those previously mentioned, and follow suit to addiction paradigms in lab animals such as the place-preference, aversion, and taste (Ferré, 2016). In exposure to lab animals in adolescence, caffeine was also able to change morphology of adenosine receptors as well as brain derived neurotrophic factors within regions of the cortex and hippocampus as well as increase the pervasiveness of substance use disorders in experimental groups (Ferré, 2016; Turgeon et al., 2016). As mentioned, in aspect of harmful cognitive repercussions the higher echelon of caffeine consumption is around 500mg. These levels of caffeine are concerning given the finding of Wilhelmus et al. that caffeine produces statistically significant effects at less than a fifth of the dose that is consumed on average, which by most studies is given between 250-350mg (2017). Given information found by Wilhelmus et al., caffeine may be used in excess by a relatively large portion of individuals (2017). With such over-use, caffeine dependence was incorporated to the

DSM-IV-TR and ICD-10, and noted by the American Psychiatric Association, accepting ‘caffeine withdrawal’ as a diagnosis that is identified through withdrawal symptoms experienced by individuals having developed a regimented use of caffeine (Ferré, 2016; Ozsunger el al., 2008). Withdrawal effects suffered by habitual users are typically fatigue, headache, dysphoric mood, and flu like stomach, although flu like stomach has been shown not to increase dose dependently (Ozsunger el al., 2008; Mills, Boakes, & Colagiuri, 2016). The symptoms can last for up to nine days of total absences and take onset in as early as six hours from cessation of caffeine consumption, depending on various factors (Ozsunger el al., 2008). In regard to our study, these described withdrawal symptoms are often used psychometrics to indicate the presence of caffeine dependence, as well as the extent to impairment on functioning (McLellan et al., 2016). While several aspects of the caffeine research remain inconclusive, we can perceive the potential benefits and hazards of caffeine. Our interest was to investigate discrepancies between these populations within the context of how student bodies from a community college and a private university display signs of caffeine dependence. Caffeine has been shown to be a form of dependence being detectable through symptoms of withdrawals (McLellan et al., 2016). While caffeine can be used to enhance certain aspects of cognition, caffeine can also produce permanent physiological changes

(Turgeon et al., 2016). Our underlying assumption is that a private university will contain more individuals showing signs for caffeine dependence, our hope is to find that levels of caffeine dependence are low for both institutions of interest. However, in the chance that one differs or that both house high levels of caffeine dependence, we will be prompted to ask further questions as to why caffeine dependence is akin to collegiate students.

Methods Participants All N = 116 participants (89 women, 42 men; Mage = 25, range = 63-16) were college students, 89 enrolled in a community college (MCC) and 27 enrolled in a private university (BU). The distribution of race is as given: African-American 13.8%, White 44%, Asian 9.5%, and Latino/Hispanic 28.5 and 4.2% as “Other”. There were 27 first years, 54 Sophomores, 25 Juniors, and 10 Seniors. We solicited inquiries of participation in the

Spring 2019 | VOL 6 | SCIENTIA | 25

common areas of each campus, utilizing convenience. Materials The psychometric we used to investigate caffeine dependence was Huntley’s CaffEQ; this scale 47-item scale was developed to assess caffeine expectancies among consumers and non-consumers (2012). Caffeine expectancies postulate individual characteristics of the participants within each item (e.g. dependence, work enhancement, etc.). These specific characteristics are identified in how respondents rate each item along a 6-point scale (1 = Very unlikely, 6 = Very Likely). Sample items include “Caffeine picks me up when I am feeling tired” and “Caffeine improves my mood”. Several questions were added to the beginning of each questionnaire in our Qualtrics online version in order to gain participant information such as; “What university do you attend?”, “How many siblings do you have?” and “From one-to-ten, how rigorous is your course load?” The coding in Qualtrics for university was 1 for BU and 2 for MCC. An Exploratory Factor Analysis revealed the underlying variables, 7-factor solution, related to caffeine consumption: withdrawal/dependence, energy/work enhancement, appetite suppression, social/mood enhancement, physical performance enhancement, anxiety/negative physical effects, and sleep disturbance; while this states that the CaffEQ is not a direct measure for caffeine dependence, the expectancy score that is produced will serve a convenient mode to collect a large sample size and quick means of subject participation. The authors reported a coefficient alpha of .80 to .94 (M =3.08, SD = 0.85) in their test-retest reliability of CaffEQ. For validity, Huntley & Juliano reported a significant correlation of item similarity along with their interbedded construct (2012). Procedure The students that were chosen for the study were sampled from BU and MCC. The participants were approached inside campus common areas at variable hours and asked if they would wish to participate in a study on caffeine consumption by taking an online questionnaire. The authors collected form MCC common areas, which we operationally define as: cafeterias, libraries, recreation centers, and lounges. Participants were informed of the basic procedures of the study before they gave consent: to abstain from caffeine on the given Saturday night, and complete the electronically-sent questionnaire the following Sunday morning before any consumption of caffeine. The participants were also informed that an email will be sent on the Saturday proceeding the study to remind them

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of their participation. Additionally, participants were informed that the study is not required of them, and their time in considering participation is appreciated. Given that the participants understood these terms, they were then asked to provide their most frequently used email, defined as the one that audibly alerts them when a message is received (to remind them of the questionnaire to complete on the specified morning). Although abstinence is not required in the CaffEQ, abstinence has been suggested to increase expectancy outcomes of caffeine dependent individuals (Ozsungur et al., 2008). The email containing the questionnaire was scheduled using the Outlook email scheduling system for 0600 on the chosen Sunday and remained open for completion for the rest of the month. The email contained a reiteration of what was initially told the participants, along with a link to Qualtrics for them to access the questionnaire. On accessing, the questionnaire the participants were initially asked to provide sex, gender, nationality, and age along with several other identifying questions. Instructions of the test were the same as that provided by the author. The time for each questionnaire was trivial (M = 10 min) and on submission of the questionnaire each participant was sent an email debriefing them on the study and thanking them for their time.

Results Exclusion criteria was based on full survey completion, after exclusions, N = 116. See table one for the distribution of our individual sample sizes. Using the Welch-Satterthwaite t-test for independent samples there was not a significant difference between caffeine expectancy for individuals attending a private university or community college (t(70.3) = .439, p = .337; see table one). With this data we concluded no reason to investigate for caffeine dependency given that expectancy scores did not yield significance (Huntley & Juliano, 2012). Table 1.

Caffeine Expectancy Across Universities

Caffeine Expectancy Scores n M SD

Baylor

138.1

41.3

MCC

134.1

49.0

Note. No p-values of significance.

.423

Discussion Our initial conclusion was the null result for any difference deriving from caffeine expectancy between our two populations of interest. To investigate the slight differences between the means for caffeine expectancy and the clear difference between perceived course load at the two universities (t(70.3) = 5.26, p < .001, g = .49), we plotted the data for caffeine expectancy onto perceived level of course load. The graphs revealed no apparent trend between individuals with high perceived levels of course load and caffeine expectancy, thus these graphs were not included and we consequently concluded investigation into our results. The observed difference between perceived levels of course rigor was not surprising, and is concurrent with challenges being faced within arguments concerning degree validity and applying programs for cognitive behavior therapies (CBTs) to different forms of higher education (Reiser & Milne, 2013). However, this observed difference in course rigor at the two universities did not appear to be related to expectancy for caffeine, and consequently dependence. While further discussion concerning the levels of perceived course rigor is appealing, the significance found in course rigor is overshadowed by the non-significant result of the main hypothesis of this study. The non-significant result of caffeine expectancy among college attending individuals was surprising in light of the research provided by McLellan et al. showing the various advantages that caffeine’s psychostimulatory effects can have on cognition (2016). Despite the available resources, being a novice in survey construction facilitated the introduction of procedures that were methodically unsound for survey creation. The specific misconstruction was our inclusion of demographic information at the beginning of the survey (Goodwin & Goodwin, 2017). With originally having 143 responses, then needing to remove 27 participants due to non-response behavior between both institutions, the inclusion of the demographic at the beginning of the survey is likely to have attributed to this possible participant ‘survey attrition’; an interesting find was that only three of the excluded data points (uncompleted datum) came from the private university. Within collecting data from our populations, several participant selection biases are likely to have attributed to our findings. The specific participant selection bias was likely to have been introduced when involving participants from the community college demographic. After the first two weeks of approaching individuals on the community college campus only seven individuals had provided consent out of possibly fifty that were approached, and of the seven only four of the

emails proved to be legitimate. This rate of individuals declining to participate from the community college appeared large when compared to the one-out-of-ten individuals approached at private university campus declining to provide consent. The difference in participation is indicative of the underlying differences between our two populations of interest and misrepresentation within our analyzed data. This misrepresentation of the community college is specifically significant considering that we can be confident 500 individuals from the community college were given access to participation in our study. When the majority of individuals from the community college opted not to participate our measurements are consequently from the minority of the population that opted to participate; this could suggest that course rigor is in fact greater at the community college but the individuals that would have responded with higher perceived course rigor were in fact too busy to participate. While as a whole, the two campuses in question did not differ inferentially in caffeine expectancy, the obstructive attitudes and perceived levels of course rigor entice further investigation. There is no postulation, outside the scope of conceptual replication, that caffeine dependence be further investigated as a priority. To exert significant effort into a type two error investigation is not a productive use of our current resources in psychology, and has become a complicated problem in many instances of research psychology (Cohen, 1994). What should be addressed following this study is the hostile attitudes and continuing increase of unrealistic self-expectations across our observed universities. This is even addressed by Dr. Marsh, the president of the student counseling service at BU, reporting to see a statistically significant increase in perceived stress among the university’s students over the past five years. While the hostile attitudes were observed at both universities on occasion, the observation was the significant increase in hostile reactions at the community college. What is apparent in these seemingly campus specific minutia problems, is that the problems between each campus is inverse. We conclude with the two different institutions that were investigated possibly being able to learn from how the other is different in aspects of perceived course rigor within the student populations.

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References [1] Cohen, J. (1994). The earth is round (p < .05). American Psychologist, 49(12), 997-1003. [2] FerrĂŠ, S. (2016). Mechanisms of the psychostimulant effects of caffeine: Implications for substance use disorders. Psychopharmacology, 233(10), 1963-1979. doi:10.1007/s00213-016-4212-2 [3] Goodwin, C. J., & Goodwin, K. A. (2017). Research in psychology: methods and design. Hoboken, NJ: John Wiley & Sons. [4] Hameleers, P. M., Van Boxtel, M. J., Hogervorst, E., Riedel, W. J., Houx, P. J., Buntinx, F., & Jolles, J. (2000). Habitual caffeine consumption and its relation to memory, attention, planning capacity and psychomotor performance across multiple age groups. Human Psychopharmacology: Clinical and Experimental, 15(8), 573-581. doi:10.1002/hup.218 [5] Huntley, E. D., & Juliano, L. M. (2012). Caffeine Expectancy Questionnaire (CaffEQ): Construction, psychometric properties, and associations with caffeine use, caffeine dependence, and other related variables. Psychological Assessment, 24(3), 592-607. [6] Kamimori, G. H., McLellan, T. M., Tate, C. M., Voss, D. M., Niro, P., & Lieberman, H. R. (2015). Caffeine improves reaction time, vigilance and logical reasoning during extended periods with restricted opportunities for sleep. Psychopharmacology, 232(12), 2031-2042. doi:10.1007/s00213-014-3834-5 [7] Lucas, M., O'Reilly, E. J., Pan, A., Mirzaei, F., Willett, W. C., Okereke, O. I., & Ascherio, A. (2014). Coffee, caffeine, and risk of completed suicide: Results from three prospective cohorts of American adults. The World Journal of Biological Psychiatry, 15(5), 377-386. doi:10.3 109/15622975.2013.795243 [8] McLellan, T. M., Caldwell, J. A., & Lieberman, H. R. (2016). A review of caffeineâ&#x20AC;&#x2122;s effects on cognitive, physical and occupational performance. Neuroscience and Biobehavioral Reviews, 71, 294-312. doi:10.1016/j. neubiorev.2016.09.001 [9] Mills, L., Boakes, R. A., & Colagiuri, B. (2016). Placebo caffeine reduces withdrawal in abstinent coffee drinkers. Journal of Psychopharmacology, 30(4), 388-394. doi:10.1177/0269881116632374 [10] Ozsungur, S., Brenner, D., & El-Sohemy, A. (2008). Fourteen well-described caffeine withdrawal symptoms factor into three clusters. Psychopharmacology, 201(4), 541-548. doi:10.1007/s00213-008-1329-y [11] Penolazzi, B., Natale, V., Leone, L., & Russo, P. M. (2012). Individual differences affecting caffeine intake. Analysis of consumption behaviours for different times of day and caffeine sources. Appetite, 58(3), 971-977. doi:10.1016/j.appet.2012.02.001

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[12] Reiser, R. P., & Milne, D. L. (2013). Cognitive behavioral therapy supervision in a university-based training clinic: A case study in bridging the gap between rigor and relevance. Journal of Cognitive Psychotherapy, 27(1), 30-41. doi:10.1891/ 0889-8391.27.1.30 [13] Richards, G., & Smith, A. (2015). Caffeine consumption and self-assessed stress, anxiety, and depression in secondary school children. Journal of Psychopharmacology, 29(12), 1236-1247. doi:10.1177/0269881115612404 [14] Turgeon, S. M., Townsend, S. E., Dixon, R. S., Hickman, E. T., & Lee, S. M. (2016). Chronic caffeine produces sexually dimorphic effects on amphetamine-induced behavior, anxiety and depressive-like behavior in adolescent rats. Pharmacology, Biochemistry and Behavior, 143, 26-33. doi:10.1016/j.pbb.2016.01.012 [15] Wilhelmus, M. M., Hay, J. L., Zuiker, R. A., Okkerse, P., Perdrieu, C., Sauser, J., & ... Silber, B. Y. (2017). Effects of a single, oral 60 mg caffeine dose on attention in healthy adult subjects. Journal of Psychopharmacology, 31(2), 222-232. doi:10.1177/0269881116668593

Original Research

The Relationship Between AttP Sites and tRNA in Cluster FF Arthrobacter Phages Mary Elizabeth Overcash, Sarah Antrich, Long Pham, Lathan Lucas, Ashley Young, Tamarah Adair, Ph.D. Baylor University, Waco, TX

Abstract

Attachment phage sites (attP sites) are relatively short regions in temperate phage genomes that are involved in the recombination of DNA strands when phages enter the lysogenic cycle. AttP sites share sequence homology with the phage host at the bacterial attachment site (attB site). AttP sites are often located near integrase genes because of the involvement of the integrase enzyme in incorporating the phage genome into the host genome. The presence of tRNA in phages has been hypothesized to result from their proximity to attB sites in the phages’ host genome. In order to locate attP sites and study their proximity to tRNA, the entire genome of each of the phages in the FF cluster was compared to the host, Arthrobacter globiformis, using NCBI BLASTn. Regions of homology near putative integrase genes and in intergenic regions were identified. A possible attP site was identified at the beginning of a tRNA sequence just upstream of the integrase gene in all three FF phages, suggesting that some tRNA may appear in phage genomes because of overlap with attP sites. The relationship between tRNA and attP sites may provide further insight into the mechanism by which phages transition between the lytic cycle and the lysogenic cycle.

Introduction Bacteriophage genomes are compact, with few non-coding elements (Delesalle, Tanke, Vill, & Krukonis, 2016). Because phages mainly utilize host machinery for replication, including host tRNA, the presence of tRNA in phage genomes is intriguing (Bailly-Bechet, Vergassola, & Rocha, 2007). One explanation for tRNA presence in temperate phage genomes is overlap with attP sites (Tan et al., 2013). There are multiple bacteriophage families, or clusters, whose tRNA genes have been found to contain attP sites (Campbell, 2003). This study examined the FF cluster, a group of Arthrobacter phages grouped together because of similarities found through comparative genomic analysis. Attachment phage sites, or attP sites, are relatively short regions in temperate phage genomes that are involved in the integrative recombination of DNA strands when the phages enter the lysogenic cycle (Campbell, 2003). A phage’s attP site shares sequence homology with the phage host at the bacterial attachment site (attB site). This area of homology, called the “core sequence,” is typically 20-45 base pairs, and the entire attP site sequence is generally between 200-500 base pairs (Fogg, Colloms, Rosser, Stark, & Smith, 2014;

Singh, Ghosh, and Hatfull, 2013). AttP sites are often located near integrase genes because of the involvement of the integrase enzyme in incorporating the phage genome into the host genome (Fogg et al., 2014). In phages coding for integrases which use a tyrosine as the catalytic residue (tyrosine integrases), overlap between attP sites and tRNA is more common. In order to gain insight into the presence of tRNA within phage genomes, this study examined if attP sites were located near tRNA within the genomes of Cluster FF Arthrobacter phages isolated on Arthrobacter globiformis B-2979.

Methods Integrase Identification Genome maps for bacteriophages Elesar, Nandita, and Ryan were accessed using Phamerator, a web-based bioinformatic tool developed for annotating and comparing Actinobacteriophage genomes (Cresawn et al., 2011). Within the phage genome, attP sites are usually located relatively close to the integrase, so Phamerator was used to locate the tyrosine integrase within the genome of each of the four phages.

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Phamerator output included a list of predicted protein product domains for each gene in each phage. Phamerator produced this output by analyzing the phage genomes with BLASTp and CLUSTALW pairwise alignments (Cresawn et al., 2011). These predicted domains indicated that the gene located at base pairs 28372-29248 in Elesar coded for a tyrosine integrase. This allowed for the identification of the tyrosine integrases in the other phages in the FF cluster because of the similarity of the sequences. These predicted gene functions were confirmed with NCBI BLAST, which was used to look for similarity between the identified genes and tyrosine integrase genes within other bacteriophage genomes (Altschul et al., 1997; Zhang et al., (1998). AttP Site Identification A full-genome sequence of the bacterial host of all these phages, Arthrobacter globiformis, was located on NCBI GenBank (NZ_BAEG01000085.1)(Benson et al., 2013; Miyazawa et al., 2011). The genome sequence for each phage was obtained from the Actinobacteriophage Database (PhagesDB). To find potential attP sites, each phage genome sequence was compared to the host genome sequence to locate areas of homology between the two sequences using NCBI BLASTn. The BLAST alignment results yielded candidates for possible attP site core sequences. These candidates were refined by eliminating sequences which were not between 20-45 base pairs, were more than 5 genes away from the tyrosine integrase gene in the phage genome, did not exhibit a level of homology higher than 90% identity, or overlapped a phage gene by 5 or more base pairs. tRNA Identification and Comparison to AttP Site Aragorn, a program which uses heuristic algorithms to detect tRNA genes within nucleotide sequences, was used to locate tRNA within the genome sequences of each of the phages and the bacterial host (Laslett & Canback, 2004). Once the base pair coordinates for the tRNA were obtained, the locations of the tRNA within the phage genomes were compared to the locations of the putative attP site core sequences.

Results Integrase Identification Elesar contained a single tyrosine integrase located at base pairs 28372-29248. Nandita contained two tyrosine integrases, the first a forward gene at base pairs 26292-27212 with low similarity to Elesarâ&#x20AC;&#x2122;s integrase (31% identity) and the second a reverse gene at 27627-28503 with high similarity (96% identity). Ryan

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also contained two tyrosine integrases, the first a forward gene at base pairs 26902-27823 (also with 31% identity to Elesarâ&#x20AC;&#x2122;s integrase) and the second a reverse gene at base pairs 28229-29105 (98% identity). AttP Site Identification When the genomes of the phages in the FF cluster were compared to their host, Arthrobacter globiformis, it was found that all three phages contained the same 25 base pair sequence that had 92% similarity with a sequence within the host (Figure 1). This sequence looked promising as an attP site because of its length, proximity to the tyrosine integrase gene, high level of homology with the host sequence, and location in an intergenic region in all three phages (Figure 2). tRNA Identification and Comparison to AttP Site When the phage genome sequences were analyzed by Aragorn, tRNA were located in all three FF phages. Elesar contained an arginine tRNA at base pairs 28252-28325, Nandita contained a serine tRNA at base pairs 753-836 and an arginine tRNA at base pairs 27504-27577, and Ryan contained an arginine tRNA at base pairs 28115-28188. In the three FF phages, the identified arginine tRNA were found to overlap the putative attP site core sequences. The 25-base pair putative attP site core sequence made up the first 25 basepairs of each 74-base pair tRNA in each of the phage genomes (Figures 3, 4, 5).

A. globiformis bp 42632-42656

GCCCTCGTAGCTCAGGGGATAGAGC GCCCCAGTAGCTCAGGGGATAGAGC Elesar bp 28252-28276 Nandita bp 27504-27528 Ryan bp 28115-28139 Figure 1: An alignment between the putative attB and attP core site sequences. The putative attB site core sequence found in the host genome is above, and the putative attP site core sequence found in all three FF phages is shown below. Lines between the two sequences indicate nucleotide conservation. Of the 25 nucleotides in each sequence, 23 are conserved, giving a 92% identity.

C A G-C C-G C-G C-G C-G A-T G-C TT T CTCCC A GA A ! +! ! ! G G CTCG GGGGG C G ! ! ! ! C TT G GAGC T ATA A G G-CGG A-T A-T G-C C T C A T A TCT

Elesar (Draft) 37

Nandita (Draft) 39

Ryan (Draft) 39

Figure 2: These Phamerator maps show the areas within the genomes of the FF phages which contain the tyrosine integrases and putative attP site core sequences. Genes are represented by rectangles, with the black rectangles indicating the tyrosine integrases and the red circles containing the 25 bp putative attP site core sequences. 28000 bp 28000

Gene Gene 35

28200 28200 bp bp

Figure 3: The predicted secondary structure of the arginine tRNA found by Aragorn within Elesar’s genome. The putative attP site core sequence, which occurs within the tRNA sequence, is indicated by the red nucleotides. Exclamation points represent hydrogen bonding between base pairs, and plus signs represent the absence of a hydrogen bond.

28400 bp bp

tRNA tRNA

28600 bp 28600 bp

Tyrosine Integrase Tyrosine IntegraseGene Gene (Gene 37) (Gene 37)

attP attP site site Figure 4: This figure shows the location of the attP site core sequence within the tRNA in Elesar’s genome. The black bar indicates the position of the genes within Elesar’s genome.

Figure 5: This WebLogo was made by inputting the sequences of the 74 bp tRNAs found within the genomes of the phages and host. Large letters indicate nucleotide conservation. In areas of discrepancy, the top letter indicates the sequence found within the phage genomes, and the bottom letter indicates the host’s sequence.

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Visualization of AttP Site A WebLogo was created to visibly show the homology between the 74 base pair tRNA found within the three FF phages and the 74 base pair tRNA found within the host (Crooks, Hon, Chandonia & Brenner, 2004; Schneider & Stephens, 1990). To create the visual alignment, the tRNA sequence found in the three FF phages was compared to the tRNA sequence found in the host. This WebLogo confirmed that the putative attP site core sequence has significantly greater nucleotide conservation than the rest of the tRNA sequence.

Discussion Without conducting wet lab experiments, it cannot be stated conclusively that the 25 base pair sequence identified is in fact an attP site core sequence. It is possible that this region only exhibits homology because the three FF phages contain the same tRNA as their host. However, this seems unlikely, as the homologous sequence occurs within variable regions of the tRNA sequence, and the remaining 49 base pairs of the tRNA demonstrate low sequence conservation. Additionally, it has been shown that it is possible for attP sites to overlap with tRNA (Tan et al., 2007). It is also possible that the 25 base pair sequence identified may not be the correct length of the true attP site. If the first five base pairs are omitted, the remaining 20 base pairs of the site are identical in the bacteriophage and in the host. Thus, the exact length of the proposed attP site cannot be determined at this time. The sequence identified does meet all the necessary requirements, being in the expected location and of the correct length. Typically, in attP sites associated with tyrosine integrases, the attP site is about 250 base pairs long, but there is a core sequence of 20-45 base pairs that closely matches with the host (Singh, Ghosh, & Hatfull, 2013). In addition, the proximity of the sequence to the tyrosine integrase gene without overlapping with any other genes indicates that it is likely an attP site. The overlap of the possible attP site core sequence with the tRNA suggests that tRNA may be an important component in the process of integrating the phage DNA into the host genome during lysogeny. Further research on attP sites could help to gain more insight concerning how phage transition between the lytic and lysogenic cycle.

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Acknowledgements We thank Kira Zack (University of Pittsburgh), Patrick Rimple (University of Pittsburgh), and Leo Rule (Baylor University) for their isolation of Nandita, Ryan, and Elesar, respectively and the Baylor University SEA-PHAGES 2017-2018 class for their genomic annotation of Elesar.

References [1] Altschul, S.F., Madden, T.L., Schaffer, A. A., Zhang, J., Zhang, Z., Miller, W. & Lipman, D.J. (1997). Gapped BLAST and PSI-BLAST: a new generation of protein database search programs, Nucleic Acids Research, 25(17), 3389-3402. doi: 10.1093/nar/25.17.3389 [2] Bailly-Bechet, M., Vergassola, M. & Rocha, E. (2007). Causes for the intriguing presence of tRNAs in phages. Genome Research. 17, 1486-1495. doi: 10.1101/ gr.6649807 [3] Benson, D. A., Clark, K., Karsch-Mizrachi, I., Lipman, D. J., Ostell, J., & Sayers, E. W. (2013). GenBank. Nucleic acids research, 42, D32-7. [4] Campbell, A. (2003). Prophage insertion sites. Research in Microbiology, 154(4), 277-282. doi: 10.1016/ S0923-2508(03)00071-8 [5] Cresawn, S.G., Bogel, M., Day, N., Jacobs-Sera, D., Hendrix, R.W. & Hatfull, G.F. (2011). Phamerator: a bioinformatic tool for comparative bacteriophage genomics. BMC Bioinformatics, 12, 395. doi:10.1186/1471-2105-12-395 [6] Crooks, G.E., Hon, G., Chandonia, J.M. & Brenner, S.E. (2004). WebLogo: A sequence logo generator. Genome Research, 14(6), 1188-90. doi: 10.1101/gr.849004 [7] Delesalle, V. A., Tanke, N. T., Vill, A. C. & Krukonis, G. P. (2016). Testing hypotheses for the presence of tRNA genes in mycobacteriophage genomes. Bacteriophage, 6(3), e1219441. doi: http://doi.org/10.1080/21597081.2016.1219441 [8] Fogg, P. C. M., Colloms, S., Rosser, S., Stark, M. & Smith, M. C. M. (2014). New Applications for Phage Integrases. Journal Of Molecular Biology, 426(15), 2703â&#x20AC;&#x201C;2716. doi: 10.1016/j.jmb.2014.05.014 [9] Laslett, D. & Canback, B. (2004). ARAGORN, a program to detect tRNA genes and tmRNA genes in nucleotide sequences. Nucleic Acids Research, 32(1), 11-16. doi: 10.1093/nar/gkh152 [10] Miyazawa, S., Hosoyama, A., Tsuchikane, K., Katsumata, H., Yamazaki, S. & Fujita, N. (2011). Arthrobacter globiformis NBRC 12137, whole genome shotgun sequence. Retrieved from https://www.ncbi.nlm. nih.gov/nuccore/NZ_BAEG01000085

[11] Schneider, T. D., & Stephens, R. M. (1990). Sequence logos: a new way to display consensus sequences. Nucleic acids research, 18(20), 6097100. [12] Singh, S., Ghosh, P. & Hatfull, G.F. (2013) Attachment Site Selection and Identity in Bxb1 Serine Integrase-Mediated Site-Specific Recombination. PLOS Genetics, 9(5), e1003490. doi: 10.1371/journal.pgen.1003490 [13] Tan, Y., Zhang, K., Rao, X., Jin, X., Huang, J., Zhu, J., …. Hu, F. (2007). Whole genome sequencing of a novel temperate bacteriophage of P. aeruginosa: evidence of tRNA gene mediating integration of the phage genome into the host bacterial chromosome. Cell Microbiology, 9(2), 479-491. doi: 10.1111/j.1462-5822.2006.00804.x [14] PhagesDB. The Actinobacteriophage Database. Retrieved from http://phagesdb.org/. [15] Zhang, Z., Miller, W., Schäffer, A. A., Madden, T.L., Lipman, D.J, Koonin, E.V., Altschul, S.F. (1998). Protein sequence similarity searches using patterns as seeds. Nucleic Acids Research. 26(17), 3986–3990. doi: 10.1093/nar/26.17.3986

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Original Research

The Influence of Imipramine on the Egg-Laying Behavior of Caenorhabditis elegans Michael Valencia, Emily Feese, Neha Hussain, Annie Luksch, Victoria Mancillas, Alyssa Alaniz, Myeongwoo Lee, Ph.D. Department of Biology, Baylor University, Waco, TX

Abstract Discovering new ways to treat mental disorders is at the forefront of scientific research due to their imposing challenges on worldwide health. A current drug therapy option is imipramine, marketed as Tofranil, a tricyclic antidepressant (TCA) used to treat various mental disorders such as depression, general anxiety, and posttraumatic stress disorder. To determine the effect of imipramine, Caenorhabditis elegans (C. elegans) were analyzed based on their important physiological process, egg laying. The effects of exogenous serotonin increase egg laying by stimulating vulval contractions within C. elegans. Wild-type (N2) C. elegans displayed increased egg-laying behavior when exposed to the imipramine solution. In order to observe the phenotype of mutant C. elegans, worms were treated with ethyl methanesulfonate (EMS) to induce point mutations with the goal of creating imi-1 (kq101) mutants. The desired recessive mutants (strain A) were determined by those that laid the least amount of eggs in the liquid egg-laying assays of imipramine. Egg-laying assays were also conducted in doxepin, serotonin, and control M9 solutions. Strain A mutants were resistant to the effects of both TCAs, doxepin and imipramine.

Keywords: C. elegans, Imipramine, Serotonin, Doxepin, mutant, egg laying

Introduction

As of today, the prevalence of any mental disorder worldwide is at 13.4%. Of this percentage, anxiety and depressive disorders make up to 6.5%, and 2.6%, respectively (Polanczyk et al., 2015). Because these diseases affect so many people, it is imperative that treatment be given to those who need it. Current mental health treatments include psychotherapy, hospitalization and drug therapy. Drugs from the tricyclic antidepressant (TCA) class are commonly prescribed for mental disorders. TCAs examined in this study include imipramine and doxepin, marketed as Tofranil and Silenor, respectively. TCAs enhance the effectiveness of the neurotransmitters, serotonin, by predominantly inhibiting their reuptake via serotonin transporter (SERT) (Barkan et al., 2004; Mitchell et al., 2006; Tatsumi et al., 1997). However, imipramine and doxepin also

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affect additional pathways such as acetylcholine, histamine, and the Îą1-adrenergic blockade. The affected secondary pathways may cause undesired side effects in humans, including, lack of coordination and blurred vision when taking imipramine, and dysrhythmia and severe hypotension may occur when taking doxepin (FDA, 2012; FDA, 2010). In C. elegans, imipramine directly inhibits the SERT, encoded by the mod-5 gene, leading to increased serotonin concentration in the synaptic cleft (Ranganathan et al. 2001). Imipramine and the increased concentration of serotonin leads to many significant behavioral effects, including inhibited locomotion in environments rich in food, stimulated pharyngeal pumping, and increased egg laying (Dempsey et al., 2005; Avery and Horvitz, 1990; Sawin et al., 2000). The activation of egg laying is the only behavioral effect

Results and Discussion First, we sought to obtain our imi-1 (kq101) mutant that showed resistance to the effects of imipramine. In order to do this, wild-type worms were exposed to a M9 control solution and imipramine to observe their behavioral changes. Wild-type worm egg laying increased from an average of 3.94 eggs in the control M9 solution to 5.89 eggs in the imipramine solution (Figure 1A and 1B). This increase had a p-value of 0.0001, confirming a very significant difference in the egg-laying behavior by imipramine stimulation. Next, after EMS-induced mutagenesis, egg-laying assays were conducted to identify the best candidates for the desired phenotype (imi-1). Suitable candidates were those that laid the least amount of eggs greater than zero. Six possible C. elegans strains were then narrowed down to one, strain A. In the imipramine solution, strain A progeny laid an average of 1.25 eggs, which is a decrease compared to the 4.72 eggs laid by the wild-type worms (Figure 1A and 1B).

Methods

20 20

Average Eggs Laid

Wild-type (N2) C. elegans were cultured at room temperature (21-23Â°C) and fed OP50 E. coli. In order to examine the influence of imipramine, C. elegans were exposed to 47 mM ethyl methanesulfonate (EMS) for four (4) hours to induce mutagenesis. Egg-laying assays using N2 and mutant worms were conducted in doxepin, serotonin, imipramine and control M9 solutions, purchased from Sigma Aldrich. A total of 704 F2/F3 EMS C. elegans mutants were incubated in 96-well microplates with 1 mg/mL imipramine solution for 70 minutes. Of all the mutants tested, one strain of C. elegans (strain A) was chosen due to its decreased egg laying in imipramine solutions as compared to N2 wild-type worms. Strain A worms underwent further egg-laying assays to compare the egg-laying effects of similar drugs. The strain A worms were incubated in a 0.016 mg/mL doxepin solution (n = 64 worms), 5 mg/mL serotonin solution (n = 32 worms), 1 mg/mL imipramine solution (n = 64 worms), and a control M9 solution (n = 32 worms) for 70 minutes. N2 wild-type worms were also incubated in the above solutions as a control. The N2 sample sizes were 24, 64, 79 and 32 worms for doxepin, serotonin, imipramine and M9 solutions, respectively. The Mann-Whitney statistics test was performed on the nonparametric, non-normally distributed data

Egg Laying Strain A N2 EMS Mutant Egg Laying Analysis Analysis of Strainof A EMS Mutant and C. elegans in M9 and and N2 Imipramine Solutions C. elegans in M9 and Imipramine Solutions 25

Average Eggs Laid

examined in this study. C. elegansâ&#x20AC;&#x2122; vulval muscles are stimulated by two types of neurons: ventral type C neurons and hermaphrodite specific motor neurons (HSN). Ventral type C neurons release acetylcholine and inhibit vulval contraction, while HSN neurons release serotonin and stimulate egg laying (Dempsey et al., 2005; Trent et al., 1983). The objective of this study was to obtain a mutant (imi-1) C. elegans strain resistant to the egg-laying effects seen when exposed to imipramine. Imi-1 (kq101) worms are characterized by decreased egg laying in the presence of imipramine, but not the absence of egg laying altogether, as that may be a result of an undesired sterile mutation. The purpose of this study was to observe the effects of TCAs on the egg-laying behavior of C. elegans. The significance of this research was to further comprehend the relationship between the nervous and reproductive systems of the model organism, C. elegans, and make connections between human and C. elegans nervous systems.

10 10

5 5

0 0

avg:

N2 (M9) N2(M9) 3.9375 3.9375

avg:

N2 (Imi) Strain AA (M9) Strain AA (Imi)(Imi) Strain (M9) Strain N2(Imi) 5.8861 3.6875 1.25 3.6875 1.25 5.8861

Figure 1A: Wild-type worms laid more eggs in the imipramine solution compared to the M9 solution because imipramine stimulated the vulva muscles to induce egg laying. Mutant strain A worms are resistant to imipramine stimulation and showed decreased egg laying when compared to wild-type worms. Strain A and N2 in imipramine were significantly different from each other by the Mann-Whitney Test.

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Egg Laying Analysis Strain A EMS N2 C. Egg Laying Analysis ofofStrain A EMS Mutant and N2Mutant C. elegans inand Doxepin, elegans in Doxepin, Imipramine, Serotonin, and M9 Solutions Imipramine, Serotonin and M9 Solutions

Average Eggs Laid

44 33 22 11 00

N2(M9) N2(M9)

A(M9) N2(Imi) N2(Imi) A(M9) C. elegans strain and solution C. elegans strain and solution

A(Imi) A(Imi)

Figure 1B: The graph displays the average amount of eggs laid by N2 and strain A worms in imipramine and control M9 solutions.

The obtained imi-1 (kq101) mutant underwent additional egg-laying assays under similar conditions to confirm the correct mutation. The egg-laying behavior of N2 and strain A worms were tested in an additional TCA, doxepin. In the doxepin solution, strain A mutants laid an average of 2.59 eggs, which shows a decrease compared to the 4.37 eggs laid by the N2 worms (Figure 2A and 2B). The similar decreases in strain A egg laying observed in both imipramine and doxepin further strengthens the idea that the mutation involves the SERT encoded by the mod-5 gene. We believe the SERTâ&#x20AC;&#x2122;s affinity for serotonin is unhindered as the difference in egg laying between the N2 (3.94 eggs) and strain A (3.69 eggs) in the M9 control solution was not significant. The addition of exogenous serotonin showed to have the greatest impact on wild-type egg laying, as the average eggs laid was 5.56. Exogenous serotonin showed the greatest impact on the strain A egg-laying behavior amongst the treatment groups (2.81 eggs), but they laid less eggs than the M9 control group (Figure 2A and 2B). Serotonin is known to increase C. elegans egg-laying behavior by stimulating vulval muscle contractions when binding serotonin receptors. In C. elegans, the known serotonin receptors are SER-1, SER-4, SER-7 and MOD-1 proteins (Hobson et al., 2005). Serotonin receptor stimulation directly modulates the vulval muscle calcium dynamic (Shyn et al., 2003).

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30 30 28 28 26 26 24 24 22 22 20 20 18 18 16 16 14 14 12 12 10 10 88 66 44 22 00

Average Eggs Laid

Egg Laying of Strain A EMS Mutant Egg LayingAnalysis Analysis of Strain A EMS Mutant and N2 C. elegansand N2 C. elegans ininM9M9 and Imipramine Solutions and Imipramine Solutions

N2(Dox) N2 (Dox) A(Dox) A (Dox) N2(Imi) N2 (Imi) A(Imi) A (Imi) N2(Ser) N2 (Ser) A(Ser) A (Ser) N2(M9) N2 (M9) AA(M9) (M9)

avg: avg: 4.375 4.375

2.594 2.594

5.886 1.250 5.563 2.813 5.886 1.250 5.563 2.813 3.938 3.938

3.688 3.688

Figure 2A: Wild-type N2 worms demonstrated increased egg laying in both imipramine and doxepin compared to M9 control. EMS strain A worms demonstrated high resistance to imipramine and doxepin. Serotonin had significant egg laying changes compared to M9 solution in both wild-type and mutant worms. Strain A and N2 in each of the solutions, besides M9, were significantly different by the Mann-Whitney Test. Increased egg-laying behavior is predominantly stimulated through inhibition of the SERT receptors by the TCAs, doxepin and imipramine. Additionally, imipramine directly stimulates the human 5-HT1 receptors and the C. elegans SER-4 receptor by acting as an activator (Haddjeri et al., 1998; Kalinnikova et al., 2013). By inhibiting serotonin reuptake, serotonin is left in the synaptic cleft to continue stimulating the serotonin receptors. With repeated stimulation of the serotonin receptor, the vulval muscle contracts more frequently in the presence of TCAâ&#x20AC;&#x2122;s, thus producing more eggs. With the EMS-induced point mutation affecting the serotonin pathway, the data demonstrates that imipramine and doxepin are unable to inhibit the serotonin transporter. As a result, a decrease in C. elegans egg-laying behavior was observed, supporting evidence for a resistance to imipramine and doxepin. This is shown by the difference in egg laying averages between wild-type worms and mutant worms exposed to the drugs. With the imi-1 phenotype, C. elegans in the imipramine and doxepin solutions displayed a decrease in the average egg laying due to the loss of function mutation rendering the serotonin transporter

Egg Laying Analysis of Strain A EMS Mutant and N2 C. elegans in Doxepin, Imipramine, Serotonin, and M9 Solutions 7 6

Average Eggs Laid

5 4 3 2 1 0

Doxepin

Imipramine

Serotonin

Solutions Strain A N2 Figure 2B: Graph displays the average amount of eggs laid by N2 and Strain A worms in doxepin, imipramine, serotonin and control M9 solutions. * = P value < 0.05, ** = P value < 0.01, *** = P value < 0.001. nonfunctional and resistant to the drug stimulation. Future studies would include sequencing the genome of the mutants by creating a gene map to identify where the mutations have taken place in the genome. Second, further imipramine trials could be conducted with wild-type worms to determine if there is a threshold concentration for the imipramine-induced behavioral effects. Finally, conducting further trials with other antidepressants of both distinct and similar classes as imipramine could be done to compare and determine their effects on the egg-laying behavior of C. elegans.

Acknowledgments We would like to thank our Lab Assistants, Angela Leung and Zhongqiang Qiu for assisting us throughout the research process and Dr. Myeongwoo Lee for his mentorship and providing support in the entirety of the project. Supplemental data available upon request from authors.

References [1] Avery, L. & Horvitz, H. R. (1990). Effects of starvation and neuroactive drugs on feeding in Caenorhabditis elegans. Journal of Experimental Zoology, 253(3), 263–270. doi: 10.1002/jez.1402530305

[2] Barkan, T., Gurwitz, D., Levy, G., Weizman, A. & Rehavi, M. (2004). Biochemical and pharmacological characterization of the serotonin transporter in human peripheral blood lymphocytes. European Neuropsychopharmacology, 14(3), 237–243. doi:10.1016/ S0924-977X(03)00107-X [3] Dempsey, C. C., Mackenzie, S. M., Gargus, A., Blanco, G. & Sze, J. Y. (2005). Serotonin (5HT), Fluoxetine, Imipramine and Dopamine Target Distinct 5HT Receptor Signaling to Modulate Caenorhabditis elegans Egg-Laying Behavior. Genetics, 169(3), 1425–1436. doi:10.1534/genetics.104.032540 [4] FDA. (2010). Retrieved from: https://www.accessdata. fda.gov/drugsatfda_docs/label/2010/022036lbl.pdf 5] FDA. (2012). Retrieved from: https://www.accessdata. fda.gov/drugsatfda_docs/label/2012/040903lbl.pdf [6] Haddjeri, N. & de Montigny, B. P. (1998). Long-term antidepressant treatments result in a tonic activation of forebrain 5-HT1A receptors. Neuropsychopharmacology, 22, 10150–10156. [7] Hobson, R. J., Hapiak, V.M., Xiao, H., Buehrer, K.L., Komuniecki, P.R. & Komuniecki, R.W (2006). SER-7, a Caenorhabditis elegans 5-HT7-like Receptor, Is Essential for the 5-HT Stimulation of Pharyngeal Pumping and Egg Laying. Genetics, 172(1), 159–169. doi: 10.1534/genetics.105.044495 [8] Kalinnikova, T. B., Kolsanova, R. R., Shagidullin, R. R., Osipova, E. B. & Gaynutdinov, M. K. (2013). On the role of gene of SER-4 serotonin receptor in thermotolerance of Caenorhabditis elegans behavior. Russian Journal of Genetics, 49, 363–366. [9] Mitchell, H. A., Ahern, T. H., Liles, L. C., Javors, M. A. & Weinshenker, D. The Effects of Norepinephrine Transporter Inactivation on Locomotor Activity in Mice. Biological Psychiatry, 60(10), 10461052. doi: https://doi.org/10.1016/j.biopsych.2006.03.057 [10] Polanczyk, G. V., Salum, G. A., Sugaya, L. S., Caye, A. & Rohde, L. A. (2015). Annual Research Review: A meta-analysis of the worldwide prevalence of mental disorders in children and adolescents. Journal of Child Psychology and Psychiatry, 56, 345–365. [11] Ranganathan, R., Sawin, E. R., Trent, C. & Horvitz, H. R. (2001). Mutations in the Caenorhabditis elegans Serotonin Reuptake Transporter MOD-5 Reveal Serotonin-Dependent and -Independent Activities of Fluoxetine. The Journal of Neuroscience, 21, 5871–5884. [12] Sawin, E. R., Ranganathan, R. & Horvitz, H. (2000). C. elegans Locomotory Rate Is Modulated by the Environment through a Dopaminergic Pathway and by Experience through a Serotonergic Pathway. Neuron, 26, 619–631. [13] Shyn, S. I., Kerr, R. & Schafer, W. R. (2003).

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Serotonin and Go Modulate Functional States of Neurons and Muscles Controlling C. elegans Egg-Laying Behavior. Current Biology, 13, 1910–1915. [14] Tatsumi, M., Groshan, K., Blakely, R. D. & Richelson, E. (1997). Pharmacological profile of antidepressants and related compounds at human monoamine transporters. European Journal of Pharmacology, 340, 249–258. [15] Trent, C., Tsuing, N. & Horvitz, H. R. (1983) Egg-laying defective mutants of the nematode Caenorhabditis elegans. Genetics, 104(4), 619–647.

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Original Research

Tape Measure Protein PCR Successfully Classifies Arthrobacter Phages Stu Mair, Tamarah Adair, Ph.D.

Department of Biology, Baylor University, Waco, TX

Abstract

The sorting of bacteriophages into clusters of high genetic similarity is currently done through full-genome analysis following isolation, DNA extraction, and full-genome sequencing. While this process accurately yields the genome for further analysis, it can be time-consuming and expensive. Relying on existing sequence alignments from bacteriophages isolated on Arthrobacter sp., it was determined that, in addition to having a high level of genome-wide sequence conservation, the clusters could be determined by comparing the sequence of the tape measure proteins. Building from this concept, alignments of the tape measure protein were created for each cluster from which forward and reverse polymerase chain reaction (PCR) primers were selected giving a specific product size for each cluster. These primers were tested against known samples of bacteriophage DNA, which confirmed the ability to identify the correct cluster based on the PCR product sizes. This biotechnology application can be used in future research by enabling cluster determination for lower-titer phage lysates and direct soil extracts. This will enhance research on the diversity of Arthrobacter phages in the soil and expand the types of research questions that can be explored. In addition, this protocol will be added to the SEA-PHAGES educational research program to extend the DNA characterization techniques that students learn and apply.

Introduction Bacteriophage genomics is a growing field in microbiology. With an increasing array of bacteriophage diversity uncovered by the Science Education Alliance-Phage Hunters Advancing Genomic and Evolutionary Science (SEA-PHAGES) program, bacteriophage clusters based on genome similarity have been created (SEA-PHAGES). Currently, when a new bacteriophage is discovered, full-genome analysis is used to sort it into a cluster. However, this method has a few drawbacks: it requires that the DNA be extracted and sequenced which takes time in the lab and can be difficult to obtain the necessary concentration of bacteriophage. Smith et al. detailed the use of a single gene, the Tape Measure Protein (TMP), to cluster Mycobacteriophagesâ&#x20AC;&#x201C; a phage type associated with the host Mycobacterium smegmatis being used in the HHMI SEA-PHAGES program (Smith, Castro-Nellar, Fisher, Breakwell, Grose & Burnett, 2013). By analyzing the genomes of the bacteriophages within each cluster, they were able to identify the tape measure protein as a conserved region of nucleotides from which inferences

about cluster identity can be made. From this knowledge, their research team designed PCR primers that were able to amplify stretches of the TMP sequences and identify a phageâ&#x20AC;&#x2122;s cluster. The tape measure protein plays an important role in the injection of the DNA strands, packaging of DNA during phage assembly, and the tail length for the bacteriophage (Mahony et al., 2016). This makes it an ideal sequence for analyses of genomic similarity as it is highly conserved within clusters and is identifiable across most bacteriophages by its characteristic size, frequently one of the largest in the genome (Belcaid, Bergeron & Poisson, 2011). By identifying this gene within a cluster of bacteriophages, researchers can find a region that is likely to exhibit significant conservation between cluster members. The host range associated with the SEA-PHAGES program has extended to include several other Actinobacteria hosts, with the use of Arthrobacter sp. ATCC 21022 being used by several university programs. At the time of publication, there were 531 phages isolated on this host, with 188 sequenced phages. Despite these

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numbers, there are some noticeable gaps in data among the 12 clusters, with just two phages in the AP cluster and four in the AT cluster in October 2018 (PhagesDB). As a way to increase the number of phages isolated and sequenced from specific clusters and increase the ease with which researchers can classify their discovered phages, analysis of TMP sequences was undertaken to confirm the reliability of gene-based clustering. As an extension, PCR primers were designed and tested, successfully classifying Arthrobacter phages into their clusters through PCR reactions targeting conserved regions of the TMP gene sequence.

Methods Dotplots In order to compare whole-genome clustering with TMP sequence-based clustering, three bacteriophages from each cluster within phages infecting Arthrobacter sp. ATCC 21022 were selected. The genomes were put together into the Gepard dotplot maker, enabling the identification of genetic similarity and cluster relationships (Krumsiek, Arnold & Rattei, 2007). In the Gepard dotplots, the sequence is placed on both the horizontal and vertical axis. Anywhere that two identical bases appear, a dot is placed. This allows for large sequences to be graphically compared for genetic similarity. The Tape Measure Protein sequence was taken from each genome and placed into a multiple-FASTA file. For genomes in which no TMP had been identified, tools including DNA Master auto-annotation and NCBI BLASTn were used to identify a gene fitting the characteristics of Tape Measure Proteins. The TMP sequences were put into Gepard and the two dotplots were compared. Primer Design The next stage of the project was to design a method for converting this information from strictly in silico applications to in vitro applications that would be more useful for future research. To this end, PCR primers were designed. For this, Mega7 alignments of the TMP sequences (three per cluster) were generated (Kumar, Stecher & Tamura, 2016). Several lengthy (25+ base pairs) sections of full alignment were selected as potential primers. Any gaps introduced were avoided as it would cause differing product sizes. Utilizing the NCBI primer design tools and IDT primer analysis, a pair of primers specific to each cluster was selected that would produce a unique product size for each cluster (Integrated DNA Technologies; Ye et al., 2012). Primers were selected with

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consideration of product size, melting temperature, off-target binding, and off-cluster binding. PCR A reference genome was selected for each cluster (AK: Nubia, AL: Shrooms, AM: Circum, AN: Laila, AO: LeeroyJ, AP: Wilde, AQ: Amigo, AR: Princess Trina, AS: Galaxy, AT: KitKat, AU: Caterpillar, AV: Jasmine) and was used to test the efficacy of the primer binding to its cluster. The PCR formulation included 10-100 ng of DNA template, 1 µL each of 10 µM forward and reverse primer, and 12.5 µL of 2X PCR mix. Each reaction was brought to a 25 µL volume with the addition of sterile deionized water. The DNA template for PCR was either purified DNA at a concentration of 10-100 ng/µL or 1µL of boiled lysate. The boiled lysate was prepared by taking a 1:5 dilution of lysate and boiling it at 100°C for 10 minutes followed by centrifugation for 30s (SEA-PHAGES). The PCR temperatures depended on the portion of the experiment. For the initial testing an annealing temperature gradient was tested. The reaction was run at 94°C for 5 minutes and then 35 cycles of 94°C for 30s, the annealing gradient for 30s, and 72°C for 45s. The reaction was concluded with 5 minutes at 72°C and a final hold at 4°C until samples were removed. The annealing gradient included four tubes at 52.4°C, 53.7°C, 55.1°C, and 57.5°C. In later reactions without an annealing temperature gradient, an annealing temperature of 55.1°C was used. Results from the PCR were analyzed by gel electrophoresis on a 2% gel run at 110V for 30-50 minutes. Imaging was done at Baylor

Species/Abbrv 1. Shrooms_AL 2. Laroye_AL 3. Salgado_AL

* * CGGC T - - -CT - - -CT

* * * TCACCG TCCAAG TCCAAG

Figure 1 – This partial alignment of the AL TMP sequences demonstrates the three features of the Mega7 alignments. The asterisks (*) along the top mark the base pairs with 100% alignment. The blanks at the top represent base pairs where a mismatch occurred. The dashes (-) in the sequence represent gaps introduced to maximize the alignment score.

Universityâ&#x20AC;&#x2122;s Molecular Biology Center on the Bio-Rad Gel Doc EZ Imager. Single-template, multiple primer PCR reactions allow researchers to avoid running multiple separate PCR reactions to test reactivity for a single template strand. By designing non-overlapping product sizes, the PCR primer sets can be combined in a single reaction to determine which of the clusters a template DNA corresponds to. A reaction mix was made with the primers specific for the AK, AL, AN, and AU clusters. This reaction mix was placed into four PCR tubes. One of the reference genomes from each cluster was added to an individual reaction and the standard PCR protocol was followed. The products were then compared by gel electrophoresis to determine if the expected product size was observed for each reaction.

Fig 2a. Dotplot of full genomes

Results Dotplots The in-silico comparison of clustering by complete genome sequences and by the TMP sequences demonstrated that clusters could be determined by using the individual Tape Measure Protein sequences as opposed to the full genomes. A Gepard dotplot with three genomes from each cluster within the bacteriophages that infect Arthrobacter sp. ATCC 21022 was created. The TMP sequences from these bacteriophages were then taken and put into a dotplot, which is then comparable to that of the full genomes. The diagonal boxes within the dotplots (Figure 2a, 2b) represent the clusters of bacteriophages, which demonstrate a high level of genetic similarity. These are visible on the dotplots using both methods. The rougher edges on the dotplot generated with the TMP sequences is likely primarily due to the shorter sequences. Despite the increased level of noise, the clusters can still be determined on that dotplot. These confirm the ability to cluster both on the full genome similarity and similarity within the TMP sequences. Primer Design Alignments of the TMP sequences within each cluster were generated and potential primers were created and analyzed. From that process, a primer pair was designed for each cluster with a variety of different product sizes, which can be resolved through gel electrophoresis. Table 1 displays each of the designed primers, their melting temperatures, and the targeted product size associated with each cluster.

Fig 2b. Dotplot of TMP sequences Figures 2a and 2b â&#x20AC;&#x201C; These two dotplots represent two different methods of clustering bacteriophages. On the top, full genomes from each cluster were used. On the bottom, only the Tape Measure Protein sequences were used. PCR The primers were designed in-silico and their binding properties and targets were checked with the DNA Master scan function and the NCBI Primer Design Tool. The primers in Table 1 were then used on known samples to validate their use in PCR reactions. Two clusters, AO and AQ, successfully worked across the annealing temperature gradient used to analyze them (Figure 3). The phage samples used for testing of the primers were genomes that had previously been sent to the University of Pittsburgh for sequencing and were sorted into the cluster for the primers they were used to test. Similar gradient testing for emperatures was conducted and confirmed the ability of the AK, AL, AN, and AU primer sets to bind to members of their clusters.

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Cluster AK AL AM AN AO AP AQ AR AS AT AU* AV

Forward Primer AACGCGGTATTCGGGTTCTTCT AAGGACTACACGGGCCTGAC GTTATGGCCGAAGAGGCTGCA GCCGTTTTGGCCGTTCGTAT TCGGCTCGAAGCTGAAGGGC ATCGCGGCCTTGAAGGACAT CGGCTCTCGGCCAGATTCTC TTGACGGCTGGGGCATGCT GACCGTGGCATTCCGATTGTCC GAACAACAGCTTCAAGCGCACC GTCTTCAAGCTCCTGAAGCGCA GGCTGTGGGAACTGGATTGCA

Reverse Primer (Rev-Comp)

Product Size (bp)

GCACCTACAATCTTTCCTGCCA CCTCAAAGGTCTTCGTGGACTT CCATCTTAGCGATGCCTTCGTCA TTAGCTCGTTCCTGGGCACC GGTTCGAGCGCGTCTTTCGTC GGCGAACTCGGTGAACATGC AATCTTGCTGGACGCACCT AACAGGGCCACGGCAATGGT GCGACCTGCACGACGAAAGC GCCAGTTCGGTCTTGGCGTC TGTTGTAGAGCTTGCGGCCCT ACAACGCCGTCACCCTTACC

590 334 742 671 487 201 899 142 827 276 418 963

Melting Temperature (Forward, C) 62.3 61.54 63.05 61.91 64.95 62.26 62.69 64.85 64.04 63.3 63.36 63.22

Melting Temperature (Reverse, C) 61.83 60.22 62.89 62.18 64.37 62.24 62.13 65.18 64.83 63.98 64.39 62.72

Table 1 – This table presents the primer set for each cluster, the expected product size from the Mega7 alignment and NCBI confirmation and the melting temperatures for each primer as provided by the NCBI primer design tools. *For cluster AU, the regions of conservation within the TMP sequence were not sufficient for primer design and so the Capsid Maturation Protease Gene was used as an alternative target for the primer sets.

By looking at changes in product presence and intensity, 55.1°C was selected as the best annealing temperature to use in PCR reactions with a single annealing temperature. The same primer sets were used to test the boiling lysate protocol, which provides a template DNA without needing to go through DNA purification. To do this, two sets of PCR reactions were run to enable side-by-side comparison of PCR with pure DNA and boiled lysate. Boiled Lysate DNA Templates The boiled lysate samples produced an identical banding pattern to that of the DNA samples, indicating an ability to use lysate as an alternative source of template DNA rather than going through the process of DNA extraction. An off-target band is displayed in the AN lanes at approximately 1200 base pairs. With the DNA samples there also appears to be an additional band well above the top reference band in the ladder of 1500 base pairs. While these were not expected as products, the largest anticipated product size is 963, much smaller than these additional bands. For that reason, the AN primer set was not redesigned and these bands were simply left as part of a characteristic banding pattern for the AN cluster. Samples of high-titer lysate for the remaining six clusters were received from the Pittsburgh Bacteriophage Institute. Boiled Lysate was used as the source of the template strands for the verification PCR reactions. The boiled lysate approach successfully yielded template DNA for all six of the samples and each primer set successfully bound to its reference bacteriophage, giving the expected product size (Figure 5). This reaction completed the testing of all twelve primer sets, which

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corresponds to all of the clusters currently identified with bacteriophages that infect the host species Arthrobacter sp. AATC 21022. Single-Template, Multiple-Primer PCR The next step within this project was to test primer specificity within single-template, multiple-primer PCR reactions. This was tested in a limited capacity using four of the primer sets (Figure 5). These PCR reactions each successfully identified the cluster of the reference phage for the DNA template in each tube. This confirms the ability to use multiple PCR primers in the same tube to identify the cluster of a single bacteriophage sequence.

Discussion Arthrobacter phages may be clustered without extensive purification through the use of PCR and the primers designed in this experiment. Cluster determination was successfully performed using boiling lysate as the DNA template. This is an improvement over current protocols because phage cluster can be determined once a lysate has been generated. Each primer pair successfully amplified the genome of a reference bacteriophage to produce the expected and unique product sizes as visualized on a 2% agarose gel. Any uncertainty in product sizes could be alleviated by using different concentrations of agarose gel to get better resolution of the expected product sizes. The design of these primers has a number of different applications within bacteriophage research. Extending the work of Smith et al., this research confirms that clustering based on PCR reactions of TMP was not a

Figure 3 – This gel electrophoresis confirms the efficacy of the AO and AQ primers across the entire annealing gradient.

Figure 4 – Verification of primer sets AK, AL, AN, and AU using both PCR templates of purified DNA and boiled lysate.

Figure 5 – The PCR templates for the reactions displayed with this gel were generated with the boiled lysate protocol. This confirms the ability of the AM, AP, AR, AS, AT, and AV cluster primer sets to bind to their target clusters.

Figure 6 – This gel electrophoresis visualizes the PCR products from an example of multiple-primer reactions confirming that, in the presence of multiple primers, the correct primer set still binds to the individual template DNA, producing the expected product size. This also demonstrates an absence of false priming from other primer sets.

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unique feature of mycobacteriophages. This extends the possibility of generating an equivalent method for bacteriophages of any host that a researcher is interested in isolating and classifying. The core protocol in determining and testing this set of primers is applicable and likely possible for bacteriophages isolated on a number of hosts. This project enables tailoring the next steps in bacteriophage discovery research, allowing researchers to more effectively fill in the gaps in the database by screening for and against phages within certain Arthrobacter phage clusters. In addition, multiplex reactions enable new and unique research directions. With single-template, multiple primer reactions, researchers can use the primers to identify the cluster of a bacteriophage isolate without going through the process of extensive purification, extraction, and sequencing. This can lower the costs associated with determining this data point and enable the researcher to get this information earlier. In addition, it is possible to conduct a soil ecology survey for bacteriophage and quickly ascertain the cluster diversity of different soil samples. This can be done by using crude soil samples, in which multiple templates could be present. Future studies could include investigating whether unenriched soil washes can produce PCR results. In the future, multiplex reactions could be set up in three tubes of four primer sets each, as shown in Table 2. This addresses volume limitations based on primer concentration while allowing one sample to be tested across all twelve clusters. The products that show up will indicate which cluster any bacteriophages in the sample would be sorted into. The primer sets were divided by both melting temperature and product size to ensure optimal reaction conditions and a wide product size range.

Tube 1 Cluster Product 142 AR 276 AT 487 AO 827 AT

Cluster AP AU AN AV

Tube 2 Product 201 418 671 963

Tube 3 Cluster Product 334 AP AK 590 742 AM AQ 899

Table 2 – This table contains the three mixes for a multiplex reaction that were optimized for melting temperature and product sizes.

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Acknowledgements My gratitude goes out to Tamarah Adair for her mentorship and effort that she has poured into helping me make this research project successful. Without her support, this project would not have been possible. Her guidance helped guide the project to completion. I would also like to thank Natalie Widdows and Niharika Koka for their valuable input during the initial planning for this project. Without their efforts, this project likely would have never taken place so I owe them both a large gratitude. With regards to the materials needed to complete the project, I owe thanks to the University of Pittsburgh. They furnished six of the samples used to confirm the functionality of the primers and manage the PhagesDB database, which was a valuable repository for sequences and clustering information. Finally, I’d like to thank the Department of Biology at Baylor University for their support of this research through enabling me to conduct my research.

References [1] Belcaid, M., Bergeron, A., & Poisson, G. (2011). The evolution of the tape measure protein: units, duplications and losses. BMC bioinformatics, 12 Suppl 9(Suppl 9), S10. doi:10.1186/1471-2105- 12-S9-S10 [2] Krumsiek, J., Arnold, R. & Rattei, T. (2007). Gepard: a rapid and sensitive tool for creating dotplots on genome scale. Bioinformatics (Oxford, England), 23(8), 1026–1028. doi:10.1093/bioinformatics/btm039 [3] Kumar, S., Stecher, G. & Tamura, K. (2016). MEGA7: Molecular Evolutionary Genetics Analysis version 7.0 for bigger datasets. Molecular Biology and Evolution, 33(7), 1870–1874. doi:10.1093/molbev/msw054 [4] Mahony, J., Alqarni, M., Stockdale, S., Spinelli, S., Feyereisen, M., Cambillau, C. & van Sinderen, D. (2016). Functional and structural dissection of the tape measure protein of lactococcal phage TP901-1. Scientific Reports, 6, 36667. doi:10.1038/srep36667 [5] Integrated DNA Technologies. OligoAnalyzer 3.1. Retrieved from http://www.idtdna.com/calc/analyze [6] SEA-PHAGES. Alternative to Purifying Bacteriophage DNA for PCR. [7] SEA-PHAGES. SEA-PHAGES | Home. (n.d.). Retrieved from https://seaphages.org/ [8] Smith, K.C., Castro-Nallar, E., Fisher, J.N., Breakwell, D.P., Grose, J.H., & Burnett, S.H. (2013). Phage cluster relationships identified through single gene analysis. BMC Genomics,14, 410. doi:10.1186/1471-2164-14-410

[9] PhagesDB. The Actinobacteriophage Database. Retrieved from http://phagesdb.org/ [10] Ye, J., Coulouris,G., Zaretskaya, I., Cutcutache, I., Rozen, S., & Madden, T.L. (2012). Primer-BLAST: a tool to design target-specific primers for polymerase chain reaction. BMC bioinformatics,13, 134. doi:10.1186/1471-2105-13-134

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2018 URSA Abstracts

Engineering The Effect of Al Doping on the Electronic Properties of Amorphous InZnO Thin Films Chandon Stone, Austin Reed, Sunghwan Lee, Ph.D.

Amorphous aluminum-doped InZnO thin films (a-IAZO) were produced using magnetron co-sputtering, where an InZnO target was deposited using DC-magnetron sputtering simultaneously with an Al target by way of RF-magnetron sputtering. The Al content of the resulting thin film was controlled by controlling the sputter power of the Al sputtering while the sputter power of the InZnO target was kept constant. The electronic properties of the a-IAZO thin films as a function of RF sputtering power were then studied in detail using Hall Effect measurements. It was found that the resistivity increased, while the carrier mobility and carrier density decreased with the addition of aluminum. The thin films with the least amount of aluminum, deposited at 10 WRF-power, exhibited a maximum as-deposited carrier mobility of roughly 35 cm 2/Vs. The thin films with optimal electrical properties deposited at 10 WRF-power were also annealed at a range of temperatures to study the effect of annealing on electronic properties. While it was found that a-IAZO thin films annealed at 100ยบC exhibited a maximum carrier mobility of approximately 48 cm 2/Vs; the a-IAZO thin films annealed at increasing temperatures were observed to exhibit correspondingly decreasing carrier mobilities. However, all the annealed a-IAZO thin films showed a correlating decrease in carrier concentration with increasing annealing temperature as a result of a decreasing number of oxygen vacancies.

Using HPC to Model Quantum-dot Cellular Automata Gabriel Hahn, Enrique Blair, Ph.D.

In an effort to reduce power dissipation in computing devices we aim to use the charge based computing paradigm of molecular Quantum-dot Cellular Automata (QCA). The interactions between neighboring cells containing quantum dots enables general-purpose computing. The clock and other inputs excite the system causing it to vibrate which requires QCA to relax the system to its ground state so calculations can be performed. This occurs primarily through environmental coupling. To help molecular designers engineer computational molecules for minimal power dissipation, we explore the design space for molecular QCA. We model clocked molecular QCA molecules and their interaction with the environment using the Lindblad equation with computational help from the Baylor Super Computer. This method was effective in determining optimal environmental coupling levels to reduce power dissipation which occurred at 22 Tvib/T1. This level is still too high so different configurations or a different molecule need to be tested.

Graphene Transfer and Spectrum Analysis Benjamin Jones, Linda Olafsen, Ph.D.

Graphene is a two-dimensional hexagonal array of carbon atoms with high optical, electrical, and thermal conductivity, making it a promising material for a variety of applications. The ultimate purpose of this work is to explore the potential usefulness of graphene contacts in the development and improvement of semiconductor optoelectronic devices, including mid-infrared lasers and light emitting diodes. The object of this project was to transfer graphene to various substrates and then perform optical analysis using a Fourier Transform Infrared Spectrometer. A major component of this research project was implementation of a new process of transferring a single layer or multiple layers of graphene to a substrate. This process involved Trivial Transfer Grapheneโ ข (TTG) and only required water, heat, and an acetone bath for the transfer to take place. We transferred graphene layers to gallium antimonide and germanium substrates and then analyzed the transmission of light through those samples in both the near-and mid-infrared ranges of the electromagnetic spectrum. While graphene is known for having strong optical transmission in the visible wavelengths (97.7%), this work aims to verify that that desirable behavior extends to longer wavelengths for infrared optoelectronic applications.

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Physics Epsilon-Near-Zero Perfect Absorber in Ultra-Thin Films

Catherine Arndt, Aleksei Anopchenko, Ph.D., Sudip Gurung, Long Tao, Ho Wai Howard Lee, Ph.D. There is a significant interest in the development of ultra-thin optical absorbers, potentially leading to layered broadband absorbers. Ultra-thin epsilon-near-zero (ENZ) materials (<100nm) such as, Indium Tin Oxide (ITO) and Aluminum Zinc Oxide (AZO) layers, support certain radiative and bound p-polarized plasmonic modes at epsilon-near-zero (ENZ) frequencies. Excitation of the radiative Berremen mode leads to resonant light absorption and perfect absorption in the near-IR spectrum. By utilizing these properties, we demonstrate perfect absorption (>99%) in <10nm thick ITO films and <55nm thick AZO films 1. The perfect absorption in deep subwavelength ITO nanolayers is due to the excitation of the Berreman mode or ENZ mode. A super continuum laser (600nmâ&#x20AC;&#x201C; 1700nm) excites modes of the ultra-thin ENZ layer. The specular reflection from the sample is collected, revealing >99% absorption in the near-IR spectrum. The perfect absorption of single layer ultrathin films may be layered to create a broadband absorber 2. Perfect absorbers facilitate the development of compact and tunable metamaterial devices and flat zero-index optics.

Geology Molecular Analysis of Flood Deposits in the Tennessee River Valley: Implications for Understanding Carbon Cycling in Fluvial Environments Emily Blackaby, Owen Craven, William Hockaday, Steve Forman, Gary Stinchcomb, Lance Stewart, William Hockaday, Ph.D. The Tennessee River Valley contains a series of well-preserved, buried flood deposits. The purpose of this study was to characterize the organic carbon in a fluvial environment and identify changes over time. Ten samples were collected from three sites: Right Bank Canal, Left Bank Canal, and Bond. Depths varied between 37cm to 350cm. Samples were dated using optically stimulated luminescence (OSL). Each was pretreated with HCl and HF, underwent solid-state cross polar 13C NMR analysis at twelve kilohertz, underwent an elemental analysis, and a molecular mixing model (MMM) was used to determine the molecular components of the organic matter present. The MMM categorized carbon molecules present in terms of carbohydrate, protein, lipid, lignin, char, or carbonyl. Char was the most prominent molecular component ranging from 28.7 to 55.9% and comprised larger percentages in older deposits while younger deposits contained more non-char constituents. The carbonyl, lipid, and carbohydrate groups are present throughout all the samples with carbonyl ranging from 9.3 to 31.4%, lipid from 5.5 to 16.7%, and carbohydrate from 4.4 to 16.9%. High amounts of carbonyl throughout the samples indicates a highly oxidizing environment. The weight percent of carbon in each sample ranged from .41 to 1.24%. Trends in the molecular data suggest that there is a consistent decrease in the presence of char in younger samples compared to older samples while there is an increase in protein, lipid, and lignin. Differences in the presence and amount of carbon groups may indicate selective degradation of molecules based on chemical stability.

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2018 URSA Abstracts

Psychology and Neuroscience Examination of cytokine expression and sickness behavior in a mouse model of Fragile X syndrome Lindsay Tomac, Samantha Hodges, Suzanne Nolan, Ilyasah Muhammad, Joaquin Lugo, Ph.D. Fragile X syndrome (FXS) is a neurodevelopmental disorder caused by a single genetic mutation in the Fragile X mental retardation 1 (FMR1) gene. Recently, there has been evidence that FXS patients may have altered immune function that could be mediating behavioral and cognitive aspects of the disorder. Previously in our lab, we found baseline hippocampal pro-inflammatory cytokine expression levels to be significantly decreased in Fmr1 knockout (KO) mice, a mouse model of FXS. To expand on these results, we investigated how Fmr1 KO mice responded to an innate immune stimulus. We first administered a single 0.33 mg/kg intraperitoneal injection of the bacterial mimetic lipopolysaccharide (LPS). Four hours after injections, brains were dissected, followed by RNA isolation and qRT-PCR on hippocampal tissue. As expected, we found LPS significantly increased pro-inflammatory cytokines in Fmr1 KO and WT mice. Additionally, Fmr1 KO mice were found to 83 have trending elevated cytokine expression after LPS administration when compared to WT mice. To examine differences in sickness behavior following innate immune stimulation, we conducted a 2nd study, where mice were tested in a burrowing paradigm with a single injection of LPS prior to testing. Twenty-four hours following injections, we examined cytokine expression and similarly to the 4hr. time point, we expect cytokine levels to be significantly altered between genotypes. While Fmr1 KO mice have altered baseline cytokines, how they respond to an innate immune stimulus will allow us to determine whether dysregulated immunity could be playing a broader role in the pathophysiology of FXS.

Does Mozart Make Memories? An Experimental Test of Targeted Memory Reactivation During Slow Wave Sleep Mary High, Taylor Luster, Daniel Zeter, Chenlu Gao, Stacy Nguyen, Michael Scullin, Ph.D. Memory encoding, processing, and consolidation occurs during sleep, particularly slow-wave sleep (SWS; Stickgold, 2005; Walker, 2009). An exciting approach for demonstrating sleep-dependent memory consolidation is the targeted memory reactivation procedure. During learning, a sensory stimulus (e.g., sounds) is presented with the memory task; if those sounds are re-presented during SWS, then next-day memory performance improves (e.g., Rasch, Buchel, Gais, & Born, 2007; Rudoy et al., 2009). The effectiveness of targeted memory reactivation in the laboratory spurs the question whether targeted memory reactivation can help students better prepare for tests. However, previous research has used only simple memory tests (e.g., word lists) and simple sensory stimuli (individual sounds). We recruited 50 healthy undergraduate students to participate in a two-night, laboratory-controlled polysomnography sleep study. On Night 1, we experimentally tested whether listening to popular lyrical music before bed impairs sleep quality. On Night 2, participants took an economics lecture while listening to classical music. When participants entered SWS, we either played white noise (control group) or classical music (experimental group). The next morning, participants took an economics test. If targeted memory reactivation is possible with complex materials (economics) and complex stimuli (classical music), then participants who listened to classical music during SWS will show higher performance on the economics test. The current work will thus shed new light on the popular notion that listening to classical music makes students more intelligent; or, in other words, that Mozart makes memories.

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Environmental Science Preliminary Results of Residential Curbside Residual Lead in West Dallas, TX

Liana DeNino, Grace Hutchinson, Julia Frandesen-DeLoach, Jonah Salazar, Clark Coneby, Trey Brown, Ph.D. In 1984 a lead smelter site in West Dallas, near downtown Dallas, Texas, was shut down after fifty years of operation. The site was subsequently placed on the Environmental Protection Agencyâ&#x20AC;&#x2122;s (EPA) National Priorities List due to lead (Pb) contamination of the surrounding area. Affordable housing was constructed near the site, however, many original residential neighborhoods remained (EPA 2016). In 2012 the Dallas Morning News and Baylor University, became aware of the presence of waste materials in residentsâ&#x20AC;&#x2122; yards and formed a partnership to begin an investigation of residual Pb contamination in the area. It was discovered that batterycasing chips from the smelter had been given to residents to be used as fill material (Martyn 2015). Although there are no records of where these chips were dumped, they have been found throughout West Dallas (Wigglesworth 2012). This led to concerns that soils contaminated by Pb emissions might still remain in the surrounding area. Our initial investigation presented here includes a survey of soil Pb concentrations near residential homes that will eventually complete an evaluation of urban Pb contamination throughout West Dallas. Once all of the data is collected it will be used to construct a geospatial map of soil Pb concentrations across West Dallas. This data will then be cross-referenced with available blood Pb concentrations that have been collected by the Texas Department of State Health Services over the last ten years to assess the potential risk of exposure of the children of West Dallas to Pb contaminated soils.

The Ketamine Epidemic and its Ecotoxicity in Aquatic Systems Kyle Wolfe, George Cobb, Ph.D.

Ketamine is a drug designed in the 1960s as a major pain killer, and it is widely used in the medical field. However, ketamine does have adverse effects in the form of mind-body dissociation and delirium. These side effects are being exploited by illicit drug users in order to experience hallucinogenic effects. Abuse of ketamine, especially in the southeastern region of Asia, is on the rise. Not only is the use of ketamine growing recreationally, but it is also being used at an increasing rate in hospitals. Whether from recreational users or patients in hospitals, the excrement they produce introduces ketamine, norketamine, and their metabolites into aquatic ecosystems where they can bioaccumulate. The purpose of this research is to examine existing literature and ascertain how the rising ketamine epidemic is affecting water quality and subsequently the health of aquatic organisms. One way that ketamine consumption is being quantified is by measuring the concentration of its metabolites in wastewater influent and extrapolating data to determine frequency of use for a given population. The fate of environmentally occurring ketamine has been examined extensively, specifically in aquatic systems that are irradiated by the sun. Through experimentation, it has been determined that phototransformation of ketamine and norketamine significantly reduces their concentrations in aquatic systems, but irradiated solutions exhibit greater toxicity in many organisms. However, overall ecotoxicological impacts of ketamine and substitutive metabolites on aquatic systems is currently unknown.

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2018 URSA Abstracts

Biology Cancer, Bacteria, and Inflammation: Outer Membrane Vesicles from Enterotoxigenic Bacteroides fragilis and Non-enterotoxigenic Bacteroides fragilis Emily Lin, Aadil Sheikh, Joe Taube, Ph.D., Leigh Greathouse, Ph.D. According to the National Cancer Institute, colon cancer is the fourth most common type of cancer and is the second leading cause of cancer death in the United States. Factors that affect inflammation and tumorigenesis in the colon include obesity, diet, and commensal bacteria all of which modulate the level of inflammation. Progression to colon cancer is associated with increasing degrees of inflammatory signaling. It is believed that certain types of bacteria can communicate with host cells to activate downstream inflammatory immune responses such as through the Pattern-Recognition Receptor (PRRs), including the activation of Toll-Like Receptors (TLRs). We hypothesize that the inflammation inducing strain of B. fragilis, Enterotoxigenic Bacteroides fragilis (ETBF), secrete Outer Membrane Vesicles (OMVs) which contain small RNAs (sRNA) that can activate TLRs, specifically TLR7, on colon cancer cell lines or immune system cells and result in downstream inflammatory signals, including inflammatory cytokines. To test this hypothesis, we extracted OMVs and characterized them through Transmitting Electron Microscopy (TEM), protein content and RNA content. We are currently using bioinformatics to analyze the differential sRNA content of the OMVs for potential regulators of mammalian gene expression. We are elucidating novel mechanisms by which ETBF contributes to the inflammation and progression of colon cancer in human hosts.

Identification of mutant C. elegans resistant to valproic acid

Quynh-An Phan, Hailey Beattie, Sihan Hu, Chi-Hung Lee, Kavya Munnangi, Sean Tran, Myeongwoo Lee, Ph.D. Valproic acid (VPA) is a generalized drug used to alleviate a broad range of conditions in humans such as bipolar disorder, Parkinsonâ&#x20AC;&#x2122;s disease, epilepsy, and other neuromuscular diseases. The use of VPA in treatment of such diseases is due to its properties as a neurotransmitter inhibitor. In studies involving the N2 strain (wild type) of C. elegans, VPA has been shown to increase their lifespans through the regulation of insulin/IGF-1 growth factor signaling pathways. However, in humans, VPA can cause serious side effects, such as liver problems, bleeding, and a reduction in blood platelet count. In addition, VPA can decrease diacylglycerol (DAG) production and inhibit IP3 signaling in C. elegans, which results in the suppression of egg laying; the IP3 signaling pathway involves the release of calcium ions from endoplasmic reticulum into the intercellular matrix. In our study, we want to create a mutant of C. elegans resistant to these egg-laying inhibitory effects that VPA causes, induced by ethyl methanesulfonate (EMS). The mutant C. elegans were then bred and screened for their ability to reproduce when exposed to VPA. This procedure was performed and repeated until a generation of consistently VPA-resistant offspring was evident. The results of the project have positive implications for the future of VPA use. By identifying the associated mutant genes in C. elegans, we hope to uncover possible ways to reduce the negative side effects caused by valproic acid in humans.

Oncomodulin modulates intracellular calcium level

Taronish Madeka, Kelsey Chaykowski, Dwayne Simmons, Ph.D. Calcium plays a critical role in hearing, particularly in regulating hearing sensitivity through active amplification mechanisms. In the inner ear, cochlear outer hair cells (OHCs), which amplify sounds, express oncomodulin (Ocm) as their predominant calcium-binding protein. Deletion of Ocm results in early progressive hearing loss. This may be because intracellular calcium levels are not effectively regulated in the absence of Ocm, which may lead to disruption of OHC function, calcium toxicity, and eventual cell death. To explore Ca2+ signals in the presence and absence of Ocm, we used HEK293 cells that constitutively express Ocm (HEK293- Ocm) as a model for Ocm+/+ cells and HEK293 cells without Ocm expression. The fluorescent Ca2+ indicator dye, Fluo-4-AM, was used to indicate the relative presence of free cytosolic Ca2+. Both cells types were challenged in independent trials with a variety of stressors known to increase intracellular free Ca2+. The data revealed that HEK293-Ocm cells responded to the stressors in distinctly different ways than normal HEK293 cells. HEK293-Ocm cells had: 1) a smaller change in fluorescence for most stressors, and 2) a more transient response to most stressors. Currently, immunohistochemistry experiments are being processed for HEK293 and HEK293-Ocm cells exposed to varying drug treatments to observe for morphological changes in their cytoskeleton and oxidative stress levels.

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Chemistry Release Kinetics of Dyes Covalently Attached to Various Tissues Allie Stinchcomb, Jerry Quintana, Kayla Murphy, Robert Kane, Ph.D.

The innate immune system can complicate transplant surgeries by causing an acute inflammatory response, threatening the health and viability of newly transplanted tissues; one significant pathway is via the stimulation of toll like receptor 4 (TLR4). A TLR4 antagonist has been identified that dramatically reduces inflammation in transplanted islets of Langerhans, and our lab seeks to expand our work with islets to include covalently modifying the surface of other tissues with the TLR4 antagonist. Over time, the TLR4 antagonist will detach from the tissue, locally inhibiting the innate immune system and protecting the tissue. In a series of preliminary studies, we have experimented with covalently attaching a fluorophore to the tissues using a cleavable linker. This allows us to measure the rate at which the fluorophore detaches from the tissue, which will mimic the release of the TLR4 inhibitor. We are currently working on synthesizing better linkers with which to treat the tissue, and after completion of the synthesis we will continue to collect data on the rate of fluorophore release. To do so, we treat the tissues with the linker molecule and a fluorophore, image the tissue at specific time points over a couple days, and use Image J to acquire quantitative data. Our previous results demonstrate that the fluorophores will covalently attach to the tissues pre-treated with a linker molecule, and the experimental linker will cleave over time in comparison to the control.

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Student Research Spotlight

A Survey of Undergraduate Research at Baylor University, Waco, Texas Kathleen Klinzing- Class of 2021 My collegiate research experience began my first semester in Dr. Harvill and Dr. Parizi's laparoscopy class. This class taught me how to design a project and refined my writing skills. The spring of my freshman year I designed and conducted an experiment with my group in Dr. Harvill's Biology 1406 class. We investigated the relationship between variance in daily light cycle on production of chlorophyll b in algae. We measured chlorophyll b as an indicator of circadian rhythm adaptability in the algae. Overall, we found that the algae on the most variant light cycles produced the most chlorophyll b. You can read more about our experiment and findings in this issue of Scientia! I learned how to think critically about controlling variables and statistics. I had a fantastic group and enjoyed exchanging ideas with my peers. We also had the opportunity to present at a research symposium at Baylor. I spent the summer purifying proteins as an intern in the Summer Program in Cancer Research at MD Anderson. I was then able to transfer these skills to my work in a lab at Baylor. Last spring, professors who research at Baylor presented their research to one of my classes. Throughout the presentations, I found myself most engaged when professors talked about proteins and intracellular interactions. I found Dr. Trakselisâ&#x20AC;&#x2122; research especially interesting and talked to him and one of the graduate students following his presentation. This fall I joined his research group, where I purify proteins and conduct other experiments to study protein interactions during DNA repair. My primary project involves investigating the interactions between Rad51 and MCM9, a DNA repair protein and a helicase, respectively. I spend most of my lab time purifying proteins, running gels, doing Westerns, and pulling down proteins. I also attend lab meetings, which have expanded my understanding of scientific articles and how to think like a scientist. I truly enjoy being immersed in science and learning from bright people all week long! Research gives me context for science. The methods that I learn in lab make class material logical instead of simply memorized. My research experiences have made my time as an undergraduate more meaningful in a practical sense. I look forward to the hours I spend in lab and am grateful for the opportunity to engage with science hands on. Research has also provided many friends and mentors who have taught me as much about patience and persistence as they have about science.

Sarah Lathrop â&#x20AC;&#x201C; Class of 2020 I started getting interested in research after I had switched my major to biology late freshman year. I knew I wanted to explore all my options, so I began to email and meet with different professors at the start of my sophomore year. I was fortunate that Dr. Sekeres accepted me into her neuroscience memory lab immediately after my interview. I started by learning how to analyze data on SMART software and slice mouse brains using a vibratome. After a few months, I became animal trained and started handling our mice for both rearing and behavioral tasks. We currently have two studies: one on aging and one on chemotherapy-induced cognitive impairments (CICI). I was able to continue my research on our CICI study throughout the summer through the Baylor Transdisciplinary Research Undergraduate Experience (BTRUE) program. This exposed me to a new side of research such as presenting my research orally, learning to dissect and evaluate research articles, and preparing a poster for conference presentations. I believe this experience is what made me want to pursue research as part of my future career since it gave me an idea of what graduate school would be like. I then took the initiative to go to several conferences the following fall and present my work both orally and in posters. Additionally, a few colleagues of mine and I decided we wanted to continue our research from a previous class and we are currently in the process of publishing our psychophysiology research. I never would have guessed I would be where I am today without the amazing opportunities I have been blessed with. Both Baylor and Dr. Sekeres has provided me with experiences and learning opportunities to grow into the person I am and develop my passion for research. Research is another way I can connect my calling to my passion and I cannot wait to see where research takes me in the next few years.

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Angelo Wong- Class of 2021 I am currently in Dr. Kebaaraâ&#x20AC;&#x2122;s research lab where we use Baker's yeast as a model to study regulation of gene expression at the messenger RNA level. We also look at how the environment influences the regulation of the genes through examining RNA accumulation and half-lives in varying conditions. Additionally, we look at the nonsense-mediated mRNA decay pathway (NMD) and how it degrades natural and truncated mRNAs. I was able to introduce myself to Dr. Kebaara through BURST (Baylor Undergraduate Research in Science and Technology). I joined the resources committee in BURST, and I was responsible for collecting information about various on-campus research opportunities. Discussing research with Dr. Kebaara was eye-opening, and I immediately expressed my interest in joining her lab. My favorite part of research is learning new protocols and procedures. Learning how to research made me enjoy how far science has come. I would recommend reading about the professorâ&#x20AC;&#x2122;s background and publications before going in to talk with them. Find out if you are actually interested in their research and it will help you excel in their lab. You will understand why you are doing certain procedures and what the lab is ultimately trying to achieve.

Emily Ziperman- Class of 2020 I am currently conducting research in the field of computational chemistry. I enjoy research because it gives me the ability to answer questions I have that have yet to be answered. Being able to ask a question, create the means to answer it, and then find your solution is a very meaningful experience. Participating in research outside the classroom has taught me many things. It has showed me where different areas of chemistry are headed, why the material we learn in class is important and how it is applied, and how to think critically about what I am learning. I think all undergraduates should try to have a research experience! Even if you end up disliking the work or process, you can learn very valuable skills that are not often taught in class lectures or labs.

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Scientia's Mission Scientia shall provide a professional platform upon which undergraduates of Baylor University are able to publish personally conducted and outstanding research in the areas of biological sciences, physical sciences, mathematics, and technology.

Accepted Formats Research Articles (maximum 4500 words including captions and references) presenting major findings performed by current undergraduate level students enrolled at Baylor University. Research articles must include an abstract, introduction, materials and methods, up to six figures or tables, results, and discussion. Review Articles (maximum 6000 words including captions and references) synthesizing developments of interdisciplinary significance written by current undergraduate level students enrolled at Baylor University. Review articles must include an abstract and an introduction outlining the topic of discussion. Abstracts (maximum 500 words) proposing research topics currently being investigated by current undergraduate level students enrolled at Baylor University.

Submitting to Scientia To submit to Scientia for publication, email your research article, review article, abstract, or Student Spotlight Submission to Sean_Ngo1@baylor.edu. To read previous editions of Scientia online, please visit https://www.baylor.edu/burst/index.php?id=930886 For more information, including the official Scientia 2020 Submission Guidelines packet, please email Scientia's Editor-in-Chief, Sean Ngo, at Sean_Ngo1@baylor.edu

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Want to become involved in research at Baylor University and beyond? Learn about Baylor Undergraduate Research in Science and Technology (BURST)! BURST is the student organization for Baylor undergraduate students interested in scientific research.

Mission

To increase awareness of undergraduate research within the Baylor campus, we aim to provide opportunities for undergraduates to optimize their research experiences, and educate them in the proper habits and techniques of research in scientific fields.

Journal Clubs

Members participate in peer-led Journal Clubs of a variety of fields. Each Journal Club reads through and discusses a selection of research articles. Some Journal Clubs consist of a discussion with a Baylor professor or another expert research about the research that they pursue.

Lab Tours

A tour of the lab in the Baylor Sciences Building (BSB), guided by the Baylor University professor or graduate student of whose research paper was read during the Journal Club preceding the tour allow members to see various research environments across campus. Members have the opportunity to ask questions, visualize the research techniques they have learned about, and occasionally gain hands-on experience with lab equipment.

Scientia

Scientia is the Baylor Undergraduate Research Journal of Science and Technology. First published in the Spring of 2014, Scientia is a yearly publication produced by BURST members and supported by the Baylor College of Arts and Sciences.

Conferences

Members who are currently doing research are encouraged to attend a variety of conferences, where they can present their findings to the scientific community in a professional environment. BURST works closely with URSA during URSA Scholars' Week, the annual Baylor conference showcasing undergraduate research. We will also promote other conference opportunities in Texas and around the nation.

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Lecture Series

Each semester, BURST organizes a lecture series featuring research experts from both Baylor and beyond. These lectures provide members with an increasing scope of knowledge about current research and how they can become involved.

Lab Technique Workshops

BURST organizes a workshop once a semester to teach its members fundamental laboratory skills, such as pipetting and gel electrophoresis. The goal of this workshop is to provide students who have never done research an opportunity to learn fundamental skills that will be useful in their first research experience.

Service in the STEM Fields

BURST organizes opportunities for members to actively engage in spreading interest for the sciences and technology in Waco. Members may choose to volunteer weekly to tutor and advise students at a high school in Waco.

Research Internship Day

BURST hosts an annual BURST Research Internship Day to increase awareness of the many research internship opportunities for undergraduate students at Baylor. This event allows students to meet with representatives from a variety of internships across Texas, listen to other students speak about their own research internship experiences, attend presentations by BURST officers regarding how to find, apply, and make the most of research internships, and also listen to lectures by Baylor University professors about the research they partake in.

For Prospective Members Are you interested in joining BURST? Please contact us at burst@baylor.edu.

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