Understanding the Gut Transcriptome Responses to Lactobacillus

Understanding the gut transcriptome responses to lactobacillus probiotics and investigating the impact of nutrition and rotavirus infection on the infant gut microbiome

DISSERTATION

Presented in Partial Fulfillment of the Requirements for the Degree Doctor of Philosophy in the Graduate School of The Ohio State University

Anand Kumar, DVM, MVSc, MS

Graduate Program in VeterinaryComparative Preventive and Veterinary Medicine Medicine

The Ohio State University

2015

Dissertation Committee:

Dr. Gireesh Rajashekara, Advisor

Dr. Linda J. Saif

Dr. Michael T. Bailey

Dr. Jordi B. Torrelles

Copyrighted by

Anand Kumar

2015

Abstract

Lactobacillus spp. have been tested in infants for the prevention or treatment of various

enteric conditions. However, to aid in rational strain selection for specific treatments,

comprehensive studies are required to delineate and compare the specific molecules and

pathways involved in a less complex but biologically relevant model (gnotobiotic pigs).

Here we elucidated Lactobacillus rhamnosus (LGG) and L. acidophilus (LA) specific gut transcriptome responses in a monocolonized pig model to simulate responses in newly colonized infants. Whole genome microarray, followed by biological pathway reconstruction, was used to investigate the host-microbe interactions at early (day 1) and later stages (day 7) of colonization. Both LA and LGG modulated common responses related to host metabolism, gut integrity, and immunity, as well as responses unique to each strain in pigs. Our data indicated that probiotic establishment and beneficial effects in the host are guided by: (1) down-regulation or upregulation of immune function- related genes in the early and later stages of colonization, respectively, and (2) alternations in metabolism of small molecules (vitamins and/or minerals) and macromolecules (carbohydrates, proteins, and lipids). Pathways related to immune modulation and carbohydrate metabolism were more affected by LGG, whereas energy and lipid metabolism-related transcriptome responses were prominently modulated by

LA. These findings are highly relevant to the improvement of probiotic-based ii interventions in enteric infections, either to moderate specific intestinal conditions or to enhance vaccine efficacy for enteric infections.

Enteric infections are attributed to millions of deaths in infants worldwide annually. Human rotavirus (HRV) is a major cause of viral gastroenteritis in infants that accounts for approximately 440,000 deaths annually worldwide, particularly in developing countries where malnutrition is prevalent. Malnutrition perturbs the infant gut microflora leading to sub-optimal functioning of the immune system. Therefore, we

hypothesized that malnutrition further exacerbates rotavirus disease severity in infants. In

this context, identifying the specific microbial composition and their probable function

during malnutrition and RV infection has a therapeutic value; however, this has not been

investigated previously. Due to various confounding factors and ethical concerns,

addressing these questions in human infants is not permissible. A growing literature

suggests that pigs are a more realistic, practical, non-primate model for transplanting human gut microflora compared to mice and rodent models. In this study, we established a microflora humanized (HM) pig model to study the effects of interactions between infant gut microbiota and diet (deficient vs sufficient) on RV disease. Clinically, HM pigs with deficient diet developed characteristic edema (like observed in infants with protein- calorie malnourishment), stunted growth rate, and also had more severe RV diarrhea compared to HM pigs with sufficient diet post RV challenge. Analysis of microbial structure and composition indicated that deficient diet suppressed the diversity and richness of gut microflora, and the microflora was further abundantly populated enteric pathogenic bacterial genus like the Clostridium compared to HRV challenged HM pigs

iii with sufficient diet. In conclusion our results demonstrate that even short-term malnutrition in the neonatal period might compromise the infant growth rate, and the gut microflora structure and composition leading to increased incidence and severity of enteric infections.

Acknowledgments

It is my great pleasure to express gratitude my advisor, Dr. Rajashekara G for his kind guidance and constant motivation throughout my PhD degree. His mentorship has a great role in framing my career and further engaging my research interest in science. I would like to thank my advisory committee members, Dr. Saif LJ, Dr. Bailey MT, and Dr.

Torrelles JB for their kind hearted guidance, close supervisions and providing me reference letters in timely during my postdoctoral job application.

I would also like extend my thanks to Rajashekara lab members- Issmat I.

Kassem, Huang-Chi Huang (Angie), Cláudio M. Vrisman, Loic Deblais, Rosario A

Candelero-Rueda, Yosra A Mohamed and Dipak Kathayat for their constant assistance and timely help during my PhD research work. I also acknowledge Dr. Vlasova AN,

Kandasamy, S., Fischer, D.D., Langel, S.N., Paim, F.C., Rauf, A., Abdulhameed, M., and

Shao, L for helping me in the animal experiment. My heart-felt gratitude to office associates Hannah Gehman, Robin Weimer, and Graduate Program Coordinator

Kathy Froilan for their kind support in academic issues. I would like to humbly acknowledge all professors, research scientists, post-doctoral researchers, and graduate students at FAHRP, Wooster, The Ohio State University, for their personal and intellectual support. I would like to take this opportunity to thank Jagadish, Mahesh,

Basavaraj and their families for cordial hospitality and helping me in my difficult time. A v

special thanks to my friend Anil K Persad who made my stay at Wooster wonderful and

cheerful.

Reaching this stage in my life would have been impossible for me without the

kind blessings of GOD, and unconditional love, support and sacrifice of my parents,

brothers and their families. My heartfelt thank and affection to my wife Meenakshi for

her constant emotional support, and sacrifice during my research. And finally a source of inspiration for me to reach pinnacle of success in the science, is my sweet and cute daughter Aadyshree.

At last, I would like thank all those other known and unknown persons, who helped me directly or indirectly during my graduate studies.

Anand Kumar

Vita

1999-2004------Doctor of Veterinary Medicine (DVM)

University of Agricultural Sciences, Dharwad, India

2004-2006------Master of Veterinary Science (MVSc)

G.B. Pant Univ. Agri & Tech, Pantnagar, India

2006-2007------Senior Research Fellow, Institute of Animal

Health & Veterinary Bio, Bangalore, India

2007-2010------Veterinary Officer, Dept of Animal

Husbandry, Govt. of Karnataka, India

2010- 2012------Graduate Research Associate (MS)

The Ohio State University

2012- Present------Graduate Research Associate (PhD)

The Ohio State University

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Publications

1) Anand Kumar, Anastasia N. Vlasova, Zhe Liu, Kuldeep S. Chattha, Sukumar Kandasamy, Malak Esseili , Xiaoli Zhang, Linda J. Saif and Gireesh Rajashekara. 2014. In vivo gut transcriptome responses to Lactobacillus rhamnosus GG and Lactobacillus acidophilus in a neonatal gnotobiotic piglet model. Gut Microbes 2014 Mar- Apr;5(2):152-64. PMID: 24637605

2) *Anandkumar Malde, Gangaiah D, Chandrashekhar K, Pina-Mimbela R, Torrelles JB, Rajashekara G.2014. Functional characterization of exopolyphosphatase/guanosine pentaphosphate phosphohydrolase (PPX/GPPA) of Campylobacter jejuni. Virulence. 2014 May 15;5(4):521-33. PMID: 24569519 * This manuscript was selected as editorial and in author surname (Malde) was used for the first time.

3) Kashoma IP, Anand Kumar, Sanad YM, Gebreyes W, Kazwala RR, Garabed R, Rajashekara G. 2014. Phenotypic and Genotypic Diversity of Thermophilic Campylobacter spp. in Commercial Turkey Flocks: A Longitudinal Study. Foodborne Pathogens and Diseases. Dec;11(12):917-9. PMID: 25184688

4) Dixie F. Mollenkopf, Johana K. Cenera, Erin M. Bryant,a Christy A. King, Isaac Kashoma, Anand Kumar, Julie A. Funk, Gireesh Rajashekara, Thomas E. Wittuma. 2014. Impact of organic or antibiotic-free labeling on the recovery of enteric pathogens and antimicrobial-resistant Escherichia coli from fresh retail chicken. Foodborne Pathogens and Diseases. 2014 Dec;11(12):920-9. PMID: 25405393

5) Annamalai. T, Pina-Mimbela. R, Anand Kumar, Binjawadagi. B, Renukaradhya. G.J and Rajashekara. G, 2013. Evaluation of nanoparticle encapsulated OMPs for the control of Campylobacter jejuni colonization in chickens. Poultry Science. 2013 Aug;92(8):2201-11. PMID: 23873570

6) Y. Sanad, G. Closs Jr, Anand Kumar, J. LeJeune, and G. Rajashekara 2013. Molecular Epidemiology and Public Health Relevance of Campylobacter Isolated from Dairy Cattle and European Starlings in Ohio, USA. Foodborne Pathogens and Diseases. 2013 Mar;10(3):229-36. PMID: 23259503

7) Anand Kumar and M. K. Saxena, 2010. Molecular typing of field isolates of Salmonella Serovar by using RAPD-PCR. Indian Journal of Animal Science.80:3 March 2010.

8) Xuilan Xu, Anand Kumar, Loic Deblais, Ruby Pina-Mimbela, Corey Nislow, James Fuchs, Sally A. Miller and Gireesh Rajashekara. Discover novel small molecules to control Clavibacter michiganensis subsp. michiganensis using a high-throughput screening approach. Frontiers in Microbiology. doi.org/10.3389/fmicb.2015.01127.

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9) *Pina-Mimbela R, Arcos J, Anand Kumar, Torrelles JB, and Rajashekara G. Polyphosphate kinases modulate the Campylobacter jejuni outer material composition altering its capacity to invade and survive in intestinal epithelial cells in vitro. Emerging Microbes and Infections-in press. This manuscript was selected for Research Summary, a significant contribution in the field of Campylobacter pathogenesis.

10) Isaac P. Kashoma, Issmat I. Kassem, Anand Kumar, Gireesh Rajashekara. Phenotypic and genotypic diversity of Campylobacter isolates from swine, dairy and beef cattle in Tanzania. Frontiers in Microbiology- accepted.

11) Sukumar Kandasamy, Anastasia Vlasova, David Fisher, Anand Kumar, Kuldeep Chattha, Lulu Shao, Gireesh Rajashekara and Linda Saif. Differential effects of Escherichia coli Nissle and Lactobacillus rhamnosus strain GG on human rotavirus binding, infection, and B cell immunity. Journal of Immunology-in revision.

12) Anand Kumar Ruby Pina-Mimbela, Xuilan Xu, James Fuchs, Corey Nislow, Patrick J. Blackall and Gireesh Rajashekara. High-throughput screening to identify novel anti- campylobacter compounds using a pre-selected enriched small molecules library. Submitted to Journal Antimicrobial Agents and Chemotherapy.

13) Anand Kumar, Linda J Saif and Gireesh Rajashekara et al. Impact of diet and/or rotavirus infection on human infant microflora in humanized piglet model (In prep).

14) Anand Kumar and Gireesh Rajashekara et al. High-throughput screening to identify novel target specific (TAT system) anti-campylobacter compounds using small molecules library (In prep).

15) Huang, Huang-Chi, Anand Kumar, Linda J Saif and Gireesh Rajashekara et al. Colonization dynamics and succession of defined commensal microflora in a gnotobiotic pigs challenged with human virulent rotavirus(In prep).

16) I. I. Kassem, O. Kehinde, Anand Kumar, Gireesh Rajashekara et al. Litter chemical amendments that reduce pH and moisture contribute to the control of Campylobacter jejuni in broilers (In prep).

Book chapters

1) Sukumar Kandasamy, Anand Kumar, Gireesh Rajashekara, Anastasia Vlasova, Linda Saif. Probiotics for Immunomodulation and treatment of Immunological disorders in children: Evaluation in a germ free animal model. Book title: “Probiotics in Children"

Editors: Marco Manfredi and Gian Luigi de’Angelis. Nova Science Publishers, Date of publication: Thursday 03 September, 2015.

2) Issmat I. Kassem, Olugbenga Kehinde, Yosra A. Helmy, Ruby Pina-Mimbela, Anand Kumar, Kshipra Chandrashekhar, Gireesh Rajashekara. Campylobacter in poultry: the conundrums of highly adaptable and ubiquitous foodborne pathogens. Book title: “'Foodborne diseases: Case studies of outbreaks in the agri-food industries”. Editors: Jan Mei Soon, Louise Manning and Carol A. Wallace. Publisher: CRC Press, Date of publication: March 18, 2016.

Fields of Study

Major Field: VeterinaryComparative Preventive and Veterinary Medicine Medicine

Probiotic biology and host-microbiota interactions

Table of Contents

Abstract ...... ii

Acknowledgments...... v

Vita ...... vii

Table of contents ...... xi

List of Tables ...... xvi

List of Figures ...... xvii

Chapter 1: Review of Literature ...... 1

1.1 Infant gut microflora and its significance ……………………………………………..2

1.2 Gut microflora colonization phases in infants………………………………………...4

1.3 Milk microbiota and milk oriented gut microbiota……………………………………7

1.4 Genesis of milk microbiota …………………………………………………………...9

1.5 Mechanisms of microflora colonization……………………………………………..10

1.6 Impact of diet, antibiotic, and probiotic on infant gut microbiota…………………...12

1.6.1 Diet…………………………………………………………………………………12

1.6.1.1 Pre-weaning……………………………………………………………………...12

1.6.1.2. Post weaning……………………………………………………………………13

1.6.2 Antibiotics…………………………………………………………………………13

1.6.3 Probiotics………………………………………………………………………….15

1.7 Role of gut microflora in intestinal immunity……………………………………….17

1.7.1 Innate immunity ……………………………………………………………………17

1.7.2 Adaptive immunity………………………………………………………………...18

1.7.3 Immune homeostasis………………………………………………………………19

1.7.4 Oral tolerance………………………………………………………………………20

1.8 Beneficial effects of probiotics on the gut…………………………………………...21

1.9 Probiotics modulate gut immunity…………………………………………………...23

1.10 Rotavirus and significance………………………………………………………….26

1.11 Pathogenesis of RV…………………………………………………………………27

1.12 RV and use of probiotics……………………………………………………………28

1.13 Pig -non-primate ideal model to study human conditions………………………….30

1.14 Specific objectives of study………………………………………………………..32

1.15 References………………………………………………………………………….44

Chapter 2: In vivo gut transcriptome responses to Lactobacillus rhamnosus GG and

Lactobacillus acidophilus in neonatal gnotobiotic piglets ...... 58

2.1 Abstract………………………………………………………………………………59

2.2 Introduction…………………………………………………………………………..60

2.3 Materials and Methods……………………………………………………………….63

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2.4 Results………………………………………………………………………………..68

2.5 Discussion……………………………………………………………………………75

2.6 Acknowledgements…………………………………………………………………..82

2.7 References……………………………………………………………………………94

Chapter 3: Investigating the impact of nutrition and rotavirus infection on the infant gut microbiota in a humanized pig model ...... 101

3.1 Abstract…………………………………………………………………………….102

3.2 Introduction…………………………………………………………………………104

3.3 Materials and Methods……………………………………………………………...107

3.4 Results………………………………………………………………………………113

3.5 Discussion…………………………………………………………………………..117

3.6 Acknowledgements…………………………………………………………………121

3.7 References…………………………………………………………………………..139

Conclusions and future directions………………………………………………..…144

Bibliography………………………………………………………………………..…150

Appendix A: Number of IPA mapped genes ‘unregulated’ by both LA and LGG ...... 168

Appendix B: Gene involved in metabolism modulated by LA and LGG in duodenum and ileum ...... 169

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Appendix C: Top 5 upstream regulators involved ...... 174

Appendix D: Genes involved in cell integrity and immunity……………………...... 175

Appendix E: Primers used for RT-qPCR………………………………………………180

Appendix F: Animal experimental design indicating different time points where variable

was introduced, samples were collected and finally animals were sacrificed…………181

Appendix G: Colonization dynamics of probiotic strain LA and LGG in Gn pigs where

limit of detection was 10 CFU/g of tissue……………………………………………...182

Appendix H: Validation of microarray results of selected genes in duodenum and ileum

by RT-qPCR……………………………………………………………………………183

Appendix I: Comparison of IPA mapped common (red numbers) and unique gene

responses modulated in duodenum and ileum on PBCD1 and 7………………………184

Appendix J: Top associated networks with their linked canonical pathways (CP) in

duodenum on PBCD1…………………………………………………………………..185

Appendix K: Top associated networks with their linked canonical pathways (CP) in ileum on PBCD7………………………………………………………………………………186

Appendix L: The tight junction signaling in duodenum and ileum for LA vs LGG generated using IPA tool………………………………………………………………..187

Appendix M: Individual animals’ community microbial profiles comparison between deficient vs sufficient HM pigs before and after RV challenged in feces at the ‘Phyla’ level……………………………………………………………………………………..188

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Appendix N: Individual animals’ community microbial profiles comparison between

deficient vs sufficient HM pigs before and after RV challenged in feces at the ‘Order’

level……………………………………………………………………………………..189

Appendix O: Individual animals’ community microbial profiles comparison between deficient vs sufficient HM pigs before and after RV challenged in feces at the ‘Class’ level…………………………………………………………………………………….190

Appendix P: Individual animals’ community microbial profiles comparison between deficient vs sufficient HM pigs before and after RV challenged in feces at the ‘Genus’ level……………………………………………………………………………………..191

Appendix Q: Individual animals’ community microbial profiles comparison between deficient vs sufficient HM pigs 14 days after RV challenged in the intestinal tissue samples at the ‘Phyla’ level……………………………………………………………192

Appendix R: Individual animals’ community microbial profiles comparison between

deficient vs sufficient HM pigs PCD14 in the intestinal tissue samples at the ‘Order’

level………………………………………………………………...... 193

Appendix S: Individual animals’ community microbial profiles comparison between

deficient vs sufficient HM pigs PCD14 in the intestinal tissue samples at the ‘Class’

level……………………………………………………………………………………..194

Appendix T: Individual animals’ community microbial profiles comparison between

deficient vs sufficient HM pigs PCD14 in the intestinal tissue samples at the ‘Genus’

level……………………………………………………………………………………..195

List of Tables

Table 2.1 Summary of gut transcriptome responses to LA and LGG ……………...... 83

Table 2.2 Top gene regulatory networks associated with LA and LGG in duodenum and ileum based on focus molecules and scores……………………………………………...84

Table 2.3 Metabolism genes influenced by administration of probiotics LA and LGG in duodenum and ileum………………………………………………………………...... 86

Table 3.1 The V4-V5 variable regions targeted barcoded primers ………………...... 123

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List of Figures

Figure 1.1 Spatial and temporal aspects of intestinal microbiota composition ...... 33

Figure 1.2 Factors influencing mother-infant symbiosis……..………………………...34

Figure 1.3 Bacterial phyla in stool of three month-old breast- and formula-fed infants..35

Figure 1.4 A simplistic schematic presentation of diversity and richness of infant the gut

microflora in first one year of life………………………………………………………..36

Figure 1.5 Factors influencing initial infant microflora colonization…………………...37

Figure 1.6 Age wise microbial colonization profiles (at the family level) of the infant and

child gut……………………………………………………………………………….....38

Figure 1.7 Genesis of milk microbiota: A hypothetical model to explain how maternal

microbiota and microbial products could be transferred from mother to the fetal and

neonatal gut………………………………………………………………………………39

Figure 1.8 Schematic illustration of how infant microbiota may influence immune

responses in particularly vaccine responses……………………………………………...40

Figure 1.9 An overview of oral tolerance induction………………………………….....41

Figure 1.10 Schematic representation of established effects of probiotics on gut

homeostasis...... 42

Figure 1.11 Simplistic view of stimulation of mucosal immunity by probiotics and

commensals………………………………………………………………………………43

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Figure 2.1 Major cellular pathways (canonical pathways) modulated by LA and LGG

were generated using ingenuity biological process analysis ...... 89

Figure 2.2 Top associated networks with their linked canonical pathways (CP) in ileum

on PBCD1 ...... 91

Figure 2.3 Top associated networks with their linked canonical pathways in duodenum

on PBCD7 ...... 92

Figure 2.4 Genes involved in cell integrity and immunity...... 93

Figure 3.1 Animal experimental design indicating different time points where variable

were introduced, samples collected and animals sacrificed ...... 124

Figure 3.2 The percentage of initial body weight gain was calculated by normalizing

birth weight of piglets ...... 125

Figure 3.3 Severity of diarrhea in HM pigs was assessed based on the fecal consistency

score ...... 126

Figure 3.4 VirHRV fecal shedding titers were determined by cell culture

immunofluorescence (CCIF) infectivity assay ...... 127

Figure 3.5 Comparison of the community microbial profile among 2 month old infant

fecal, HM pig ileum and HM pig fecal samples using16S rRNA variable region targeted

sequencing...... 128

Figure 3.6 Comparison of the microbial community profiles in feces between HM pigs with deficient vs sufficient before and after HRV challenge. The data presented at the

‘Phyla’ level...... 130

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Figure 3.7 Comparison of the microbial community profiles in feces between HM pigs with deficient vs sufficient before and after HRV challenge. The data presented at the

‘Order’ level ...... 131

Figure 3.8 Comparison of the microbial community profiles in feces between HM pigs with deficient vs sufficient before and after HRV challenge. The data presented at the

‘Class’ level …………………………………………………………………………..132

Figure 3.9 Comparison of the microbial community profiles in feces between HM pigs with deficient vs sufficient before and after HRV challenge. The data presented at the

‘Genus’ level ...... 133

Figure 3.10 Comparison of the microbial community profiles in intestinal tissue sections between HM pigs with deficient vs sufficient after 14 days (PCD14) of RV challenge.

The data was presented at the ‘Phyla’ level…………..………………………………...134

Figure 3.11 Comparison of the microbial community profiles in intestinal tissue sections between HM pigs with deficient vs sufficient after 14 days (PCD14) of RV challenge.

The data was presented at the ‘Order’ level………………………………………..…...135

Figure 3.12 Comparison of the microbial community profiles in intestinal tissue sections between HM pigs with deficient vs sufficient after 14 days (PCD14) of HRV challenge

The data was presented at the ‘Class’ level ...... 136

Figure 3.13 Comparison of the microbial community profiles in intestinal tissue sections between HM pigs with deficient vs sufficient after 14 days (PCD14) of HRV challenge.

The data was presented at the ‘Genus’ level ...... 137

xix

Figure 3.14 Comparison of the microbial community in fecal and tissue samples from deficient and sufficient HM pigs using mean square distance (MSD) plot by bray distance method...... 138

CHAPTER 1

Review of literature

Anand Kumar and Gireesh Rajashekara

Food Animal Health Research Program; Department of Veterinary Preventive Medicine;

Ohio Agricultural Research and Development Center; The Ohio State University;

Wooster, OH USA.

1.1 Infant gut microflora and its significance

‘We are not we’. The human body is composed of 10 times more bacterial cells compared to our own cells whose collective genome ‘microbiome’ contains at least 100 times as many genes as in the human genome (Collado, Cernada, Bauerl, Vento, & Perez-

Martinez, 2012). Hence, the microflora is an integral part of our body and we are referred as ‘superorganisms’. The microflora consists of not only the bacterial populations but also is composed of abundant yeasts, single cell eukaryotes, viruses and small parasitic worms (Conlon & Bird, 2014). One well characterized microflora is the gut microflora.

Evolution of the gut microflora from birth till death is a complex process and an intense area of investigation in recent years due to its life promoting implications. Coevolution of the microflora and the intestine has facilitated the evolution of numerous tissue types which ultimately are well adapted to the host survival (Sanderson, 1999). Traditionally, the human fetus was considered microbiologically sterile, with the first microbial exposure taking place at vaginal birth and from the surrounding environment (Mackie,

Sghir, & Gaskins, 1999; Thum et al., 2012). However, based on recent findings, it is unlikely that the fetus in the uterus is sterile, thereby refuting the traditional concept. The beginning of bacterial exposure for the developing fetus was seen as early as the third trimester (Jimenez et al., 2005; Jimenez et al., 2008; Pettker et al., 2007). The maternal gut and/ or urogenital signature bacteria have been isolated and/or detected in several tissues including umbilical cord blood (Jimenez et al., 2005), amniotic fluid (Hitti et al.,

1997), meconium (Jimenez et al., 2008), placental (Pettker et al., 2007)and fetal membranes (Pettker et al., 2007; Satokari, Gronroos, Laitinen, Salminen, & Isolauri,

2009; Steel et al., 2005), even in the absence of infection or inflammation in the mother or infant. The presence of low levels of bacteria in fetal membranes suggests that bacterial exposure of the fetus is necessary to educate the fetal immune system to prepare for life outside the uterus before colonizing the fetal gut (Thum et al., 2012). As a consequence and due to the maternal immunosuppressive microenvironment, the first exposure to microbes during birth does not evoke adverse neonatal immune responses.

This supports the existence of complex regulatory mechanisms that are likely to commence during fetal development (Wagner, Taylor, & Johnson, 2008). An overview of spatio-temporal composition of intestinal microbiota is depicted in Figure 1.1.

In utero, the fetal gut closely resembles the germ-free animal intestine and is characterized by an immature epithelial surface, prolonged cell turnover times and less developed gut associated lymphoid tissue (GALT). In contrast, the intestine of the colonized newborn infant is characterized by an active epithelial surface with more rapid turnover, subtypes of epithelial cells and numerous developing GALT (Walker, 2013).

This observation not only emphasizes the importance of the presence of the microflora for development and maturation of the gut, but also for the immune system. The microbiota within the gut provides a spectrum of functions, such as digestion of essential nutrients, maturation of intestinal epithelial cells and development of the immune system

(Sekirov, Russell, Antunes, & Finlay, 2010; Varankovich, Nickerson, & Korber, 2015).

Studies of animal models have shown a number of significant effects of the microbiota on the host, such as intestinal epithelium maturation and development, increased concentrations of short-chain fatty acids (SCFA), and proper development of immunological responses (Aureli et al., 2011). The microbiota also has the ability to

3 affect physiologic parameters, with systemic effects on blood lipids, influences on the immune system, and inhibition of pathogens(Mikelsaar, 2011). Pathogen inhibition by intestinal microbiota provides significant human health benefits; i.e. protection against infection as a natural barrier against pathogen exposure in the gut(Wallace et al., 2011).

1.2 Gut microflora colonization phases in infants

Numerous factors have been shown to influence the spatiotemporal pattern of gut microflora colonization in mothers and infants (Figure 1.2) (Mackie et al., 1999). These include the weight, medication, environment, presence of microbes and the mother’s physiological condition (Collado et al., 2012; Penders et al., 2006).

In infants, intestinal colonization by the microflora occurs broadly during four phases. Phase-I is the shortest (a few hours) and has a significant impact on health postnatally, as well as on Phase-II colonization. In the phase-I, infants leave the intrauterine environment and pass through the microflora rich birth canal, with ingestion of a bolus of vaginal and colonic microflora. Phase-I varies between full term vs pre-term delivery and vaginal delivery vs caesarian, resulting in variability in the spatio-temporal colonization of the initial signature microflora. Bacteria that initially colonize the gut of infant are facultative anaerobes such as Escherichia coli and Streptococcus sp. These species metabolize the oxygen present in the gut, thereby creating anaerobic conditions(Mountzouris, McCartney, & Gibson, 2002). Thus, subsequent colonizers are anaerobes and profiles of such bacteria largely depend on type of food and the environmental factors(Varankovich et al., 2015). For example, after caesarian sections, where the fetus bypasses the vaginal and colonic microflora exposure, which may be then

followed by antibiotic treatment affecting the composition of the gut microbiota, the gut

microflora of these infants tends to have a completely different pattern compared to a full

term vaginally-delivered infant. In infants born by cesarean delivery, the colonization

dynamics of Bifidobacterium and Lactobacillus-like bacteria varied compared to vaginally delivered infants. Further, infants born by cesarean delivery were significantly less often colonized with bacteria of the Bacteroides fragilis group than were vaginally delivered infants (Gronlund, Lehtonen, Eerola, & Kero, 1999). Oral breast milk feeding marks Phase-II, where the milk microflora (Rodriguez, 2014) and milk oligosaccharides

(potential prebiotics) help in activating the ingested bolus of microflora in phase-I. The milk oligosaccharides are resistant to gastric acid and to intestinal enzymatic digestion.

Therefore, the majority of milk oligosaccharides reach the large intestine intact, where they directly influence neonatal gut microbiota by serving as substrates, favoring the growth of specific commensal gut bacteria(e.g. Bifidobacterium and Bacteroides spp)

(Jost, Lacroix, Braegger, & Chassard, 2015; Kunz, Rudloff, Baier, Klein, & Strobel,

2000; Marcobal & Sonnenburg, 2012). Thus, the predominant and beneficial members of the early gut microbiota in breast fed neonates comprised of

Bifidobacterium and Bacteroides spp., which possess the gene repertoire that are necessary for efficient use of a broad range of milk oligosaccharides as the sole carbon source(Jost et al., 2015). This eventually results in establishing a new niche as well as preparing suitable conditions for the secondary invading microflora. It has been hypothesized that nutrition has a strong influence in determining microflora colonization during the newborn period when the infant is initially establishing its lifetime signature microflora (Walker, 2013). A prospective study has shown that breastfed infants in their

first month have an increased population of health promoting bacteria like

Bifidobacterium infantis, Lactobacillus acidophilus and Bacteroides fragilis compared to

formula fed infants in which Enterococci species prevailed (Ogawa, Ben, Pons, de Paolo,

& Bustos Fernandez, 1992; Rubaltelli, Biadaioli, Pecile, & Nicoletti, 1998). In a recent study it was highlighted that the bacterial phyla that are dominant in three-month-old breast fed infants are Actinobacteria, Proteobacteria and Bacteroidetes while age matched formula fed infants in contrast have Firmicutes, Actinobacteria and complete absence of

Bacteroidetes (Figure 1.3).

Phase-III begins after weaning and this phase further enriches the microflora colonization. At this phase the infant gut microbiota develops toward an adult like signature microbiota that is usually seen around 2 to 3 years. Based on the founder effect, i.e. initial pioneer colonizing microbiota are instrumental in the successional directional microbial assemblage; hence, the type of complementary food (carbohydrate rich vs fiber rich) and exposure to agents like probiotics or antibiotics during this phase further orchestrate the dynamics of the microflora colonization processes. During suckling and the post weaning period, nutrition also has a significant impact on infant gut development and immune maturation. Nutrition may serve as: (a) a source of foreign antigens, thereby inducing tolerance mechanisms; (b) nutrients and micronutrients themselves may modulate immune maturation; and (c) it provides factors that influence the microflora and indirectly promotes gut homeostasis (Calder et al., 2006). In this context, Narayan et al.,

showed that breast-fed vs formula-fed rhesus macaques develop significantly different gut microbial communities, which in turn are associated with different immune systems in infancy, where breast-fed animals manifested greater T cell activation and proliferation

and harbored robust pools of T helper 17 (TH17) cells(Narayan et al., 2015). A simplistic

schematic presentation of diversity and richness of the infant gut microflora in the first

year of life along with different scenarios and their impact on the assembly of gut microflora is depicted in Figure 1.4 (Dogra, Sakwinska, Soh, Ngom-Bru, Bruck, Berger,

Brussow, Karnani, et al., 2015).

When infants reach 24-36 months of age (Phase-IV), the intestinal microflora begins to resemble the adult like flora consisting of more than 1,000 unique signature species (Palmer, Bik, DiGiulio, Relman, & Brown, 2007). However, infants with an abnormal microflora (dysbiosis) (see Figure 1.5) may take 4-5 years to develop an adult-

like microflora, during which the infants are increasingly susceptible to various

pathophysiological conditions. The normal gut microflora sequential colonization pattern

(at the family level) in 2- to- 3 year old children is depicted in Figure 1.6 (Arrieta,

Stiemsma, Amenyogbe, Brown, & Finlay, 2014).

1.3 Milk microbiota and milk oriented gut microbiota

Traditionally all mammalian body fluids including secretions like milk and urine were

considered to be sterile unless contaminated during collection, storage or processing, or

there was pathology associated with the secreting gland(Lewis et al., 2013; McGuire &

McGuire, 2015). This central dogma was largely conceived due to methodological

limitations (culture dependent techniques) that were employed to detect the presence of

microbes. The advent of culture independent techniques (Next Generation Sequencing,

NGS) led to a paradigm shift in this context (Cabrera-Rubio, Mira-Pascual, Mira, &

Collado, 2015; Martin et al., 2003). As no technique is fool proof, NGS techniques also

7 suffer from numerous flaws like: (i) viability and load of microbes cannot be assessed accurately; (ii) relative quantification of microbes may be compromised by gene copy numbers (eg, 16sRNA) and (iii) the possibility of nucleic acid contamination by personnel or reagents used. For the first time, the presence of a milk microbiome using culture independent techniques was reported using 16S rDNA sequencing where it was shown that breast-feeding can be a significant source of lactic acid bacteria to the infant gut microflora (Martin et al., 2003). Thereafter numerous other studies confirmed the existence of a human milk microbiome exploiting hypervariable regions of the bacterial

16S ribosomal gene. Types of bacteria commonly found in milk are the both skin and enteric associated organisms.. Like the gut microbiome, inter-individual variation in richness and diversity of the human milk microbiome was observed. However, most commonly 9 genera were regularly detected in human milk samples by using 454- pyrosequencing and primers targeting the V1-V2 hypervariable region of 16S rRNA gene. Thus these 9 genera are believed to compose the core of human milk microbiome comprised of Staphylococcus, Streptococcus, Serratia, Pseudomonas, Corynebacterium,

Ralstonia, Propionibacterium, Sphingomonas and Bradyrhizobium(Hunt et al., 2011). A study was conducted in Finnish lactating women to characterize milk microbiome samples collected at different time points using qPCR and 454 pyrosequencing targeting the V1-V3 region. Results indicated high levels of Lactobacillus populations in colostrum and also an abundant population of Staphylococcus, Streptococcus, Veillonella,

Leptotrichia, and Prevotella (Cabrera-Rubio et al., 2012).

However existence of a microbiome core was not described, unlike the previous study(Hunt et al., 2011). Of the numerous factors expected to contribute to observed

8 differences between studies, the obvious ones include;, primer used, sample preparation method, and bioinformatics analysis(McGuire & McGuire, 2015). In summary it is now indisputably established that milk from healthy individuals contains a diverse pool of live microbes and is a constant seeding source to the infant gut microbiota. Accumulative evidence also suggests that the presence of milk microbiota is seen not only in humans, but also in other mammals (Callon et al., 2007; Oikonomou et al., 2014).

1.4 Genesis of the milk microbiota

Existence of the milk microbiome raises the question, how does milk acquire this microbiome? Although not convincingly shown there are three hypotheses (Figure 1.7)

(Cacho & Neu, 2014) tested experimentally by diverse research groups. The first, is that hormonal changes during late pregnancy causes the gastrointestinal tract of the mother to be more permeable, and thus facilitates passive bacterial uptake by the bloodstream

(translocation), which transports the bacteria to the mammary gland. The second is known as the contamination theory. This postulates that the human-milk microbes come from direct contamination of the mother’s skin and oral secretions from the infant; hence we observe microbes in mammary gland and eventually in the milk(Latuga, Stuebe, &

Seed, 2014). This hypothesis and mechanism may explain the presence of bacteria that have been found in the oral cavity of both neonates and breast milk such as Gemella,

Veillonella, Staphylococcus, and Streptococcus (Hunt et al., 2011; Lif Holgerson,

Harnevik, Hernell, Tanner, & Johansson, 2011). However, the bacteria that are typically observed in the oral cavity of neonates like Actinomyces have not been consistently seen in breast milk and also the initial colostrum contains bacteria before breast-feeding by

9 infants (Cabrera-Rubio et al., 2012). Thus, it does not fully explain the composition of the breast milk microbiota(Latuga et al., 2014). The third, involves active migration of bacteria with the aid of dendritic cells and through entero-mammary trafficking (EMT) to milk. Recent evidence suggests that intestinal dendritic cells regularly take-up the intestinal bacteria, which are subsequently trafficked into the systemic circulation(Stagg,

Hart, Knight, & Kamm, 2003). It is likely that in the pregnant and lactating women, these leukocytes with intracellular bacteria may be trafficked to the mammary gland and contributes to the milk microbiome. Thus the organisms may directly seed the infant gut(Latuga et al., 2014). In this regard, studies to characterize the microbiota among mother and their infants have shown that a subset of microbe profiles especially the one belonging to Bifidobacterium longum, Streptococcus thermophilus, and Bifidobacterium pseudocatenulatum, were common to maternal stool, maternal blood, breast milk, and infant stool samples(Donnet-Hughes et al., 2010; Perez et al., 2007; Turroni et al., 2012).

Though none of the hypotheses are mutually exclusive in explaining the milk microbiota

(Jeurink et al., 2013) , it is believed that a combination of factors are responsible for the presence of the milk microbiota. Undoubtedly delineating the probable mechanism(s) involved in genesis of the milk microbiome may lead to ways to exploit the transport of certain favorable bacterial strains from the mother to the infant to further boost infant health(Cacho & Neu, 2014).

1.5 Mechanisms of microflora colonization

The normal gut microbiota of an adult consists of approximately 100 trillion microorganisms, comprising 500 to 3000 species and nearly 5 million genes(Groer et al.,

2014). Colonization of microflora is a multifaceted process where numerous factors play

significant roles. From the host perspective, protective and mucus physical layers of the

gut play a critical role in the colonization process. Mucus is made of a glycoprotein

substance called mucin whose glycobiology dictates the nature of mucus. A thick gel-like

water insoluble mucus that adheres strongly to epithelial cells forms the inner mucus

layer, while water soluble mucus containing the microflora forms the outer mucous layer

(see Figure 1.1B) (Viggiano et al., 2015) . A wide array of potential adhesion sites

(adhesins) for microbes are present in the mucus. The innate repertoire of adhesins is genetically controlled by the host; therefore, the host genome governs the colonization of the intestinal microflora (Bourlioux, Koletzko, Guarner, & Braesco, 2003; R. S. Miller &

Hoskins, 1981). As the diversity of the innate repertoire is governed by the genetic make- up of an individual, the colonization of the gastrointestinal tract of neonates with the first signature microflora depends on the innate repertoire. Colonization progresses with the partial or total modifications of adhesins under the action of bacterial glycosidases

(Hoskins et al., 1985), until the adult repertoire emerge. If the pioneer colonizer bacteria are endowed with glycolytic potential, the new repertoire evolves and new species bind to the new adhesion sites as they appear. Therefore, every person has his or her own signature intestinal microflora. Additionally, cross-talk between the microflora and host

was also shown to play a critical role in colonization. The microflora communicates with

the host to inhibit or activate host glycosyltransferases, thus inducing changes in the

carbohydrate repertoire of mucins (Hooper & Gordon, 2001). Hence, the evolved new

site may allow adherence of commensal or pathogenic bacteria. Early microbiota also

generate bioactive metabolites like folate, butyrate, and acetate that could epigenetically

alter the gut epithelium and other cells leading to a type of developmental programming

which might translate into risk for a variety of human diseases(Groer et al., 2014).

1.6 Impact of diet, antibiotics, and probiotics on the infant gut microbiota

1.6.1.1Diet: (Pre-weaning) In the breastfed infants, human milk is the principle source of

infant nutrition before weaning. In the Chinese infant (aged 1-6 months) population,

researchers compared the fecal microbiota of breast-fed (BF), formula-fed (FF), and mixed-fed (MF) infants by using 16S rDNA sequencing targeting variable (V6) region

(Fan, Huo, Li, Yang, & Duan, 2014). The number of genera detected in FF infants was

lowest and showed higher Enterobacteriaceae enrichment than BF and MF infants. The

abundance of the Bifidobacteriaceae was highest in the feces of BF infants compared to

MF and FF infants. Previously the dominance of Bifidobacteria in breast fed infants was

speculated due to presence of a bifidus factor (N-acetylglycosamine–containing

oligosaccharides) in the human milk(Gauhe et al., 1954). However with the advancement

of analytical techniques, it was revealed that the human milk contains a rich diversity of

human oligosaccharides (HMO) and their concentrations exceed that in other mammalian

milk by 10- to 100-fold, suggesting a potentially unique role of HMO in infant gut

microbiota development that cannot be replaced or supplemented with formula. The

present literature suggests that the bifidogenic activity is likely attributed to both the

protein and carbohydrate components in human milk (Donovan et al., 2012; Pacheco,

Barile, Underwood, & Mills, 2015). Further the Bifidobacterium genus share several

large clusters of genes that are necessary for transport and enzymatic degradation of

HMO. Thus, the microbiota of the breast fed infant contains Bifidobacteria that are

specialized to metabolize HMO(Pacheco et al., 2015).

1.6.1.2 (Post weaning), When infant switch to a solid diet, a significant and rapid

shift in the gut microbe population occurs characterized by a decline in the Lactobacillus

and Bifodobacteria populations. According to diet, a wide diversity of microbes that

utilize a variety of nutrients present in the diet flourishes; for example, a fiber rich diet

favors Prevotella and Xylanibacter that are known to contain a set of genes utilizing

cellulose and xylan(De Filippo et al., 2010). In this regard, an elegant study was

conducted to compare the gut microbiota of children aged 1–6 years living in a village of

rural Africa(developing nation) with the gut microbiota of western European (developed

nation) children of the same age(De Filippo et al., 2010). Results suggest that the diet has a dominant role over other possible variables such as ethnicity, sanitation, hygiene, geography, and climate, in shaping the gut microbiota. Researchers further observed a correlation between polysaccharide-degrading microbiota and the calories that the host can extract from diet. In the same study, the authors hypothesized that the diversified microbiota co-evolved with the diet of African infants, allowing them to maximize the energy intake from indigestible components while, the reduction in microbiota richness in

European children could indicate how the consumption of sugar, animal fat, and calorie-

dense foods is rapidly limiting the adaptive potential of the microbiota in developed

countries (De Filippo et al., 2010).

1.6.2 Antibiotics: Antibiotics are ubiquitously found in nature and higher eukaryotes are

constantly exposed to a spectrum of these antibiotics at lower concentrations (Finley et

13 al., 2013; Modi, Collins, & Relman, 2014). In an important study, an ancient human oral microbiome was profiled to uncover the antiquity of antibiotic resistance in human microbiota before the use of therapeutic antibiotics had been documented (Warinner et al., 2014). Results identified the presence of numerous DNA sequences with homology to antibiotic resistance genes including genes for multidrug efflux pumps and native resistance genes to aminoglycosides, β-lactams, bacitracin, bacteriocins and macrolides, as well as a near-complete plasmid encoded conjugative transposon carrying efflux pump genes found in oral and pathogenic bacteria(Warinner et al., 2014). Thus it is believed that a spectrum of antibiotics displaying growth inhibition properties have evolved long ago to mediate interspecies signaling in the microbial world and ultimately to shape and confer the stability of the microbial communities(Modi et al., 2014). The diversity and number of small molecules and potential antibiotics produced by gut microflora is far greater than previously expected. This claim is well appreciated with advancement of

“Genome-mining” approaches that have revealed a number of novel, potent small molecules present in both environmental and gut microflora(Modi et al., 2014).

Extensive use of high concentrations of antibiotics for over 70 years has resulted in changes in the communities’ composition which is sufficient to cause large scale disturbances in inter-species interactions in the microbial ecosystem. Additionally, antibiotics induce a strong selection pressure leading to dissemination of antibiotic resistance genes in the microbial ecosystem. The mammalian gut microbiota is an important site of horizontal gene transfer due to a potential reservoir of antimicrobial resistance. One of the earliest studies that describe the effect of antibiotics on the gut was the loss of colonization resistance, i.e. loss of competitive exclusion using mouse models

(Bohnhoff & Miller, 1962; C. P. Miller, Bohnhoff, & Rifkind, 1956). This eventually

leads to colonization of pathogenic bacteria like Salmonella. A recent study further

confirmed this phenomenon where it was demonstrated that antibiotic treatment leads to

increase in abundance of host derived free sialic acids in the gut that are later exploited

by pathogenic bacteria like S. typhimurium and C. difficle to establish in the gut(Ng et al.,

2013). The broad spectrum antibiotics have been shown to affect the gut anaerobe

population, for example use of clindamycin treatment for 7 days significantly reduced the

clonal diversity of the Bacteroides isolates(Dethlefsen, Huse, Sogin, & Relman, 2008).

Ciprofloxacin, which has relatively little activity against cultivable anaerobes, has profound effects on the gut microbiota composition when used for five days.

Ciprofloxacin influenced the abundance of a third of the bacterial taxa and decreased taxonomic richness in the gut (Dethlefsen et al., 2008). Some human subjects exhibited

an abrupt decrease in bacterial diversity and depletion of many of the Ruminococcaceae,

while in others the degree or timing of community composition recovery had unique

trends after ciprofloxacin treatment. Four weeks was sufficient to recover almost similar

profiles in most of the cases but some compositional effects lasted for six months

(Dethlefsen & Relman, 2011). In an interesting study, the early-life exposure of mice to subtherapeutic doses of antibiotics resulted in an altered composition of the intestinal microbiota, increased total and relative body fat mass, bone density, and altered SCFA and hepatic fatty acid metabolism(Cho et al., 2012).

1.6.3 Probiotics: The most popularized definition of probiotics according to Food and

Agriculture Organization/World Health Organization, includes “Live microorganisms

that, when administered in adequate amounts, confer a health benefit on the host” (Hill et al., 2014; Varankovich et al., 2015). Generally probiotics include representatives of

genera of microbes that belong to Lactobacilli, Enterococci, Bifidobacteria, and yeasts. A

recent review highlighted the effects of probiotic bacteria on the human immune system

and on the microflora of the gut(Varankovich et al., 2015). The microbiome plays a

central role in other crucial aspects of health functionality, including beneficial impacts

on the treatment of metabolic disorders (e.g. obesity and type 2 diabetes), improvement

of bowel function, potential cognitive and mood-enhancing benefits, antidepressant and

anxiolytic (antianxiety) effects (Bravo et al., 2011; Delzenne, Cani, Everard, Neyrinck, &

Bindels, 2015; Desbonnet, Garrett, Clarke, Bienenstock, & Dinan, 2008; Dinan, Stanton,

& Cryan, 2013; Varankovich et al., 2015). The anxiolytic effect of probiotics has led to the emergence of the new term, known as ‘psychobiotic’ and defined in a similar fashion as a ‘live organism that, when ingested in adequate amounts, produces a health benefit in patients suffering from psychiatric illness’(Dinan et al., 2013). Probiotics have been shown to reduce the incidence of lactose in-tolerance, constipation, diarrheal diseases and allergic conditions in children and it was expected that probiotics likely induce these effect by interacting with the gut microflora (Bezirtzoglou & Stavropoulou, 2011;

Parracho, McCartney, & Gibson, 2007). Increasingly the scientific literature has demonstrated the possibility of modulating the early infant gut microflora composition by means of consumption of probiotics by the mother in her advanced stage of pregnancy(Rautava, Luoto, Salminen, & Isolauri, 2012; Tannock, 2004). However until now the effect of probiotics in modulating the infant microbiota and its long term consequences has not been described.

1.7 Role of gut microflora in intestinal immunity

The GALT is broadly divided into two categories consisting of : (a) aggregated lymphoid tissue like Peyer’s patches (PP), tonsils, mesenteric lymph nodes (MLN); and (b) diffuse lymphoid tissues that are composed of plasma cells (producing IgA), mature T cells and effector cells, scattered throughout the intestine. Lamina propria (LP), a connective tissue layer underlying the epithelial lining, has myeloid and lymphoid cells including IgA producing B cells. Thus, LP is considered as the major effector site of the intestinal mucosal immune system (Bailey et al., 2001). Intestinal intraepithelial lymphocytes

(IELs), members of the T cell compartment, play an important role in maintaining the integrity of intestinal epithelial cells and mounting protective immune responses to various pathogens (Cheroutre, Lambolez, & Mucida, 2011). The PP is an active immune inductive site usually located in the distal small intestine (ileum). The PP is more amenable to selective sampling of microbes or antigens as it has lower numbers of mucus producing cells and also possesses M cells and dendritic cells (DCs). M cells and DCs selectively phagocytose soluble antigens and microbes that are then presented to immunocompetent cells located at the sub-epithelial surface. Thus overall, M cells and

DCs orchestrate immune maturation and immune homeostasis.

1.7.1 Innate immunity: Existence of the normal microflora in the gut results in a symbiotic association between microbes and host cells, mainly intestinal epithelial and lymphoid cells. The microbe-specific interactions occur through pattern recognition receptors (PRR) present on the surface of host cells. Toll-like receptors (TLR) and

nucleotide-binding and oligomerization domain (NOD)-like receptors are the two well-

characterized members of the PRR. Thirteen families of TLRs have been identified so far

(De Nardo, 2015). These TLRs interact with diverse species of Gram positive and Gram

negative bacteria, viruses and other microbes, thereby mediating both cell mediated and

humoral immune responses in addition to impacting the gut mucosal barrier (Mazmanian,

Liu, Tzianabos, & Kasper, 2005). The signature surface molecules of microbes recognized by TLRs are called microbe associated molecular patterns (MAMPs).

Numerous MAMPs are associated with diverse microbes, for example, lipopolysaccharides (LPS) in Gram negative bacteria. PRR and MAMP interactions are specific and wide array of MAMPs are seen on both commensals and pathogens; however, the host discriminates pathogens vs commensals to maintain normal gut homeostasis. The intestinal commensals are separated from the lamina propria and underlying tissues by the epithelial cell barrier and thick mucus layer (Figure 1.1B).

Further, TLRs present on the basolateral surfaces of polarized intestinal epithelial cells

(IECs) mount inflammatory responses, but TLRs present on the apical surface induce anti-inflammatory responses upon recognition of MAMPs (Abreu, 2010). Thus commensals that breach the protective epithelial and mucosal barriers are eliminated by induction of innate immunity. Sustained interaction between commensal Gram negative bacterial LPS and TLR4 leads to negative regulation of inflammation at various levels without an adverse effect (Walker, 2013); hence, a symbiotic association is seen.

1.7.2 Adaptive immunity: The colonized microflora also activates adaptive immunity that is generally observed as early as in Phase-I and II colonization that corresponds to 1

month postpartum in infants. Underlying mechanisms involve the selective uptake of

antigens or colonized bacteria through appendages of gut DC and M cells, followed by

migration of activated DCs to MLN where IgA responses are induced through a class

switch recombination process with or without T cell help (Fagarasan & Honjo, 2003; A.

J. Macpherson et al., 2000). The dimeric IgA produced is directed against antigens

presented in the PP or MLN and is then transported across epithelial cells via the

interaction with polymeric immunoglobulin receptor (IgR) on epithelial cells leading to

secretion of secretory IgA (sIgA)into the gut lumen. The sIgA coats the microvillus or

interacts with pathogens to protect against invading pathogens (A. J. Macpherson & Uhr,

2004) . Further, commensals that penetrate the protective layers also induce an IgA

response which eventually leads to coating of those commensals with the elicited IgA.

Subsequently the antibody coated commensals are restricted to the lumen of the intestine

by reducing motility of the commensals (Andrew J Macpherson, Geuking, & McCoy,

2005). An illustration of how the infant microflora may influence immune responses especially vaccine responses, is highlighted in Figure 1.8.

1.7.3 Immune homeostasis: Balanced innate and adaptive immune responses results in immune homeostasis. Interaction of DCs and colonized bacteria results in secretion of cytokines that influence naïve T helper cells (Th0) to differentiate into Th1, Th2, Th17 and T regulatory cells (Treg) cells. Th1 and Th2 cells mediate cellular and humoral immunity, respectively. Th17 cells mediate tissue inflammation and clearance of

extracellular pathogens. Treg cells are known to mediate oral tolerance and anti-

inflammation (Walker, 2013). Certain microbial communities such as Clostridium

clusters IV and XIVa (Atarashi et al., 2011) and Bacteroides fragilis (Round &

Mazmanian, 2010) are involved in induction of Treg cells which help to maintain intestinal homeostasis.

1.7.4 Oral tolerance:

Suppression of both the gut and systemic immune responses to orally administered antigens results in the oral tolerance phenomenon. Tolerance to intestinal microflora and tolerance to ingested food proteins differs by its effects on the immune system. Oral tolerance utilizes mechanisms that include active impact of Tregs, clonal deletion, and clonal anergy of T cells. An antigen dose is shown to influence the choice of tolerogenic mechanisms(Chistiakov, Bobryshev, Kozarov, Sobenin, & Orekhov, 2015). A single high dose exposure of antigen results in the clonal deletion or anergy tolerogenic mechanisms whereas numerous low antigen doses exposure for long time induces T cell anergy as briefly detailed in Figure 1.9 (Chistiakov et al., 2015).

Tolerance to food protein is induced through the small intestine influencing both local and systemic immune responses, while tolerance to gut microflora in particularly the colonic microflora does not suppress the systemic responses(Pabst & Mowat, 2012).

Mechanisms of oral tolerance differ with the nature and the dose of the antigen being presented to DC. Low-molecular antigens such as haptens and peptides could diffuse through the intestinal epithelium via pores in inter-epithelial tight junctions (Chistiakov et al., 2015; Hossain & Hirata, 2008), while high-molecular complexes can be taken up across enterocytes by transcytosis or through an exosome-mediated pathway associated with major histocompatibility complex (MHC) class II-dependent recognition and

antigen processing(Menard, Cerf-Bensussan, & Heyman, 2010). Particulate material and

selected microbes of the gut microflora are mainly delivered into the GALT by M cell transcytosis while soluble antigens induce oral tolerance through DC-mediated uptake mostly in the LP and then in the GALT(Chistiakov et al., 2015). DCs located at the submucosa secrete specialized cytokines upon exposure to antigens or commensal bacteria that drive preferential differentiation of Th0 cells into Treg cells. These Treg cells produce TGF-β that reduces the Th1, Th2 and Th17 responses to luminally exposed antigens or bacteria, hence achieving oral tolerance (Spiekermann & Walker, 2001).

1.8 Beneficial effects of probiotics on the gut

The healthy human gut comprises more than 1,000 bacterial species of which

Lactobacillus and Bifidobacterium species are tested probiotics. Several experimental

(both in vivo and in vitro) settings have demonstrated the benefits of use of probiotics.

Established effects of probiotic strains on the gut are depicted in Figure 1.10 which

include;

I. Competitive exclusion of pathogens: competition for establishment in the gut by

restricting nutrient availability or adhesion sites on mucus.

II. Improvement in gut barrier functions: modulate proteins or factors that decrease

permeability and thereby prevent translocation of pathogens.

III. Improve villous structure and enterocyte proliferation: villus and enterocyte

destruction caused by invasive enteropathogens are neutralized, further preventing

pathogen spread.

IV. Production of inhibitory peptides (microcine, colicine): inhibitory peptides

selectively inhibit pathogen multiplication and establishment in the gut.

V. Production of host induced anti-microbial peptides (AMPs) that aid in increased

secretion of AMPs like defensins that prevent pathogen multiplication.

VI. Suppression of toxin production: secrete diffusible molecules that suppress toxin

production by pathogens thereby nullifying the toxins’ effect.

VII. Modulation of gut motility: increased exposure to toxins present in the gut is

prevented by improved gut motility by secretory molecules that affect the central

nervous system.

VIII. Regulation of nutrient absorption and utilization: enhanced absorption and proper

utilization of essential macro and micro (minerals and vitamins) nutrients.

IX. Cholesterol clearance: restricts lipid absorption and increases cholesterol

metabolism in the liver.

X. Influence on the mucosal immune responses: modulate both innate and adaptive

immune responses (Figure 1.11).

The altered intestinal microbial composition is implicated in the development of

food allergy and increased susceptibility to enteroinvasive pathogens (Yurist-Doutsch,

Arrieta, Vogt, & Finlay, 2014). Infants have increased susceptibility to infectious

gastroenteritis due to an immature gut and immune system(Sharma, Jen, Butler, &

Lavoie, 2012), which contributes to a higher incidence of infectious gastroenteritis

(accounts for approximately 6 million deaths annually) (Black et al., 2010). Enteric

disease conditions are manifested by inflammation in the intestine, the primary cause of

which is infectious agents (bacteria, viruses and fungi). Perpetuation of inflammation

leads to a compromised gut barrier, resulting in loss of Na/Cl ions (diarrhea) and

translocation of pathogens (secondary infections). Thus, treatment of infectious

gastroenteritis requires comprehensive approaches such as regulating immune responses

(like inflammation), restoring barrier functions, and eliminating pathogens. Therefore,

probiotics are used in combination with oral rehydration therapies in intestinal disorders

as they often fulfill the prerequisite for a multipronged treatment. Most of the probiotics

are transient colonizers of the gut for variable time periods. Hence, to achieve beneficial

effects, probiotics need to be consumed repeatedly in sufficiently high numbers (1010

CFU; colony forming units).

1.9 Probiotics modulate gut immunity

Probiotics are known to promote host defense systems that include innate and adaptive

immune responses. Probiotics have been shown to enhance innate, humoral and T cell

immune responses and promote a protective intestinal immunological barrier (Chattha,

Vlasova, Kandasamy, Rajashekara, & Saif, 2013; Isolauri et al., 1993; Kandasamy,

Chattha, Vlasova, Rajashekara, & Saif, 2014; Preidis et al., 2012; Vlasova et al., 2013).

Probiotic bacteria like Lactobacillus and Bifidobacterium species have also been shown to stimulate nonspecific host resistance to microbial pathogens and to aid in down regulating hypersensitive reactions to pathogens. (Sutas et al., 1996; Vlasova et al.,

2013).

Innate immune modulation by probiotics is demonstrated in several studies where cytokines such as TNFα, IL-12, IL-18, and IFN-γ are shown to be produced by human peripheral blood mononuclear cells (MNC) after probiotic treatment (Haller, Blum, Bode,

Hammes, & Schiffrin, 2000; Y. G. Kim et al., 2006; Miettinen et al., 1998). In an elegant

in vitro culture system, human umbilical cord blood derived DCs (similar to lamina

propria gut DCs) were used to study the immunological effects and anti-inflammatory

properties of the L. rhamnosus isolated from feces of breast-fed newborn infants

(Bermudez-Brito, Munoz-Quezada, Gomez-Llorente, Romero, & Gil, 2014). Pathogenic

E.coli induced pro-inflammatory cytokines effects were suppressed in human DCs

challenged with L. rhamnosus or its supernatant. Further using a mouse model, it was

shown that both NF-κB and p38 MAPK signaling pathways play important roles in the

augmentation of innate immunity after probiotic treatment (Y. G. Kim et al., 2006). The

downregulation of Glucocorticoid receptor (GR) signaling by Lactobacillus species, that

otherwise leads to enhanced release of pro-inflammatory cytokines IL-1β, TNF-α, and

IL-6 (Coutinho & Chapman, 2011), was observed. The critical downregulated molecules

in GR signaling were JNK, NFκB [complex] and TNF [family] (Kumar et al., 2014).

Therefore, it is conceivable that downregulation of GR signaling at an early stage by

probiotic Lactobacillus spp. could suppress the initial host response that suppresses

probiotic colonization. Further, Lactobacillus spp. at an early stage showed

downregulation of various genes related to innate immune responses, such as anti-

microbial peptides (REG3G, surfactant protein D), lysozyme, antigen presentation (MHC

class 1 and 2), and immune cell trafficking (CCL20/28, only LGG), which may allow

early colonization and adaptation of these probiotics in the host. In addition, higher

expression of soluble (complement component 9 and 8G genes) and cellular innate

mediators (non-classical MHC class I CD1d expressed on NKT cells) was also observed.

Due to species and further strain specificity of probiotic effects, the beneficial

effect of one strain cannot be extrapolated to another probiotic strain of the same species

(Kumar et al., 2014; Rask, Adlerberth, Berggren, Ahren, & Wold, 2013; P. van Baarlen,

Wells, & Kleerebezem, 2013). Further the same probiotic strains given at different growth phases had varying effects (P. van Baarlen et al., 2009). Strain specificity of the probiotic species of Lactobacillus are partly attributed to the MAMPs, in particular the lipoteichoic acids (LTA). Different Lactobacillus species produce different LTA that vary in polymer composition, length, and substitution (P. van Baarlen et al., 2013). The

LTA D-alanylation loss in L. plantarum was shown to decrease its ability to elicit pro-

inflammatory responses and significantly protect against colitis in a murine model

compared with its wild type counterpart strain (Grangette et al., 2005). However, a

similar mutation in L. rhamnosus did not affect cytokine production (Perea Velez et al.,

2007).

The gut immune barrier function is provided by polymeric immunoglobulin IgA

(pIgA) that is actively produced by effector plasma B cells on exposure to antigens or

microbes in the MLN and then dimeric IgA attaches to the polymeric Ig receptor

(secretory component) on epithelial cells and is secreted as sIgA into the gut lumen. In

an animal model, pregnant mice fed with Bifidobacteria showed significantly higher

levels of fecal total IgA and higher levels of anti-β-lactoglobulin IgA in milk and fecal

extracts compared to the control group. These results suggest that the intake of

Bifidobacteria can enhance local production of IgA in milk and in the intestine, which

may help to protect both neonates and the mother from sensitization to food antigens

(Fukushima, Kawata, Mizumachi, Kurisaki, & Mitsuoka, 1999). In healthy Japanese

children, intake of formula containing viable Bifidobacteria significantly increased total

fecal IgA levels, suggesting that the increase in local IgA levels may contribute to

enhancement of mucosal resistance against gastrointestinal infections in infants

(Fukushima, Kawata, Hara, Terada, & Mitsuoka, 1998). In monoassociated germfree

mice, oral dosing of Saccharomyces boulardii resulted in increased serum IgA levels,

accompanied by increased levels of TNF-α, IFN-γ and IL-12 compared to control germ free mice (Rodrigues et al., 2000).

1.10 Rotavirus and its significance

Rotavirus (RV) is a major cause of childhood diarrhea worldwide. RV infection accounts for 23 million outpatients, 2.3 million hospitalizations and is responsible for at least 450,

000 deaths annually in children across the globe (Parashar, Nelson, & Kang, 2013; P. J.

Smith et al., 2003). In the US alone annually, RV infection contributed to losses of about

$1 billion in healthcare costs, besides loss of human productivity (Widdowson et al.,

2007). Typical symptoms include vomiting, nonbloody diarrhea, and fever. Infected individuals shed RV as high as 1010 virus particles per gram of feces; however healthy

individuals can be susceptible with an infectious dose of 10 or less particles(Greenberg &

Estes, 2009) (Blutt & Conner, 2007). Hence, places where sanitation is an issue (eg. developing countries) or compromised individuals, (eg. day care centers) frequently report RV outbreaks (Binns, Lee, Harding, Gracey, & Barclay, 2007). The fecal-oral

route is the established mode of transmission, although a respiratory mode (by inhalation)

is also infrequently described.

1.11 Pathogenesis of RV

RV is a nonenveloped, double-stranded RNA virus and has a segmented (11 segments) genome that encodes six structural (VP) and six nonstructural proteins (NSP). One segment has overlapping open reading frame (ORF) for NSP5 and NSP6, that results in synthesis of 12 proteins (Greenberg & Estes, 2009). On ingestion, rotavirus primarily infects mature villus enterocytes in the gut. Disease progression is often multifactorial leading to malabsorptive diarrhea in infants. Diarrhea is attributed to virus mediated destruction of enterocytes, virus induced downregulation of the absorptive enzymes, and compromised gut barrier functions (Greenberg & Estes, 2009; Parashar et al., 2013).

Activation of the enteric nervous system and effects of non-structural protein (NSP4)

were also suggested to mediate a secretory rotavirus diarrhea. NSP4 mobilizes

intracellular Ca2+ in enterocytes from the endoplasmic reticulum (ER) which is critical

for rotavirus replication(Parashar et al., 2013). It is also suggested that increased

intracellular Ca2+ perturbs the cytoskeleton and tight junction integrity independent of the phospholipase C pathway (PLC) leading to compromised gut barrier. NSP4 was also shown to activate cellular Cl- channels resulting in the secretion of Cl- and water(Parashar

et al., 2013). RV replication is restricted to specialized electron-dense structures called

‘viroplasms’, located adjacent to the cell nucleus and near the ER(Greenberg & Estes,

2009). Studies have indicated a requirement for lipid droplets at the site of viroplasms

for RV replication and specific inhibition of fatty acid metabolism results in significantly

decreased virus replication (Gaunt, Cheung, Richards, Lever, & Desselberger, 2013).

1.12 RV and use of probiotics

RV induced diarrhea is a vaccine preventable condition; however, vaccine cost, reduced

efficacy of vaccines and rare but life threatening vaccination outcomes limit the

widespread application of RV vaccines, particularly in developing countries ((CDC).

1999; Parashar et al., 2013). Since there are no other effective safe methods for

preventing diarrhea caused by RV, over the last few years, researchers have focused their

efforts on the potential use of beneficial bacteria known as probiotic agents to moderate

RV diarrhea (Das, Salam, & Bhutta, 2014). Probiotics modulate mucosal repair and gut

homeostasis; hence, they are valuable treatment options in preventing RV induced

damage in the intestine. Thus, probiotics can be used: a) before RV infection, along with

RV vaccine as they have immune potentiating effects; b) after RV infection, along with

oral rehydration therapy as they aid in gut homeostasis; and c) in pregnant mothers that

eventually transfer them to the infant intestine and hence influence resistance to RV

disease postnatally.

Live attenuated vaccines (Rotarix and Rotateq) are the prophylactic measures

used to control RV infection(Parashar et al., 2013). Unfortunately the RV vaccines protective mechanisms are unclear; however, studies have suggested B- cells producing

IgA and IgG either systemically or mucosally are critical for clearing RV infections(Angel, Franco, & Greenberg, 2012; Greenberg & Estes, 2009; Lowe, 2013).

Lactobacillus probiotic sps are the most commonly studied strains in ameliorating human

RV diarrhea in children. Yuan et al., have demonstrated that IgA and IgG RV-specific antibodies secreting cells (ASC) were significantly increased in the attenuated-RV infected pigs in systemic circulation and in the local gut environment, in order to induce

complete protection against RV disease(Yuan, Ward, Rosen, To, & Saif, 1996). Further

conclude that protective immunity in RV infection depends on the magnitude, location,

viral protein-specificity, and isotype of the antibody responses induced by

vaccination(Yuan & Saif, 2002). Lactobacillus rhamnosus GG (LGG) strain is effective

in decreasing both duration and frequency of RV diarrhea in neonates but mechanisms

are not clearly understood (Lowe, 2013; Mrukowicz, Szajewska, & Vesikari, 2008). LGG

reduce the pro-inflammatory cytokine (IFN-γ and TNF-α) induced alterations in gut

barrier integrity. Two soluble proteins of LGG (p40 and p70) were also shown to inhibit

cytokine induced apoptosis and oxidative stress damage to the tight junction complex

(Liu et al., 2013; Lowe, 2013). Previously it was demonstrated co-colonization with L.

acidophilus and L. reuteri in Gn pigs develop similar levels of RV specific B cell responses as those of HRV-infected un colonized Gn pigs. . This suggests co-colonized with two strains of lactobacilli colonization just alone was alone more efficient in stimulating intestinal B cell responses than HRV infection alone in Gn pigs (W. Zhang et al., 2008). The intestinal immune system is largely immunotolerent of LAB colonization alone; however, LAB can significantly modulate cytokine responses induced by HRV infection in Gn pigs(Azevedo et al., 2012). Further, a study by Vlasova et al. demonstrated that dual colonization by probiotics (L. rhamnosus GG and Bifidobacterium animalis subsps. lactis Bb12) in a neonatal Gn piglet moderated VirHRV infection, suggesting a potential role for these probiotics in neonatal immune homeostasis(Vlasova et al., 2013). Findings from Chattha et al. have indicated that colostrum/milk components affect initial probiotic colonization, and together, they modulate neonatal antibody responses to oral attenuated HRV vaccine (Chattha, Vlasova, Kandasamy, Esseili, et al.,

2013). Colonization by LGG and Bb12 probiotics significantly reduced the severity of

HRV diarrhea and fecal HRV shedding compared to uncolonized piglets (Chattha,

Vlasova, Kandasamy, Rajashekara, et al., 2013). Further protective effects of these probiotics coincided with higher HRV specific intestinal IgA antibody responses and increased frequencies of T helper and cytotoxic T cells (Chattha, Vlasova, Kandasamy,

Rajashekara, et al., 2013; Kandasamy et al., 2014). These two probiotics also enhanced immunomaturation as suggested by changes in maturation markers on mononuclear cells in LGG and Bb12 colonized piglets(Vlasova et al., 2013) . Thus, LGG and Bb12 enhanced both innate and adaptive immune responses in Gn piglets (Chattha, Vlasova,

Kandasamy, Rajashekara, et al., 2013; Vlasova et al., 2013).

1.13 Pig as a non-primate model to study human enteric conditions

Animal models are proposed and established in order to appropriately replicate the human conditions under investigation and are expected to respond in similar fashion as humans. Although primate models are closer to humans, due to costs and ethical acceptability, non-primate models are preferred. The accumulating literature suggests that pigs are a more authentic non-primate model to study the human conditions than rodents

(Heinritz, Mosenthin, & Weiss, 2013; F. Meurens, A. Summerfield, H. Nauwynck, L.

Saif, & V. Gerdts, 2012). Pigs are closely related to humans in terms of anatomy, genetics, physiology and immunology and are omnivores like humans(F. Meurens et al.,

2012). Due to this fact, various surgical and non-surgical procedures that are typically used in human medicine and are impossible to perform in other animal models, including catheterization, heart surgery, valve manipulation, endoscopy and broncho-alveolar

lavages have been developed and improved in pigs(F. Meurens et al., 2012). Further the

pig genome and protein sequence homologies >80% vs <10% with human the

counterparts respectively, and similar immune responses compared to mice (F. Meurens

et al., 2012).

The pig’s intestinal tract physiology is very similar to humans in terms of digesta transit

time, analogous digestion and absorption processes, and similar per Kg dietary

requirements(Heinritz et al., 2013). Both pigs and humans are colon fermenters and they

have a similar composition of the colonic microbiota. Overall, greater gut microbial

similarity are shared between the pig and humans in comparison to rodents(Heinritz et

al., 2013). The gut microbiota of pigs is mainly composed of Firmicutes and

Bacteroidetes phyla similar to the human gut microflora and hence pig models have been

used long term for human and biomedical research (Heinritz et al., 2013; Leser et al.,

2002). Because of these similarities, pig models are proposed and established and are

used regularly for research in dietary modulation of the human gut microbiota, amino

acid metabolism, total parenteral nutrition, RV infection, bacterial and viral

pneumonias(Heinritz et al., 2013; F. Meurens et al., 2012).

Neonatal piglets are similar to infants in size and are susceptible to various human infant conditions, like weaning diarrhea and necrotising enterocolitis (NEC) conditions in infants. Pigs are naturally susceptible to weaning diarrhea and upon induction, can exhibit

similar a course and symptoms of NEC(Heinritz et al., 2013). More importantly, like in infants-post weaning, changes in the composition of the gut microflora, are similar post weaning in pigs particularly lactobacilli population(Krause, Easter, White, & Mackie,

1995). Further, germ free piglets are susceptible to repeated infections by multiple RV

serotypes with pathognomonic symptoms as seen in infants(L. J. Saif, Ward, Yuan,

Rosen, & To, 1996). Results generated using outbred pigs more accurately mirror the

human population heterogeneity and do not have inbred bias as those in inbred mouse

model(F. Meurens et al., 2012). In summary, pigs are emerging as non-primate,

translational animal models to study interactions of the human microbiota-diet-pathogen.

The overall goal in this study was to investigate species specific gut transcriptome responses to Lactobacillus probiotics in a monocolonized model and to investigate the impact of diet, and infant gut microflora on rotavirus severity in an animal model.

1.14 Specific objectives were;

. To determine the Lactobacillus rhamnosus GG (LGG) and L. acidophilus (LA)

colonization dynamics and gut transcriptome responses in mono-colonized

neonatal piglets. Our hypothesis was that ‘Colonization of Lactobacillus

rhamnosus GG (LGG) and L. acidophilus (LA) induces species specific host

responses’.

. To characterize the microbial profiles in a microflora humanized (HM) Gn pig

model and to investigate the interactions of diet, infant gut microflora and RV

infection. Our hypothesis was that ‘Transplantation of infant microbiota results in

a similar assembly of gut microflora as that of infant microbiota signifying the

translation value of the pig model. Further malnutrition perturbs the HM and thus

may exacerbate RV severity in the HM pigs’.

Figure 1.1: Spatial and temporal aspects of intestinal microbiota composition. (A)

Variations in microbial numbers and composition across the length of the gastrointestinal tract. (B) Longitudinal variations in microbial composition in the intestine. (C) Temporal aspects of microbiota establishment and maintenance and factors influencing microbial composition. This figure was modified and adapted from journal Physiological

Review(Sekirov et al., 2010).

Figure 1.2: Factors influencing mother-infant symbiosis. This figure was adapted from

Pediatric Research Review (Putignani, Del Chierico, Petrucca, Vernocchi, &

Dallapiccola, 2014)

Figure 1.3: Bacterial phyla in stool of three month-old breast- and formula-fed infants.

Breast-fed infants had a higher percentage of Bacteroidetes and lower Firmicutes and

Verrucomicrobia compared with formula-fed infants. This figure was adapted from

(Donovan et al., 2012).

Figure 1.4: (a) A simplistic schematic presentation of diversity and richness of infant the gut microflora in first one year of life. (b-e) The representation of different scenario and their impact on the assembly of gut microflora. This diagram was adapted from previous literature(Dogra, Sakwinska, Soh, Ngom-Bru, Bruck, Berger, Brussow, Karnani, et al., 2015).

Normal Altered colonization colonization

• During Normal delivery C-section Environment Phase-I birth Full-term Pre-term Genetics Delivery Phase-II • Nursing Breast milk Formula Infant microflora Diet Solid food Solid food Mother Phase-III • Weaning Antibiotics physiology

1. Adult-like 1. Delayed Agents Phase-IV 24-36 months flora normal flora 2. Beneficial 2. Less · Beneficial microbes 3. Less microbes pathogenic 3. More microbes pathogenic 4.Resistant to microbes allergens 4.Suceptable to allergens

Figure 1.5: Factors influencing initial infant microflora colonization. Phase wise evolution of microflora, normal vs altered colonization of microflora and the factors that drive these dynamics are highlighted. This figure was generated using information from previous literature (Butler, Sun, Weber, Navarro, & Francis, 2000; Dogra, Sakwinska,

Soh, Ngom-Bru, Bruck, Berger, Brussow, Lee, et al., 2015; Satokari et al., 2009).

Figure 1.6: Age wise microbial colonization profiles (at the family level) of the infant and child gut. The most abundant bacterial families were depicted in circles, where the size of the circle was proportional to the relative abundance of the bacterial taxa. The intestinal microflora of the newborn was initially colonized by Enterobacteria, days after strict anaerobic bacteria dominate the microbial community. During the first month, Bifidobacterial spp. predominate in the gut, but the introduction of solid foods at around 4–6 months was accompanied by an expansion of clostridial spp (Lachnospiracea, Clostridiaceae, and Ruminococcaceae). Members of the Ruminococcaceae family continue to increase in abundance in the following months. By 2–3 years of age, the microbiota composition consists of mainly Bacteroidaceae, Lachnospiraceae, and Ruminococcaceae, which then remains stable into adulthood. This diagram was adapted from previous literature (Arrieta et al., 2014)

Figure 1.7: Genesis of milk microbiota: A hypothetical model to explain how maternal microbiota and microbial products could be transferred from mother to the fetal and neonatal gut. Where GUM, genitourinary tract mucosa; LMGM, mucosa of the lactating mammary gland; MLN, mesenteric lymph node; RM, respiratory tract mucosa; SLGM, mucosa of the salivary and lacrimal gland. The figurewas adapted from(Thum et al.,

2012).

Figure 1.8. Schematic illustration of how infant microbiota may influence immune responses in particularly vaccine responses. Numerous studies have shown an interconnection between the intestinal microbiota and the immune response. This figure was adapted from (Yanet Valdez, Brown, & Finlay).

Figure 1.9: An overview of oral tolerance induction. Intestinal antigens (like bacteria or food protein) pass from the gut to the GALT through M cells, DCs, and enterocytes.

Depending upon the dose and the nature of antigen, a particular tolerogenic mechanism is selected. This figure was adapted from previous literature(Chistiakov et al., 2015).

Immunity Competitive (Innate & Adaptive) exclusion Maintenance of Gut barrier Cholesterol & motility clearance

Villus & Probiotics Enterocyte Nutrients availability

Inhibitory peptide Toxin production suppression AMP production

Figure 1.10: Schematic representation of established effects of probiotics on gut homeostasis. This figure was generated from previous literature (Borchers, Selmi,

Meyers, Keen, & Gershwin, 2009).

Microflora Host-Commensal/Probiotics interaction

Outer layer Mucus Inner layer Paneth cells AMPs Enterocytes DC Antigens/microbes M cells sampling IgA+ Plasma Antigen sampling cells PP Goblet cells Mucus secretion T0

Th1 Th2 Th17 Treg

Cellular Oral tolerance Humoral Inflammation immunity Anti-inflammation immunity Pathogen clearance

MLN T0

Th1 Th TregT Thoracic Th2 17 reg Blood duct B cell Mucosal immunity Figure 1.11: Simplistic view of stimulation of mucosal immunity by probiotics and

commensals.

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CHAPTER 2

In vivo gut transcriptome responses to Lactobacillus rhamnosus GG and

Lactobacillus acidophilus in neonatal gnotobiotic piglets

Anand Kumar1, Anastasia N Vlasova1, Zhe Liu1, Kuldeep S Chattha1, Sukumar

Kandasamy1, Malak Esseili1, Xiaoli Zhang2, Gireesh Rajashekara1,2*, and Linda J Saif1,*

1Food Animal Health Research Program; Department of Veterinary Preventive Medicine;

Ohio Agricultural Research and Development Center; The Ohio State University;

Wooster, OH USA. 2Center for Biostatistics; The Ohio State University; Columbus, OH,

USA

Abbreviations: LGG, Lactobacillus rhamnosus; LA, Lactobacillus acidophilus; PBCD,

post bacterial colonization day; CFU, colony forming unit; IPA, Ingenuity pathway

analysis; IPKB, Ingenuity pathways knowledge base, MLN, mesenteric lymph node; CP,

canonical pathway.

Citation: Kumar A, Vlasova AN, Liu Z, Chattha KS, Kandasamy S, Esseili M, Zhang X,

Rajashekara G, and Saif LJ. In vivo gut transcriptome responses to Lactobacillus rhamnosus GG and Lactobacillus acidophilus in neonatal gnotobiotic piglets. Gut

Microbes. 2014 Mar-Apr; 5(2):152-64. doi: 10.4161/gmic.27877.

2.1 Abstract

Probiotics facilitate mucosal repair and maintain gut homeostasis. They are often used in adjunct to rehydration or antibiotic therapy in enteric infections. Lactobacillus spp have

been tested in infants for the prevention or treatment of various enteric conditions.

However, to aid in rational strain selection for specific treatments, comprehensive studies

are required to delineate and compare the specific molecules and pathways involved in a

less complex but biologically relevant model (gnotobiotic pigs). Here we elucidated

Lactobacillus rhamnosus (LGG) and L. acidophilus (LA) specific effects on gut

transcriptome responses in a neonatal gnotobiotic (Gn) pig model to simulate responses

in newly colonized infants. Whole genome microarray followed by biological pathway

reconstruction was used to investigate the host-microbe interactions in duodenum and

ileum at early (day 1) and later stages (day 7) of colonization. Both LA and LGG

modulated common responses related to host metabolism, gut integrity and immunity as

well as responses unique to each strain in Gn pigs. Our data indicated that probiotic

establishment and beneficial effects in the host are guided by: (1) down or upregulation

of immune function related genes in the early and later stages of colonization,

respectively; and (2) alternations in small molecules (vitamins, minerals) and

macromolecules (carbohydrates, proteins and lipids) metabolism. Pathways related to

immune modulation and carbohydrate metabolism were more affected by LGG, whereas

energy and lipid metabolism related transcriptome responses were prominently

modulated by LA. These findings imply that identification of probiotic strain specific gut

responses could facilitate the rational design of probiotic-based interventions to moderate

specific enteric conditions.

2.2 Introduction

Enteric pathogens cause diarrheal diseases in both adults and infants, with the latter being

more susceptible due to an immature gut with increased permeability to antigens as well

as immature immune system(Axelsson, Ivarsson, & Raiha, 1989; Eastham, Lichauco,

Grady, & Walker, 1978). Given the scenario, particularly in developing countries where enteric vaccine availability or lower efficacy of vaccines (e.g., rotavirus vaccine) are

problems, other means of treatment in adjunct to oral rehydration are often required.

Probiotics that facilitate mucosal repair and maintain gut homeostasis are invaluable

treatment modalities for neonatal enteric infections including rotavirus diarrhea (Chattha,

Vlasova, Kandasamy, Rajashekara, et al., 2013). Recent research has highlighted the

effective role of probiotics in modulation of the gut microbiota thereby alleviating

intestinal disorders like inflammatory bowel disease, travelers’ diarrhea, colitis, Crohn

disease, and antibiotic associated diarrhea(Boirivant & Strober, 2007; de Vrese &

Schrezenmeir, 2008). However, pathways and mechanisms by which probiotics mediate

their beneficial effects, especially in neonates are largely undefined.

Among several probiotic bacteria, Gram positive bacteria dominate, especially ones belonging to the Lactobacillus and Bifidobacterium genera. Lactobacillus species are probably the most widely consumed and marketed in the world because of the long history of safety in the food and dairy industry(Petrof, 2009). However, overall they comprise only a small portion of the natural gut microflora(Walter, 2008). Many

Lactobacillus spp. have been tested for the prevention or treatment of various pathological conditions. Numerous clinical studies have shown the beneficial role of

Lactobacillus rhamnosus GG (LGG) in treatment of acute diarrhea in pediatrics

patients(Guandalini et al., 2000; Szajewska, Kotowska, Mrukowicz, Arma′nska, &

Mikolajczyk, 2001; Vanderhoof et al., 1999). LGG was effective in decreasing both

duration and frequency of rotaviral diarrhea and also antibiotic associated diarrhea in

neonates (Fang et al., 2009; Majamaa, Isolauri, Saxelin, & Vesikari, 1995; Rosenfeldt et

al., 2002). People consuming L. acidophilus (LA) NCFM, had reduced fever, cough, and

runny nose(Lee, 2009). Additionally, LA strains also have been shown to possess anti- inflammatory and breast cancer inhibition effects(Lee, 2009). These studies demonstrate that probiotics mediate their effects in a species and strain specific manner; however, mechanisms are still largely undefined (Plantinga et al., 2011).

The Lactobacillus strains exert their beneficial effects in multiple ways: producing antimicrobial substances, enhancing barrier functions (i.e., improved gut integrity), producing cryoprotective heat shock protein (Hsp72), antiapoptotic proteins

(p40 and p75), regulating Th1-Th2 balance, enhancing B cell and IFN responses, and

inhibiting cytokine induced apoptosis(Preidis et al., 2012; Silva, Jacobus, Deneke, &

Gorbach, 1987; Troost et al., 2008; Peter van Baarlen et al., 2011; Wen et al., 2011; Yan

& Polk, 2002; W. Zhang et al., 2008). Further, the type and timing of colonization of

Lactobacillus strains influence the early neonatal gut mucosal immune responses and

have a significant impact on resistance to diseases postnatally; however, further studies

are required to understand the underlying molecular mechanisms. Investigations into

these basic mechanisms will identify specific molecules and pathways mediating the

beneficial effects on the host which in turn aid in probiotic strain selection to modulate

the specific clinical conditions.

Investigation of in vivo probiotic-host cell interactions is difficult considering the complex gut microflora in infants. Germ free animal models are critical to dissect the molecular interactions of probiotics. The neonatal piglets are similar to infants in terms of anatomy, physiology and mucosal immunology and serve as a useful model to elucidate mechanisms of probiotic modulation of neonatal host responses(François Meurens, Artur

Summerfield, Hans Nauwynck, Linda Saif, & Volker Gerdts, 2012). In the present study,

we used neonatal Gnotobiotic (Gn) pig model to better understand L. rhamnosus (LGG)

and L. acidophilus (LA) strain specific responses. In vivo strain specific colonization

dynamics were investigated by conventional bacterial culturing and host-microbe

interactions by in vivo transcriptome analysis of the small intestine (SI). We used

duodenum and ileum for transcriptome analyses because they represent both inductive

(ileum) and effector (duodenum) mucosal sites to assess probiotic-host interactions. The

duodenum is the first segment of the SI to come in contact with probiotic bacteria and it

also helps in minimizing adaptive changes that probiotic might go through during passage

of intestinal tract(Peter van Baarlen et al., 2009). The ileum contains specialized

structures (Peyer’s patches) for sampling and induction of immune responses to antigens

and is also the site of enteropathogenic bacterial invasion(Kleerebezem & Vaughan,

2009). We investigated both early (post bacterial colonization day 1: PBCD1) and later

(PBCD7) transcriptome responses to probiotics. Comprehensive analysis of our

microarray data indicated that both LA and LGG modulated expression of genes

regulating host metabolism, mucosal cell integrity and immunity in neonatal Gn pigs.

Immune modulation and carbohydrate metabolism genes were most affected in LGG,

whereas energy and lipid metabolism genes were pronounced in LA colonized Gn pigs.

Collectively, our study provides new insights to the distinct and common cellular

pathways modulated in Gn pigs by LA and LGG.

2.3 Materials and Methods

Bacterial strains and growth conditions

The Lactobacillus rhamnosus GG (ATCC 53103) and L. acidophilus NCFM™ (ATCC

700396) strains were routinely cultured in Lactobacilli MRS media (Hardy Diagnostics) at 37 °C with BD GasPak EZ anaerobic container system (Becton, Dickinson and

Company). The freezer stocks of strains were stored at -80 °C in MRS medium with 30% glycerol (v/v). The cells from the freezer stock were plated on MRS agar and overnight grown culture was used to inoculate 5 ml of MRS broth and grown overnight in anaerobic conditions. The OD600nm was measured to calculate the colonization dose of 1

× 108 CFU/pig.

Ethical statement and animal experiment

All animal experiments were approved by the Institutional Animal Care and Use

Committee (IACUC) of The Ohio State University. Piglets were derived surgically from

near-term sows (Landrace x Yorkshire x Duroc) and maintained in sterile isolators as

previously described(Meyer, Bohl, & Kohler, 1964). Conventional culture methods

(blood agar plates and thioglycolate broth culturing) were used to confirm the sterility of

Gn pigs before probiotic administration. Gn pigs were divided randomly into three

groups: Group 1-LGG (n = 8); Group 2-LA (n = 8); and Group 3 non-colonized controls

(n = 6). The pigs were inoculated orally with 1.0 × 108 CFU/pig at 3 d of age (Appendix-

F). Throughout the experiment, animals received the same type of feed (ultra-high temperature processed commercial cow milk [Parmalat]). The animals were euthanized on PBCD1 and 7 and tissue samples (duodenum, jejunum, ileum, cecum, colon, rectum, spleen, liver and mesenteric lymph nodes [MLN]) were collected. In addition, rectal swabs were also collected on PBCD1, 3 and 7 to monitor colonization dynamics. Tissue samples were suspended in buffered peptone water, homogenized, suspensions were 10- fold serially diluted; then 100 μL of each dilution was spread onto lactobacilli MRS agar plates and incubated for 24 h. The CFU/g of tissue or per ml of rectal swab was determined. The statistical analysis was performed using independent group t test or paired t test to compare colonization between strains, tissues, and days. The P value of alpha 0.05 was considered as significant.

RNA isolation and analysis

We investigated both early (PBCD1) and late (PBCD7) transcriptome responses to probiotics in the duodenum and ileum. Since probiotic bacteria begin to colonize by day

1 and attain the maximum colonization by day 7 in these pigs, we selected these time points for analysis. A Modified Trizol method was followed for RNA extraction. Briefly, the tissue samples were homogenized with Trizol reagents (Life Technologies) followed by chloroform treatment. Subsequently, the samples were centrifuged at 12,000 × g to obtain a clear lysate. The lysates were mixed immediately with an equal volume of 70% ethanol (v/v) and RNA was extracted using RNeasy mini kit (Qiagen Inc.) followed by

DNase treatment. The concentration and purity of RNA samples were determined using

NanoDrop ND2000c spectrophotometer (Thermo Scientific) and agarose gel

electrophoresis. RNA integrity and purity was further confirmed using Agilent 2100

bioanalyzer (Agilent Technologies). The samples with ratio of 18S:28S (Sus scrofa rRNA subunits) closer to 2 were used for microarray analyses.

Transcriptome analysis

The expression analysis was performed using Sus scrofa Sureprint 4 × 44k arrays

(Agilent Technologies). The Agilent Sureprint 4 × 44k high density arrays contain in situ synthesized 60mer oligo’s representing 44 000 pig expressed sequence tags. Microarray hybridizations were performed according to the manufacturer’s instructions at the

Biomedical Genomics Core facility, The Research Institute at Nationwide Children's

Hospital, Columbus, Ohio. In brief, samples were labeled with Cy3, purified using

Qiagen columns, and checked for labeling efficiency using the Nanodrop. The labeled test and control samples were fragmented and hybridized to the array overnight.

Microarray slides were then washed and scanned with Agilent G2505C Microarray

Scanner, at 2 µM resolution. Subsequently, images were analyzed with Feature

Extraction 10.10 (Agilent Technologies). Median foreground intensities were obtained for each spot. The data set was filtered to remove positive control elements and any elements that had been flagged as bad.

Statistical analysis

For the microarray intensity data, the data was background corrected and summarized with a quantile normalization method (Irizarry et al., 2003; Rao et al., 2008). A filtering

method was applied to filter out low expression genes if more than 80% of arrays had

expression level at or below the noise cutoff. Linear mixed effects’ models were used to

account for the dependencies among observations from the same subject (each subject

provided both ileum and duodenum samples). Treatment types, organ types, treatment

days, and interactions among them were included as fixed effects in the model. A time

factor was also included to control for the batch effects because the microarray samples

were run on two different days from samples obtained from 2 independent experiments.

For each comparison, type I error was controlled by allowing for number of false

positives among the tested genes(A. Gordon, Glazko, Qiu, & Yakovlev, 2007). Because of the small sample size, to determine the expression of a gene is significantly different between conditions, we controlled type I error at 0.0003 by controlling 10 false positives out of the tested genes (around 30 000) for each comparison (cutoff for p-value =

0.0003). Again, due to the nature of the study, for the pathway analysis, we also included some genes with more than 2-fold changes, but with p-values larger than the cutoff.

Ingenuity pathway analysis (IPA)

The web-based pathway analysis tool, IPA (www.ingenuity.com, Ingenuity Systems®)

was used to identify biological functions and molecular networks modulated in

duodenum and ileum by LA and LGG. The significant (± < or > 2) regulated genes were

uploaded into IPA along with the gene identifiers and corresponding fold change values.

Each gene identifier was mapped to its corresponding gene object in the Ingenuity

Pathways Knowledge Base (IPKB) and a set of relevant networks and focus genes and

canonical pathways were identified. The significance of the association between the

genes from the data set and the canonical pathways was measured as described in IPA.

For network analysis, networks of differentially expressed genes were algorithmically

generated based on their connectivity. Two genes are considered to be connected if there

is a path in the network between them and the highly-interconnected networks likely represent significant biological function. The functional analysis of a network identifies the biological functions and/or diseases that were most significant to the genes in the network, and the functional analysis of the entire data set identifies the biological functions and/or diseases that are most significant to the data set. Fischer’s exact test was used to calculate a P value for each biological function and/or disease assigned to that network.

Reverse-transcription quantitative PCR (RT-qPCR)

To validate the microarray data we tested the expression profiles of selected genes using

RT-qPCR. The available similar sequences were searched using Tentative Consensus number (TC #) with Sus scrofa deposited reference sequence at

(http://compbio.dfci.harvard.edu/cgi-bin/tgi/gimain.pl?gudb=pig) to design primers for

RT-qPCR. Primers were designed and commercially synthesized using Integrated DNA

Technologies (IDT). The primers used with their gene description are listed (Table S5).

The cDNA was synthesized from total RNA using SuperScript® III First-Strand

Synthesis SuperMix kit (Invitrogen). cDNA concentration was normalized and RT-qPCR was performed using SensiMixPlus® SYBR RT-PCR Kit (Bioline) in a realplex2

mastercycler (Eppendorf). The difference in expression of genes was calculated using the

comparative threshold cycle (∆∆Ct) method(Livak & Schmittgen, 2001) to yield fold-

difference in transcript levels.

2.4 Results

Colonization begins at early stage (PBCD1) and increases at later stage (PBCD7)

On PBCD1, LA colonized duodenum (1.0 × 103 CFU/g), jejunum (9.4 × 102), and ileum

(3.3 × 104) with higher CFU counts in the large intestine, reaching a maximum in the

cecum (1.8 × 105 CFU/g). Surprisingly, LA was also detected in the spleen, liver and

MLNs in two of the three pigs. On the other hand, for LGG, on PBCD1, the CFU counts

were one log higher than LA in duodenum (2.6 × 104) and ileum (2.8 × 105) and the

colonization in jejunum was similar to LA (1.5 × 102) (Appendix G). Similarly LGG also colonized large intestine in higher numbers than LA with maximum colonization in cecum (1.7 × 106). Unlike LA, no LGG was detected (detection limit; 10 CFU/g) in the

extra-intestinal tissues.

On PBCD7, the LA colonization pattern remained similar to PBCD1 except more

bacteria (>1-log) were recovered from all intestinal tissues and was also recovered in

higher numbers from all the extra-intestinal tissues (Appendix G). However, LGG

colonization was similar to PBCD1, and was also recovered from spleen (1 pig), liver (3

pigs), and MLNs (1 pig) on PBCD7. Translocation of both LA and LGG to secondary

lymphoid organs (spleen, MLN) may serve for development of appropriate protective and

controlled immune responses in the host (Belkaid & Naik, 2013; Ibnou-Zekri, Blum,

Schiffrin, & von der Weid, 2003). Rectal swab cultures indicated that both LA and LGG

shedding increased from PBCD1 (LA, 2.2 × 104; LGG, 8 × 104) to PBCD7 (LA, 6 × 106;

LGG, 6.5 × 105) consistent with the increased intestinal colonization.

Ileum in early (PBCD1) and duodenum in later (PBCD7) stages are the major sites

of transcriptome changes in response to probiotic colonization

A summary of transcriptome responses following probiotic administration is listed in

Table 2.1. Comparison of differentially expressed transcripts revealed more changes in

the ileum for PBCD1 and in the duodenum for PBCD7, irrespective of probiotic bacteria

treatment. On PBCD1, LA modulated 286 genes in ileum whereas, LGG modulated 292

genes. On PBCD7 in duodenum, LA affected 137 genes, and in contrast LGG affected

569 genes. Another trend noted for both lactobacilli strains at both times was that more genes were downregulated in duodenum and upregulated in ileum with exception of ileum for LGG at PBCD 7. The transcriptional changes observed by microarray analyses were further confirmed by RT-qPCR for selected genes (Appendix H), which also indicated a similar overall trend in the transcription.

The microarray generated transcripts were further mapped to available gene annotations in the IPA. Only up to 55% of transcripts were mapped due to lack of complete pig genome annotation. The number of IPA mapped genes was further compared across probiotic strains, tissues, and days post colonization. On PBCD1, numbers of mapped genes were not significantly different across strains and tissues; however, significant differences were observed in duodenum on PBCD7 where LGG modulated higher numbers (2.1 fold) of unique as well common mapped genes compared with LA (Appendix A; Appendix I).

Cellular growth and proliferation, inflammatory response, and immune cell

trafficking pathways were modulated by LA and LGG

To determine the relationship of genes identified in our microarray analysis, using IPA,

we compared the major well defined molecular (canonical) pathways that were

significantly affected by probiotic administration. Our results highlighted the distinct

modulation of host gene expression in duodenum and ileum by both LA and LGG

(Figure 2.1 A and B). Specifically, in most categories, LGG induced pronounced response in duodenum particularly on PBCD7, whereas an opposite trend was observed with LA, which induced pronounced response on several pathways in ileum on PBCD7.

On PBCD1, in duodenum LGG modulated pathways associated with inflammatory

response, hematological system development and immune cell trafficking; however, both

LA and LGG modulated cell tocell signaling and interaction, cellular growth and

proliferation pathways. On PBCD7, LGG also modulated cellular growth and

proliferation, inflammatory response, and immune cell trafficking; whereas both LA and

LGG affected cell signaling and cellular growth and proliferation pathways.

Contrastingly, on PBCD7, in ileum, LA modulated host tissue development, cellular

movement and immune cell trafficking and cell to cell signaling pathways, while both LA

and LGG modulated inflammatory response, and free radical scavenging pathways.

Collectively, our analyses highlight the unique and common major cellular pathways

modulated in duodenum and ileum by LA and LGG.

In the early stage at PBCD1, LA and LGG administration affected cell morphology,

cellular movements, and anti-inflammatory genes in ileum

To better understand the biological relevance of transcriptome responses observed after probiotic colonization, we analyzed the networks resulting from these transcriptome responses using IPA. The top affected networks are shown in Table 2. The top network in each category was compared (LA vs LGG) and further analyzed using “Molecular

Activity Predictor” (MAP). The predicated activities of molecules were in turn linked to significant canonical pathways representing the major underlying biological processes.

The gene regulatory networks with allied canonical pathways are depicted (Figure 2.2;

Appendix J and Appendix K).

On PBCD1, the top network associated with LA in ileum was Cellular Movement,

Hematological System Development and Function, Immune Cell Trafficking. This network was linked to the canonical pathway involved in Glucocorticoid Receptor (GR)

Signaling. The majority of molecules involved (A2M, Akt, ERK, ERK1/2, GPCR, IL1,

JNK, MAP2K1/2, MAPK, Mmp, NFκB (complex), P38 MAPK, PI3K (complex), Pka,

TNF (family) were predicted to be inhibited (Fig. 2). MAPK, ERK1/2 and JNK have an important function in dynamic regulation of the cell cytoskeleton, tight junction (TJ) and epithelia barrier function(Lebeer, Vanderleyden, & De Keersmaecker, 2008). Selective interaction of cytoskeleton protein networks provide a structural framework for cell morphology, stabilize the other membrane systems and mediate cellular movements(Doherty & McMahon, 2008). This suggests that LA modulates the host cell morphology and cellular movement processes by selectively inhibiting these proteins.

Further, glucocorticoids act through GR signaling and thereby mediate anti-

inflammatory, anti-proliferative and immunomodulatory activity(Miner, Hong, & Negro-

Vilar, 2005). Dampening of GR signaling by LA indicated probable early anti- inflammatory and immune modulatory responses in the ileum permitting more effective colonization with higher CFU observed vs duodenum.

Similarly, the top network associated with LGG in ileum was Cell Morphology,

Cellular Function and Maintenance, Cell Cycle which was also linked to GR signaling canonical pathway (Cbp/p300, ERK1/2, Histone h3, HMGB1, IFNβ, MAPK, MEK, P38

MAPK, and TGFβ). The majority of the molecules involved in this pathway were predicted to be inhibited (Figure 2.2). Cbp/p300 is a transcriptional co-activator protein that besides exerting tumor-suppressive effects, it also modulates proliferation, differentiation, and apoptosis in various cell types(Chan & La Thangue, 2001). It is likely that inhibition of Cbp/p300 by LGG stimulates cell proliferation, differentiation and anti- apoptosis. LGG also seems to mediate host anti-inflammatory effects like LA by modulating genes associated with GR signaling in ileum.

In the later stage at PBCD7, LA and LGG modulated cell signaling and metabolism genes in duodenum

On PBCD7, the top network associated with LA in duodenum was Cell-To-Cell

Signaling and Interaction, Tissue Development, Cell Death and Survival. This network was associated with canonical pathway involved in Integrin Linked Kinase (ILK)

Signaling (CDH1, ERK1/2, FOS, JUN, VIM). The associated molecules were predicted to be inhibited (Figure 2.3). The ILK signaling connects integrins to the cytoskeleton, thereby mediating cell signaling and also has important roles in cancer progression, and is

a valid therapeutic target in cancer research (Hannigan, Troussard, & Dedhar, 2005;

Legate, Montanez, Kudlacek, & Fassler, 2006). Downregulation of ILK signaling is likely to favor more colonization (> 1X105) vs PBCD1. Also inhibited status of

transcriptional factors such FOS and JUN, which downregulate the release of

inflammatory cytokine (anti-inflammatory effects)(Bladh et al., 2005), likely to decrease

innate immunity to promote LA colonization.

The top network associated with LGG in duodenum was Cell Cycle,

Carbohydrate Metabolism, Cellular Function and Maintenance which was linked to the

canonical pathway involved in Thyroid Receptor complex (TR/RXR) Activation

signaling (Figure 2.3). The corresponding molecules (Akt, G6PC, mTORC1, mTORC2,

N-Cor, NCOA3, PEPCK, Rxr, thyroid hormone receptor, TSH) were predicted to be

activated except TSH (Figure 2.3). Activation of TR/RXR signaling affects various

processes including lipid/carbohydrate/steroid metabolism, thermogenesis and CNS

function(Harvey & Williams, 2002). In addition, functional Akt and mTORC1 complex

promotes lipid anabolism in liver(Wan et al., 2011) and activation of G6PC (Glucose-6-

phosphatase), has consequences for host carbohydrate metabolism, as it is a key enzyme

in glucose homeostasis(Chou & Mansfield, 2008) (Table 2.3; Appendix B).

The genes associated with metabolism, cellular integrity, and immune responses

were either commonly or uniquely modulated by LA and LGG

Probiotic strain-specific host responses are well documented (Kleerebezem & Vaughan,

2009; Preidis et al., 2012). The growth phase specific role for probiotic strains has also

been reported recently(Peter van Baarlen et al., 2009). In this regard, our data showed

73 differentially expressed genes in different functional categories, particularly those involved in metabolism (Table 2.3; Appendix B). Both LA and LGG modulated the genes involved in energy metabolism whereas LA and LGG negatively regulated cytochrome subunits genes (e.g., COX1) in duodenum and positively regulated ATPase subunits (e.g., ATP5F1) in ileum. Similarly, LGG downregulated the (pyruvate kinase isozymes and phosphoglycerate mutase 2 (PGAM2) genes involved in glycolysis, but LA upregulated the gluconeogenesis gene phosphoenolpyruvate carboxykinase (PCK1).

Conversely in protein metabolism, LA downregulated the 3-oxoacid CoA transferase 1 gene (OXCT1) involved in branched chain amino acid synthesis whereas LGG upregulated the (branched chain amino-acid transaminase 2, mitochondrial (BCAT2) gene also involved in branched chain amino acid synthesis. Genes involved in dietary fat metabolism (PNLIP) and cholesterol clearance (APOA1) were upregulated by both LA and LGG. LA and LGG also modulated genes involved in vitamin and mineral metabolism. A majority of genes were induced for vitamin A (e.g., RDH) and vitamin D

(e.g., CYP24A1) metabolism. LA also induced the gene responsible for iron storage

(FTH1).

Our data also support common pathway modulation in duodenum and ileum: for example, the tight junction signaling on day 1, although the mechanistic regulation may vary for both LA and LGG due to involvement of different up stream regulators

(Appendix C). In duodenum, transcription of catenin, claudin and FOS molecules were affected where catenin was downregulated and claudin and FOS molecules were upregulated. However, in ileum, catenin, actin (α, αl and α2), FOS, myosin (heavy and light chain) were involved and all molecules were predicted to be activated except NF-

κB. The NF-κB signaling inhibits MLCK (Myosine Light Chain Kinase) to decrease the

permeability of tight junctions in the ileum (Appendix L) and this may suggest that

MLCK inhibition may limit the extraintestinal dissemination of LGG to spleen, liver and

MLN on PBCD1.

Both LA and LGG induced genes involved in cellular integrity such as sialomucin

(CD164 molecule), integrin, β 3 (ITGB3), heat shock 27kDa protein 8 (Hsp27) and

glucagon-like peptide-2 receptor (GLP2R) (Figure 2.4; Appendix D). In addition, both

LA and LGG tended to downregulate the innate immune response genes in the early

stages (PBCD1) to favor colonization. For example, cytosolic and membrane bound form

of Glutathione S transferase (GSTA1), Lysozyme (LYZ), polymeric immunoglobulin

receptors (PIGR), surfactant protein D (SFTPD) and an antibacterial protein regenerating

islet-derived 3 gamma (REG3G) were downregulated in duodenum (Figure 2.4;

Appendix D). However, in the later stages (PBCD7) complement component C9, and

C8G, cellular innate mediators; CD1d, non-classical MHC I and Th1 specific IFN

induced chemokine CXCL9, and were induced by both LA and LGG.

2.4 Discussion

Beneficial role of probiotics in nutritional physiology is less documented compared with

its role in immune stimulation and anti-diarrheal effect(Wolvers et al., 2010). However few studies highlighted the beneficial role of lactobacilli in enhancing mineral and nutrients absorption, vitamin metabolism, or modulation of intestinal physiology(Turpin,

Humblot, Thomas, & Guyot, 2010). Human interventional studies in children using L. rhamnosus and L. acidophilus showed improvement in weight and size of the subjects

likely due to increased feed conversion ratio(Saran, Gopalan, & Krishna, 2002; Vendt et

al., 2006); however, mechanisms remain still unclear(Turpin et al., 2010). Our study provides mechanistic insights into the role of both LA and LGG in nutrient metabolism.

LA upregulated two important host genes ferritin (FTH1) and ceruloplasmin (CP), which

are involved in iron and copper homeostasis(de Silva & Aust, 1993). Further, upregulation of antimicrobial peptide hepcidin (HAMP) and ferritin, the host iron sequestration genes, limits iron availability to intruding pathogens, thereby prevent pathogen establishment in host(Ganz, 2011). Both LA and LGG may also play a role in

bioavailability of small molecules, calcium and phosphorus. Host intestinal absorption

and homeostasis of calcium and phosphorus requires the vitamins A and D. The gene

product of CYP2D25 and CYP24A1 encodes vitamin D 25-hydroxylase and 1, 25-

dihydroxyvitamin D3 24-hydroxylase respectively, the key enzymes in the vitamin D

metabolism. Our data support the strain specific induction of CYP2D25 gene by LGG in

duodenum on PBCD7, and common induction of CYP24A1 gene on PBCD1. It is

interesting to note that LGG on PBCD1 differentially modulated the CYP24A1 gene in

the duodenum and ileum suggesting calcium absorption was favored in duodenum (Table

2.3; Appendix B). Likewise, β-carotene 15,15'-monooxygenase (BCMO1) gene involved in vitamin A metabolism was upregulated by LA in duodenum (PBCD1); conversely,

LGG in both tissues upregulated RDH16, retinol dehydrogenase 1, an enzyme involved in the visual cycle (Table 2.3; Appendix B). Both Vitamin A and D can modulate the innate and adaptive immune responses as well as metabolites of vitamin A and D can induce tissue specific immune responses and have been investigated for preventing and/or treating inflammation and autoimmunity(Wintergerst, Maggini, & Hornig, 2007).

Previously, colonization of B. thetaiotaomicron in germ free mice produced

changes in host genes involved in breakdown and absorption of complex carbohydrates

and lipids(Hooper, Midtvedt, & Gordon, 2002). In our study, lactobacilli administration

altered expression of genes involved in glycolysis/gluconeogenesis. For example,

pyruvate kinase isozymes R/L-like (LOC100621940 and phosphoglycerate mutase 2

(PGAM2) were negatively regulated whereas phosphoenolpyruvate carboxykinase 1

(PCK1) and glucose-6-phosphatase, catalytic subunit (G6PC) were positively regulated.

This suggests a potential role for lactobacillus in host carbohydrate metabolism (Table

2.3). Lactobacillus contributes to host lipid metabolism through their ability to metabolize bile acids, the primary function of which is to emulsify fats. Hence, probiotics seem to regulate how much fat the body can absorb(Lebeer et al., 2008). LA on PBCD1 in duodenum induced genes, pancreatic lipase-related protein 1 (PNLIPRP1) involved in breakdown of triacylglycerols, and pancreatic colipase (CLPS) that prevents the inhibitory effect of bile salts on lipase whereas, on PBCD7 in duodenum, LGG upregulated pancreatic lipase (PNLIP) the primary enzyme required for hydrolyzing dietary fat. Recently, it was shown that yogurt containing probiotic bacterial strains, L. acidophilus and B. lactis, had a cholesterol-lowering effect in hypercholesterolemic subjects(Ataie-Jafari, Larijani, Alavi Majd, & Tahbaz, 2009). Both LA and LGG on

PBCD7 in duodenum induced a 300 fold upregulation of apolipoprotein A-I (APOA1), a major protein component of high density lipoprotein (HDL) in plasma promoting cholesterol efflux from tissues to the liver for excretion and helping to clear cholesterol from arteries(Wasan, Brocks, Lee, Sachs-Barrable, & Thornton, 2008). L. casei Shirota and B. breve (De Preter et al., 2008) have been shown to enhance colonic protein and

ammonia metabolism in healthy humans. In our study, genes involved in catabolism of

branched chain amino acids (3-oxoacid CoA transferase 1 OXCT1), tyrosine (4-

hydroxyphenylpyruvate dioxygenase HPD) and arginine (arginine decarboxylase SADC)

were negatively regulated, whereas genes involved in amino acid synthesis (5-

methyltetrahydrofolate-homocysteine methyltransferase reductase MTRR) and branched

chain amino acid synthesis (branched chain amino-acid transaminase 2, mitochondrial

BCAT2) were positively regulated (Table 2.3). This sugests that both LA and LGG may

play a role in host protein metabolism and this maybe mediated through activation of

TR/RXR signaling (Figure 2.3) as in the case of LGG. TR/RXR signaling is attributed in

metabolic signaling pathways (glucose, fatty acid, and cholesterol metabolism) besides

morphogenesis, cell proliferation, cell differentiation, cell death(Harvey & Williams,

2002).

Alterations in the epithelial barrier functions are implicated in various intestinal disorders and probiotics are suggested to enhance or maintain the epithelial barrier, but the molecules mediating these effects are only partially defined. In our study, both LGG and LA altered gene expression in canonical pathways involving cell signaling, tissue development, cellular growth and proliferation. For example, both LA and LGG ingestion enhance expression of glucagon-like peptide-2 receptor (GLP2R) gene. The GLP2R regulates intestinal growth stimulation and upregulation of villus height in the small intestine, concomitant with increased crypt cell proliferation and decreased enterocyte apoptosis. Enterocyte transcriptome profiling in neonatal mice has shown that ingestion of probiotic L. reuteri strains altered expression in multiple canonical pathways involving cell motility as well as increased enterocyte migration and proliferation in the ileum in a

strain-specific manner(Preidis et al., 2012). ILK signaling stabilizes enterocyte migration

and has been implicated in intestinal epithelial cell proliferation regulation and certain

bacteria hijack ILK signaling to stabilize focal adhesions and prevent cell detachment

(Gagne et al., 2010; Y. B. Kim et al., 2008; McDonald, Fielding, & Dedhar, 2008). It is

possible that LA might exploit ILK signaling to colonize the Gn pig intestine

(Figure 2.3). LGG also induced expression of claudin-8, which is associated with

decreased paracellular gut permeability(Markov, Veshnyakova, Fromm, Amasheh, &

Amasheh, 2010), (Appendix C). Further, our results suggested an anti-apoptotic role for both LA and LGG on the host cell through induction of heat shock proteins (eg.Hsp27).

Increased expression of Hsp27 has been shown to suppress stress and receptor induced apoptotic pathways. Also Hsp27 increases the antioxidant defense of cells by decreasing intracellular reactive oxygen species (ROS)(Schmitt, Gehrmann, Brunet, Multhoff, &

Garrido, 2007). Thus, both LA and LGG may contribute to maintaining gut homeostasis; however, LGG may play a more significant as a probiotic in influencing gut epithelial integrity.

Our study also highlighted the immunomodulatory functions of LA and LGG.

Both LA and LGG on PBCD1 showed downregulation of various genes related to innate immune responses such as, anti-microbial peptides (REG3G, Surfactant protein D), lyzozymes, antigen presentation (MHC class 1 and 2) and regulation (CD32), and immune cell trafficking (CCL20/28, only LGG Fig. 4 and Table S4), which may allow early colonization and adaptation of these probiotics to the host. GR signaling leads to enhanced release of pro-inflammatory cytokines IL-1β, TNF-α and IL-6(Busillo, Azzam,

& Cidlowski, 2011) hence, it may be conceivable that downregulation of GR signaling at

early stage by both LA and LGG could suppress the initial host response that otherwise

prevents probiotic colonization (Figure 2.2). Once colonized on PBCD7, both probiotics induced stimulatory effects in duodenum and ileum as indicated by higher expression of soluble (complement component 9 and 8 G genes) and cellular innate mediators (non- classical MHC class I CD1d expressing NKT cells). Furthermore, both LA and LGG induced CCL9 expression, which is a Th1-specific IFN induced chemokine similar to

CXCL10 and CXCL11 gene(Xanthou, Duchesnes, Williams, & Pease, 2003) and LA also increased granzyme A/B, which is expressed by cytotoxic T cells and NK cells (both associated with Th1 responses) suggesting that these probiotics may favor Th1 immune responses. Interestingly, in our previous studies we have shown Th1 immunomodulating effects of LA and LGG (in combination with Bb12) on attenuated HRV vaccine in a Gn pig model(Chattha, Vlasova, Kandasamy, Rajashekara, et al., 2013; W. Zhang et al.,

2008) suggesting that the initial Th1 microenvironment (observed on PBCD7) induced by these probiotics may result in Th1 biased adaptive immune response to specific microbial agents. Thus both LA and LGG may be useful as adjuvant for viral vaccines and infections, where Th1 immune responses are critical.

Summary and conclusions: Though our study used a Gn pig model, similar observations

have been made using other conventional animal models as well as in clinical trials,

supporting the usefulness of Gn pigs in elucidating mechanisms underlying the beneficial

roles of probiotics. For example, comparison of transcriptome responses of germfree

piglets vs. the piglets with intestinal microbiota indicated that genes involved in

biological processes like epithelial cell turnover, nutrient transport and metabolism,

xenobiotic metabolism, JAK-STAT signaling pathway, and immune response were

altered(Chowdhury et al., 2007). Similarly, administration of LA and LGG in a human

interventional study identified mucosal transcriptional responses associated with immune

response, tissue growth and development, and ion homeostasis(Peter van Baarlen et al.,

2011). In our study, genes involved in nutrient transport and metabolism, immune

responses, and epithelial cell turnover are also the major classes of genes that were

differentially regulated by the colonization of Gn pigs with lactobacillus species. In

addition, previous studies using Gn and conventionalized mice revealed major molecular

responses in metabolism, intestinal morphology and cell proliferation and immunity as

early as day 1–4 post conventionalization, with a pronounced changes occurring after day

4 post conventionalization(El Aidy et al., 2013; El Aidy et al., 2012). Consistent with

these findings, in our study more robust responses were observed on PBCD7, particularly

in duodenum, illustrating the tissue-specific changes in the biological processes. However detailed temporal analysis of transcriptome responses to lactobacilli colonization in neonatal Gn and conventional pigs (mimicking the infants) would provide better understanding of the biological processes modulated by these known probiotics. In addition, studies of the host proteome and metabolome should further enhance our understanding of how probiotic bacteria confer health benefits. Understanding these

interactions will provide information for the rational treatment of disease phenotypes

with known probiotics and will have significant implications in personalized healthcare

medicine.

2.6 Acknowledgements

AV, KC, SK and ME assisted in the animal experiment; ZL assisted in performing real- time PCR; XZ performed statistical analysis of microarray data. We gratefully

acknowledge the technical assistance of Dr. Juliette Hanson, Rich McCormick, Lindsey

Good, Joshua Amimo, Isaac Kashoma, and Kyle T. Scheuer. This work was supported by

grants from the NIH, NCCAM R21AT004716 and NIAID R01 A1099451 (Linda J Saif-

PI, Anastasia N Vlasova/Gireesh Rajashekara-Co-PI) and federal funds appropriated to

the Ohio Agricultural Research and Development Center (OARDC) of The Ohio State

University.

Group PBCD Tissue All genes* Up regulated Down regulated

LA 1 Duodenum 157 73 84 LA 1 Ileum 286 161 125 LGG 1 Duodenum 183 66 117 LGG 1 Ileum 292 189 109 LA 7 Duodenum 137 58 79 LA 7 Ileum 108 74 34 LGG 7 Duodenum 569 259 310 LGG 7 Ileum 145 64 81

Table 2.1. Summary of gut transcriptome responses to LA and LGG*

*The numbers represent the total number of transcripts that are differentially expressed in response to probiotic inoculation compared with uninoculated controls. The upregulated and downregulated genes were determined using a cutoff ± < 2 or > 2, respectively compared with the control uninoculated control group, PBCD refers to post bacterial colonization day.

LA Top network involved Score Focus molecules Day-1 Duodenum 1. Free Radical Scavenging, Small Molecule Biochemistry, Cell- To-Cell 52 23 Signaling and Interaction 2. Protein Synthesis, Renal and Urological System Development and Function, 29 14 Developmental Disorder

Day-1 Ileum 1. Cellular Movement, Hematological System Development and Function, 35 16 Immune Cell Trafficking 84 2. Neurological Disease, Molecular Transport, Cellular Development 33 15 3. Cancer, Reproductive System Disease, Cellular Development 22 11 Day-7 Duodenum 1. Cell-To-Cell Signaling and Interaction, Tissue Development, Cell Death and 46 21 Survival 2. Carbohydrate Metabolism, Lipid Metabolism, Small Molecule Biochemistry 35 18 3. Cell-To-Cell Signaling and Interaction, Cellular Assembly and 29 15 Organization, Cellular Function and Maintenance 4. Cell Signaling, Lipid Metabolism, Small Molecule Biochemistry 29 15

Day-7 Ileum 1. Cellular Assembly and Organization, Skeletal and Muscular System 38 16 Development and Function, Tissue Development 2. Cellular Movement, Renal and Urological System Development and 32 14 Function, Cardiovascular System Development and Function Continued

Table 2.2. Top gene regulatory networks associated with LA and LGG in duodenum and ileum based on focus molecules and scores.

Table 2.2 (Continued)

LGG Top network involved Score Focus molecules Day-1 Duodenum 1. Cell-To-Cell Signaling and Interaction, Hematological System Development and 45 20 Function, Immune Cell Trafficking 2. Cancer, Cell-To-Cell Signaling and Interaction, Tumor Morphology 35 16 3. Immunological Disease, Organismal Injury and Abnormalities, Cell-To-Cell Signaling 22 11 and Interaction Day-1 Ileum 1. Cell Morphology, Cellular Function and Maintenance, Cell Cycle 46 19 85 2. Developmental Disorder, Hereditary Disorder, Metabolic Disease 26 13

Day-7 Duodenum 1. Cell Cycle, Carbohydrate Metabolism, Cellular Function and Maintenance 27 16 2. Developmental Disorder, Hereditary Disorder, Neurological Disease 27 16 3. Hereditary Disorder, Organismal Injury and Abnormalities, Reproductive System Disease 27 16 4. Lipid Metabolism, Small Molecule Biochemistry, Molecular Transport 25 15 5. Lipid Metabolism, Molecular Transport, Small Molecule Biochemistry 24 15 Day-7 Ileum 1. Free Radical Scavenging, Hereditary Disorder, Ophthalmic Disease 41 17 2. Cardiovascular System Development and Function, Tissue Morphology, Cellular 26 12 Development *Only networks with focus molecules 10 or higher are shown, where “score” reflects number of network eligible molecules; the higher scores indicate that the given network is more likely modulated by probiotic treatment; Focus molecules’ are genes and molecules that are affected by the probiotic treatment and are considered for generating network.

Gene symbol and functions PBCD1 PBCD1 PBCD 7 PBCD7 LGG LA LGG LA Small molecules metabolism Duo Ile Duo Ile Duo Ile Duo Ile a).Vitamin and mineral BCMO1, Vitamin A metabolism ↑ CYP24A1, Vitamin D metabolism ↑ ↑ ↓ CYP4A21, Vitamin D metabolism ↑ DIO3, Thyroid hormone metabolism ↑ RDH16, Vitamin A metabolism ↑ ↑ FTH1, Iron storage ↑ HAMP, Macrophage iron storage ↑

86 CP, Copper and iron homeostasis ↑

CYP2D25, Vitamin D metabolism ↑ b). Drug metabolism TPMT, Metabolizes thiopurine drugs ↓ DPEP1, Renal metabolism of glutathione ↓ ↓ and conjugates FMO1, Metabolism of drugs and ↑ xenobiotics DHRS7, Metabolism of steroid, ↓ prostaglandins, retinoids, lipids and xenobiotics Macro molecules metabolism a). Carbohydrate PCK1, Gluconeogenesis ↑ GPD1, Link between carbohydrate and ↑ lipid metabolism Continued Table 2.3. Metabolism genes influenced by administration of probiotics LA and LGG in duodenum and ileum.*

Table 2.3 Continued PDK4, Inactivate pyruvate ↑ dehydrogenase G6PC, Key enzyme in glucose ↑ homeostasis AMY2, Hydrolyze oligo and ↑ polysaccharides LOC100621940, Plays a key role in ↓ glycolysis PGAM2, Involved in glycolysis ↓ 87

b). Lipid metabolism CLPS, prevent the inhibitory effect of ↑ bile salt on lipase PNLIPRP1, Role in dietary fat digestion ↑ APOA1, Role in cholesterol metabolism ↑ ↑ PNLIP, Primary enzyme in hydrolyzing ↑ dietary fat LIPE, Hydrolyze the first fatty acid from ↑ a triacylglycerol c). Protein metabolism OXCT1, Branched chain aa degradation, ↓ synthesis and degradation of ketone bodies MTRR, Helps in process of aa synthesis ↑ OAZ2, ornithine decarboxylase inhibitor ↑ thereby prevent polyamine synthesis Continued

Table 2.3 Continued SADC, Amino acid (aa)metabolism and ↓ urea cycle BCAT2, Synthesis of the branched chain ↑ aa OAZ1, ornithine decarboxylase ↑ inhibitor,prevent polyamine synthesis HPD, Break down the aa tyrosine ↓ d). Energy metabolism COX3, Cytochrome c oxidase sub unit ↓ COX1, cytochrome c oxidase subunit I ↓ ↓ ↓

88 ATP5B, Mitochandrial ATPase synthase ↓

ATP5F1, Mitochandrial ATPase synthase ↑ ↑

*Only selected genes in each category are shown with ‘↓’ and‘↑’ referring to down or upregulation, respectively. Comprehensive list of all the genes involved in metabolism with fold change values and description are listed in Appendix B. The upregulated/downregulated genes were determined using a cut off ± < 2 or > 2, respectively compared with the uninoculated control group.

Duodenum 64 40 64 17 42 27 9 17 16 10 5 28 1A 17 5 4 18 23 23 19 8 2 10 9 16 12 11 5 8 7 4 1 4 2 8 5 3 3 1 3 2 1 4 - value) 1 13

- log(p

89 Cell signaling

Gene expression immune response immune interactions Antigen presentation Antigen Antimicrobial response response Antimicrobial Immune Immune traffickingcell Inflammatory responseInflammatory Free scavenging radicals Free Cell to cell signalingand functions and Humoral PBCD1 LA Cellular growth andproliferation response immune mediated Cell PBCD7 LA Hematological system development PBCD1 LGG PBCD7 LGG

Continued Figure 2.1. Major cellular pathways (canonical pathways) modulated by LA and LGG were generated using ingenuity biological process analysis. These pathways were compared in duodenum and ileum for both days. The selected pathways were the significantly modulated ones and were above the threshold [-log (p value)] analyzed using IPA. Numbers on the top of the bars represent the total number of genes involved in a given canonical pathway. The distinction between probiotic strains was based on log p value and number of genes involved in (1A) duodenum and (1B) ileum.

Figure 2.1 Continued

PBCD1 LA PBCD7 LA PBCD1 LGG PBCD7 LGG

Ileum 14 1B 16 8 10 3 8 10

) 13 11 16 9 13 11 5 13 7 6 14 14 1 11 2 1 5 7 12 3 7 value 9 3

- 6 2 12 2 2 1 4 3 4 6 4 2 2 1 1 log (p log

Gene expression immune response immune interactions

Cellular movements Antigen presentation Antigen Tissue development Antimicrobial response response Antimicrobial Immune Immune traffickingcell Inflammatory responseInflammatory Free scavenging radicals Free Cell to cell signalingand and functions and

Humoral Cell mediated immune response immune mediated Cell Hematological system development

LA LGG

Figure 2.2. Top associated networks with their linked canonical pathways (CP) in ileum on PBCD1. In the graphical representation of a network, genes or gene products are represented as nodes, and the biological relationship between two nodes is represented as an edge (line). Human, mouse, and rat orthologs of a gene are represented as a single node in the network. The intensity of the node color indicates the degree of up- (red) or down- (green) regulation; and more confidence predicted activation (blue) or inhibition (brown) of a given gene. Nodes are displayed using various shapes that represent the functional class of the gene product. Edges are displayed with various labels that describe the nature of the relationship between the nodes. The higher number of molecules contribution in the discerned canonical pathway decided the pathway assigned for that particular probiotic strain; in the case of approximately equal numbers of molecules contributing to the same pathway it was assigned for both LA and LGG. Both LA and LGG were associated with Glucocorticoid Receptor (GR) Signaling canonical pathway. The connecting sky blue line indicates the associated molecules with their predicted biological status, whereas the pink lines highlight the direct relation (solid line) between the genes. Only genes that are involved in canonical path are shown and indirect interactions are depicted with broken arrow. Predicated relationship between molecules are indicated with color of line (solid/broken) as orange, blue, yellow and grey color to indicate activation, inhibition, finding in consistent and effect not predicted respectively. 91

LA LGG

Figure 2.3. Top associated networks with their linked canonical pathways in duodenum on PBCD7. The higher number of molecules contribution in the discerned canonical pathway decided the pathway assigned for that particular probiotic strain; in the case of approximately equal numbers of molecules contributing to the same pathway it was assigned for both LA and LGG LA was associated Integrin Linked Kinase (ILK) and LGG with Thyroid Receptor complex (TR/RXR) Activation canonical pathway. The connecting sky blue line indicates the associated molecules with their predicted biological status whereas the pink lines highlight the direct relation between the genes. Only genes that are involved in canonical path are shown and indirect interactions are depicted with broken arrow. Predicated relationship between molecules are indicated with color of line (solid/broken) as orange, blue, yellow and grey color to indicate activation, inhibition, finding in consistent and effect not predicted respectively.

LA LGG Common Up Dn Up Dn Up Dn Day 1 Duodenum Cytosolic and membrane bound form of Glutathioane S transferase (GS Lysozyme (LYZ) polymeric immunoglobin receptors (PIGR) surfactant protein D (SFPTD) Regenerating islet-derived 3 gamma (REG3G) M HC class I/II histocompatibility antigen chemokine ligand 20/28 Day -1 Ileum CD40 molecule, TNF receptor superfamily member 5 Autophagy related 4D, cysteine peptidase M ucin 13, cell surface associated Fc fragment of IgG, low affinity IIb, receptor (CD32) Day-7 Duodenum complement component C9 complement component C8G CD1d molecule CD164 molecule, sialomucin tumor protein D52 collagen, type VII, alpha 1 glucagon-like peptide 2 receptor (GLP2R) protocadherin 1 Suppressor of cytokine signaling 4 (SOCS4) Hepcidin antimicrobial peptide (HAM P) claudin 8 (CLDN8) mucolipin 3 Granzyme A/B Day- 7 Ileum integrin, beta 3 (platelet glycoprotein IIIa, antigen CD61) (ITGB3) chemokine (C-X-C motif) ligand 9 activated leukocyte cell adhesion molecule heat shock 27kDa protein 8 (Hsp27) surfactant protein B chemokine (C-X-C motif) ligand 2 clusterin claudin 12 polymeric immunoglobulin receptor Figure 2.4. Genes involved in cell integrity and immunity. The gene functions were determined using IPA and NCBI database and were manually compared with their regulated status. The genes involved were color coded to indicate regulated status (green- down regulation; red-up-regulation). The genes responsible for innate immunity were commonly regulated in both LA and LGG. 93

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100

CHAPTER-3

Investigating the impact of nutrition and rotavirus infection on the infant gut

microbiota in a humanized pig model

Kumar, A1, Vlasova, A.N1., Huang, H-C1., Wijeratne, A2., Kandasamy, S1., Fischer,

D.D1., Langel, S.N1., Paim, F.C1., Rauf, A1., Abdulhameed, M1., Shao, L1, Saif, L.J1 and

Rajashekara G1.

1Food Animal Health Research Program; Department of Veterinary Preventive Medicine;

2The Molecular and Cellular Imaging Center; Ohio Agricultural Research and

Development Center; The Ohio State University; Wooster, OH USA.

101

3.1 Abstract: Enteric infections attribute to millions of infant deaths worldwide annually.

Human rotavirus (HRV) is a major cause of viral gastroenteritis in infants that accounts

for approximately 440,000 deaths annually worldwide, particularly in developing

countries where malnutrition is prevalent. Malnutrition perturbs the infant gut microflora

leading to sub-optimal functioning of the immune system. Therefore, we hypothesized

that malnutrition further exacerbates rotavirus disease severity in infants. In this context,

identifying the specific microbial composition and their probable function during

malnutrition and RV infection has a therapeutic value; however, this has not been

investigated previously. Due to various confounding factors and ethical concerns,

addressing these questions in human infants is not permissible. A growing literature

suggests that pigs are a more realistic, practical, non-primate model for transplanting human gut microflora compared to mice and rodent models. In this study, we established a microflora humanized (HM) pig model to study the effects of interactions among infant gut microbiota, diet (deficient vs sufficient) and RV disease. Clinically, HM pigs with deficient diet developed characteristic edema (like observed in infants with protein- calorie malnourishment), stunted growth rate, and also had more severe RV diarrhea compared to HM pigs with sufficient diet post RV challenge. Analysis of microbial structure and composition indicated that deficient diet suppressed the diversity and richness of the gut microflora, and the microflora was further abundantly populated with bacterial genus like the Clostridium compared to HRV challenged HM pigs with sufficient diet. In conclusion our results demonstrate that even short-term malnutrition in

the neonatal period might compromise the infant growth rate, and the gut microflora

102 structure and composition leading to increased incidence and severity of enteric infections.

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3.2 Introduction

There is increasing research interest in understanding the effects of diet in infancy and the subsequent implications in later life (Calder et al., 2006; Groer et al., 2014; Mountzouris et al., 2002). Human breast milk is a nutritious complete food and considered as a ‘gold’ standard for infant nutrition (Jeurink et al., 2013; McGuire & McGuire, 2015). In conditions where breast feeding is not possible or breast milk is not available in adequate quantity, infant formula provides an alternative safe, and nutritious diet(Mountzouris et al., 2002). Deprivation of a nutritious diet (infant formula or breast milk) and for various other reasons, (sanitation, infection, poverty etc) in infants in developing countries leads to malnutrition (Bain et al., 2013). Malnutrition is characterized by devastating health consequences in infants. These include serious infections such as diarrhea, measles, pneumonia and malaria, as well as HIV and high fatality rates from common childhood illnesses (Pelletier, Frongillo, & Habicht, 1993). It has been shown that infection by enteric pathogens and malnutrition have a repetitive vicious cycle (Brown, 2003;

Guerrant, Oria, Moore, Oria, & Lima, 2008). Infections especially of gastrointestinal origin, adversely affects nutritional status through malabsorption of dietary intake, electrolyte imbalance, and secretary diarrhea leading to dehydration and finally undernourishment. On the other hand, malnutrition leads to increased gut permeability, and sub-optimal immune functions resulting in proliferation and translocation of pathogenic bacteria and secondary infections (Brown, 2003; Guerrant et al., 2008). At the either end of the vicious cycle, ‘infection or malnutrition’ the gut microflora acts as a bridge to communicate responses. Thus, role of the gut microflora is increasingly recognized in health and disease conditions(Wallace et al., 2011). Now it is evident that

104 the composition and activities of the gut microflora drive various local and systemic effects, and in turn the gut microflora can be modulated by diet (Marchesi et al., 2015).

The factors like xenobiotics (eg. probiotics, prebiotics or antibiotics) and enteric pathogen infections (eg rotavirus) are also known to perturb the normal gut microflora

(Marchesi et al., 2015; H. Zhang et al., 2014).

With the advent of next generation sequencing technology and the availability of bioinformatic tools, studies exploring microbial ecology and their relevant functions are increasing(Langille et al., 2013) (Marchesi et al., 2015). Recent studies investigated the consequences of malnutrition on the microflora dynamics and suggested a role for the gut microflora in obesity. Malnutrition’ increases the occurrence of diarrheal diseases in infants (Gupta et al., 2011; M. I. Smith et al., 2013; Turnbaugh et al., 2009). These conditions are characterized by relative enrichment of specific populations of bacteria, like energy harvesting bacteria in obesity and the presence of enteric pathogens like

Campylobacter in diarrheal diseases (Gupta et al., 2011; M. I. Smith et al., 2013). Human rotavirus (HRV) gastroenteritis is a vaccine preventable disease in infants that accounts for approximately 440,000 deaths annually worldwide. Though effective vaccines are available, their efficacy is low in developing countries (Das et al., 2014; Greenberg &

Estes, 2009). The poor efficacy of HRV vaccines in developing countries is attributed to numerous reasons including malnutrition and the gut microflora (Patel et al., 2009) (Das et al., 2014). Malnutrition perturbs the gut microflora and thereby induces negative effects on the immune system. Therefore malnutrition is expected to contribute to HRV vaccine failure in developing countries (Y. Valdez, Brown, & Finlay, 2014). Identifying the specific microbial composition and their probable functions under malnutrition and/or

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HRV infection has both diagnostic and therapeutic value which unfortunately has not yet

been investigated. Due to complex factors and ethical concerns, addressing these

questions and modulating relevant factors in human infants is not permissible. Microflora

humanized (HM) animal models are used whereby selective microbial communities can

be modeled under controlled conditions; however, not all HM animal models recapitulate

most of the donor microflora (mouse microflora humanized model)(Gootenberg &

Turnbaugh, 2011; F. Meurens et al., 2012). A growing literature suggests that pigs are more advantageous non-primate models to study human conditions than rodents, because pigs are more closely related to humans in terms of anatomy, genetics, physiology, and immunology and they are omnivores like humans. Furthermore, recent studies have demonstrated that transplantation of the human microbiota into germ free piglets resulted in similar microbial community structure (Gootenberg & Turnbaugh, 2011; F. Meurens et al., 2012). In contrast, humanizing germ free mice with human microbiota did not recapitulate most of the microbial profiles as seen in human donor (Turnbaugh et al.,

2009; Wos-Oxley et al., 2012). This signifies the translational value of a microflora humanized pig model to study enteric disorders(Heinritz et al., 2013). Importantly,

neonatal gnotobiotic (Gn) pigs infected with HRV exhibit symptoms and clinical

intestinal lesions similar to those seen in human infants, unlike lack of HRV infection,

lesions and symptoms seen in an adult mouse model (F. Meurens et al., 2012; L. J. Saif et al., 1996). We hypothesized that the transplantation of infant microbiota into Gn pigs would result in a similar assembly and composition of the gut microflora in HM pigs and furthermore that undernutrition would perturb the gut microflora leading to sub-optimal functioning of the immune system, and exacerbating HRV disease severity.

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Our results indicated that Gn pigs transplanted with human infant microflora

recapitulated very similar microbial community structure, and that malnutrition

suppressed the diversity and richness of the gut microflora and enhanced HRV disease

severity.

3.3 Materials and Method

Source of infant fecal microbiota (HM): Multiple fecal samples were aseptically

collected in sterile fecal cups from a healthy, two-month-old, breast-fed, full-term, and male infant. Neither infant nor mother had any recent history of disease or antibiotic treatment at the time of sample collection. Consent was obtained from the mother before collecting the fecal samples from the infant. Collected fecal samples were pooled and stored immediately at -80oC until processed. Before transplantation to Gn pigs, a small

aliquot of sample was tested for the presence of HRV before freezing using cell culture immunofluorescence (CCIF) assay as described previously(Costantini et al., 2007). Fecal samples were weighed, diluted 1:20 (w/v) in phosphate buffer solution containing 0.05% cysteine (v/v) and 30% sterile glycerol as described previously(H. Zhang et al., 2014).

Homogenized fecal suspension was used to prepare two ml HM inoculuea in an

anaerobic working station (Microbiology International, MD). Prepared HM inoculums

were stored at -80oC until inoculation.

Transplantation of HM: Neonatal Gn piglets were surgically derived from near- term

sows (Landrace × Yorkshire × Duroc) and maintained in sterile isolators as described

previously (Meyer et al., 1964). Classical culture methods such as plating fecal samples

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on blood agar plates and thioglycolate broth culturing were used to confirm the sterility

of Gn piglets prior to HM transplantation. From derivation and during the course of

experiment, piglets were maintained on either the Sufficient (Suf) diet or protein-calorie deficient (Def) diet. The Suf group received the ‘Parmalat feed’ (ultra-high temperature processed commercial cow milk) while the Def group received half of the Suf group

Parmalat feed (i.e. 50% Parmalat + 50% sterile water). Required numbers of HM inoculuae were thawed prior to oral inoculation of Gn neonatal piglets.

As a proof of concept, we performed oral inoculation of HM to Gn neonatal piglets with sufficient diet. Post transplantation (PBCD3, PBCD7) in fecal samples were analyzed for microbial ecology. Finally HM pigs with sufficient diet were sacrificed one week of transplantation to assess the recapitulating capacity of HM pig’s community composition in tissue level when compared to HM.

Ethical statement and experimental design: All animal experiments were approved and performed in accordance to the Institutional Animal Care and Use Committee of The

Ohio State University. Four-day-old Gn piglets (n=9) orally inoculated with two ml one dose of HM inoculum were randomly divided into two groups (n=4 or 5), (1) Deficit

(Def) diet +RV (n=4) and (2) Sufficient (Suf) diet + RV groups (n=4). Both groups were challenged with Wa (G1P[8] human strain ten days after HM transplantation (Figure

3.1). For microbial ecology analysis, fecal samples were collected at three days (PBCD3,

PBCD6 PBCD9 or PCD-1) before HRV challenge and three days post HRV challenge

(PCD2, PCD5 and PCD12). Two weeks post HRV (PCD14) challenge all pigs were euthanized and small intestinal (duodenum, jejunum, and ileum) and colon tissue samples

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were collected aseptically in liquid nitrogen. For long term storage, samples were stored

at -80oC until processed for DNA extraction.

Assessing clinical and pre-clinical correlates: Clinical signs and parameters (like changes in body weight, diarrhea severity and duration and vomition) were recorded by trained animal technicians during the experiment. The severity of diarrhea was assessed based on the fecal consistency score (W. Zhang et al., 2008). Scores were recorded 0, normal; 1, pasty; 2, semiliquid; and 3, liquid and pigs with daily fecal consistency scores of ≥1.5 considered as diarrheic. The mean cumulative score was calculated as sum of daily fecal scores from each group from PCD0 t0 PCD7. . RV shedding in fecal samples was measured using a CCIF technique as described previously(L. Saif, Yuan, Ward, &

To, 1997). Cells were examined using an inverted fluorescence microscope and titers were expressed as FFU/ml.

Genomic DNA extraction: (a) Fecal samples- fecal swabs collected from HM piglets

were suspended in 2 ml sterile buffered peptone water. Suspensions were centrifuges at

10,000 X g for10 mint to collect sediments and used for genomic DNA extraction using

PowerFecal DNA Isolation Kit (Mo Bio Laboratories, Carlsbad, CA) in accordance with the manufactures instruction. Briefly, sediments (≈ 0.25 gram) were transferred tubes containing dry beads along with bead solution and vortexed briefly. Subsequently, steps were followed as mentioned in the PowerFecal DNA Isolation Kit. Finally, DNA was eluted from spin column using 100 µl nuclease free water. (b) Tissue sample- genomic

DNA was extracted using Dneasy Blood and Tissue Kit (Qiagene, Valencia, CA).

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Briefly, ≈ 0.5 gram of tissue samples were cut into small pieces and suspended in buffer with proteinases K, and incubated at 56oC for 3 h with intermittent vortexing.

Subsequently samples were treated for RNase A (100mg/ml) and ethanol precipitated.

Suspension was transferred to spin column and washed. Finally, 200 µl of nuclease free water was used to elute DNA from the spin column. Quantity and quality of eluted DNA was assessed using NanoDrop 1000 Spectrophotometer V3.7.1 (Fisher Scientific,

Pittsburgh, PA) and also by agarose gel electrophoresis.

Amplicon library preparation and MiSeq sequencing: Extracted DNA samples were subjected for 16S rRNA variable region sequencing. As a first step of targeted sequencing, amplicon libraries were prepared by using Phusion® High-Fidelity PCR Kit

(New England Biolabs Inc, Ipswich, MA) in a 96 well plate. Twenty five µl of PCR reactions were prepared using 5 µl (5X) of PCR buffer, 4 µl (5 ng/µl) of DNA sample, and 2.5 µl (2 µM) primer, 0.5 µl (10 mM) dNTPs, 0.2 µl of enzyme and finally nuclease free water is added to make-up the final volume. The barcoded primers targeted the region between V4-V5 variable regions (Table 3.1). Following PCR conditions were used for amplifications: initial denaturation was at 96oC for 2 min, and 25 cycles of 96oC for 30 sec, 55oC for 30 sec, 72oC for 30 sec, and final extension of 72oC for 5 min.

Following PCR amplification, PCR products were cleaned using AMPure XP PCR

(Beckman Coulter Inc, Beverly MA). Samples’ concentrations were measured and equal volumes of all samples were pooled into one flow cell and sequenced using Illumina

MiSeq 300-base, paired-end kit at the Molecular and Cellular Imaging Center located at the Ohio Agricultural Research and Development Center in Wooster, OH.

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Sequence analysis using bioinformatics tool: MiSeq sequencing data was downloaded from Base space server and processed using updated Mothur-1.35 pipeline as described previously (Kozich, Westcott, Baxter, Highlander, & Schloss, 2013). Briefly, as a first step, pair end data set was demultiplexed, quality checked and was trimmed to remove adaptor, spacer and primer sequences using cutada tool in Linux terminal. Trimming followed by contigs formation resulted in sequence length of 280 bp and chimeric sequences were checked before alignment. Clustering into operational taxonomic units

(OTU) at a 97% sequence similarity was performed against Silva reference database(Pruesse et al., 2007). Taxonomy file was converted to Biom file in Mothur and further imported along with all the variables into R using Phyloseq (McMurdie &

Holmes, 2013). Alpha and beta diversity indices were calculated after normalizing the data and ignoring low abundant (1 time in at least 20%) OTU’s of the samples. To further predict the functional categorization of the sequences, each data set submitted to

PICRUSt tool at the Galaxy server (Langille et al., 2013). Hence generated OTUs were subjected for microbial phenotype analysis using alpha diversity and beta diversity indices as described in Mothur Miseq SOP.

Statistical analysis:

Statistical analysis of the clinical and para-clinical correlates was done in GraphPad

Prism 5 (GraphPad Software, Inc., CA, USA). Mean RV fecal shedding, diarrhea scores, and pre and para-clinical correlates were compared by one-way ANOVA (ANOVA-

111 general linear model), followed by Duncan’s multiple range test. P value of ≤ 0.05 was considered as significant.

For microbiome analysis, statistical analysis was performed as described previously with few modifications(Debenport et al., 2015). Briefly, OTU containing read counts (for each OTU in each sample), taxonomy information (for each OTU), and sample metadata (for each sample) were exported from Mothur into R using phyloseq(McMurdie & Holmes, 2013). The sequences were excluded that were observed at very low frequencies (i.e. OTUs representing less than 0.001% of the total number of sequences from each library were removed). OTU tables were placed in a subset for each comparison and formatted for the DESeq2 package in R(Love, Huber, & Anders, 2014).

The differential abundance of each OTU by sample type was determined using DESeq2 and sequences for all OTUs belonging to genera shared between experiments were extracted from representative sequence files in Mothur. Sequences for each genus were subsequently aligned using ClustalW tool (Larkin et al., 2007), and trees were constructed using the neighbor-joining method with 1,000 bootstrap replicates in

Geneious 6.0.3 (Biomatters). Further subsets of OTUs within a genus were defined by bootstrap values over 90(Debenport et al., 2015).

Functional metagenomics: To further predict the functional categories of the sequences, each data set was submitted to PICRUSt tool at the Galaxy server (Langille et al., 2013).

Generated OTUs were subjected for microbial phenotype analysis using alpha and beta diversity indices as described in Mothur Miseq SOP. Protein sequences similarity ≥ 65% and alignment length of 50 bp was used to categorize functions. The percentages of

112 specific sequences associated with functional categories were obtained and compared between different treatments as described previously (Langille et al., 2013).

3.4 Results

HM pigs with deficient diet exhibited more severe HRV diarrhea

As nutritional status has a direct correlation with the weight gain in initial stages of infancy (Vieira et al., 2014), we measured the HM pigs’ weights that were provided with deficient and sufficient diet since birth. The piglets’ weight at birth contributes to the cumulative body weight gain; thus, we calculated the percentage normal body weight gain during the experiment and compared it between the two different diet treatment groups. From the beginning to the end of the experiment, HM pigs maintained on deficient diet showed diminished normalized weight gain compared to HM pigs on sufficient diet. Interestingly, following HRV challenge, on day-10 (PBCD10), HM pigs with deficient diet exhibited a severe decline in body weight gain; significant differences were observed on days 19, 22 and 28. End point (day-28) body weight measurement showed that HM pigs with deficient diet were severely stunted which was characterized by approximately half of the body weight (220%) gain compared with the HM pigs with sufficient diet (420%) (Figure 3.2). Although it is difficult to attribute the major decline in body weight in HRV infected HM pigs solely to deficient diet, as also seen in malnourished infants in developing countries, we anticipate that the both RV infection and/ or malnutrition might have contributed to the stunted growth.

Malnutrition affects the gut barrier environment and further worsens the diarrheal symptoms in infants (Guerrant et al., 2008); however, few studies have investigated the

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HRV diarrheal severity with respect to malnutrition. To rule out the possibility of deficient diet itself inducing diarrhea before HRV challenge, we monitored the diarrheal scores at three different time points (days 5, 7 and 9) prior to HRV challenge. Neither sufficient nor deficient diet induced any diarrhea prior to HRV challenge in the HM pigs, suggesting that the dietdid not play a role in inducing diarrhea (Figure 3.3). To determine the effect of diet post HRV challenge in HM pigs, we monitored diarrhea scores for seven days after HRV challenge. Post HRV challenge, HM pigs with deficient diet exhibited higher diarrheal scores compared to HM pigs with sufficient diet.

Although expected, our study further highlighted the significance of sufficient diet in

HRV severity as HM pigs with deficient diet suffered from severe diarrhea (i.e. diarrheal score of 3 for three days compared to less than 2 diarrheal score for 2 days in HM pigs with sufficient diet). In particular, post HRV challenge days 3 and 5 showed a significant difference in the diarrheal scores in comparison to HM pigs with sufficient diet (Figure

3.3). In summary clinical parameters like body weight gain and HRV diarrheal severity in undernourished HM pigs most likely mirror the malnourished infants with HRV diarrhea.

HM pigs with deficient diet showed increased fecal HRV shedding post HRV challenge

An infectious dose, as low as 10 wild type RV particles is sufficient to cause diarrhea in a susceptible individual and furthermore, an attack rate as high as 50% in children in close contact with infected children, increases susceptibility(Anderson & Weber, 2004; Bishop,

1996; Ward et al., 1986; Yen et al., 2011). Hence, the amount of HRV shedding (per gram of feces) and the duration of shedding in infected individuals are of paramount

114 importance. HRV challenged HM pigs with deficient diet showed 15-fold increases in the level of HRV shedding in feces compared with HM pigs with sufficient diet on PCD2

(Figure 3.4), suggesting that the undernourishment in HRV infected children could further facilitates dissemination of HRV infection to a wider susceptible population compared to well-nourished RV infected children.

Transplantation of HM resulted in a similar microbial ecology in the HM pigs

We transplanted HM four days after birth into neonatal piglets. In our pilot study, HM pigs feces on sufficient diet one week (PBCD7) after HM transplantation showed almost similar microbial composition as seen in the original HM (Figure 3.5). Further we verified the similar microbial structure presence at the tissue level in the ileum of HM pigs suggesting that one week of time is enough to establish most of the microbial communities present in the HM. Firmicutes and Proteobacteria dominated the microbial composition in both HM and HM pigs following transplantation. Overall the ileum more closely mirrored the microbial fecal HM communities as the low abundant

Alteromonadales (2%) bacterial taxa structure was recapitulated as that of HM, and was completely absent in the HM pigs fecal samples. One specific bacterial community

‘Coriobacteriacea / Coriobacteriales’, although present in the HM in low abundance, was not observed in HM pig samples (feces or ileum) suggesting that complete recapitulation of microbial communities structure does not occur in the HM pig model or it may be the

Coriobacteriacea community is more labile (from frozen culture) or needs more time to establish in the pig gut. There were also minor differences in the percentage of relative

115 abundance in bacterial taxa distribution when original HM, HM pig feces and ileum samples were compared (Figure 3.5).

HRV challenge increases Clostridium and unclassified genus load in HM pigs with deficient diet

Fecal samples collected at three different time points (PBCD3, PBCD6, PBCD9 or PCD-

1) before and after HRV challenge (PBCD12 or PCD2, PBCD15 or PCD5 and PBCD 22 or PCD12) were analyzed for the microbial structure. Microbial structures of feces samples between deficient and sufficient HM pigs, before HRV challenge were similar at the phyla, order, and class levels where Proteobacteria, Enterobacteriales,

Gammaproteobacteria were predominant, respectively (Figure 3.6, 3.7 and 3.8;

Appendix M, N and O). At the genus level, the major populations of bacteria were unclassified except for the presence of the Sphingomonas genus at PBCD9/PCD-1 in the deficient HM compared to the sufficient HM pigs. At post challenge days 2 and 5 (PCD2 and PCD5), an increase in the relative abundance of the enteric pathogen Clostridium genus was observed in HM pigs on deficient diet but not on PCD12 after challenge compared to the HRV challenged HM pigs on sufficient diet (Figure 3.9; Appendix P).

Overall, an increase in the relative abundance of unclassified bacteria and eClostridium genus were characteristic of the deficient HM group compared to the sufficient challenged HM pigs.

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Decreased diversity and richness in intestinal tissue samples was characteristic of

HM pigs on deficient diet post HRV challenge

In HRV challenged HM pigs on deficient diet, across all the intestinal tissues the diversity and richness of microbes were suppressed. At the phyla level, Proteobacteria was more abundant irrespective of the gut tissues in the HM pigs on deficient diet

(Figure 3.10, Appendix Q). In contrast, the more abundant Bacteroidetes and unique presence of Deinococcus-Thermus phyla in HM pigs with sufficient diet was observed in all gut tissues. Similarly, the unique presence of Thermales and more abundant

Bacteroidales were signatures of the sufficient HM pigs; while, Clostridiales and

Enterobacteriales were present in HM pigs with deficient diet (Figure 3.11; Appendix

R). At the class and genus levels, the unique presence of Deinococci-Meiothermus and more abundance of Betaproteobacteria-Alistipes were seen in HM pigs on sufficient diet.

Whereas greater abundance of Gammaproteobacteria-Clostridium and an unclassified genus were the hallmark of HM pigs on deficient diet (Figure 3.12 and 3.13; Appendix

S and T).

3.5 Discussion

The enteric pathogens and/or their toxins can breach the complex gut barrier leading to leakage of luminal contents into the underlying lamina propria. The immune cells located in the lamina propria potentially mediate the deleterious inflammatory effects resulting in diarrhea or disrupted absorptive functions (Su et al., 2009). On the other hand, nutritional status, gut-microflora, and lack of stress have been shown to play roles in maintaining gut barrier function (Guerrant et al., 2008; Kolling, Wu, & Guerrant, 2012; Zareie et al.,

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2006). Perturbations of the nutritional status and alteration of the gut-microflora is evident in persistent malnutrition and enteric infections (Guerrant et al., 2008; Mondal et al., 2012). Altered levels of intraepithelial lymphocytes result in sub-optimum immune responses, and blunted villi, crypt hypertrophy, and further malabsorption, are the intestinal changes seen during the malnourished state in infants that ultimately lead to decline in infant growth(Guerrant et al., 2008; Kolling et al., 2012).

The triad of ‘diet-gut microflora-host response’ increasingly investigated by scientific communities, especially in infants due to the recent concept of ‘first 100days of life’, is critically important in an individuals’ overall development(J. I. Gordon,

Dewey, Mills, & Medzhitov, 2012). The 1000 days spans the beginning of pregnancy to 2 years of age in infants. This window influences health and physiology lifelong where gut microflora is very fragile and can be easily altered. On either end of the human cycle i.e. infant age (< 3 years) and old age (> 65 years) the plasticity of gut microflora is well documented(Kolling et al., 2012). The physiology of the intestine, dietary habits, and level of exercise account for the low buffering capacity of the gut microbiota to resist external insults at either end of the human cycle(Kolling et al., 2012). The adult gut microflora tends to have increased buffering capacity to resist external insults. Although until now the normal perception is that increased diversity and richness of gut microflora promotes good health, this has not been conclusively proven. Due to the increased diversity of the gut microflora among individuals, it is difficult to establish a core healthy microbiome concept, although researchers have tried to categorize the gut-microflora of individuals based on the predominant phyla ‘enterotypes’ like Bacteriodes, Prevotella, and Ruminococcus (Gerritsen, Smidt, Rijkers, & de Vos, 2011) (Arumugam et al., 2011).

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The present concept is that the buffering capacity of the gut microflora dictates the healthy vs susceptible gut microflora(Soen, 2014). Cumulative evidence using HM animal models have indicated that the different populations of the microbiota members can have either positive or negative impacts on the host depending upon their relative abundance and community structure and the host status. For example, when the host defense system was compromised especially in the gut, the normal microbial community structure was disrupted, resulting in normal innocuous microbes becoming pathobionts if they carry virulence factors (J. I. Gordon et al., 2012). The further release of bacterial components like LPS stimulates the immune system, thereby mediating inflammation.

The levels of disturbance and the duration of disturbance in the gut microflora accounts for the outcome in the host(Kau, Ahern, Griffin, Goodman, & Gordon, 2011). Agents like diet, fecal transplant, antibiotics, and probiotics have also been shown to significantly moderate the composition and structure of the gut microbiota in beneficial or deleterious ways(Heinritz et al., 2013; Marchesi et al., 2015; Nagpal, Yadav, & Marotta, 2014;

Penders et al., 2006).

In the elderly, the individual gut microflora tends to have decreased abundance of beneficial microbial populations like Bifodobacteria and Firmicutes with increased enterpathogens like facultative anaerobes (Kolling et al., 2012). This dramatic change in the gut microflora increases the susceptibility of the elderly to C. difficile infection (CDI).

Antibiotic treatment leads to lack of colonization resistance, with outgrowth of C. difficile, leading to dramatic decreases in diversity and richness of the gut microflora(Cucchiara, Iebba, Conte, & Schippa, 2009). Hence as a prophylactic measure, probiotics are used and as a therapeutic measure, fecal transplants are used in recurrent

119 antibiotic resistant CDI subjects(Gerritsen et al., 2011). In children, changes in the gut microbial composition have been linked to childhood allergies and inflammatory bowel diseases (IBD), obesity, and type I diabetes (De Filippo et al., 2010). For example in the case of allergies, it has been established that the gut microbiota in infants modulates the mucosal immune response to environmental allergens , and thereby dictates the host outcome(Tannock, 2007). Similar to CDI conditions in elderly individuals, decrease in the beneficial Bifodobacterial populations have been associated with the allergic conditions in children(Kolling et al., 2012). In IBD, such as Crohn's disease and ulcerative colitis, altered gut microflora characterized by loss of microbial diversity and richness, especially Clostridial cluster IV members (e.g., Faecalibacterium prausnitzii) was documented (Kolling et al., 2012). An increase in the Bacteroidetes:Firmicutes ratios and a predominance of opportunistic Proteobacteria have been reported in pediatric and adult IBD patients(Cucchiara et al., 2009). Similarly in our study, HRV challenged malnourished HM pigs showed decreased diversity as well as richness in the intestinal tissue microbial ecology. This was accompanied by a significant increase in the population of Proteobacteria where the ratios of Bacteroidetes: Proteobacteria, especially at the jejunum and ileum were 0.28 and 0.57, respectively. On the contrary, the sufficient diet HRV challenged HM pigs had Bacteroidetes: Proteobacteria ratios of 1.13 and 0.93, in jejunum and ileum, respectively (Figure 3.10). Even the mean square distance (MSD) plot analysis for microbial communities’ distribution in the tissue showed clear clustering of sufficient diet microbial communities mainly in the small intestinal sections (Figure

3.14). The changes in gut microflora in our study may be due to the combined effects of any of the following factors: (i) malnutrition, as malnutrition was shown to affect gut

120 microflora structure and composition (ii) RV pathogenesis- previously studies have shown that enterpathogens including RV have significant effects on the gut flora(H.

Zhang et al., 2014); and (iii) the host response or immune response(J. I. Gordon et al.,

2012), host natural defense system is essential for maintaining the normal organization of the gut microflora. A logical question to answer is which among these factors has a significant effect on the infant gut microflora and in turn on the infant physiology in the natural setting? Extensive literature reviews performed by Guerrant et al suggests that recurrent episodes of diarrhea caused by enteropathogens has a major effect on the gut microflora (Guerrant et al., 2008). To substantiate this claim, previous studies have shown that malnourished children who did not have a diarrheal burden, likely due to enteric infections, did indeed gain weight normally compared to nourished children, while the increasing incidence of diarrhea in malnourished children progressively decreased the weight gain(Petri et al., 2008; Schorling et al., 1990). Hence, in natural settings, it is clear that the recurrent episodes of diarrhea have the greatest effects on children's growth due to the cumulative effects on gut microflora and intestinal absorptive function, which is especially problematic in undernourished children

(Guerrant et al., 2008).

3.6 Acknowledgements

We greatly acknowledge Dr. Vlasova AN for developing the HM Gn pig model and developing and performing the malnutrition studies. We thank to Saranga Wijeratne at the Molecular and Cellular Imaging Center at the Ohio State University Ohio

Agricultural Research and Development Center (http://oardc.osu.edu/mcic/) who

121 provided assistance with the Illumina sequencing and analysis. We gratefully acknowledge the technical assistance of Dr. Juliette Hanson, and valuable assistance of

Juliet Chepngeno, Marcia V Lee, Ronna S Wood, John Blankenship and Rachelle Root during our animal studies. This work was supported by grants from the NIH, R01 and

NIAID AI099451 (Linda J Saif-PI, Anastasia N Vlasova/Gireesh Rajashekara-Co-PI) and federal funds appropriated to the Ohio Agricultural Research and Development Center

(OARDC) of The Ohio State University.

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Primer Sequence 515F- 5’ tcgtcggcagcgtcagatgtgtataagagacag NNN GAGTGCCAGCMGCCGCGGTAA 3 806R-5' gtctcgtgggctcggagatgtgtataagagacag NNNACGGACTACHVGGGTWTCTAAT 3' Table3.1: The V4-V5 variable regions targeted barcoded primers information. * Where lowercase letters indicate adaptor sequences, NNN barcodes and bold letters 16S RNA priming site.

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Infant fecal HRV microbiota Euthanasia challenge Piglets transplant derived by Day-32 cesarean Day-4 Day-14

PBCD3, 6 & 9 PBCD0 PBCD10 PBCD12 & 15 Sufficient/ PBCD22 or PBCD28 or PCD0 or PCD2 & 5 Deficient PCD12 or PCD14

Samples collection (fecal & blood) and measuring clinical parameters (diarrhea & weight)

Figure 3.1: Animal experimental design indicating different time points where variable were introduced, samples collected and animals sacrificed. Treatment groups includes:

Sufficient diet (Parmalat, bovine milk), virulent human rotavirus (VirHRV) challenge,

(n=5); Deficient diet (50% Parmalat, 50% water), HRV challenge, (n=4). HM pigs were challenged with 106 FFU/pig of VirHRV Wa (G1P[8] human strain. PBCD-post bacterial colonization day and PCD-post HRV challenge day.

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Normalized weight gain

450 400 350

300 *** 250 Deficient 200 Sufficient 150

% of initial bodyweight 100 HRV challenge 50

0 Day 1 Day 2 Day 5 Day 8 Day 12 Day 15 Day 19 Day 22 Day 28

Figure 3.2: The percentage of initial body weight gain was calculated by normalizing birth weight of piglets. Deficient diet HM pigs challenged with HRV showed a dramatic decline in the % initial body weight gain compared to the HRV challenged sufficient diet

HM pigs.

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Diarrhea severity 3.5

2.5

2 ** *** Deficient 1.5 Sufficient Diarrhea score Diarrhea 1

HRV challenge 0.5

0 Day 5 Day 7 Day 9 Day 10 Day 11 Day 12 Day 13 Day 14 Day 15 Day 16 Day 17

Figure 3.3: Severity of diarrhea in HM pigs was assessed based on the fecal consistency score. Score of 0 - normal; 1- pasty; 2 - semiliquid; and 3 -liquid consistency. HM pigs with fecal consistency scores of ≥ 1.5 were considered diarrheic. Increased severity of

HRV diarrhea in HM pigs nourished with deficit diet was observed compared to sufficient diet HM pigs.

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Rotavirus shedding 5 2.5×10

2.0×10 5

1.5×10 5

5 FFU/ml 1.0×10

5.0×10 4

0 0 1 2 3 4 5

Post-challenge day Deficient diet Sufficient diet

Figure 3.4: VirHRV fecal shedding titers were determined by cell culture immunofluorescence (CCIF) infectivity assay. The fecal HRV shedding was more in HM pigs with deficient diet compared to HM pigs with sufficient diet.

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Figure 3.5: Comparison of the community microbial profile among 2 month old infant fecal, HM pig ileum and HM pig fecal samples using16S rRNA variable region targeted sequencing. Transplantation of infant fecal microflora resulted in very similar microbial profiles in HM pigs at 1 week post transplantation .

128

Figure 3.5 continued

"Enterobacteriales Oceanospirillales Halomonadaceae " 1% 1% Alteromonadales 4% Enterobacteriacea 0% Coriobacteriaceae e 0% 4% Coriobacteriales 0% Ruminococcaceae Firmicutes 4% 21% Lachnospiraceae 1%

Bacillales 4%

Lactobacillales 4% Clostridia 12% Actinobacteria 3% "Actinobacteria" Oceanospirillales 3% 2% Halomonadaceae Clostridiales 12%

129 2% Enterobacteriaceae Alteromonadales Gammaproteobact 5% 1% eria Bacilli "Enterobacteriales" 7% 6% "Proteobacteria"

5% 10% HM pig fecal microflora Coriobacteriaceae Firmicutes 1% Coriobacteriales 17% Alteromonadales Oceanospirillales Halomonadaceae 1% Enterobacteriaceae 2% 2% 2% Ruminococcaceae 4% 3% "Enterobacteriales" Coriobacteriales 4% Lachnospiraceae 0% Firmicutes 2% Coriobacteriaceae Clostridia 0% 17% Ruminococcaceae 10% Bacillales 1% 3% Lachnospiraceae Lactobacillales 1% 5% Bacillales 1% Clostridiales Lactobacillales Clostridia 10% 5% 9% Actinobacteria Actinobacteria 2% 2% "Actinobacteria" "Actinobacteria" 2% "Proteobacteria" 2% 10% Clostridiales Bacilli Gammaproteobacter Gammaproteobacte 9% 6% ia Infant fecal microflora ria 12% 9%

"Proteobacteria" Bacilli 19% 6%

HM pig ileum microflora

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Figure 3.6: Comparison of the microbial community profiles in feces between HM pigs with deficient vs sufficient before and after HRV challenge. The data presented at the

‘Phyla’ level, where an increased Proteobacteria population was observed in deficient

HM pigs compared to sufficient HM pigs.

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Figure 3.7: Comparison of the microbial community profiles in feces between HM pigs with deficient vs sufficient before and after HRV challenge. The data presented at the

‘Order’ level, where an increased Enterobacteriales population was observed in deficient

HM pigs in particularly after HRV challenge compared to sufficient HM pigs.

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PBCD 3 PBCD 6 PCD -1 PCD2 PCD5 PCD12

Def Suf Def Suf Def Suf Def Suf Def Suf Def Suf

Before HRV After HRV

Figure 3.8: Comparison of the microbial community profiles in feces between HM pigs with deficient vs sufficient before and after HRV challenge. The data presented at the

‘Class’ level, where an increased Gammaproteobacteria population was observed in deficient HM pigs in particularly after HRV challenge compared to sufficient HM pigs.

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PBCD 3 PBCD 6 PCD -1 PCD2 PCD5 PCD12

Def Suf Def Suf Def Suf Def Suf Def Suf Def Suf

Before HRV After HRV

Figure 3.9: Comparison of the microbial community profiles in feces between HM pigs with deficient vs sufficient before and after HRV challenge. The data presented at the

‘Genus’ level, where an increased Clostridium and unclassified genus was observed in deficient HM pigs in particularly after HRV challenge compared to sufficient HM pigs.

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Figure 3.10: Comparison of the microbial community profiles in intestinal tissue sections between HM pigs with deficient vs sufficient after 14 days (PCD14) of RV challenge.

The data was presented at the ‘Phyla’ level, where an increased abundance of

Proteobacteria population was observed in HM pigs with deficient diet irrespective of tissues while the Bacteroidetes population was abundant in HM pigs with sufficient diet especially in jejunum and ileum.

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Figure 3.11: Comparison of the microbial community profiles in intestinal tissue sections between HM pigs with deficient vs sufficient after 14 days (PCD14) of RV challenge.

The data was presented at the ‘Order’ level, where an increased relative abundance of

Enterobacteriales population was observed in HM pigs with deficient diet irrespective of tissues while the Bacteroidales population was abundant in HM pigs with sufficient diet especially in jejunum and ileum.

135

Figure 3.12: Comparison of the microbial community profiles in intestinal tissue sections between HM pigs with deficient vs sufficient after 14 days (PCD14) of HRV challenge.

The data was presented at the ‘Class’ level, where an increased relative abundance of

Gammaproteobacteria population was observed in HM pigs with deficient diet irrespective of tissues while the abundant Betaproteobacteria in jejunum and ileum, unique presence of Deinococci in all tissue in HM pigs with sufficient diet.

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Figure 3.13: Comparison of the microbial community profiles in intestinal tissue sections between HM pigs with deficient vs sufficient after 14 days (PCD14) of HRV challenge.

The data was presented at the ‘Genus’ level, where an increased relative abundance of enteric pathogen Clostriduim and unclassified genus was observed in HM pigs with deficient diet irrespective of tissues while the abundant Alistipes in jejunum and ileum, unique presence of Meiothermus in all tissue in HM pigs with sufficient diet.

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Feces Tissue

Figure 3.14: Comparison of the microbial community in fecal and tissue samples from deficient and sufficient HM pigs using mean square distance (MSD) plot by bray distance method. In tissue, HM pigs with sufficient diet clustered more uniformly together compared with HM pigs with deficient diet. In feces, some degree of clustering was evident between two diets in PCD2, but not at PBCD3 and PCD5 time points.

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CONCLUSIONS AND FUTURE DIRECTIONS

Objective 1: Although in our first objective we used a Gn pig model to understand the gut transcriptome responses, similar observations have been made using other conventional animal models as well as in clinical trials, supporting the usefulness of Gn pigs in elucidating mechanisms underlying the beneficial roles of probiotics. For example, comparison of transcriptome responses of germfree piglets vs. the piglets with intestinal microbiota indicated that genes involved in biological processes like epithelial cell turnover, nutrient transport and metabolism, xenobiotic metabolism, JAK-STAT signaling pathway, and immune response were altered(Chowdhury et al., 2007).

Similarly, administration of LA and LGG in a human interventional study identified mucosal transcriptional responses associated with immune response, tissue growth and development, and ion homeostasis(van Baarlen et al., 2011). In our study, genes involved in nutrient transport and metabolism, immune responses, and epithelial cell turnover are also the major classes of genes that were differentially regulated by the colonization of

Gn pigs with lactobacillus species. In addition, previous studies using Gn and conventionalized mice revealed major molecular responses in metabolism, intestinal morphology and cell proliferation and immunity as early as day 1–4 post conventionalization, with a pronounced changes occurring after day 4 post conventionalization(El Aidy et al., 2013; El Aidy et al., 2012). Consistent with these findings, in our study more robust responses were observed on PBCD7, particularly in duodenum, illustrating the tissue-specific changes in the biological processes. However

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detailed temporal analysis of transcriptome responses to lactobacilli colonization in

neonatal Gn and conventional pigs (mimicking the infants) would provide better

understanding of the biological processes modulated by these known probiotics. In

addition, studies of the host proteome and metabolome should further enhance our

understanding of how probiotic bacteria confer health benefits. Understanding these

interactions will provide information for the rational treatment of disease phenotypes

with known probiotics and will have significant implications in personalized healthcare medicine.

Objective 2: The ecology of gut microflora and the factors that influence its niche are increasingly recognized as a potential signature to diagnose and/or to treat the gut disorders. Continuous advancement in the field of ‘Omics’ has transformed our

understanding of the interactions between microbial communities and their niche. A

recent review highlighted the significance of an increased prevalence of the bacterial

phylum Proteobacteria as a marker for dysbiosis (an unstable microbial community) and

a potential diagnostic criterion for diseases in the humans(Shin, Whon, & Bae, 2015).

The Proteobacteria are facultative anaerobes and known to influence the ratios of other

resident bacterial phyla thereby the gut homeostasis (Shin et al., 2015). Balanced gut

microbiota that has symbiotic association with the host immune system and metabolism,

known to suppress the uncontrolled expansion of Proteobacteria(Shin et al., 2015).

Increase in Proteobacteria has shown to reflect an unstable gut microbial community in

the intestinal disorders (Fei & Zhao, 2013; Morgan et al., 2012). An increase in the

Bacteroidetes: Firmicutes ratios and a predominance of opportunistic Proteobacteria have

been reported in pediatric and adult IBD patients(Cucchiara, Iebba, Conte, & Schippa,

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2009). Similarly in our study, HRV challenged malnourished HM pigs showed decreased

diversity as well as richness in the intestinal tissue microbial ecology. This was

accompanied by a significant increase in the population of Proteobacteria where the ratios

of Bacteroidetes: Proteobacteria, especially at the jejunum and ileum were 0.28 and 0.57,

respectively. On the contrary, the sufficient diet HRV challenged HM pigs had

Bacteroidetes: Proteobacteria ratios of 1.13 and 0.93, in jejunum and ileum, respectively.

Even the mean square distance (MSD) plot analysis for microbial communities’

distribution in the tissue showed clear clustering of sufficient diet microbial communities

mainly in the small intestinal sections.

The relation between the metabolic disorders and the expansion of Proteobacteria

has been recently described (Fei & Zhao, 2013). Increase in the one such family of

Proteobacteria ‘Enterobacteriaceae’ was characterized by the decrease in the weight-loss

in obese subjects (Fei & Zhao, 2013). Further it was shown that the mono-colonization

with Enterobacter cloacae B29, the one isolated species from the obese human gut, was sufficient to induce obesity(Fei & Zhao, 2013). These finding supports that the gut dysbiosis characterized by an abundance of Proteobacteria, likely to represent an active feature, rather than a passive consequence in the metabolic disturbances (Fei & Zhao,

2013; Shin et al., 2015). Recent studies have demonstrated that the distinct presence of the microbiota and the microbiome in malnourished children from the developing

countries (Smith et al., 2013; Subramanian et al., 2014). In these studies, a dominance of

Proteobacteria and a low diversity of gut microbiota were commonly observed in

undernourished children, (as also seen in our HM pigs with deficient diet, second

objective) and further regarded as a major hindrance to postnatal maturation of the gut

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microbiota. Additionally, a recent study has revealed a mechanistic interrelation between

the Enterobacteriaceae and the sub-optimal mucosal immunity under malnutrition (Kau et

al., 2015). Thus the agents like probiotics that are known to restore the gut homeostasis

are regularly used to ameliorating the intestinal disorders. In this context, our first

objective also indicated that the LA and LGG stimulate the gut immunity and host

metabolism (Kumar et al., 2014), hence it is likely that use of these probiotics may restore the gut dysbiosis conditions in intestinal disorders.

Future directions: The observed changes in the gut microbial ecology in our study are difficult to attribute to diet or HRV. Therefore age matched control groups (deficient and sufficient diets no HRV challenge) will be used in our future animal experiments. Further significance of changes in the microbial ecology of the gut to the host physiology will be predicated by analyzing probable biological function (functional metagenomics). It also helps us to identify a particular biological function (s) that is signature of healthy or diseased microbial community. Identifying unique genus in HRV challenged HM pigs with sufficient diet and testing their probably role in restoring the dysbiosis can also be exploited in future mono-colonization Gn pig experiment to moderate the HRV severity in malnourished pigs. Alternatively, probiotics can also be tested to restore the gut dysbiosis. We also would like to investigate the effect of antibiotic treatment in malnourished HM pigs on HRV severity and on the gut microflora to simulate the natural setting conditions in developing countries.

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Fei, Na, & Zhao, Liping. (2013). An opportunistic pathogen isolated from the gut of an obese human causes obesity in germfree mice. ISME J, 7(4), 880-884. doi: http://www.nature.com/ismej/journal/v7/n4/suppinfo/ismej2012153s1.html

Kau, A. L., Planer, J. D., Liu, J., Rao, S., Yatsunenko, T., Trehan, I., . . . Gordon, J. I. (2015). Functional characterization of IgA-targeted bacterial taxa from undernourished Malawian children that produce diet-dependent enteropathy. Sci Transl Med, 7(276), 276ra224. doi: 10.1126/scitranslmed.aaa4877 Kumar, A., Vlasova, A. N., Liu, Z., Chattha, K. S., Kandasamy, S., Esseili, M., . . . Saif, L. J. (2014). In vivo gut transcriptome responses to Lactobacillus rhamnosus GG and Lactobacillus acidophilus in neonatal gnotobiotic piglets. Gut Microbes, 5(2), 152-164. doi: 10.4161/gmic.27877

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Shin, Na-Ri, Whon, Tae Woong, & Bae, Jin-Woo. (2015). Proteobacteria: microbial signature of dysbiosis in gut microbiota. Trends in Biotechnology, 33(9), 496-503. doi: http://dx.doi.org/10.1016/j.tibtech.2015.06.011

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Subramanian, S., Huq, S., Yatsunenko, T., Haque, R., Mahfuz, M., Alam, M. A., . . . Gordon, J. I. (2014). Persistent gut microbiota immaturity in malnourished Bangladeshi children. Nature, 510(7505), 417-421. doi: 10.1038/nature13421 van Baarlen, Peter, Troost, Freddy, van der Meer, Cindy, Hooiveld, Guido, Boekschoten, Mark, Brummer, Robert J. M., & Kleerebezem, Michiel. (2011). Human mucosal in vivo transcriptome responses to three lactobacilli indicate how probiotics may modulate human cellular pathways. Proceedings of the National Academy of Sciences, 108(Supplement 1), 4562-4569. doi: 10.1073/pnas.1000079107

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Appendix A: Number of IPA mapped genes ‘unregulated’ by both LA and LGG.

* PBCD- post bacterial colonization day. Unregulated’ refers to number of genes that are commonly modulated by both LA and LGG.

PBCD* Tissue Number of genes 1 Duodenum 12

Ileum 12

7 Duodenum 28

Ileum 13 .

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Appendix B. Gene involved in metabolism modulated by LA and LGG in duodenum and ileum. * FC refers to fold change

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Appendix B Continued LA Gene symbol and description FC* Tissue Known functions and day Genes involved in energy metabolism COX3, cytochrome c oxidase III -20.45 Duo 1 Cytochrome c oxidase sub units CYTB, cytochrome b -8.04 Duo 1 Sub unit of cytochrome b complex COX1, cytochrome c oxidase subunit I -21.07 Ile 1 Cytochrome c oxidase sub units ATP5B, ATP synthase, H+ transporting, mitochondrial F1 complex, beta polypeptide -11.04 Ile 1 Mitochandrial ATPase synthase ATP5F1, ATP synthase, H+ transporting, mitochondrial Fo complex, subunit B1 7.8 Ile 1 Mitochandrial ATPase synthase CYP3A39, cytochrome P450 3A39 2.74 Ile 1 Electron carrier activity Genes involved in vitamin and mineral metabolism ACE, Angiotensin I converting enzyme (peptidyl-dipeptidase A) 1 2.12 Duo 1 Electrolyte balance 170 BCMO1, beta-carotene 15,15'-monooxygenase 1 2.64 Duo 1 Vitamin A metabolism

CYP24A1, cytochrome P450, family 24, subfamily A, polypeptide 1 86.94 Duo 1 Vitamin D metabolism CYP4A21, cytochrome P450, family 4, subfamily A, polypeptide 21 2.16 Ile 1 Vitamin D metabolism FTH1, ferritin, heavy polypeptide 1 147.65 Duo 7 Iron storage HAMP, Hepcidin antimicrobial peptide 1.8 Duo 7 Macrophage iron storage CP, Ceruloplasmin (Ferroxidase) 1.7 Duo 7 Copper and iron homeostasis Carbohydrate metabolism PCK1, phosphoenolpyruvate carboxykinase 1 (soluble) 2.13 Duo 1 Gluconeogenesis GPD1, glycerol-3-phosphate dehydrogenase 1 (soluble) 2.126 Ile 1 Major link between carbohydrate and lipid metabolism CHST15, carbohydrate (N-acetylgalactosamine 4-sulfate 6-O) sulfotransferase 15 1.73 Duo 7 Synthesis of proteoglycans and also acts as a B-cell surface signaling receptor Lipid metabolism DGAT2, diacylglycerol O-acyltransferase 2 2.1 Duo 1 Triglyceride synthesis CLPS, colipase, pancreatic 2.9 Duo 1 prevent the inhibitory effect of bile salt on lipase KPLA2G1B, phospholipase A2, group IB (pancreas) 3.8 Duo 1 Fatty acid release PNLIPRP1, pancreatic lipase-related protein 1 8.7 Duo 1 Role in dietary fat digestion SCD, stearoyl-CoA desaturase (delta-9-desaturase) 2.5 Ile 1 Key enzyme in fatty acid metabolism Continued

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Appendix B Continued PNPLA4, patatin-like phospholipase domain containing 4 2.8 Ile1 Lipid hydrolase MGLL, monoglyceride lipase 1.8 Duo 7 Hydrolyze intracellular triglyceride stores in adipocytes and other cells FAAH, fatty acid amide hydrolase 2.0 Duo 7 Catabolizes bioactive lipids called the fatty acid amides APOA1, apolipoprotein A-I 303.5 Duo 7 Role in cholesterol metabolism Protein metabolism OXCT1, 3-oxoacid CoA transferase 1 -1.83 Duo 7 Branched chain aa degradation, synthesis and degradation of ketone bodies MTRR, 5-methyltetrahydrofolate-homocysteine methyltransferase reductase 1.7 Duo 7 Helps in process of aa synthesis OAZ2, ornithine decarboxylase antizyme 2 1.777 Duo 7 ornithine decarboxylase inhibitor 171 thereby prevent polyamine

synthesis SDS, Serine dehydratase 2.1 Ile 7 Catalyzes deamination of L- serine to yield pyruvate PHGDH, phosphoglycerate dehydrogenase 3.201 Ile 7 Involved in serine biosynthesis Drug metabolism TPMT, thiopurine S-methyltransferase -2.0 Duo 1 Metabolizes thiopurine drugs DPEP1, dipeptidase 1 (renal) -2.3 Duo 1 Renal metabolism of glutathione and conjugates CA3, carbonic anhydrase III, muscle specific -2.6 Ile 1 Reversible hydration of carbon dioxide CA3, carbonic anhydrase III, muscle specific 3.3 Ile 7 Reversible hydration of carbon dioxide CKM, creatine kinase, muscle 5.6 Ile 7 energy reservoir in muscle LGG Gene symbol and description FC* Tissue Known functions and day Genes involved in energy metabolism

COX1, cytochrome c oxidase subunit I -10.69 Duo 1 Cytochrome c oxidase sub units COX17, cytochrome c oxidase assembly homolog (S. cerevisiae) 7.28 Ile1 Cytochrome c oxidase sub units Continued

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Appendix B Continued ATP5F1, ATP synthase, H+ transporting, mitochondrial Fo complex, subunit B1 7.82 Ile1 Mitochandrial ATPase synthase COX1, cytochrome c oxidase subunit I -2.02 Ile7 Cytochrome c oxidase sub units Genes involved in vitamin and mineral metabolism CYP24A1, cytochrome P450, family 24, subfamily A, polypeptide 1 86.94 Duo 1 Vitamin D metabolism DIO1, deiodinase, iodothyronine, type I -2.1 Duo 1 Activation and de- activation of thyroid hormone CYP24A1, cytochrome P450, family 24, subfamily A, polypeptide 1 -12.38 Ile 1 Vitamin D metabolism GIF, gastric intrinsic factor (vitamin B synthesis) -4.78 Ile 1 Vitamin B12 absorption DIO3, deiodinase, iodothyronine, type III 2.25 Ile 1 Thyroid hormone metabolism RDH16, retinol dehydrogenase 1 2.14 Ile 1 Vitamin A metabolism IGFBP3, insulin-like growth factor binding protein 3 2.54 Duo 7 Stimulates IGF1 production INSIG1, insulin induced gene 1 2.66 Duo 7 Cholesterol homeostasis IGF1, insulin-like growth factor 1 (somatomedin C) -2.65 Duo 7 Growth hormone

172 PTGS2, prostaglandin-endoperoxide synthase 2 -2.25 Duo 7 Prostaglandin metabolism IGFBP2, insulin-like growth factor binding protein 2, 36kDa 2.1 Duo 7 Activate or inhibits IGF1 effect

RDH16, retinol dehydrogenase 1 2.71 Duo 7 Vitamin A metabolism CYP2D25, vitamin D3 25-Hydroxylase 4.02 Duo 7 Vitamin D metabolism CYP2D25, vitamin D3 25-Hydroxylase 3.93 Duo 7 Vitamin D metabolism Carbohydrate metabolism PDK4, pyruvate dehydrogenase kinase, isozyme 4 2.5 Duo 7 Inactivate pyruvate dehydrogenase MIOX, myo-inositol oxygenase 3.1 Duo 7 Oxidizes myo-inositol to glucuronic acid G6PC, glucose-6-phosphatase, catalytic subunit 3.3 Duo 7 Key enzyme in glucose homeostasis AMY2, amylase, alpha 2B (pancreatic) 5.13 Duo 7 Hydrolyze oligo and polysaccharides LOC100621940, pyruvate kinase isozymes R/L-like -2.4 Duo 7 Plays a key role in glycolysis PGAM2, phosphoglycerate mutase 2 (muscle) -2.0 Ile 7 Involved in glycolysis. Lipid metabolism APOA1, apolipoprotein A-I 300.1 Duo 7 Role in cholesterol metabolism. SCD, stearoyl-CoA desaturase (delta-9-desaturase) -2.1 Duo 7 Hydrolyzes the inositol ring of phosphatedylino-sitol Continued

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Appendix B Continued ALOX12, arachidonate 12-lipoxygenase 2.8 Duo 7 Involved in arachidonic acid metabolism PNLIP, pancreatic lipase 4.3 Duo 7 Primary enzyme in hydrolyzing dietary fat LIPE, lipase, hormone-sensitive 2.0 Ile 7 Hydrolyze the first fatty acid from a triacylglycerol molecule CBR1, carbonyl reductase 1 2.4 Ile 7 Participates in arachidonic acid metabolism Protein metabolism SADC, arginine decarboxylase -2.0 Duo 1 Amino acid (aa)metabolism and urea cycle BCAT2, branched chain amino-acid transaminase 2, mitochondrial 2.5 Ile 1 Synthesis of the branched chain aa

173 TDH, L-threonine dehydrogenase 2.8 Ile 1 Facilitates the catabolism of threonine OAZ1, ornithine decarboxylase antizyme 1 15.67 Duo 7 ornithine decarboxylase inhibitor,prevent polyamine synthesis HPD, 4-hydroxyphenylpyruvate dioxygenase -2.03 Ile 7 Break down the aa tyrosine Drug metabolism FMO1, flavin containing monooxygenase1 2.1 Duo 1 Metabolism of drugs and xenobiotics DPEP1, dipeptidase 1 (renal) -4.1 Duo 1 Renal metabolism of glutathione and its conjugates CA2, carbonic anhydrase II -2.5 Duo 1 Reabsorption of sodium ions in the proximal tubule DHRS7, dehydrogenase/reductase (SDR family) member 7 -2.5 Duo 7 Metabolism of steroid, prostaglandins, retinoids, lipids and xenobiotics. CA2, carbonic anhydrase II -2.1 Duo 7 Reabsorption of Na+ in the proximal tubule GATM, glycine amidinotransferase 3.5 Duo 7 Synthesis of creatine CA1, carbonic anhydrase I 2.9 Ile 7 Transport carbon dioxide out of tissues CA3, carbonic anhydrase III, muscle specific -2.3 Duo 7 Reversible hydration of CO2

173

Appendix C: Top 5 upstream regulators involved.

Group Days PBCD Tissue Upstream regulators LA 1 Duodenum CEBPB, C/ebp , RELA, TWIST2, RXRG LGG 1 Duodenum CEBPA, SOX4, RXRG, Rxr, ELF3 LA 1 Ileum FOXO1, PPARGC1A, PPARA, MLXIPL, FKHR LGG 1 Ileum MYOD1, PAX8, GABPB1, POU5F1, ZNF91Δ 174 LA 7 Duodenum KLF3, USF2, HOXD3, UBE2K, IRX5

LGG 7 Duodenum FOS, SMAD3, NR0B2, FOXA2, STAT6 LA 7 Ileum NCOA2, RNA polymerase II, Id, SMARCA4, MEF2A LGG 7 Ileum JUNB, BHLHA15, RELA, TFAP2A, ALYREF * Common upstream regulators are indicated in bold; PBCD refers to post bacterial colonization day.

174

Appendix D: Genes involved in cell integrity and immunity. * FC refers to fold change

Gene symbol and description LA LGG Day 1 Duodenum GSTA1, Cytosolic and membrane bound form of Glutathioane S -10 -7.6 transferase LYZ, Lysozyme -2.4 -3.8 PIGR, polymeric immunoglobin receptors -2.2 -2.8 SFTPD, surfactant protein D -2.0 -2.0 REG3G, Regenerating islet-derived 3 gamma -2.5 -6.8 175 LOC100622689, MHC class I histocompatibility antigen -2.9

SLA-DQA1, MHC class II histocompatibility antigen -2.6 CCL20, chemokine ligand 20 -2.0 CCL28, chemokine ligand 28 -2.4 LOC396867, Stefin A8 -2.0 GZMA, Granzyme A (granzyme 1, cytotoxic T-lymphocyte- -2.0 associated serine esterase ) Day 1 Ileum CD40, CD40 molecule, TNF receptor superfamily member 5 -3.5 ATG4D, Autophagy related 4D, cysteine peptidase -2.2 MEG3, Maternally expressed 3 (non-protein coding) -2.0 LOC780409, chemokine ligand 126-like protein -3.0 LOC396866/67, Stefin A1/A8 2.5 CFH, complement factor H 2.0 XCR1, Chemokine (C motif) receptor 1 2.1 LOC100038010, CCL11 chemokine -2.4 SLA-5, MHC class I antigen 5 -2.9 MUC13, Mucin 13, cell surface associated -12.4 FCGR2B, Fc fragment of IgG, low affinity IIb, receptor (CD32) -2.8 BRCA1, Breast cancer 1, early onset -2.3 Continued

175

Appendix D Continued TP53BP1, Tumor protein p53 binding protein 1 2.0 Day 7 Duodenum EGF, epidermal growth factor -3.5 -3.1 LOC100037951, complement component C9 2.5 2.6 LOC100037955, complement component C8G 3.4 2.6 CD1D, CD1d molecule 1.9 2.0 CD164, sialomucin 20.1 18.0 TPD52, tumor protein D52 6.3 4.9 COL7A1, collagen, type VII, alpha 1 2.4 2.3 GLP2R, glucagon-like peptide 2 receptor 2.0 2.4 PCDH1, protocadherin 1 2.3 2.4 SOCS4, Suppressor of cytokine signaling 4 1.8 HAMP, Hepcidin antimicrobial peptide 1.8

176 GZMH, Granzyme H (cathepsin G-like 2, protein h-CCPX) -3.1 IL4 , interleukin 4 -2.7

CD274, CD274 molecule -2.6 CDH24, cadherin 24, type 2 -2.4 CLDN18, claudin 18 -2.5 CLDN8, claudin 8 2.0 MCOLN3, mucolipin 3 2.0 GZMA/GZMB, granzyme A/B (granzyme 1/2, cytotoxic T- 2.3 lymphocyte-associated serine esterase 3/1) LY75, lymphocyte antigen 75 2.5 CUL7, cullin 7 2.6 TCTP, translationally controlled tumor protein 17.6 LOC100155338, heat shock 27kDa protein 8 40.0 Day 7 Ileum CALCB, calcitonin-related polypeptide beta -2.1 -2.6 ITGB3, integrin, beta 3 (platelet glycoprotein IIIa, antigen CD61) 163 132 CXCL9, chemokine (C-X-C motif) ligand 9 5.0 3.9 ALCAM, activated leukocyte cell adhesion molecule 4.2 4.5 CYBA, cytochrome b-245, alpha polypeptide 2.2 2.242 CYBA, cytochrome b-245, alpha polypeptide 2.3 Continued

176

Appendix D continued SFTPB, surfactant protein B -2.4 CXCL2, chemokine (C-X-C motif) ligand 2 -2.1 CLU, clusterin 2.3 CLDN12, claudin 12 -2.12 PIGR, polymeric immunoglobulin receptor -3.6 LOC396866/67, stefin A1/A8 -2.2 -2.6 LOC100155338, heat shock 27kDa protein 8 51.6 38 SLA-3, MHC class I antigen 3 -2.2 SLA-6, MHC class I antigen 6 -3.0 SLA-DQA, MHC class II, DQ alpha -2.0 SLA-DQA1, MHC class II histocompatibility antigen SLA-DQA -4.3 SLA-DRB1, MHC class II histocompatibility antigen SLA-DRB1 -2.6

177 Functions

Day 1 Duodenum Detoxification of carcinogens, drugs, environmental toxins and products of oxidative stress thereby protecting cells from the ROS Anti- microbial agent whose natural substrate is the bacterial cell wall Facilitates the secretion of IgA and IgM Innate immune system collectin Antibacterial activity against Gram-positive bacteria Involved in antigen presentation in every nucleated cell Present on antigen-presenting cells and lymphocytes Chemoattractant of lymphocytes and dendritic cells towards the epithelial cells Chemotactic activity for resting CD4 or CD8 T cells and eosinophils Epidermal development and maintenance and proposed as prognostic and diagnostic tools for cancer Lysis of target cells by cytotoxic T lymphocytes and natural killer cells Day 1 Ileum Essential for broad variety of immune and inflammatory responses including Ig class switching, memory B cell development Cell homeostasis and cell remodeling during differentiation, metamorphosis and aging Are long non-coding RNAs (lncRNAs), demonstrated that these are tumor suppressor No know function Epidermal development and maintenance and proposed as prognostic and diagnostic tools for cancer Function is to regulate the Alternative Pathway of the complement system Continued

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Appendix D continued Transduces a signal by increasing the intracellular calcium ions level Selectively recruits eosinophils by inducing chemotaxis and implicated in allergic responses Involved in antigen presentation in every nucleated cell Lubrication, cell signalling to forming chemical barriers Phagocytosis of immune complexes and regulation of antibody production by B-cells Maintaining genomic stability, and it also acts as a tumor suppressor Tumor suppressor genes Day 7 Duodenum Inhibition of gastric acid secretion as well as GIT mucosal protection Member of the Membrane Attack Complex (MAC) which induces pores on membranes Formation of membrane attack complex (MAC) on bacterial cell membranes Mediate the presentation of primarily lipid and glycolipid antigens of self or microbial origin to T cells Cytoprotective or antiadhesive agents and as adhesion receptors

178 A novel molecular marker in ovarian cancer Anchoring fibril between the external epithelia and the underlying stroma

Stimulates intestinal growth and up-regulates villus height in the small intestine, concomitant with increased crypt cell proliferation and decreased enterocyte apoptosis. Membrane protein found at cell-cell boundaries Suppress follicle growth and development Regulation of iron storage in macrophages, and for intestinal iron absorption Constitutively expressed in NK cells and may play a role in the cytotoxic arm of the innate immunity Stimulation of activated B-cell and T-cell proliferation, and the differentiation B cells into Plasma Cells. immunosuppressive cell-surface protein Cadherin-24 mediate strong cell-cell adhesion Forms tight junction strands in epithelial cells Components of tight junction strands Cation channel proteins Lysis of target cells by cytotoxic T lymphocytes and natural killer cells Recently been designated CD205 Role in ubiquitin-proteasome system breaks down unwanted proteins cell cycle, apoptosis, Cell proliferation, growth, stress response, gene regulation, and heat shock Cell growth, differentiation and also involved in stress resistance and actin organization Day 7 Ileum Potent peptide vasodilator and can function in the transmission of pain Continued

178

Appendix D continued Integrins are known to participate in cell adhesion as well as cell-surface-mediated signaling CXCL9 is a T-cell chemoattractant induced by IFN-γ Interactions between thymic epithelial cells and CD6+ cells during intrathymic T cell development and also been used as a potential cancer stem cell marker Involved in superoxide production and phagocytosis Component of the microbicidal oxidase system of phagocytes essential for lung function and homeostasis chemotactic for polymorphonuclear leukocytes and hematopoietic stem cells Numerous functions such as phagocyte recruitment, aggregation induction, complement attack prevention, apoptosis inhibition, membrane remodelling, lipid transport, hormone transport and/or scavenging and matrix metalloproteinase inhibition Components of tight junction strands and also play critical roles in maintaining cell polarity and signal transductions Facilitates the secretion of IgA and IgM Epidermal development and maintenance and proposed as prognostic and diagnostic tools for cancer

179 Cell growth, differentiation and also involved in stress resistance and actin organization Involved in antigen presentation in every nucleated cell

Involved in antigen presentation in every nucleated cell Present on antigen-presenting cells and lymphocytes Present on antigen-presenting cells and lymphocytes Present on antigen-presenting cells and lymphocytes

179

Appendix E: Primers used for RT-qPCR.

EST number Gene symbol Sequence NM_214394 -F CYP2D25-F TGAACTTCCAAGACCCACGTCTCA NM_214394 -R CYP2D25-R TCCAGATCTGCTGCAGGCTGAATA NM_001114289-F CXCL9-F TGCATCAACACCAGCCAAAGGATG NM_001114289-R CXCL9-R TTAGGCTGACCTGTTTCTCCCACT ENSSSCT00000013072-F ALCAM-F TGCTAGTAGGAACGCAACTGTGGT ENSSSCT00000013072-R ALCAM-R AGTCCTTCAACCTCTTGCAGAGCA NM_001110428-F CNPY3-F CCAAGGCCTTCCTGTTTGTTGTGT NM_001110428-R CNPY3-R AAGCACTGAGTGATCTGGTGTGGT ENSSSCT00000011031-F SMARCB1-F ACCACCATTGCATACAGCATTCGG ENSSSCT00000011031-R SMARCB1-R TTTCCATCTCGGCATCTGTCAAGG NM_001113286 -F LOC396897-F ACTCCTGCCAGAACACTGGTTTCA NM_001113286 -R LOC396897-R TCACGGGTGAAGGTTTGCAACTAC ENSSSCT00000001616-F SLA-DQA1-F ATCATTCAAGGCCTACGCTCAGGT ENSSSCT00000001616-R SLA-DQA1-R TGAGCACATCCACTCTTCAGCTCT NM_001113704-F SLA-6-F CATTGGCAACCACAACCATAGCCA NM_001113704-R SLA-6-R GTGAATCTTGGACCCAGAAACGCA ENSSSCT00000019677-F COX3-F TCGAGAAAGCACTTTCCAAGGCCA ENSSSCT00000019677-R COX3-R TAATTCGGGTGTTGGTGCTAGGCT NM_214389-F GSTA1-F ATGATCTTGCTGTTGCCACTGTGC NM_214389-R GSTA1-R TGCCCACAAGGAAGTCTTGTCCAT TC597602-F ACCAGGAGCTTCAAAGGAGCTCTA TC597602-R AGGAGTCACTTCAAAGAGCGGAGA NM_001001867-F LGALS1-F AGACAGATGAGACAGGCAGGACAT NM_001001867-R LGALS1-R TCTTGCAGACAGGTTTGTGTCCCA NM_001134823-F SEPP1-F AGATTGCCAGTCGTGGAAGATAGG NM_001134823-R SEPP1-R GCCAATTGTGTACTGCATTCTTGC NM_214367-F CTNNB1-F TATCCAGTTGATGGGCTGCCAGAT NM_214367-R CTNNB1-R ACAGGTCAGTATCAAACCAGGCCA NM_001102679 -F SFTPB-F TTGTCATCCTGCTGAACATGCTGC NM_001102679 -R SFTPB-R TCTCTGCCTGAGTGGTCACAAACA NM_001101030 -F BPIFB2-F ACCGTGGTCAGACTCAACAAGGAA NM_001101030 -R BPIFB2-R AATTTGGGACAGTGACTTGCAGGG NM_214020 -F EGF-F TTGTATTGGTGCGATGCCAAGCAG NM_214020 -R EGF-R AAACACAGCTACCGCAAATGGGTG NM_001144847 - F REG3-F AATGGCTATAGCAGGTGAAGGGCA NM_001144847 - R REG3-R AGGCAGCATCATGTTTGTGACGTG

180

Appendix F: Animal experimental design indicating different time points where variable were introduced, samples were collected and finally animals were sacrificed.

Hysterectomy Gn.Pig sterility LA/LGG Euthanasia-1 Euthanasia-2 colonization

Day-2 Day 0 Day 1 Day 3 Day 7

Samples SI, LI, MLN, Spleen, Rectal swab Rectal swab SI, LI, MLN, Spleen, Liver & Rectal swab Liver & Rectal swab

Dose ≈1X108 CFU SI and LI refers to small and large N=3 to 4/group intestinal tissue sections MLN, mesenteric lymph node

181

Appendix G: Colonization dynamics of probiotic strain LA and LGG in Gn pigs. Where

limit of detection was 10 CFU/g of tissue.

LA LGG 1.0×10 8 1.0×10 8 1.0×10 7 Day-1 1.0×10 7 Day-1 1.0×10 6 Day-7 1.0×10 6 Day-7 1.0×10 5 1.0×10 5 1.0×10 4 1.0×10 4 1.0×10 3 1.0×10 3 Log 10 CFU/gLog 10 1.0×10 2 CFU/gLog 10 1.0×10 2 1.0×10 1 1.0×10 1 1.0×10 0 1.0×10 0

Liver MLN MLN Ileum Colon Ileum Colon Liver Cecum Rectum Spleen Cecum Spleen Jejunum Jejunum Rectum Duodenum Duodenum

182

Appendix H. Validation of microarray results of selected genes in duodenum and ileum

by RT-qPCR.

50 RT-qPCR 40 Microarray 30 20 change 10 Fold Fold 0 -10

-20 -30 -40

-50

183

Appendix I: Comparison of IPA mapped common and unique gene responses modulated

in duodenum and ileum on PBCD1 and 7.

Where microarray transcripts were mapped using orthologs of human, rat and mouse genes in IPA and also common and unique genes were noted using IPA comparison tool.

IPA missing transcripts annotations were further manually compared and gene number

added in specific categories. Increased numbers of genes were modulated by LGG on

PBCD7.

Duodenum Ileum

LA LGG LA LGG

56 12 56 61 12 67 PBCD - 1

LA LGG LA LGG

60 28 163 38 13 38

PBCD - 7

184

Appendix J. Top associated networks with their linked canonical pathways (CP) in duodenum on PBCD1. Where the higher number of molecules involved in the discerned canonical pathway decided the canonical pathway assigned for that particular probiotic strain; in the case of approximately equal numbers of molecules contributing to the same pathway it was assigned for both LA and LGG. LA with associated CP indicated where (IL-12 Signaling and Production in Macrophages). LGG with associated CP (Cell-To-Cell Signaling and Interaction, Hematological System Development and Function, Immune Cell Trafficking). The connecting sky blue line indicates the associated molecules with their predicted biological status.

LA LGG

185

Appendix K: Top associated networks with their linked canonical pathways (CP) in ileum on PBCD7. Where the higher number of molecules involved in the discerned canonical pathway decided the canonical pathway assigned for that particular probiotic strain; in the case of approximately equal numbers of molecules contributing to the same pathway it was assigned for both LA and LGG. (4A) LA with associated CP (was ILK Signaling). (4B) LGG with associated CPs (IL-12 Signaling and Production in Macrophages).The connecting sky blue line indicates the associated molecules with their predicted biological status.

186

Appendix L: The tight junction signaling in duodenum and ileum for LA vs LGG

generated using IPA tool.

Where molecules involved and their predicted status highlighted the difference in mechanism of tight junction signaling, in duodenum catenin, claudin and FOS molecules involved whereas in ileum catenin, actin, FOS, myosin (heavy and light chain) involved.

187

Appendix M: Individual animals’ community microbial profiles comparison between deficient vs sufficient HM pigs before and after RV challenged in feces at the ‘Phyla’

level.

Before HRV After HRV

188

Appendix N: Individual animals’ community microbial profiles comparison between deficient vs sufficient HM pigs before and after RV challenged in feces at the ‘Order’

level.

189

Appendix O: Individual animals’ community microbial profiles comparison between deficient vs sufficient HM pigs before and after RV challenged in feces at the ‘Class’

level.

Before HRV After HRV

190

Appendix P: Individual animals’ community microbial profiles comparison between deficient vs sufficient HM pigs before and after RV challenged in feces at the ‘Genus’

level.

PBCD3 PBCD6 PCD-2 PCD2 PCD5 PCD12

Before HRV After HRV

191

Appendix Q: Individual animals’ community microbial profiles comparison between

deficient vs sufficient HM pigs 14 days after RV challenged in the intestinal tissue

samples at the ‘Phyla’ level.

192

Appendix R: Individual animals’ community microbial profiles comparison between

deficient vs sufficient HM pigs 14 days after RV challenged in the intestinal tissue

samples at the ‘Order’ level.

193

Appendix S: Individual animals’ community microbial profiles comparison between deficient vs sufficient HM pigs 14 days after RV challenged in the intestinal tissue

samples at the ‘Class’ level.

194

Appendix T: Individual animals’ community microbial profiles comparison between

deficient vs sufficient HM pigs 14 days after RV challenged in the intestinal tissue

samples at the ‘Genus’ level.

195