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The effect of the microbiome and short-chain fatty acid metabolites on early life immune development… Cait, Alissa 2018

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    THE EFFECT OF THE MICROBIOME AND SHORT CHAIN FATTY ACID METABOLITES ON EARLY LIFE IMMUNE-DEVELOPMENT WITH LONG TERM CONSEQUENCES FOR ATOPY AND ASTHMA       by Alissa Cait BSc. University of Guelph, 2011         A THESIS SUBMITTED IN PARTIAL FULFILLMENT OF THE REQUIREMENTS FOR THE DEGREE OF  DOCTOR OF PHILOSOPHY in The Faculty of Graduate and Postdoctoral Studies (Microbiology and Immunology)          THE UNIVERSITY OF BRITISH COLUMBIA (Vancouver)  July 2018   © Alissa Cait, 2018 ii  The following individuals certify that they have read, and recommend to the Faculty of Graduate and Postdoctoral Studies for acceptance, the dissertation entitled: The effect of the microbiome and short chain fatty acid metabolites on early life immune development with long term consequences for atopy and asthma  submitted by Alissa Cait in partial fulfillment of the requirements for  the degree of Doctor of Philosophy  in Microbiology and Immunology.   Examining Committee:  William W. Mohn, Microbiology and Immunology Supervisor   Pauline Johnson, Microbiology and Immunology Supervisory Committee Member   Ninan Abraham, Microbiology and Immunology University Examiner   Bruce Vallance, Experimental Medicine University Examiner   Additional Supervisory Committee Members:  Kelly M. McNagny, Experimental Medicine Supervisory Committee Member  iii  Abstract  Asthma is the most common childhood medical condition and it accounts for nearly two million hospital visits and thousands of deaths per year.  Despite its immense societal burden, there is no cure, there are very few treatment options, and no prevention strategies.  In fact, the etiology of asthma remains elusive.    There is an emerging understanding that an association exists between the gut microbiome and asthma.  Herein, we provide evidence that the microbiome impacts early-life immune development with consequences for asthma via the production of short chain fatty acid (SCFA) metabolites.   We found that mice treated with vancomycin have an altered microbiome and metabolite profile, exhibit exacerbated Th2 responses, and are more susceptible to allergic lung inflammation. Here we show that dietary supplementation of SCFAs ameliorates this enhanced asthma susceptibility by modulating the activity of T cells and dendritic cells.   Informed by this animal model, we sought to determine whether alterations in microbiome carbohydrate fermentation pathways could also be identified in human infants prior to developing atopic disease using shotgun metagenomic sequencing of the gut microbiome.  We found that the microbiome of infants that went on to develop asthma later in childhood lacked genes encoding key functional enzymes for carbohydrate breakdown and butyrate production.   To better understand the imprint of SCFAs on immune development, we successfully transferred the phenotypes of both heightened and dampened Th2 skewing via bone marrow transplants to iv  irradiated recipient mice. Consistent with the hypothesis that the transferred phenotype is encoded within the epigenome, we found unique regulatory states, as defined by DNA acetylation, within the genomes of purified hematopoietic stem and progenitor cells of recipient mice that received BM transplants from dysbiotic mice.   Altogether, this research highlights the role of microbially-derived metabolites, SCFAs, in the development of asthma and atopy.    We present a new understanding of the intricate relationship between the microbiome, microbial metabolites and asthma. Knowledge of this process will have potential practical applications in the prevention and treatment of disease.     v  Lay Summary  Asthma is the most common childhood medical condition and it accounts for nearly two million hospital visits and thousands of deaths per year.   Despite its immense societal burden, there is no cure, there are very few treatment options, and no prevention strategies.  In fact, the etiology of asthma remains elusive.   Recently, evidence has suggested the bacteria that live symbiotically in our intestines (the microbiome) play a role in shaping the immune system and preventing or predisposing an individual to developing asthma.   The work described in this thesis focuses on the role of the microbiome, through production of a group of chemicals called short chain fatty acids (SCFAs), in both mouse models of asthma and in the human disease.  We present a new understanding of the intricate relationship between the microbiome, SCFAs, and asthma. Knowledge of this process will have practical applications in the prevention and treatment of disease.   vi  Preface  All work presented here was conducted at the Life Sciences Institute and the Biomedical Research Centre at the University of British Columbia, Point Grey Campus.   Chapter 2:  A version of chapter 2 has been previously published in A Cait, MR Hughes, F Antignano, J Cait, PA Dimitriu, KR Maas, LA Reynolds, L Hacker, J Mohr, BB Finlay, C Zaph, KM McNagny, and WW Mohn.  Microbiome-driven allergic lung inflammation is ameliorated by short chain fatty acids.  Mucosal Immunology.  2017l; doi:10.1038/mi.2017.75.  An electronic version of this manuscript can be accessed here:   https://www.nature.com/articles/mi201775.epdf?author_access_token=3NPUfYLg0mj2iYHB3vTcQNRgN0jAjWel9jnR3ZoTv0PHCgkEeJ7N4WQywKxmfCnn8yrjLMaV5Ph-9xQvOe-iAbxhMrlxh_1MJR-LQZ9yYIvghcjGhh5L6KCLoTliKLVU.    I was principally responsible for designing the study, carrying out experiments, analyzing data, and interpreting results in collaboration with WW Mohn, KM McNagny, and MR Hughes.  The project conception was initiated in collaboration with MR Hughes, who continued to provide technical and theoretical guidance throughout the project.  F Antignano and LA Reynolds helped with planning and performing experiments, and data analysis.  J Cait, L Hacker, and J Mohr helped with sample collection.  PA Dimitriu and KR Maas aided in bioinformatics analysis. C Zaph, BB Finlay, KM McNagny, and WW Mohn co-supervised the study.   KM McNagny and WW Mohn edited the manuscript. All experiments were carried out in accordance with the vii  Canadian Council on Animal Care guidelines are were approved by the University of British Columbia committee on Animal Care (protocol no. A15-0113).     Chapter 3: A manuscript is in preparation for this chapter.  A. Cait, E. Cardenas, N Amenyogbe, The CHILD Study Investigators*, S. E. Turvey, T.R. Kollmann, P.A. Dimitru, W.W. Mohn. Early infancy microbial alterations in SCFA production pathways are predictive of asthma.  2018.    I was principally responsible for data analysis and interpretation of results.  The CHILD Study*, including Turvey, provided the samples and clinical data.  E. Cardenas was principally responsible for bioinformatic analysis of metagenomic data. I wrote the manuscript, W.W. Mohn edited it, and all the other authors read the manuscript and provided corrections. W.W. Mohn conceived and planned this investigation with the collaboration of myself, E. Cardenas, P.A. Dimitriu, N. Amenyogbe, and T.R. Kollmann.  Each parent or legal guardian gave signed informed consent and all research protocols for the following studies in human samples were approved by The University of British Columbia/Children’s and Women’s Health Centre of British Columbia Research Ethics Board (ethics certificate number: H07-03120).  I would like to acknowledge Amee Manges for her statistical advice.  *List of CHILD study investigators: Sears MR, (Director), McMaster University; Subbarao P (co-Director), The Hospital for Sick Children; Allen R, Simon Fraser University; Anand SS, McMaster University; Becker AB, University of Manitoba; Befus AD, University of Alberta; Brauer M, University of British viii  Columbia; Brook JR, University of Toronto; Chen E, Northwestern University, Chicago; Cyr M, McMaster University; Daley D, University of British Columbia; Dell S, The Hospital for Sick Children; Denburg JA, McMaster University; Elliott S, University of Waterloo; Grasemann H, The Hospital for Sick Children; HayGlass K, University of Manitoba; Hegele R, The Hospital for Sick Children; Holness DL, University of Toronto; Lou WYW, University of Toronto; Kobor MS, University of British Columbia; Kollman TR, University of British Columbia; Kozyrskyj AL, University of Alberta; Laprise C, Université du Québec à Chicoutimi; Larché M, McMaster University; Macri J, McMaster University; Mandhane PM, University of Alberta; Miller G, Northwestern University, Chicago; Moqbel R (deceased), University of Manitoba; Moraes T, The Hospital for Sick Children; Paré PD, University of British Columbia; Ramsey C, University of Manitoba; Ratjen F, The Hospital for Sick Children; Sandford A, University of British Columbia; Scott JA, University of Toronto; Scott J, University of Toronto; Silverman F, University of Toronto; Takaro T, Simon Fraser University; Tang P, University of British Columbia; Tebbutt S, University of British Columbia; To T, The Hospital for Sick Children; Turvey SE, University of British Columbia.  Chapter 4: A manuscript is in preparation for the epigenetic sections of this chapter. A Cait, M.R. Hughes, M. Bilenky, J Cait, M. Moksa, M. Hirst, K.M. McNagny, and W.W. Mohn.    Exposure to SCFAs in the early-life window leaves a life-long epigenetic imprint on hematopoietic stem and progenitor cells. 2018.   I was principally responsible for data analysis and interpretation of results.  MR Hughes provided technical and theoretical guidance throughout the project. M. Bilenky was principally ix  responsible for bioinformatic analysis of ChIP-seq data.  J Cait provided technical support. M Moksa was responsible for sample preparation for ChIP-seq. W.W. Mohn conceived and planned this investigation with the collaboration of myself, M.R. Hughes, M. Hirst, and K.M. McNagny. All experiments were carried out in accordance with the Canadian Council on Animal Care guidelines are were approved by the University of British Columbia committee on Animal Care (protocol no. A15-0113, A16-006).     The fecal transplant experiment  in chapter 4 was performed by LA Reynolds in B.B. Finlay’s research group.  I did the microbiome analysis.  x  Table of Contents  Abstract .......................................................................................................................................... ii Lay Summary .................................................................................................................................v Preface ........................................................................................................................................... vi Table of Contents ...........................................................................................................................x List of Tables .............................................................................................................................. xvi List of Figures ............................................................................................................................ xvii List of Abbreviations ................................................................................................................. xix Acknowledgements .................................................................................................................... xxi Dedication ................................................................................................................................. xxiii  Introduction ................................................................................................................1 1.1 The Microbiome.............................................................................................................. 1 1.1.1 An introduction to the Microbiome ............................................................................ 1 1.1.2 In Health and Disease ................................................................................................. 2 1.1.3 Acquisition of the microbiome ................................................................................... 3 1.1.4 Tools to study the microbiome ................................................................................... 4 1.2 Microbial derived metabolites ........................................................................................ 7 1.2.1 Short Chain Fatty Acids .............................................................................................. 9 1.2.2 SCFA fermentation ..................................................................................................... 9 1.2.3 SCFAs: mechanism of action.................................................................................... 14 1.2.3.1 SCFAs: mechanism of action.  G-protein coupled receptors ............................ 14 1.2.3.2 SCFAs: mechanism of action: histone modification ........................................ 15 xi  1.3 Asthma .......................................................................................................................... 16 1.3.1 Immune processes in type 2 responses ..................................................................... 17 1.3.2 Animal models of asthma ......................................................................................... 23 1.4 Microbiome in asthma and atopy .................................................................................. 24 1.4.1 Epidemiological evidence ......................................................................................... 24 1.4.2 Exposure to microbes ................................................................................................ 25 1.4.3 Microbiome depletion in animal models .................................................................. 26 1.4.4 The critical window .................................................................................................. 28 1.4.5 SCFAs and the immune response ............................................................................. 30 1.4.6 SCFAs in asthma....................................................................................................... 34 1.5 Research outline ............................................................................................................ 35 1.5.1 Hypothesis and objectives......................................................................................... 35 1.5.2 Anticipated Impact and Significance ........................................................................ 36  Microbiome-driven allergic lung inflammation is ameliorated by short chain fatty acids ......................................................................................................................................37 2.1 Synopsis ........................................................................................................................ 37 2.2 Introduction ................................................................................................................... 37 2.3 Materials and Methods .................................................................................................. 39 2.3.1 Mice .......................................................................................................................... 39 2.3.2 Antibiotic and SCFA treatment and quantification. ................................................. 40 2.3.3 Microbiome analysis ................................................................................................. 40 2.3.4 Antibodies and flow cytometry ................................................................................. 41 2.3.5 OVA-induced allergic lung inflammation ................................................................ 41 xii  2.3.6 Papain-induced allergic lung inflammation .............................................................. 42 2.3.7 RNA isolation and quantitative RT-PCR .................................................................. 42 2.3.8 Histology ................................................................................................................... 43 2.3.9 Determination of serum IgE...................................................................................... 43 2.3.10 Isolation of lymphocytes ....................................................................................... 43 2.3.11 Isolation of dendritic cells ..................................................................................... 44 2.3.12 Transcriptome analysis ......................................................................................... 44 2.3.13 DC in vivo migration assay ................................................................................... 45 2.3.14 T cell activation assay ........................................................................................... 45 2.3.15 DC transwell migration assay ............................................................................... 46 2.3.16 Statistics ................................................................................................................ 46 2.4 Results ........................................................................................................................... 46 2.4.1 Vancomycin treatment depletes SCFA-producing bacteria ...................................... 46 2.4.2 SCFA attenuate allergic lung inflammation in vancomycin-treated mice ................ 52 2.4.3 SCFA attenuate vancomycin-induced IgE production ............................................. 59 2.4.4 SCFA attenuate vancomycin-induced IL-4 production ............................................ 62 2.4.5 SCFA exposure alters the gene expression profile of DCs ....................................... 66 2.4.6 SCFAs attenuate DC activation ................................................................................ 70 2.4.7 SCFAs attenuate DC chemotaxis .............................................................................. 73 2.5 Discussion ..................................................................................................................... 75  Early infancy microbiome short chain fatty acid production pathways are predictive of asthma .....................................................................................................................80 3.1 Synopsis ........................................................................................................................ 80 xiii  3.2 Introduction ................................................................................................................... 80 3.3 Materials and methods .................................................................................................. 82 3.3.1 CHILD Study design, and diagnoses ........................................................................ 82 3.3.2 Sample selection ....................................................................................................... 82 3.3.3 DNA extraction and metagenome generation ........................................................... 83 3.3.4 Butyrate gene identification ...................................................................................... 84 3.3.5 CAZy gene identification .......................................................................................... 84 3.3.6 Data analysis and statistics........................................................................................ 85 3.4 Results ........................................................................................................................... 86 3.4.1 Clinical phenotype groups ........................................................................................ 86 3.4.2 The relationship between fecal microbiome and clinical phenotypes. ..................... 88 3.4.3 A deficit of CAZyme genes in the 3-month microbiome and a deficit of butyrate production genes in the 1-year microbiome are associated with the risk of developing asthma. .................................................................................................................................. 92 3.4.4 CAZyme genes are depleted in children who develop asthma. ................................ 93 3.4.5 CAZymes that degrade human milk oligosaccharides were depleted in children who develop asthma...................................................................................................................... 95 3.4.6 Enzymes required for butyrate production were depleted in the 3-mo microbiome of children who develop atopy. ................................................................................................. 98 3.5 Discussion ................................................................................................................... 100  Exposure to short chain fatty acids in the early-life window leaves a life-long epigenetic imprint on hematopoietic stem and progenitor cells ............................................107 4.1 Synopsis ...................................................................................................................... 107 xiv  4.2 Introduction ................................................................................................................. 108 4.3 Materials and methods ................................................................................................ 109 4.3.1 Mice ........................................................................................................................ 109 4.3.2 Antibiotic and SCFAs treatment ............................................................................. 110 4.3.3 Antibodies and Flow Cytometry ............................................................................. 110 4.3.4 Fecal transplant ....................................................................................................... 111 4.3.5 Determination of serum IgE.................................................................................... 111 4.3.6 Microbiome analysis ............................................................................................... 111 4.3.7 Bone marrow transplant .......................................................................................... 112 4.3.8 Papain-induced allergic lung inflammation ............................................................ 113 4.3.9 RNA isolation and quantitative reverse transcriptase PCR .................................... 113 4.3.10 Histology ............................................................................................................. 114 4.3.11 HSPC ChIP-seq ................................................................................................... 114 4.3.12 Statistics .............................................................................................................. 115 4.4 Results ......................................................................................................................... 115 4.4.1 Fecal transplant rescues microbiome-driven atopic disease if administered in early life. ……………………………………………………………………………………..115 4.4.2 Success of fecal transplant is independent of age at transplant .............................. 117 4.4.3 Atopic susceptibility is transferred by hematopoietic transplant ............................ 120 4.4.4 SCFAs alter the epigenetic state of hematopoietic progenitors .............................. 124 4.5 Discussion ................................................................................................................... 129  Discussion ................................................................................................................138 5.1 Relevance and contribution to the field ...................................................................... 138 xv  5.1.1 Introduction ............................................................................................................. 138 5.1.2 Microbiome-driven allergic lung inflammation is ameliorated by short-chain fatty acids ……………………………………………………………………………………..138 5.1.3 Early infancy microbial alterations in SCFA production pathways are predictive of atopic disease. ..................................................................................................................... 140 5.1.4 Exposure to SCFA in the early-life window leaves a life-long epigenetic imprint on hematopoietic stem and progenitor cells............................................................................. 141 5.2 Future direction ........................................................................................................... 142 5.2.1 Microbiome-driven allergic lung inflammation is ameliorated by short-chain fatty acids ……………………………………………………………………………………..142 5.2.2 Early infancy microbial alterations in microbiome SCFA production pathways are predictive of atopic disease. ................................................................................................ 143 5.2.3 Exposure to SCFAs in the early-life window leaves a life-long epigenetic imprint on hematopoietic stem and progenitor cells............................................................................. 145 5.3 Final Conclusions........................................................................................................ 145 Works Cited ................................................................................................................................147  xvi  List of Tables  Table 1-1: Overview of known effects of microbial products on the host. .................................... 8 Table 1-2: Summary of cell-specific responses of immune cells to SCFAs. ................................ 32 Table 4-1: Genes with relevance to atopy that are differentially acetylated in dysbiotic mice. . 134  xvii  List of Figures Figure 1-1: Pathways for butyrate synthesis. ................................................................................ 12 Figure 1-2 Overview of immune processes in atopic disease. ...................................................... 20 Figure 1-3 T helper cell subsets. ................................................................................................... 22 Figure 1-4 Summary of findings related to the early-life window during which microbes and microbial products impact atopic disease. .................................................................................... 29 Figure 2-1 Vancomycin depletes SCFA-producing bacteria in the gut. ....................................... 50 Figure 2-2: Vancomycin treatment does not alter levels of acetate or propionate in the serum. .. 51 Figure 2-3: Exogenous SCFA ameliorate vancomycin-induced asthma severity. ....................... 55 Figure 2-4 Exogenous SCFAs ameliorate vancomycin induced asthma severity. ....................... 57 Figure 2-5 BAP attenuates vancomycin induced epsilon-GLT production. ................................. 58 Figure 2-6 Vancomycin treatment does not alter baseline inflammation or Th2-associated cytokine transcripts in the lung. .................................................................................................... 60 Figure 2-7: SCFA-supplementation (BAP) attenuates vancomycin-induced IgE production. ..... 61 Figure 2-8: SCFAs attenuate vancomycin-induced IL4 production. ............................................ 64 Figure 2-9: SCFAs prevent vancomycin induced loss of Tregs in colonic LP and mesenteric LNs....................................................................................................................................................... 65 Figure 2-10: Transcriptomes reveal anti-inflammatory effect of butyrate on DCs. ..................... 69 Figure 2-11 SCFAs attenuate DC activation ................................................................................ 71 Figure 2-12 SCFAs attenuate DC migration. ................................................................................ 74 Figure 2-13: Summary model. ...................................................................................................... 79 Figure 3-1 Clinical phenotypes. .................................................................................................... 87 xviii  Figure 3-2 Differences in fecal microbiome taxonomic composition at 3 mo and 1 yr between atopic and control children. ........................................................................................................... 89 Figure 3-3: Bacterial species associated with children who develop atopy. ................................. 91 Figure 3-4: CAZyme genes are depleted from the 3-mo microbiome in children who develop atopy. ............................................................................................................................................. 94 Figure 3-5: CAZyme genes that degrade HMOs and resistant starch were depleted in the microbiomes of children who developed atopy. ........................................................................... 97 Figure 3-6: Enzymes required for fermentation of butyrate are depleted from the 3 mo microbiome in children who develop atopy.................................................................................. 99 Figure 3-7: Summary model. ...................................................................................................... 102 Figure 4-1: Fecal transplant rescues microbiome-driven atopic disease if administered in early life. .............................................................................................................................................. 116 Figure 4-2 Success of fecal transplant is dependent on age at transplant. .................................. 119 Figure 4-3: Atopic susceptibility is transferred by hematopoietic transplant. ............................ 122 Figure 4-4: Atopic susceptibility is transferred by hematopoietic transplant. ............................ 123 Figure 4-5: Gating strategy for HSPC population. ..................................................................... 125 Figure 4-6: Unique regulatory states (H3K27ac) within the genomes of HSPCs from dysbiotic mice. ............................................................................................................................................ 128 Figure 4-7: Summary model. ...................................................................................................... 131 xix  List of Abbreviations AA Auxiliary activities AAI allergic lung inflammation AW atopic + wheeze BAP butyrate, acetate, and propionate bp base pair CAZymes carbohydrate active enzymes CHILD Canadian healthy infant longitudinal design ChIP chromatin immuno-precipitation ChIP-seq chromatin immuno-precipitation sequencing CI confidence intervals coenzyme A coA C-section caesarean section  DC dendritic cell  FcεR Fcε receptor FFA free fatty acid  FMT fecal microbiota transplantation FOXP3 forkhead box protein p3  GATA GATA-binding protein GF germ-free GH Glycoside hydrolase GPCR G-protein coupled receptors  GT Glycosyl transferase GWAS genome wide association studies HDAC histone deacetylases  HDACi histone deacetylases inhibitor HDM house dust mite HMO human milk oligosaccharides  HSC hematopoietic stem cell HSPC hematopoietic stem and progenitor cell Ig Immunoglobulin IL Interleukin ILC  innate lymphoid cells IN  intranasal iNKT inducible Natural Killer T cells  IP intraperitoneal   LP lamina propria LPS lipopolysaccharide  MAMPs molecular associated microbial patterns  xx  MHC major histocompatibility complex mo month MYD88 myeloid differentiation primary response protein 88 NMDS non-metric multidimensional scaling  OAA ovalbumin model of allergic asthma  OVA Ovalbumin PCR polymerase chain reaction PL Polysaccharide lyase PPs Peyer's patches PSA Polysaccharide A ROS reactive oxygen species RS resistant starch SCFA short chain fatty acid SPF specific pathogen free  STAT signal transducer and activator of transcription  TFH Follicular helper T cells Th helper T cell Th2 T helper type 2 TLR  Toll-like receptor Treg regulatory T cell  TRIF TIR domain-containing adaptor protein inducing INFβ  TSLP thymic stromal lymphopoietin vanc vancomycin vanc+BAP vancomycin + butyrate, acetate, and propionate  WGS whole genome sequencing wks weeks yr year   xxi  Acknowledgements  The experiments and research in this thesis would not have been possible without the support and guidance of my incredible supervisor, Dr. William Mohn. Throughout my time at UBC he continually provided encouragement, ideas, advice, and thoughtful discussion. Bill, thank you for providing me the opportunity to learn in your lab.    Thank you to the members of my committee, Dr. Pauline Johnson and Dr. Kelly McNagny for providing valuable discussion and guidance.   I owe a special thank you to Dr. Kelly McNagny for welcoming me into his lab like one of his own students.  Kelly, thank you for sharing your enthusiasm and passion for science – it’s contagious.   I’d like to acknowledge the CHILD study and all the CHILD collaborators- thank you for your important contributions to my thesis and to the field.    I would like to thank all members of the Mohn lab, past and present.  A special thank you to Dr. Kendra Maas, Dr. Pedro Dimitru, Dr. Erick Cardenas, Dr. Roli Wilhelm, Hilary Leung, and Nelly Amenyogbe for your help, support, and friendship. I also owe many thanks to the members of the McNagny lab, past and present: Kim Snyder, Dr. Bernard Lo, Melina Messing, Ido Refaeli, Diana Canals, and Jessie Cait.   I’d like to single out the incredible contributions to this thesis and to my development as a scientist by Dr. Michael Hughes.  Thank you for always giving me the advice (and criticism) that I needed to become a better scientist… and for always usually trying to sugar coat it.  This work would not have been possible without the help of my scientific collaborators over the years. Thank you to Dr. Brett Finlay. Dr. Lisa Reynolds and Dr. Shannon Russell, thank you for being my partners in the war on vancomycin.  A special thank you to Frann Antignano for being xxii  an incredible scientific mentor and for imparting your wisdom on me both in the lab and on the trail.    To everyone at the BRC- thank you for making this such a special place to work.   You’ve made this my home away from home and I’m going to miss this place immensely.    To the sport of synchronized swimming (#sportforlife)- what an amazing outlet for all the frustrations of science. Thank you to all the strong, smart, incredible women that I’ve been lucky to share the pool with over the years.  Thank you to Jordan for your motivational quotes which have been applicable on the pool deck and at the benchtop.    To my cheer squad and best friends- Katie, Juliana, Jessica, Amanda, Diana. Thank you for all the laughter and adventures.  You guys kept me sane. I really truly couldn’t have done this without your love and support.    To Rob for his endless and optimistic presumption that I would, in fact, complete this thesis.  Thank you for the constant encouragement and for always being a source of enthusiasm for science and curries.  And finally, to Jessie, who made it possible to dance and laugh through every rough patch.  You’ve been by my side through every step of this journey.  I owe the bulk of my gratitude to you.     xxiii  Dedication  To my family who supported and encouraged me through every failure and celebrated every success.  1   Introduction 1.1 The Microbiome 1.1.1 An introduction to the Microbiome  Mammals have co-evolved with a complex community of microorganisms that inhabit every environmentally exposed surface of the body. The mammalian gastrointestinal tract harbors one of the densest microbial communities on earth.  It is estimated that there are approximately 3.9 x 1013 microbial cells in the body1, making them approximately equal in cell numbers to our own human cells2.  Microbiota describes all the microbial taxa associated with a host including viruses, eukaryotes, archaea, and bacteria.  Operationally for this thesis, microbiota and microbiome will refer to only the bacterial component of the microbiota, which outnumbers eukaryotes and archaea by 2 to 3 orders of magnitude3.  Further, all discussion of microbiota will be limited to the intestinal microbiota, unless explicitly stated otherwise. The microbiome consists of all the genetic material harboured by these cells4.  In humans and in mice a healthy gut microbiota is dominated by Bacteroidetes and Firmicutes but also includes smaller proportions of Actinobacteria, Proteobacteria, and Verrucomicrobia4.   The microbiome has co-evolved along-side the host over millions of years, during which many complex interdependences formed. It is now clear that the microbiome is critical for health and survival of the host.  The most obvious functions our resident microbiota plays are in digestion and metabolism5. Beyond this, the intestinal microbiota protects against invading pathogens6, 2  affects host-cell proliferation7 and vascularization8, regulates intestinal endocrine functions9, affects neurological signalling10, regulates adiposity bone density11, synthesizes essential vitamins12, metabolizes bile salts13, and modifies drugs14.  It is becoming clear that the microbiota also has a considerable influence on the development of the immune system, and that microbes and microbial products exert a profound effect on nearly every aspect of immunological networks to maintain healthy homeostasis.  Disruption of this balance may importantly contribute to the rise in allergic diseases such as asthma. 1.1.2 In Health and Disease We have long recognized and studied bacteria for their pathogenic properties, and since the discovery of antibiotics we have attacked them as the enemy. It is now clear that we have greatly overlooked how our practices of Western hygiene and liberal antibiotic use can be a detriment to our health. The microbiota has a considerable influence on the development of the immune system that is relevant to human health and disease.   The concept that the microbiota can influence the development of the immune system was first suggested by Louis Pasteur in 1885 15, 16,17.  The use of germ free (GF) animals nearly 60 years later proved him to be correct16, 17. These early comparisons of GF and conventional animals showed striking physiological differences including an enlarged cecum and reduced gastrointestinal motility in the former – both due to the loss of critical digestive functions performed by the microbiome. The absence of commensals also profoundly affected the development of immune cells and lymphoid tissue18. 3   Specific microbes and microbial products exert a profound effect on nearly every aspect of immunological networks that maintain healthy homeostasis. Disruption of this balance contributes to the development of intestinal inflammation and disease in distant tissues: In the lung, this mechanism is called the gut-lung axis.  1.1.3 Acquisition of the microbiome Establishing a microbiome is critical for development and survival of the host.   Given this importance, there are mechanisms that have evolved to facilitate the reliable transmission of microbiome from parent to offspring. Our early understanding of this transmission process was gained from detailed invertebrate studies where mechanisms include vertical transmission (maternal to offspring), horizontal (transfer within a species or from the environment), or a mixed model of both 19.  Acquisition of the human microbiome is considerably more complex than these models, and studying this process is complicated because we cannot do so experimentally.  Nevertheless, many studies exist that shed light on this process.   Traditionally, it was believed that the womb was a sterile environment and that the infant’s first contact with microbes was at birth.  Recently, this concept has become controversial as studies proclaiming evidence of bacteria in the womb have been published and argue that colonization begins in utero 20,21, 22, 9.   The evidence for in utero bacterial colonization is controversial, and arguably insufficient to substantiate such claims 23.   To date, the best evidence supports the notion that acquisition of the microbiome normally begins during birth.    4   During labour and delivery, the neonate is rapidly colonized by organisms.  Neonates born by both laboured caesarean procedure and vaginal delivery are colonized initially by microbes from the maternal vagina and skin in approximately equal parts.  Neonates born by unlaboured caesarean procedure are initially populated only by microbiota found in the maternal skin.  By 6-weeks of age the infant fecal microbiome closely resembles the mother’s, regardless of delivery mode.  This convergence between individuals by body site suggests an evolutionary pressure towards a shared set of microbial metabolic pathways and activities.  The infant gut microbiome is dominated by Lactobacillus, Bifidobacterium and Bacteroides.  There is also an important contribution of Enterobacteriace in the intestine24.  Breastfeeding practice is the only environmental factor found to substantially contribute to the abundance of Bacteroides, with exposure to formula resulting in increased levels of this genus25.   Over the next years of life, the infant microbiota increases in richness and diversity.  This maturation process is reviewed in ref26.  The developing microbiota is highly heterogeneous and unstable until 2-3 years of age27, 28, 29.  During this time, environmental factors such as maternal factors30, 31, 32, 33, birth mode34, 35, 25, nutrition36, 25, and antibiotics37,38 influence microbiome development.   1.1.4 Tools to study the microbiome Traditionally, microbial communities were probed with culture-based techniques. Though culture based approaches are still used today and play a vital role in our understanding of microbiology, the recent improvement in sequencing technology coupled with the reduction in 5  sequencing costs have revolutionized the way we study the microbiome39. These advances have made it possible to study microbial communities independent of culture.  There are two major types of experimental approaches to studying the microbiome that are relevant to this thesis.  In an amplicon-based approach a highly conserved gene is PCR amplified and the sequence is determined.  This approach allows determination of which organisms are in the sample, and how those organisms differ between samples.  Amplicon-based approaches to study the microbiome primarily rely on a highly conserved gene, the 16s rRNA gene40.  The use of this gene as a marker of phylogeny was first described by Olsten et al in 198641 and later gained traction when Carl Woese used this molecular tool to dissect the three domains of life42.    This gene continues to increase in usefulness as powerful databases and computational tools to process 16S based amplicon data, such as mothur43 and QIIME44, continue to improve.  This type of amplicon-based analysis is the most common sequencing approach to analyze the microbiome.    In a metagenomic approach the entirety of the DNA in a sample is sequenced, to determine what genes are present in a sample and if the overall functional capacity of the organisms differ between samples 45.   The major advantage of shotgun metagenome sequencing  over the amplicon approach is that it provides much richer data on the functional potential of a community40. Additionally, shotgun metagenome sequencing amplifies the non-bacterial DNA including eukaryotes and viruses, understudied components of the microbiome40.   6  These DNA-based approaches to microbial ecology require the use of high-throughput sequencing methodology.  454 Life Sciences significantly advanced this field by introducing a sequencing strategy that integrated pyrosequencing on a pico-titer platform46.  Briefly, DNA libraries are fragmented between 400 and 800 base pairs (bps), ligated to adaptors, and denatured into single strands.  These strands are then captured by amplification beads and amplified by emulsion PCR.  The beads are transferred to a pico-tire plate and dNTPs are washed over the plate one at a time.  When a dNTP is incorporated light is released and the sequence is determined by detecting the emission47. One weakness of this technology is the imprecision in detecting homopolymeric tracts (regions where one nucleotide is repeated many times)48.  The 454 platform was used broadly in studies of the microbiome and served as a major tool of the Human Microbiome Project49.  However, this platform has fallen out of favour more recently after emergence of the Illumina next-generation sequencing platform.   Like the 454 platform, Illumina technology also uses sequencing by synthesis after DNA amplification.  Briefly, after DNA fragmentation adapters are ligated onto the fragments.  DNA is denatured into single strands and bound to a flowcell via the adaptor.  The DNA is then rapidly amplified via bridge PCR to form clusters identical to the original fragment.  Fluorescently labelled dNTPs are washed over the flowcell and release light when incorporated.  The specific wavelength of emission is detected and used to determine the DNA sequence45.  This technology is superior to previous sequencing technologies in both output and reagent cost and does not have the same weakness with homopolymeric tracks as its predecessor.  The major disadvantage is the relatively short read lengths50.  This technology was also employed for the Human 7  Microbiome Project49  and is the main platform used for DNA-based microbiome studies currently.  1.2 Microbial derived metabolites The molecular mechanisms that account for the interaction between the microbiota and the immune system are not completely understood.  The best understood host-microbe interactions are mediated by pattern recognition receptors (PRRs), present on innate immune cells which recognize microbe-associated molecular patterns (MAMPs) to initiate a pro-inflammatory defense response. These interactions are essential for host immunity against infection51.  However, a more complete picture of host-microbe interactions needs to also account for tolerogenic signals that allow for high numbers of microbes living in equilibrium at mucosal surfaces.  Small molecules produced by the microbiota as byproducts of metabolism (metabolites) are gaining traction as a plausible link in this context.  The exchange of small molecules between the intestinal bacteria and the host’s mucosal surfaces and immune cells all over the body via circulation allows for cross talk between the resident microbiota and the host. Several of these metabolites have been identified and their specific effects on different arms of the immune system have been elucidated (reviewed in refs 52,53).  A summary is presented in table 1.  One class of molecules that serves as an important link between the microbiota and the cells of the immune system are short chain fatty acids (SCFAs), which are not included in the table, but will be expanded on in the next sections.    8  Table 1-1: Overview of known effects of microbial products on the host. Metabolite Effect on Host Reference  Niacin anti-inflam on DCs and macrophages 54  suppress colonic inflammation      Indole promotes IL-22 from ILCs 55,56,57  promotes production of antimicrobial peptides   enhances epithelial barrier   protects against infection     Retinoic acid promotes gut homing of lymphocytes via DC factors 58,59, 60,61  promotes differentiation of Tregs   suppresses differentiation of Th17   required for induction of proinflam Th responses     Polysaccharide A suppress proinflam cytokines 62,63,64  promotes anti-inflam cytokines     Bile acids regulates bacterial growth 65,66,67  suppresses NF-KB induction     Taurine activates inflammasome activation 68   promotes intestinal homeostasis       AHR ligands promotes IL-22 production 55, 69, 70,71  protective against colitis   protective against central nervous system disease                9  1.2.1 Short Chain Fatty Acids Short chain fatty acids (SCFAs) are small carboxylic acids containing 1 to 6 carbon-atoms. Acetic acid (C2), propionic acid (C3), and butyric acid (C4) are the most abundant SCFAs in the human and murine gut, produced in the colon by bacterial fermentation of indigestible fibers and amino acids72, 73, 74. The colons of GF mice have higher levels of fermentation substrates and diminished levels of SCFAs 75 compared to specific pathogen free (SPF) mice, highlighting the essential role of the microbiota in the production of these compounds. In the large intestine of SPF mice, SCFAs are present at a molar ratio of 60 acetate: 20 propionate: 20 butyrate76, which is reflective of the balance between the rates of production by the microbes and the absorptive capacity of the gut mucosa. Because rates of production are dependent on fermentation substrate availability, SCFAs are highest in the cecum and lowest in the colon where substrates have been depleted 77.  The role of butyrate in the health of the colonic epithelium is well documented where it acts as an energy source for colonic enterocytes and may induce cell cycle arrest and apoptosis in colonic carcinoma cells78. More recently, there has been interest in uncovering the role of SCFAs in regulating the immune system.  1.2.2 SCFA fermentation Our human genomes do not contain the enzymes required for the break-down of most of the structural polysaccharides in plant material which represent a nutrient dense, rich source of energy in our diets. Collectively, our microbes contain a far larger repertoire of degradative enzymes and metabolic capabilities than we, the hosts. Nutritionally specialized bacteria play a 10  critical role in microbial degradation of complex non-digestible carbohydrates by initiating the degradation of substrates such as plant cell walls79,80. Understanding the impact of our diet on our health requires an in-depth understanding of the relationship between the metabolic capacity of our microbes and the metabolic output of their processes.   Most dietary starch is completely digested in the small intestine by host enzymes81.  Those carbohydrates that cannot be hydrolyzed by the host digestive enzymes, known as resistant starch (RS) enter the colon79.  The anaerobic breakdown of RS and other carbohydrates by bacteria is known conventionally as fermentation.  In humans the major end products are carbon dioxide and the SCFAs acetate, propionate, and butyrate.  Formate, valerate, caproate and the branched-chain fatty acids isobutyrate, 2-methyl-butyrate and isovalerate are also produced in lesser amounts82.  It is estimated that, depending on the diet, approximately 10% of calories are supplied to the host in the form of SCFAs from microbial fermentation83.  Reduced overall intake of complex dietary carbohydrates decreases SCFA production, with greatest reduction in fecal butyrate84.    Plant cell wall carbohydrates make up the largest proportion of RS in an omnivore diet79.  The structural features consist of cellulose fibrils embedded in a matrix of hemicellulose and pectin, with lignin also present in secondary walls79 .  Enzymes capable of attacking these structures and releasing free polysaccharide subunits are known collectively as carbohydrate active enzymes (CAZymes)85.  Dietary plant fiber, due to its insolubility, is found in the particulate fraction of human feces.  It was found that in human fecal material certain Runinococcaceae are found to be enriched in the particulate fraction whereas Bacteroides are found in higher prevalence in the 11  liquid phase.  This may reflect ecological niches and roles in the breakdown of carbohydrates  and suggests that the primary breakdown of these substrates may be restricted to certain groups of specialized bacteria73 .  Despite the wide expression of CAZymes by Bacteroides and the studies detailing the polysaccharide-degrading activity of these bacteria, this work suggests that the in vivo breakdown of these substrates in the human gut may in fact largely be the work of the Ruminococcaceae.   Comparatively, these bacteria have received far less attention for their importance in polysaccharide breakdown. Solubilization of the matrix polysaccharides in RS results in cross-feeding to other groups of bacteria, including the SCFA-producing bacteria.     SCFAs are generated in the gut via both the glycolytic pathway and the pentose phosphate pathway, predominantly by the former pathway82,86, 87.    The bacteria that contribute to SCFA production do not form a monophyletic group, but rather a functional guild88.  There are a wide range of pathways bacteria can use to generate SCFA end products.  The major pathway of acetate production is via the Wood-Ljungdahl pathway, whereas propionate is mainly generated via a carbon dioxide fixation pathway89.  Butyrate is primarily synthesized via acetyl-coenzyme A (CoA). In a four-step pathway acetyl-CoA is converted to butyryl-CoA and then to butyrate.  The final reaction is catalyzed by butyryl-CoA: acetate coA transferase (but) or butyrate kinase (buk)90, 91. qPCR for these terminal enzymes is often used as the biomarker of butyrate-producing capacity in microbial communities92, 93.  There are three other pathways by which butyrate can be fermented from amino acids: the lysine, glutarate, and 4-aminobutyrate pathways.  These pathways are summarized in Figure 1-1, from ref93.   12   Figure 1-1: Pathways for butyrate synthesis.  Four different pathways for butyrate synthesis and corresponding genes (protein names) are displayed. Major substrates are shown. Terminal genes are highlighted in red. L2Hgdh, 2-hydroxyglutarate dehydrogenase; Gct, glutaconate CoA transferase; HgCoAd, 2-hydroxy-glutaryl-CoA dehydrogenase; Gcd, glutaconyl-CoA decarboxylase; Thl, thiolase; hbd, β-hydroxybutyryl-CoA dehydrogenase; Cro, crotonase; Bcd, butyryl-CoA dehydrogenase; KamA, lysine-2,3-aminomutase; KamD,E, β-lysine-5,6-aminomutase; Kdd, 3,5-diaminohexanoate dehydrogenase; Kce, 3-keto-5-aminohexanoate cleavage enzyme; Kal, 3-aminobutyryl-CoA ammonia lyase; AbfH, 4-hydroxybutyrate dehydrogenase; AbfD,4-hydroxybutyryl-CoA 13  dehydratase; Isom, vinylacetyl-CoA 3,2-isomerase (same protein as AbfD): 4Hbt, butyryl-CoA:4-hydroxybutyrate CoA transferase; But, butyryl-CoA:acetate CoA transferase; Ato, butyryl-CoA:acetoacetate CoA transferase; Ptb, phosphate butyryltransferase; Buk, butyrate kinase. Co-substrates for individual butyryl-CoA transferases are shown.  14  1.2.3 SCFAs: mechanism of action SCFAs exert their effects on target cells predominantly through two known mechanisms:  G-protein coupled receptors (GPCR) and inhibition of histone deacetylases (HDAC). GPR43/41/109a are expressed in a wide variety of tissues including adipocytes, intestinal epithelial cells and several blood cell lineages.  HDACs are expressed in all cells of the body, and interactions between SCFAs and HDACs has broad implications for gene expression. 1.2.3.1 SCFAs: mechanism of action.  G-protein coupled receptors  GPR43 (aka FFA2/FFAR2) is the primary receptor for acetate, although it also recognizes other SCFAs including propionate, butyrate, caproate, and valerate94.  GPR43 is expressed on colonocytes and enterocytes of the small and large intestine, as well as on adipose tissue, where it is thought to play a role in modifying body weight95 and diabetes96.   GPR43 is also widely expressed on immune cells including eosinophils and basophils94, neutrophils, monocytes, dendritic cells94,97, and mast cells98, suggesting a broad role for SCFA signaling via GPR43 in immune responses. In general, signaling through GPR43 has anti-inflammatory effects99.    GPR41 (aka FFA3/FFAR3) is activated most potently by acetate and propionate, and to a lesser extent by butyrate.  GPR41 is also expressed in the colonic mucosa, and in the colonic smooth muscle where it is thought to act as a sensor for luminal SCFAs100. GPR41 is expressed in spleen and peripheral blood mononuclear cells101, as well as intestinal epithelial cells102. GPR41 plays a role in promoting intestinal immune responses against infection102. 15  GPR109a is a high-affinity niacin (vitamin B3) receptor that is also activated by butyrate (with low-affinity binding)103, which is expressed on immune cells such as dendritic cells, monocytes, macrophages, and neutrophils104, ILC3s105, and intestinal epithelial cells54.  GPR109a has a well-defined role in regulating adiposity104.  GPR109a also plays an essential role in mucosal immunoregulatory functions54, 105, 106.  1.2.3.2 SCFAs: mechanism of action: histone modification Chromatin is a complex macromolecule composed of DNA, protein, and RNA.  The primary protein component of chromatin is the histone, an octamer made up of two copies of each core histone (H2A, H2B, H3 and H4), which acts to package DNA.  Covalent chemical modifications to the histone occur post transcriptionally and change the way it interacts with DNA, determining the accessibility of the DNA for transcription107.  The main histone modifications are methylation, phosphorylation, acetylation, and ubiquitination (reviewed in ref 108). The resulting chromatin structure is classified broadly as either euchromatin (loose and transcriptionally active) or heterochromatin (dense and transcriptionally repressed)109.     In the 1970s it was first reported that treating erythroleukemic cells with butyrate was accompanied by histone hyperacetylation110.   Histone acetylation occurs at lysine residues by the enzymatic addition of an acetyl group (COCH3) from acetyl coenzyme A, neutralizing the positive charge.  Histone acetylation is associated with increases in transcriptional activation.   This reversible reaction is modified by histone acetyltransferases (HATs), and the removal of an acetyl group is catalyzed by histone deacetylase enzymes (HDACs).  There are eleven HDAC isoforms (HDAC1-11) that are divided into four classes (HDAC class I-IV)109.  SCFAs inhibit 16  HDAC activity. Butyrate is the most potent inhibitor of HDACs (HDACi); propionate is also an HDACi, although to a lesser extent.  Acetate has little or no effect on HDAC activity111.   The exact mechanism by which SCFAs inhibit HDACs is unknown.  It is thought that butyrate may act as a competitive inhibitor of HDACs.  Two molecules of butyrate can occupy the hydrophobic cleft on the active site of HDACs112.  It is also possible that SCFAs may have an indirect effect on histone acetylation via GPCRs.   The overwhelming result of HDAC inhibition via SCFAs is anti-inflammatory (reviewed in ref101).  1.3 Asthma  Asthma is a disease of the airways that is characterized by chronic inflammation and airway obstruction. It represents a major health and socioeconomic issue worldwide as this disease afflicts 300 million people and accounts for 250,000 deaths annually113. Of all age groups, children are the most affected by asthma and account for the greatest number of deaths proportionally114. This disease etiology and pathophysiology is heterogeneous, but the most common manifestation is allergic asthma115. In humans, allergic asthma commonly begins early in childhood after sensitization to aeroallergens, most commonly: House dust mite (HDM), cockroaches, animal dander, fungi and pollen116.  17  1.3.1 Immune processes in type 2 responses Although this is a simplified stratification, the mammalian immune response is often grouped into a type 1 or a type 2 response117.  These responses are effective against different pathogens but left unchecked can both lead to pathology and disease. Type 1 responses protect the host against bacteria, viruses, fungi, and protozoa.  They are mediated by CD4+ T helper 1(Th1) and Th17 subsets as well as cytotoxic CD8+ T cells.  An inappropriate type 1 response can lead to autoimmunity when directed against a self-antigen118.    Type 2 responses are effective in protecting the host against large extracellular parasites, such as helminths.  In an appropriate type 2 response Th2 cells direct enhanced barrier defenses at mucosal sites and induce the expulsion and/or killing of parasites.  These responses are also appropriately induced by venoms and vaccine adjuvants119.  An inappropriate type 2 response can lead to asthma117. The immunological processes that give rise to asthma can also give rise to diseases such as food allergy, atopic dermatitis (eczema), allergic rhinitis (hay fever), and anaphylaxis120.   Atopy is an umbrella term for these conditions and is characterized by an immune propensity to produce IgE antibodies in response to antigens121. Allergic responses that are not mediated by IgE (ie- non-atopic) also occur122. This thesis will focus on atopic asthma.   The initiation of a type 2 or atopic response begins after damage to the mucosal epithelium, pathogen recognition, or allergen exposure. Signals released from the epithelial cells such as; thymic stromal lymphoprotein (TSLP), interleukin-25 (IL-25), and IL-33, recruit eosinophils, basophils, mast cells, monocytes, and activate dendritic cells (DCs) and type 2 innate lymphoid 18  cells (ILC2s).   DCs uptake allergens, process them into small peptides, and present them to T cells via major histocompatibility (MHC) complexes MHC class I or MHC class II, initiating an adaptive response123. There is evidence that DCs exposed to TSLP preferentially polarize T cells towards a Th2 effector phenotype124. Additional signals enhance polarization towards a type 2 response but remain poorly understood.  A population of recently discovered innate cells, ILC2s, can also serve as an important source of IL-5, IL-9 and IL-13 in response to IL-25 and IL-33.  Nasal polyps isolated from patients with chronic rhinosinusitis and lesions from patients with atopic dermatitis are enriched for ILC2s, suggesting that this cell type also plays a meaningful role in human atopy125,126.  Notably, IgE production is entirely dependent on T cell-derived cytokines127.  Although ILC2s cannot play a direct role in atopic disease by driving IgE production, they play a key role in creating an environment that directs Th2 polarization.   In both humans and mouse models it is well established that CD4+ Th2 cell responses are central to atopy115 .   Th2 cells are characterized by their expression of the transcription factors GATA-3, STAT5 and STAT6 and their production of the canonical type 2 cytokines: IL-4, IL-5, IL-9, and IL-13. In the early phase of the adaptive response, type 2 cytokines drive the allergic response by promoting immunoglobulin E (IgE) production by B cells (IL-4, IL-13), mast cell differentiation and maturation (IL-3, IL-9, IL-13), eosinophil maturation and survival (IL-3, IL-5, GM-CSF), basophil recruitment (IL-3, GMCSF), and the expression of vascular cell adhesion molecule-1 (VCAM-1) on endothelial cells (IL-4)116. Later phases of asthma including airway remodeling, hyper-responsiveness, and mucus hyper production are mediated by IL-13116. 19   IgE is central to atopic disease.  Allergen specific IgE binds to the high-affinity Fcε receptor (FcεRI) on basophils and mast cells. Expression of FcεRI has been observed in DCs after antigen exposure128.  Expression of FcεRI and the low affinity receptor, FcεRII, has been reported on eosinophils129. When this IgE is cross-linked upon subsequent antigen exposure these granulocytes become activated and release inflammatory mediators, such as cytokines, chemokines, histamine, heparin, serotonin, and proteases.  This response is most typical of mast cells, although it is also observed in eosinophils, and basophils.  These inflammatory mediators act on smooth muscle resulting in constriction, increased vascular permeability, and recruitment of additional inflammatory cells, resulting in the symptoms of an asthma attack117.  20             Naïve T cells are presented with antigen by DCs and differentiate into Th2 effector T cells.  These cells are characterized by their production of the cytokines IL4, IL5, and IL13.  In the presence of IL4, B cells class switch to producing IgE.  IgE then coats mast cells basophils and eosinophils on the high-affinity Fc-epsilon receptor.  When this IgE is cross-linked upon subsequent antigen exposure, mast cells and certain granulocytes including eosinophils and basophils, become activated and release inflammatory mediators, such as cytokines, chemokines, histamine, heparin, serotonin, and proteases.  These inflammatory mediators act on smooth muscle resulting in constriction, increase vascular permeability, and recruit additional inflammatory cells and resulting in the symptoms of an asthma attack   IL4 IL5 IL13 Naïve T cell Th2 cells class  switching IgE B cell DC degranulation mast cell Figure 1-2 Overview of immune processes in atopic disease. 21  The T helper subsets are briefly reviewed in figure 1-3.   Acting to dampen Th2 responses in the context of asthma are regulatory T cells (Tregs).  These are an important subset of T helper cells that function to maintain immunological unresponsiveness and suppress immune responses in part by their production of the anti-inflammatory cytokine IL-10. These cells are characterized by their expression of transcriptional regulator forkhead box protein p3 (Foxp3)130. Inducible Tregs, which are derived extra-thymically, exert critical control over Th2 driven mucosal inflammation in asthma, and are protective131. Asthmatic patients have fewer Tregs and they are less active130,132. Additionally, follicular helper T cells (Tfh) are specialized to help B cells in the germinal center.  The IL-4 that is secreted from these cells is critical for IgE during allergy development 133,134,135.  Finally, Th9 cells are found in abundance in the bronchoalveolar lavage fluid from asthmatic patients and express high levels of IL-9 and IL-4 136,137. Thus, T helper subsets other than the canonical Th2 cells coexist and play important roles in the pathogenesis of atopy.    22      Cytokines inside the arrows drive differentiation of the effector type.  The transcription factors expressed by each effector type are listed in the nucleus.  The signature cytokines produced by each subset are in bold.  Adapted from ref 118,138 .   Figure 1-3 T helper cell subsets.   23  1.3.2 Animal models of asthma To dissect the cellular, molecular, and genetic mechanisms that contribute to asthma several mouse disease models have been developed. The most commonly used is the ovalbumin (OVA) model of allergic asthma is (OAA). This model is not without limitations, but it well recapitulates the early phase response, driven by crosslinking of IgE on mast cells and the resulting degranulation, and airway hyperresponsiveness of asthma 139. There are several ways this model deviates from human atopy. First, the sensitization is performed systemically by intraperitoneal injection of the allergen. Additionally, OVA is not a physiologically relevant allergen. Finally, the model typically uses an adjuvant (alum) during sensitization.  Two models that are gaining favour that utilize locally administered physiologically relevant allergens and do not require the use of an adjuvant.  The most popular is the house-dust mite (HDM) model which utilizes the HDM- derived protease Derp1.  This model induces an adaptive immune response and airway hyper-reactivity that is Th2-dependent initiated by innate inflammatory responses after protease-mediated mucosal damage.  This model induces airway hyperresponsiveness and antigen specific IgE140.  The second is the papain model, which utilizes the cystine-protease allergen from papaya.  Papain causes occupational asthma 141. The allergenic activity of papain is protease dependent.  The protease activity of the enzyme causes damage to the mucosa resulting in the release of danger cytokines from the epithelium141.  This model induces eosinophilic inflammation and robust levels of IgE. Studies show that ILC2s are a critical mediator of this response142, 143,144.  ILC2s play a key role in initiating the memory Th2 cell response by creating a chemokine milieu that promotes Th2 cell recruitment145.   24  1.4 Microbiome in asthma and atopy 1.4.1 Epidemiological evidence There is an undeniable genetic component to atopy.  Genome wide association studies (GWAS) have determined that the most common of the genetic variants linked to atopy encode genes for epithelial cell-derived cytokines (TSLP and IL-33) and those involved in Th2 cell differentiation (STAT6)146.  However, there is a growing body of evidence to suggest that the genetic component may be secondary to the environmental triggers of this disease.   Many epidemiological observations have pointed towards a role for the microbiome in the etiology of asthma. Known modifiers of asthma risk include: race/ethnicity, sex, exposure to farm animals and domestic cats and dogs, family size and birth order, daycare attendance in early childhood, microbial exposures, use of antibiotics and antipyretics, mode of delivery, breastfeeding, diet and nutrition, and obesity (reviewed in ref 146, 147, 121).  These are all factors also known to affect the composition of the microbiota148.  This, combined with the rapid increase in the incidence of allergic diseases in recent times149 suggests a strong environmental contribution to asthma susceptibility.   Compelling evidence for an environmental factor having a greater influence over the development of asthma than genetic factors comes from research on migrant populations.  The prevalence of atopic disease in immigrants moving from low-middle income countries to high income countries is lower than the overall prevalence in those host countries.  However, the 25  atopic risk of second generation migrants  converges with the general prevalence in the host country150,151.  The importance of early-life exposure to environmental factors in high income countries, or lack of exposure in low income countries, is supported by studies of adopted children who move to high income countries in early life 152.   This strongly suggests that there is a limited window of opportunity for environmental factors to influence disease outcome.  1.4.2 Exposure to microbes There is evidence that immune detection of microbial components or products influences asthma development.  Polymorphisms in CD14 (co-receptor for lipopolysaccharide (LPS)), Toll-like receptor 2 (TLR2), and TLR4 are risk factors for atopy development153.  Further, children of farmers are at a decreased risk of developing atopy, which was attributed to higher expression of the genes encoding Toll-like receptor 2 (TLR2) and CD14 on immune cells isolated from the peripheral blood 154.  More recently, a study of two agricultural populations with similar genetic ancestries and lifestyles but distinct farming practices, revealed that the microbial composition of and endotoxin level in house dust explained the dramatic difference in prevalence of asthma between the two populations.  The study convincingly shows that innate sensing of molecular associated microbial patterns (MAMPs) is intricately tied to allergic sensitization155.     Several studies have emphasized that the composition and diversity of the early life microbiota is associated with allergy development. Two independent studies showed that low intestinal microbiota diversity in Scandinavian children in the first month of life is associated with allergy156 and asthma157. In the United Kingdom it was found that colonization with 26  Bifidobacterium catenulatum  in newborns was associated with increased risk of atopic dermatitis158.  An American birth cohort study identified changes in the microbiome in early life (3-6 mo) that were associated with milk allergy resolution by age 8159.  Three large birth cohort studies have independently confirmed that the composition of the microbiota and associated changes in fecal and urinary metabolites in early life are predictive of allergic sensitization and asthma development 160, 161, 162.  These three studies all indicate that there is a microbial signature present in the first 100 days of life that is predictive of asthma susceptibility.  However, the exact microbial taxa that are predictive differ between the three studies, consistent with the strong influence of biogeography on the microbiome.   Further, these differences are not limited to the gut microbiome.  The microbial communities in the nasal cavities differ in asthmatic and control children and adults163.  The persistence of the nasal microbiome-difference suggests that it may be caused by atopy, while the transient nature of the gut microbiome difference suggests that it may have a causal role in atopy.  More information is required to dissect the mechanisms underpinning these associations.  1.4.3 Microbiome depletion in animal models The role of the microbiome in atopic disease has been studied using microbiome-depletion in mice.  The most extreme microbiome studies employ the use of a germ-free (GF) or anexic animals, which are devoid of all microorganisms164.  These mice are produced by hysterectomy rederivation and maintained under rigorous sterile conditions in isolators to maintain their GF status. Antibiotics can be used as a tool to create controlled disturbances of the microbiota. Many groups have used a cocktail of broad spectrum antibiotics (commonly containing all or some of 27  ampicillin, vancomycin, neomycin, metronidazole, and gentamycin) to cause severe disturbances to the microbiome in both composition and total numbers of bacteria165.  Single antibiotics have also been used in low doses to create more subtle disturbance to the microbiota.  Vancomycin is a glycopeptide antibiotic that sterically hinders peptidoglycan polymerase to prevent the formation of the backbone glycan chains and interferes with cell wall synthesis in many Gram-positive bacteria166. Vancomycin has low oral bioavailability167. Consequently, orally administered vancomycin is poorly absorbed into the blood stream and therefore acts mainly on gut bacterial communities. Typically reserved as an antibiotic of last resort, the rates of vancomycin administration are on the rise as resistance to other front-line antibiotics becomes more common place168. Mice treated with low-dose vancomycin displayed dramatic changes in their bacterial microbiota. Although vancomycin treated mice have only slightly fewer total intestinal bacterial numbers, the composition of the community shifted profoundly169.  In mice, it has been robustly demonstrated that a lack of microbial colonization in early life increases sensitivity to atopic models. GF mice or mice treated with a cocktail of antibiotics are more susceptible to peanut allergy than their SPF counterparts, and this susceptibility can be alleviated by colonization with Clostridia170.   A diverse microbiota is required early in life to limit oral-induced systemic anaphylaxis171.    Several studies have emphasized that appropriate microbial colonization in early life is required to protect against mouse models of asthma.    Mice on a cocktail of antibiotics produce high steady-state levels of IgE and circulating basophils, which is mediated via MyD88 on B cells.  These mice are more susceptible to the HDM-model of allergic lung inflammation172.  Both GF and vancomycin treated mice are highly susceptible to the OAA model of allergic lung inflammation169, 173.  Both studies emphasized the importance of 28  early-life exposure to establishing a normal microbiome, and that the effect on health persisted into adulthood.  In GF mice, an accumulation of inducible Natural Killer T cells (iNKT) in lung tissue is one factor increasing asthma pathology173.  In vancomycin treated mice the mechanism is not yet elucidated, but may be in part because of decreased levels of Tregs in the intestinal lamina propria (LP)169.  1.4.4 The critical window Several groups have independently described an early life critical window during which alterations in the microbiome are predictive of atopic disease.   These studies are highly suggestive that the microbiome has a causal role in the later development of atopy during this window (reviewed in refs  174, 121 ).   In mice, studies show that alterations to the microbiome before weaning (~3 weeks of age) impact disease susceptibility for life.  In humans, studies find that features of the microbiome in the first-year are associated with disease susceptibility for life.  Several studies emphasize the first 3 months as being a critical window.  The major findings are summarized in figure 1-4 adapted from ref 174.   29                        (A) Insight from mouse models.  (B) The critical window as defined in human studies.  1 week 2 week 3 week 4 week birth Maternal intake of acetate reduces AAI in adult offspring Differences in airway microbiome associated with decreased AAI Neonatal exposure to H. pylori protects against AAI exposure to B. longum protects against AAI Vancomycin treatment exacerbates AAI Exposure of GF mice to SPF conditions protects against AAI  microbial diversity =   IgE production and     oral anaphylaxis 1 month 2 month birth microbial diversity =    incidence of asthma Differences in bacterial and fungal taxa associated with asthma risk Streptococcus = chronic wheezing Differences in bacterial taxa associated with asthma risk (CHILD study) 3 month Differences in bacterial and fungal taxa associated with asthma risk (Ecuador study) Differences in bacterial and fungal taxa associated with asthma risk (US birth cohort)  6 month Microbiome composition associated with milk-allergy resolution A B Figure 1-4 Summary of findings related to the early-life window during which microbes and microbial products impact atopic disease. 30  1.4.5 SCFAs and the immune response Understanding the specific effects of SCFAs on the different cells of the immune system has been an area of intense interest over the last 15 years.  To date, our understanding relies primarily on in vitro work.  Despite the advances made in the field, many aspects of SCFA-immune cell interactions remain elusive, and conflicting results are described in the literature.   Overall, the effects of SCFAs on immune cells is overwhelmingly anti-inflammatory.  A broad review of the cell-specific interactions is summarized in table 1-2. A more detailed explanation of these interactions is given below and limited to those cell types most relevant to this thesis.  GF and vancomycin treated mice lack the bacteria required to ferment SCFAs75, 175, 176.  These mice have been shown by multiple groups to have fewer colonic Tregs177, 169, 178, 179.  Reintroduction of indigenous Clostridium species, known producers of SCFAs, restored these Treg numbers179,177.  Oral gavage of a rationally selected mixture of 17 Clostridium strains into GF animals was sufficient to restore Treg numbers and cecal SCFA levels177. Smith et al. found that oral administration of SCFAs alone was capable of restoring gut Treg populations but had no effect on Treg numbers in the spleen, mesenteric lymph node, thymus or blood178. Conversely, Apraia al., found that oral administration of butyrate alone was able to restore systemic Treg numbers but had no effect on colonic Tregs180. Notably, in this study, SCFAs administered by enema induced more Tregs locally (intestinal tissue) and systemically than oral administration. Furusawa et al. found that feeding the mice butylated high-amylose maize starches restored gut Treg numbers but had no effect on systemic Treg numbers181. Smith et al. found propionate treatment of mice reduced colonic Treg expression of HDAC6 and HDAC9178.  Apraia et al. and Furusawa et al. both identified chromatin modification of the Foxp3 locus after 31  administration of butyrate181,180. This body of work suggests that SCFAs may regulate Tregs through their action as an HDAC inhibitor, but the relative contributions of the various SCFAs and how they influence Tregs locally and systemically are still undetermined.  DCs are the most direct line of communication between the gut lumen, where the microbiota resides, and the rest of the immune system. How SCFAs influence DCs may be integral to understanding how the microbiota influences systemic immune responses. In vitro experiments have shown that butyrate treatment of DCs prevented IL-12 production, homotypic DC aggregation, and NF-κB binding to target DNA sites182. These DCs cells were less mature and expressed lower levels of co-stimulatory molecules183 ,184, 185, 186. Butyrate also inhibited the ability of DCs to activate T cells and inhibited the production of the proinflammatory cytokine IFN-γ but promoted production of cytokines IL-10185 and IL-23184. In addition, pre-treatment of DCs with butyrate enhanced Treg induction in vitro and attenuated Th17 differentiation180.   32   Table 1-2: Summary of cell-specific responses of immune cells to SCFAs. Cell type  Response* Mechanism** Ref T cell  Polarize towards a Treg fate HDAC 178,180, 181   Increased number of Tfh - 187   Inhibit proliferation - 188   Induce apoptosis - 188  B cell  Boost antibody response  HDAC 187 DC  Inhibit production of inflammatory cytokines  - 182, 185, 184, 189,190    Promote production of anti-inflammatory cytokines - 185, 184, 189   Inhibit functional differentiation  HDAC, GPCR 183,186, 191   Inhibit expression of costimulatory molecules - 184, 185, 189,190,54   Modified hematopoiesis GPCR 192    Enhanced Treg induction HDAC, GPCR 180, 54, 193 Neutrophil  Induce chemotaxis GPCR 99, 194, 195 33  Cell type  Response* Mechanism** Ref   Suppress production of inflammatory cytokines HDAC 196   Promote production of inflammatory cytokines - 197   Inhibit phagocytosis - 198, 199, 200    Inhibit production of ROS  - 198, 199, 200 Monocyte/Macrophage  Suppress production of inflammatory cytokines HDAC, GPCR 97, 185,201    Promote proinflammatory cytokines - 197,    Inhibit proliferation - 188    Inhibit migration HDAC 202 ILCs  Suppresses group3 ILCs - 105   *Responses in black are broadly attributed to SCFAs.  Responses in blue are elicited by propionate alone, responses in red are elicited by butyrate alone.  Responses in bold font have been studied in vivo, those that are not bolded have been demonstrated only in vitro.  **Mechanisms that are largely speculative are in italics.  – means no mechanism is known.    34  1.4.6 SCFAs in asthma There are several lines of evidence that indicate the microbiota can influence asthma susceptibility via the production of SCFAs.  Researchers have identified a correlation between low fiber consumption (the substrate required for generation of SCFAs) and incidence of asthma203, 204, 205, 206 in both humans and mice.   The first group to directly test the effect of a high fiber diet in mice found that it was protective against allergic airway disease.  This effect, which was reproduced by feeding propionate alone, was mediated via DCs that were abnormal in their hematopoiesis and their ability to induce a robust Th2 response.  This effect was GPR41 and GPR43 dependent192.  Mice deficient in GPR43 are also more susceptible to allergic airway disease99.  The significant role of Tregs in asthma (reviewed in section 1.3.1) coupled with the profound effect of SCFAs on Treg biology (reviewed in section 1.4.4) has led many groups to believe that diet and SCFAs may impact asthma via this mechanism.  Indeed, Thorburn et al., found that a high-fiber diet or acetate-feeding led to decreased allergic airway disease via this enhancement in Treg numbers through an epigenetic mechanism, specifically HDAC9 inhibition31.    35  1.5 Research outline 1.5.1 Hypothesis and objectives The literature outlined here suggests a role for SCFAs produced by the microbiota in asthma and the progression of atopic disease.  I hypothesize that SCFAs are protective against the development of asthma and atopic disease and alterations to the microbiota that disrupt the SCFA producing bacteria will promote disease. I tested this hypothesis by completing the following objectives:   Objective I: Use antibiotics to deplete the SCFA producing bacteria in a mouse model. Analyze the immune perturbations that result from this depletion. Add back the SCFAs to understand their role in asthma immunobiology.   Objective II: Compare the functional potential of the intestinal microbiome in children who are diagnosed with asthma at early life timepoints: 3 months and 1 year with a focus on SCFA-fermentation pathways.   Objective III: Identify how exposure or lack of exposure the SCFAs in the early life window systemically imprints the host for life.   36  1.5.2 Anticipated Impact and Significance The research described has exciting potential for beneficial impact on human health.  Numerous associations have been described among the microbiome, microbial metabolites and autoimmune diseases such as asthma.  This body of work provides compelling evidence for the involvement of an epigenetic mechanism that is not only relevant to mouse models.  The occurrence of asthma is increasing as an epidemic in developed countries, and it has become the most prevalent disease among children in those countries.  The social and economic costs of asthma are enormous.  This project generates new understanding of asthma with immense potential to lead to innovative approaches to prevent and treat the disease, such as manipulation of the microbiome or provision of essential microbial metabolites.      37   Microbiome-driven allergic lung inflammation is ameliorated by short chain fatty acids  2.1 Synopsis  The mammalian gastrointestinal tract harbors a microbial community with metabolic activity critical for host health, including metabolites that can modulate effector functions of immune cells. Mice treated with vancomycin have an altered microbiome and metabolite profile, exhibit exacerbated Th2 responses, and are more susceptible to allergic lung inflammation. Here we show that dietary supplementation with short chain fatty acids (SCFAs) ameliorates this enhanced asthma susceptibility by modulating the activity of T cells and dendritic cells (DCs). Dysbiotic mice treated with SCFAs have fewer IL-4-producing CD4+ T cells and decreased levels of circulating IgE. In addition, DCs exposed to SCFAs activate T cells less robustly, are less motile in response to CCL19 in vitro and exhibit a dampened ability to transport inhaled allergens to lung draining nodes. Our data thus demonstrate that gut dysbiosis can exacerbate allergic lung inflammation through both T cell- and DC-dependent mechanisms, which are inhibited by SCFAs. 2.2 Introduction Short chain fatty acids (SCFA) are small carboxylic acids containing one to six carbon atoms. Acetic acid, propionic acid and butyric acid are the most abundant SCFA in the human and  38  murine gut and are produced by bacterial fermentation of indigestible fibers and amino acids72,73, 74. The colons of germ-free mice have higher levels of fermentable substrates and diminished levels of SCFAs compared to those of conventionally raised mice colonized by commensal intestinal flora75. This phenomenon highlights the essential role of the microbiome in the production of SCFAs in the gut. These SCFAs are then absorbed and circulate in the serum. Recently, there has been interest in uncovering potential roles of SCFAs in regulating the immune system.   SCFAs generated by the gut microbiome have been implicated in modulation of the immune system and in the development of allergic disease207. For example, SCFAs were previously shown to play a role in modulating the immune response in a mouse model of allergic asthma99,192. Likewise, we recently found that, at 3 months of age, lower concentrations of fecal acetate correlated with a high propensity for the development of atopy in childhood160.   Previous work from our laboratories found that vancomycin dramatically alters the host microbiome, leading to disrupted immune homeostasis and increased susceptibility to allergic inflammation169. Disruption of the microbiome after antibiotic treatment results in an altered metabolome with attenuated production of SCFAs208,209. We hypothesized that a reduction of SCFA metabolites in vancomycin-treated mice contributes to their enhanced susceptibility to allergic airway inflammation.  Here, we studied the effect of vancomycin on SCFA-production by the gut microbiome and the ability of exogenous SCFAs to prevent exacerbation of lung inflammation by vancomycin  39  treatment in two models of asthma. We examined effects of SCFAs on Th2 skewing associated with asthma both in vivo and in vitro. Our results show that gut microbiome metabolites can have an instructive effect on critical immune cells, both T cells and DCs, associated with Th2 immunity. This study provides novel mechanistic insights into the communication between the gut microbiome and immune system, the mediation of systemic health, and the maintenance of homeostasis. These insights may lead to a therapeutic approach to prevent or limit the severity of allergic asthma.  2.3 Materials and Methods 2.3.1 Mice C57BL/6J and C.129-IL-4tm1Lky/J (4get) mice (The Jackson Laboratory, ME) were bred and maintained in a specific pathogen-free facility at the Biomedical Research Centre. Mice were housed in autoclaved cages and received irradiated chow ad libitum (equal parts mixture of PicoLab Mouse Diet 20 and Picolab Rodent Diet 20) and autoclaved tap water. All experiments were carried out in accordance with the Canadian Council on Animal Care (CCAC) guidelines and were approved by the University of British Columbia Committee on Animal Care (Protocol# A15-0113). At experimental end-points mice were humanely euthanized by Avertin (2,2,2-tribromoethanol) overdose followed by cardiac puncture or removal of lungs.     40  2.3.2 Antibiotic and SCFA treatment and quantification.  As indicated, breeding pairs and nursing dams were administered vancomycin (Sigma-Aldrich, MO) at 200 mg/L in drinking water. Pups born from respective breeding pairs were reared on antibiotic-treated water with for the duration of the experiment. As indicated, a cocktail of SCFA: 40 mM butyrate, 67.5 mM acetate plus 25.9 mM propionate (Sigma-Aldrich) was included in drinking water. Alternatively, 40 mM butyrate was included.   SCFA solutions were prepared and changed weekly.  To quantify SCFA levels, mouse fecal and cecal samples were combined by vortexing with 25% phosphoric acid followed by centrifugation until a clear supernatant was obtained. Supernatants were submitted for GC analysis to the Department of Agricultural, Food and Nutritional Science of the University of Alberta. Samples were analyzed as previously described160. 2.3.3 Microbiome analysis Fecal pellets were collected from individual mice and stored at     -70°C. Pellets were homogenized using a bead-beating method (FastPrep bead matrix E, MP Biomedicals, OH), and total DNA was extracted (Ultra Clean Fecal DNA kit, Mo Bio Laboratories, CA). 16S rRNA gene fragments were amplified using bar-coded primers160, with the following primer regions (5’ to 3’):  27F:  AGAGTTTGATCMTGGCTCAG, 519R: GWATTACCGCGGCKGCTG. Amplicons were pooled in equal molar concentrations and pyrosequenced using a 454 Titanium platform (Roche, CT). Sequence data was trimmed, quality filtered, and clustered at 97% identity into Operational Taxonomic Units (OTUs) using a modified MOTHUR standard operating  41  procedure43. OTUs were taxonomically annotated using the SILVA database210. Global community structure comparisons were made using DESeq2 with an adjusted (Benjamini-Hochberg method) P-value of less than 0.01 and Phyloseq211 2.3.4 Antibodies and flow cytometry Staining and antibody dilutions were prepared in PBS containing 2% fetal calf serum, 2 mM EDTA, and 0.05% sodium azide. Samples were first blocked in buffer containing 5 mg/mL anti-CD16/32 (clone 2.4G2). Antibodies used were as follows: PE-conjugated Siglec-F (E50-2440) from BD Biosciences (San Jose, CA), PE-Cy7–conjugated CD3e from eBioscience (San Diego, CA), fluorescein isothiocyanate–conjugated anti-neutrophil (7/4) from Abcam (Cambridge, MA), Alexa Fluor 647-conjugated CD4 (RM4-5) from eBioscience (Santa Clara, CA), Pacific Blue–conjugated CD45 (I3/2) made in-house, and Alexa Fluor 647–conjugated CD11c (N418) made in-house. Samples were run on a BD LSRII, and data analysis was performed with FlowJo software (TreeStar, CA).  2.3.5 OVA-induced allergic lung inflammation On days 0 and 7 mice were injected intraperitoneally with 50 mg of chicken ovalbumin (OVA) (grade III; Sigma) and 650 mg of aluminum hydroxide (Sigma). On days 21, 22, 23, 25, and 27 mice were intranasally challenged with 50 mg of OVA. On day 28 mice were sacrificed by avertin overdose. Bronchoalveolar lavage fluid was collected by 3 washes with 1 mL of sterile  42  saline. Blood was collected by cardiac puncture. Cells were enumerated and differentiated by flow cytometry using antibodies to CD3e, CD11c, CD45, B220, Siglec-F, and 7/4. 2.3.6 Papain-induced allergic lung inflammation On days 0, 1, 14, and 20, lightly anesthetized mice were administered intranasally 10 µg of papain (Sigma) prepared in a volume of 40 µL PBS. Experimental mice were sacrificed 16 h after the final papain treatment (on day 21). Bronchoalveolar lavage fluid was collected by 3 washes with 1 mL of sterile saline. Blood was collected by cardiac puncture. Cells were enumerated and differentiated by flow cytometry using antibodies to CD3e, CD11c, CD45, B220, Siglec-F, and 7/4.  2.3.7 RNA isolation and quantitative RT-PCR   Using a Qiagen TissueLyser (Valencia, CA), tissues were homogenized in Trizol (Life Technologies, CA). Total RNA was extracted and reversed transcribed with a high-capacity cDNA kit (Life Technologies). QPCR was performed with SYBR® green technology (KAPA Biosystems, MA) on an ABI 7900 real-time PCR instrument (Life Technologies). Primer sequences were as follows (5’- 3’): ε-GLT: forward GCCTGCACAGGGGGCAGAAG and reverse ATGACCCTGGGCTGCCTGGT, GAPDH:  forward CATCAAGAAGGTGGTGAAGC and reverse CCTGTTGCTGTAGCCGTATT, beta-actin forward ACTAATGGCAACGAGCGGTTC and reverse GGATGCCACAGGATTCCATACC.  43  2.3.8 Histology The left lung lobe was fixed in 10% buffered formalin, embedded in paraffin, and stained with haematoxylin and eosin (H&E) or periodic acid-Schiff (PAS). H&E stained sections were blindly scored on a scale of 0 to 12 as described212.  Mucus was quantified on PAS sections using the color threshold function in ImageJ (National Institute of Health) to create a binary image. 2.3.9 Determination of serum IgE Mice were sacrificed by Avertin overdose and blood was collected via cardiac puncture.  Serum was separated from whole blood by centrifugation after clotting overnight at 4°C.  ELISA for total serum IgE was performed according to the manufacturer’s instructions (BD Biosciences).  2.3.10 Isolation of lymphocytes   Lymph nodes were collected from mice and passed through a 70-μM strainer to obtain a suspension of free cells.  Lamina propria cells were isolated by digestion in 0.5mg/ml collagenase (Sigma-Aldrich) and 0.05% DNAse (StemCell Technologies, British Columbia) in Dulbecco’s Modified Eagle Medium (DMEM) for 30 min.  Leukocytes were enriched by Percoll separation. Cells were cultured for up to 4 days in Th2 conditions: DMEM supplemented with 10% FBS, 2 mM glutamine, 1% penicillin/ streptomycin, 25 mM HEPES, and 5 × 10–5 M 2-mercaptoethanol with 1 μg/ml each anti-CD3 (145-2C11) and anti-CD28 (37.51) in the presence  44  of anti–IFNγ (10 μg/ml), and IL-4 (80 ng/ml), and then stained with antibodies against CD45 and CD4 and analyzed by flow cytometry. Alternatively, cells isolated from LNs were not incubated and were prepared directly for flow cytometry.  2.3.11 Isolation of dendritic cells Flt3L ligand–producing B16 melanoma cells were grown in vitro in complete DMEM medium supplemented with 10% FBS and 1% penicillin/streptomycin. 5 × 104 cells were injected subcutaneously into mice. After tumor growth to approximately 1 cm diameter, splenic DCs were isolated using a CD11c positive selection kit (StemCell Technologies). The cell purity was verified by flow cytometry and found to be between 96% and 98%. 2.3.12 Transcriptome analysis DCs were incubated for 3 h with or without 50 µM butyrate in complete DMEM medium, buffered at pH 7 with bicarbonate, supplemented with 10% FBS and 1% penicillin/streptomycin. Cells were then stimulated with 100 ng/ml of LPS for 18 h. Total RNA was extracted using an RNeasy Mini Kit according to manufacturer’s instructions (Qiagen). Approximately 500 ng of RNA was prepped with the Truseq Stranded mRNA kit from Illumina and sequenced on an Illumina MiSeq 75x75 Paired End v3 run. Using TopHat2, reads were aligned to the mm10 transcript reference and Cufflinks was used to determine differential expression. Differential  45  expression tables were analyzed by the “core analysis” function in Ingenuity Pathways Analysis (Qiagen). Heat maps were generated using the open source R and VisR platforms213 2.3.13 DC in vivo migration assay DC migration to the lymph nodes upon antigen acquisition was tested in vivo via intranasal administration of 50 μl DQ-OVA (Thermo Fisher Scientific, MA). The percentage of FITC+ DCs in the mediastinal nodes was determined 24 h after the administration of FITC-OVA via flow cytometry using antibodies to CD11c.  2.3.14 T cell activation assay  Flt3L-derived splenic dendritic cells were incubated for 3 h with or without 50 µM sodium butyrate in the culture media (complete DMEM supplemented with 10% fetal bovince serum, 2mM glutamine, 1% penicillin/streptomycin, 25mM HEPES, and 5x10-5 M 2-mercaptoethanol) and stimulated with 100 µg/ml ovalbumin peptide (Worthington Biochemical Corporation) in the presence of 100 ng/ml LPS.  DCs were then washed extensively.  This was followed by the addition of CFSE (Invitrogen)-labeled OT-II CD4+ T cells (10:1 T cell:DC ratio) for 7 days.  CD4+ T cells were isolated from spleen by negative selection using RoboSep (StemCell Technologies Inc.). Purities were routinely 90–95% CD4+ cells.  46  2.3.15 DC transwell migration assay DCs were added to in the upper chamber of a 6-µm pore transwell (Corning, NY). CCL19 was added in the lower chamber as a chemoattractant. Chambers were incubated at 37°C + 5% CO2 for 3 h. Cells in lower chambers were recovered and counted by flow cytometry.  2.3.16  Statistics  Unless otherwise specified, differences between treatment groups were compared using a paired and unpaired Student's t-test, as appropriate, or one-way ANOVA (GraphPad Prism software, version 4.0, CA). 2.4 Results  2.4.1 Vancomycin treatment depletes SCFA-producing bacteria Our objective was to identify mechanisms downstream of vancomycin-induced gut dysbiosis responsible for promoting susceptibility to allergic asthma. To do this, we further examined the effect of vancomycin treatment on the mouse microbiome. Vancomycin-treated mice were generated from pregnant dams maintained on autoclaved drinking water containing vancomycin (200 mg/L) starting between day 10 and 14 of gestation. Pups were subsequently maintained on such water for life. Control mice were generated from pregnant dams maintained on autoclaved  47  water. We analyzed the fecal microbiome by sequencing 16S rRNA gene amplicons. The microbiome was significantly altered by vancomycin treatment (permanova results p < 0.001).  Analyses of differential abundance of operational taxonomic units (OTUs) showed that vancomycin-treated mice had a significant reduction in OTUs belonging to several families, many of which are among the major SCFA-producers in gut, namely Clostridiaceae, Lachnospiraceae and Ruminococcaceae (Fig 2.1). Notably, vancomycin treatment reduced the overall abundance, diversity and species composition of the class Clostridia, the dominant butyrate-producing component of the intestinal microbiome (Fig 2-1)214,215,88, 91.   To assess whether the microbiome shift led to lower SCFA production, we measured the concentrations of butyrate, propionate and acetate in the feces and cecum of treated mice. We observed reduced levels of butyrate in the cecum and feces of vancomycin-treated mice but not of acetate or propionate compared to control mice (Fig 2-1).  Butyrate levels were below the limit of detection in the serum. Propionate and acetate were not significantly reduced in the serum of vancomycin treated mice (Fig 2-2). Supplementing the drinking water of vancomycin-treated mice with a mixture of butyrate, acetate, and propionate (BAP) partially restored the concentration of butyrate detected in the cecum but did not restore butyrate in the feces (Fig 2-1).  These treatments did not significantly alter propionate concentrations, but the trends for propionate were similar to those for butyrate but the differences in concentration among treatments were smaller.   Because SCFAs, including butyrate, have been reported to stimulate growth of intestinal microbes216, we assessed whether supplementing drinking water with BAP altered the fecal  48  microbiome of mice. Hierarchical clustering based on the similarity of bacterial microbiome composition at the OTU level confirmed a major shift in composition due to vancomycin treatment with a Bray-Curtis dissimilarity index of 0.8 (Fig 2-1). However, treatment with BAP did not substantially alter the microbiome of vancomycin-treated or control mice (PERMANOVA P > 0.05). As seen in Fig 1D, the bacterial community at the class level is dramatically altered with vancomycin treatment, but not by the addition of BAP. These results show that exogenous SCFA (in the form of BAP) had no detectable effect on the fecal microbiome composition    49    050010001500BAP control vancovanco.BAPDescriptionAbundanceRank5Clostridiaceae_1Clostridiales_Incertae_Sedis_XIIClostridiales_Incertae_Sedis_XIIIGracilibacteraceaeLachnospiraceaePeptostreptococcaceaeRuminococcaceaeunclassifiedPorphyromonadaceae Burkholderiaceae Clostridiaceae 0 .00 .20 .40 .60 .8A C1 3. 02 3. 4A C1 3. 02 3. 5A C1 3. 02 3. 6A C1 3. 02 3. 8A C1 3. 02 3. 12A C1 3. 02 3. 3A C1 3. 02 3. 1A C1 3. 02 3. 7A C1 3. 02 3. 9A C1 3. 02 3. 2A C1 3. 02 3. 10A C1 3. 02 3. 11A C1 3. 02 5. 14A C1 3. 02 5. 11A C1 3. 02 5. 12A C1 3. 02 5. 10A C1 3. 02 5. 13A C1 3. 01 6. 20A C1 3. 01 6. 19A C1 3. 01 6. 21A C1 3. 02 5. 18A C1 3. 02 5. 19A C1 3. 02 5. 15A C1 3. 02 5. 16A C1 3. 02 3. 17A C1 3. 02 3. 19A C1 3. 02 3. 13A C1 3. 02 3. 15A C1 3. 02 3. 18A C1 3. 02 3. 20A C1 3. 02 3. 14A C1 3. 02 3. 160   5 -10 -15  -5 A Log2(fold change) 10 0.6 0.4 0.2 0 0.8 0 1 2 3 4 μmol/ml μmol/ml butyrate acetate feces feces cecum cecum C * * * Vanc Con Vanc + BAP ControlVancVanc BAP0.6 0.4 0.2 0 0.8 μmol/ml propionate feces cecum B 050010001500BAP control vancovanco.BAPDescriptionAbundanceRank5Clostridiaceae_1Clostridiales_Incertae_Sedis_XIIClostridiales_Incertae_Sedis_XIIIGracilibacteraceaeLachnospiraceaePeptostreptococcaceaeRuminococcaceaeunclassified050010001500BAP co tr l vancovanco.BAPDescriptionAbundanceRank5Clostridiaceae_1Clo tridiales_Incertae_Sedis_XIIClo tridiales_Incertae_Sedis_XIIIGracilibacteraceaeLachnospiraceaePeptostreptococcaceaeRuminococcaceaeunclassifiedclassified uminococcaeae lostridiales XII chnospirace  lostridiaceae lostridiales XIII eptos reptococcaceae racilib teraceae 10  0   500 150  relative abundance Vanc Con * Con   CBAP   Con   CBAP   CBAP   Con   Con Con     Con     Con   Con   Con   CBAP   CBAP   CBAP   CBAP   CBAP   Con   Con   CBAP   VBAP   VBAP   Vanc   VBAP   VBAP   Vanc   Vanc   VBAP   VBAP   Vanc   Vanc   Vanc   D 0.8 0.6 0.4 0.2 0 Bray-Curtis dissimilarity 0.000.250.500.751.00C1C11C12C2C3C4C5C6C7C8C9CBAP1CBAP2CBAP3CBAP4CBAP5CBAP6CBAP7CBAP8CBAP9V1V2V3V4V5V6VBAP1VBAP2VBAP3VBAP4VBAP5VBAP6nameAbundanceRank3ActinobacteriaAlphaproteobacteriaBacilliBacteroidiaBetaproteobacteriaClostridiaDeltaproteobacteriaErysipelotrichiaGammaproteobacteriaActinobacteria Alphaproteobacteria Bacilli 0.000.250.500.751.00C1C11C12C2C3C4C5C6C7C8C9CBAP1CBAP2CBAP3CBAP4CBAP5CBAP6CBAP7CBAP8CBAP9V1V2V3V4V5V6VBAP1VBAP2VBAP3VBAP4VBAP5VBAP6nameAbundanceRank3ActinobacteriaaproteobacteriaBacilliBacteroidiaBetaproteobacteriaClostridiaDeltaproteobacteriaErysipelotrichiaGammaproteobacteria75 50 0 25 100 % abundance Betaproteobacteria Clostridia Bacteroidia 0.000.250.500.751.00C1C11C12C2C3C4C5C6C7C8C9CBAP1CBAP2CBAP3CBAP4CBAP5CBAP6CBAP7CBAP8CBAP9V1V2V3V4V5V6VBAP1VBAP2VBAP3VBAP4VBAP5VBAP6nameAbundanceRank3ActinobacteriaAlphaproteobacteriaBacilliBacteroidiataproteobacteriaClostridiaDeltaproteobacteriaErysipelotrichiaGammaproteobacteriaGammaproteobacteria Deltabroteobacteria Epsilonproteobacteria 0.000.250.500.751.00C1C11C12C2C3C4C5C6C7C8C9CBAP1CBAP2CBAP3CBAP4CBAP5CBAP6CBAP7CBAP8CBAP9V1V2V3V4V5V6VBAP1VBAP2VBAP3VBAP4VBAP5VBAP6nameAbundanceRank3ActinobacteriaAlphaproteobacteriaBacilliBacteroidiaBetaproteobacterialostridiaDeltaproteobacteriarysipelotrichiaammaproteobacteria●●●●●●●●●●●●●●●●●●●● ●●●●●●●●●●●●●●●●●●●● ●●●● ●●●● ●●●●−10−50510BurkholderiaceaeClostridiaceae_1EnterobacteriaceaeErysipelotrichaceaeLachnospiraceaeLactobacillaceaePaenibacillaceae_1PorphyromonadaceaeRuminococcaceaeunclassifiedRank5log2FoldChangeRank3●●●●●●●BacilliBacteroidiaBetaproteobacteriaClostridiaErysipelotrichiaGammaproteobacteriaunclassified●●●●●●●●●●●●●●●●●●●● ●●●●●●●●●●●●●●●●●●●● ●●●● ●●●● ●●●●−10−50510BurkholderiaceaeClostridiaceae_1EnterobacteriaceaeErysipelotrichaceaeLachnospiraceaeLactobacillaceaePaenibacillaceae_1PorphyromonadaceaeRuminococcaceaeunclassifiedRank5log2FoldChangeRank3●●●●●●●BacilliBacteroidiaBetaproteobacteriaClostridiaErysipelotrichiaGammaproteobacteriaunclassified●●●●●●●●●●●●●●●●●●●● ●●●●●●●●●●●●●●●●●●●● ●●●● ●●●● ●●●●−10−50510BurkholderiaceaeClostridiaceae_1EnterobacteriaceaeErysipelotrichaceaeLachnospiraceaeLactobacillaceaePaenibacillaceae_1PorphyromonadaceaeRuminococcaceaeunclassifiedRank5log2FoldChangeRank3●●●●●●●BacilliBacteroidiaBetaproteobacteriaClostridiaErysipelotrichiaGammaproteobacteriaunclassified●●●●●●●●●●● ●●●●●●●● ●●●●●●●●●●●●● ●●●● ●● ●●● ●●●●● ●●●−10−50510BurkholderiaceaeClostridiaceae_1EnterobacteriaceaeErysipelotrichaceaeLachnospiraceaeLactobacillaceaePaenibacillaceae_1PorphyromonadaceaeRuminococcaceaeunclassifiedRank5log2FoldChangeRank3●●●●●BacilliBacteroidiaBetap 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dChangeRank3●●●BacilliBacteroidiaBetaproteobacteriaClostridiaErysipelotrichiaGammaproteobacteriaunclassified 50   Mice were reared for 6-8 weeks (Con), where indicated, with vancomycin (Vanc) or Vanc +  BAP in their water. (A) DESeq analysis of differentially abundant OTUs in control versus vancomycin-treated mice grouped by family and color-coded by class; negative fold change values indicate OTUs reduced by vancomycin, p < 0.01 after correction for false discovery (Benjamini-Hochberg method) (B) Relative abundance of OTUs belonging to the class, Clostridia, color-coded by family. (C) Concentration of the three most abundant SCFA measured in cecum and feces of mice, measured by gas chromatography and normalized to volume of sample. (D) Community composition at the class level of each sample is shown with hierarchical clustering based on Bray-Curtis dissimilarity of OTU profiles. Drinking water treatments of mice: Con, control; Vanc, vancomycin; Vanc + BAP or VBAP, vancomycin plus butyrate, acetate and propionate.  Samples from replicate experiments were combined for analysis (n=6); error bars show SEM. * P < 0.05   Figure 2-1 Vancomycin depletes SCFA-producing bacteria in the gut.  51           Concentration of acetate and propionate measured in blood serum of mice, measured by gas chromatography and normalized to volume of sample.   Vanc Con Vanc + BAP ControlVancVanc BAP0.6 0.4 0.2 00.8 μmol/ml 1 propionate acetate Serum level SCFA Figure 2-2: Vancomycin treatment does not alter levels of acetate or propionate in the serum.  52  2.4.2 SCFA attenuate allergic lung inflammation in vancomycin-treated mice The ovalbumin (OVA) model of allergic asthma is a useful model to recapitulate many of the hallmarks of a Th2 cell-driven lung inflammatory disease. The results from our microbiome and SCFA analyses led us to hypothesize that the reduction of bacterially derived SCFAs in vancomycin-treated mice makes them more susceptible to OVA-induced asthma and that exogenous SCFAs (ie, BAP water) would make them less susceptible.   As expected, mice treated with vancomycin displayed enhanced allergic inflammation with an increase of inflammatory cells and eosinophils in the lung airways and increased serum IgE (Fig 2-3). And as hypothesized, supplementation of vancomycin-treated mice with BAP in their drinking water attenuated the infiltration of inflammatory cells, including eosinophils, into the lung airways and abolished elevation of IgE. Allergic inflammation was further verified by transcript-level analysis of Th2 cytokines.  We found Il4 transcript levels were significantly higher in the vancomycin-treatment group relative to controls and to mice treated with vancomycin plus BAP, and trends consistent with this phenotype were observed for both Il5 and Il13 transcripts (Fig 2-4).  We examined lungs from all treatment groups for histopathology consistent with allergic inflammation and found vancomycin-treated mice had elevated pathology and increased mucus in the lungs, this elevation was prevented by SCFA supplementation (Fig 2-4).     53  We found that butyrate supplementation, alone, was sufficient to attenuate OVA-induced airway inflammation (Fig 2-3). SCFA supplementation did not significantly alter severity of airway inflammation in mice raised on control water (Fig 2-3).   The OVA-allergic asthma mouse model relies on systemic antigen priming followed by induction of lung inflammation by direct airway challenge. It is possible that the influence of the intestinal microbiome and SCFAs may only be relevant in this systemic priming asthma model. Therefore, we also evaluated the effect of vancomycin and SCFAs in a model of papain-induced lung inflammation, which relies entirely on repeated intranasal antigen exposure. We again evaluated asthma severity by quantifying cell infiltration into the airway and serum IgE levels.  By both measures, vancomycin treatment increased susceptibly to papain-induced lung inflammation, and exogenous BAP completely prevented the enhanced inflammation attributable to vancomycin (Fig 2-3). Allergic inflammation was further verified by transcript-level analysis of Th2 cytokines.  We found Il13 transcript levels were significantly higher in the vancomycin-treatment group, and trends consistent with this were seen for both Il4 and Il5 (Fig 2-4).  We examined lungs from all treatment groups for histopathology consistent with allergic inflammation and found vancomycin-treated mice had elevated pathology and increased mucus in the lungs relative to controls.  Mice on vancomycin supplemented with SCFA did not have elevated pathology scores or mucus production (Fig 2-4).  To further understand how BAP and vancomycin treatment influences IgE levels, we looked for evidence of IL-4-driven IgE isotype-switching in lymphoid tissues following papain induction. Expression of the sterile germline transcript (εGLT) in B cells is indicative of ongoing isotype switching to IgE171. We found εGLT  54  to be significantly higher in mesenteric lymph nodes (LN), Peyer’s patches, and mediastinal LNs of vancomycin-treated mice, while this elevated transcription was prevented by BAP (Fig 2-5).    55              Assessment of asthma severity in OVA treated mice (A – C). (A) total cell counts in the bronchiolar lavage fluid (BALF), serum level IgE, and total number of eosinophils in BALF.  (B) Total cell counts in the bronchiolar lavage of OVA induced mice; Vanc + B, treated with vancomycin plus only butyrate. (C) Total cell counts in the bronchiolar lavage of OVA treated mice; control water vs. control water supplemented with BAP. (D) Assessment of asthma severity in papain treated mice: total cell counts in the BALF, and serum level IgE. The data in A comes from 3 independent experiments with 4 to 6 mice per group, data in B-C comes from 2 independent experiments with 4 to 6 mice per group.  In A-C data shows a representative experiment.  Data from D is from 4 independent experiments with 5 mice per group, samples from replicate groups were combined for data analysis.  Error bars in all panels show SEM.    * P < 0.05  B Cells/ml  x 105 BALF BALF 15 10 0   5 20 25   10   30   20 * * C Con Con + BAP ControlVancVanc+BAPdongVanc 0 Con Vanc+ only B ControlVancVanc+BAPdo gCells/ml  x105 A BALF * 30 20 0  10 40 50 * * 15 10 0   5 Eosinophils Vanc Con Vanc + BAP ControlVancVanc BAP* * IgE 500 0 1500 1000 cells/ml  x105  ng/ml * * OVA Cells/ml  x105 D BALF 15 10 0   5 20 * *  ng/ml Vanc Con Vanc + BAP ControlVancVanc BAPIgE 200 600 0 400 800 * * Papain 0 Figure 2-3: Exogenous SCFA ameliorate vancomycin-induced asthma severity.  56    IL13   0 0.1 0.2 0.3 0.4 0.5 IL4   0 0.2 0.4 0.6 0.8 1 mRNA expression (relative to beta-actin) B papain lung cytokine levels   0 0.1 0.4 0.2 0.3 IL5 IL4   0 0.02 0.04 0.06 0.08 IL5   0 0.001 0.002 0.003 0.004 IL13   0 0.005 0.01 0.015 mRNA expression (relative to beta-actin) A OVA lung cytokine levels lung histology lung histology   6 7 8 9 10 11 12 score * * * * * *   0 5 10 15 20 25 % of lung with mucus * score   0 5 10 15 20 25 % of lung with mucus   0 2 4 6 8 10 * * * # Control Vanc Vanc+BAP Control Vanc Vanc+BAP Control Vanc Vanc+BAP Control Vanc Vanc+BAP C D E F # Vanc Con Vanc + BAP ControlVancVanc BAPVanc Con Vanc + BAP ControlVancVanc BAP 57   Assessment of asthma severity in OVA treated mice (A – C). (A) Transcript levels of Th2 cytokines (IL-4, IL-5, IL-13) measured in lung tissue normalized to the house-keeping gene beta-actin. Drinking water treatments of mice: Con, control; Vanc, vancomycin; Vanc + BAP, vancomycin plus butyrate, acetate and propionate. (B) Representative images of hemotoxylin and eosin stained lung sections and total pathological scores. (C) Representative images of Periodic acid–Schiff stained lung sections and mucus quantification.  Assessment of asthma severity in papain treated mice (D-F) (D) Transcript levels of Th2 cytokines (IL-4, IL-5, IL-13), as above. (E) Representative images of hemotoxylin and eosin stained lung sections and total pathological scores. (F) Representative images of Periodic acid–Schiff stained lung sections and mucus quantification.  The data in this figure comes from 1 experiment with 4 to 6 mice per group.  Error bars in all panels show SEM. * P < 0.05, # P < 0.1   Figure 2-4 Exogenous SCFAs ameliorate vancomycin induced asthma severity.  58           (A) Transcript levels of ε-GLT measured in mesenteric LN, PPs, and mediastinal LN normalized to the housekeeping gene, beta-actin, in mice treated as in Supplementary Fig. 1. In each graph the order of treatments is: Con, Vanc, Vanc + BAP. Data is from 2 independent experiments with 4-6 mice per group, one representative experiment is shown. Error bars show SEM.    * p < 0.05   ε-GLT expression (relative to beta-actin) mediastinal 0.01   0 0.02 0.03 0.04 PPs 0.1   0 0.2 0.3 0.4 mLN 0.05   0 0.10 0.15 post-papain ε-GLT transcript levels * * * * * * Vanc Con Vanc + BAP ControlVancVanc BAPFigure 2-5 BAP attenuates vancomycin induced epsilon-GLT production.  59  2.4.3 SCFA attenuate vancomycin-induced IgE production  To determine the mechanisms by which SCFAs attenuate lung inflammation, we evaluated antibiotic-treated, but otherwise naïve mice (not exposed to OVA or papain) for indicators of a helper T cell polarization bias.  We first examined the naïve lung for elevated levels of Th2 cytokines, immune cell infiltrate, and histopathology and found no indications of inflammation due to vancomycin treatment (Fig 2-6).  We next looked systemically for a marker of Th2 polarization bias, elevated serum IgE. Vancomycin-treated mice had significantly elevated levels of circulating IgE, but not IgA or IgG2a compared to control mice, and BAP supplementation attenuated this elevated IgE production (Fig 2-7).   We additionally looked for evidence of isotype switching to IgE in the naïve mice by measuring εGLT expression in lymphoid tissues. In vancomycin-treated mice, εGLT expression was not substantially elevated in the spleen compared to control mice but was dramatically elevated in Peyer’s patches [18]  (Fig 2-7). These findings are consistent with the IL-4-driven class switching observed in the PP of germ-free mice171. In fact, the elevated serum IgE in germ-free mice is largely dependent on the development of PP171, suggesting that PP are a major source of circulating IgE in naïve mice. Importantly, supplementation with BAP prevented the elevation of εGLT expression in the PPs of vancomycin-treated mice.   Finally, we found that the maximal dampening of IgE levels in vancomycin-treated mice occurred only if SCFA supplementation was introduced at or before weaning (3 weeks) (Fig 2-7).     60                  Assessment of lungs for signs of inflammation or Th2 disease in mice treated as in Supplementary Fig. 1. (A) Transcript levels of Th2 cytokines (IL-5, IL-13) measured in lung tissue normalized to the housekeeping gene Gapdh.  (B) Total cell counts and cell differential in the bronchiolar lavage of mice. (C) Representative image of hemotoxylin and eosin stained lung sections (D) Representative image of Periodic acid–Schiff stained lung sections and mucus qualification.  The data in this figure comes from 1 experiment with 5 mice per group.  Error bars in all panels show SEM.   IL5 2   0 4 6 A mRNA expression (relative to GAPDH) X 10--5 IL13 5   0 10 15 cells/ml X 104 2   0 4 6 8 B BALF BALF differential   0 50 100 % of CD45+   0 5 10 15 20 25 % of lung with mucus Control Vanc Vanc+BAP C D Control Vanc Vanc+BAP Naive Vanc Con Vanc + BAP ControlVancVanc BAPFigure 2-6 Vancomycin treatment does not alter baseline inflammation or Th2-associated cytokine transcripts in the lung.  61                Naïve mice were reared for 8 weeks. (A) Concentrations of circulating antibodies in blood serum of naïve mice. (B) Transcript levels of ε-GLT measured in spleen normalized to the house keeping gene, glyceraldehyde 3-phosphate dehydrogenase (GAPDH). (C) Circulating IgE in blood serum of vancomycin-treated mice with exogenous BAP commencing at birth, at weaning (3 weeks) or at adulthood (7 weeks).  Data from A is from 3 independent experiments with 4-6 mice per group, samples from replicate groups were combined for data analysis.  Data from B is from 3 independent experiments with 5 mice per group, one representative experiment is shown. Data from C is from 2 independent experiments with 5-6 mice per group, samples from replicate groups were combined for data analysis.  Error bars in all panels show SEM.    * P < 0.05   A  ng/ml 200 100 500 300 0 400 40 0 80 100  ng/ml A450 B 1.5 1 0.5 0 2  mRNA expression (relative to GAPDH) Spleen ε-GLT   Vanc Con Vanc + BAP ControlVancVanc BAP6 4 2 0 8 * * PP ε-GLT  IgE IgA IgG2a Vanc Con Vanc + BAP ControlVancVanc BAP0 0.1 0.2 0.3 0.4 0.5 ng/ml * 300 200 100 0 C IgE * * Naive Figure 2-7: SCFA-supplementation (BAP) attenuates vancomycin-induced IgE production.   62  2.4.4 SCFA attenuate vancomycin-induced IL-4 production  Interleukin-4 (IL-4) plays a pivotal role in shaping the environment of the immune response in allergic disease by promoting isotype switching from IgM to IgG1 and IgE. Th2-polarized T cells are an important source of IL-4 (in addition to other cell types including basophils)217. Using an IL-4-GFP reporter mouse strain (4get mice)218, we determined if CD4+ T cells from vancomycin-treated mice were predisposed to producing IL-4. We could only detect low levels of IL-4 expression in T cells taken directly from the spleen of naïve 4get mice reared on vancomycin water or control water (not shown). However, when stimulated ex vivo under Th2 cell-polarizing conditions, splenocytes harvested from vancomycin-treated 4get mice yielded a higher proportion GFP-positive CD4+ T cells than splenocytes from control mice (Fig 2-8). Furthermore, butyrate supplementation in vitro reduced the proportion of IL-4-producing CD4+ T cells from vancomycin-treated mice to below that of control mice.   These results indicate that SCFA inhibit IL-4 expression in T cells.   We next assessed the effects of vancomycin and SCFAs on IL-4 expression in T cells in vivo using the 4get mice. Consistent with the IgE class switching activity we observed in the PP of vancomycin-treated mice, we also observed in these mice an increase in the number of IL-4-producing CD4+ T cells in the PP but not in the colonic lamina propria (LP), mesenteric lymph nodes or mediastinal lymph nodes (Fig 2-8). Further, supplementation with BAP caused a dramatic decline in the number of IL-4-producing CD4+ T cells in the PP but not in the other tissues we examined.    63  Because previous reports have focused on the role of SCFAs in Treg polarization we also examined levels of Tregs in the mesenteric lymph nodes (mLN), colonic LP, PP, spleen, and lung in naïve mice.  In mLN and colonic LP we found lower levels of Tregs in the vancomycin treated mice relative to controls (Fig 2-9). BAP supplementation prevented the decrease in Tregs in these tissues. A similar trend was observed in the PPs, but the numbers of Tregs were not statistically different. We did not observe altered levels of Tregs in the spleen and lung relative to control mice by treatment with vancomycin or vancomycin plus BAP.     64           Percentage of CD4+ T cells from naïve 4get mice producing IL4 gene expression by GFP-expression via flow cytometry. (A) Cells from spleens of mice incubated in vitro under TH2 stimulating conditions; mice were treated as in Fig. 2B (i.e., B, only butyrate). (B) In vivo expression in unstimulated cells from lymph nodes as indicated.   Data from A is from 3 independent experiments with 3 mice per group, one representative experiment is shown. Data from B is from 2 independent experiments with 5-6 mice per group, samples from replicate groups were combined for data analysis.  Error bars in all panels show SEM.    * p < 0.05   Vanc Con Vanc + B ControlVancVanc BAPA 15 10 0   5 % GFP+ Vanc Con Vanc + BAP ControlVancVanc BAP% GFP+ B Colonic LP   9   6  0   3 PPs 10 0 20 40 30 15 10 0   5 20 15 10 0   5 Mediastinal  LN Mesenteric LNs * * * * Stimulated spleen cells Figure 2-8: SCFAs attenuate vancomycin-induced IL4 production.  65             (A) Percentage of CD4+ T cells from naïve mice expressing FoxP3, Mice were treated as in Supplementary Fig. 1., and cells were from tissues indicated. Data is from 2 independent experiments with 4-6 mice per group, one representative experiment is shown. Error bars show SEM.    * p < 0.05   PPs   0 4 6 8 10 2 spleen 5   0 10 15 lung 5   0 10 15 5   0 10 15 mLN 5   0 10 15 20 25 colonic LP % of CD45+ CD4+ cells expressing FOXP3 A * * * * Vanc Con Vanc + BAP ControlVancVanc BAPFigure 2-9: SCFAs prevent vancomycin induced loss of Tregs in colonic LP and mesenteric LNs  66  2.4.5 SCFA exposure alters the gene expression profile of DCs We next investigated the immune processes that occur after the introduction of allergen. DCs play an essential role in the initial recognition of antigen and provide a critical link between the innate and adaptive branches of the immune response. These cells are mainly responsible for the uptake and transfer of the allergen from the airway to the local (mediastinal) LN for presentation to and activation of T cells in initiation of an adaptive response219.  SCFAs have previously been shown to have anti-inflammatory effects on DCs.   In the presence of butyrate, both human and mouse DCs express lower levels of co-stimulatory molecules and lower levels of inflammatory gene transcripts186. Because DCs have previously been shown to be both necessary and sufficient for the induction of a robust Th2 cell-mediated immune response220,128,221, we investigated whether the presence of SCFAs altered the ability of DCs to prime Th2 mediated immunity. To look broadly at these mechanisms, we used a transcriptomic approach.  By injecting control mice with Flt3L-expressing B16-melanoma tumors we were able to harvest large numbers of splenic DCs for ex vivo culture222,223,224,180.  Flt3L-induced splenic DCs were isolated by immunomagnetic bead selection, incubated ex vivo in the presence or absence of butyrate and stimulated with lipopolysaccharide (LPS). Of the 15,183 genes represented in the transcriptome, transcript levels of only 74 genes were significantly altered by butyrate exposure in LPS-activated DCs (butyrate treatment primarily dampened upregulation of these genes) (Fig 2-10). Using Ingenuity Pathway Analysis225 we were able to integrate expression profiles for both down- and up-regulated genes into known biological pathways. This analysis revealed a strong immunomodulatory effect of butyrate on two pathways known to impact susceptibility to  67  allergic disease. First, we found a significantly altered expression profile suggesting that butyrate decreases the ability of DCs to activate lymphocytes (Fig 2-10). Although our transcriptome data identified a mix of both down and up-regulated genes after butyrate treatment, the overall pattern indicated decreased function of the pathway (ie, some genes will decrease T cell activation when up-regulated, and some will decrease activation when down-regulated). Second, we found a profile suggesting that butyrate dampens DC trafficking mechanisms (Fig 2-10).     68                          ●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●−4−202−4 −2 0 2log1log2significant●●noyesA Log(RPKM(butyrate treated)) Log(RPKM(control)) 0 -5 -2.5 2.5 0 -2 -4 2 Significantly changed Expression rel to con Expression rel to con −2−1010 25 50 75OrderRatiopredictionABPredicted increased function Predicted decreased function zscore = - 2.296 p = 1.12x10-10   0 -2 1 -1 Cell trafficking C Predicted increased function Predicted decreased function −3−2−1010 50 100 150orderLogRatio PredictionDecreasedIncreasedzscore = - 3.322 p = 7.32x10-25   0 -2 -3 1 -1 Activation of lymphocytes FPR2NUP85TNFFCGR2ACCL4CXCL3CCL3L3ITGAMMYD88CAPGCDKN1AAPPPLA2G7PTGESICAM1SLPIMIFCSF1FCER1GIL1BPFN1PPIAITGB2C1QBPAPOETGFB1ITGALCCDC88ACCR7CSF3RRPS19CX3CL1ANO6NINJ1GNAI3PIK3R5DOCK5PTK2BOPRD1LILRB3PTENPTPRCTSC1trafficking activation B 1 2 0 -1 -2 row Z-score D  69   (A) Log ratio of RPKM values of LPS stimulated DCs, not treated (control) or treated with butyrate.  Transcripts in red were found to differ significantly (p < 0.05). (B) Heat-map showing relative expression of genes involved in cell migration and chemotaxis. Log ratio of RNA-expression of genes involved in lymphocyte activation (C) and cell trafficking (D). Color-coding indicates genes whose expression is predicted to increase or decrease with the corresponding function.   DCs from two control mice were subjected to 4 treatments.  Data are pooled in A and C and individual duplicates are shown in B.   Figure 2-10: Transcriptomes reveal anti-inflammatory effect of butyrate on DCs.  70  2.4.6 SCFAs attenuate DC activation To verify the pathways implicated by transcriptome analysis, we first incubated Flt3L-elicited splenic DCs with SCFAs ex vivo. Incubation with butyrate did not affect DC viability (Fig 2-11). After stimulation with LPS, however, Flt3L-derrived DCs incubated with butyrate displayed attenuated expression of co-stimulatory molecules CD80 and CD86 (Fig 2-11). Furthermore, DCs isolated from vancomycin-treated mice displayed increased expression of CD80 and CD86 after stimulation with LPS (Fig 2-11), which could be attenuated by oral BAP supplementation. Our results are consistent with previous work suggesting that human monocyte derived and mouse bone marrow derived butyrate-treated DCs are less mature and express lower levels of co-stimulatory molecules 186,183,184. . DCs from control mice were isolated and cultured +/- butyrate in culture media for 3 hours. These were then washed, pulsed with OVA, and incubated with CFSE labeled OTII T cells.  We found that untreated DCs activated T cells, thus inducing more proliferation, with greater efficiency than those that were pre-incubated with butyrate (Fig 2-11). Thus, butyrate has a cell-intrinsic effect on DCs activation and ability to stimulate the adaptive immune response following exposure.    71                    Flt-3L derived DCs were harvested from naïve mice. (A) Viability of DCs after incubating 48 h, without (-) or with (B) butyrate. (B) Expression levels by DCs of co-stimulatory molecules CD80 and CD86 after stimulation with LPS in vitro, as determined via mean fluorescent intensity by flow cytometry.  Results are expressed as fold change from the paired control cells, incubated with no LPS or butyrate. (C) Expression of co-stimulatory molecules CD80 and CD86 by DCs isolated from mice with drinking water treatments as in Fig. 1C, and DCs subsequently stimulated ex vivo with LPS for 18 h. (D) Activation of T cells by DCs. DCs from control mice LPS LPS B 4000 3000 2000 1000 0 MFI CD80 CD86 B - B - B - B - * * Vanc Con Vanc + BAP ControlVancVanc BAPCD86 D 0 60 20 40 * * % viability 80 60 40 20 0 B - A CD80 10 0 5 15 20 MFI * * C B - 80 60 40 20 0 100 % proliferated relative to control E Figure 2-11 SCFAs attenuate DC activation  72  were isolated and cultured without (-) or with (B) butyrate in culture media for 3 h, washed, pulsed with OVA, and incubated with CFSE labeled OTII Tcells.  Proliferation of CD4+ T cells was assessed by flow cytometry and percent proliferation was assessed relative to its paired no-butyrate control.  Data from A and B are from 3 independent experiments with 3 mice per group, samples from replicate groups were combined for analysis. Data from C is from 2 independent experiments, one representative experiment is shown.  Data from D is from 2 independent experiments, samples from replicate groups were combined for analysis.  Error bars in all panels show SEM.    * p < 0.05    73  2.4.7  SCFAs attenuate DC chemotaxis Since the CCL19/21 and CCR7 chemokine axes mediate DC migration from inflamed tissue to collecting LNs226, we tested whether this migration is altered by exposure of Flt3L-elicited splenic DCs to butyrate in vitro. In a transwell chemotaxis assay, DCs harvested from control mice and incubated with butyrate for 3 hours migrated less efficiently in response to CCL19 than corresponding DCs incubated without butyrate (Fig 2-12). Furthermore, cells isolated from vancomycin-treated mice migrated with greater efficiency than DCs isolated from control mice or mice treated with vancomycin plus BAP (Fig 2-12). Our results show that butyrate attenuates CCL19-induced DC migration.   We next tested whether the trafficking behavior of lung tissue DCs is altered in dysbiotic mice in vivo using the papain inflammatory model. To track DCs in vivo we included DQ-OVA in our papain challenge. DQ-OVA fluorescently labels antigen-presenting cells (APCs) after phagocytosis and proteolytic processing. Upon necropsy, we enumerated DCs that had acquired antigen (DQ-OVA) in the lung and subsequently migrated to the local draining LN (mediastinal). Vancomycin-treated mice had significantly higher numbers of DCs in the mediastinal LN compared to controls. Further, exogenous BAP reduced trafficking of DCs in vancomycin-treated mice to levels comparable to control mice (Fig 2-12). We conclude that both the sensitivity to activation and the migratory behavior of DCs is altered by SCFA exposure.    74         (A) Flt-3 derived DCs were harvested from naïve mice, pooled, and split into treatment groups: incubated with butyrate (B) or without butyrate (-) in the culture media; or with no CCL19 (Neg). The percentage of total CD11c+ cells plated that migrated in a CCL19 chemokine gradient were determined using a transwell assay and flow cytometry. (B) Flt-3 derived DCs were harvested from mice treated as in Fig. 2-1.  Cells from each mouse were split into two treatment groups: incubated with (B) or without (-) butyrate in the culture media.  The percentage of DCs that migrated to a CCL19 chemokine gradient was determined using a transwell assay and flow cytometry. Data is expressed as a percentage of total DCs plated. (C) In vivo experiment enumerating DC migration after DQ-OVA/papain challenge.  DCs were from mice treated as in Fig. 2-1. Percentage of DQ labeled DCs in the lung draining (mediastinal) lymph node three days after intranasal DQ-OVA/papain challenge. Data is expressed as a percentage of total CD11c+ DQ-labeled cells counted in both the lung and lymph node.   Data from A - C are from 2 independent experiments with 4-6 mice per group, samples from replicate groups were combined for analysis.  Error bars in all panels show SEM.    * p < 0.05   10 0 5 15 % migrated DCs A B - Neg * Con mouse 20 0 10 30 B - B - B - Neg B * * * * * Con Vanc Vanc+BAP Vanc Con Vanc + BAP ControlVancVanc+BAP8 6 4 2 0 10 * * C % migrated DCs % migrated DCs Figure 2-12 SCFAs attenuate DC migration.  75  2.5 Discussion  It has been widely recognized that there is a relationship between antibiotic use and the development of asthma227,228,229.  Our study provides novel insight to how SCFAs, as a product of a specific subset of the microbiome, may serve as a mechanistic link for this association, and highlights the importance of a healthy microbiome in dampening asthma susceptibility or severity. We provide evidence that the exacerbation of OVA-induced allergic lung inflammation by vancomycin treatment is due to depletion of gut microbiome populations responsible for the production of SCFAs, particularly butyrate. Consistent with this observation, supplementation of vancomycin-treated mice with exogenous SCFAs partially restores butyrate levels in the cecum, and completely attenuates the exacerbation of lung inflammation by vancomycin in two mechanistically distinct asthma models. In contrast to other reports99,192, we did not find evidence that SCFAs alter asthma severity in the presence of a healthy microbiome. We postulate that the effect of SCFAs are highly dependent on the environment (animal unit) and the variation that exists in a “normal” microbiome and SCFA levels. Similar to our findings, Arpaia et al. found that the oral administration of SCFAs did not have an effect on the generation of Tregs in the presence of a healthy microbiome180.  Although we cannot rule out the possibility that additional aspects of the gut microbiome other than SCFA production can attenuate asthma, we show here that SCFAs are sufficient to completely prevent the exacerbation of lung inflammation due to vancomycin treatment. It is known that antibiotic treatment can cause an outgrowth of the fungal microbiome- which has been shown to impact asthma susceptibility. In our model the addition of an antifungal treatment to the vancomycin treatment did not change asthma susceptibility (data not shown).  Notably SCFAs did so without altering the dysbiotic  76  microbiome composition resulting from vancomycin treatment. Our results also provide evidence that the effect of vancomycin in the two models of asthma is via its effect on the microbiome and not via some direct effect of the antibiotic on the host.  How SCFAs mediate this effect, either via HDAC inhibition or via GPCRs should be investigated in future studies.  More mechanistic insight could be garnered from the use of genetic approaches, such as GPCR41/43 knock out animals.    The current paradigm for the function of the gut microbiome in influencing Th2 mediated disease focuses predominantly on the induction of regulatory T cells.  Our study provides novel evidence for two additional and distinct processes by which SCFAs link the gut microbiome to the immune response in asthma. We show that vancomycin treatment leads to an increase in IL-4-producing CD4+ T cells in mice naïve to specific antigen, which consequently promotes IgE class switching activity in the PP. Supplementation of vancomycin-treated mice with exogenous SCFA dampens the induction of IL-4-producing CD4+ T cells and class-switching activity in the PP. Additionally our in vitro data show that exposure of splenic cells to butyrate under Th2 cell-polarizing conditions restrains the development of IL-4-producting CD4+ T cells. Our findings are consistent with observations by others that serum IgE levels are elevated in germ-free171 and antibiotic-treated mice172. We propose that elevated IgE in both models is due to reduced SCFA production in the gut.  Interestingly, we found that the ability of SCFAs to reduce IgE levels in vancomycin-treated mice was greatest if SCFAs were administered before weaning. This finding supports the hypothesis that there is a perinatal window during which the microbiome plays a key role in the development of allergic susceptibility, and that effective interventions must be started before this critical window closes169,230,173, 38,160.  77   It was previously shown that SCFAs have a T cell autonomous-effect on the extrathymic generation of regulatory T cells (Treg) via histone modification181,180. Our findings suggest a novel effect of SCFAs on T cell fate: That SCFAs, butyrate in particular, may also restrain T cell polarization towards a Th2 cell fate. Both the previously described Treg phenotype and our proposed mechanism will contribute towards preventing asthma susceptibility. We have shown that in a naïve state, SCFAs play an instructive role in setting up a healthy immune environment. The lack of SCFAs primes the host for the later development of allergy by facilitating a Th2-skewed immune environment.   We found that the effects of SCFAs on the development of Th2-skewed T cells are systemic (spleen) but most dramatic in the PPs. Although this may seem counter intuitive (SCFA production begins in the cecum), it is consistent with data from several independent studies.  Others have found that in a germ-free mouse, the IgE levels are most dramatically affected in the PPs171,231.  The strength of this T cell phenotype in the PPs may be explained by the fact that specific IgE responses originate in this tissue232.  In addition, we identified a second DC-dependent immuno-modulatory effect of SCFAs that is consistent with an attenuated response to airway allergens in vivo.  Consistent with previous observations, we found that SCFAs attenuate DC activation in vitro at the transcription level and at the protein level. Although these experiments were done on splenic DCs, we believe that this is likely true for lung DCs as well based on functional data for lung migration and asthma severity.  For the first time, we demonstrate that a lack of SCFA exposure in vivo has a persistent  78  effect on DC activation. Further, we demonstrate that SCFA exposure in vivo dramatically attenuates DC migration and that butyrate attenuates DC migration and activation in vitro. Attenuation of DC migration by SCFAs could have far reaching consequences for many diseases, including allergic disease, autoimmune disease, and infection.   In summary, we find that SCFAs modulate the systemic immune response by dampening Th2 responses through direct effects on both T cells and DCs (Fig 2-13).  These findings highlight the importance of assessing the appropriate use of antibiotics, especially in young children, and identify potential approaches for the development of probiotics or metabolite therapy to prevent childhood asthma and other Th2-driven disease.     79   Figure 2-13: Summary model. Vancomycin treatment results in a shift in composition of the microbiota. The overall abundance, diversity, and species composition of the class Clostridia, the known dominant burtyrate producer, is reduced. Consequently low butyrate levels are restored in the cecum by BAP supplementation. SCFAs modulate the activity of T cells and dendritic cells (DCs). Dysbioitc mice treated with SCFAs have fewer IL4-producing CD4+ T cells and decreased levels of circulating IgE prior to antigen challenge. DCs exposed to SCFAs activate T cells less robustly, are less motile in response to CCL19 in vitro and exhibit a dampened ability to transport inhaled allergens to lung draining nodes. As a result, mice on vancomycin are more susceptible to allergic lung inflammation.  These effects are attenuated by SCFA supplementation.   80   Early infancy microbiome short chain fatty acid production pathways are predictive of atopy 3.1 Synopsis Atopy is the most frequent chronic health issue in children and has been linked to early life gut microbiome dysbiosis.  Recently, we showed that, in animal models of antibiotic-induced asthma susceptibility, SCFAs can compensate for microbial dysbiosis.  Here, we sought to determine whether dysbiosis in microbiome carbohydrate and amino acid fermentation pathways could also be identified in human infants, prior to their developing atopic disease, using shotgun metagenomic analysis of the gut microbiome.  We found that the microbiome of infants that went on to develop atopy later in childhood lacked genes encoding key enzymes for carbohydrate breakdown and butyrate production. Our findings support the importance of deficiency in microbial carbohydrate metabolism in early infancy on the development of atopy and suggest metabolite therapy as a potential prevention strategy. 3.2  Introduction The Canadian Healthy Infant Longitudinal Development (CHILD) Study is a birth cohort that followed children from the prenatal period until 5 years of age with the goal of identifying early-life host and environmental the factors that predict or drive development of asthma and atopic disease later in childhood, ultimately leading to the development of intervention strategies to reduce disease incidence. Our seminal finding from this cohort was that four genera  81  (Faecalibacterium, Lachnospira, Veillonella, and Rothia) in the intestinal microbiome at 3 months were predictive of developing atopic disease later in life160.  In this study, we also found that lower concentrations of the fecal metabolite, acetate, correlated with a high propensity for the development of atopy160.   SCFAs are exclusively generated in the colon by bacterial fermentation of carbohydrates, collectively known as resistant starch, that escape digestion by the host enzymes in the upper digestive tract.  Human milk oligosaccharides (HMOs) are the primary source of resistant starch for breastfed infants, and plant cell wall carbohydrates from solid foods are the primary source later in life.  Resistant starch is broken down by bacterial carbohydrate active enzymes (CAZymes) into mono- and disaccharides, which are then cross-fed to and fermented by SCFA-producing bacteria.  In chapter 2 we showed that mice treated with antibiotics have an altered microbiome and metabolite profile, exhibit exacerbated Th2 responses, and are more susceptible to allergic airway inflammation (AAI) in animal models of asthma.  Dietary supplementation with a cocktail of SCFAs, or butyrate alone, was sufficient to compensate for this microbial dysbiosis and protect against AAI.  This finding, combined with our earlier findings from the CHILD study, led us to hypothesize that SCFA fermentation pathways in the gut microbiome during infancy are protective against the later development of atopic disease in children.  This study is the first investigation to link metabolic potential of the microbiome to atopic disease using metagenomics.  82  3.3 Materials and methods 3.3.1 CHILD Study design, and diagnoses  The CHILD study is a multicenter longitudinal, prospective, general population birth cohort study.  This study followed 3624 pregnant mothers across four enrollment centers during their pregnancy and their infants from birth to 5 years of age.  Detailed characteristics of the CHILD study are described elsewhere 233, 234, 235.  Briefly, at age 1 and age 3 trained staff performed skin prick testing using ten standardized inhalant allergens and common food allergens (Alternaria tenuis, cat hair, dog epithelium, Dermatophagoides pteronyssinus, Dermatophagoides farinae, German cockroach, peanut, soybean, egg white, and cow’s milk), a positive control (histamine), and a negative control (glycerin). If the child developed a wheal ≥ 2 mm in diameter in response to any allergen, they were scored as positive for atopy.  If a child tested positive for glycerin, the wheal size for glycerin was subtracted from the wheal sizes from the allergens.  3.3.2  Sample selection This study used a nested case-controlled design to analyze the functional potential of the fecal microbiome in infants enrolled in the CHILD Study.  Based on sample availability, we selected all available 3-month and 1-year stool samples from subjects from one enrollment center (Vancouver) with asthma diagnosis alongside a random selection of non-asthmatic children  83  selected as controls.  A total of 105 subjects were selected. Stool samples were stored between ~3.5 and 4 years at -80°C. 3.3.3 DNA extraction and metagenome generation DNA was extracted from stool (~ 50 mg) using the PowerSoil for KingFisher kit (MO Bio, Carlsbad, CA, USA) following manufacturer’s instructions. DNA extracts were submitted for shotgun sequencing at the UBC Sequencing and Bioinformatics Consortium, Vancouver, Canada.   Libraries were prepared using the Nextera XT kit (Illumina, San Diego, CA).  The average fragment size was 505 bp, ranging from 250 to 900 bp.   DNA concentration was measured by fluorescence using Qubit and extracts pooled at equal concentration.  Samples were sequenced in two runs on an Illumina NextSeq instrument.  After sequencing, reads were separated according to the barcode used in the library preparation. Initial quality evaluation was done using FastQC v0.11.5236.   Processing took part in three steps: Paired-ends read joining, removing of contaminants, and trimming.  Paired-end reads were joined using FLASH v1.2.11237.  Reads were then compared to the Human Genome (hg19, GRCh37 Genome Reference Consortium Human Reference 37), and sequences that mapped to it were removed. Finally sequences were trimmed according to their quality values using Trimmomatic v0.36238 using custom parameters (LEADING:5 TRAILING:5 SLIDINGWINDOW:4:15 MINLEN:70). The average genome size for members of the microbiome was calculated using MicrobeCensus v1.0.7239.   84  3.3.4 Butyrate gene identification To identify genes involved in butyrate production, we created a protein database based on a published report91 which identified 3030 genes involved in butyrate production from different bacteria. We retrieved 3021 of those proteins from public repositories using custom Biopython scripts240   Proteins were then associated to a butyrate production pathway using the Vital et al. report and to a taxonomy using the NBCI taxonomy from the protein record. QC-reads were compared to the database using diamond v0.7.11.60241 and a 1E-5 E-value threshold. Gene and pathway counts were normalized by the total metagenome size and by the average genome. 3.3.5 CAZy gene identification QC-reads were compared against the Carbohydrate Active enzyme database242 using diamond and a local version of the protein database. The local protein database was generated from the CAZy website (as May 6th, 2014) by downloading all the protein IDS which were then used to retrieve the proteins from Genbank using custom scripts. The proteins in the database were annotated using the CAZy scheme. We used a 1E-5 E-value threshold. Gene counts were normalized by the total metagenome size and by the average genome. CAZy gene families associated with the processing of human milk oligosaccharides were defined using a published report243.   85  3.3.6 Data analysis and statistics  All data analysis and statistics were performed using R-based computational tools.  All figures were generated using ggplot2244 and RColorBrewer245, unless otherwise stated.   All sample comparisons using a t-test were first subjected to a Shapiro-Wilk test for normality, and log transformed when necessary to meet statistical assumptions for normality.  Multiple comparisons were corrected using the Benjamini-Hochberg method.  Corrected P-values are represented as q-values. Differences were considered significant with a P- or q-value < 0.05.  Distance matrices (Bray-Curtis) were calculated using Vegan246 to assess compositional dissimilarity between samples, and visualized using ggord247.  Permutational multivariate analysis of variance (PERMANOVA) was performed using the Adonis function from the vegan package to determine factors that significantly explained variation in microbial β-diversity.   Relative abundance of kingdoms was determined using MetaPhlan2248 and statistically confirmed by Wilcoxon rank-sum test.  The Shannon diversity index was calculated and visualized using the phyloseq R package (version 1.20.0)211 and statistically confirmed by Welch’s t-test.   Differentially abundant species between atopic and control subjects at each time point were identified using the Wald test implemented through the DESeq2 package 249.   Detailed descriptions of each test used for comparisons among carbohydrate-fermentation genes are described in text.  86  3.4 Results 3.4.1 Clinical phenotype groups 97 We selected 105 children from the CHILD study for analysis of the gut microbiome metabolic potential. The study participants were grouped by clinical phenotypes based on data collected at 1 and 3 years of age.  At year 1, children were diagnosed with atopy based on allergy skin prick testing.  Given the longitudinal nature of this study, we also had clinical data from the same subjects at 3 years of age when the children were diagnosed with asthma, atopy, or as a control.  From the subjects we analyzed fecal samples collected at subject age 3 months and 1-year (Fig 3-1).  For this analysis, we looked only at atopy diagnosis.   We therefore excluded 8 subjects from the control group that were asthmatic but non-atopic, as we considered asthma to be a confounder.  Low-quality samples and those with no comparable timepoint were removed leaving 157 samples for the final analysis. The two clinical phenotypes are as follows: control, negative for both skin prick tests and asthma diagnosis(n = 27 at 3 mo and n = 38 at 1 yr); atopic, positive for at least one skin prick test (n = 45 at 3 mo and n = 47 at 1 yr).      87            Sample collections and diagnostic tests relevant to this analysis are summarized on a time-line.     Figure 3-1 Clinical phenotypes. 3 months fecal sample 1 year 3 years skin prick test ----- ----- Sample collected: Atopy test administered: fecal sample skin prick test  88  3.4.2 The relationship between fecal microbiome and clinical phenotypes.  Our first objective was to determine if, using shotgun metagenome analysis, we could identify microbiome taxonomic composition features were associated with the development of atopy.  To meet this objective, we used MetaPhlAn2, which relies on unique clade-specific marker genes to identify viruses, archaea, microbial eukaryotes, and, with species-level resolution, bacteria 250,248.   Overall gut community composition at 3 months or 1 year did not differ significantly among the clinical phenotype groups.  Contrary to findings in other studies160, 162, age was not a substantial driver of species-level microbial shifts in this cohort (Bray-Curtis, PERMANOVA, R2 = 0.0293, p > 0.05).  Also contrary to findings in other studies 157, our analysis did not reveal any significant differences in alpha diversity among clinical phenotype groups (Welch’s t-test).  (Fig 3-2).  Overall, the microbial communities were dominated by bacteria.  Eukaryotic and viral DNA combined contributed less than 1% of the total diversity.  No archaeal DNA was identified.  There were no significant differences in abundance of viral or eukaryotic DNA identified among clinical phenotype groups.  Analysis of the bacterial microbiome identified species level differences between the atopic and control microbiomes (DESeq2 Wald test, q < 0.05, |log2(foldchange)| > 1.5).  Most of the differentially abundant species were depleted in children who became atopic.  Many of the identified species have been associated with the fermentation of resistant starch in the human gut, specifically production of butyrate89, 212. (Fig 3-3).   89                 (A) Multivariate analysis by non-metric multidimensional scaling (NMDS).  Circles denote 95% CIs. Bacterial beta diversity was not driven by age or 3 yr atopy diagnosis. (C) Alpha diversity (Shannon diversity index). Boxplot shows standard summary statistics.  Color denotes atopy diagnosis.    Figure 3-2 Differences in fecal microbiome taxonomic composition at 3 mo and 1 yr between atopic and control children. 3mo microbiome, atopic 1yr microbiome, atopic 1yr microbiome, control 3mo microbiome, control A B 3mo microbiome 1yr microbiome atopic control  90                                    3mo microbiome 1yr microbiome Butyrate fermenting Log2(Fold Change) Depleted in atopics Enriched in atopics Log2(Fold Change) Depleted in atopics Enriched in atopics  91     Differentially abundant species in the 3-mo samples from atopic children vs. controls.   Species in purple are known to produce butyrate.  Figure 3-3: Bacterial species associated with children who develop atopy.  92  3.4.3 A deficit of CAZyme genes and butyrate production genes in the 3-month microbiome are associated with the risk of developing atopy.  We next looked at the metabolic potential of these microbiomes with respect to genes associated with fermentation of resistant starch in the human gut, specifically carbohydrate degradation and butyrate production. We found a deficit in genes coding for carbohydrate active enzymes (CAZymes) and of genes coding for enzymes in butyrate production in the 3-month microbiome.  We accessed the odds ratio associated with these genes and found no significant increase in risk of developing atopy.     93  3.4.4 CAZyme genes are depleted in children who develop atopy. Our next objective was to directly profile carbohydrate degradation potential.  The 3-month microbiome of children who became atopic was depleted in the relative abundance of total CAZyme genes relative to controls (Wilcoxon rank-sum test) as well in abundance of the classes Glycoside Hydrolases (GH), and Glycosyl Transferases (GT). The former class contains the families of enzymes that catalyze the cleavage of the glycosidic bonds in the major diet-derived polysaccharides79 .  These differences were not detectable in the 1-year microbiome (Fig 3-4).     94               (A) Total number of CAZy genes detected.  (B) CAZy genes are subdivided by CAZy class: AA, auxiliary activity; CE, Carbohydrate Esterases; GH Glycoside Hydrolases; GT, Glycosyl transferases; PL, Polysaccharide Lysases.  Normalized count, hits normalized to genome equivalents per sample. Boxplot shows standard summary statistics. * q < 0.053mo  microbiome 1yr  microbiome A: Total number of CAZyme genes detected * B: Class assignment of CAZyme genes CE GH GT PL AA 3mo microbiome 1yr microbiome * * atopic control Figure 3-4: CAZyme genes are depleted from the 3-mo microbiome in children who develop atopy.  95  3.4.5 CAZymes that degrade human milk oligosaccharides were depleted in children who develop atopy Our next objective was to identify the substrates of the CAZymes whose genes were depleted in the infants who became atopic. Since all the subjects selected for this study were breast fed, we compared the abundance distributions of  CAZyme families with activity against human milk oligosaccharides (HMOs)243.  These oligosaccharides cannot be degraded by host enzymes and are exclusively degraded by the microbiome251.   In the 3-month microbiome, we found that the total number of HMO CAZyme genes were depleted in children who became atopic relative to controls (Welch’s t-test, total q = 0.004) (Fig 3-5).  When we compared the taxonomic affiliation of these genes, there were no differences among the clinical phenotype groups. Therefore, the differences were due to relative abundance of the genes and not specific taxa of bacteria (data not shown).  The difference in HMO CAZyme gene abundance was no longer detectable in the 1-year microbiomes.  This data suggests that the microbiomes of children who became atopic were less capable of degrading the HMOs in their diet at 3 months.   The length of time our subjects were exclusively breast-fed was variable, but all were introduced to solid food within a year of birth.  Our second analysis was for CAZymes with activity against resistant starch in the omnivore human diet252.    In the 1-year microbiome we found that the total number of CAZymes active against: plant cell wall carbohydrates, animal carbohydrates, and fungal carbohydrates were depleted in children who became atopic relative to controls (Wilcoxon rank-sum test; q = 0.046, 0.053, 0.055 respectively) (Fig 3-5).  The dominant source  96  of resistant starch from an omnivore diet is plant cell walls. This data suggests that the microbiome of children with atopy is less capable of degrading the resistant starch in their diet at 1year.     97                         (A) Total number of HMO CAZyme genes detected (hits normalized to genome equivalents per sample) in 3-mo or 1-yr microbiomes.  (B) CAZymes stratified by predicted substrate.  Boxplot shows standard summary statistics. Color denotes atopy diagnosis    * q < 0.1 3mo  microbiome 1yr  microbiome animal carbs dextran fungal  carbs peptido- glycan plant  cell walls starch glycogen sucrose fructans * * * HMOs * atopic control Figure 3-5: CAZyme genes that degrade HMOs and resistant starch were depleted in the microbiomes of children who developed atopy.  98  3.4.6 Enzymes required for butyrate production were depleted in the 3-mo microbiome of children who develop atopy.  We next looked at the metabolic potential of microbiomes to produce butyrate. To do this, we utilized a database of genes from the major known butyrate-producing pathways generated by Vital et al91 (overview of pathways and genes in this database in section 1.2.2).    In the 3 month microbiome, we found that the total number of butyrate pathway genes detected was depleted in the children who developed atopy (Wilcoxon rank sum test, P = 0.018) (Fig 3-6).     Butyrate is produced via four pathways; the pyruvate pathway is the main one.  Our analysis indicated that the amino butyrate and glutarate and pyruvate pathways were significantly depleted in the 3-mo microbiome of the children who become atopic. None of the pathways were significantly depleted in their 1-yr microbiome (Wilcoxon rank-sum test, Benjamin-Hochberg, q < 0.1) (Fig 3-6).   When we compared the taxonomic identity of the butyrate genes there were no differences between the controls and children who became atopic (Wilcoxon rank-sum test). Thus, the overall abundance of butyrate producers, rather than a difference in the responsible taxonomic groups, appears to account for the difference in metabolic potential (data not shown).      99                            (A) Total number of butyrate pathway genes detected (hits normalized to genome equivalents per sample. Boxplot shows standard summary statistics. (B) Total butyrate pathway genes grouped by pathway assignment. *  q < 0.1           3mo  microbiome 1yr  microbiome 3mo  microbiome 1yr  microbiome * amino butyrate glutarate lysine pyruvate B: Pathway assignment of butyrate genes A: Total number of butyrate genes detected atopic control * * * * * * Figure 3-6: Enzymes required for fermentation of butyrate are depleted from the 3 mo microbiome in children who develop atopy  100  3.5 Discussion  Our study suggests that the microbiome during early life plays a critical role in the development of atopic disease.  This finding is consistent with other reports that microbial dysbiosis early in life (i.e., during a critical window of development) increases the risk of developing asthma and other atopic diseases later in life (see section 1.4.5).   We found that this early life-dysbiosis was characterized by the decreased relative abundance of several bacterial taxa, many of which are known to play a role in butyrate production.  Further, we identified a deficiency in genes encoding the intimately linked processes of carbohydrate degradation and butyrate production.  We found a decrease in relative abundance of the genes for butyrate production in the 3-month microbiome. Further, we found a dramatic decrease in CAZymes in the 3-month microbiome, particularly those with HMO-activity. Resistant starch that escapes digestion by the host enters the colon and is broken down by the microbiome with extracellular CAZymes.  The initial dietary source of resistant starch comes in the form of HMOs, and later is dominated by plant-derived glycans. Interestingly, others have emphasized the importance of being able to degrade plant-cell walls as the substrates become available.  Koening et al. found that the functional capacity to utilize plant-derived glycans is present before the introduction of solid foods27.  The resulting oligo and mono-saccharides are degraded by primary fermenters to substrates that are used by butyrate-producing bacteria, among others. The importance of this metabolic cross-feeding in butyrate-production has been demonstrated in several in vitro systems 253, 254. In food chains such as these, the presence or absence of certain keystone members could dramatically impact the overall metabolic output.  Recent investigations found that Ruminococcus bromii is a keystone species in this regard255.  R. bromii, which we found to be depleted in children who  101  become atopic, possesses a superior ability to degrade resistant starch and is critical for releasing products from resistant starch used by other gut bacteria.   Overall, we conclude that microbiomes of children who became atopic may have decreased relative potential to produce butyrate at 3 months and at 1 year. A model summarizing these findings is presented (Fig 3-7).       102  Healthy         Dysbiotic               In a healthy gut, resistant starches are degraded into smaller polymers by CAZymes.  These polymers are fermented into butyrate.  Butyrate gets absorbed and circulated around the body in the blood.  Butyrate interacts with various immune cells, the effects of which are generally to limit inflammation and to prevent Th2 polarization.  In a dysbiotic gut, CAZymes are not abundant and therefore resistant starches are poorly degraded.  The butyrate-producing bacteria lack the substrates they require for fermentation.  Dysbiosis can also be characterized by a lack of butyrate-producing genes.  Without the anti-inflammatory signals required from butyrate, immune processes polarize Th2, resulting in atopy. CAZyme Monosaccharide Butyrate Butyrate-fermenting bacterium T cell Dendritic cell Figure 3-7: Summary model.  103  An interesting question that arises from this work is: what environmental factors drive the differential abundance of carbohydrate metabolism genes?  Known modifiers of atopy risk include: race/ethnicity, sex, exposure to farm animals, domestic cats and dogs, family size and birth order, gestational age, prenatal antibiotics, postnatal antibiotics, mode of delivery, breastfeeding, and intake of diet and nutrition146.  These are all factors known to affect the composition of the microbiome148.   We were unable to test how these factors influence the metagenome in our study because we lacked sufficient power in our subset of samples.  But, understanding how these environmental factors influence the metabolic potential of the microbiome should be investigated in the future.  The CHILD study evaluated these factors in 2695 subjects and found that none of them were significantly associated with risk of atopy at 1 year, and that only postnatal antibiotics significantly increased the risk of developing asthma at 3 years160.   The fact that we were able to identify a strong microbiome signature in a small subset of the CHILD study is consistent with the hypothesis that there is a stronger link between the metabolic potential of the microbiome and atopy development than any of the environmental factors studied.   Different environmental factors and combinations of environmental factors may converge on their influence on atopy via changes to the microbiome. Our data is consistent with the hypothesis that the microbiome impacts the immune processes leading to atopy and that butyrate is a central component of this mechanism.   It was initially surprising to us that we did not identify an overall difference in beta-diversity of metagenomes, when comparing subjects at 3 months and 1 year of age.   We found large heterogeneity among individual genes present in our samples (data not shown).  The variability of the individual genes masked significant differences – which were immediately apparent when  104  groups of genes were aggregated by function.  This underscores the redundancy that exists within the microbiome where diverse taxa are capable of important functional pathways.    There is mounting evidence that microbial dysbiosis, particularly in the gut, is linked to atopy 156, 157, 158, 160, 161, 162, 163.   In contrast to our findings, other groups have found age is a main driver of microbial alpha diversity160,161.  This apparent discrepancy may be in part due to different experimental approaches, 16S rRNA gene amplicon sequencing versus shotgun metagenomics. Each approach has strengths and weaknesses. Amplicon datasets have greater sequencing depth and therefore more completely identify community composition, particularly with respect to less-abundant species256,257. On the other hand, shotgun metagenomics avoids PCR bias, gives greater taxonomic resolution and provides information about metabolic potential. Also in contrast to our findings, other groups have found children who develop atopy have lower microbial diversity.  This may also be explained by the difference in experimental approach but may also reflect differences in the cohorts studied.  Like our study, Arrieta et al.  found that overall microbial diversity did not differentiate control from atopic children in a larger subset of the CHILD study160.    Several groups have independently described an early life critical window during which the microbiome may have the most influence preventing atopic disease (reviewed in  174, 121 ).   In mice, studies identified that differences in the microbiome before weaning (~3 weeks of age) impact disease susceptibility for life.  Studies in humans found that differences in the microbiome during the first-year, specifically the first three months, impact disease susceptibility for life (section 1.4.1).  In agreement with these findings, our study found that differences in  105  carbohydrate fermentation potential at as early as three months impacts the later development of atopy.  How these early-life microbiome differences impart a life-long influence on the immune system is not yet understood.  Our data suggests that the products of carbohydrate fermentation, particularly butyrate, may play a role in this mechanism.    The complex mechanisms linking the intestinal microbiome to systemic disease remain elusive, but microbially derived metabolites have been identified as plausible modulators, and work linking metabolites to atopy shows promise.  Research from Arrieta et al  in two separate birth cohort studies (CHILD and an Ecuadorian study) used PICRUSt 258 to predict carbohydrate metabolism from 16S rRNA gene datasets 160,162.  The findings in both studies support an association between SCFA production and atopy.   Notably, both studies identified a decrease in the fecal metabolite acetate in atopic samples in a targeted metabolite analysis.   These findings do not preclude the possibility that other SCFAs were also a factor.  Measurement of fecal butyrate is unreliable in samples that have been stored for long periods, because of the volatile nature of this compound 259.  Fujimura et al 161 recently found a microbiome composition state with a unique metabolite profile associated with atopy at two years and asthma at four years.  When T cells were incubated with sterile fecal water (metabolites) isolated from these samples, the atopic samples induced a much more robust Th2 response than the control samples.    Our study is the first to compare the functional potential determined by shotgun metagenome sequencing between children who develop atopy versus ones that do not.  This fills an important mechanistic gap in our understanding of how the microbiome impacts health and disease via carbohydrate fermentation.  In chapter 2 we show that SCFAs in general, and butyrate  106  specifically, protect against airway inflammation.  This is the first study to link butyrate production pathways to atopy in children. Thus, providing a compelling link between findings in animals and clinical studies.   In our previous mouse studies, we found that oral supplementation of butyrate could compensate for extreme microbiome dysbiosis, reducing lung inflammation.  The present study suggests that metabolic therapy in humans may have similar success.  These results enhance the potential for future metabolite-based therapy to prevent the development of atopic disease in children. These results may also be of potential use for early-diagnosis of children at a higher risk of developing atopic disease, so that intervention strategies can be implemented early.      107   Exposure to short chain fatty acids in the early-life window leaves a life-long epigenetic imprint on hematopoietic stem and progenitor cells  4.1  Synopsis Asthma has become the most common childhood disease in developed countries, with major social and economic consequences. The causes of asthma are complex and poorly understood. We and others have recently shown an association between the gut microbiome and asthma. Subsequently, we found that SCFAs, fermentation products of the gut microbiome, dampen Th2 skewing of the immune system. Mice lacking exposure to SCFAs early in life, due to gut microbiome dysbiosis, are atopic and have a pro-inflammatory immune phenotype with heightened Th2 responses in a model of asthma. We successfully transferred the phenotypes of both heightened and dampened Th2 skewing via bone marrow (BM) transplants to lethally-irradiated recipient mice. Consistent with the hypothesis that the transferred phenotype is encoded within the epigenome, we found unique regulatory states, as defined by DNA acetylation, within the genomes of purified hematopoietic stem and progenitor cells (HSPC) of recipient mice reconstituted with BM from dysbiotic donor mice. This work demonstrates a novel epigenetic process linking the microbiome and the immune system.  This is an important advance in our fundamental understanding of how microbial processes can drive systemic immune development and will have applications in the prevention and treatment of asthma.    108  4.2 Introduction There is a growing body of evidence that implicates early-life dysbiosis of the gut microbiome as a driving force in the development and severity of asthma and allergy. The hygiene hypothesis postulates that lack of early-life exposure to microbial products results in impaired development of the immune system and an increased risk of developing atopic disease260.  Support for this phenomenon includes observations that mode of birth (caesarean-section), feeding method (formula), and early-life antibiotic exposure increase relative risk of developing atopic disease (reviewed in ref 121, 174).  In both humans and mice, the association of asthma with the gut microbiome composition in early life has been reported by many groups (reviewed in 1.4.1).   We have shown that a selective depletion of gut microbiota alters the immune response in a mouse model of allergic asthma. In our work (chapter 2), we found that vancomycin treatment causes gut dysbiosis marked by a selective depletion of commensal bacterial diversity (but not overall quantity). Selective loss of these fiber-fermenting taxa leads to an altered metabolome marked by reduced production of SCFA metabolites. We subsequently found that exogenous supplementation with SCFAs (or with only butyrate), did not significantly alter the gut dysbiosis in vancomycin-treated mice but was sufficient to ameliorate allergic severity due to vancomycin. Furthermore, we identified a microbiome signature in samples from asthmatic children characterized by alterations in butyrate production pathways (chapter 3).  Together, this work suggests that the influence of the microbiome on the development of atopic disease may be in part through early-life exposure to microbially derived SCFAs.  SCFAs exert regulatory effects on target mammalian cells through HDAC inhibition (HDACi), which has been extensively studied as a mechanism of immune suppression and tolerance.  By altering the epigenetic state of  109  hematopoietic progenitors, SCFAs may impart lasting effects with downstream consequences in mature immune lineages.    We hypothesized that the early-life microbiome could be associated with immunotolerance in two possible ways. 1) The gut microbiome is associated with asthma at different ages, but confounders obscure the association as subjects age.  If this is true a fecal transplant with a healthy microbiome should reverse allergic disease in a previously dysbiotic mouse.  2) The microbiome imprints the host in early life, and this imprint persists in the absence of continued dysbiosis.  Herein, we show evidence for the latter explanation, and that the imprint from the microbiome is driven by epigenetic marks on hematopoietic stem and progenitor cells (HSPCs) via SCFAs.  4.3  Materials and methods  4.3.1 Mice C57BL/6J mice (The Jackson Laboratory, ME) were bred and maintained in a specific pathogen-free facility at the Biomedical Research Centre. Mice were housed in autoclaved cages and received irradiated chow ad libitum (equal parts mixture of PicoLab Mouse Diet 20 and Picolab Rodent Diet 20) and autoclaved tap water. All experiments were carried out in accordance with the Canadian Council on Animal Care (CCAC) guidelines and were approved by the University of British Columbia Committee on Animal Care (Protocol# A15-0113). At experimental end- 110  points mice were humanely euthanized by Avertin (2,2,2-tribromoethanol) overdose followed by cardiac puncture or removal of lungs.   4.3.2 Antibiotic and SCFAs treatment As indicated, breeding pairs and nursing dams were administered vancomycin (Sigma-Aldrich, MO) at 200 mg/L in drinking water. Pups born from respective breeding pairs were reared on antibiotic-treated water for the duration of the experiment. As indicated, a cocktail of SCFAs was included in drinking water at final concentrations of 40 mM butyrate, 67.5 mM acetate plus 25.9 mM propionate (Sigma-Aldrich)178. SCFAs solutions were prepared and changed weekly.   4.3.3  Antibodies and Flow Cytometry Staining and antibody dilutions were prepared in PBS containing 2% fetal calf serum, 2 mM EDTA, and 0.05% sodium azide. Samples were first blocked in buffer containing 50 µg/mL anti-CD16/32 (clone 2.4G2). Antibodies used were as follows: PE-conjugated Siglec-F (E50-2440) from BD Biosciences (San Jose, CA), PE-Cy7–conjugated CD3e from eBioscience (San Diego, CA), fluorescein isothiocyanate–conjugated anti-neutrophil (7/4) from Abcam (Cambridge, MA), Alexa Fluor 647-conjugated CD4 (RM4-5) from eBioscience (Santa Clara, CA), Pacific Blue–conjugated CD45 (I3/2) made in-house, and Alexa Fluor 647–conjugated CD11c (N418) made in-house. Samples were run on a BD LSRII, and data analysis was performed with FlowJo software (TreeStar, CA). Alexa Fluor-647 conjugated CD45.2 made in-house, lineage cocktail  111  containing fluorescein isothiocyanate–conjugated d CD11c, CD11b, Gr1, B220, CD19, CD3e, Ter119, and NK1.1 (all made in house), PE-Cy7-conjugated Ly6A from Biolegend (San Diego, CA), PerCP-Cy5.5-conjugaetd CD117 from Biolegend, and Pacific Blue-conjugated CD48 from Biolegend.  4.3.4 Fecal transplant A fecal slurry was prepared by combining donor feces with 1 mL of PBS reduced with 0.05% of cysteine-HCl to protect anaerobic feces. Mice were administered 30 µL fecal slurry by oral gavage.   4.3.5 Determination of serum IgE Mice were killed by avertin overdose and blood was collected via cardiac puncture.  Serum was separated from whole blood by centrifugation after clotting overnight at 4°C.  Enzyme-linked immunosorbent assay for total serum IgE was performed according to the manufacturer’s instructions (BD Bioscience, San Jose, CA). 4.3.6  Microbiome analysis  Fecal pellets were collected from individual mice and stored at -70°C.   DNA was extracted using the PowerSoil for KingFisher kit (MO Bio, Carlsbad, CA, USA) following manufacturer’s  112  instructions. 16S rRNA V4 gene fragments were amplified using bar-coded primers as described261 with the following primer regions (5’ to 3’):  fwd: GTGCCAGCMGCCGCGGTAA, rev: GGACTACHVGGGTWTCTAAT.  Pooled PCR amplicons were diluted to 20 ng/ml and sequenced using MiSeq 2000 bi-directional Illumina sequencing and Cluster Kit v4 (Macrogen Inc.).  Library preparation was done using TruSeq DNA Sample Prep v2 Kit (Illumina) with 100 ng of DNA per sample. The library was quantified, and quality checked using Qubit (ThermoFisher scientific, Waltham, MA, USA). Sequence data was trimmed, quality filtered, and clustered at 97% identity into Operational Taxonomic Units (OTUs) using a modified MOTHUR standard operating procedure43. OTUs were taxonomically annotated using the SILVA database210. Global community structure comparisons were made in an R environment using Phyloseq211.    4.3.7  Bone marrow transplant  Recipient mice were lethally irradiated with 10 Gy split over two doses with a cobalt-60 source and were reconstituted by intravenous injection of bone marrow from donor mice (5.0 x 106 cells/recipient).  In experiments where recipients were pre-treated with antibiotics and/or SCFAs, treatment was discontinued on day of transplant.  113  4.3.8  Papain-induced allergic lung inflammation On days 0, 1, 14, and 20, lightly anesthetized mice were administered intranasally 10 µg of papain (Sigma) prepared in a volume of 40 µL PBS. Experimental mice were sacrificed 16 h after the final papain treatment (day 21). Bronchoalveolar lavage fluid was collected by 3 washes with 1 mL of sterile saline. Cells were enumerated and differentiated by flow cytometry using antibodies to CD3e, CD11c, CD45, B220, Siglec-F, and 7/4.  4.3.9  RNA isolation and quantitative reverse transcriptase PCR Using a Qiagen TissueLyser (Valencia, CA), tissues were homogenized in Trizol (Life Technologies, CA). Total RNA was extracted and reversed transcribed with a high-capacity cDNA kit (Life Technologies). QPCR was performed with SYBR® green technology (KAPA Biosystems, MA) on an ABI 7900 real-time PCR instrument (Life Technologies). Primer sequences were as follows (5’- 3’): beta-actin forward ACTAATGGCAACGAGCGGTTC and reverse GGATGCCACAGGATTCCATACC. IL-4 forward TCGGCATTTTGAACGAGGTC and reverse CAAGCATGGAGTTTTCCCATG.  114  4.3.10 Histology The left lung lobe was fixed in 10% buffered formalin, embedded in paraffin, and stained with haematoxylin and eosin (H&E). H&E stained sections were blindly scored on a scale of 0 to 12 as described212.   4.3.11 HSPC ChIP-seq Approximately 10,000 viable HSPCs (live-gatedCD45+Lin-Sca1(Ly6A/E)+cKit+) were sorted from the BM of two pooled naïve C57BL/6 mice.  Cells were sorted on a FACSAria II sorter (Becton Dickinson Biosciences, San Jose, CA). Native ChIP-seq of the HSPCs was performed using standard operating procedures for ChIP-seq library construction as previously described262 and sequenced on an Illumina MiSeq 75x75 Paired End v3 run.  Sequence reads were aligned to the mm10 reference genome using BWA aligner version 0.7.6a263. Duplicated reads were collapsed, reads that mapped to multiple locations were discarded.  We focused on the analysis of H3K27ac histone modification and compared it with DNA input control.  To reduce signal to noise we looked only at reads that mapped to promoter regions (defined as transcriptional start site +/- 1.5Kb) using Ensembl version 81264. Differentially marked H3K27ac regions were identified between the control and two treatment groups.  We assigned the P-value for the enrichment in the ChIP-seq signal by using corresponding Input control.  The Input control signal was re-scaled to match read coverage distribution in the ChIP-seq experiment.     115  4.3.12 Statistics  Unless otherwise specified, differences between treatment groups were compared using a paired and unpaired Student's t-test, as appropriate, or one-way ANOVA (GraphPad Prism software, version 4.0, CA). 4.4  Results 4.4.1  Fecal transplant rescues microbiome-driven atopic disease if administered in early life.  To better understand the influence on atopy of the early-life microbiome, we evaluated antibiotic-treated but otherwise naïve mice (not exposed to specific allergen) for indicators of a helper T cell polarization bias.  As expected, mice on a life-long course of vancomycin had significantly higher concentrations of circulating IgE compared to controls.  Mice that were treated for the first 4 weeks of life with vancomycin and then put on control water (vancomycin pre-treatment) had similarly elevated levels of circulating IgE (Welch’s t-test, P < 0.05) (Fig 4-1).  However, if vancomycin pre-treated mice received a fecal microbiota transplantation (FMT) with a healthy (control) microbiome at 4 weeks when they were moved to control water, their circulating IgE levels dropped significantly.  FMT was no longer effective at reducing IgE levels when administered in later life (7 weeks).  All mice were assayed for IgE at 10 weeks of age.      116           Concentrations of circulating IgE in blood serum of naïve 10-week-old mice. Data is pooled from 2 independent experiments with 3-6 mice per group.  Boxplot shows standard summary statistics.   * p < 0.05    length of vanc treatment: age at fecal transplant: ----- ----- ----- 4 wks 4 wks 7 wks 4 wks 7 wks ----- life long * * Figure 4-1: Fecal transplant rescues microbiome-driven atopic disease if administered in early life.  117  4.4.2 Success of fecal transplant is independent of age at transplant We profiled the microbiome in the five treatment groups in Fig 4-1 as well as the donor feces by sequencing 16S rRNA gene amplicons.  The microbiome was significantly altered by vancomycin treatment in the absence of FMT (Bray-Curtis, PERMANOVA, P < 0.01).  Microbiomes of mice pre-treated with vancomycin that received FMT therapy at 4 weeks or 7 weeks were both similar to control mice and donor feces but distinct from vancomycin-treated mice that did not receive FMT therapy (Fig 4-2A).  Also, α-diversity of microbiomes of mice with life-long vancomycin treatment or vancomycin pre-treatment in the absence of FMT was significantly lower than that of controls or donor feces (Welch’s t-test, P< 0.05).  There was no significant difference in α-diversity among controls and mice that received FMT therapy (Fig 4-2B).  The microbiome of control mice and those that received FMT therapy was dominated by Bacteroida and Clostridia classes and a smaller proportion of Bacilli (Fig 4-2C).  Mice on life-long vancomycin were dominated entirely by Gammaproteobacteria.  Mice pre-treated with vancomycin that did not receive FMT were the most variable group.  From this analysis we were unable to resolve a significant difference in the efficacy of early or later life microbiome transplant.    118                         ----- ----- ----- 4 wks 7 wks ----- donor  feces ----- A 4 wks 4 wks 7 wks life long length of vanc treatment: age at fecal transplant: ----- ----- ----- 4 wks 4 wks 7 wks 4 wks 7 wks ----- life long -----  donor  feces B C length of vanc treatment: age at fecal transplant: ----- ----- ----- 4 wks 4 wks 7 wks 4 wks 7 wks ----- life long -----  donor  feces relative abundance Actinobacteria Bacilli Bacteroidia Clostridia Gammaproteobacteria Mollicutes Verrucomicrobiae unclassified  Class: * * ----- -----  119    (A) Multivariate analysis by non-metric multidimensional scaling (NMDS).  Circles denote 95% CIs. (B) α diversity (Shannon diversity index). Boxplot shows standard summary statistics.  (C) Community composition at the class level of each sample is shown.     Figure 4-2 Success of fecal transplant is dependent on age at transplant.  120  4.4.3 Atopic susceptibility is transferred by hematopoietic transplant  In chapter 2 we demonstrate that exogenous SCFAs restrains Th2 polarization in dysbiotic mice only when they are exposed early in life (at or near birth). We posited that this effect of SCFAs was via an epigenetic mechanism. Epigenetic imprinting due to environmental exposures and diet may modify gene expression in hematopoietic cell lineages265  or other tissues, including intestinal266 and lung epithelial cells267. To test whether SCFA exposure alters the behavior of blood cells or other tissues, we generated cohorts of experimental chimeric mice by reconstituting congenic recipient mice with bone marrow from donors (Fig 4-3A and Fig 4-4A).  Eight weeks after transplant, peripheral blood in chimeras contained 90-97% circulating CD45+ donor cells (data not shown).  We found that Th2 skewing, as measured by elevated IgE, due to gut microbiome dysbiosis (i.e., paucity of SCFAs), could be transferred via transplant of unfractionated total BM from donors into lethally-irradiated congenic recipient mice with a “normal” gut microbiome (Fig 4-3A). We subjected these chimeric mice to a papain model of AAI and found that recipients reconstituted with BM from vancomycin-reared donors had significantly higher levels of inflammatory read-outs, including increased lung tissue pathology scores, increased immune infiltrates in lung tissue and airways, and higher levels of IL-4 transcript in lung tissue.  SCFA supplementation of vancomycin-treated donors with exogenous SCFA abrogated the transfer phenotype significantly in some experimental readouts (IgE levels and IL-4 transcript) (Fig 4-3B).  In this experiment, the skewed hematopoietic compartment in donor mouse BM cells presumably over-rides the normal (control) environment.  In this context the environment includes the microbiome, butyrate exposure levels, epithelial and stroma.    121  Conversely, we found that, if we transplanted total BM from a control mouse (normal microbiome and butyrate exposure) into a previously Th2 skewed (vancomycin treated) mouse, we could dampen that Th2 skewing to control levels (Fig 4-4A and Fig 4-4B). Thus, we conclude that the Th2 proclivity in vancomycin-treated mice can be transferred to control mice by BM transplant. Transfer of BM from control donors, ameliorate the Th2 pro-clivity of vancomycin-treated donors. Thus, the Th2 proclivity and pro-inflammatory phenotype always correlated with the SCFAs exposure of the donor but not the recipient. These findings support the hypothesis that early-life gut dysbiosis has long-term effects on the hematopoietic tissue that is transplantable.    122   .          (A) Schematic overview of experiment.  Whole bone marrow (BM) is collected from Ly5.2 control, vancomycin, or vancomycin + BAP treated donors and transplanted into irradiated Ly5.1 control water recipients. (B) Concentration of circulating IgE in blood serum of naïve mice 8-10 weeks after transplant. (C-E) Assessment of asthma severity in papain treated mice.  (C) Total cell counts in the bronchiolar lavage fluid (BALF).  (D) Total pathological score of hematoxylin and eosin stained lung sections. (E) Transcript level of IL-4.   The data from (B) comes from 2 experiments with 7 mice/group.  The data from (C-E) comes from 1 experiment with 7 mice per group.  Error bars in all panels show SEM.  * P < 0.05.    Control Vanc Vanc +  BAP Control bone marrow  transplant A B: serum IgE Donor Recipient control vanc vanc + BAP C: BAL D: Histological score E: IL-4 transcript levels 250 200 150 100 50 0 [IgE]ng/ml * * 15 10 5 0 20 rel exp x 10-4 * * 6 4 2 0 8 score * 6 4 2 0 8 10 Cells/ml x 104 * Figure 4-3: Atopic susceptibility is transferred by hematopoietic transplant.  123           (A) Schematic overview of experiment.  Whole bone marrow (BM) is collected from Ly5.1 control donors and transplanted into irradiated Ly5.2 control, vancomycin, or vancomycin + BAP treated recipients. (B) Concentration of circulating IgE in blood serum of naïve mice 8-10 weeks after transplant.  The data from comes from 2 experiments with 3 mice/group.  Error bars in all panels show SEM.   A B 500 400 300 200 100 0 [IgE]ng/ml con vanc vanc  + BAP Control Vanc Vanc +  BAP Control bone marrow  transplant Donor Recipient Figure 4-4: Atopic susceptibility is transferred by hematopoietic transplant.  124  4.4.4  SCFAs alter the epigenetic state of hematopoietic progenitors  To test if the microbiome (specifically SCFA exposure) alters the epigenome of a transplantable hematopoietic compartment, we used native ChIP-seq to analyze DNA acetylation in snap frozen Lin-Sca1+cKit+ (LSK) cells (Fig 4-5) isolated from mice raised from birth on control, vancomycin, or vancomycin + BAP drinking water. LSK cells are a heterogeneous population of hematopoietic stem and progenitor cell (HSPC) consisting of long- and short-term repopulating cells and multipotent progenitors but not lineage-committed progenitors (Sca1+).  An HSPC in this work is defined as hematopoietic precursors with multipotent differentiation and at least short-term engraftment potential in congenic recipients. .    125                HSPCs were defined as live, CD45+, Lin-, Sca1+, cKit+.    FSC SSC CD45 viability CD48 cKit Sca1 Lineage      % of  total = 1.2 Figure 4-5: Gating strategy for HSPC population.  126  Consistent with the hypothesis that the transferred immune phenotype is encoded within the epigenome of dysbiotic mice, we found unique regulatory states, as defined by DNA acetylation (H3K27ac occupancy) within the genomes of HSPCs from vancomycin treated mice.    We identified 5812, 6238, and 6781 acetylated regions in genomes of HSPCs from the control, vancomycin, and vancomycin + BAP treated mice, respectively.  After applying a coverage based threshold and a false discovery correction to our dataset262, we found high similarity for the coverage distribution at promoters for all three donor-mouse treatment conditions (Fig 4-6A). However, there were 248 genes, 1.2% of all genes, that were differentially acetylated in HSPCs isolated from vancomycin treated mice that were consistent with SCFA-dependent regulation (Fig 4-6B). Differentially acetylated genes were dominated by immune functions that were predicted to be up-regulated in vancomycin treated mice (194/248), including numerous functions associated with an allergic phenotype (Fig 4-6C & E).  We also found a subset of genes (59/248) that were predicted to be down-regulated in vancomycin treated mice, including functions associated with immunotolerance (Fig 4D & F).  Taken together these results suggest that exposure to SCFAs alters the regulatory network in the genome of HSPCs (LSK cells) through alterations to H3K27ac occupancy.     127                         265 194 54 59 65 154 5528 12512 1.2 0.9 0.3 0.3 0.3 0.7 25.7 58.2 gene counts gene % A B Log10[fraction of bases] 0 -1 -2 -3 -4 -5 -6 0 10 20 30 40 Base coverage 10 20 30 40 Input H3K27 Mean  signal D Control Control (input) Vanc (input) Vanc + BAP (input) Vanc Vanc + BAP F C Mean  signal Control Control (input) Vanc (input) Vanc + BAP (input) Vanc Vanc + BAP E base coverage @ TSS+/-1.5Kb C  V  V+BAP C  V  V+BAP C   V   V+BAP  128   (A) Coverage distribution of ChIP-Seq aligned fragments for DNA Input (left) and H3K27ac (right), after quality filtering, for LSK (Lin-Sca1+cKit+) bone marrow cells from control (blue), vancomycin treated (orange), or vancomycin + BAP treated (green) mice.  (B) Eight possible patterns of acetylation when comparing the three treatment groups.  A circle in the high position denotes an acetylated region (q < 0.05); low position denotes an un-acetylated region.  Left y-axis indicates number of significant hits with each pattern.  Right y-axis indicates percentage of hits with each pattern.  Red indicates genes that are uniquely acetylated in HSPCs from vancomycin treated mice, and blue indicates genes that are uniquely un-acetylated in those HSPCs. (C-D) Heatmaps showing mean acetylation signal intensity of the top 20 most significantly marked promoters ranked by increasing q value.  (C) Red indicates genes that are uniquely acetylated in HSPCs from vancomycin treated mice.  (D) Blue indicates genes that are uniquely non-acetylated in those HSPCs (E-F) Visualization at UCSC genome browser of a representative example gene across H3K27ac ChIP-seq signal track.  Red arrow indicates region of differential acetylation. (E) TMEM201, uniquely acetylated in HSPCs from vancomycin treated mice.  (F) SNRPA1, uniquely not-acetylated in HSPCs from vancomycin treated mice.    Figure 4-6: Unique regulatory states (H3K27ac) within the genomes of HSPCs from dysbiotic mice.  129  4.5 Discussion Our study found that mice treated with vancomycin during early life were Th2 skewed.  We found that early-life intervention with a FMT could partially rescue this phenotype; this intervention was no longer effective at 7 weeks of age.  Our evaluation of the microbiome led us to believe that this was not driven by a failure of the microbiome to engraft.  However, we cannot rule out the possibility that there are important strain level microbiome differences that our study didn’t detect.  Furthermore, a future metagenomic evaluation could reveal differences in the metabolic potential of microbes that engraft during FMT.   The data collected thus far leads us to believe that the microbiome imparts a lasting (up to 3 weeks post-transplant) effect on the host in early life that persists in the absence of dysbiosis in later life.  How long this effect persists is a question that should be addressed in the future. Our data suggests that FMT therapy would not be an effective treatment option for adults suffering with atopic disease.  We found that atopic and AAI-susceptibility, due to gut microbiome dysbiosis (i.e., paucity of SCFAs), could be transferred via BM transplant into lethally-irradiated congenic recipient mice with a “normal” gut microbiome.  We found that the presence of SCFAs in the donor mouse were protective against atopic susceptibility in the BM recipient.  The Th2 proclivity in recipient mice always correlated with the early-life (first 4 weeks) SCFA exposure of the donor, but not the recipient. Collectively, these findings support the hypothesis that early-life gut dysbiosis may alter the immune response by an epigenetic mechanism.   130  Our study found SCFAs systemically alter the epigenome of HSPCs.  Our unique experimental system allowed us to parse out the specific effect of SCFAs on the epigenetic state of HSPCs -  i.e., by looking at marks that are present in control mice, lost with antibiotic treatment (loss of SCFA), and then return with SCFA supplementation (and vice versa) our data provided convincing evidence that SCFAs drive the observed changes in epigenetic programming.   This mechanism is summarized (Fig 4-7).         131                Figure 4-7: Summary model. SCFAs are fermented by the microbiota from resistant starch in the diet.  These SCFAs are absorbed and circulate in the blood, which perfuses the bone marrow.  HSPCs in the bone marrow are exposed to SCFAs.  Unique regulatory states are imprinted in the absence of the SCFAs, which are pro-inflammatory.   Resistant starch SCFAs Vancomycin SCFAs  132  Although a cocktail of SCFAs was used in this study,  our previously published results268 suggest that butyrate alone is sufficient to compensate for microbiome driven Th2 skewing in vancomycin-treated mice.  Thus, butyrate, which is known to have the strongest HDACi-activity of the SCFAs, may be sufficient to epigenetically program HSPCs.  The role of each individual SCFA, particularly butyrate, should be investigated in future studies.  There is mounting evidence that the microbiome can impact the hematopoietic compartment.  It has been previously demonstrated that the microbiome can have direct effects on hematopoiesis through microbial products and microbial metabolites in the circulation. GF mice exhibit global defects in the their innate-immune compartment269. Further, it has been known for 30 years that mice on antibiotics display aberrant hematopoiesis, specifically in reduced frequency of granulocyte-macrophage colony forming cells270. Early in development, at the yolk-sac stage, there is evidence that the mother’s microbiome impacts myeloid cell development 271, 272.  Trompette et al found that microbially-derived SCFAs increase the production of DC precursors in the BM192.  More recently, it was demonstrated that antibiotic-fed animals had aberrant numbers of HSPCs, and that these could be restored to normal levels by the administration of NOD1 ligands (bacterial peptidoglycan) via sensing and signaling through mesenchymal stem cells273.   However, our study is the first to identify a mechanism by which the microbiome influences hematopoietic changes via an epigenetic mechanism.  Although there are more experiments needed, our data suggests that there may be a relationship between the epigenetic signature seen in the HSPCs, and the ability of the BM to transplant the atopic phenotype.  The epigenetic signature unique to HSPCs isolated from vancomycin-treated  133  mice suggested differential regulation of many genes associated with an atopic phenotype.  Ten of the most intriguing genes are summarized in Table 4-1. Patterns of acetylation highlighted in this table are consistent with the expected phenotype, i.e. the list is dominated by genes that are uniquely acetylated in the vancomycin group and have functions predicted to increase asthma susceptibility.  The differential regulation of these asthma-linked genes provides strong support for the conclusion that an epigenetic mechanism confers the immune phenotypes transferred via BM transplant.  How these epigenetic marks are maintained on the cells that differentiate from these HSPCs should be investigated in the future.  Our data suggests that the epigenetic marks imparted on HSPCs in the presence of SCFAs will influence the development of immunotolerant differentiated immune cells, but more experiments will be needed to fully understand this process. 134  Table 4-1: Genes with relevance to atopy that are differentially acetylated in dysbiotic mice.   Gene name Acetylation pattern*  q-value Relevance to atopy ref CCR7 acetylated 0.001056 CCR7 drives chemotaxis of dendritic cells to lymph-node in context of allergic challenge.  We have shown that this is a mechanism for enhanced susceptibility to asthma in vanc mice Chapter2 Alox5ap  acetylated 8.83E-05  codes for FLAP, required for production of Leukotrienes (chemical mediators of inflammation in asthma), several FLAP inhibitors show promise in phase II trials for treatment of asthma 274 Il17re  acetylated 8.83E-05  IL-17E is a Th2 cell-promoting cytokine, commonly known as IL-25.  Produced by mucosal epithelial cells.  Transgenic over expression of IL-25 promotes eosinophilia and stimulates production of TH2 cytokines (such as IL-13) 275 Ccdc88b  acetylated 8.83E-05  Regulator of T cell function, loss results in impaired maturation, activation, and cytokine production by T cells 276  135  Gene name Acetylation pattern*  q-value Relevance to atopy ref Ttf2  acetylated 8.83E-05  an epithelial cell-derived repair molecule required for rapid production of IL-33, a Th2 promoting cytokine.  Increased TTF2 results in worse HDM asthma.  Also shown to be upregulated during allergic lung disease in children 277 Nfkbia  unacetylated  0.000109  blocks the ability of NF-KB transcription factors to bind and activate DNA 278 Gcnt4  acetylated 0.001056  preferentially expressed in peanut specific memory Th cells from donors with peanut allergy 279 Scarf1  acetylated 8.83E-05  involved in antigen uptake by dendritic cells, KO mice have impaired uptake of apoptotic cells and develop a lupus-like disease associated with dermatitis 280 Myb  acetylated 0.001056  involved in regulating class switch to IgE production 281 Fhl4  unacetylated  0.000109  codes for Syntaxin, a negative regulator of phagocytosis in monocytes and macrophages 282 *Acetylated, uniquely acetylated in HSPCs from vancomycin treated mice. Unacetylated, uniquely unacetylated in those HSPCs. 136  HSPCs are a heterogeneous population, and the complete BM contains an even more heterogenous population of cells.   Our data shows an atopic immune phenotype is transplantable via bone marrow.  This phenotype may be transmitted via a subset of cells within the HSPC population, such as the hematopoietic stem cells or multipotent progenitor sub population283.  A second possibility is that the BM transplant may confer the pro-inflammatory phenotype via engrafted mature lineages such as polarized T cells and/or memory B cells. The ability of BM to transplant atopic disease in humans is well documented284, 285,286 and has been hypothesized to be mediated via the latter group of cells.   Understanding which populations of cells, their exact epigenetic and phenotypic signatures, and the absolute number of these cells that exist in the various conditions will be critical to our understanding of this process.  Our epigenetic data provides compelling evidence that the HSPCs are responsible (in part or fully) for this phenomenon.   Future work should focus on elucidating the exact population(s) within the BM capable of conferring the phenotype.     These findings have immense potential for beneficial impact on human health.  Numerous associations have been identified among the microbiome, microbial metabolites and asthma.  Early life microbiome dysbiosis has been linked to arthritis, IBD, leukemia, obesity, autism spectrum disorder, diabetes and more 287,288.   The underlying functional mechanisms by which microbial cues are conveyed to the developing immune system may be broadly applicable to all or many of these diseases. Dissecting the basic science that underlies this process may have broad implications for how we think about many different diseases.  Despite the importance of this basic scientific question - we still lack an understanding of what happens during this early-life window and how the effects of this interaction persist and develop into disease over time.   137  This study is among the first to identify mechanisms for such associations, and it is the first to provide evidence for the involvement of epigenetics in such a mechanism.   Understanding the early life window has important translational impact.   Knowledge of which metabolites are key in early-life may allow prevention of diseases via numerous strategies to ensure exposure.  For example, exposure could be achieved naturally with probiotics, or therapeutically with enzyme or metabolite supplements.   Furthermore, appreciation for the role of a healthy microbiome in preventing disease may dissuade doctors from certain medical practices (C-sections, prescribing antibiotics) unless absolutely necessary.  Although current evidence suggests that there is a narrow therapeutic window in which endogenous microbial metabolites protect the host from asthma, this work may provide a fuller understanding of the epigenetic mechanisms underlying this protection that may then allow us to develop therapeutic interventions (such as more potent or specific HDACi) with therapeutic benefit in asthma patients at all life stages.      138   Discussion 5.1  Relevance and contribution to the field 5.1.1 Introduction  Asthma is the most common childhood medical condition.  It accounts for nearly two million hospital visits and thousands of deaths per year in the United States alone289.   Despite its immense societal burden, there is no cure, very few treatment options, and no prevention strategies.  In fact, the etiology of asthma and atopic disease remains elusive.   Recently, evidence has suggested the gut microbiome may play a role in shaping the immune system and preventing or predisposing an individual to developing the disease.   The work described in this thesis focuses on the role of the intestinal microbiome, through production of short chain fatty acids, in both mouse models of asthma and in the human disease. The novel contributions made to the field by this thesis are presented in the next sections. 5.1.2 Microbiome-driven allergic lung inflammation is ameliorated by short-chain fatty acids In chapter 2 we show how SCFAs serve as a mechanistic link between microbial dysbiosis and the development of asthma.   We provide evidence that the increased susceptibility of vancomycin-treated mice to models of AAI is due to depletion of gut microbiome populations  139  responsible for production of SCFAs.  At the time we started this work there was no publication linking microbially derived metabolites to atopic disease.  Our work was initially driven only by the observation that mice on vancomycin were more susceptible to OAA-asthma169, and mice on a cocktail of antibiotics had elevated IgE172.  Today, the paradigm for how the microbiome impacts susceptibility to allergic disease focuses on the induction of Tregs.  In this chapter we challenge that paradigm by finding evidence for two additional and distinct immunological processes by which SCFAs link the microbiota to immune processes in atopy.    Firstly, we show that microbial dysbiosis leads to increased IL-4-producing T-cells in naïve mice promoting IgE class switching activity in the PPs.  SCFAs mediate this response and prevent this Th2 polarization.  Our finding that SCFAs restrain T-cell polarization towards a Th2 fate is a novel effect of SCFAs on T-cell fate.   We show that for SCFAs to be effective at dampening IgE levels, they need to be present during early life.   Secondly, we show that microbial dysbiosis leads to a DC-dependent immunomodulatory process.  Although other groups have previously found that SCFAs can modulate both DC activation and trafficking when cultured in their presence, we are the first to demonstrate that SCFA exposure in vivo has a persistent effect on DCs, dramatically attenuating DC migration and activation ex vivo. Furthermore, our studies are the first to show how microbial dysbiosis impacts DC migration in vivo in the context of allergic disease.    140  5.1.3 Early infancy microbial alterations in SCFA production pathways are predictive of atopic disease.  In chapter 3 we present the first whole shotgun metagenome study to compare the microbiome metabolic potential in healthy versus atopic children.  We identified a unique signature in fecal samples from atopic children that is characterized by alterations in carbohydrate-fermentation pathways.  We showed that children who become atopic lack bacterial taxa known to play a role in carbohydrate fermentation, including Ruminococcus bromii, a keystone species in this process.  We identified a functional deficiency in the enzymes required for carbohydrate metabolism, particularly human breast milk oligosaccharide metabolism, and butyrate production.  This is the first study to show that metabolic potential of the gut microbiome is predictive of atopy.    There is mounting evidence that microbial dysbiosis, particularly intestinal dysbiosis, is linked to atopy in humans.   We present convincing evidence in chapter 3 that microbially derived SCFAs, particularly butyrate, impact immune processes in atopy.   Our findings from this metagenomic study link butyrate fermentation pathways to asthma and atopy in children.  This work provides a novel and compelling link between findings in animal studies and the human disease.   141  5.1.4 Exposure to SCFA in the early-life window leaves a life-long epigenetic imprint on hematopoietic stem and progenitor cells In chapter 4 we further investigated the early-life window of opportunity.   We are not the first group to identify an early life window during which a microbiome signature predicts whether children will become atopic.   One of the major questions in the field is how and why this window exists.  Two possible hypotheses are: (1) the gut microbiota is associated with atopic disease throughout life, but as the microbiome matures and confounders increase, this signature is no longer discernable, and (2) early life represents a key developmental window during which the microbiome imprints on the infant immune system159.    In chapter 4 we addressed this question and presented novel evidence that the second interpretation is the most likely answer.    We found that SCFAs dampen Th2 skewing of the immune system. Mice lacking exposure to SCFA early in life, due to gut microbiome dysbiosis, were atopic and had a pro-inflammatory immune phenotype with heightened Th2 responses in a model of asthma.   This phenotype could not be rescued by a fecal transplant with a healthy microbiome after the early-life window closed – thus rejecting the first hypothesis.  However, we successfully transferred the phenotypes of both heightened and dampened Th2 skewing via bone marrow (BM) transplants to irradiated recipient mice.  We provide the first evidence that the phenotype is encoded within the epigenome.  We found unique regulatory states, as defined by H3K27ac, within the genomes of purified hematopoietic stem and progenitor cells (HSPC) of recipient mice that received BM transplants from dysbiotic mice.  The unique regulatory state identified in SCFA-deficient HSPCs identified several candidate genes known to play a significant role in atopic disease  142  progression.  The data presented in this chapter is among the first to identify an epigenetic mechanism that links the microbiome, microbial metabolites, and allergic disease.  5.2 Future direction This work has immense potential for beneficial impact on human health by changing the way we view the role of the microbiome and microbially derived metabolites in the context of asthma and atopic disease.  However, there are many new questions raised by these findings that should be addressed in future studies. 5.2.1 Microbiome-driven allergic lung inflammation is ameliorated by short-chain fatty acids In chapter 2 we use a cocktail of SCFAs containing butyrate, acetate, and propionate for all in vitro studies.  However, the results of serum SCFA analysis in the vancomycin treated mice indicate that the only SCFA significantly depleted by vancomycin treatment is butyrate.   Furthermore, our in vivo work shows a key role for butyrate alone in the phenotypes investigated. The three major SCFAs are known to interact with cells via different mechanisms and with different affinities for different subsets of immune cells.  The SCFAs have been studied by multiple groups in the context of allergic disease, and controversy exists on the relative importance of butyrate, acetate, and propionate.  Future mouse studies should continue to investigate the relative contribution of the various SCFAs in vivo.    143   Chapter 2 focuses on mechanisms in two cell types: T cells and DCs. Despite recent interest in the role of SCFAs on various populations of immune cells, very little is known about the effects of in vivo SCFA exposure on specific subsets of immune cells, particularly in the context of atopic disease.  Many of the key cell types involved in the allergic response (mast cells, eosinophils, basophils) express high-levels of receptors for SCFAs. How these and other cell types are influenced by paucity of SCFAs should be investigated in the future. In particular, this work should make use of the 4Get mouse strain to look at IL-4 production in different cell types such as ILC2s and basophils.   Finally, continued work using the vancomycin/ vancomycin +BAP model should focus on better defining the window of opportunity.   We found that SCFA-intervention was most effective when administered before 3 weeks and no longer effective after 7 weeks.  In the life-span of a mouse this is a broad window.  A better understanding of this early-life window is essential for translating effective interventions to humans.  5.2.2 Early infancy microbial alterations in microbiome SCFA production pathways are predictive of atopic disease. Others have shown that environmental factors that are known to drive disturbances in the microbiota (e.g., antibiotics, C-section birth, formula feeding) increase a child’s risk for developing asthma.  There is still uncertainty about the causal role of the microbiome in immune development leading to asthma. This thesis provides some mechanistic insight to how the  144  microbiota may be mediating this immune development via SCFAs. Future studies should continue to chip away at this gap in our understanding by determining how infants are acquiring or not acquiring the genes associated with protection (ie- CAZy and butyrate production genes).  Larger sample sizes will allow us to assess the relative contributions of specific environmental factors on humans.   To test the effect of these environmental factors on the acquisition of specific genes will require controlled mouse experiments.    Our metagenomic analysis of CHILD samples is just the beginning of work that should be done.  There are many other microbial processes that have been demonstrated to impact host immune processes including: bile acid modification, vitamin synthesis, fatty acid metabolism, and generation of other short chain fatty acids.  Future work should investigate additional pathways and their possible association with atopic disease.   Finally, continued work with additional longitudinal human cohort studies should focus on narrowing the window of opportunity for microbiome exposure.   We found a metagenomic signature present at 3 mo and at 1 yr.  It is possible that an even stronger signature exists at a different time-point.   A better understanding of the physiological and developmental boundaries of this window is essential for effective therapeutic interventions.   145  5.2.3 Exposure to SCFAs in the early-life window leaves a life-long epigenetic imprint on hematopoietic stem and progenitor cells Chapter 4 implicates HSPCs as a key population of cells responsible for the life-long imprint of the microbiome on the development of allergic disease via an epigenetic mechanism.  As discussed, our data does not preclude the possibility that a subset of cells within the HSPC population, such as hematopoietic stem cells or a multipotent progenitor sub-population; or a mature lineage in the BM such as polarized T cells and/or memory B cells are the population responsible for transmitting the phenotype.  Future work should focus on elucidating the exact population within the BM capable of conferring the phenotype.     Future studies should also include a comprehensive epigenomic analysis of primary purified cell populations determined to confer the pro-inflammatory phenotype.  This work should prioritize H3K4me2, H2k4me1, H3K27me3, and H3K27ac, given their importance in the specification of a self-renewing state and marking enhancer regions in combination with DNA methylation status.  Targets identified by these studies, and the candidates identified in chapter 4 should be validated.  5.3 Final Conclusions In conclusion, this thesis highlights the role of microbially-derived metabolites, SCFAs, in the development of asthma and atopy.    We present a new understanding of the intricate relationship between the microbiome, microbial metabolites and asthma. 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