The Role of the Human Gut Microbiome in Ankylosing Spondylitis

The role of the human gut microbiome in ankylosing spondylitis

Mary-Ellen Clare Costello Bachelor of Applied Science (Microbiology), Masters of Science (Research)

A thesis submitted for the degree of Doctor of Philosophy at The University of Queensland in 2015 University of Queensland Diamantina Institute

Abstract

Intestinal microbiome dysbiosis and microbial infections have been implicated in a number of immune mediated diseases, including multiple sclerosis, inflammatory bowel disease (IBD), and type 1 diabetes. Bacterial infections in the gut and urogenital tract are known to trigger episodes of reactive arthritis, a form of spondyloarthropathy (SpA), a group of related inflammatory arthropathies of which ankylosing spondylitis (AS) is the prototypic disease. There is a close relationship between the gut and SpA, exemplified in reactive arthritis patients, where a typically self-limiting arthropathy follows either gastrointestinal infection with Campylobacter, Salmonella, Shigella or Yersinia, or urogenital infection with Chlamydia. Microbial involvement has been suggested in AS, however, no definitive link has been established. Multiple genes associated with AS also play a role in gut immunity, such as genes involved in the IL-23 pathway, which are important regulators of intestinal ‘health’. There is a marked over-representation of genes associated with Crohn’s disease (CD) that are also associated with AS suggesting the two diseases my have similar aetiopathogenic mechanisms, possibly involving gut dysbiosis. Up to 70% of AS patients have subclinical gut inflammation with 5-10% of these patients developing clinically defined IBD resembling CD. Therefore unravelling the relationship between underlying host genetics, the intestinal microbiome and the immune system is imperative for furthering out understanding of AS pathogenesis.

This thesis sought to examine the role of the gut microbiome in AS by characterising the AS microbiome, examining how changes in host genetics influences intestinal microbial composition and then exploring at the association between AS susceptibility loci and microbiome composition in healthy twins.

To date, no comprehensive characterisation, using culture-independent methods, of intestinal microbiota from terminal ileal (TI) biopsies from AS patients has been performed. I firstly optimised the methods required for 16S rRNA characterisation of human intestinal tissue. This included the intestinal biopsies extraction method, 16S PCR development, next generation library construction and sequencing as well as the analysis methods employed for characterisation and comparison of the intestinal microbiome.

Secondly I profiled the microbiome of TI biopsies from patients with recent-onset, TNF- antagonist naïve AS and healthy controls (HC), using the methods detailed in Chapter 2 of this thesis, to investigate if the AS gut carries a distinct microbial signature. Results showed that AS patients carried a clear and distinct microbial signature when compared to HC (P<0.001) higher abundance of five families of bacteria Lachnospiraceae (P=0.001), Ruminococcaceae (P=0.012), Rikenellaceae (P=0.004), Porphyromonadaceae (P=0.001), and Bacteroidaceae (P=0.001), and a decreases in abundance of two families Veillonellaceae (P=0.01) and Prevotellaceae (P=0.004). The microbial composition was found to correlate with disease status and greater differences were observed between than within disease groups. Further investigation of the AS intestinal microbiome demonstrated that an assemblage or ‘core’ of five families of bacteria were driving the intestinal profile. These results are consistent with the hypothesis that genes associated with AS act at least in part through effects on the gut microbiome.

I then further examined the role of host genetics in microbiome composition by examining the effect of Endoplasmic reticulum aminopeptidase-1 (ERAP1) in a murine model. Genome wide association studies have currently identified over 40 loci associated with ankylosing spondylitis, with ERAP1 is one of the strongest associations, along with HLA- B27. ERAP1 plays a critical role in overall immune system modulation with systemic effects. In this chapter, I asked how a lack or down regulation of ERAP1 influences the inherent gut microbiome composition, and how this impacts on the antigen repertoire and ensuing affects the immune system.

In the final component of this thesis I further investigated the influence of host genetics, specifically AS risk loci, on microbiome composition by examining the association between AS SNPs and intestinal microbiome composition in a non-AS, otherwise healthy individuals. To investigate the relationship between genes known to be associated with AS and the gut microbiome, results of matched genetic and 16S rRNA profiling of 982 twins from the United Kingdom Adult Twin Registry (TwinsUK) were examined to determine if SNPs, either individually or in combination, were associated with specific microbes. We have shown, in an otherwise healthy Twin cohort, that genes associated with developing AS were linked to families of bacteria known to be inflammatory and key indicator species in the AS microbiome. The allelic risk score linked an AS genetic profile with the families of Ruminococcaceae, Lachnospiraceae and the order Clostridiales, which are keystone species of the AS microbiome and members of the core microbiome. Further investigation 3 of individual genes involved in microbial response, peptide processing, trimming and presentation were also correlated with these same families of bacteria. This intimates that host genetics play a role in not only overall microbiome composition, but also structure of core microbiome.

Collectively the work presented in this thesis highlights of the role the human gut microbiome in AS. It demonstrates evidence for a discrete microbial signature in the TI of patients with AS compared to HC. The microbial composition was found to correlate with disease status and driven by a core group of five bacterial families. Genes associated with developing AS were linked to families of bacteria known to be inflammatory and key indicator species in the AS microbiome, and investigation of individual genes involved in microbial response, peptide processing, trimming and presentation were also correlated with these same families of bacteria. This indicates that underlying host genetics plays a role in core microbiome as well as overall microbiome structure. Final discussion chapter of this thesis discusses implications of these associations, limitations of the research as well as future directions to be explored.

Declaration by author

This thesis is composed of my original work, and contains no material previously published or written by another person except where due reference has been made in the text. I have clearly stated the contribution by others to jointly-authored works that I have included in my thesis.

I have clearly stated the contribution of others to my thesis as a whole, including statistical assistance, survey design, data analysis, significant technical procedures, professional editorial advice, and any other original research work used or reported in my thesis. The content of my thesis is the result of work I have carried out since the commencement of my research higher degree candidature and does not include a substantial part of work that has been submitted to qualify for the award of any other degree or diploma in any university or other tertiary institution. I have clearly stated which parts of my thesis, if any, have been submitted to qualify for another award.

I acknowledge that an electronic copy of my thesis must be lodged with the University Library and, subject to the policy and procedures of The University of Queensland, the thesis be made available for research and study in accordance with the Copyright Act 1968 unless a period of embargo has been approved by the Dean of the Graduate School.

I acknowledge that copyright of all material contained in my thesis resides with the copyright holder(s) of that material. Where appropriate I have obtained copyright permission from the copyright holder to reproduce material in this thesis.

Publications during candidature

Publications in peer-reviewed journals

Costello M-E, Ciccia F, Willner D, Warrington N, Robinson PC, Gardiner B, Marshall M, Kenna TJ, Triolo G, Brown MA (2014). Intestinal dysbiosis in ankylosing spondylitis. Arthritis & Rheumatology 10.1002/art.38967 Available online

Rosenbaum J, Lin P, Asquith M, Costello M-E, Kenna T, Brown M (2014). Does the microbiome play a causal role in spondyloarthritis? Clinical Rheumatology 33: 763-767.

Kenna TJ, Lau MC, Keith P, Ciccia F, Costello M-E, Bradbury L et al (2014). Disease- associated polymorphisms in ERAP1 do not alter endoplasmic reticulum stress in patients with ankylosing spondylitis. Genes Immun. 10.1038/gene.2014.62

Costello M-E, Elewaut D, Kenna T, Brown M (2013). Microbes, the gut and ankylosing spondylitis. Arthritis Research & Therapy 15: 214.

Publications included in this thesis

Costello M-E, Ciccia F, Willner D, Warrington N, Robinson PC, Gardiner B, et al. Brief Report: Intestinal Dysbiosis in Ankylosing Spondylitis. Arthritis & Rheumatology. 2015;67(3):686-91.– incorporated as Chapter 3 (complete article in Appendices)

Contributor Statement of contribution Author - Costello M-E (Candidate) Conceptualised the study (20%) Designed experiments (60%) Designed microbial analysis strategy (40%) Performed the experiments (95%) Performed microbial and statistical analysis (75%) Wrote the paper (80%)

Author – Ciccia F Conceptualised the study (5%) Patient recruitment and collected human 6

Tissue samples (70%) Author – Willner D Designed microbial analysis strategy (60%) Wrote and edited the paper (5%) Author - Warrington N Performed statistical analysis (5%) Author - Robinson PC Performed statistical analysis (5%) Wrote and edited the paper (5%) Author – Gardiner B Designed experiments (20%) Author – Marshall M Performed analysis (5%) Author – Kenna TJ Conceptualised the study (10%) Wrote and edited the paper (10%) Author – Triolo G Conceptualised the study (5%) Patient recruitment and collected human tissue samples (30%) Author – Brown MA Conceptualised the study (60%) Designed experiments (20%) Wrote and edited the paper (5%)

Costello M-E, Elewaut D, Kenna T, Brown M (2013). Microbes, the gut and ankylosing spondylitis. Arthritis Research & Therapy 15: 214 – incorporated and adapted in Chapter 1 (complete article in Appendices)

Contributor Statement of contribution Author - Costello M-E (Candidate) Wrote and edited the paper (40%)

Author – Elewaut D Wrote and edited the paper (10%) Author – Kenna TJ Wrote and edited the paper (25%) Author – Brown MA Wrote and edited the paper (25%)

Contributions by others to the thesis

Chapter 2: Associate Professor Graham Radford-Smith, Queensland Institute of Medical Research, provided the colon biopsies used for the methods development. Dr Francesco Ciccia and Professor Giovanni Triolo from the University of Palermo provided the terminal ileal biopsies for the pilot study. Dr Dana Willner provided significant guidance and technical expertise in the planning and analysis of the experiments.

Chapter 3: Dr Francesco Ciccia and Professor Giovanni Triolo from the University of Palermo provided all the terminal ileal biopsies and metadata.

Chapter 4: Dr Tony Kenna and Patricia Keith, The University of Queensland Diamantina Institute, provided assistance in the collection of mouse tissue. Dr Tony Kenna also provided significant contributions to the conception, design and analysis of the mouse work in this chapter.

Chapter 5: The 16S rRNA sequencing was performed at Cornell University, in the Lab of Professor Ruth Ley. 16S rRNA sequenced the faecal samples of all the Twins used in the study. Julia Goodrich from Professor Ley’s Lab analysed the 16S rRNA sequencing, filtered, corrected and regressed the OTU table used through out this chapter. Genotyping was conducted in Professor Tim Spector’s Lab at St Thomas’, King’s College London, and Michelle Beaumont extracted the genotypes. Dr Jordana Bell also from Professor Tim Spector’s Lab at St Thomas’, King’s College London, provided technical assistance and the approach using Merlin to assess association between genes and the microbiome. Professor David Evans, The University of Queensland Diamantina Institute, also provided significant technical assistance in the overall methods and design of this chapter. Dr Adrian Cortes, The University of Queensland Diamantina Institute, provided technical assistance with the genotype imputation. Dr Nicole Warrington, The University of Queensland Diamantina Institute provided technical assistance with the allelic risk scores.

Significant contributions to the thesis as a whole have been made by Professor Matt Brown and Dr Tony Kenna in their capacity as my PhD supervisors. This includes conceptual and experimental design, writing, editing and advice regarding specific content in this thesis.

Statement of parts of the thesis submitted to qualify for the award of another degree

None.

Acknowledgements

From beginning to end, and all the parts in the middle, this thesis has been a collective endeavor, so I’d like to start off by thanking my supervisor Professor Matt Brown for taking me on and giving me the opportunity to work at UQDI and Brown lab in a field and on a project that I’ve become very passionate about. I was just a classical microbiologist with an interest in infections diseases when I started. I’m very grateful for the space and the latitude to think and experiment and for all the encouragement, reassurance and support through out this thesis. I also no longer think that normal flora is boring (Thank you!). I also owe many, many thanks to my supervisor Dr Tony “Boss man” Kenna. You taught me how to write, how to present, and the value of practice and persistence; you challenged me to think, really think. I know it wasn’t meant to turn out this way but I’m very glad you were my supervisor. Thanks Boss man. Many thanks also goes to my supervisor Dr Gethin Thomas who is always good for an honest chat and some frank advice. Thanks Gethin. Sometimes there are people who come and go in your life and indelibly leave their mark. I know it hasn’t always been easy, but I’m indebted to you Dana for your friendship and support. You’re an amazing teacher who inspires me to try that little be harder and work that little bit harder. Thank you.

I am very grateful to all members of Brown Lab past and present. Many of you have provided crucial experimental help and support as well as friendship. My work would not have been possible without the support of our Nursing and Admin staff Linda, Kelly and Kim. I know my biopsies caused a few headaches and dramas, and I just want you know how appreciative I am of your time and support over the years.

Much of the work presented here would not have been possible without the consent and participation of the many patients who contributed to the work presented in this thesis – I am very grateful for their contribution. I would also like to acknowledge the financial support that I received during my PhD candidature from The University of Queensland Diamantina Institute, and for travel through the Training fellowship. The fellowship enabled me to further my studies, scientific education as well as collaborations. I am grateful for this financial support and opportunity.

Having said that, special mention goes to those in my life who have kept me sane during this PhD journey. Firstly to Helen “Prof. Brenham” Benham for the coffee, cake and chats. 10

Your support and advice has been invaluable and will continue to be. To my Brown Lab coffee group members Adrian and Phil, I am forever grateful for your support. I’d like to thank Adrian for all the grilled cheese and coffee that got me through my final immunology experiments. You always made time and helped me, even when you didn’t have to. So thank you for your patience and your friendship; it means more to me than you know. To Phil for always being there for a whinge, a graph, to hastily read something I’ve written, or a high-five. Not to mention telling me to suck it up princess when I really needed to hear it. Thanks Robinson! To my climbing partners Roland, Kylie and Claudio, a massive thank you for all the climbing and for the thesis-free space. I owe many thanks and probably several beers to a number of my lab mates that have become very good friends over the last few years. To Katie “KC” Cremin, Jess “J-Rab” Harris and Lawrie “Lawrence” Wheeler, your friendship and support means the world to me (yes Lawrence, even during the rugby season). Thanks to Rabbit for reading several chapters of this thesis, you’re fantastic! To KC, J-Rab and Ryan, you made coming to work so much fun and our brunches and dinners such a blast. I do believe we’ve all become ridiculous foodies over the last few years. I give the whole thing 4.5/5 Survey Co’s and look forward to it continuing next year! Last but not least to Nicole for all of your time, help and patience with this non-statistical geneticist. You’ve been fabulous and I’m so thankful for your technical experience but more importantly your friendship over the last year (especially during thesis write-up and submitting time).

A massive thank you to my mum and dad for all their love and support, not to mention for feeding me for the majority of this last year and looking after Bella. I am forever grateful. To my sister who has had to live with me throughout the duration of my thesis, I know I wasn’t always the easiest person to live with so the biggest of thanks for putting up with me and my special kind crazy.

Thanks to my family: Mum, Dad, Katie, Sean and Patrick this wouldn’t have been possible without you.

Keywords

Ankylosing spondylitis, community profiling, microbiome,

Australian and New Zealand Standard Research Classifications (ANZSRC)

110703 Autoimmunity, 40% 060408 Genomics, 30% 060504 Microbial Ecology, 30%

Fields of Research (FoR) Classification

0605 Microbiology, 60% 0604 Genetics, 20% 1107 Immunology, 20%

Si Vis Pacem, para bellum

If you wish for peace, prepare for war

Vegetius, 4th Centaury Roman military author

Table of Contents Abstract ...... 2 Declaration by author ...... 5 Publications during candidature ...... 6 Contributions by others to the thesis ...... 8 Statement of parts of the thesis submitted to qualify for the award of another degree ...... 9 Acknowledgements ...... 10 Keywords ...... 12 Australian and New Zealand Standard Research Classifications (ANZSRC) ...... 12 Fields of Research (FoR) Classification ...... 12 1 Chapter 1 Introduction and Literature Review ...... 23 1.1 Introduction ...... 23 1.2 Ankylosing spondylitis ...... 23 1.2.1 The gastrointestinal tract and AS ...... 24 1.3 Genetics and overlap between AS and gut disease ...... 25 1.4 The gut, barrier regulation and intestinal epithelial cells ...... 27 1.4.1 The physical barrier ...... 27 1.4.1.1 Permeability and Disease ...... 28 1.4.1.2 Innate immunity ...... 29 1.4.2 Adaptive Immunity ...... 30 1.4.2.1 IL-23-responsive cells ...... 30 1.4.2.2 γδ T cells ...... 31 1.4.2.3 Natural killer T cells ...... 32 1.4.2.4 Mucosal-associated invariant T cells ...... 32 1.4.2.5 Lymphoid tissue inducer- like cells ...... 33 1.4.3 Cytokines and gut homeostasis ...... 33 1.4.3.1 IL-17 ...... 33 1.4.3.2 IL-22 ...... 33 1.4.4 Interaction between intestinal microbes and the immune system ...... 34 1.5 Interrogating the human gut microbiome ...... 35 1.5.1 Microbial amplicon sequencing ...... 35 1.5.2 The 16S rRNA ...... 36 1.5.3 Metagenomic sequencing ...... 37 1.5.4 Advances in tools for metagenomic studies ...... 37 1.6 Dynamics of the gut microbiome ...... 38 1.7 The normal microbiome ...... 40 1.7.1 Enterotypes ...... 41 1.7.2 Homeostasis ...... 41 1.8 Influence of underlying host genetics on the intestinal microbiome ...... 42 1.9 The microbiome in immune mediated disease ...... 43 1.9.1 Ankylosing spondylitis ...... 43 1.9.2 Rheumatoid arthritis ...... 44 1.9.3 Inflammatory bowel disease ...... 45 1.9.4 Psoriasis ...... 45 1.10 The Chicken and the egg ...... 47 1.11 Thesis outline...... 49 1.12 Aims ...... 49 1.13 Significance ...... 49 2 Chapter 2 Community profiling the terminal ileal microbiome ...... 50 14

2.1 Introduction ...... 50 2.2 Materials and Methods ...... 53 2.2.1 Optimisation of 16S primers 347F 803R and 517F 803R ...... 53 2.2.2 16S rRNA PCR ...... 53 2.2.3 In silico assessment of Primer pairs ...... 53 2.2.4 Pipeline optimisation Intestinal Biopsies ...... 54 2.2.5 Sequencing of Pilot intestinal biopsies ...... 54 2.2.6 Bioinformatics ...... 55 2.2.7 Community profiling the terminal ileal microbiome ...... 55 2.2.7.1 Ethics statement ...... 55 2.2.7.2 Patient clinical data ...... 56 2.2.7.3 DNA extraction and sequencing ...... 56 2.2.7.4 Bacterial Mock communities ...... 58 2.2.7.5 16S rRNA PCR ...... 59 2.2.7.6 Sequencing Library preparation ...... 59 2.2.7.7 Illumina MiSeq Sequencing ...... 59 2.2.7.8 Bioinformatics and statistics ...... 60 2.3 Results ...... 61 2.3.1 In silico assessment of Primer Pairs ...... 61 2.3.2 Sequencing of Pilot intestinal biopsies ...... 62 2.3.3 Sequence depth analysis ...... 63 2.3.4 Community profiling the terminal ileal microbiome ...... 65 2.4 Discussion ...... 70 3 Chapter 3 Intestinal dysbiosis in ankylosing spondylitis ...... 73 3.1 Abstract ...... 73 3.2 Introduction ...... 74 3.3 Materials and Methods ...... 75 3.3.1 Patient clinical data ...... 75 3.3.2 DNA extraction and sequencing ...... 77 3.3.3 Mock community ...... 77 3.3.4 16S rRNA PCR ...... 77 3.3.5 Sequencing Library preparation ...... 78 3.3.6 Illumina MiSeq Sequencing ...... 78 3.3.7 Bioinformatics and statistics ...... 78 3.4 Results ...... 81 3.5 Discussion ...... 90 4 Chapter 4 Effect of ERAP1 genotype on the intestinal microbiome and the impact on the immune system in the ERAP mouse model ...... 93 4.1 Introduction ...... 93 4.1.1 Peptide presentation and infection ...... 94 4.1.2 Intestinal cytokines and gut homeostasis ...... 96 4.1.3 Interactions between intestinal microbes and the immune system ...... 97 4.2 Methods ...... 98 4.2.1 Antibodies and Flow Cytometry ...... 98 4.2.1.1 Mice ...... 98 4.2.1.2 Antibodies ...... 99 4.2.1.3 Cell preparation ...... 99 4.2.1.4 Statistics ...... 99 4.2.2 Sequencing and analysis ...... 100 4.2.2.1 DNA extraction ...... 100 4.2.2.2 Mock community ...... 100 4.2.2.3 16S rRNA PCR ...... 100 4.2.2.4 Sequencing Library preparation ...... 100 4.2.2.5 Illumina MiSeq Sequencing ...... 101 4.2.2.6 Bioinformatics and statistics ...... 101 4.3 Results ...... 101 4.3.1 Alterations in host genetics influences gut microbiome composition ...... 101 15

4.3.2 Loss of ERAP does not change overall proportions of Immune cell subsets ...... 106 4.3.3 Loss of ERAP leads to differences in cytokine potential ...... 109 4.4 Discussion ...... 113 5 Chapter 5 Effect of underlying host genetics on intestinal microbiome composition ...... 117 5.1 Introduction ...... 117 5.2 Methods ...... 121 5.2.1 TwinsUK dataset ...... 121 5.2.2 Sample collection ...... 121 5.2.3 DNA extraction and 16S rRNA amplification ...... 121 5.2.4 16S rRNA filtering and analysis ...... 121 5.2.5 SNP selection, genotyping and imputation ...... 122 5.2.6 Statistical analysis ...... 123 5.3 Results ...... 123 5.3.1 Distribution of OTUs is not linked to infection or Phylum ...... 123 5.3.2 AS susceptibility variants are associated with overall microbiome structure ...... 125 5.3.3 Genes involved in peptide presentation and microbial sensing are associated with AS core families of bacteria ...... 137 5.4 Discussion ...... 157 5.5 Supplementary Tables ...... 161 6 Chapter 6 Final Discussion ...... 183 6.1 Overview and Conclusions ...... 183 6.2 Future directions ...... 185 7 References ...... 191 Appendices ...... 205

Tables Table 1.1 Genes associated with ankylosing spondylitis (AS) and their association with inflammatory bowel disease (IBD)...... 26 Table 2.1 Intestinal biopsies utilised for pipeline optimisation ...... 54 Table 2.2 Pipeline optimisation sequencing metrics ...... 55 Table 2.3 Clinical details of patients used in preliminary community profiling studies...... 57 Table 2.4 Mock1 construction using bacteria at varying concentrations...... 58 Table 2.5 Mock2 – 16S copy number normalised to 10ng/ul at varying ratios...... 59 Table 2.6 Counts of 16S rRNA sequence reads obtained per biopsy...... 60 Table 2.7 Taxon output by Grinder...... 62 Table 3.1. Clinical details of patients supplying biopsies at time of collection. Disease duration is from date of onset of symptoms...... 76 Table 3.2 Bacterial mock community composition...... 77 Table 3.3 16S rRNA sequence read counts per sample post quality control and filtering .... 79 Table 4.1 Innate lymphoid cells including cytokine expression and immune function...... 98 Table 4.2 Differences in average percentage OTU counts between C57BL/6 and ERAP-/- mice showing that changes in proportions of key bacteria contribute to the ERAP-/- microbial signature...... 103 Table 4.3 Frequencies of cell subsets showing no significance between ERAP+/+ (WT) and ERAP-/- (KO) strains in spleen and mesenteric lymph nodes...... 107 Table 4.4 Frequencies of cytokine expression from Immune cell subsets in the spleen of ERAP-/- (KO) and ERAP+/+ (WT) mice. There was no significance in expression between ERAP WT and ERAP KO strains in spleen as determined by unpaired t test...... 110 Table 4.5 Frequencies of cytokine expression from immune cell subsets in the MLN. There was no significant difference in expression between ERAP+/+ (WT) and ERAP-/- (KO) strains in spleen as determined by unpaired t test...... 113 Table 5.1 Reported AS associated loci including correlation of genes associated with inflammatory bowel disease...... 127 Table 5.2 OTUs with taxonomy associated at P<0.05 from the weighted allelic risk score, including beta and standard error. Six of the OTUs associated belong to the family of Ruminococcaceae, three belong to the family Lachnospiraceae and the remainder belonging to Bifidobacteriaceae and the order Clostridiales. Taxonomy is shown with full classification from phylum through class (c_), order (o_), family (f_), genus (g_) and species (s_)...... 130 Table 5.3 OTUs reaching nominal significance (P<0.05) in the unweighted risk score. The major families of bacteria represented are Ruminococcaceae, Lachnospiraceae, Bifidobacteriaceae, the order Clostridiales and Veillonellaceae, which are the same families of bacteria observed in the weighted analysis and in the core microbiome in Chapter 3 of this thesis. Taxonomy is shown with full classification from phylum through class (c_), order (o_), family (f_), genus (g_) and species (s_)...... 131 Table 5.4. AS associated loci investigated for association with each OTU in the microbiome...... 137 Table 5.5 OTUs reaching nominal significance (P<0.05) in the ERAP locus. 53 OTUs, predominantly Ruminococcaceae and Lachnospiraceae families associated with the ERAP1- ERAP2 SNP, rs10045403; 44 OTUs belonging to the same families of bacteria associated with the ERAP1-ERAP2-LNPEP SNP, rs2910686 and 50 OTUs all belonging to Ruminococcaceae and Lachnospiraceae associated with ERAP1 SNP, rs30187. Taxonomy is shown with full classification from phylum through class (c_), order (o_), family (f_), genus (g_) and species (s_)...... 141 Table 5.6 OTUs reaching nominal significance (P<0.05) associated with the IL-23R SNP, rs11209026, predominantly Ruminococcaceae, Lachnospiraceae and Bacteroidaceae families. . Taxonomy is shown with full classification from phylum through class (c_), order (o_), family (f_), genus (g_) and species (s_)...... 151 Table 5.7 OTUs reaching nominal significance (P<0.05) associated with the HLA-B27 allele, predominantly Clostridiaceae, Ruminococcaceae and Lachnospiraceae families. Taxonomy is 17

shown with full classification from phylum through class (c_), order (o_), family (f_), genus (g_) and species(s_)...... 153 Supplementary table 5.8 OTUs reaching nominal significance (P<0.05) associated with BACH2. 50 OTUs associated with rs17765610 and 81 with rs639575; the majority of which belong to Ruminococcaceae, Lachnospiraceae and Bacteroidaceae families. Taxonomy is shown with full classification from phylum through class (c_), order (o_), family (f_), genus (g_) and species (s_)...... 161 Supplementary table 5.9 OTUs reaching nominal significance (P<0.05) associated with NOS2. NOS2 rs2297518 showed 65 OTU associations and rs2531875 with 18 OTUs associations, all dominated by Ruminococcaceae, Lachnospiraceae and Bacteroidaceae families along with Rikenellaceae and Clostridiaceae Taxonomy is shown with full classification from phylum through class (c_), order (o_), family (f_), genus (g_) and species (s_)...... 167 Supplementary table 5.10 OTUs reaching nominal significance (P<0.05) associated with CARD9 SNP, rs1128905, predominately Lachnospiraceae, Veillonellaceae and Ruminococcaceae, including Faecalibacterium prausnitzii. Taxonomy is shown with full classification from phylum through class (c_), order (o_), family (f_), genus (g_) and species (s_)...... 171 Supplementary table 5.11 OTUs reaching nominal significance (P<0.05) for NKX2-3, ICOSLG and SH2B3. The families of Ruminococcaceae, Lachnospiraceae dominating the associations. Taxonomy is shown with full classification from phylum through class (c_), order (o_), family (f_), genus (g_) and species (s_)...... 177

Figures Figure 1.1 The physical barrier. Separating the intestinal lumen and its inhabiting commensal bacteria from the underlying lamina propria is a single layer of intestinal epithelial cells (IECs). These IECs are stitched together, creating a tight junction and regulating the paracellular flux. IECs also secrete soluble factors that are crucial to intestinal homeostasis, such as mucins and anti-microbial peptides (AMPs), including lysozymes, IgA, defensins, and C-type lectins such as RegIIIγ. Release of these molecules into luminal crypts is thought to prevent microbial invasion into the crypt microenvironment as well as limit bacteria-epithelial cell contact. Toll- like receptors (TLRs) are also expressed on IECs to sense a breach of barrier or bacterial invasion. Underneath the IECs, the lamina propria contains T cells, bacteria-sampling dendritic cells (DCs), and macrophages. Figure from: Costello et al. Arthritis Research & Therapy 2013 15:214 doi:10.1186/ar4228 ...... 28 Figure 1.2 The immune barrier. Dendritic cells (DCs) and macrophages patrol the gastrointestinal tract in high numbers. They densely populate the intestinal lamina propria and form a widespread microbe-sensing network. Activated DCs can secrete a number of cytokines and chemokines, including interleukin-23 (IL-23), IL-6, and IL-1, activating IL-23-responsive cells. IL-23-, IL-17-, and IL-22-producing cells are enriched in gut mucosa, and IL-17 and IL-22 are known to be important regulators of intestinal 'health'. IL-17 plays important roles in intestinal homoeostasis in several ways, including maintenance of epithelial barrier tight junctions. LTi, lymphoid tissue inducer; MAIT, mucosa-associated invariant T; NKT, natural killer T; T reg, regulatory T; TNF, tumour necrosis factor. Figure from: Costello et al. Arthritis Research & Therapy 2013 15:214 doi:10.1186/ar4228 ...... 31 Figure 1.3 Cartoon depiction of the 16S rRNA gene. Modified 16S rRNA gene highlighting the variable regions of the gene commonly used for taxonomy. Circled in red are the two constant commonly used and circled in yellow is the V3-V5 region commonly used for amplicon sequencing in the gut...... 37 Figure 2.1 Phyla represented in gut biopsies showing percentage abundance. Comparison of primer pairs 347F 80R and 517F 803R demonstrating no significant differences when trialled in five colon biopsies for the purpose of pipeline optimisation ...... 63 Figure 2.2 Rank abundance graph showing number of reads (read depth) with number of species identified for the sample Q1462...... 64 Figure 2.3 Rarefaction curves for microbial communities in AS patients (red curve), CD patients (blue curve), healthy controls (orange curve) and the mock communities (green curve)...... 65 Figure 2.4 Hierarchical clustering of microbial communities based on Weighted Unifrac distance clustering of terminal ileal biopsies of patients with ankylosing spondylitis (AS), Crohn’s disease (CD), healthy controls (HC) as well as the two mock community controls. ... 66 Figure 2.5 Principal coordinates analysis of technical and biological replicates (transparent points) showing the grouping of ankylosing spondylitis (AS) samples compared to Crohn’s disease (CD) and healthy controls (HC) with the grouping of the replicates shown in the red circles...... 67 Figure 2.6 Comparison of variation between and within samples and affection status showing greater significance between and within affection status than between biological and technical replicates. Significance was assessed using Mann-Whitney test (P<0.001 = ***) ...... 68 Figure 2.7 Microbial community profiles of terminal ileal (TI) biopsies from ankylosing spondylitis (AS), Crohn’s disease (CD) patients and healthy controls (HC). Biological replicates are indicated as ‘a’ and ‘b’. Each row represents a different OTU with the abundance as a percentage of the total population as represented by colour. Phyla and selected families are noted on the left, and a subset of OTU classifications on the right. Microbial biomass of each biopsy is shown by the bar graph at the bottom of the heat map. . 69 Figure 3.1 Differences between AS and HC microbial communities. Bar chart showing the difference in taxonomy at the Phylum level between AS and HC noting the increase in Bacteroides and the change in the Bacteroides to Firmicutes ratio in the AS samples...... 82 Figure 4.1 Principal coordinate analysis and taxonomy showing community differences between ERAP-/- mice and control C57BL/6. (A) Unweighted Principal Coordinate Analysis 19

(PCoA) showing distinct clustering of ERAP-/- mice from control C57BL/6. (B) Bar chart showing the difference in taxonomy at the Phylum level between C57BL/6 and ERAP-/-, noting the increase in Bacteroides in C57BL/6 and the change in the Bacteroides to Firmicutes ratio between the samples...... 103 Figure 4.2 ERAP-/- Intestinal community Co-occurrence network showing the positive (blue lines) and negative (red lines) correlations between indicator species. The thickness of the line denotes the strength of the correlation (Pearson correlation)...... 104 Figure 4.3 Unweighted Principal coordinates analysis (PCoA) of a single litter of ERAP mice comprising three ERAP+/+, four ERAP+/- and one ERAP-/- showing distinct grouping of ERAP+/+ away from the other genotypes and separate clustering of ERAP-/- and ERAP+/-...... 105 Figure 4.4 Example gating strategy for isolation, identification and characterisation of T cells. Doublets were removed and dead cells were excluded using Aqua Viability dye discrimination. Cell subset specific analysis of cell phenotype and activation status was performed by analysing cell surface protein expression, with transcription factor expression determined intracellularly following cell permeabilisation. Cytokine production was determined intracellularly following in vitro stimulation...... 106 Figure 4.5 Frequencies of major immune cell subsets in ERAP+/+ (WT) and ERAP-/- (KO) from the Spleen (A) and mesenteric lymph nodes (C) and activation status respectfully (B and D) shown as mean ± SEM and compared by unpaired t test...... 108 Figure 4.6 Immune cell subsets and expression of cytokine from the spleen of ERAP-/- (KO) mice compared to ERAP+/+ (WT). (A) Expression of IL-17 in the spleen, noting the increase in secretion from γδ T cells, and the absence of IL-17 secretion from CD3+ T cells as a whole and CD8+ T cells. (B) Expression of IL-22 showing a slight decrease across all cell types except for NK cells and no significant differences in expression of either IL-10 (C) or TNF (D). Data shown as mean ± SEM and compared by unpaired t test...... 111 Figure 4.7 Immune cell subsets and expression of cytokine from the mesenteric lymph nodes (MLN) of ERAP-/- (KO) mice compared to ERAP+/+ (WT). (A) Expression of IL-17 in the MLN, showing an increase in secretion from macrophages. (B) Expression of IL-22 showing an increase in secretion from CD3+ T cells, but undetectable levels across other T cell subsets. A slight increase in IL-10 secretion was detected in macrophages (C) and TNF secretion in the MLN mirrored what was seen in the spleen (D)...... 112 Figure 5.1 Representative OTUs prior to normalisation, demonstrating the range of distributions present within the OTU table and the same order of bacteria, showing the range of mean, skew and kurtosis of each OTU. Taxonomy is shown with full classification from phylum through class (c_), order (o_), family (f_), genus (g_) and species (s_)...... 124 Figure 5.2 Distributions of the family Lachnospiraceae demonstrating variation of distribution patterns within the same family of bacteria. Taxonomy is shown with full classification from phylum through class (c_), order (o_), family (f_), genus (g_) and species (s_)...... 125 Figure 5.3 Distributions of selected members of the family Enterobacteriaceae, which contains a number of pathogenic bacteria including Shigella, Klebsiella and E.coli, showing no clear pattern of distribution. Taxonomy is shown with full classification from phylum through class (c_), order (o_), family (f_), genus (g_) and species (s_)...... 125 Figure 5.4 Histogram of weighted allelic risk score demonstrating the effect of HLA-B27 genotype (GG, AG, AA)...... 126

List of commonly used Abbreviations

16S rRNA 16S ribosomal RNA AS ankylosing spondylitis BLAST basic local alignment search tool CD Crohn’s disease CIA collagen-induced arthritis DC dendritic cell ER endoplasmic reticulum ERAP1 endoplasmic reticulum aminopeptidase-1 FoxP3 forkhead box P3 γδ T cell gamma-delta T cell GF germ free GWAS genome wide association study HC healthy control HLA human leukocyte antigen IBD Inflammatory bowel disease IL-23 interleukin-23 IL-17 interleukin-17 IL-22 interleukin-22 IL-23R interleukin-23 receptor ILC innate lymphoid cell IFNγ interferon gamma LTi cell lymphoid tissue inducer cell MAIT mucosal-associated invariant T cells MHC major histocompatibility complex NSAIDs non-steroidal anti-inflammatories NK cell natural killer cell NKT cell natural killer T cell PCoA principal coordinates analysis OTU operational taxonomy unit RA rheumatoid arthritis RORyt retinoic acid related orphan receptor gamma SFB segmented filamentous bacteria SNP single nucleotide polymorphism

SpA spondyloarthropathy Th17 Thelper 17 TI terminal ileal TLR toll-like receptor Tregs regulatory T cells TNF tumour necrosis factor TGF-β transforming growth factor beta UC ulcerative colitis

1 Chapter 1 Introduction and Literature Review

1.1 Introduction

It is increasingly clear that the interaction between host and microbiome profoundly affects health. There are ten times more bacteria in and on our bodies than the total of our own cells, with the human intestine containing ~100 trillion bacteria. Our microbial communities are not passive bystanders, and we are only just beginning to appreciate the influence our microbial residents have on our health. Until recently interrogation of our microbial communities using classical microbiology techniques offered a very restricted view of these communities, only allowing us to see what we can grow in isolation. However, recent advances in sequencing technologies have greatly facilitated systematic and comprehensive studies of the microbiome, and elucidate its role in human health and disease.

1.2 Ankylosing spondylitis

Ankylosing spondylitis (AS) is a common and highly heritable autoimmune disease belonging to a group of arthritides called spondyloarthropathies (SpA) and affects over 22,000 Australians (1). This group of seronegative diseases comprises of AS, psoriatic arthritis (PsA), reactive arthritis (ReA), Juvenile onset SpA, and Undifferentiated SpA associated with inflammatory bowel diseases including Crohn’s disease (CD) and ulcerative colitis (UC) (2). All members of SpA are characterised by a strong association with the human leukocyte antigen (HLA) allele HLA-B*27, combined with the absence of an association with rheumatoid factor or anti-nuclear antibodies. Additionally, they all show a certain degree of enthesitis, which is inflammation of the site of insertion of ligaments, tendons or joint capsules to bone, such as the Achilles tendon (3).

AS is a life long condition with first symptoms appearing at a young age. AS is characterised by excessive bone formation (ankylosis) that gradually bridges the gap between joints, eventually fusing and causing stiffness, inflexibility, pain, significant morbidity and increased mortality. The mechanism that triggers AS in a genetically susceptible person is poorly understood. There is a close relationship between the gut and 23

SpA, exemplified in reactive arthritis patients, where a typically self-limiting arthropathy follows either gastrointestinal infection with Campylobacter, Salmonella, Shigella or Yersinia, or urogenital infection with Chlamydia. AS has a global distribution, with no outbreaks or clustering being reported. The gene identified as having the strongest association with AS development is the major histocompatibility complex (MHC) gene HLA-B27. However, only 1-5% of HLA-B27+ individuals go on to develop AS. Monozygotic twin studies suggest that the trigger for AS in a genetically susceptible person is a ubiquitous environmental factor that is most likely widely distributed (4, 5).

1.2.1 The gastrointestinal tract and AS

The co-existence of AS and intestinal inflammation has been known for some time (6), with between 5-10% of AS patients developing clinically diagnosed inflammatory bowel disease (IBD). Up to 70% of AS patients have either clinical or subclinical gut disease, which suggests that intestinal inflammation is important in disease (7, 8). The human gastrointestinal tract is not a complete barrier and can become permeable to especially when there is a loss or impairment of modulation of tight junctions (9). Permeability of the gut is increased in conditions including IBD and celiac disease (10), and also in AS (11, 12). The increased gut permeability in AS has been mostly attributed to the use of nonsteroidal anti-inflammatories (NSAID) (6) however, first-degree relatives of AS patients have also demonstrated increased gut permeability when compared with unrelated healthy controls (6, 11-13). This is consistent with the hypothesis that increased ‘leakiness’ of the gut may increase the microbial dissemination and drive inflammation in the disease.

In reactive arthritis (ReA), a member of the SpA family, inflammatory arthritis develops following urogenital infection with Chlamydia trachomatis or gastrointestinal infection with Campylobacter, Salmonella, Shigella or Yersinia (14). Such cause and effect relationships are not established for other SpA. Studies in the B27 transgenic rat model of AS, supports the idea of a ubiquitous environmental trigger. The B27 transgenic rat when exposed to normal commensal bacteria, developed disease while the rats that were maintained in a germ-free environment remained disease free (15). Microbial involvement, especially arising from the gut, has been associated in both diseases however in AS, no definitive link has been established (16, 17).

1.3 Genetics and overlap between AS and gut disease

Strong genetic overlap exists between AS and IBD (Table 1.1), particularly CD, with the two conditions commonly occurring together in families (18, 19). Danoy et al., 2010 examined genes known to be associated with IBD in a large AS cohort (20) identifying new loci and genes. Of particular interest were genes involved in the IL-23 pathway, such as STAT3, IL23R, and IL12B (which encodes IL-12p40, the share subunit of IL-23 and IL-12 (20-22). How these genetic lesions influence gut homeostasis and function still remains unclear although, many of the genes associated with disease are associated with microbial handling, processing and response such as IL23R, NOD2, NOS2 and CARD9. Genetic studies in CD, one of the two common forms of IBD, shows a marked over-representation of genes including NOD2 and TNFRSF18, that correlated with an increased risk of developing mycobacterial diseases, including leprosy and tuberculosis, (23). Seven of the eight known genetic variants, which include SLC11A1, VDR and LGALS9, are associated with increased risk of developing leprosy and were also found to increase and individual’s risk of CD (23), A high proportion of genes associated with AS are genes involved in mucosal immunity (24). Variants in transcription factors RUNX3, EOMES, and TBX21, as well as the cytokine receptors IL7R and IL23R (24), are key regulators of the differentiation and activation of innate lymphoid cells, which are themselves critical components of mucosal immune defences.

Major differences also exist between the genetics of IBD and AS, with AS showing no association to date with NOD2/CARD15 or the autophagy gene ATG16L1, which are major susceptibility factors in IBD, whereas IBD shows no association with HLA-B or ERAP1 which are the strongest AS-susceptibility genes (18, 24).

Table 1.1 Genes associated with ankylosing spondylitis (AS) and their association with inflammatory bowel disease (IBD).

Reported gene SNP Locus AS IBD Risk/Non- association risk Alleles RUNX3 rs6600247 1p36 C/T - IL23R rs11209026 1p31 G/A Concordant rs12141575 1p31 A/G GPR25-KIF21B rs41299637 1q32 T/G Concordant PTGER4 rs12186979 5p13 C/T Concordant ERAP1-ERAP2-LNPEP rs30187 5q15 T/C - rs2910686 5q15 G/A Concordant ERAP1-ERAP2 rs10045403 5q15 A/G - IL12B rs6871626 5q33 T/G Concordant rs6556416 5q33 G/T Concordant CARD9 rs1128905 9q34 C/T Concordant LTBR-TNFRSF1A rs1860545 12p13 C/T - rs7954567 12p13 A/G - NPEPPS-TBKBP1- rs9901869 17q21 T/C - TBX21 21q22 rs2836883 21q22 G/A Concordant IL6R rs4129267 1q21 G/A - FCGR2A rs1801274 1q23 T/C Concordant rs2039415 1q23 C/T - UBE2E3 rs12615545 2q31 C/T - GPR35 rs4676410 2q37 A/G - NKX2-3 rs11190133 10q24 C/T Concordant ZMIZ1 rs1250550 10q22 C/A Concordant SH2B3 rs11065898 12q24 A/G - GPR65 rs11624293 14q31 C/T Concordant IL27-SULT1A1 rs7282490 16p11 A/G Concordant rs35448675 16p11 A/G - TYK2 rs35164067 19p13 G/A Concordant rs6511701 19p13 A/C - ICOSLG rs7282490 21q22 G/A Concordant EOMES rs13093489 3p24 A/C - IL7R rs11742270 5p13 G/A - UBE2L3 rs2283790 22q11 C/T - BACH2 rs17765610 6q15 G/A Discordant rs639575 6q15 A/T - NOS2 rs2531875 17q11 G/T - rs2297518 17q11 A/G - HHAT rs12758027 1q32 C/T - IL1R2-IL1R1 rs4851529 2q11 G/A Concordant

rs2192752 2q11 C/A - 2p15 rs6759298 2p15 C/G Concordant GPR37 rs2402752 7q31 T/C - HLA-B27 rs4349859 6p21 A/G - ANTRX2 rs4333130 4q21 A/G -

1.4 The gut, barrier regulation and intestinal epithelial cells

Homeostasis of the normal microbial flora in the gut is essential for intestinal health. The gastrointestinal tract is heavily populated with microbes and is the primary site for interaction between these microorganisms and the immune system (25, 26). Furthermore, microbes found in the gut help to shape host immune systems from an early age. The incomplete development of the immune system in neonates and under germ-free conditions tells us that microbiota play a role in sculpt the host immune system (27, 28).

Maintenance of intestinal and microbial homeostasis is being increasingly recognised as playing a pivotal role in overall health (29), and dysregulation of either gut or microbial homeostasis may play a role in autoimmunity. Physiological processes in the host that maintain gut homeostasis and respond to perturbance in the gut microenvironment involve both the adaptive and innate immune system, and the barrier function of the intestines themselves.

1.4.1 The physical barrier

The human gastrointestinal tract is not a complete barrier. It is comprised of a single layer of intestinal epithelial cells (IECs), which form a physical barrier separating the intestinal lumen from the lamina propria (Figure 1.1). IECs secrete soluble factors that are crucial to intestinal homeostasis, such as mucins, anti-microbial peptides (AMP), including lysozymes, defensins, cathelicidins, lipocalins and C-type lectins such as RegIIIγ (30-32). Release of these molecules into luminal crypts is thought to prevent microbial invasion into the crypt microenvironment as well as limit bacteria-epithelial cell contact (31, 33). CD patients have pronounced decreases in the human α-defensins DEFA5 and DEFA6 in the ileum compared to healthy controls, resulting in altered mucosal function and overgrowth or dysregulation of commensal microbial flora (33, 34). Furthermore, depletion of the mucin layer leads to an IBD-like phenotype and endoplasmic reticulum stress, potentially driving IL-23 production (35). IL-23 excess alone is sufficient to induce spondyloarthritis in

27 mice (36), and genetic evidence in particular indicates that the cytokine plays a key role in development of spondyloarthritis in humans.

Figure 1.1 The physical barrier. Separating the intestinal lumen and its inhabiting commensal bacteria from the underlying lamina propria is a single layer of intestinal epithelial cells (IECs). These IECs are stitched together, creating a tight junction and regulating the paracellular flux. IECs also secrete soluble factors that are crucial to intestinal homeostasis, such as mucins and antimicrobial peptides (AMPs), including lysozymes, IgA, defensins, and C-type lectins such as RegIIIγ. Release of these molecules into luminal crypts is thought to prevent microbial invasion into the crypt microenvironment as well as limit bacteria-epithelial cell contact. Toll-like receptors (TLRs) are also expressed on IECs to sense a breach of barrier or bacterial invasion. Underneath the IECs, the lamina propria contains T cells, bacteria-sampling dendritic cells (DCs), and macrophages. Figure from: Costello et al. Arthritis Research & Therapy 2013 15:214 doi:10.1186/ar4228

1.4.1.1 Permeability and Disease

The dynamic crosstalk between IECs, microbes and the local immune cells is fundamental to intestinal homeostasis but is also suggested to play a part in disease pathogenesis (37, 38). There are a multitude of factors capable of altering the permeability of epithelial junctions. Once considered fixed and unchanging, epithelial tight junctions are remarkably dynamic and responsive component of the intestines. The tight junctions constantly open and close in response to a number of stimuli including diet, neural signals, mast cell

28 productions as well as a variety of cellular pathways, all of which can be hijacked by microbial as well as viral pathogens (39-42).

In IBD and celiac disease, epithelial tight junctions are dysregulated causing increased permeability between IECs and resulting in a ‘leaky gut’ (10, 43). While the causes and ramifications of a ‘leaky gut’ in the disease setting are still under investigation, It has been suggested that an increase in intestinal permeability may lead to bacteria, either pathogens or normal resident bacteria, entering the lumen and triggering an inflammatory immune response (43, 44). Intestinal permeability in AS patients has always been considered a side effect due to the long term use of NSAIDs (6). However, studies looking at first-degree relatives of AS patients showed they also have increased gut permeability, suggesting that there may be an underlying genetic process operating in the gut (11, 45).

1.4.1.2 Innate immunity

Dendritic cells (DC) densely populate the intestinal lamina propria and form a widespread microbial sensing network. DC recognise a broad repertoire of bacteria, sensing with receptors such as toll-like receptors (TLRs) and monitoring the bacteria on the mucosal surface (46). Intestinal DC orchestrate production of intestinal specific IgA to limit bacterial contact with the intestinal epithelial cell surface (47). Activated DC can secrete a number of cytokines and chemokines, including IL-23 and IL-6, that are involved in inflammation and migration of DC (48).

Macrophages patrol the gastrointestinal systems in high numbers. They frequently come in contact with ‘stray’ bacteria, including commensals that have breached the epithelial cell barrier. Circulating macrophages phagocytose and rapidly kill such bacteria using mechanisms that include production of antimicrobial proteins and reactive oxygen species (49). However, intestinal macrophages have several unique characteristics including the expression of the anti-inflammatory cytokine IL-10, both constitutively and after bacterial stimulation (50, 51). This makes intestinal macrophages non-inflammatory cells that still have the capacity to phagocytose. The importance of this pathway is highlighted by the fact that loss-of-function mutations in IL10R lead to early onset IBD (52). Intestinal macrophages also play a role in the restoration of the physical integrity of the epithelial cell barrier following injury or bacterial insult (9). Re-establishing this barrier post injury is

29 imperative to avoid bacterial penetration and sepsis in such a microbe-laden environment (53).

1.4.2 Adaptive Immunity

1.4.2.1 IL-23-responsive cells

IL-23 is a key cytokine in the development of IL-17 and IL-22-secreting cells. IL-23 signals through a receptor consisting of the specific IL-23 receptor (IL-23R) subunit and IL-12Rβ1, also shared with IL-12R (54). Loss of function polymorphisms in IL23R are associated with protection from AS (55), psoriasis (56) and IBD (57), and many other genes in the IL-23 pathway are associated with these diseases. Under physiological conditions, IL-23, IL-17 and IL-22 producing cells are enriched in gut mucosa and IL-17 and IL-22 are known to be important regulators of intestinal ‘health’. IL-17 plays important roles in intestinal homoeostasis in several ways, including maintenance of epithelial barrier tight junctions (58) and induction of anti-microbial proteins such as β-defensins, S100 proteins and REG proteins. IL-22 induces secretion of anti-microbial peptides (59). In the gut, innate-like immune cells act as sentinels, responding very rapidly to alterations to the microbial composition of the gut with rapid secretion of IL-17. Key among these sentinels are γδ T cells, Natural killer T (NKT) cells, mucosal-associated invariant T (MAIT) cells and lymphoid tissue inducer (LTi)-like cells (Figure 1.2).

Figure 1.2 The immune barrier. Dendritic cells (DCs) and macrophages patrol the gastrointestinal tract in high numbers. They densely populate the intestinal lamina propria and form a widespread microbe-sensing network. Activated DCs can secrete a number of cytokines and chemokines, including interleukin-23 (IL-23), IL-6, and IL-1, activating IL-23-responsive cells. IL-23-, IL-17-, and IL-22-producing cells are enriched in gut mucosa, and IL-17 and IL-22 are known to be important regulators of intestinal 'health'. IL-17 plays important roles in intestinal homoeostasis in several ways, including maintenance of epithelial barrier tight junctions. LTi, lymphoid tissue inducer; MAIT, mucosa-associated invariant T; NKT, natural killer T; T reg, regulatory T; TNF, tumour necrosis factor. Figure from: Costello et al. Arthritis Research & Therapy 2013 15:214 doi:10.1186/ar4228

1.4.2.2 γδ T cells

γδ T cells are found in large numbers at epithelial surfaces such as the gut and skin, where they can account for up to 50% of T cells. γδ T cells not only bear an antigen- specific T cell receptor (TCR), but also have many properties of cells of the innate immune system, including expression of the major innate immunity receptors and TLRs (60) and dectin-1(61), which recognises microbial β-glucans. Expression of these receptors supports a role for γδ T cells in early responses to microbes. Of further relevance, we and others, have recently confirmed that CARD9, part of the dectin-1 response pathway, is a susceptibility gene for AS and for IBD (62). γδ T cells are potent producers of inflammatory

31 cytokines such as IFN-γ, TNF-α and IL-17 (63, 64). They are pathogenic in the collagen- induced arthritis model (65) and mouse models of colitis (66), and IL-17-secreting γδ T cells are expanded in patients with AS (67). Intraepithelial γδ T cells also play an important role in modulating intestinal epithelial growth through secretion of fibroblast growth factor (68). Alterations to γδ T cell numbers or functions may, therefore, have profound effects on intestinal health.

1.4.2.3 Natural killer T cells

Natural killer T (NKT) cells are characterised by expression of an invariant T cell receptor, Vα24Jα18 in humans and the orthologous Vα14Jα18 in mice. NKT cells recognise glycolipd structures presented to them by the non-classical antigen presenting molecule CD1d. Like γδ T cells, NKT cells are rapid responders to antigenic stimuli and are capable of producing a range of immunoregulatory cytokines (69-72). Within the gut, NKT cells are protective in Th1-mediated models of inflammatory bowel disease but pathogenic in Th2 models (73, 74). Recently, it has been shown that microbial stimulation of NKT cells in the gut of mice affects NKT cell phenotypic and functional maturation (75). Given that NKT cells have protective roles in models of arthritis (76) and spondyloarthropathy (77, 78) their functional maturation in the gut provides evidence for a role for mucosal T cell priming in inflammatory joint disease.

1.4.2.4 Mucosal-associated invariant T cells

Mucosal-associated invariant T (MAIT) cells are a population of innate-like T cells that are abundant in human gut, liver and blood and secrete a range of inflammatory cytokines such as IL-17 and IFN-γ in response to antigenic stimulation (79-81). Like NKT cells, MAIT cells bear an invariant T cell receptor (Vα7.2 in humans) that recognises antigen presented by the non-classical MHC-like molecule, MR1 (82). In blood, MAIT cells display a memory phenotype (81) and express the transcription factor ZBTB16 (83), allowing them to rapidly secrete cytokine in response to antigenic stimuli. Furthermore, they express high levels of IL-23R (84). MAIT cells react to a wide range of microbial stimuli, including Gram positive and Gram negative bacteria as well as yeasts (79, 80). While the precise role of MAIT cells in maintenance of the mucosal barrier remains unclear, the rapid postnatal expansion of MAIT cells and their acquisition of phenotypic markers of memory likely represents interaction with developing commensal microflora (85).

1.4.2.5 Lymphoid tissue inducer- like cells

Lymphoid tissue inducer (LTi)-like cells are found in spleen, lymph node and gut lamina propria. LTi-like cells constitutively express hallmarks of IL-17-secreting cells including IL- 23R, RORγt, AHR and CCR6 (86, 87). LTi cells in the intestine represent an increasingly interesting and heterogeneous population of innate lymphoid cells (ILC) with or without CD4 expression (88). A common characteristic of ILCs is the expression RORγt, a transcription factor important for the development of these cells. ILC have been linked to gut inflammation through colitis models where IL-23 responsive ILC secrete IL-17, IL-22 and IFN-γ and to promote intestinal inflammation (89). RORγt, ILCs are abundant in the intestinal lamina propria and produce IL-17 and/or IL-22 in order to preserve mucosal integrity against extracellular pathogens. These NKp46+ ILCs have been found to be critical for host defence against pathogens such as Citrobacter rodentium infection through secretion of IL-22 (87, 90).

1.4.3 Cytokines and gut homeostasis

1.4.3.1 IL-17

IL-17 is primarily described as a T cell-secreted cytokine however much of the IL-17 released during an inflammatory response is in fact produced by innate immune cells in response to injury, stress or to pathogens (91). IL-17 has been shown to be produced 4- 8hrs after a microbial infection and is secreted by T cells including γδ T cells (63, 92, 93). Innate IL-17 producing cells have been found to reside mainly in the skin and mucosal tissues, especially the gastrointestinal tract, and act as a first line of defence. They also promote mobilisation of neutrophils and cytokines that are produced by epithelial cells for protective immunity as well as controlling physiological process such as epithelial tight junctions and angiogenesis. The hallmark of the innate immune response is its rapid reaction to pathogens by secreting factors that recruit large numbers of neutrophils through increasing the activity of IL-1, IL-6 and tumour necrosis factor (TNF), which promote tissue infiltration, which is crucial for effective and rapid control of bacterial and fungal pathogens (91).

1.4.3.2 IL-22

IL-22 has been shown to play a critical role in host defence, tissue homeostasis and inflammation and the IL-22-IL-22R pathway contributes to regulating immunity, 33 inflammation and tissue repair. IL-22 is expressed by innate and adaptive cells and seems to act almost exclusively on non-haematopoietic cells, with basal IL-22R expression in the skin, pancreas, intestine, liver, lung and kidney (94). First described in the skin by Dumoutier et al., 2000, who showed that the stimulation of keratinocytes with IL-22 resulted in marked induction of genes encoding several proteins involved in anti-microbial host defences including S100A7, S100A8, S100A9, β-defensin-2 and β-defensin-3 (95- 97). Further studies have shown that the induction of two intestinal antimicrobial peptides that regulate gut homeostasis, RegIIIβ and RegIIIγ are also dependent on the production of IL-22 (98, 99). IL-22 can act synergistically or additively with IL-17A, IL-17F or TNF to promote the expression of many of the genes that encode molecules involved in host defence in the skin, airway or intestine. This demonstrates the functional importance of IL- 22 in promoting barrier immunity and mucosal integrity, particularly against Gram negative pathogens, where IL-22 is critical for limiting bacterial replication and dissemination, possibly in party by inducing the expression of antimicrobial peptides from epithelial cells at these barrier surfaces (98, 99). Containment of normal flora is another important arm of homeostasis. IL-22 producing innate lymphoid cells (ILCs) found throughout the gut play a critical role in the physical containment of resident microbes (100). Loss of epithelial integrity leads to dissemination of intestinal commensals and systemic inflammation.

1.4.4 Interaction between intestinal microbes and the immune system

There is strong evidence from murine studies to indicate that interaction between the gut microbiome and the host determines the overall level of activation of the immune cells producing these cytokines. Segmented filamentous bacteria (SFB) are commensal bacteria that induce IL-17 secretion. Mice lacking SFB have low levels of intestinal IL-17 and are susceptible to infection with pathogenic Citrobacter spp. (27, 101) Restoration of SFB in these mice increased the number of gut-resident IL-17-producing cells and enhanced resistance to infection through induction of specific epithelia cell-specific genes, as well as inflammatory response host genes, which were found to be up regulated by the bacteria leading to an inflammatory environment (27). Lachnospiraceae, Ruminococcaceae and Prevotellaceace are families of bacteria that have been observed in IBD gut microbiomes and are strongly associated with colitis and CD (102-104), with Prevotellaceace especially known to elicit a strong inflammatory response in the gut (104). The demonstration that bacteria can directly influence the host response highlights the effect commensal bacteria have on the immune response and their role in inflammation. Conversely, members of the gut microbiota such as Clostridium have been found to play a 34 protective role by inducing T regulatory cells (Tregs) in the colonic mucosa. Tregs are an important counter balance in the immune system and promote homeostasis. It was found that a specific mix of cluster IV and XIVa of Clostridium was sufficient to promote Treg accumulation in the colon (105).

1.5 Interrogating the human gut microbiome

Microbiome refers to the totality of microbes, their genetic elements and environmental interaction in a defined environment (106). The aim of metagenomic studies is to profile the microbes within the community, identify functions and pathways of the communities and how the communities compare. Projects such as the Human Microbiome Project run by the National Institute of Health, and the Metagenomics of the Human Intestinal Tract (MetaHit) consortium, aim to determine what constitutes a ‘normal’ or healthy microbiome. Research in this field has recently been greatly accelerated by the development of methods for microbial profiling without the requirement to culture organisms, using high- throughput sequencing methods.

1.5.1 Microbial amplicon sequencing

Culture independent molecular investigations using the 16S rRNA have become the foundation for identifying and characterising microbes. With the advent of next generation sequencing, 16S rRNA profiling has taken a back seat to newer approaches such as metagenomics. Metagenomics has been favoured as it facilitates a more comprehensive view of the microbial community by providing the communities overall functional potential as opposed to simply a list of microbial inhabitants. However, 16S rRNA marker genes studies are making a return to more widespread use and are becoming the standard method for community profiling. This is due to significant advances in sequencing methodology and analysis programs (107).

1.5.2 The 16S rRNA

16S ribosomal RNA (rRNA) is a section of prokaryotic DNA found in all bacteria and Archaea. 16S rRNA sequences are used to differentiate between organisms across all major phyla of bacteria and to classify strains down to species level (108). 16S rRNA sequencing has dramatically changed our understanding of phylogeny and microbial diversity because it provides an unbiased assessment of the microbiome, and is not restricted by the ability to culture the bacteria present. This commonly used ribosomal RNA gene sequence analysis is a powerful method for identifying and quantifying members of microbial communities (109). The 16S rRNA gene can be compared not only among all bacteria but also with the 16S rRNA gene of Archaebacteria and the 18S rRNA gene of Eukaryotes (109). The 16S rRNA sequence is ~1,550 bp long, highly conserved and is composed of both variable and conserved regions (Figure 1.3). The gene is large enough and has sufficient interspecific polymorphisms as to provide distinguishing and statistically valid measurements with the variable portions of the 16S rRNA is used for comparative taxonomy (110). There are nine diverse, species-specific variable regions denoted as V1-9 (Figure 1.3), which can be used to infer phylogenetic relationships. This gene also allows the analysis of microbial communities by way of high-throughput sequencing. The 16S rRNA gene sequences allows for differentiation between organisms across all major phyla of bacteria and has the ability to classify strains at multiple levels including the species and subspecies level. Similar 16S sequences are then clustered together to form operational taxonomy units (OTUs). Sequences can be clustered two ways; the first is open reference where sequences are clustered by similarity with respect to the other sequences clustered at the same time. The second is closed reference where sequences are clustered at a certain threshold of sequence similarity, such as 97% (111) against a specific reference database. Clustering at a maximum sequence identity threshold of 97% has been widely adopted as a measure to avoid over estimating diversity by inadvertently mapping sequence error (112). The sequence clusters are then mapped to a database, such as Greengenes or ARB/Silva, and assigned taxonomy based on sequence similarity (113). One drawback of the 16S rRNA technique cannot be used to compare strains for epidemiological purposes or to detect a specific strain virulence factors. The 16S rRNA gene does not contain enough variation for strain resolution and does not encode virulence factors. However variations on this gene analysis using variable regions, can infer functionality including metabolism (114, 115).

Figure 1.3 Cartoon depiction of the 16S rRNA gene. Modified 16S rRNA gene highlighting the variable regions of the gene commonly used for taxonomy. Circled in red are the two constant commonly used and circled in yellow is the V3-V5 region commonly used for amplicon sequencing in the gut.

1.5.3 Metagenomic sequencing

Further improvements in sequencing technologies have reduced the need for targeted studies such as 16S sequencing, and enabled high throughput shotgun sequencing. This type of sequencing randomly samples all genes present in a habitat rather than just 16S rRNA. Shotgun sequencing provides more information about the microbiome than just the 16S characterisation, which is of particular benefit in determining the functional capacity of the microbial community, rather than just its phylogeny. However shotgun sequencing is more complex to analyse, including the challenge of distinguishing between host and bacterial genomic material, and requires far more sequencing to be performed. Therefore most studies to date have relied on 16S rRNA sequencing approaches.

1.5.4 Advances in tools for metagenomic studies

Several sequencing technologies have been developed for human genetic studies and have since been adapted to metagenomics. Roche’s 454 sequencing platform uses large- scale parallel pyrosequencing to produce reads of between 450bp to 1000bp length. Read

37 lengths produced by the 454 platform are well suited to 16S rRNA amplicon metagenomics studies as well as shotgun sequencing as they are easily aligned to reference bacterial genetic datasets. Illumina’s sequencing platforms, the HiSeq2000 and MiSeq, produce shorter reads than the Roche 454 however, in the last 2yrs or so, the MiSeq has over taken from Roche as the standard platform for 16S rRNA amplicon sequencing. The HiSeq was primarily designed for human genomic sequencing and current chemistry produces 100bp paired-end reads. Illumina sequencing is best suited for shotgun sequencing or indexed amplicon sequencing of multiple samples. The MiSeq however has rapidly become the go to platform for amplicon sequencing due to its reduced shorter sequencing runs, longer amplicon reads

This is a rapidly developing field. Advances in chemistry are predicted to increase both read-lengths and output for both of these platforms over the next 12-24 months, particularly for the Illumina platforms that are less mature than the Roche 454. New platforms coming to the market are likely to have a major impact on metagenomics study design. For example, the Pacific Biosystems sequencing technology provides reads of ~3000 bases, which will make it particularly suited to this field, despite its lower overall output (~450Mb – 1GB of sequence per run). Life Technologies Ion Torrent technology is reported to produce up to 400 base reads and up to 1Gb of sequence per run. The relative position of these competing technologies in metagenomics has yet to be established.

1.6 Dynamics of the gut microbiome

There are more microbes on the human body than cells, and about 29% of all these reside in the gut. The human gut microbiome contains a dynamic and vast array of microbes that are essential to health as well as provide important metabolic capabilities. Until recently studying this community has been difficult and generally limited to classical phenotypic techniques (109, 116). The application of molecular techniques, particularly sequencing, has shown the remarkable amount of diversity in the human gut, exceeding all previous held beliefs. Originally the dominant species was thought to be E. coli, pathogen detection was the primary aim with normal flora disregarded and considered inconsequential. Recent studies using sequencing as the investigative tool have shown that members of the phyla Firmicutes, Actinobacteria and Bacteroidetes are abundant in healthy individuals (114, 116). Given that the gut plays a major role in metabolism, pathogen resistance, as well as the body’s immune response, it is important to ascertain what role the human gut

38 microbiome plays with respect to illness and disease; and what influence these communities have on disease pathogenesis. To explore this, it must be possible to compare microbial communities within and across individuals in different states of disease and health (116).

Understanding how the intestinal microbiome is assembled and maintained is becoming increasingly relevant not only to general healthy, but also potentially for the treatment of complex chronic diseases. In recent years only a handful of studies have examined the composition, diversity and function of the human gut microbiome.

Studies comparing the gut microbiota of lean and obese twins have shed light on the importance of intestinal microbes and how a change in microbiome composition can affect food metabolism in the gut (114, 117, 118). Turnbaugh et al showed that even with a similar genetic make-up, obese twins had substantial differences in the composition and diversity of their gut flora with a dominance of Gram Positive bacteria from the phylum Firmicutes, compared with discordant or lean twins (114). This shift in gut flora composition altered how food was broken down and metabolised in the gut, leading to increased body mass index, adiposity and obesity (114, 117, 119). This demonstrates that shifts in the intestinal microbiome have functional consequences on the health of the individual (120). To further interrogate structure and function of the gut microbiome, and the consequences of shifts and alterations, faecal samples from four twin pairs discordant for obesity were transplanted into germ free mice (121). The authors found that the Firmicute phylum dominated microbial communities from the obese twin lead to obesity in the germ-free mice. The Bacteroides dominated microbial communities from the lean mice kept the germ-free mice lean. When the mice were co-housed, the invasion of Bacteroides from the lean mice lead to a decrease in adiposity and body mass in obese mice and transformed their microbiota’s metabolic profile to a lean-like state (121).

Maslowski, Vieira et al. 2009 suggests that not only can diet induce changes in the gut microbial community, but these associated changes may also be underpinning inflammatory disease. The authors suggest that a lower intake of fibre from complex plant polysaccharides adversely affects the makeup of the intestinal microbiota, which leads to less production of immunomodulatory products in particular short-chain fatty acids (SCFA). These SCFA are produced by the phylum Bacteroidetes, so a shift in the flora from predominantly Bacteroidetes to Firmicutes due to a more western diet with less fibre, 39 reduces the amount of SCFA secreted. The effect SCFA has on the immune response was investigated in a mouse strain deficient in single G protein–coupled receptor, GPR43 (122). Mice lacking GPR43 failed to suppress inflammation in models of colitis, arthritis and asthma, as did germ-free wild-type mice also lacking SCFA. Wild type mice raised in non-germ-free conditions were able to suppress inflammation. This suggests that diet- induced reduction in faecal SCFA leads to a reduced ability to suppress a variety of inflammatory conditions.

1.7 The normal microbiome

These studies have shown that shifts in microbiome can change metabolism; however, they do not provide details of what constitutes a ‘normal’ microbiome or how it behaves. Two large studies interrogating and cataloguing microbiomes from various regions of the body in health and disease have been undertaken by the National Institute of Health Human Microbiome Project in the USA and the European MetaHit project (123-125). The Human Microbiome Project was established for the purpose of identifying and cataloguing the human microbiome by sequencing samples from 242 individuals (129 males, 113 females) across five major body sites, including the mouth, skin, stool and vaginal tract, multiple times with the aim of interrogating within subject variation, between subject variation as well as microbiome variation over time. A total of 4,788 specimens were sequenced with females sampled from 18 different habitats and males from 15 habitats (126). Diversity and abundance of each habitat’s signature microbes was found to vary greatly amongst the healthy subjects, with strong niche specialisation found both within and between individuals, demonstrating the dynamic nature of the microbiome (126-128). Metagenomic carriage of metabolic pathways was found to be stable amongst individuals, despite the variation seen within community structure. When clinical metadata was present, ethnic background proved to be one of the strongest predictors of both pathways and microbes. This suggests that environmental factors such as diet, location/region and host genetics could play a role in sculpting the microbiome. These studies further define the range of structural and functional configurations that are present in normal microbial communities in a healthy population (123, 126).

1.7.1 Enterotypes

The European MetaHit consortium combined published data sets from across the world and added 22 newly sequenced faecal metagenomes from four different European countries. They identified three robust clusters, termed ‘enterotypes’, that were not nation or continent specific. These enterotypes characterised the microbial phylogenetic variation as well as the function variation of the clusters at gene and functional class levels (123).

Each enterotype had a dominant bacterial genus: Enterotype 1 dominated by the genus Bacteroides; enterotype 2 by Prevotella; and enterotype 3 by Ruminococcus. The three enterotypes were also shown to be functionally different. For example enterotype 2, which is Prevotella dominant, also contains Desulfovibrio, which may act in synergy with Prevotella to degrade mucin glycoproteins present in the mucosal layer of the gut. It may be that different enterotypes may be associated with diseases such as obesity and IBD, rather than necessarily specific bacterial species, given their differing functional capacities. However, recent work suggests that the boundaries between the enterotypes are more vague than initially described. Multiple datasets including the Human Microbiome Project 16S rRNA gene sequence data and metagenomes with similar published data were interrogated for the existence of enterotypes across multiple body sites. However, rather than forming enterotypes, many of the samples examined fell into sliding scale based on taxonomic abundances of bacteria such as Bacteroides, rather than discrete enterotypes (129). Moreover, metatanalysis demonstrated that many of the methods used in the analysis of enterotypes, such as clustering approaches and distance metrics affected the likelihood of identifying enterotypes in particular body habitats. This suggests enterotypes are not discrete but are continuous and vary widely amongst individuals (130).

1.7.2 Homeostasis

Microbes and humans have evolved over time to live in symbiosis with each other. Many of these diverse communities carry out specific tasks that benefit the host, as well as the microorganisms A balancing act between war and peace exists within the gut between resident microbial members of the gut and potential pathogenic and non-pathogenic. It is well documented that changes in the microbial composition in the gut by a pathogen, elicit an innate immune responses (131). However, it is still unknown if a shift or dysbiosis in the gut flora has the capacity to illicit the same response as an infection as the gut tries to defend its self and restore homeostasis (132). Homeostasis of the normal flora in the gut

41 microbiome is essential, and both the bacteria that inhabit the gut and the human immune system have developed strategies to regulate and protect it. Bacteria in the intestine have several techniques including competitive exclusion, biosurfactant production and modulation of tight junctions (9). The human body has a number of physiological processes that respond to a disruption in gut homeostasis or bacterial insult. If a bacterial insult is detected by antigen presenting cells (APC), such as a dendritic cell, naïve T cells are then activated and recruited to the area. IL-17 secreting Th17 cells are sequestered to induce epithelial cells to recruit neutrophils to the area to neutralise the disruption and in concert, IL-22 is secreted to promote epithelial repair and antibacterial proteins (133). The epithelial cells lining the intestine can also respond to a disturbance or insult and attempt to restore homeostasis by secreting a range of soluble factors such as mucins and AMPs that aim to prevent the invasion of microbes into the intestinal crypt (30-32, 134).

1.8 Influence of underlying host genetics on the intestinal microbiome

A key issue in microbiome research is determining whether host genetics directly influences changes in microbiome composition or indirectly via the immune system. The extent that underlying host genetics influences intestinal microbial community composition in humans is unclear. Host gene deletions have been shown in animal models to cause shifts in microbiota composition (135). Moreover, a recent quantitative trait locus mapping study linked specific genetic polymorphisms with microbial abundances (136). These studies highlight the importance of underlying host genetics in community composition in animals. The role of host genetics is of interest in diseases such as IBD, where many of the genes associated with disease are involved in handling, processing and response to microbes such as IL23R, NOD2, NOS2 and CARD9. Genetic studies in CD, one of the two common forms of IBD, shows a marked over-representation of genes, notably NOD2 and TNFRSF18, which are associated with an increased risk of developing mycobacterial diseases, including leprosy and tuberculosis (23). There is significant overlap between the risk of developing leprosy and CD, with seven of the eight known genetic variants associated with an increased risk of leprosy, including SLC11A1, VDR and LGALS9, also associated with an increase the risk of CD; compared with only two being associated with risk of any other immune-mediate disease. Combined with dramatic shifts in the gut microbiota composition associated with IBD (23, 135), this strongly indicates a potential link between host genetics and the microbiome in IBD patients. The genetic influence over microbiome composition is further highlighted in a study of 416 monozygotic and dizygotic 42 twin pairs investigating the heritability of the gut microbiome in obesity. The authors found the most heritable taxon was the family Christensenellaceae (137).This particular family of bacteria is only recently described and was shown to be central to a network of co- occurring heritable microbes associated with lean body mass index. Animal studies showed that the introduction of any of the members of the Christensenellaceae family had the capacity to protected the mice from weight gain (137). Variation and shifts in the gut microbiome have until recently been associated with diet and the environment or natural variation. Even though the exact gene-microbe association with the Christensenellaceae family still remains unknown, this study demonstrates that microbiome composition may in part be due to host genetics (137).

1.9 The microbiome in immune mediated disease

1.9.1 Ankylosing spondylitis

To date no study has been reported investigating the gut microbiome using sequencing based methods in AS. One study using denaturing gradient gel electrophoresis to profile the microbiome using faecal samples found no differences between AS cases and healthy controls (16). . Many studies largely using antibody tests have suggested an increased carriage of Klebsiella species in AS patients, however this has not been universally supported (17). Several specific bacteria have been suggested to have a role in AS pathogenesis particularly Klebsiella pneumoniae (138, 139) and Bacteroides vulgatus (140) although current studies using antibody tests have been unable to produce convincing evidence demonstrating increased infection with any specific triggering agent influencing AS development. K. pneumoniae and B. vulgatus have been of considerable focus for several reasons. Increased levels of B. vulgatus have linked to colitis in the HLA‐

B27/β2 microglobulin transgenic rats. When the HLA‐B27/β2 microglobulin transgenic rats were raised germ free and subsequently colonised with an intestinal bacterial cocktail, the rats developed more several colitis when increased amounts of B. vulgatus were present (141). Substantial research effort has been directed towards the possible involvement of K. pneumoniae. Early work examining the faeces of AS patients during active disease, reported the presence of K. pneumoniae (142). As well as during active disease, Studies have also reported elevated levels of serum antibodies against K. pneumoniae in patient sera (143). Adding to the suspected involvement of K. pneumoniae, the antigenic similarity

43 noted between Klebsiella and HLA‐B27 (144) have been noted. However, recent studies have been unable to confirm a role of Klebsiella AS pathogenesis.

As well as specific bacteria triggering disease, it has also been speculated that antimicrobial reactivity can develop during intestinal inflammation. There have been a number of antibodies described over the years, including anti-Saccharomyces cerevisiae antibodies (ASCA), anti-I2 antibodies (which are associated with anti- Pseudomonas activity), perinuclear antineutrophil cytoplasmic antibodies (pANCA), anti- Escherichia coli outer membrane porin C (anti-OmpC) antibodies, as well as anti-flagellin antibodies (anti-CBir1), that have shown to have clinical significance in IBD (145, 146). Given the overlap between IBD and AS, the prevalence of several of the above antimicrobial antibodies were examined in the serum of 80 AS patients and healthy controls. While there was no difference in the number of antibody positive results between AS patients and healthy controls, AS patients were more likely than healthy controls to have elevated levels of anti-I2 and ASCA antibodies (147). A recent study has shown of anti-CBir1 antibodies are elevated in patients with AS with IBD compared to patients who only have AS (148). Moreover, patients with AS only had increased levels of anti-CBir1 antibodies when compared to healthy controls. This data suggests that AS patients have an increased exposure of the immune system to commensal bacteria, causing the antibody production. Increased levels of anti-CBir1 antibody in AS patients, compared to healthy controls, could be accounted for, at least in part, by microscopic gut inflammation even in the absence of clinical gastrointestinal symptoms (149). While this observation requires further investigation, the development of anti-CBir1 antibodies in patients with AS raises the intriguing question of their pathophysiological significance with regards to commensal bacteria, and more specifically commensal bacteria with flagella.

1.9.2 Rheumatoid arthritis

Rheumatoid arthritis (RA) is the only inflammatory arthritis for which modern metagenomic studies have been reported. Community profiling studies of RA patients’ gut flora reveal differences in the composition of RA patient’s gut microbiota compared with healthy controls, with a lower abundance of Bifidobacterium and Bacteroides bacteria observed in RA cases (150, 151). However, these studies were undertaken using faecal samples and not intestinal biopsies, possibly influencing the populations and the proportions observed (152, 153). It is not just the intestinal microbiome that has an implicated in RA pathogenesis. There is microbial profiling data that suggests that the oral microbiome, 44 specifically a periodontal infection with the common pathogen Porphyromonas gingivalis, may be important in RA pathogenesis. P.gingivalis, which has also been identified in synovial fluids of RA patients (154), has the ability to citrullinate host peptides like smoking, which subsequently generates auto antigens that drive autoimmunity in RA (155).

1.9.3 Inflammatory Bowel Disease

Several lines of evidence indicate that the gut microbiome plays an important role in IBD, including the association of genes involved in mucosal immunity with IBD (such as CARD15, CARD9, IL23R and ATG16L1), the therapeutic effect of antibiotics on the condition, and the beneficial effect of faecal stream diversion in CD. Previous studies of human IBD have been undertaken using standard culture techniques (156) or molecular analysis (157, 158). These studies noted alterations in intestinal microbiota when compared to non-IBD patients, a finding recently confirmed using 16S rRNA sequencing of intestinal biopsies (159). A recent study examined the intestinal microbiome in treatment naïve new-onset CD, and described microbial signature. 16S community profiling intestinal and faecal samples showed that the signature comprised of an increased abundance in bacteria including Enterobacteriaceae, Pasteurellaceae, Veillonellaceae, and Fusobacteriaceae, and decreased in abundance of Erysipelotrichales, Bacteroidales, and Clostridiales and correlate strongly with disease status (153).

1.9.4 Psoriasis

The microbiome is thought to play a significant role in psoriasis, another AS-related condition. It has long been suggested that streptococcal infection, especially throat infections, may trigger psoriasis in a genetically susceptible individual (160). Recent studies using 16S rRNA sequencing have found significant differences between the cutaneous microbiota of psoriasis cases and controls, and in psoriasis involved and control skin in psoriasis cases, with less Staphylococci and Propionibacteria observed in cases and in affected skin (161). Again, further studies will be required to determine if there is a particular microbial profile or specific bacterial species involved in psoriasis. These studies are thus consistent with the hypothesis that the microbiome contributes to the aetiopathogenesis of the immune mediated arthritis or seronegative diseases like IBD and

45 psoriasis. However at this point, with the exception of reactive arthritis, there is no definitive evidence of specific bacterial infections or changes in microbial profile that play a causative role in these conditions.

1.10 The Chicken and the egg

Whether the changes noted in these microbiome studies of immune mediated disease are a consequence of disease or are involved in its development or persistence still remains unclear. This distinction may prove impossible to dissect in human studies, but there is considerable evidence in studies in mice to support a role for the microbiome in driving immune mediated diseases.

In the B27 rat model of AS, rats housed under germfree (GF) conditions did not develop disease (141) demonstrating that microbes in this model are important disease penetrance. In contrast, in the New Zealand black (NZB) model of Systemic lupus erythematosus, mice maintained under GF conditions produced higher levels of antinuclear antibodies and developed worse disease (162) demonstrating a protective role for commensal microbes.

Considering the IL-17/IL-22 cytokines, which are of relevance to AS, IBD and psoriasis in particular, there is strong evidence from murine studies to indicate that interaction between the gut microbiome and the host determines the overall level of activation of the immune cells producing these cytokines. Segmented filamentous bacteria (SFB) are a commensal bacteria that induce IL-17 secretion. Mice lacking SFB have low levels of intestinal IL-17 and are susceptible to infection with pathogenic Citrobacter spp. Restoration of SFB in these mice increased the number of gut-resident IL-17-producing cells and enhanced resistance to infection (27). Further, using recolonisation studies, investigators have recently demonstrated that in neonatal mice, commensal microbes influence iNKT cell intestinal infiltration and activation, establishing mucosal iNKT cell tolerance to later environmental exposures (163).

The mechanism by which the microbiome influences IL-17 producing cell activation is still being determined. Ivanov and colleagues demonstrated that serum amyloid A, produced in the TI, can induce TH17 differentiation of CD4+ T-lymphocytes (27). It has also been shown that development of TH17 lymphocytes in the intestine is stimulated by microbiota induced IL-1b (but not IL-6) production (164). Colonisation with Clostridia species has been shown to stimulate intestinal TGFb production, in turn increasing IL-10+ CTLA4high Treg activation (105). Clostridia colonisation of neonatal mice reduced severity of induced colitis using DSS or oxazolone, and reduced serum IgE levels in adulthood. So it is likely 47 that alterations to the gut microflora, or invasion of the gut by pathogenic bacteria, influence the balance of IL-17- and IL-22-producing cells, and other immune cells, influencing susceptibility to local and systemic immune mediated disease.

Dissecting out what is genetic influence on microbiome composition and what is environmental is inherently complicated. A recent study by Hildebrand et al., 2013, investigated exactly that across five common laboratory healthy mouse strains BALB/c, FVB, B6, NOD and Swiss. They found that the gut microbiota differed according to genotype, caging and inter-individual variation (165). These factors contributed on average 19%, 31.7% and 45.5% respectively to the variance in the microbial composition. Genetic distance was also found to positively correlate to microbiota distance, indicating that strains that are more closely related genetically have more similar microbiota than distantly related strains. Microbiota composition was also correlated with inflammation marker calprotectin. Mice that displayed low microbial richness had higher levels of calprotectin but no obvious signs of inflammation suggesting low-grade inflammation. This study demonstrates the influence of genetic background on microbiota composition as well as the environmental influences albeit in a controlled setting. This work highlights the complexity of host-environment-microbiota interactions (165).

The above studies highlight that changes in the microbiome can lead to inflammation which may have far reaching effects, and demonstrate experimental approaches by which findings of metagenomic studies in mice and humans can be explored to successfully dissect the role of the microbiome in human immune mediated diseases.

1.11 Thesis outline

This thesis investigates the role of the human gut microbiome in AS by characterising the AS microbiome, examining how changes in host genetics influences intestinal microbial composition and then exploring at the association between AS susceptibility loci and microbiome composition in healthy twin cohort.

1.12 Aims

Aim 1: To establish a pipeline for 16S community profiling the microbial communities present in the terminal ileum of patients with AS, compared to CD and HC.

Aim 2: To characterise the terminal ileum of patients with AS and determine if the AS gut carries a distinct microbial signature in comparison to HC.

Aim 3: To investigate how loss of ERAP influences the gut microbiome composition, the impact this has on the antigen repertoire presented and ensuing affects on the immune system.

Aim 4: To investigate the causality between AS susceptibility loci and gut microbiome composition.

The above aims will be discussed in separate results chapters that will form the body of this thesis.

1.13 Significance

The human gut microbiome is a dynamic and complex ecosystem that is only now beginning to be understood. It is increasingly clear that the interaction between host and microbiome profoundly affect health. It is still unclear how interactions between host genes, microbes and environmental factors can predispose patients to the development autoimmune diseases such as AS. We are only beginning to grasp the influence the microbiome has on health. Improved knowledge of the composition and function of the gut microbiome in AS patients and how the microbiome shapes the immune response and influences inflammation, both local and systemic, will likely provide important insights into early events in the pathogenesis of AS.

2 Chapter 2 Community profiling the terminal ileal microbiome

2.1 Introduction

It is increasingly clear that the interaction between host and microbiome profoundly affects health. We are more microbe than we are human with over ten times more bacteria and their genes living in and on our bodies, with the human intestine containing ~100 trillion bacteria. Our ‘second genome’ of microbes are not passive bystanders and only now are we beginning to appreciate the influence our microbial residents have on our health. Until recently, interrogation of our microbial communities using classical microbiology techniques offered a very restricted view of these communities, only allowing us to see what we can grow in isolation. Advances in sequencing technologies in the past few years have greatly facilitated systematic and comprehensive studies of the microbiome, and elucidate its role in human health and disease.

AS has a global distribution, with no outbreaks or clustering being reported. The gene identified as having the strongest association with AS development is the major histocompatibility complex (MHC) gene HLA-B27. However only 1-5% of HLA-B27+ individuals go on to develop AS. Monozygotic twin studies suggest that the trigger for AS in a genetically susceptible person is a ubiquitous environmental factor that is most likely widely distributed (4, 5). There is a close relationship between the gut and SpA, exemplified in reactive arthritis patients, where a typically self-limiting arthropathy follows either gastrointestinal infection with Campylobacter, Salmonella, Shigella or Yersinia, or urogenital infection with Chlamydia. The mechanism by which AS is triggered, in a genetically susceptible individual, is not well understood. Studies in the B27 transgenic rat model of AS, supports the idea of a ubiquitous environmental trigger. The B27 transgenic rat when exposed to normal commensal bacteria, develop disease while the rats that were maintained in a germ-free environment remain disease free (15). CD is a common form of IBD where, again, interplay between host genetic factors and largely undefined environmental factors are thought to drive the disease. Microbial involvement, especially from the gut, has been associated in both diseases however in AS, no definitive link has been established (16, 17).

There is a strong overlap between AS and CD, 70% of AS patients have subclinical gut inflammation with 5-10% of these patients with more severe intestinal inflammation developing clinically defined IBD resembling CD (7). Multiple genes associated with AS also play a role in gut immunity, and there is a marked over-representation of genes associated with CD also being associated with AS (20). Of these loci and genes identified, of particular interest are genes involved in the IL-23 pathway including STAT3, IL23R, and IL12B (which encodes IL-12p40, the share subunit of IL-23 and IL-12 (20-22). In the intestinal mucosa IL-23, IL-17 and IL-22 producing cells are enriched with IL-17 and IL-22 are known to be important regulators of intestinal ‘health’. IL-17 plays important roles in intestinal homoeostasis. In the gut, innate-like immune cells act as sentinels, responding very rapidly to alterations to the microbial composition of the gut with rapid secretion of IL- 17. Loss of function polymorphisms in IL23R are associated with protection from AS (55) and IBD (57), and many other genes in the IL-23 pathway are associated with these diseases. This suggests that similar aetiopathogenic mechanisms may likely involve the gut microbial communities and that dysbiosis of the gut flora is a significant contributor to disease. There have been several studies have shown that AS patients and their first- degree relatives have increased intestinal permeability relative to unrelated healthy controls, again consistent with a role for the gut microbiome in the disease (11). Whilst there are significant genetic similarities, major differences also exist between the genetics of IBD and AS, with AS showing no association to date with NOD2/CARD15 or the autophagy gene ATG16L1, which are major susceptibility factors in IBD, whereas IBD shows no association with HLA-B or ERAP1 which are the strongest AS-susceptibility genes (166).

Together, the multi-factorial components of the immune system shape the microbial population that inhabits the gut and, to an extent, vice versa, with each side pushing to establish a stable state. Understanding the yin and yang of this relationship is at the heart of current microbiome research. Microbiome refers to the totality of microbes, their genetic elements and environmental interaction in a defined environment (106). The human body has more microbes living on it or in it, than it does human cells with approximately 29% of all these microbes residing in the gut. Homeostasis of the intestinal microbiome and relationship between the microbes that inhabit the gut and the immune system is critical. The gastrointestinal tract is heavily populated with microbes and is the primary site for interaction between these microorganisms and the immune system (25, 26). Furthermore, microbes found in the gut help to shape host immune systems from an early age. The 51 incomplete development of the immune system in neonates and under germ-free conditions tells us that microbiota sculpt the host immune system (27, 28). Maintenance of intestinal and microbial homeostasis is being increasingly recognised as playing a pivotal role in overall health (29), and dysregulation of either gut or microbial homeostasis may play a role in autoimmunity. Physiological processes in the host that maintain gut homeostasis and respond to perturbance in the gut micro-environment involve both the adaptive and innate immune system, and the barrier function of the intestines themselves, have been implicated in a number of immune mediated diseases, including multiple sclerosis, inflammatory bowel disease, and type I diabetes (25, 102, 167, 168).

The human gut microbiome contains a dynamic and vast array of microbes that are essential to health as well as provide important metabolic capabilities. Until recently studying these complex communities has been difficult and generally limited to classical phenotypic techniques (109, 116). The application of molecular techniques, particularly sequencing, has shown the remarkable amount of diversity in the human gut, exceeding all previous held beliefs and expectations. Development of high-throughput sequencing methods has greatly improved our ability to profile complex microbial communities without the requirement to culture organisms. The gene commonly used for community profiling is the 16S ribosomal RNA (rRNA). This ~1,550bp section of prokaryotic DNA is found in all bacteria and archaea, is highly conserved and is composed of both variable and conserved regions. The 16S rRNA is able to differentiate between organisms across all major phyla of bacteria and to classify strains down to species level (108). 16S rRNA sequencing has dramatically changed our understanding of phylogeny and microbial diversity because it provides an unbiased assessment of the microbiome, and is not restricted by the ability to culture the bacteria present.

The aim of this chapter was to establish a pipeline to characterise the microbial community present in the terminal ileum of patients with AS compare to CD and healthy controls (HC). This included tissue extraction methods for optimal microbial recovery, choice of 16S amplicon sequencing as well as analysis strategy. To validate the pipeline, a pilot study was undertaken consisting of four AS patients, three CD patients and four HC to determine whether the gut microbiome of AS patients is different and distinct from CD patients and HC. This pilot study also included the use of two mock communities consisting of well characterised bacteria of known concentrations that served as internal sequencing and analysis controls. In addition to exploring community-level composition and differences 52 between affection states, we also evaluated technical replication by sequencing from both forward and reverse primers and analysing them separately. Biological replication was assessed by halving biopsies, then processing and analysing them independently. The resulting sequence libraries were analysed using the QIIME pipeline as described in the Material and Methods (169). To determine differences in microbial load, total microbial biomass in biopsy samples was quantified using real-time qPCR of the 16S rRNA gene as described in Willner et al., 2013 (170).

2.2 Materials and Methods

2.2.1 Optimisation of 16S primers 347F 803R and 517F 803R These two primer pairs spanning the V3-V5 region were optimised using a mock community of enteric bacteria, a human cell line control and a TI biopsy. The mock community consists of five enteric bacteria Escherichia coli, Klebsiella pneumonia, Klebsiella oxytoca, Enterobacter spp., Citrobacter spp. and as well as Listeria monocytogenes. A human cell line control was used to test if the primer set amplified human DNA as well as bacterial DNA. The primer pairs 347F 803R and 517F 803R were taken through to sequencing as their amplicon size was compatible with the requirements of Illumina sequencing of 300-500bp including linkers and adaptors.

2.2.2 16S rRNA PCR The master mix for the reactions in a 25ul total volume using Bioline 10x reaction buffer, dNTP(0.625mM), mgcl2 (2.0mM), 1U of Bioline TAQ, both primers 5uM each and 2ul DNA. Thermocycling conditions comprised 10 min 94ºC then 30 cycles at 94ºC 50 s, 54º 30 s, 72ºC 45 s and 72ºC 10 min.

2.2.3 In silico assessment of Primer pairs To compare the spectrum of diversity of our two primer sets in silico library simulation was conducted using Grinder was used to generate mock data sets from the microbial refseq library (NCBI) aligning to whole genomes (171). These data sets consisted of 10 patients that contained 1000 reads per library. Reads were generated this way to best represent what will likely happen with patient gut biopsies as well as to cache the microbial diversity.

Data sets were analysed using the QIIME pipeline (qiime.sourceforge.net) (172). OTU assignment was based on 97% similarity with taxonomic assignments made using BLAST to Greengenes (113) as implemented in QIIME.

2.2.4 Pipeline optimisation Intestinal Biopsies Resected colon Biopsies were collected from patients with diagnosed with either Ulcerative colitis (UC) or Crohn’s disease (CD) courtesy of A/Prof Graham Radford-Smith for the purpose of the pilot study and protocol optimisation (Table 2.1).

Table 2.1 Intestinal biopsies utilised for pipeline optimisation.

Biopsy ID Sample Histology Diagnosis DNA description Concentration ng/ul Q1462 Ileum Non-Inflamed UC 128.15 A457 Ileum Non-inflamed UC 54.57 12.001.08 Ileum Inflamed CD 48.95 53.001 Colon Inflamed UC 9.77 83.001.03 Ileum Inflamed CD 113.82

2.2.5 Sequencing of Pilot intestinal biopsies To compare the two primer sets five gut biopsies were amplified with 347F 803R and the same six were amplified with 517F 803R, multiplex and run on the HiSeq. 10 16S rRNA amplicon libraries were generated from five gut biopsies and converted to Illumina HiSeq compatible prior to adaption of ligation of barcodes the amplicon libraries were QC checked using the bioanalyzer (Agilent) looking for discrete peaks at 286bp and 456bp. 1µg amplicon library were constructed using the TruSeq DNA sample preparation kit (Illumina) as per manufacturer’s instructions, commencing at the end repair stage and omitting both the fragmentation and gel purification steps. The final library underwent QC on the Agilent Bioanalyzer to confirm that indexing was successful, by looking for a shift in the discrete peaks to 386bp and 556bp respectfully. These libraries were then pooled for multiplexing using equimolar proportions of each library to a final concentration of 2µM.

Table 2.2 Pipeline optimisation sequencing metrics.

Gut biopsy Primer set Number of sequences per library Q1462347F 347F 803R 2681259 Q1462517F 517F 803R 2341819 A457347F 347F 803R 2281754 A457517F 517F 803R 2263814 12.08347F 347F 803R 2739502 12.08517F 517F 803R 2198014 53.01D347F 347F 803R 2265574 53.01517F 517F 803R 2347683 83.03347F 347F 803R 1384798 83.03517F 517F 803R 1421586

2.2.6 Bioinformatics 10 16S rRNA amplicon libraries were generated from five gut biopsies using the two primer sets and sequenced on the Illumina HiSeq (Table 2.2). The multiplexed reads were demultiplexed using the Illumina program CASAVA. Fastq reads were converted to QIIME format using custom script. Samples were analysed using the QIIME pipeline (qiime.sourceforge.net) (169). OTU assignment was based on 97% similarity with taxonomic assignments made using BLAST to Greengenes(113) as implemented in QIIME.

Sequence depth analysis was conducted by sub setting samples at –n 1000, 5000, 10 000, 50 000, 100 000, 500 000 and 1 000 000 reads per sample and then graphed using the plot_rank_abundance_graph.py as implemented in QIIME.

2.2.7 Community profiling the terminal ileal microbiome

2.2.7.1 Ethics statement Written informed consent was obtained from all subjects, and the study protocol was approved by the local ethics committee of the University of Palermo.

2.2.7.2 Patient clinical data AS patients were diagnosed according to the modified New York criteria for the disease (173). No AS and CD patients were receiving biological agents such as TNF-inhibitors, and only one AS patient was on nonsteroidal anti-inflammatory (NSAIDs) drugs at time of biopsy sampling. Healthy controls were undergoing ileocolonoscopy for routine evaluation (Table 2.3).

2.2.7.3 DNA extraction and sequencing TI biopsies were thawed and then divided to create biological replicates. Each was then manually disrupted using a polypropolene disposable pestle from Sigma. DNA extractions were performed using the Qiagen DNeasy tissue extraction kit as per manufacturer’s instructions.

Table 2.3 Clinical details of patients used in preliminary community profiling studies.

ID Diagnosis Biopsy Age Disease CRP mg/l HLA-B27 Treatment Site duration (0-10mg) AS01 Ankylosing Ileum 56 7 years 18 Pos NSAID spondylitis AS02 Ankylosing Ileum 33 4 years 1 Pos None spondylitis AS03 Ankylosing Ileum 24 5 years 5 Pos None spondylitis AS04 Ankylosing Ileum 22 3 years 3 Pos None spondylitis CD01 Crohn’s Ileum 44 12 months 12 N/A Mesalazine disease CD02 Crohn’s Ileum 32 4 months 6 N/A none disease CD03 Crohn’s Ileum 47 7 months 8 N/A Mesalazine disease HC01 None Ileum 58 N/A 0.3 N/A N/A

HC02 None Ileum 43 N/A 0.5 N/A N/A

HC03 None Ileum 38 N/A 0.05 N/A N/A

HC04 None Ileum 65 N/A 0.8 N/A N/A

2.2.7.4 Bacterial Mock communities Mock communities consisting of six different bacteria, Gram positive and Gram negative, was employed as a control for both sequencing and bioinformatic analysis and was processed alongside the TI biopsies. The six bacteria used were Staphylococcus aureus (ATCC 25923), Enterococcus faecalis (ATCC 51299), Haemophilus influenzae (ATCC49247), Klebsiella oxytoca (ATCC 700324), Escherichia coli (ATCC 35218) and Yersinia enterocolitica (ATCC2 3715). Bacteria were extracted using the Qiagen DNeasy tissue extraction kit as per manufacturer’s instructions. Two communities were then constructed using differing and varying concentrations of the seven bacteria and labelled Mock1 and Mock2.

Two mock communities (Tables 2.4 and 2.5) were created as PCR and sequencing controls. The first mock community (Mock1) contains six bacteria at varying concentrations and then pooled. The second mock community (Mock2) contains the same six bacteria and was normalised for 16S copy number to 10ng/ul and then pooled at varying ratios.

Table 2.4 Mock1 construction using bacteria at varying concentrations.

Bacteria Concentration Dilution Pooled Relative ng/ul amount abundance % ul Y. enterocolitica 28.39 Neat 10 89.1

E. faecalis 7.49 1:10 10 8.9

K. oxytoca 31.08 1:100 10 0.9

H. influenzae 5.47 1:100 10 0.9

S. aureus 17.89 1:1000 10 0.1

E. coli 12.47 1:1000 10 0.1

Table 2.5 Mock2 – 16S copy number normalised to 10ng/ul at varying ratios.

Bacteria Concentration 16S copy Pooled Relative ng/ul number amount abundance % ul Y. enterocolitica 28.39 7 3 21.4 E. faecalis 7.49 4 1 7.1 K. oxytoca 31.08 8 4 28.7 H. influenzae 5.47 6 2 14.3 S. aureus 17.89 5 1 7.1 E. coli 12.47 4 3 21.4

2.2.7.5 16S rRNA PCR Bacterial 16S rRNA amplicon PCR was conducted using the V4 primer spanning the region 517F 803R of the 16S gene. The master mix for the reactions in a 25ul total volume using Bioline 10x reaction buffer, dNTP(0.625mM), mgcl2 (2.0mM), 1U of Bioline TAQ, both primers 5uM each and 2ul DNA. Thermocycling conditions comprised 10 min 94ºC then 30 cycles at 94ºC 50 s, 54º 30 s, 72ºC 45 s and 72ºC 10 min.

2.2.7.6 Sequencing Library preparation 16S rRNA amplicon libraries were assembled and indexed for multiplexing using the Illumina TruSeq DNA library protocol as per manufacturer’s instructions. Samples were then normalized using the Agilent Bioanalyzer and pooled for sequencing at a concentration of 2nM. To create the required complexity for sequencing, PhiX was added to the pool at 50/50 for a final pool concentration of 4pM.

2.2.7.7 Illumina MiSeq Sequencing Barcoded 16S amplicon sequences were sequenced with 2x150bp paired-end on an Illumina MiSeq with approximately 100 000 reads per sample (Table 2.6).

Table 2.6 Counts of 16S rRNA sequence reads obtained per biopsy.

Sample name Total sequences AS01a 279,668 AS01b 170,508 AS02a 132,010 AS02b 262,060 AS03a 115,598 AS03b 178,534 AS04a 237,744 AS04b 219,418 CD01a 103,896 CD01b 124,414 CD02a 185,934 CD02b 200,200 CD03a 237,004 CD03b 272,198 HC01a 688,184 HC01b 121,098 HC02a 316,964 HC02b 327,222 HC03a 235,822 HC03b 241,690 HC04a 226,356 HC04b 242,772 Mock1 284,316 Mock2 244,840

2.2.7.8 Bioinformatics and statistics Reads were de-multiplexed using the Illumina program CASAVA. Fastq reads were converted to QIIME format using and amplicon orientated using custom scripts. Samples were normalised at 10,000 reads per sample and then analysed using the QIIME pipeline (qiime.sourceforge.net) (169). OTU assignment was based on 97% similarity with taxonomic assignments made using BLAST to Greengenes(113) as implemented in QIIME.

Alpha diversity metrics, which look at the diversity within a sample, were generated using the QIIME workflow alpha_rarefaction.py with five repetitions at each sampling point. Rarefaction curves for the observed number of OTUs were based on the outputs of alpha_rarefaction.py. UniFrac distances between microbial communities were evaluated using FastUniFrac (174). UPGMA clustering was employed both weighted and unweighted, to detect significant differences between microbial communities between AS, CD and HC (175). FastUniFrac was also used to generate sample distance metrics as well as Principle Coordinates Analysis.

PERMANOVA analysis was conducted on the genus level OTU table using the R package vegan to test the relationship between the whole microbial community and disease (176). Indicator species analysis, using the ‘labdsv’ R package, was employed to investigate what microbial species are driving the microbial community and driving the signature (177).

2.3 Results

2.3.1 In silico assessment of Primer Pairs

In silico library simulation of the primer pairs 347F 803R and 517F 803R returned 14 and 21 different phyla respectively. The dominant phyla previously reported to inhabit the gut are Bacteroidetes and Firmicutes with lower abundance of Actinobacteria, Proteobacteria, Fusobacteria and Verrucomicrobia (152, 178). The purpose of this experiment was to demonstrate that the primer pairs covered the range of expected phyla as well as the capacity to discriminate other phyla. The results show that, as expected, both primer sets were able to classify all dominant and minor phyla as previously reported to inhabit the gastrointestinal tract (Table 2.7). This demonstrates that either primer set would adequately profile the intestinal microbiome. However, 517F 803R identified seven more phyla, including some phyla that have been reported as being low in abundance such as Verrucomicrobia and Lentisphaerae (178). Based on the in silico simulation, the primer set 517F 803R would be better suited to interrogate human gut biopsies moving forward.

Table 2.7 Taxon output by Grinder.

Phyla 347f 803R Phyla 517F 803R Acidobacteria Acidobacteria Actinobacteria Actinobacteria Bacteroidetes Bacteroidetes Flavobacteria Flavobacteria Sphingobacteria Sphingobacteria Chlorobia Chlorobia Chrysiogenetes Chloroflexi Deferribacteres Chrysiogenetes Fibrobacteres Deferribacteres Firmicutes Deinococci Fusobacteria Dictyoglomi Nitrospira Fibrobacteres Proteobacteria Firmicutes Mollicutes Fusobacteria Lentisphaerae Nitrospira Planctomycetacia Proteobacteria Spirochaetes Mollicutes Verrucomicrobia

2.3.2 Sequencing of Pilot intestinal biopsies

Preliminary analysis of primer pairs in a pilot study using five colon biopsies showed that there were three major phyla observed across the gut biopsies were Bacteroidetes, Firmicutes and Proteobacteria (Figure 2.1). This is consistent with what has been reported in the literature (114, 152). The minor phyla present include Actinobacteria, Fusobacteria, Cyanobacteria, and Acidobacteria with a very low unclassified ‘other’. These ‘others’ may be members of the phylum Archea, such as Methanobacteria, which are also common in the gut. Using an alternate reference database, such as Silva, may possibly resolve these unclassified sequences or they may not be present in the reference databases.

The phylum level classification illustrates that the 517F 803R primer set seems to classify slightly more members of the Firmicutes phylum, while the 347F possibly classified Bacteroidetes better. The Komolgorov-Smirnov test was run at the phylum level in order to evaluate the differences between the two primer sets and to compare the similarities and differences between the overall sequences corresponding to the same phyla. Neither of the primer pairs came up significantly different from each other as all p-values were greater than 0.90. This means that using one primer compared to the other did not cause significant differences detected in the community profiles at the phylum level.

55.02347F Firmicutes

Q1462517F Bacteroidetes Q1462347F

A457517F Proteobacteria

A457347F Actinobacteria

83.03517F

Fusobacteria 83.03347F

53.01517F Cyanobacteria

53.01D347F Acidobacteria 12.08517F

12.08347F Other

0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100%

Figure 2.1 Phyla represented in gut biopsies showing percentage abundance. Comparison of primer pairs 347F 80R and 517F 803R demonstrating no significant differences when trialled in five colon biopsies for the purpose of pipeline optimisation

2.3.3 Sequence depth analysis

Illumina high-throughput sequencing is capable of producing millions of reads, so ascertaining the appropriate number of reads required per sample for amplicon sequencing is important not only for taxonomy assignment but sample multiplexing as well.

Whilst deep sequencing of PCR amplicons enables the detection of rare or low abundant organisms, this can also lead to over estimation of microbial diversity by the generation of reads containing errors (112, 179). Recent work by Bokulich et al., 2012 demonstrated that strict quality filtering of reads greatly improves taxonomy assignment and α-diversity measures for microbial community profiling (180). The five samples from the pilot study were sampled at varying read depths ranging from 1000 reads to 1 000 000 reads and compared (Figure 2.2). The analysis showed that 1000 and 5000 reads per sample detected the least number of species with 500 000 and 1 000 000 reads per sample detecting the greatest number. The concern is that the more species being detected may not be true and may in fact be sequence error (112). With this in mind the taxonomy assignment from 1000 to 50 000 reads was compared, with on minor differences found. Based on this 10 000 reads was the depth selected to continue with as it identified the same taxa as 50 000 reads and allowed for more samples to be multiplexed.

Figure 2.2 Rank abundance graph showing number of reads (read depth) with number of species identified for the sample Q1462.

2.3.4 Community profiling the terminal ileal microbiome

The microbial communities present in the TI of AS patients were found to be significantly different and more diverse than those communities associated with CD and healthy controls (P<0.001; PERMANOVA). Alpha diversity, a measure of richness and/or evenness of taxa within an individual community, was increased in AS samples. The CD and HC communities were more similar to each other than AS, but were less diverse overall than the AS microbial community (Figure 2.3).

Figure 2.3 Rarefaction curves for microbial communities in AS patients (red curve), CD patients (blue curve), healthy controls (orange curve) and the mock communities (green curve).

Communities were compared using weighted and Unweighted UniFrac distances, a phylogenetic metric that accounts for differences in both community membership and relative abundance (174). The Weighted UniFrac distance metric is used to detect changes in how many organisms are present in a community. It takes into account the relative abundance of microbes present, which is particularly important for looking at changes in bacterial abundances between communities. The Unweighted UniFrac distances metric informs us about community membership is used to measure the core differences between communities in terms of presences or comparative absence of taxa. 65

Hierarchical clustering based on both Weighted and Unweighted UniFrac distances indicated the primary factor influencing differentiation of communities is disease, and that the microbial communities present in AS patients are distinct from those in CD and HC (Figure 2.4).

Figure 2.4 Hierarchical clustering of microbial communities based on Weighted Unifrac distance clustering of terminal ileal biopsies of patients with ankylosing spondylitis (AS), Crohn’s disease (CD), healthy controls (HC) as well as the two mock community controls.

Figure 2.5 Principal coordinates analysis of technical and biological replicates (transparent points) showing the grouping of ankylosing spondylitis (AS) samples compared to Crohn’s disease (CD) and healthy controls (HC) with the grouping of the replicates shown in the red circles.

PERMANOVA analysis confirmed the presence of a significant relationship between disease status and microbial community composition (P<0.001). This was not due to heterogeneity in biopsy samples as the biological replicates showed no significant difference between each other (P<0.001) (Figure 2.6). Nor was it due to a lack of technical reproducibility, as the technical replicates, like the biological, were not statistically different from each other (P<0.001) and cluster tightly together (Figure 2.5).

Figure 2.6 Comparison of variation between and within samples and affection status showing greater significance between and within affection status than between biological and technical replicates Significance was assessed using Mann-Whitney test (P<0.001 = ***).

Average UniFrac distances demonstrated that the differences between both biological and technical replicates were significantly less than differences between individuals and disease states (Figure 2.6). Total microbial load was investigated using Biomass q-PCR. Total microbial load is of interest as this is an indicator of the overall amount of bacteria that are present in the community. In our samples, Biomass q-PCR showed that the apparent microbial signature in AS patients was not due to bacterial overgrowth as there was no significant difference on average in 16S rRNA copy number between AS, CD, and HC (Figure 2.7). One sample, AS02, displayed a higher microbial load than the other AS samples, while AS04 exhibited a lower overall microbial mass, illustrating the variability of microbial community load across people and the disease. The biomass of CD and HC samples were relatively similar to each other, with no outstanding increases or decreases in microbial load.

Figure 2.7 Microbial community profiles of terminal ileal (TI) biopsies from ankylosing spondylitis (AS), Crohn’s disease (CD) patients and healthy controls (HC). Biological replicates are indicated as ‘a’ and ‘b’. Each row represents a different OTU with the abundance as a percentage of the total population as represented by colour. Phyla and selected families are noted on the left, and a subset of OTU classifications on the right. Microbial biomass of each biopsy is shown by the bar graph at the bottom of the heat map.

2.4 Discussion

Whether the changes in the microbiome are a consequence of disease or are involved in its development or persistence is unclear. This distinction may prove difficult to dissect in human studies, but there is considerable evidence in studies in mice to support a role for the microbiome in driving immune mediated diseases such as AS. In order to understand the role of the gut microbiome in human disease, it is important to first be able to accurately and confidently profile the microbiome. This requires confidence in tissue extraction techniques, 16S rRNA primer choice to ensure diversity is sufficiently captured and that the analysis strategy employed is appropriate (181).

During 16S pipeline validation, nine different primer sets spanning the 16S rRNA gene were evaluated to determine which set provided the best coverage and discrimination of intestinal flora. Emerging literature coupled with in silico analysis of the primer sets, we focused two primer sets in the V3-V5 region of the 16S rRNA gene (127, 169). Two primer sets 347F 803R and 517F 803R demonstrated ability to classify all expected major and minor phyla, as previously reported to inhabit the intestinal microbiome, not only in silico but also when trialled on human colonic biopsies (127, 128, 152, 182, 183). As well as capturing the diversity of the bacteria in the intestinal microbiome, ability to selectively amplify bacteria over human DNA was crucial in when considering primers. 16S rRNA shares significant homology with human mitochondrial DNA and amplification of human DNA is an issue. The final decision to move forward with the 517F 803R primer set was also influenced by choice of sequencing platform and chemistry. Sequencing of the TI biopsies was 2x150bp on the Illumina MiSeq, therefore sequencing read length was an important determinant when considering primer sets for amplicon sequencing. Two bacterial mock communities, consisting of six well-characterised bacteria at varying concentrations, were used as internal standards to determine reproducibility across multiple runs. These two mock communities enabled us to accurately compare PCRs and sequencing runs, as well as serving as internal controls to validate the pipeline and analysis approach.

There is increasing evidence that these indigenous microbes are also driving the development and/or function of different of immune cells in the intestine. In the intestinal mucosa, innate lymphoid cells (ILCs) have emerged as an important innate lymphocyte population involved in immunity to intestinal infections, especially in conjunction with IL-22 70

(184). The transcription factor T-bet, encoded by the TBX21 gene, controls the fate of ILCs and is yet another gene in the list of genes found to be controlled by cues from the intestinal microbiome and IL-23. Meaning that the commensal microbiota are instructing T- bet expression and therefore ILC development. (185).

Inflammation is a critical component in autoimmune disease. In the K/BxN mouse model of arthritis it was shown that the introduction of a single gut-residing species, segmented filamentous bacteria (SFB), into germ free animals was sufficient to cause proliferation of Th17 cells, which led to the production of autoantibodies and rapidly progressed to arthritis. Neutralisation of IL-17 in specific-pathogen-free K/BxN mice prevented arthritis development. Thus, a single commensal microbe, via its ability to promote a specific T helper cell subset, can drive autoimmune disease (101). Conversely, members of the gut microbiota such as Clostridium spp., have been found to play a protective role by inducing T regulatory cells (Tregs) in the colonic mucosa. Tregs are an important counter balance in the immune system and promote homeostasis. It was found that a specific mix of cluster IV and XIVa of Clostridium was sufficient to promote Treg accumulation in the colon (105).

To date no comprehensive characterisation of intestinal microbiota in AS patients has been performed. Here we described the optimisation and validation of 16S rRNA culture- independent microbial sequencing for the purpose of profiling TI biopsies. The choice to examine intestinal biopsies, rather than less invasive stool samples, was in order to fully characterise the mucosal microbiome at a site of initial inflammation. Several studies have shown that while stool is routinely used for large scale investigations of the intestinal microbiome (123, 126, 137), examining the mucosal microbiome has tremendous value as the mucosal microbes are in a position to influence the immune system (152, 153, 186). Four AS patients, three individuals with CD, four HC and two microbial mock communities were profiled using the optimised 16S rRNA pipeline to determine if the AS gut carries a distinct microbial signature. Hierarchal clustering showed that the microbiome of AS patients is distinct and different from CD and HC, with the PCoA confirming that the AS cases group separately. Statically the relationship between disease status and microbial community composition was confirmed indicating that the differences between the microbial community compositions were not due to heterogeneity in the biopsy or lack of technical reproducibility, but that the driving force is disease state. These results demonstrate that the tissue extraction method coupled with choice of 16S rRNA primers, was able to capture and profile the intestinal microbiome in human TI samples. 71

Given what we know from murine experiments, the differences we see in the microbial composition in AS gut compared to CD and HC may have wider reaching implications. The evidence is mounting that not just the overall composition of the intestinal microbiome, but presences and/or absences of specific microbes that have a substantial impact on host response, regulation of inflammation and development of intestinal cells. What is clear is that the interactions between our genetics, immune system and resident microbiota are complex but critical. Our intestinal microbiota are clearly not just bystanders but are in fact orchestrators of our health and disease, so having the capacity to accurately profile the microbiome is imperative. Further studies are needed into whether the changes in intestinal microbial composition are due to genetics, and how this affects the overall function of the gut microbiome in AS patients, including how the microbiome then goes on to shape the immune response and influence inflammation. Now have the tools to sequence and analyse intestinal microbial communities, these investigations will likely provide new insights and help us to better understand AS disease pathogenesis.

3 Chapter 3 Intestinal dysbiosis in ankylosing spondylitis

Accepted and published online as a Brief Report in Arthritis and Rheumatology 2015,

2015;67(3):686-91.

Mary-Ellen Costello1, Francesco Ciccia2*, Dana Willner3*, Nicole Warrington1, Philip C.

Robinson1, Brooke Gardiner1, Mhairi Marshall1, Tony J. Kenna1*, Giovanni Triolo2*, Matthew

A. Brown1

1The University of Queensland, The University of Queensland Diamantina Institute, Translational Research Institute, Princess Alexandra Hospital, Woolloongabba, Brisbane, QLD 4102, Australia. 2University of Palermo, Palermo, Italy. 3Australian Centre for Ecogenomics, School of Chemistry and Molecular Biosciences, University of Queensland, Brisbane, QLD 4072, Australia. *These authors contributed equally to this work.

Modified article forms Chapter 3, full brief report (PDF) in appendices

3.1 Abstract

Objective. Ankylosing spondylitis (AS) is a common highly heritable immune mediated arthropathy that occurs in genetically susceptible individuals exposed to an unknown but likely ubiquitous environment trigger. There is a close relationship between the gut and SpA, exemplified in reactive arthritis patients, where a typically self-limiting arthropathy follows either gastrointestinal or urogenital infection. Microbial involvement has been suggested in AS, however, no definitive link has been established. We sought to determine if the AS gut carries a distinct microbial signature, in comparison to healthy controls (HC).

Methods. Microbial profiles from terminal ileal (TI) biopsies from subjects with recent- onset, TNF-antagonist naïve AS and HC, were generated using culture-independent 16S rRNA gene sequencing and analysis techniques.

Results. Our results show the TI microbial communities of patients with AS differ significantly (P<0.001) from HC, driven by higher abundance of five families of bacteria Lachnospiraceae (P=0.001), Ruminococcaceae (P=0.012), Rikenellaceae (P=0.004), Porphyromonadaceae (P=0.001), and Bacteroidaceae (P=0.001), and a decreases in abundance of two families Veillonellaceae (P=0.01) and Prevotellaceae (P=0.004).

Conclusions. We show evidence for a discrete microbial signature in the TI of cases with AS, compared to HC. The microbial composition was found to correlate with disease status and greater differences were observed between than within disease groups. These results are consistent with the hypothesis that genes associated with AS act at least in part through effects on the gut microbiome.

3.2 Introduction

Up to 70% of AS patients have subclinical gut inflammation with 5-10% of these patients with more severe intestinal inflammation go on to develop clinically defined IBD resembling Crohn’s disease (CD) (7). The high heritability of AS (5), the global disease distribution and the absence of outbreaks of the disease, suggest that AS is triggered by a common environmental agent in genetically susceptible individuals (187). Multiple genes associated with AS also play a role in gut immunity such as genes involved in the IL-23 pathway (22, 24), which are important regulators of intestinal ‘health’. There is a marked over- representation of genes associated with CD that are also associated with AS (18, 20); this suggest the two diseases my have similar aetiopathogenic mechanisms, possibly involving gut dysbiosis (188). 74

Several studies have shown that AS patients and their first-degree relatives have increased intestinal permeability relative to unrelated healthy controls, again consistent with a role for the gut microbiome in the disease (6, 11, 12). To date, no comprehensive characterization of intestinal microbiota in AS patients has been performed. Here we performed culture-independent microbial community profiling of terminal ileal (TI) biopsies from nine AS and HC to determine to investigate differences the gut microbiome in these disease states in comparison with each other and with HC.

3.3 Materials and Methods

3.3.1 Patient clinical data Terminal ileal biopsies were collected at colonoscopy from nine consecutively enrolled TNF-inhibitor naïve, recently diagnosed AS cases (defined according to the modified New York classification criteria for AS (173)) and nine healthy controls (Table 3.1). All patients gave written informed consent and the study protocol was approved by the relevant Universities of Palermo and Queensland research ethics committees. Disease duration listed in Table 3.1 is from onset of symptoms, as reported by the patients, not duration since diagnosis. One AS patient was on NSAIDs at the time of biopsy. AS03, AS04 and AS10 had reported occasional although interrupted use of NSAIDs due to gastrointestinal upset. Other AS patients did not report use of NSAIDs but were using either paracetamol and/or tramadol.

Table 3.1. Clinical details of patients supplying biopsies at time of collection. Disease duration is from date of onset of symptoms.

ID Diagnosis Biopsy Age Gender Disease ESR CRP BASDAI HLA-B27 NSAID Histological site duration mm/Hr mg/l status treatment inflammation AS01 AS Ileum 56 M 7 years 34 18 7 Pos Current Chronic AS02 AS Ileum 33 M 4 years 22 1 5.5 Pos None Acute AS03 AS Ileum 24 F 5 years 18 5 4.8 Pos None Normal AS04 AS Ileum 22 M 3 years 33 3 5.5 Pos None Normal AS05 AS Ileum 33 M 5 years 41 2.7 6 Pos None Chronic AS06 AS Ileum 28 M 6 years 28 1.5 6.2 Pos None Normal AS08 AS Ileum 36 M 8 years 34 1.5 5.6 Pos None Acute AS09 AS Ileum 38 F 6 years 45 2.2 6.7 Pos None Chronic AS10 AS Ileum 31 M 11 years 27 1.3 8 Pos None Normal HC01 HC Ileum 58 F N/A N/A 0.3 N/A N/A N/A N/A HC02 HC Ileum 43 M N/A N/A 0.5 N/A N/A N/A N/A HC03 HC Ileum 38 F N/A N/A 0.05 N/A N/A N/A N/A HC04 HC Ileum 65 M N/A N/A 0.8 N/A N/A N/A N/A HC05 HC Ileum 48 F N/A N/A 0.02 N/A N/A N/A N/A HC06 HC Ileum 34 F N/A N/A 0.03 N/A N/A N/A N/A HC07 HC Ileum 45 M N/A N/A 0.5 N/A N/A N/A N/A HC08 HC Ileum 44 F N/A N/A 0.8 N/A N/A N/A N/A HC09 HC Ileum 56 F N/A N/A 0.02 N/A N/A N/A N/A

3.3.2 DNA extraction and sequencing Samples were preserved in liquid nitrogen or on dry ice till processed. TI biopsies were thawed and divided in two to create biological replicates. Each sample was manually disrupted using a polypropylene disposable pestle from Sigma. DNA was extracted from tissue biopsies using Qiagen DNeasy tissue extraction kits after manual disruption as per manufacturer’s instructions.

3.3.3 Mock community A mock community consisting of six different bacteria, Gram positive and Gram negative, was employed as a control for both sequencing and bioinformatic analysis and was processed alongside the TI biopsies. The mock community contained Staphylococcus aureus (ATCC 25923), Enterococcus faecalis (ATCC 51299), Haemophilus influenzae (ATCC49247), Bacteroides fragilis (ATCC 25285), Corynebacterium renale (ATCC 19412) and Yersinia enterocolitica (ATCC2 3715). The bacteria were extracted using the Qiagen DNeasy tissue extraction kit as per manufacturer’s instructions. The bacterial mock community was constructed using differing amounts of the six bacteria and labelled Mock (Table 3.2).

Table 3.2 Bacterial mock community composition.

Bacteria Concentration ng/ul Pooled amount

Y. enterocolitica 28.39 1ul E. faecalis 7.49 2ul B. fragilis 7.3 4ul H. influenzae 5.47 4ul S. aureus 17.89 1ul C. renale 2.4 2ul

3.3.4 16S rRNA PCR Bacterial 16S rRNA amplicon PCR was conducted using the V4 primer spanning the region 517F GCCAGCAGCCGCGGTAA and 803R CTACCRGGGTATCTAATCC of the 16S rRNA gene. All samples, including the samples presented previously in Chapter 2 (AS01-AS04 and HC01-HC04), were processed in one batch together in this experiment.

The master mix for the reactions in a 50ul total volume using Invitrogen Platinum PCR SuperMix High Fidelity. Mastermix consisted of 45ul of SuperMix, 1ul each of both primers at 10uM and 3ul of DNA. Thermocycling conditions comprised of 2min at 94ºC then 25 cycles of 94ºC for 30s, 54ºC for 30s, 68ºC for 45 s and a single 72ºC for 10 min.

3.3.5 Sequencing Library preparation 16S rRNA amplicon libraries for all samples presented in this chapter, including the samples presented previously in Chapter 2 (AS01-AS04 and HC01-HC04), were assembled and indexed for multiplexing using the Illumina 16S Metagenomic Sequencing library protocol as per manufacturer’s instructions. All samples were normalised using the Agilent Bioanalyzer and pooled for sequencing at a concentration of 2nM. To create the required complexity for sequencing, PhiX was added to the pool at 25/75 for a final pool concentration of 4pM.

3.3.6 Illumina MiSeq Sequencing Barcoded 16S amplicon sequences were sequenced with 2x250bp paired-end reads on a single Illumina MiSeq run. A total of 6.1M reads were generated with an average of 200,000 reads per sample.

3.3.7 Bioinformatics and statistics Samples were demultiplexed and quality filtered using split_libraries_fastq.py (Table 3.3), then analysed using the QIIME package and workflow scripts (qiime.sourceforge.net) (169). Operation Taxonomy Unity (OTU) assignment was based on 97% similarity with taxonomic assignments made using BLAST to Greengenes (113) as implemented in QIIME.

Table 3.3 16S rRNA sequence read counts per sample post quality control and filtering Sample names Sequence counts AS01a 82,363 AS01b 132,597 AS02a 117,012 AS02b 77,983 AS03a 123,111 AS03b 126,647 AS04a 131,851 AS04b 150,298 AS05a 127,339 AS05b 81,085 AS06a 238,920 AS06b 121,211 AS07a 105,164 AS08a 113,490 AS08b 94,585 AS09a 117,119 AS09b 139,181 AS10a 120,418 AS10b 124,143 HC01a 111,807 HC01b 103,234 HC02a 161,879 HC02b 168,001 HC03a 166,127 HC03b 172,902 HC04a 92,936 HC04b 132,558 HC05a 100,554 HC05b 99,696 HC06a 121,273 HC06b 137,985 HC07a 91,674 HC07b 119,069 HC08a 94,205 HC08b 63,347 HC09a 110,494 HC09b 112,864 Mock 103,896

Hierarchical clustering was employed using Unweighted Pair Group Method with Arithmetic mean (UPGMA) both weighted and Unweighted UniFrac distance, to detect significant differences within microbial communities between AS and HC (175). The Weighted UniFrac distance metric detects changes in how many organisms are present in a community, taking into account the relative abundance of microbes present. The Unweighted UniFrac distance metric describes community membership. UniFrac enabled in QIIME was used to generate sample distance metrics as well as Principal Coordinate Analysis (PCoA) (189). To determine differences in microbial load, the total microbial biomass in biopsy samples was quantified using real-time qPCR of the 16S rRNA gene as described in Willner et al., 2013 (170).

PERMANOVA analysis was conducted on the genus level OTU table using the R package ‘vegan’ to test the relationship between the whole microbial community and disease (176). Indicator species analysis, using the ‘labdsv’ R package, was employed to investigate what microbial species are driving the microbial community and driving the signature (177). Co-occurrence network analysis, utilizing the R package ‘Spaa’, was carried out to further investigate correlations between microbial families in the AS microbiome. Core microbiome analysis, as well as supervised learning, was performed to further characterise the intestinal microbial signature using the workflows compute_core_microbiome.py and supervised_learning.py in QIIME.

To interrogate the function of bacteria and communities, Phylogenetic Investigation of Communities by Reconstruction of Unobserved States (PICRUSt) was used to predict the metagenome functional content from the 16S rRNA OTU table (115). Using the workflow normalize_by_copy_number.py in PICRUSt, the OTU table was normalised by copy number to limit copy number bias by dividing each OTU by the known or predicted 16S copy number. The metagenome was predicted using predict_metagenomes.py, which creates the prediction by multiplying each normalized abundance by the predicted functional trait abundance, to produce a table of function by samples. The table produced by the predict_metagenomes.py workflow is then analysed as a metagenome table. The 80 functions were collapsed into categories using the categorize_by_function.py script for further analysis in R. Metagenomic pathways were compared between AS and HC using generalized linear model glm in R.

The core microbiomes at varying thresholds were also investigated for metagenomic pathways of interest. As described above, the Core 100, 90, 75 and 50 OTU tables were processed through PICRUSt and then compared using Wilcox test and glm in R.

3.4 Results

The TI microbial and mock communities were profiled by high-throughput barcoded amplicon sequencing. In addition to exploring community-level differences between disease states, we evaluated biological replication by halving biopsies, then processing and analysing them independently

The microbial communities in the TI of AS cases were significantly different (Figure 3.1) and more diverse (Figure 3.2C) than those communities associated with HC (P<0.001; PERMANOVA). It was noted that three of the HC samples had higher levels of Proteobacteria (HC01, HC08 and HC09) (Figure 3.1). The mock community profile was consistent with control components (Table 3.2, Figure 3.1) showing 55% Proteobacteria (Y. enterocolitica and H. influenzae), 37% Firmicutes (E. faecalis and S. aureus), 7.5% Bacteroides (B. fragilis) and 0.5% Actinobacteria (C. renale).

Figure 3.1 Differences between AS and HC microbial communities. Bar chart showing the difference in taxonomy at the Phylum level between AS and HC noting the increase in Bacteroides and the change in the Bacteroides to Firmicutes ratio in the AS samples.

Figure 3.2 Shift in microbiome composition driven by disease status. (A) Weighted Principal Coordinates Analysis, which detects differences in organisms present in a community and taking into account their relative abundance, showing the distinct clustering of AS samples compared to HC, and supported by the supervised learning predicting disease status based off microbiome composition (B) Weighted Principal Coordinates Analysis, having removed the four outlying HC, still showing the distinct clustering of AS samples compared to HC (C) Alpha diversity box plot showing an increase of microbial diversity (observed species) in AS samples compared to CD and HC. (D) Comparison of variation between and within samples and affection status showing greater significance between and within affection status than between biological and technical replicates significance was assessed using Mann-Whitney test (P<0.001 = ***).

Figure 3.3. Weighted and Unweighted Principal Coordinates Analysis of the samples and their biological replicates (A) Weighted Principal Coordinates Analysis of the samples and their biological replicates (open symbols) showing not only distinct clustering samples by disease status, but tight clustering of the biological replicates. The bacterial (Mock) control included as a known positive control. (B) Unweighted Principle Coordinates Analysis, which detects differences in the presence and absences of microbes in a community, of samples and their biological replicates showing general but not distinct discrimination of AS samples and their biological replicates to HC, indicating there are some differences in the AS microbial community due to presence/absence of microbes.

PCoA showed AS samples group separately to HC (Figure 3.2), including biological replicates (Figure 3.3), indicating that disease is the primary factor influencing community differences. Four HC samples group separately and away from the other HC, and closer to the mock community control. Three of the four HC samples contained increased proportion of Proteobacteria (HC01, HC08 and HC09) and HC04 that had increased levels of Actinobacteria, compared to the other HC samples. Therefore to examine if these outlying samples were affecting the clustering seen on PCoA (Figure 3.2A), the four outlying samples were removed and the remained samples reanalysed (Figure 3.2B). The reanalysed PCoA showed the AS samples still clustered separately from HC, with the exception of AS02 which also contained more Proteobacteria than the other AS samples (Figure 3.2B).

To further demonstrate the distinct grouping of AS samples to HC, supervised learning was conducted to test the predictive capacity of the microbiome differences, with all nine AS cases predicted as AS (Figure 3.2A). PERMANOVA analysis further confirmed the 84 presence of a significant relationship between disease status and microbial community composition (P<0.001). This was not due to heterogeneity in biopsy samples as biological replicates showed no significant difference between each other (Figure 3.2D). Biomass q- PCR showed no significant difference on average in 16S rRNA copy number between AS and HC; indicating that the observed differences were not due to overgrowth or dominance of bacteria driving community differences (Mann-Whitney test, P>0.05 = NS). Average UniFrac distances demonstrated that differences between both biological and technical replicates were significantly less than differences between individuals and disease states (P<0.001; Mann Whitney) (Figure 3.2D).

16S community profiling, where sequencing reads were clustered against the Greengenes reference database at 97% similarity (112, 113) showed 51 genera present across all biopsies, with major differences observed at the phylum level between AS and HC (Figure 3.1). Indicator species analysis was performed to determine if alterations in specific species were driving the differences observed between communities in cases with AS and HC. Analysis showed that compared to HC, the microbial communities in AS cases were characterized by higher abundances of Lachnospiraceae including Coprococcus spp. and Roseburia spp. (P=0.001), Ruminococcaceae (P=0.012), Rikenellaceae (P=0.004), Porphyromonadaceae including Parabacteroides spp. (P=0.001), and Bacteroidaceae (P=0.001). Decreases were noted in Veillonellaceae (P=0.01) and Prevotellaceae (P=0.004) (Figure 3.4A). Further drilling down into the AS microbiome signature, co- occurrence analysis, which examines at the correlation between microbes, showed that bacterial interactions further shape the AS microbial community signature with positive correlations observed between the indicator species Lachnospiraceae and Ruminococcaceae, and negative correlations observed between Veillonellaceae and Prevotellaceae (Figure 3.4B).

Figure 3.4 Differences in bacterial family abundances and co-occurrence network. (A) Differences in relative abundances of bacterial families in AS (orange) microbiome compared to HC (red). The first seven families of bacteria are AS indicator species, including P-values, with the last three bacterial families being HC indicator species. (B) Co-occurrence network showing the positive (blue lines) correlations, between AS indicator species Lachnospiraceae and Ruminococcaceae families, and negative (red lines) correlations between families such as Veillonellaceae, Porphyromonadaceae and Prevotellaceae. The thickness of the line denotes the strength of the correlation (Pearson correlation).

Given the distinct differences seen between the AS and HC intestinal microbiome composition, we were interested in seeing how changes in composition affect microbiome function. Microbial function was inferred from the 16S rRNA data using the prediction software PICRUSt (115). PICRUSt analysis indicated 31 significant pathways with differential representation in AS compared to HC (Bonferroni corrected P<0.02) (Table 3.4). These included a decrease in Bacterial invasion of epithelial cells (P=4.33x10-5), an increase in antimicrobial production in the Butirosin and neomycin biosynthesis pathway

(P=0.002), which is consistent with an increase in bacteria from Bacteroidaceae family, and an increase in the Secondary bile acid biosynthesis pathway (P=0.004) consistent with an increase in the order Clostridia and Ruminococcaceae species (190).

Table 3.4 Significant KEGG pathways comparing AS to HC as identified by PICRUSt analysis P<0.02.

KEGG Pathway name AS to HC Pvalue Bacterial invasion of epithelial cells 0.0001 Electron transfer carriers 0.0009 Fluorobenzoate degradation 0.0021 Butirosin and neomycin biosynthesis 0.0021 Germination 0.0021 Transcription related proteins 0.0030 Caprolactam degradation 0.0030 alpha-Linolenic acid metabolism 0.0030 Secondary bile acid biosynthesis 0.0041 Primary bile acid biosynthesis 0.0041 Ether lipid metabolism 0.0041 Flavone and flavonol biosynthesis 0.0057 Flavonoid biosynthesis 0.0057 Phenylpropanoid biosynthesis 0.0057 Glycan biosynthesis and metabolism 0.0076 Linoleic acid metabolism 0.0076 Adipocytokine signaling pathway 0.0101 Phosphonate and phosphinate metabolism 0.0101 Sporulation 0.0101 Sphingolipid metabolism 0.0101 Polycyclic aromatic hydrocarbon degradation 0.0101 Pathogenic Escherichia coli infection 0.0113 Carotenoid biosynthesis 0.0133 Cyanoamino acid metabolism 0.0133 Phosphotransferase system (PTS) 0.0172 Amoebiasis 0.0172 Bisphenol degradation 0.0172 Photosynthesis 0.0172 Photosynthesis proteins 0.0172 Cell cycle - Caulobacter 0.0172 Other glycan degradation 0.0172

Further interrogating the AS microbiome signature, which is the overall combination of microbes which distinguishes AS from HC, by seeing if there was an assemblage or ‘core’ microbes present in all AS and HC samples at varying levels (127) and to probe their functional capacity. Exploring the core microbiome is imperative, to better understand the stable and consistent components across complex microbial communities, given the distinct AS microbial signature. We began by looking at Core 100, which requires microbes to be present in all samples 100% of the time, which included Clostridia, Actinomycetaceae, Bacteroidaceae, Lachnospiraceae, Porphyromonadaceae, Rikenellaceae, Ruminococcaceae and Veillonellaceae families of bacteria (Table 3.5). These families of bacteria are also AS indicator species suggesting that the core microbiome is driving the AS microbial signature (Figure 3.4).

Table 3.5 Significant KEGG pathways comparing in Core100 microbiome comparing AS to HC as identified by PICRUSt analysis P<0.02.

KEGG Pathway name AS to HC Pvalue Basal transcription factors 0.0003 Atrazine degradation 0.0004 Renal cell carcinoma 0.0015 Flavone and flavonol biosynthesis 0.0021 Germination 0.0021 African trypanosomiasis 0.0041 Bacterial invasion of epithelial cells 0.0041 Bladder cancer 0.0041 Chagas disease (American trypanosomiasis) 0.0041 Butirosin and neomycin biosynthesis 0.0076 Electron transfer carriers 0.0076 Phenylpropanoid biosynthesis 0.0079 Carotenoid biosynthesis 0.0101 Caprolactam degradation 0.0101 Flavonoid biosynthesis 0.0101 Pathogenic Escherichia coli infection 0.0126 Shigellosis 0.0126 Fluorobenzoate degradation 0.0133 Primary bile acid biosynthesis 0.0133 Secondary bile acid biosynthesis 0.0133 Alpha-Linolenic acid metabolism 0.0133 Sphingolipid metabolism 0.0172

Given that these microbes were present in all samples 100% of the time, we were interested in their function, the biological pathways they were involved in and whether they involved in known AS associated pathways. For Core 100 there were 22 significant pathways (Table 3.5) (P< 0.02) that again included Bacterial invasion of epithelial cells (P= 0.004), antimicrobial production in the Butirosin and neomycin biosynthesis pathway (P=0.007) and the Secondary bile acid biosynthesis pathway (P=0.013). Members of Bacteroides, Clostridium and Ruminococcus are known to be involved in cholesterol metabolism and secondary bile synthesis with this pathway also implicated in colorectal cancer.

As the threshold for core membership was reduced to 90, 75 and 50% of the time, the families of bacteria present remained the same however the number of genus and species within these families increased, especially in the order Clostridiales, Lachnospiraceae and Ruminococcaceae. This demonstrates the structure of the core microbiome is robust and that decreasing the threshold only expands the number of genera and species of bacteria within these families. However, as the core threshold was decreased the number of significant pathways also decreased suggesting that as the threshold is relaxed, the expansion of genera and OTUs dilutes pathway signals. This was reflected in the metagenome prediction with PICRUSt. As the core threshold was relaxed, the number and types of pathways that were significantly different between AS and HC was reduced.

No significant differences were noted between AS cases and controls in abundance of bacteria known to be associated with reactive arthritis such as Campylobacter, Salmonella, Shigella, Yersinia and Chlamydia. Also, there was no significant difference in abundance of Klebsiella in AS cases, which has been proposed to play a role in triggering AS (142).

3.5 Discussion

Here we present the first characterisation and identification of intestinal dysbiosis is the AS microbiome using 16S rRNA community profiling of TI biopsies. We performed culture- independent microbial community profiling of TI biopsies from nine AS patients, nine HC and a bacterial mock community, and show evidence for a discrete microbial signature in the TI of cases with AS. A mock community consisting of six bacteria was constructed and used as an internal positive control for the purpose of determining if the methods used accurately captured the microbial communities examined (112, 191). All members of the mock community belong to bacterial families that are commonly found in the gut, with the exception of Pasteurellaceae. Y. enterocolitica is a known intestinal pathogen belonging to Enterobacteriaceae family, E. faecalis and B. fragilis are both known and well documented intestinal bacteria. Corynebacteriaceae and Staphylococcaceae bacterial families, to which C. renale and S. aureus belong, are also documented residents of the gastrointestinal tract. H. influenzae is a member of the Pasteurellaceae family and whilst H. influenzae specifically is not found in the gut, members of the Pasteurellaceae family are found in oral microbiome and upper respiratory tract. There is increasing evidence of a link between the oral and gut microbiome, particularly if patients are on proton-pump inhibitors (192), indicating that microbial communities may not be site specific discrete environment, but fluid communities so (129, 193, 194). Analysis of the mock community returned the expected 16S profile dominated by Proteobacteria (Y. enterocolitica and H. influenzae), Firmicutes (E. faecalis and S. aureus), Bacteroides (B. fragilis) and the low abundant Actinobacteria (C. renale). This demonstrates that the resulting community profile is consistent with the known inputs and that the sequencing and analysis methods are producing accurate profiles. This also demonstrates that the methods used are not introducing unwanted bias to the community profiling results (195), nor are the less abundant bacteria being swamped by more dominate bacteria.

Taken together, this demonstrates that the observed microbial profile differences are not due to differences in overall bacterial quantity between cases with different diseases, but are qualitative. PCoA was able to show the clear and distinct grouping of AS cases from HC; larger studies will be required to define the individual bacterial species involved. Four HC samples were found to group separately and away from the other HC, closer to the mock community control. Three of the four HC samples contained increased proportion of Proteobacteria (HC01, HC08 and HC09) and HC04 that had increased levels of

Actinobacteria which has been associated with obesity (114, 196), compared to the other HC samples, skewing their profiles. Increased levels of Proteobacteria can be associated with issues such as poor sequencing quality, however all samples were processed and sequenced at the same time, and thus would all have potentially been affected by any such bias. With only these there HC samples seemingly affected, poor sequencing quality is an unlikely explanation for the observations. There is limited metadata on the HC patients with regards to medications, gastrointestinal issues, diet or other co-morbidities, therefore a biological reason for the increased Proteobacteria cannot be excluded. There is evidence to suggest that a reduction in overall microbiome diversity can lead to increased proportions of Proteobacteria (196), with experiments in mice demonstrating that increased dietary fat leads to the reduction of microbial diversity and the expansion of Proteobacteria leading to a diet induced dysbiosis (197-199).

To examine if these outlying samples were affecting the clustering seen on PCoA, the four outlying were removed and the remaining samples reanalysed. Even excluding the four HC samples, clear clustering of AS samples away from the HC was seen with the exception of AS02, which upon further investigation also had higher percentage Proteobacteria compared to the other AS samples. As well as diet induced dysbiosis, increased Proteobacteria has also been linked to colitis in animal studies (200), demonstrating there are several possible biological reasons for an increase in this phyla. Despite the incidence of Proteobacteria in several of the samples, statistically the relationship between disease status and microbial community composition was confirmed indicating that the differences between the microbial community compositions were not due to heterogeneity in the biopsy or lack of technical reproducibility, but that the driving force is disease state.

Of the seven families of microbes with differences in abundance within the AS microbiome, Lachnospiraceae, Ruminococcaceae and Prevotellaceace are strongly associated with colitis and CD (102-104) with Prevotellaceace especially known to elicit a strong inflammatory response in the gut (104). Further investigations showed that correlations between these families of bacteria further shape the AS microbial signature, and that these microbes are present in all AS samples studied suggesting that they are not only driving the microbial signature, but they are at the core of the AS microbial signature. Increases in Prevotellaceae and decreases in Rikenellaceae have also recently been reported in the intestinal microbiome in the HLA-B27 transgenic rat model of spondyloarthritis, suggesting 91 that underlying host genetics may play a role in sculpting the gut microbiome in this animal model (201).

There is an increase in the diversity of the AS community without an overall change in microbial load, showing there is not an overgrowth or dominance of a particular microbe driving the signature. However, murine experiments demonstrate that both the overall composition of the intestinal microbiome, and the presences and/or absence of specific microbes can have a substantial impact on host response, regulation of inflammation and development of intestinal cells. In the K/BxN mouse model of arthritis it was shown that the introduction of a single gut-residing species, Segmented Filamentous Bacteria, into GF animals was sufficient to reinstate the Th17 cells, leading to the production of autoantibodies and arthritis. If IL-17 was neutralised in specific-pathogen-free K/BxN mice, arthritis development was prevented. Thus, a single commensal microbe, via its ability to promote a specific T helper cell subset, can drive immune mediated disease (101).

Further studies are needed into whether the changes in intestinal microbial composition are due to host genetics, and how this affects the overall function of the gut microbiome in AS cases, including how the microbiome then goes on to shape the immune response and influence inflammation. In particular, given the strong association of HLA-B27 with AS, the hypothesis has been raised that HLA-B27 induces AS by effects on the gut microbiome, in turn driving spondyloarthritis-inducing immunological processes such as IL-23 production (67, 202). Further experiments comparing the intestinal microbiome of HLA-B27 negative and positive patients would shed light of the influence of HLA-B27 on overall intestinal microbiome composition, particularly given the work in B27 transgenic rats showing that HLA-B27 was associated with altered caecal microbiota (201).

Our data here, showing intestinal dysbiosis in AS cases, is consistent with this hypothesis however further studies are clearly required to distinguish cause and effect interactions between the host genome and immune system, and the gut microbiome. These investigations will provide new insights and help us to better understand AS disease pathogenesis.

4 Chapter 4 Effect of ERAP1 genotype on the intestinal microbiome and the impact on the immune system in the ERAP mouse model

4.1 Introduction

To date, over 40 loci have been associated with ankylosing spondylitis, and one of the strongest associations, along with HLA-B27, is Endoplasmic reticulum aminopeptidase-1 (ERAP1) (21, 24, 55, 62). Interactions between HLA-B27 and ERAP1 have been described in ankylosing spondylitis (AS) such that variants of ERAP1 were shown only to influence AS disease risk in the presence of HLA-B27 (62), suggesting that ERAP1 operates through effects on peptide processing prior to MHC class I presentation. Fine mapping studies indicate that the ERAP1 single nucleotide polymorphism (SNP) rs30187 is directly associated with disease risk and that this loss of function variant is protective in AS (62, 187). Therefore AS-associated ERAP1 variants may change either the amount and length of peptides presented or even influence the stability of the peptide loaded onto the MHC class I, or the peptide MHC complex and its cell surface retention (203). Consequently, ERAP1 could influence AS disease risk via changes in the type or amount of volume of peptide presented. In this chapter we are asking very different questions of ERAP’s function and role in AS pathogenesis by exploring how loss of ERAP1 impacts on the gut microbiome and how that subsequently affects the immune system.

Early functional studies suggested two roles for ERAP1, one as a ‘molecular ruler’ trimming peptides prior to presentation on the MHC class I, and the second as a ‘sheddase’, which acts by cleaving cytokine receptors off the cell surface, including IL-6R, IL-1R2 and tumour necrosis factor receptor (TNFR). However studies in mice showed no difference in the levels of these receptors over time, indicating that ERAP1 does not have a major influence on cytokine receptor trimming, at least in mice. Further, ERAP1 SNPs were shown not to be associated with variation in serum cytokine receptor levels, confirming in humans that this gene does not play a significant role in cytokine receptor shedding (204). This strengthens the case that ERAP1 acts as a ‘molecular ruler’ and is responsible for the trimming of peptides prior to presentation (205). ERAP1 is a ubiquitously expressed, multifunctional enzyme that trims peptides and antigen for presentation by MHC class I to the immune system. The endoplasmic reticulum (ER)

93 localised ERAP1 is critical in the MHC class I presentation pathway. Once peptides have been process through the proteasome, the Transporter associated with Antigen Processing (TAP) takes the subsequent peptide from the cytoplasm into the ER. ERAP1 then further trims any N-terminally extended peptides to no longer than 9 amino acids in length which generates or destroys antigenic epitopes prior to loading onto MHC class I molecules (187, 206-208). Interaction between ERAP and the AS associated MHC class I HLA-B27, was the focus of a recent study by Akram et al., 2014. The authors suggest that ERAP may play a role in stabilising the heavy chain B27 during assembly of the MHC class I by presenting it with appropriate B27-specific peptides(209). Interestingly, this stabilisation seems to be B27 specific. When compared to B7, presence/absence of ERAP had no effects on host immune response, whereas the absence of ERAP significantly altered the immune response to infection with B27 was present (209).

The establishment and maintenance of beneficial interactions between the host and their microbiota are key requirement for overall health. The bacteria that inhabit the gut and the human immune system have both developed a number of strategies to maintain gut homeostasis. The gastrointestinal tract is dynamic environment heavily populated with microbes and is the primary site for interaction between microbes and the host immune system. Furthermore, the microbes found in the gut help to shape host immune systems from an early age (210, 211). The incomplete development of the immune system in neonates and mice raised under germ free conditions, tells us that microbiota sculpt the host immune system (28, 212). The dysregulation of either gut or microbial homeostasis is increasingly recognised as playing a pivotal role in autoimmunity. Fluctuations in microbial composition due to changes in diet, environment, hygiene and use of antibiotics, presents a major challenge to the immune system (30). Controlling activation of the immune system in response to these changes in gut microbiota has to be tightly regulated as to avoid chronic inflammation. The body has a number of physiological processes that respond to a disruption in gut homeostasis or a bacterial insult, involving both the adaptive and innate immune system, and the barrier function of the intestines themselves (94, 213).

4.1.1 Peptide presentation and infection

Aminopeptidases play integral roles in modulating the adaptive immune responses to both host and pathogen derived antigens. Unlike humans, mice only carry one isoform of ERAP1, which will be referred to from here as ERAP. Recent animal studies have

94 revealed how ERAP shapes the antigenic peptide repertoire against microbial and viral infections. This is dramatically demonstrated in ERAP-/- mice infected with the obligate intracellular parasite Toxoplasma gondii, that causes toxoplasmosis. Mice lacking ERAP are unable to trim and present immunodominant and appropriate length peptides derived from T. gondii. This leads to aberrant peptide presentation, an inability to mount an effective immune response to infection with fatal consequences (214). ERAP-/- mice infected with lymphocytic choriomeningitis virus (LCMV) were shown to present drastically different immunodominant epitopes derived from LCMV for presentation than wild type (WT) mice (215). ERAP’s role in modulating the immune response to viral pathogens is further validated by the finding that human cytomegalovirus (HCMV) has evolved an immune evasion strategy where by a micro RNA miR-US4-1 is expressed and specifically targets ERAP, down regulating expression and altering peptide trimming and presentation as to prolong viral infection (206). Down regulation or the loss of ERAP not only affects peptide presentation during infection, it also significantly alters the composition of the endogenous MHC class I peptidome. Murine studies have shown that a lack of ERAP, in the absence of infection, presents unstable and structurally unique peptide-MHC complexes and that these complexes elicited a potent CD8+ T cell response (207, 208).

Antigen presenting cells (APC), such as dendritic cells (DCs), internalise antigens from the environment, process and present these exogenous antigens to MHC class II for presentation to CD4+ T cells. However, many viruses and bacteria have developed a number of evasion strategies and presentation of peptide from DCs to MHC class I mitigates this by eliciting a potent CD8+ T cell immune response. This cross presentation is critical for immune surveillance defence against viruses, bacteria and even tumour cells (216). Peptides presented by DCs in this manor are processed mainly via the phagosome to cytosol pathway. There is recent evidence to suggest that peptides generated in the cytosol may be transported by transporter associated with antigen processing (TAP) to MHC class I in the ER via ERAP1, or may do so in phagosomes (214). One phagosome peptidase of interest is Insulin regulated aminopeptidase (IRAP), a homolog of ERAP1, which also trims amino acids from the N terminus and its important for cross presentation (217). Studies have shown that DCs that lack IRAP are capable of presenting antigen normally however, their ability to cross present antigen is substantially reduced; not unlike what is seen with the loss of ERAP1 (217). This further highlights the vital role ERAP1 plays in modulating the adaptive immune responses to both host and pathogen derived antigens. 95

4.1.2 Intestinal cytokines and gut homeostasis

IL-23 is a key cytokine in the development of IL-17 and IL-22-secreting cells. IL-23 signals through a receptor consisting of the specific IL-23 receptor p19 subunit (IL-23R) and the IL-12 shared subunit p40 (54). Loss of function polymorphisms in IL23R are associated with protection from AS (24, 55), psoriasis (56) and IBD (23, 57), and there are many other genes in the IL-23 pathway that are known to be associated with these diseases. IL-23, IL- 17 and IL-22 producing cells are enriched in intestinal mucosa and have been shown to be overexpressed in the intestinal mucosa of AS patients with sub-clinical gut disease (218). IL-17 and IL-22 work together in concert to regulate intestinal health with IL-17 playing important roles in intestinal homoeostasis in several ways including maintenance of epithelial barrier tight junctions (58); as well as the induction of anti-microbial proteins such as β-defensins, S100 proteins and REG proteins. Innate IL-17 producing cells have been found to reside mainly in the skin and mucosal tissues, especially the gastrointestinal tract, and act as a first line of defence. IL-17 released during an inflammatory response is produced by innate immune cells in response to injury, stress or to pathogens (91). IL-17 has been shown to be produced 4-8hrs after a microbial infection and is secreted by T cells including T helper cells (Th17) and γδ T cells (63, 92, 219). IL-22 is known to induce secretion of anti-microbial peptides (98) and has been shown to play a critical role in host defence, tissue homeostasis and inflammation with the IL-22-IL-22R pathway contributing to regulating immunity, inflammation and tissue repair. IL-22 is expressed by innate and adaptive cells and seems to act almost exclusively on non-haematopoietic cells, with basal IL-22R expression in the skin, pancreas, intestine, liver, lung and kidney (94). IL-22 can act synergistically or additively with IL-17A, IL-17F or tumour necrosis factor (TNF) to promote the expression of many of the genes that encode molecules involved in host defence in the skin, airway or intestine. This demonstrates the functional importance of IL- 22 in promoting barrier immunity and mucosal integrity, particularly against Gram negative pathogens, where IL-22 is critical for limiting bacterial replication and dissemination, possibly in part by inducing the expression of antimicrobial peptides from epithelial cells at these barrier surfaces (98, 99). Containment of normal flora is another important arm of homeostasis. IL-22 producing innate lymphoid cells (ILCs) found throughout the gut, play a critical role in the physical containment of resident microbes (100). Loss of epithelial integrity leads to dissemination of intestinal commensals and systemic inflammation.

The initial immunological characterisation of the IL-23/Th17 axis was performed largely in the collagen-induced arthritis (CIA) mouse model of autoimmune arthritis, which phenotypically resembles rheumatoid arthritis (RA). In the CIA model, mice that lack IL-23 are protected against the development of disease. In IL-17 and IL-22 deficient mice CIA is suppressed, implicating the IL-23 pathway in pathogenesis of disease (220, 221).

4.1.3 Interactions between intestinal microbes and the immune system

A number of immune cells play critical roles in inflammation, cytokine production and homeostasis within the gut. Natural killer T (NKT) cells are protective in Th1-mediated models of inflammatory bowel disease but pathogenic in Th2 models (73, 74). Recently, it has been shown that microbial stimulation of NKT cells in the gut of mice affects NKT cell phenotypic and functional maturation (75). NKT cells recognise glycolipd structures presented to them by the non-classical antigen-presenting molecule CD1d. NKT cells are rapid responders to antigenic stimuli and are capable of producing a range of immunoregulatory cytokines. Given that NKT cells have protective roles in models of arthritis (76) and spondyloarthropathy (77, 78) their functional maturation in the gut provides evidence for a role for mucosal T cell priming in inflammatory joint disease.

Lymphoid tissue inducer (LTi)-like cells are found in spleen, lymph node and gut lamina propria. LTi-like cells constitutively express hallmarks of IL-17-secreting cells including IL- 23R, RORγt, AHR and CCR6 (86, 87). A common characteristic of ILCs, specifically ILC3, is the expression RORγt, a transcription factor important for the development of these cells. ILCs were initially described as important for development of lymphoid tissues and more recently in the initiation of inflammation at barrier surfaces in response to infection or tissue damage. It has become apparent that the family of ILCs (Table 4.1) plays more complex roles throughout the duration of immune responses, participating in the transition from innate to adaptive immunity across varying insults and contributing to chronic inflammation. ILCs have been linked to gut inflammation through colitis models where IL- 23 responsive ILCs secrete IL-17, IL-22 and IFN-γ and to promote intestinal inflammation (89). RORγt expressing ILC3s are abundant in the intestinal lamina propria and produce IL-17 and/or IL-22 in order to preserve mucosal integrity against extracellular pathogens and maintain equilibrium between host and microbes. These NKp46+ ILCs have been found to be critical for host defence against pathogens such as Citrobacter rodentium infection through secretion of IL-22 (87, 90).

Table 4.1 Innate lymphoid cells including cytokine expression and immune function.

Classification Transcription Cytokine expression Immunity roles factors ILC group 1 IFNγ, TNF-α Bacteria, intracellular (NK cells) parasites, autoimmune disorders, ILC group 2 RORα, GATA 3 IL-5, IL-9, IL-13 Parasitic worms, allergic (Natural helper disease, obesity, cells) ILC group 3 RORγt IL-22 and/or IL-17 Bacteria, autoimmune (Lymphoid disorders, homeostasis tissue inducers (LTi) γδ T cells IL-17, IFN-γ, TNF-α Bacteria, mediate innate and adaptive immunity

Taken together, this suggests ERAP1 plays a critical role in overall immune system modulation with systemic effects. In this chapter we are asking how a lack, or down regulation, of ERAP1 influences the inherent gut microbiome composition, and how this impacts on the antigen repertoire and consequently the local and systemic immune system. We hypothesise that the loss of ERAP1 leads to altered T cell selection, impairing immune recognition of inherent gut microbiota, and leading to an overall shift in microbial community composition.

4.2 Methods

4.2.1 Antibodies and Flow Cytometry

4.2.1.1 Mice C57BL/6 mice were purchased from Animal Resources Centre, Perth, Australia. The generation of ERAP-/-, which are on a C57BL/6 background, has been described previously (207). ERAP-/- mice were a kind gift from Dr N. Shastri (University of California, Berkeley). All mice were housed in the specific pathogen–free animal facility and fed standard rodent chow at Translational Research Institute (TRI) Biological Research Facility, according to the guidelines of the Australian NHMRC code for ethical use of animals. All animal studies have been reviewed and approved by The University of Queensland Animal Ethics Committee. F2-intercross mice were bred to generate littermates that were ERAP-/- (KO), ERAP+/- (HET) or ERAP+/+ (WT). To minimise cage-

98 effects on the gut microbiome, littermates were raised together throughout the experiment, and analyses were adjusted for cage.

4.2.1.2 Antibodies Antibodies against CD4 (GK1.5), CD3 (145-2C11), CD69 (H1.2F3), CD44 (IM7), CD335 (29A1.4), CD25 (PC61), CD11b (M1/70), NK1.1 (PK136), IFNγ (XMG1.2), IL-10 (JESS- 16E3) were purchased from Biolegend (San Diego, CA). Antibodies against γδ (eBioGL3), RORγt (AFKJS-9), FOXP3 (FJK16s) and IL-22 (IH8PWSR) were obtained from eBiosciences (San Diego, CA). IL-17 (TC11-18H10) and TNF (MP6-XT22) were purchased from BD Pharmagin (San Diego, CA).

4.2.1.3 Cell preparation Spleen and mesenteric lymph node (MLN) cells were prepared from freshly harvested ERAP-/- and C57BL/6 mice. Whole spleens and MLN were disrupted and strained using 70mL filters (BD Pharmingen) to create single cell suspensions. Splenic cells were lysed using ACK lysis buffer for 2 minutes. Cells were washed in PBS prior and half transferred to 48-well plate for stimulation with PMA (phorbol myristate acetate, AKA phorbol ester, Biochimika) and Ionomycin (Ca2+ salt, Calbiochem) as previously described (222), with the other half being used for activation marker and transcription staining.

Splenic and MLN cells were stained for extracellular markers, transcription factors and intracellular cytokines. For detection of T cell markers cells were stained for CD3, CD4, CD8 and γδ. For detection of non-T cells, NK1.1 and CD11b were used. Activation status was determined with CD69, CD44, CD335 and CD25. Transcription factors were assessed through FoxP3 and RORgt. Cells were fixed (Fixation Buffer, Biolegend), permeabilised (Perm/Wash buffer, BD Pharmingen) and stained for intracellular cytokines TNF, IFNγ, IL- 10, IL-22 and IL-17. Cytometric data was acquired on the Beckman Coulter Gallios cytometer.

4.2.1.4 Statistics All data are presented as the mean ± standard error of the mean (SEM). Data were compared by unpaired T test, using GraphPad Prism 6 (GraphPad Software). P≤0.05 were considered significant. Power calculations were computed using an on-line power

99 calculator (http://www.statisticalsolutions.net/pss_calc.php) with effect size and Cohen’s d computed as outlined by Olejnik and Algina, 2003 (223).

4.2.2 Sequencing and analysis

4.2.2.1 DNA extraction Faecal pellets were collected and DNA extraction was performed using the Qiagen DNeasy tissue extraction kit as per manufacturer’s instructions.

4.2.2.2 Mock community A mock community consisting of seven different bacteria, Gram positive and Gram negative, was employed as a control for both sequencing and bioinformatic analysis and was processed alongside the mouse faecal samples. The seven bacteria used were Staphylococcus aureus (ATCC 25923), Enterococcus faecalis (ATCC 51299), Haemophilus influenzae (ATCC49247), non-mucoid Pseudomonas aeruginosa (ATCC 27853), Klebsiella oxytoca (ATCC 700324), Escherichia coli (ATCC 35218) and Yersinia enterocolitica (ATCC2 3715). Bacteria were extracted using the Qiagen DNeasy tissue extraction kit as per manufacturers instructions. The community was then labelled Mock1.

4.2.2.3 16S rRNA PCR Bacterial 16S rRNA amplicon PCR was conducted using V4 primers spanning the region 517F 803R of the 16S rRNA gene. The master mix for the reactions was conducted in a

25ul total volume using Bioline 10x reaction buffer, dNTP(0.625mM), MgCl2 (2.0mM), 1U of Bioline TAQ, both primers at 5uM each and 2ul DNA. Thermocycling conditions comprised 10 min at 94ºC then 30 cycles at 94ºC for 50 s, 54º for 30 s, 72ºC for 45 s and a 72ºC single step for 10 min.

4.2.2.4 Sequencing Library preparation 16S rRNA amplicon libraries were assembled and dual indexed for multiplexing using the Nextera XT dual indexing library protocol as per manufacturer’s instructions. Samples were then normalised and pooled for sequencing at a concentration of 2nM. To create the required complexity for sequencing, PhiX was added to the pool at 75/25 for a final pool concentration of 3.5pM.

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4.2.2.5 Illumina MiSeq Sequencing Dual barcoded 16S amplicon sequences were sequenced using 2x250 base paired-end protocols on the Illumina MiSeq. There were 6.1million reads generated overall with an average of 200,000 reads per sample.

4.2.2.6 Bioinformatics and statistics Samples were demultiplexed using CASAVA. Reads were quality filtered for Q30 and above using split_library.py and then analysed using the QIIME pipeline (qiime.sourceforge.net). OTU assignment was based on 97% similarity with taxonomic assignments made using BLAST to Greengenes as implemented in QIIME (Caporaso et al., 2011).

Alpha diversity metrics were generated using the QIIME workflow alpha_rarefaction.py with five repetitions at each sampling point. Rarefaction curves for the observed number of OTUs were based on the outputs of alpha_rarefaction.py. UniFrac distances between microbial communities were evaluated using FastUniFrac (Louzapone and Knight 2005). UPGMA clustering was employed both weighted and unweighted, to detect significant differences between microbial communities. UniFrac enabled in QIIME was used to generate sample distance metrics as well as Principal Co-ordinate Analysis (PCoA) (189).

PERMANOVA analysis was conducted at the genus level using the R package ‘vegan’ (version 3.0.2) to test the relationship between the whole microbial community, genotype, cage and litter effect (176). Co-occurrence network analysis was conducted using the ‘Spaa’ package in R (version 3.0.2).

4.3 Results

4.3.1 Alterations in host genetics influences gut microbiome composition

The effect of underlying host genetics on the microbiome structure and function, especially in AS, is still poorly understood. Taking a reductionist approach, we examined the effect that loss of ERAP1 has on the gut microbiome and subsequent effects on the local and systemic immune system using a mouse model of ERAP-/-. The loss of ERAP1 is protective in AS, we therefore explored if the loss of ERAP in a mouse model caused a shift in the intestinal flora and if that shift was anti-inflammatory in nature. 101

Faecal pellets from eight ERAP-/- and 11 wild-type C57BL/6 mice were collected and bacterial DNA was extracted, amplified for microbial 16S rRNA and sequenced. This proof of principle study aimed to determine if the loss of a single gene in is sufficient to cause a shift in the intestinal microbiome.

16S rRNA community profiling of the mouse gut microbiome revealed distinct clustering of communities separating ERAP-/- from wild-type C57BL/6 mice (Figure 4.1). Microbial communities were compared using Weighted and Unweighted UniFrac distances, a phylogenetic metric that accounts for differences in both community membership and relative abundance (174). These distance metrics allow us explore whether it is the presence or absence of bacteria that is driving the overall community structure, or if it is simply a change in the relative abundances of particular microbes in the community (189). Unweighted UniFrac analysis indicated that overall structure, the presence or absence of bacteria, was driving the distinct clustering between these mice. The Phylum level differences between C57BL/6 and ERAP-/- (Figure 4.1B) included changes in the Bacteroides and Firmicutes ratio that constitutes fundamental changes in community structure, driving the clustering on PCoA (Figure 4.1A). Several of the species observed driving the clustering of these mice are also indicator species driving the clustering of microbial communities in terminal ileum of AS patients as described in Chapter 2 of this thesis (Table 4.2). Whilst there are environmental effects such as background and cage effect, this suggests that a single change in underlying host genetics may be sufficient to influence microbial community structure in the gut.

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Figure 4.1 Principal coordinate analysis and taxonomy showing community differences between ERAP-/- mice and control C57BL/6. (A) Unweighted Principal Coordinate Analysis (PCoA) showing distinct clustering of ERAP-/- mice from control C57BL/6. (B) Bar chart showing the difference in taxonomy at the Phylum level between C57BL/6 and ERAP-/-, noting the increase in Bacteroides in C57BL/6 and the change in the Bacteroides to Firmicutes ratio between the samples.

Table 4.2 Differences in average percentage OTU counts between C57BL/6 and ERAP-/- mice showing that changes in proportions of key bacteria contribute to the ERAP-/- microbial signature.

Taxonomy C57BL/6 ERAP-/- Difference Result (OTU Ave) (OTU Ave) Bacteroidaceae 0.008 0.024 0.016 Increase in ERAP-/- (genus Bacteroides) Prevotellaceae 0.012 0.008 -0.004 Decrease in ERAP-/- (genus Prevotella) Rikenellaceae 0.011 0.028 0.017 Increase in ERAP-/- (genus Rikenella) S24-7 0.680 0.442 -0.238 Decrease in ERAP-/- (order Bacteroidales) Lactobacillaceae 0.032 0.014 -0.018 Decrease in ERAP-/- (genus Lactobacillus) Clostridiaceae 0.129 0.301 0.172 Increase in ERAP-/- (genus Clostridium) Lachnospiraceae 0.031 0.062 0.031 Increase in ERAP-/- Ruminococcaceae 0.007 0.016 0.009 Increase in ERAP-/- Desulfovibrionaceae 0.024 0.012 -0.012 Decrease in ERAP-/- (genus Desulfovibrio)

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Thus the overall structure of the gut microbiome in the ERAP-/- mice was different to control C57BL/6. Additionally, key indicator species also seen in AS TI biopsies shown to be driving the clustering between mouse strains differing in AS-associated genetic makeup. Therefore I sort to explore how the microbial community members are interacting and how these interactions shape the community structure and signature. Co-occurrence analysis, which looks at the correlation between microbes, was undertaken to explore correlations between keystone members of the ERAP-/- intestinal community compared to controls. Positive correlations, shown with blue lines, were seen between the indicator families Lachnospiraceae, Prevotellaceae, Clostridiaceae and Rikenellaceae with negative correlations, shown with red lines observed between S24-7, Prevotellaceae, Ruminococcaceae (Figure 4.2). The thickness of the line denotes the strength of correlation between the bacteria and demonstrates that bacterial interactions further shape the ERAP-/- microbial community signature (Table 4.2).

Figure 4.2 ERAP-/- Intestinal community Co-occurrence network showing the positive (blue lines) and negative (red lines) correlations between indicator species. The thickness of the line denotes the strength of the correlation (Pearson correlation).

Dissecting the effects of genetic variation, in this case comparing ERAP-/- with C57BL/6 mice, it is important to consider that these experiments can be subject to variation due to other factors such as strain differences, cage effect and founder effect may be driving the

104 differences seen between these mice (165). To minimise such potential confounders, we explored the gut microbiome differences within a single litter of ERAP mice comprising of ERAP-/-, ERAP+/- and ERAP+/+. Given that these mice were the offspring of sibling ERAP HET founder mice, were from the same litter, and were cohoused from birth, variation due to background strain and cage/litter effects were minimized or excluded. Faecal pellets were collected and processed as described previously.

Figure 4.3 Unweighted Principal coordinates analysis (PCoA) of a single litter of ERAP mice comprising three ERAP+/+, four ERAP+/- and one ERAP-/- showing distinct grouping of ERAP+/+ away from the other genotypes and separate clustering of ERAP-/- and ERAP+/-.

The unweighted PCoA, which shows structural differences between microbial communities (Figure 4.3), illustrating that genotype does alter the structure of the intestinal microbiome (P=0.001; PERMANOVA). The three ERAP+/+ mice, our controls, are seen to cluster together and away from the ERAP+/-and the ERAP-/- mice. As expected, the ERAP+/- and the ERAP-/- cluster away from the WT and are more similar to each other than to the ERAP+/+ mice. It is apparent that a change in genotype, from ERAP+/+ to ERAP+/-, is enough to influence the community structure of the microbiome. The clustering of the ERAP+/- and the ERAP-/- together, but separately from the ERAP+/+, suggests that change of one allele is sufficient to alter the microbiome structure. Further litters will need to be studied to confirm this apparent dominant effect.

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4.3.2 Loss of ERAP does not change overall proportions of Immune cell subsets

Given that we observed that the loss of a single gene influences gut microbiome composition, and the gut is the primary interface between microbes and the immune system, we were interested in how changes in the microbiome affect the immune system both locally and systemically. We began by examining the major immune cell subsets in eight ERAP-/- and eight ERAP+/+ mice to determine if an altered gut microbiome altered the immune system (Figure 4.4). Following removal of doublets, dead cells were excluded using Aqua Viability dye discrimination. Cell subset specific analysis of cell phenotype and activation status was performed by analysing cell surface protein expression. Transcription factor expression was determined intracellularly following cell permeabilisation. Cytokine production was determined intracellularly following in vitro stimulation for 4 hours with PMA and ionomycin in the presence of the Golgi-transport blocker monensin.

Figure 4.4 Example gating strategy for isolation, identification and characterisation of T cells. Doublets were removed and dead cells were excluded using Aqua Viability dye discrimination. Cell subset specific analysis of cell phenotype and activation status was performed by analysing cell surface protein expression, with transcription factor expression determined intracellularly following cell permeabilisation. Cytokine production was determined intracellularly following in vitro stimulation.

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A post hoc power calculation indicated our study had 80% power to detect an effect with Cohen’s d >0.2 α =0.05 between ERAP-/- and ERAP+/+ cells isolated from the spleen, however our results indicated effect sizes of Cohen’s d <0.34. In cells derived from the MLN our study had 80% power to detect an effect size Cohen’s d >1 α =0.05 and our results indicated effect sizes with Cohen’s d >1.3. No significant differences were seen in overall numbers of T cells (CD3+, CD4+, CD8+ or γδ), NK cells (CD3- NKp46+), Macrophages (CD11b+) or regulatory T cells (CD4+ CD25+) either systemically in the spleen or locally in the MLN (Figure 4.5, Table 4.3).

Table 4.3 Frequencies of cell subsets showing no significance between ERAP+/+ (WT) and ERAP-/- (KO) strains in spleen and mesenteric lymph nodes.

Spleen Mesenteric lymph nodes

Immune cell PValue Mean± Mean± PValue Mean± Mean± subset SEM WT SEM KO SEM WT SEM KO CD3+ >0.1 15.08± 14.74± >0.05 32.62 ± 27.83 ± 1.197 1.514 1.129 2.432

CD4+ >0.1 22.99± 22.84± 0.054 44.02 ± 37.39 ± 2.110 0.733 2.546 1.865

CD8+ >0.1 16.24± 14.97± >0.1 22.77 ± 21.74 ± 1.314 0.499 1.435 1.314

γδ >0.1 0.66± 0.57± >0.1 0.77 ± 0.76 ± 0.061 0.045 0.069 0.063

NK cells >0.1 3.11± 2.98± >0.1 21.30 ± 22.27 ± 0.240 0.343 2.603 1.283

Macrophages >0.1 6.056± 4.93± >0.1 3.34 ± 3.78 ± 0.956 0.540 1.213 1.091

Treg cells >0.1 0.96± 0.96± >0.1 3.07 ± 3.46 ± 0.110 0.083 0.218 0.696

There is trend towards fewer CD3+, CD4+ T cells and NK cells in the MLN, this is countered by a slight increase in regulatory T cells. A non-significant reduction in overall numbers of Macrophages and CD3+ NKp46+ cells and increase in NK cells in the spleen of ERAP-/- is observed. There were no other notable differences in any of the immune cell subsets investigated suggesting that at least systemically, the loss of ERAP does not lead to significant shifts or changes in the T cell and non T cell compartment. There were no differences functionally in T cell activation as measured by CD69 expression (Figure 4.5). CD69 is a marker of early activation and involved in inducing T cell proliferation and 107 lymphocyte migration, and is normally expressed at low levels on resting CD4+ or CD8+ T cells. Increased activation in CD4+ and CD8+ T cells in either the spleen and MLN of ERAP-/- mice, may have suggested T cell activation and mobilisation, possibly in response to the shift in gut flora. Together this data suggests that the effect of the loss of ERAP on the immune system is not systemic; the effect may be more subtle and localised to the intestinal tissue at the vanguard of immune system and microbe interaction.

Figure 4.5 Frequencies of major immune cell subsets in ERAP+/+ (WT) and ERAP-/- (KO) from the Spleen (A) and mesenteric lymph nodes (C) and activation status respectfully (B and D) shown as mean ± SEM and compared by unpaired t test.

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4.3.3 Loss of ERAP leads to differences in cytokine potential

Although the intestinal flora of ERAP-/- mice are skewed more towards containing families of bacteria known to elicit a strong immune response, it is notable that there is minimal evidence of an immune response to this shift in flora. There are no detectable differences in the immune cell subsets either in the spleen or the MLN, so the lack of response is not due to an absence of immune cell subset. Therefore, we were interested to see if there were differences in how the immune cells responded to the intestinal microbes, and the cytokines produced.

There was no change observed in overall numbers of immune cells between ERAP-/- and C57BL/6 control mice. However, there was a trend observed in ERAP-/- mice away from an inflammatory IL-17 dominated environment, towards increased levels of IL-22 creating a less inflammatory and a more homeostatic gut environment.

In the spleens of the ERAP-/- mice there is a non-significant trend towards a reduction in the amount of IL-17 expressed especially from CD3- and CD4+ T cells (Figure 4.6). However, there is a statistically significant decrease in production of IL-17 from NK cells (P=0.04) in ERAP-/- mice, suggestive of a response by the innate lymphoid system in response to the microbiome changes (Figure 4.6, Table 4.4). With a reduction of IL-17, there would be an expected increase in the homeostatic cytokine IL-22. However there was no IL-22 detected from CD8+ T cells or γδ T cells. There were no differences in expression of IL-10, TNF or IFNγ (Figure 4.6, Table 4.4).

In the MLN, IL-17 expression was mixed across the immune subsets however there was an increase in expression in macrophages, although not significant (Figure 4.7, Table 4.5). There was an increase of IL-22 expression from CD3+ T cells, although not significant, and unexpectedly a decrease across all other T cell and non T cell subsets. This demonstrates that the IL-22 is being expressed predominately from CD3+ T cells with a slight increase in IL-10 expression from macrophages, adding to the homeostatic environment (Figure 4.7, Table 4.5).

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Table 4.4 Frequencies of cytokine expression from Immune cell subsets in the spleen of ERAP-/- (KO) and ERAP+/+ (WT) mice. There was no significance in expression between ERAP WT and ERAP KO strains in spleen as determined by unpaired t test.

Spleen IL-17 IL-22 IL-10 TNF Immune subsets PValue Mean± PValue Mean± PValue Mean± PValue Mean± SEM SEM SEM SEM WT, KO WT, KO WT, KO WT, KO CD3+ >0.1 0.052± >0.1 6.220± >0.1 1.043± >0.1 0.262± 0.019, 1.164, 0.437, 0.092, 0.061± 5.800± 0.940± 0.636± 0.015 1.251 0.796 0.348 CD3- >0.05 28.55± - - - - >0.1 2.615± 5.290, 0.528, 15.93± 5.583± 4.043 3.012 CD4+ >0.1 16.44± >0.1 0.491± - - >0.1 21.86± 1.131, 0.204, 5.255, 13.82± 0.218± 17.86± 2.155 0.068 3.030 CD8+ ------>0.1 9.890± 2.858, 7.486± 1.601 γδ >0.1 4.183± - - - - >0.1 9.519± 2.108, 2.199, 6.671± 10.23± 1.671 1.239 NK cells 0.0397 0.698± >0.1 8.346± >0.1 1.948± >0.1 17.14± 0.279, 2.317, 0.375, 2.191, 0.061± 9.755± 2.105± 17.30± 0.026 3.467 0.439 4.316 Macrophage >0.1 1.501± >0.1 12.16± >0.1 3.983± >0.1 30.05± 0.645, 1.545, 0.807, 2.961, 1.369± 11.25± 3.098± 28.35± 0.318 2.608 0.480 4.323

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Figure 4.6 Immune cell subsets and expression of cytokine from the spleen of ERAP-/- (KO) mice compared to ERAP+/+ (WT). (A) Expression of IL-17 in the spleen, noting the increase in secretion from γδ T cells, and the absence of IL-17 secretion from CD3+ T cells as a whole and CD8+ T cells. (B) Expression of IL-22 showing a slight decrease across all cell types except for NK cells and no significant differences in expression of either IL-10 (C) or TNF (D). Data shown as mean ± SEM and compared by unpaired t test.

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Figure 4.7 Immune cell subsets and expression of cytokine from the mesenteric lymph nodes (MLN) of ERAP-/- (KO) mice compared to ERAP+/+ (WT). (A) Expression of IL-17 in the MLN, showing an increase in secretion from macrophages. (B) Expression of IL-22 showing an increase in secretion from CD3+ T cells, but undetectable levels across other T cell subsets. A slight increase in IL-10 secretion was detected in macrophages (C) and TNF secretion in the MLN mirrored what was seen in the spleen (D).

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Table 4.5 Frequencies of cytokine expression from immune cell subsets in the MLN. There was no significant difference in expression between ERAP+/+ (WT) and ERAP-/- (KO) strains in spleen as determined by unpaired t test.

MLN IL-17 IL-22 IL-10 TNF Immune subsets PValue Mean± PValue Mean± PValue Mean± PValue Mean± SEM SEM SEM SEM WT, WT, WT, WT, KO KO KO KO CD3+ >0.1 0.008± >0.1 5.150± >0.1 0.7500 >0.1 3.061± 0.006, 1.253, ± 1.368, 1.244± 5.536± 0.3543, 3.068± 1.221 1.956 1.619± 0.823 1.069 CD3- >0.1 20.03± - - - - >0.1 8.066± 3.159, 2.134, 18.96± 8.850± 4.603 2.024 CD4+ >0.1 0.976± >0.1 - - - - - 0.656, 0.926± 0.277 CD8+ ------>0.1 29.16± 4.637, 33.87± 8.483 γδ >0.1 4.083± ------0.951, 3.334± 0.661 NK cells 0.059 7.023± 0.013 2.675± >0.1 11.80± 0.055 20.92± 1.794, 0.562, 3.954, 4.451, 3.023± 0.921± 10.59± 11.31± 0.767 0.254 1.788 1.186 Macrophage >0.1 21.67± >0.1 4.595± >0.1 66.10± 0.054 51.38± 3.942, 1.117, 8.980, 9.148, 23.71± 2.455± 70.75± 31.34± 3.701 0.997 7.556 2.772,

4.4 Discussion

The association of variants of ERAP1 is one of the strongest non-MHC associations seen in AS. In this chapter I investigated if the loss of a single AS-associated gene was sufficient to alter the intestinal microbiome and the immune response. Given that loss of function genetic variants of ERAP1 in AS are protective, and that an estimated 70% of AS patients have either clinical or sub clinical gut disease, we explored how a lack or down regulation of ERAP1 influences the inherent gut microbiome composition, and given the gut microbiome is the educator of the immune system, the subsequent impact this has on the antigen repertoire and immune system. 113

To investigate this hypothesis that ERAP1 leads to altered T cell selection, impairing immune recognition of inherent gut microbiota, and leading to an overall shift in microbial community composition; we examined if the loss of ERAP, in a mouse model, caused a shift in the intestinal flora. Consistent with this hypothesis, 16S community profiling revealed that the gut microbiome in ERAP-/- did shift, however it was surprisingly towards a more inflammatory profile, with increases of bacteria such as Rikenellaceae, Lachnospiraceae and Ruminococcus which are not only known to elicit a strong immune response, but are also key bacteria in the TI of AS patients (102-104). The shift in the ERAP-/- mouse microbiome was further explored to determine if it was the overall structure or simply the presence or absence of bacteria that was driving the signature. Unweighted UniFrac analysis revealed that the loss of ERAP triggered a fundamental shift of bacteria at the phylum level, causing an overall shift in community structure. Interestingly, this also included a shift in the Bacteroides-Firmicutes ratio leading to an increase in Firmicutes in the ERAP-/- mice, which is consistent with what we observe in the AS gut microbiome as described in Chapter 3 of this thesis. Indicator species analysis revealed that alterations in specific species, and not just entire Phyla, were contributing to the microbial signature. The bacterial families that varied in ERAP+/+ and ERAP-/- mice were Rikenellaceae, Lachnospiraceae, Ruminococcaceae, Bacteroidaceae and Clostridiaceae which are also bacteria associated with the microbial signature in AS. Whilst the association of particular bacterial families seen in both AS intestinal biopsies and ERAP-/- gut microbiome is interesting, it is however incidental.

Given the loss of ERAP caused such dramatic change in gut microbiome structure an experiment for cage and litter effects was conducted. In order to minimise such potential confounders, the intestinal microbial composition of a single litter of ERAP mice comprising of ERAP+/+, ERAP+/- and ERAP-/- were explored. These mice were the offspring of sibling ERAP+/- founder mice, from the same litter, and were cohoused from birth so that variation due to background strain and cage/litter effects were minimized or excluded as much as possible. It is clear from the unweighted PCoA that the ERAP+/- and ERAP-/- mice were more similar to each other and clustered away from the WT mice. This suggests that a change in genotype, from WT to HET, is sufficient to influence the overall structure and composition of the gut microbiome. The clustering of the ERAP+/- and ERAP-/- together, but separately from the ERAP+/+, suggests that change of one allele is sufficient to alter the microbiome structure, but as only one ERAP-/- mouse was present in the litter, whether homozygous loss of ERAP has additional effects compared 114 with heterozygotes remains unclear. While only one litter was studied, and larger numbers are needed to better characterise the impact of genotype on the intestinal microbiome, these results are consistent with the hypothesis that AS-associated genes contribute to disease aetiopathogenesis through effects of the underlying host genetics on intestinal microbial composition.

The intestine is the front line between the immune system and the gut flora, and a shift in gut flora is usually met with a strong and swift inflammatory immune response dominated by IL-17 in an effort to return to balance and homeostasis. ERAP-/- mice demonstrated a shift towards microbiome composition akin to what was observed in AS cases. Therefore, we examined the affect the loss of ERAP had on the local and systemic immune system, and how the immune system in turn sculpted the gut microbiome. There was no significant change or alteration in the T cell or non T cell compartments. This is in keeping with previous work that has shown that there are no differences in the profile of CD4+ T cells or CD8+ T cells of ERAP-deficient mice when compared to ERAP competent mice (208, 209, 214, 215). There was also no significant change in the cytokine potential in ERAP-/- mice compared to control ERAP+/+, although there was a trend towards an increase in IL-22 secretion from CD3+ T cells as well as an increase in IL-10 secretion from macrophages in the MLN. Coupled together, this data suggests that the effect of the loss of ERAP on the immune system is subtle and site specific. To dissect the effect of the loss of ERAP, not only on microbiome composition but the immune system, the local intestinal tissue environment needs to be explored. Examining the lamina propria (LP) cells from the intestinal walls of ERAP-/- and ERAP+/+ mice would shed light on the front line interactions between the immune system and the gut microbiota. However, this would require significantly more mice to be studied, as LP cells are isolated in small numbers requiring mice to be pooled to obtain sufficient cell numbers for analysis.

The impact the establishment and maintenance of beneficial interactions between the host and their microbiota is rapidly becoming a key focus as a requirement for overall health. The bacteria that inhabit the gut and the human immune system have developed a number of strategies to maintain gut homeostasis in dynamic and every changing environment. The gut also is the primary site for interaction and education between gut microbes and the host immune system. The dysregulation of gut homeostasis is increasingly recognised as playing a pivotal role in autoimmunity and controlling activation of the immune system in response to these changes in gut microbiota has to be tightly regulated as to avoid chronic 115 inflammation. The role that underlying host genetics plays in gut microbiome composition and the flow on effect to the immune system requires more interrogation. In this chapter we described how the loss of a single AS-associated gene, ERAP1, led to dramatic shift in the overall composition and structure of gut microbiome. Of particular interest, was the finding that the clustering in the murine experiments was driven by bacteria that are AS also seen in the microbiome of patients who have AS. Further investigation into the impact of genotype on microbiome composition revealed that the change in even one allele led to detectable shift in relative abundance of microbes. The role the immune system has in helping shape these microbiome changes is unclear. By better understanding the interactions locally in the intestinal tissue at the interface between gut microbes and the immune system, we may better understand ERAP mechanism in conferring protection from AS and possibly harnessing and modulating an anti-inflammatory immune response.

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5 Chapter 5 Effect of underlying host genetics on intestinal microbiome composition

5.1 Introduction

The more we learn about the microbes that live in and on our body, the more we understand about how they interact with our genes and the environment, to affect overall health and wellbeing (211). There is an estimated 10-fold increase in the number of bacterial cells in our body relative to human cells, essentially making us more microbe than human (125). The majority of these microbes reside in the gut, the primary site for interaction between microbes and the host immune system (38, 123, 126). The exact composition of a ‘healthy’ gut microbiome differs markedly between individuals, however microbiome composition is increasingly viewed as a risk factor in a number of chronic diseases such as rheumatoid arthritis (224), obesity (114), IBD (102, 225), and diabetes (226). The gut microbiota is increasingly being looked at as a target for novel therapies, including microbiome modulation with pre- and probiotics and even faecal transplants. These novel therapies are being increasingly used to treat patients with CD (227) and Clostridium difficile infections (228), where conventional treatments and antibiotics have failed. The successful use of the gut microbiome as a therapeutic target requires a deeper understanding of factors that shape the community composition including age, diet, gender as well as underlying host genetics.

The role of the host genetics in shaping intestinal microbial community composition in humans is unclear. In animal models, host gene deletions have been shown to result in shifts in microbiota composition (135). In addition, a recent quantitative trait locus mapping study linked specific genetic polymorphisms with microbial abundances (136). These studies highlight the importance of underlying host genetics in community composition in animals. This is of great interest in diseases such as IBD, where many of the genes associated with disease are associated with handling, processing and response to microbes such as IL23R, NOD2, NOS2 and CARD9. Genetic studies in CD, one of the two common forms of IBD, shows a marked over-representation of genes, especially NOD2 and TNFRSF18, which are associated with an increased risk of developing mycobacterial diseases, including leprosy and tuberculosis (23). For example, seven of the eight known genetic variants associated with increased risk of leprosy, including SLC11A1, VDR and

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LGALS9, also increase the risk of Crohn’s disease, compared with only two being associated with risk of any other immune-mediate disease. Coupled with the dramatic shifts in the gut microbiota that have been associated with IBD (23, 135), this strongly indicates a potential link between host genetics and the microbiome in IBD patients. The genetic influence over microbiome composition is further highlighted in a study of 416 monozygotic and dizygotic twin pairs that found the most heritable taxon was the recently described family Christensenellaceae (137).This family of bacteria was also shown to be central to a network of co-occurring heritable microbes associated with lean body mass index. In mice it was shown that the introduction of any of the members of the Christensenellaceae family of protected the mice from weight gain (137). Until recently variation and changes in the gut microbiome have been explained by diet and environment. While the exact genes associated with the Christensenellaceae family are unknown, this study demonstrates that microbiome composition may in part be due to host genetics.

AS is a common inflammatory arthritis that affects over 22,000 Australians (1). Up to 70% of AS patients have either clinical or subclinical gut disease, which suggests that intestinal inflammation is important in the disease (7, 8). Increased gut permeability has been demonstrated in both AS patients and their first-degree relatives compared with unrelated healthy controls (11-13, 229). This is consistent with the hypothesis that increased ‘leakiness’ of the gut may increase the microbial dissemination and drive inflammation in the disease.

In the SKG mouse and B27-transgenic rat models of spondyloarthritis, animals raised in sterile conditions remain healthy and do not develop disease (230, 231). When the gut of these mice and rats are recolonised with bacteria, arthritis often develops (15, 232). Whilst this data supports a role for gut pathogens or the gut microbiome in AS, current studies using antibody tests have not produced convincing evidence to demonstrate that increased infection with any specific triggering agent influences the development of AS. Several specific bacteria have been suggested to have a role in AS pathogenesis particularly Klebsiella pneumoniae (138, 139), Bacteroides vulgatus (140). These two bacteria have been of considerable focus for several reasons. When HLA‐

B27/β2 microglobulin transgenic rats, that have been raised germ free, are colonised with an intestinal bacterial cocktail with increased amount of Bacteroides vulgatus, the rats developed more several colitis (141). Significant research has been directed towards the 118 involvement of K. pneumoniae. Early work reported the presence of K. pneumoniae in the faeces of AS patients during active disease (142). Studies have reported the presence of K. pneumoniae in faeces during active inflammatory disease (142) elevated levels of serum antibodies against K. pneumoniae in AS patient sera have been detected (143), as well as the antigenic similarity noted between Klebsiella and HLA‐B27 (144). Recent studies however have been unable to confirm a role of Klebsiella AS.

Understanding how the gut microbiota communities are assembled and maintained is becoming increasingly relevant not only to general healthy, but also potentially to the treatment of complex chronic diseases. In recent years only a few studies have examined the composition, diversity and function of the human gut microbiome. Studies comparing the gut microbiota of lean and obese twins have shed light on the importance of intestinal microbes and how a change in microbiome composition can affect food metabolism in the gut (114, 117, 118). Turnbaugh et al showed that even with a similar genetic make-up, obese twins had substantial differences in the composition and diversity of their gut flora with a dominance of Gram positive bacteria from the phylum Firmicutes, compared with discordant or lean twins (114). This shift in gut flora composition altered how food was broken down and metabolised in the gut, leading to increased body mass index, adiposity and obesity (114, 117, 119). This demonstrates that shifts in the intestinal microbiome have functional consequences on the health of the individual (120). To further investigate structure and function of the gut microbiome, as well as the consequences of shifts and alterations, faecal samples from four twin pairs discordant for obesity were transplanted into germ free mice (121). The authors found that the Firmicute phylum dominated microbial communities from the obese twin lead to obesity in the germ-free mice. The Bacteroides dominated microbial communities from the lean mice kept the germ-free mice lean. When the mice were co-housed, the invasion of Bacteroides from the lean mice lead to a decrease in adiposity and body mass in obese mice and transformed their microbiota’s metabolic profile to a lean-like state (121).

These studies have shown that shifts in microbiome can change metabolism; however, they do not provide details of what constitutes a ‘normal’ microbiome or how it behaves. Two major studies into the ‘normal’ microbiome have been undertaken by the European MetaHit project and the National Institute of Health Human Microbiome Project in the USA (123, 126, 233). The European MetaHIT project was developed to catalogue all the bacteria living in the intestine. The consortium combined published data sets from across 119 the world, in addition to 22 newly sequenced faecal metagenomes from four different European countries. They identified three clusters, or groups of bacteria, that they termed ‘enterotypes’. The authors suggested that particular enterotypes, due to their functional capacities, might be associated with diseases such as obesity or IBD (123) Enterotypes to date have not been confirmed in other datasets; however, the idea of groups or clusters of bacteria being related to disease because of function is gaining popularity. The Human Microbiome Project was developed to identify and catalogue the human microbiome by sequencing samples from 242 individuals (129 males, 113 females) from five major body areas. A total of 4,788 specimens were sequenced with females sampled from 18 different habitats and males from 15 habitats (126). Metagenomic carriage of metabolic pathways was stable among individuals, despite the variation seen in community structure. When clinical metadata was present, ethnic background proved to be one of the strongest predictors of both pathways and microbes. This suggests that environmental factors such as diet, location/region and host genetics could play a role in sculpting the microbiome. These studies further define the range of structural and functional configurations that are present in normal microbial communities in a healthy population (123, 126).

Genes are the primary determinant of the risk of developing AS, with heritability about 97% (4), implying that the trigger for the disease is environmental. CD is closely related to AS with a similar prevalence and high heritability. The two commonly co-occur with an estimated ~5% of AS patients developing CD, and 4-10% of CD patients developing AS (234, 235). The propensity to occur in families implies shared aetiopathogenic factors, and extensive sharing of genetic risk factors. There is strong suggestive evidence to support a role for the gut microbiome in the aetiopathogenesis of both diseases. Understanding the influence of host genetics on the bacterial composition of the microbiome is vital because if the underlying genetics dictates the response of the immune system to the microbiome, or how the body tolerates or processes microbes, then no amount of microbiota modulation or probiotics will permanently shift the intestinal microbiome; not without doing something to influence the gene actions. However, if host genetics favours microbiota modulation, then the potential for the microbiome as a therapeutic target via faecal microbiota transplants (FMT) or probiotics presents a new avenue for therapeutic developments for AS.

We hypothesise that the major genetic predisposing factors to AS influence the gut microbiome in healthy and affected individuals, and that genes predisposing to AS do so 120 by influencing the gut microbiome composition. We explored the association between AS associated single nucleotide polymorphisms (SNPs) and intestinal bacteria. The intestinal communities of 982 faecal samples were characterised in 287 DZ pairs and 204 MZ pairs, from the TwinsUK registry as described by Goodrich et al (137).

5.2 Methods

5.2.1 TwinsUK dataset As detailed in Goodrich et al, 982 faecal samples from as many otherwise healthy individuals, none of which were known to be diagnosed with AS, were sequenced. Most subjects were female aged from 23 to 86 years (average age: 60.6 ± 0.3 years). 78,938,079 quality-filtered sequences were generated and mapped to the Bacteria and Archaea in the Greengenes database (average sequences per sample: 73,023 ± 889).

5.2.2 Sample collection All work involving human subjects was approved by the Cornell University IRB (Protocol ID 1108002388). Faecal samples were collected at home by participants in the United Kingdom Adult Twin Registry (TwinsUK); (Moayyeri et al., 2013) in 15 ml conical tubes and refrigerated for 1-2 days prior to the participants’ annual clinical visits at King’s College London (KCL). Upon arrival at KCL, the samples were stored at -80ºC and shipped by courier on dry ice to Cornell University, where they were stored at -80ºC until processing.

5.2.3 DNA extraction and 16S rRNA amplification Genomic DNA was isolated from an aliquot of ~100 mg from each sample using the PowerSoil® - htp DNA isolation kit (MoBio Laboratories Ltd, Carlsbad, CA). 16S rRNA genes were amplified by PCR from each of the 982 samples (287 DZ twin pairs, 204 MZ twin pairs) using the 515F and 806R primers for the V4 hypervariable region of the 16S rRNA as previously described (127).

5.2.4 16S rRNA filtering and analysis As described in Goodrich et al matching paired-end sequences (mate-pairs) were merged using the fastq-join command in the ea-utils software package and merged sequences over 275 bp in length were filtered out of the dataset (137). The remaining merged sequences were analysed using the open-source software package QIIME 1.7.0 (Quantitative Insights Into Microbial Ecology; (169)).

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Quality filters were used to remove sequences containing uncorrectable barcodes, ambiguous bases, or low quality reads (Phred quality scores ≤ 25). Closed-reference Operational Taxonomy Unit (OTU) picking at 97% identity against the May 2013 Greengenes database (97% OTU reference sequences), which excluded 15.6% of total sequences. The taxonomic assignment of the reference sequence was used as the taxonomy for each OTU. α-diversity, Chao 1, Observed Species as well as β-diversity (Unweighted and Weighted UniFrac) (189) metrics, and Bray-Curtis Dissimilarity using the Greengenes phylogenetic tree where necessary, were calculated. β-diversity was calculated with a rarefied OTU table containing 10,000 sequences per sample and PCoA on the distance matrices. α-diversity metrics were calculated from 100 iterations using a rarefaction of 10,000 sequences per sample. Summaries of the taxonomic distributions of OTUs were generated at six levels from genus to phylum from the non-rarefied OTU table.

5.2.5 SNP selection, genotyping and imputation Individuals were genotyped on the HumanHap 300k Duo array or the HumanHap610- Quad array. Of the 491 twin pairs, 4 were genotyped on both chips, 252 were genotyped only using the Illumina HumanHap 300 duo array, with the remaining 235 genotyped using the Illumina HumanHap 610k duo array. Previous studies have identified 43 SNPs to be associated with AS, some of which are in the MHC (24). Genotype data was extracted 1MB around the 43 confirmed susceptibility loci; SNPs with a MAF <0.05 were excluded. For each locus we phased data with MACH (236) using the default parameters. For each locus we imputed missing genotypes from the European panel of the 1,000 Genomes Project (2010-11 data freeze, 2012-02-14 haplotypes as downloaded from the MACH website*) using minimac (237). Imputed SNPs with low imputation quality score (Rsq < 0.5) were removed from all analyses. The reported index SNP for each of the susceptibility loci was then used for subsequent analyses, unless otherwise stated. Genotyped data was used where available otherwise the dosages from the imputed data were used (i.e. expected number of copies of a given allele, ranging from 0 to 2).

Due to the large effect of HLA-B27, two allelic risk scores were constructed using AS associated imputed SNPs (Table 5.1). The first score for each of the twins was derived as a sum of AS-risk alleles across the imputed dosages for all 43 SNPs. The second score weighted the risk allele dosage by the effect size reported in the relevant published papers and then summed the weighted dosages. Allelic risk scores were calculated using custom scripts in R 3.0.2 (238). 122

* http://www.sph.umich.edu/csg/abecasis/MACH/download/1000G.2012-02-14.html

5.2.6 Statistical analysis We assessed whether each of the 768 16S rRNA OTUs followed a Gaussian distribution through plotting histograms and calculating the mean, skew and kurtosis. The mean allowed the assessment of central tendency for each OTU. Skewness and kurtosis describe the shape of the distribution for each OTU; skewness quantifies the asymmetry of the distribution and kurtosis quantifies the peakedness of the distribution. Based on these summary statistics and the histograms, it was determined that some of the OTUs did not follow a normal distribution. The statistical tests used in the remainder of this chapter assume the data follows a normal distribution; we therefore applied an inverse normal transformation to each of the OTUs to ensure normality before analysis.

Linear mixed effects models were used to investigate the association between each of the OTUs and the 43 individual AS SNPs or allelic scores. These models account for the correlation between twins within a family. Analysis of the single SNPs was conducted in Merlin (239) using the following command line:

merlin -d zygosity dat file,SNP dat file –p Twins zygosity pedigree file,SNP pedigree file -m SNP map file --assoc --tabulate Results-chr22-assoc-Jan14

This uses a likelihood ratio test to compare a model including the individual SNP to a model without the SNP. Descriptive statistics and the analysis of the allelic score was conducted in R (version 3.0.2) using the nlme library (238).

5.3 Results

5.3.1 Distribution of OTUs is not linked to infection or Phylum The distribution of OTUs is often ignored because currently used methods of normalisation and quality control are deemed sufficient. However, the statistical tests used to investigate the relationship between host genetics and the microbiome assume the OTU is normally distributed. Figure 5.1 shows the histograms for a selection of the OTUs, and Table 5.2 shows the mean, skew and kurtosis. Histograms demonstrated a mixture of modalities across the OTUs, with some OTUs following a normal distribution but the majority showing 123 bimodal distributions. Table 5.2 indicates that there is a large range of skewness and kurtosis in the OTUs (Skew: -0.503 to 0.952, Kurtosis: -1.616 to 0.744), further emphasising the departure from normality for many OTUs. This demonstrates the need for normalisation prior to applying a statistical model.

Figure 5.1 Representative OTUs prior to normalisation, demonstrating the range of distributions present within the OTU table and the same order of bacteria, showing the range of mean, skew and kurtosis of each OTU. Taxonomy is shown with full classification from phylum through class (c_), order (o_), family (f_), genus (g_) and species (s_).

To establish if the variation of distributions of the OTUs was due to pathogenic bacteria, such as E. coli, or if particular modality patterns were specific to Gram negative or Gram positive bacteria, OTUs within a family or genus of bacteria were compared. Comparisons showed that OTUs within the same family or genus have very different distributions (Figure 5.2). Distributions of known pathogenic bacteria, such as pathogenic E. coli, were also compared and no trend or pattern was observed (Figure 5.3). This demonstrates that the variability seen across the OTU table was not driven by bacterial pathogenicity or by taxonomy. This also indicated that OTU distribution cannot be predicted through taxonomy, and variation seen within the OTUs may be a due to temporal variance or normal variation with the bacteria and the microbiome.

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Figure 5.2 Distributions of the family Lachnospiraceae demonstrating variation of distribution patterns within the same family of bacteria. Taxonomy is shown with full classification from phylum through class (c_), order (o_), family (f_), genus (g_) and species (s_).

Figure 5.3 Distributions of selected members of the family Enterobacteriaceae, which contains a number of pathogenic bacteria including Shigella, Klebsiella and E.coli, showing no clear pattern of distribution. Taxonomy is shown with full classification from phylum through class (c_), order (o_), family (f_), genus (g_) and species (s_).

5.3.2 AS susceptibility variants are associated with overall microbiome structure

AS is a complex highly heritable disease with over 40 loci known to be associated with AS. Due to the large effect of HLA-B27, two allelic risk scores were constructed to investigate the combined effect of AS susceptibility variants on microbiome composition (Figure 5.4). No OTUs were significantly associated with either the weighted or unweighted allelic risk score, using a Bonferroni corrected experiment-wide significance level. However, 13 OTUs 125 reached nominal significance (P<0.05) on the weighted risk score (Table 5.2) and 102 OTUs reached nominal significance with the unweighted risk score (including the 13 OTUs from the weighted score) (Table 5.3). Within the 13 OTUs that were nominally associated with the weight risk allelic score, six OTUs belonged to the family of Ruminococcaceae, three belong to the family Lachnospiraceae and the remaining four OTUs belonged to Bifidobacteriaceae and the order Clostridiales. The families of Ruminococcaceae, Lachnospiraceae and the order Clostridiales are known to be associated with AS and are members of the core microbiome as described in Chapter 3 of this thesis. Of the 102 OTUs reaching nominal significance in the unweighted risk score, the major families of bacteria represented were again Ruminococcaceae, Lachnospiraceae, Bifidobacteriaceae, the order Clostridiales and Veillonellaceae. This demonstrates that regardless of whether the risk score is weighted for effect size or not, the same AS core families of Ruminococcaceae, Lachnospiraceae, and the order Clostridiales are associated with AS SNPs, with only the membership and number of OTUs expanding. This is similar to what we observed in intestinal dysbiosis in AS cases in Chapter 3. As the threshold for core membership was reduced to 90, 75 and 50% of the time, the families of bacteria present remained the same however the number of genus and species within these families increased, especially in the order Clostridiales, Lachnospiraceae and Ruminococcaceae families. This suggests the structure of the core microbiome is not only robust, but may be influenced by underlying host genetics.

Figure 5.4 Histogram of weighted allelic risk score demonstrating the effect of HLA-B27 genotype (GG, AG, AA).

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Table 5.1 Reported AS associated loci including correlation of genes associated with inflammatory bowel disease.

Reported gene SNP Locus AS Position Risk/Non- risk Alleles RUNX3 rs6600247 1p36 C/T 25,177,701 IL23R rs11209026 1p31 G/A 67,478,546 rs12141575 1p31 A/G 67,520,024 GPR25-KIF21B rs41299637 1q32 T/G 199,144,473 PTGER4 rs12186979 5p13 C/T 40,560,617 ERAP1-ERAP2-LNPEP rs30187 5q15 T/C 96,150,086 rs2910686 5q15 G/A 96,278,345 ERAP1-ERAP2 rs10045403 5q15 A/G 96, 173,489 IL12B rs6871626 5q33 T/G 158,759,370 rs6556416 5q33 G/T 158,751,323 CARD9 rs1128905 9q34 C/T 138,373,660 LTBR-TNFRSF1A rs1860545 12p13 C/T 6,317,038 rs7954567 12p13 A/G 6,361,386 NPEPPS-TBKBP1- rs9901869 17q21 T/C 42,930,205 TBX21 21q22 rs2836883 21q22 G/A 39,388,614 IL6R rs4129267 1q21 G/A 152,692,888 FCGR2A rs1801274 1q23 T/C 159,746,369 rs2039415 1q23 C/T 159,121,069 UBE2E3 rs12615545 2q31 C/T 181,756,697 GPR35 rs4676410 2q37 A/G 241,212,412 NKX2-3 rs11190133 10q24 C/T 101,268,715 ZMIZ1 rs1250550 10q22 C/A 80,730,323 SH2B3 rs11065898 12q24 A/G 110,346,958 GPR65 rs11624293 14q31 C/T 87,558,574 IL27-SULT1A1 rs7282490 16p11 A/G 28,525,386 rs35448675 16p11 A/G 28,236,248 TYK2 rs35164067 19p13 G/A 10,386,181 rs6511701 19p13 A/C 10,486,067 ICOSLG rs7282490 21q22 G/A 44,440,169 EOMES rs13093489 3p24 A/C 27,769,911 IL7R rs11742270 5p13 G/A 35,917,200 UBE2L3 rs2283790 22q11 C/T 20,286,653 BACH2 rs17765610 6q15 G/A 90,722,494 rs639575 6q15 A/T 91,047,852 NOS2 rs2531875 17q11 G/T 23,172,294 rs2297518 17q11 A/G 23,120,724 HHAT rs12758027 1q32 C/T 208,861,431

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IL1R2-IL1R1 rs4851529 2q11 G/A 102,013,732 rs2192752 2q11 C/A 102,135,805 2p15 rs6759298 2p15 C/G 62,421,949 GPR37 rs2402752 7q31 T/C 124,226,835 HLA-B27 rs4349859 6p21 A/G 31,365,787 ANTRX2 rs4333130 4q21 A/G 81,168,853

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Table 5.2 OTUs with taxonomy associated at P<0.05 from the weighted allelic risk score, including beta and standard error. Six of the OTUs associated belong to the family of Ruminococcaceae, three belong to the family Lachnospiraceae and the remainder belonging to Bifidobacteriaceae and the order Clostridiales. Taxonomy is shown with full classification from phylum through class (c_), order (o_), family (f_), genus (g_) and species (s_).

OTU Beta Standard Error P-value Taxonomy

178760 -0.0256 0.0107 0.0172 Firmicutes; c__Clostridia; o__Clostridiales; f__; g__; s__

179572 -0.0261 0.0112 0.0202 Firmicutes; c__Clostridia; o__Clostridiales; f__Lachnospiraceae; g__Blautia; s__

681370 -0.0487 0.0211 0.0216 Actinobacteria; c__Actinobacteria; o__Bifidobacteriales; f__Bifidobacteriaceae; g__Bifidobacterium; s__pseudolongum 324894 -0.0392 0.0176 0.0267 Firmicutes; c__Clostridia; o__Clostridiales; f__Ruminococcaceae; g__; s__

330469 -0.0307 0.0138 0.0267 Firmicutes; c__Clostridia; o__Clostridiales; f__Ruminococcaceae; g__; s__

4347159 -0.1122 0.0505 0.0269 Actinobacteria; c__Actinobacteria; o__Bifidobacteriales; f__Bifidobacteriaceae; g__Bifidobacterium; s__adolescentis 178642 -0.0235 0.0106 0.0275 Firmicutes; c__Clostridia; o__Clostridiales; f__Lachnospiraceae; g__; s__

2688035 -0.0313 0.0143 0.0292 Firmicutes; c__Clostridia; o__Clostridiales; f__Lachnospiraceae; g__; s__

16054 -0.0590 0.0272 0.0310 Firmicutes; c__Clostridia; o__Clostridiales; f__Ruminococcaceae; g__Ruminococcus; s__callidus 232828 -0.0340 0.0162 0.0367 Firmicutes; c__Clostridia; o__Clostridiales; f__Ruminococcaceae; g__; s__

185814 -0.0589 0.0282 0.0376 Firmicutes; c__Clostridia; o__Clostridiales; f__Ruminococcaceae; g__; s__

48084 -0.0950 0.0465 0.0419 Firmicutes; c__Clostridia; o__Clostridiales; f__Ruminococcaceae; g__; s__

193946 -0.0339 0.0172 0.0498 Firmicutes; c__Clostridia; o__Clostridiales; f__Lachnospiraceae; g__Blautia; s__

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Table 5.3 OTUs reaching nominal significance (P<0.05) in the unweighted risk score. The major families of bacteria represented are Ruminococcaceae, Lachnospiraceae, Bifidobacteriaceae, the order Clostridiales and Veillonellaceae, which are the same families of bacteria observed in the weighted analysis and in the core microbiome in Chapter 3 of this thesis. Taxonomy is shown with full classification from phylum through class (c_), order (o_), family (f_), genus (g_) and species (s_).

OTU Beta SE P-value Taxonomy 16054 -0.0292 0.0085 0.0006 Firmicutes; c__Clostridia; o__Clostridiales; f__Ruminococcaceae; g__Ruminococcus; s__callidus 324894 -0.0190 0.0055 0.0007 Firmicutes; c__Clostridia; o__Clostridiales; f__Ruminococcaceae; g__; s__ 330469 -0.0141 0.0043 0.0012 Firmicutes; c__Clostridia; o__Clostridiales; f__Ruminococcaceae; g__; s__ 2617854 -0.0525 0.0164 0.0015 Bacteroidetes; c__Bacteroidia; o__Bacteroidales; f__Rikenellaceae; g__; s__ 189937 -0.0116 0.0039 0.0031 Firmicutes; c__Clostridia; o__Clostridiales; f__Ruminococcaceae; g__Faecalibacterium; s__prausnitzii 177207 -0.0224 0.0078 0.0045 Firmicutes; c__Clostridia; o__Clostridiales; f__; g__; s__ 185558 -0.0123 0.0043 0.0047 Firmicutes; c__Clostridia; o__Clostridiales; f__; g__; s__ 193477 -0.0123 0.0044 0.0051 Firmicutes; c__Clostridia; o__Clostridiales; f__Lachnospiraceae; g__; s__ 198947 -0.0141 0.0051 0.0058 Firmicutes; c__Clostridia; o__Clostridiales; f__Ruminococcaceae; g__; s__ 185814 -0.0244 0.0088 0.0060 Firmicutes; c__Clostridia; o__Clostridiales; f__Ruminococcaceae; g__; s__ 197698 -0.0203 0.0074 0.0061 Firmicutes; c__Clostridia; o__Clostridiales; f__; g__; s__ 348642 -0.0289 0.0105 0.0061 Firmicutes; c__Clostridia; o__Clostridiales; f__Lachnospiraceae; g__[Ruminococcus]; s__gnavus 196513 -0.0191 0.0070 0.0066 Firmicutes; c__Clostridia; o__Clostridiales; f__Ruminococcaceae; g__; s__ 319275 -0.0187 0.0069 0.0077 Firmicutes; c__Clostridia; o__Clostridiales; f__Ruminococcaceae; g__Faecalibacterium; s__prausnitzii 182036 -0.0233 0.0087 0.0080 Firmicutes; c__Clostridia; o__Clostridiales; f__Ruminococcaceae; g__; s__ 193969 -0.0242 0.0091 0.0084 Firmicutes; c__Clostridia; o__Clostridiales; f__Lachnospiraceae; g__Coprococcus; s__ 193968 -0.0284 0.0109 0.0094 Firmicutes; c__Clostridia; o__Clostridiales; f__Ruminococcaceae; g__; s__ 197343 -0.0134 0.0051 0.0094 Firmicutes; c__Clostridia; o__Clostridiales; f__Ruminococcaceae; g__; s__ 184114 -0.0325 0.0126 0.0103 Firmicutes; c__Clostridia; o__Clostridiales; f__Ruminococcaceae; g__; s__ 181826 -0.0125 0.0048 0.0105 Firmicutes; c__Clostridia; o__Clostridiales; f__Ruminococcaceae; g__; s__

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3530697 -0.0174 0.0067 0.0105 Firmicutes; c__Clostridia; o__Clostridiales; f__Ruminococcaceae; g__; s__ 190169 -0.0231 0.0091 0.0116 Firmicutes; c__Clostridia; o__Clostridiales; f__Ruminococcaceae; g__Faecalibacterium; s__prausnitzii 189092 -0.0320 0.0128 0.0127 Firmicutes; c__Clostridia; o__Clostridiales; f__Ruminococcaceae; g__Faecalibacterium; s__prausnitzii 191734 -0.0072 0.0029 0.0132 Firmicutes; c__Clostridia; o__Clostridiales; f__Lachnospiraceae; g__Blautia; s__ 535955 -0.0210 0.0085 0.0144 Firmicutes; c__Clostridia; o__Clostridiales; f__Clostridiaceae; g__SMB53; s__ 295258 -0.0243 0.0099 0.0145 Firmicutes; c__Clostridia; o__Clostridiales; f__Ruminococcaceae; g__; s__ 180721 -0.0159 0.0065 0.0152 Firmicutes; c__Clostridia; o__Clostridiales; f__Ruminococcaceae; g__; s__ 175729 -0.0233 0.0096 0.0160 Firmicutes; c__Clostridia; o__Clostridiales; f__Lachnospiraceae; g__; s__ 183970 -0.0162 0.0067 0.0165 Firmicutes; c__Clostridia; o__Clostridiales; f__Lachnospiraceae; g__; s__ 181139 -0.0222 0.0092 0.0167 Firmicutes; c__Clostridia; o__Clostridiales; f__Ruminococcaceae; g__Faecalibacterium; s__prausnitzii 185583 -0.0271 0.0113 0.0171 Firmicutes; c__Clostridia; o__Clostridiales; f__Ruminococcaceae; g__; s__ 310979 -0.0167 0.0070 0.0178 Firmicutes; c__Clostridia; o__Clostridiales; f__; g__; s__ 2943548 -0.0299 0.0125 0.0178 Firmicutes; c__Clostridia; o__Clostridiales; f__Ruminococcaceae; g__Ruminococcus; s__ 44151 -0.0103 0.0043 0.0180 Firmicutes; c__Clostridia; o__Clostridiales; f__Ruminococcaceae; g__; s__ 187924 -0.0203 0.0086 0.0185 Firmicutes; c__Clostridia; o__Clostridiales; f__Ruminococcaceae; g__; s__ 4425669 -0.0354 0.0150 0.0186 Firmicutes; c__Clostridia; o__Clostridiales; f__Lachnospiraceae; g__Coprococcus; s__ 175836 -0.0077 0.0033 0.0194 Firmicutes; c__Clostridia; o__Clostridiales; f__Ruminococcaceae; g__; s__ 192127 -0.0143 0.0061 0.0197 Firmicutes; c__Clostridia; o__Clostridiales; f__Lachnospiraceae; g__; s__ 186772 -0.0181 0.0077 0.0199 Firmicutes; c__Clostridia; o__Clostridiales; f__Ruminococcaceae; g__; s__ 185575 -0.0488 0.0209 0.0200 Firmicutes; c__Clostridia; o__Clostridiales; f__Ruminococcaceae; g__; s__ 4466275 -0.0108 0.0047 0.0216 Firmicutes; c__Clostridia; o__Clostridiales; f__Ruminococcaceae; g__; s__ 199279 -0.0089 0.0039 0.0220 Firmicutes; c__Clostridia; o__Clostridiales; f__Lachnospiraceae; g__Blautia; s__ 192370 -0.0098 0.0043 0.0227 Firmicutes; c__Clostridia; o__Clostridiales; f__Ruminococcaceae; g__; s__ 193769 -0.0169 0.0074 0.0233 Firmicutes; c__Clostridia; o__Clostridiales; f__Lachnospiraceae; g__[Ruminococcus]; s__gnavus

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178760 -0.0076 0.0034 0.0235 Firmicutes; c__Clostridia; o__Clostridiales; f__; g__; s__ 182577 -0.0189 0.0084 0.0246 Firmicutes; c__Clostridia; o__Clostridiales; f__Ruminococcaceae; g__; s__ 189924 -0.0114 0.0050 0.0249 Firmicutes; c__Clostridia; o__Clostridiales; f__Ruminococcaceae; g__; s__ 179572 -0.0079 0.0035 0.0252 Firmicutes; c__Clostridia; o__Clostridiales; f__Lachnospiraceae; g__Blautia; s__ 179200 -0.0196 0.0087 0.0252 Firmicutes; c__Clostridia; o__Clostridiales; f__Ruminococcaceae; g__; s__ 317814 -0.0206 0.0092 0.0259 Firmicutes; c__Clostridia; o__Clostridiales; f__; g__; s__ 305318 -0.0157 0.0071 0.0268 Firmicutes; c__Clostridia; o__Clostridiales; f__Lachnospiraceae; g__; s__ 193709 -0.0226 0.0101 0.0269 Firmicutes; c__Clostridia; o__Clostridiales; f__Ruminococcaceae; g__; s__ 187504 -0.0258 0.0116 0.0269 Firmicutes; c__Clostridia; o__Clostridiales; f__Ruminococcaceae; g__; s__ 4347159 -0.0350 0.0158 0.0273 Actinobacteria; c__Actinobacteria; o__Bifidobacteriales; f__Bifidobacteriaceae; g__Bifidobacterium; s__adolescentis 4425663 -0.0216 0.0098 0.0278 Firmicutes; c__Clostridia; o__Clostridiales; f__; g__; s__ 295485 -0.0116 0.0053 0.0280 Firmicutes; c__Clostridia; o__Clostridiales; f__Ruminococcaceae; g__Faecalibacterium; s__prausnitzii 110192 -0.0117 0.0053 0.0282 Firmicutes; c__Clostridia; o__Clostridiales; f__Ruminococcaceae; g__Oscillospira; s__ 183439 -0.0297 0.0135 0.0288 Firmicutes; c__Clostridia; o__Clostridiales; f__Ruminococcaceae; g__; s__ 1820513 -0.0218 0.0099 0.0291 Proteobacteria; c__Betaproteobacteria; o__Burkholderiales; f__Alcaligenaceae; g__Sutterella; s__ 189877 -0.0102 0.0047 0.0310 Firmicutes; c__Clostridia; o__Clostridiales; f__Lachnospiraceae; g__Blautia; s__ 2688035 -0.0097 0.0045 0.0312 Firmicutes; c__Clostridia; o__Clostridiales; f__Lachnospiraceae; g__; s__ 178642 -0.0071 0.0033 0.0320 Firmicutes; c__Clostridia; o__Clostridiales; f__Lachnospiraceae; g__; s__ 179486 -0.0081 0.0037 0.0320 Firmicutes; c__Clostridia; o__Clostridiales; f__Ruminococcaceae; g__; s__ 3709990 0.0132 0.0061 0.0327 Firmicutes; c__Erysipelotrichi; o__Erysipelotrichales; f__Erysipelotrichaceae; g__[Eubacterium]; s__dolichum 174695 -0.0193 0.0090 0.0335 Firmicutes; c__Clostridia; o__Clostridiales; f__Lachnospiraceae; g__; s__ 174818 -0.0215 0.0100 0.0335 Firmicutes; c__Clostridia; o__Clostridiales; f__Ruminococcaceae; g__; s__ 48084 -0.0311 0.0146 0.0341 Firmicutes; c__Clostridia; o__Clostridiales; f__Ruminococcaceae; g__; s__ 180473 -0.0172 0.0081 0.0341 Firmicutes; c__Clostridia; o__Clostridiales; f__Ruminococcaceae; g__; s__

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180352 -0.0128 0.0060 0.0343 Firmicutes; c__Clostridia; o__Clostridiales; f__; g__; s__ 357261 -0.0140 0.0066 0.0348 Firmicutes; c__Clostridia; o__Clostridiales; f__Ruminococcaceae; g__Ruminococcus; s__ 191214 -0.0131 0.0062 0.0348 Firmicutes; c__Clostridia; o__Clostridiales; f__Lachnospiraceae; g__; s__ 177581 -0.0169 0.0080 0.0349 Firmicutes; c__Clostridia; o__Clostridiales; f__Lachnospiraceae; g__; s__ 179201 -0.0103 0.0049 0.0352 Firmicutes; c__Clostridia; o__Clostridiales; f__Ruminococcaceae; g__; s__ 177758 -0.0136 0.0065 0.0357 Firmicutes; c__Clostridia; o__Clostridiales; f__Lachnospiraceae; g__Dorea; s__ 193946 -0.0114 0.0054 0.0364 Firmicutes; c__Clostridia; o__Clostridiales; f__Lachnospiraceae; g__Blautia; s__ 181754 -0.0146 0.0070 0.0368 Firmicutes; c__Clostridia; o__Clostridiales; f__Lachnospiraceae; g__; s__ 192461 -0.0079 0.0038 0.0371 Firmicutes; c__Clostridia; o__Clostridiales; f__Lachnospiraceae; g__; s__ 4414476 -0.0269 0.0129 0.0375 Firmicutes; c__Clostridia; o__Clostridiales; f__Ruminococcaceae; g__; s__ 216710 -0.0216 0.0104 0.0377 Firmicutes; c__Clostridia; o__Clostridiales; f__Ruminococcaceae; g__; s__ 181008 -0.0145 0.0069 0.0378 Firmicutes; c__Clostridia; o__Clostridiales; f__; g__; s__ 189828 -0.0227 0.0109 0.0383 Firmicutes; c__Clostridia; o__Clostridiales; f__Ruminococcaceae; g__; s__ 185584 -0.0173 0.0083 0.0384 Firmicutes; c__Clostridia; o__Clostridiales; f__Ruminococcaceae; g__; s__ 681370 -0.0136 0.0066 0.0399 Actinobacteria; c__Actinobacteria; o__Bifidobacteriales; f__Bifidobacteriaceae; g__Bifidobacterium; s__pseudolongum 178965 -0.0137 0.0067 0.0411 Firmicutes; c__Clostridia; o__Clostridiales; f__; g__; s__ 4465124 -0.0310 0.0151 0.0412 Firmicutes; c__Clostridia; o__Clostridiales; f__Clostridiaceae; g__Clostridium; s__ 300620 -0.0235 0.0115 0.0422 Firmicutes; c__Clostridia; o__Clostridiales; f__Ruminococcaceae; g__; s__ 188676 -0.0254 0.0125 0.0424 Firmicutes; c__Clostridia; o__Clostridiales; f__Ruminococcaceae; g__; s__ 198909 -0.0099 0.0049 0.0425 Firmicutes; c__Clostridia; o__Clostridiales; f__Lachnospiraceae; g__Lachnospira; s__ 182116 -0.0138 0.0068 0.0431 Firmicutes; c__Clostridia; o__Clostridiales; f__Lachnospiraceae; g__; s__ 4357811 -0.0074 0.0037 0.0434 Bacteroidetes; c__Bacteroidia; o__Bacteroidales; f__Bacteroidaceae; g__Bacteroides; s__ 288651 -0.0178 0.0088 0.0443 Firmicutes; c__Clostridia; o__Clostridiales; f__Ruminococcaceae; g__; s__ 196957 -0.0229 0.0114 0.0451 Firmicutes; c__Clostridia; o__Clostridiales; f__Ruminococcaceae; g__; s__ 193148 -0.0072 0.0036 0.0455 Firmicutes; c__Clostridia; o__Clostridiales; f__; g__; s__

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4458959 -0.0102 0.0051 0.0462 Firmicutes; c__Clostridia; o__Clostridiales; f__Veillonellaceae; g__Veillonella; s__ 1952 -0.0356 0.0179 0.0470 Bacteroidetes; c__Bacteroidia; o__Bacteroidales; f__Porphyromonadaceae; g__Parabacteroides; s__ 190772 -0.0168 0.0084 0.0474 Firmicutes; c__Clostridia; o__Clostridiales; f__; g__; s__ 198127 -0.0112 0.0057 0.0482 Firmicutes; c__Clostridia; o__Clostridiales; f__Lachnospiraceae; g__; s__ 232828 -0.0101 0.0051 0.0489 Firmicutes; c__Clostridia; o__Clostridiales; f__Ruminococcaceae; g__; s__ 187404 -0.0182 0.0092 0.0489 Firmicutes; c__Clostridia; o__Clostridiales; f__Ruminococcaceae; g__; s__ 214036 -0.0245 0.0124 0.0490 Firmicutes; c__Clostridia; o__Clostridiales; f__[Mogibacteriaceae]; g__; s__ 189176 -0.0130 0.0066 0.0499 Firmicutes; c__Clostridia; o__Clostridiales; f__Lachnospiraceae; g__[Ruminococcus]; s__gnavus

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5.3.3 Genes involved in peptide presentation and microbial sensing are associated with AS core families of bacteria

We were interested in examining the association between individual AS associated SNPs and the microbiome. We began by investigating the strongest associations to AS susceptibility HLA-B27, ERAP and IL-23R as well as NKX2-3, ICOSLG, NOS2, CARD9, BACH2 and SH2B3. These genes are of interest because of their strong relative effect in AS and for their roles in processing, trimming, presenting antigens in the class I antigen presentation, microbial sensing and immunity. A total of 13 SNPs across the 10 genes (Table 5.4) were each tested against the each individual OTU in the microbiome.

Table 5.4. AS associated loci investigated for association with each OTU in the microbiome.

Locus SNP Chr Risk /Non- risk Allele IL23R rs11209026 1p31 G/A ERAP1-ERAP2- rs30187 5q15 T/C

LNPEP rs2910686 5q15 G/A ERAP1-ERAP2 rs10045403 5q15 A/G HLA-B27 rs4349859 6 NKX2-3 rs11190133 10q24 C/T ICOSLG rs7282490 21q22 G/A

NOS2 rs2531875 17q11 G/T rs2297518 17q11 A/G CARD9 rs1128905 9q34 C/T BACH2 rs17765610 6q15 G/A rs639575 6q15 A/T SH2B3 rs11065898 12q24 T/C

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Whilst none of the OTUs were associated with the SNPs involved in processing, trimming, presenting antigens at a Bonferroni corrected significance level, a total of 212 OTUs reached nominal significance P<0.05. SNPs in the ERAP1 locus were more significant with OTUs than IL23R SNPs or the HLA-B27 allele. There were 53 OTUs, predominantly Ruminococcaceae and Lachnospiraceae families (Table 5.5), associated with the ERAP1-ERAP2 SNP, rs10045403, and 44 OTUs belonging to the same families of bacteria associated with the ERAP1-ERAP2-LNPEP SNP, rs2910686. The aminopeptidase ERAP1 SNP, rs30187, which is known to interact with HLA-B27 in AS, showed association with 50 OTUs all belonging to Ruminococcaceae and Lachnospiraceae bacterial families. There were 25 OTUs associated with the IL-23R SNP, rs11209026, predominantly Ruminococcus, Lachnospiraceae and Bacteroidaceae families (Table 5.6). An additional 40 OTUs belonging to the order Clostridiales, Ruminococcaceae and Lachnospiraceae families were associated with the HLA-B27 allele (Table 5.7).

Microbial sensing genes NKX2-3, ICOSLG, NOS2, CARD9, BACH2 and SH2B3 were also examined to assess for association with individual microbes within the intestinal microbiome (Table 5.4). These six loci are of interest because they are involved in sensing and responding to microbes and microbial antigen with four of the six loci also associated with dysregulated microbial sensing and response in Crohn’s disease (23). Whilst none of the OTUs reached Bonferroni corrected significance, a total of 404 OTUs reached nominal significance P<0.05 across all six microbial sensing SNPs. BACH2 was the most associated of all the microbial sensing genes with 50 OTUs associated with rs17765610 and 81 with rs639575; the majority of which belong to Ruminococcaceae, Lachnospiraceae and Bacteroides families (Supp table 5.8). NOS2 rs2297518 showed 65 OTU associations and rs2531875 with 18 OTU associations, all dominated by Ruminococcaceae, Lachnospiraceae and Bacteroides families along with Rikenellaceae and Clostridiaceae (Supplementary table 5.9). There were 105 OTUs associated with the CARD9 SNP, rs1128905, predominately Lachnospiraceae, Veillonellaceae and Ruminococcaceae, including Faecalibacterium prausnitzii

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(Supplementary table 5.10). The remaining genes NKX2-3, ICOSLG and SH2B3 were associated with 36, 30 and 18 OTUs respectively, with the families of Ruminococcaceae, Lachnospiraceae dominating the associations (Supplementary table 5.11). This demonstrated that no one SNP, or gene, is associated with an individual family or genes of bacteria; rather, these genes are linked to the three major families of Ruminococcaceae, Lachnospiraceae and Bacteroidaceae as well as members of the Rikenellaceae and Veillonellaceae families that have been shown to dominate the core microbiome in AS, as detailed in Chapter 3 of this thesis.

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Table 5.5 OTUs reaching nominal significance (P<0.05) in the ERAP locus. 53 OTUs, predominantly Ruminococcaceae and Lachnospiraceae families associated with the ERAP1-ERAP2 SNP, rs10045403; 44 OTUs belonging to the same families of bacteria associated with the ERAP1-ERAP2-LNPEP SNP, rs2910686 and 50 OTUs all belonging to Ruminococcaceae and Lachnospiraceae associated with ERAP1 SNP, rs30187. Taxonomy is shown with full classification from phylum through class (c_), order (o_), family (f_), genus (g_) and species (s_).

SNP Gene OTU Beta Pvalue Taxonomy rs30187 ERAP 4420408 0.108 1.87E-03 Bacteroidetes; c__Bacteroidia; o__Bacteroidales; f__Bacteroidaceae; g__Bacteroides; s__ rs30187 ERAP 4256470 0.167 1.87E-03 Bacteroidetes; c__Bacteroidia; o__Bacteroidales; f__Bacteroidaceae; g__Bacteroides; s__ovatus rs30187 ERAP 182073 0.102 1.87E-03 Firmicutes; c__Clostridia; o__Clostridiales; f__; g__; s__ rs30187 ERAP 199677 0.046 1.87E-03 Firmicutes; c__Clostridia; o__Clostridiales; f__; g__; s__ rs30187 ERAP 4419459 0.079 1.87E-03 Firmicutes; c__Clostridia; o__Clostridiales; f__; g__; s__ rs30187 ERAP 2724175 0.122 1.87E-03 Firmicutes; c__Clostridia; o__Clostridiales; f__Lachnospiraceae; g__; s__ rs30187 ERAP 4457427 0.118 1.87E-03 Firmicutes; c__Clostridia; o__Clostridiales; f__Lachnospiraceae; g__Lachnospira; s__ rs30187 ERAP 208739 0.088 1.87E-03 Firmicutes; c__Clostridia; o__Clostridiales; f__Ruminococcaceae; g__Faecalibacterium; s__prausnitzii rs30187 ERAP 185420 -0.197 7.89E-03 Bacteroidetes; c__Bacteroidia; o__Bacteroidales; f__Bacteroidaceae; g__Bacteroides; s__ rs30187 ERAP 352747 -0.111 7.89E-03 Bacteroidetes; c__Bacteroidia; o__Bacteroidales; f__Bacteroidaceae; g__Bacteroides; s__ovatus rs30187 ERAP 178760 -0.061 7.89E-03 Firmicutes; c__Clostridia; o__Clostridiales; f__; g__; s__ rs30187 ERAP 182167 -0.103 7.89E-03 Firmicutes; c__Clostridia; o__Clostridiales; f__; g__; s__

141 rs30187 ERAP 192676 -0.054 7.89E-03 Firmicutes; c__Clostridia; o__Clostridiales; f__; g__; s__ rs30187 ERAP 194236 -0.042 7.89E-03 Firmicutes; c__Clostridia; o__Clostridiales; f__; g__; s__ rs30187 ERAP 197274 -0.106 7.89E-03 Firmicutes; c__Clostridia; o__Clostridiales; f__; g__; s__ rs30187 ERAP 553080 -0.156 7.89E-03 Firmicutes; c__Clostridia; o__Clostridiales; f__; g__; s__ rs30187 ERAP 178642 -0.043 7.89E-03 Firmicutes; c__Clostridia; o__Clostridiales; f__Lachnospiraceae; g__; s__ rs30187 ERAP 187196 -0.08 7.89E-03 Firmicutes; c__Clostridia; o__Clostridiales; f__Lachnospiraceae; g__; s__ rs30187 ERAP 198626 -0.068 7.89E-03 Firmicutes; c__Clostridia; o__Clostridiales; f__Lachnospiraceae; g__; s__ rs30187 ERAP 329597 -0.047 7.89E-03 Firmicutes; c__Clostridia; o__Clostridiales; f__Lachnospiraceae; g__; s__ rs30187 ERAP 3856408 -0.117 7.89E-03 Firmicutes; c__Clostridia; o__Clostridiales; f__Lachnospiraceae; g__; s__ rs30187 ERAP 189176 -0.079 7.89E-03 Firmicutes; c__Clostridia; o__Clostridiales; f__Lachnospiraceae; g__[Ruminococcus]; s__gnavus rs30187 ERAP 348642 -0.119 7.89E-03 Firmicutes; c__Clostridia; o__Clostridiales; f__Lachnospiraceae; g__[Ruminococcus]; s__gnavus rs30187 ERAP 179557 -0.036 7.89E-03 Firmicutes; c__Clostridia; o__Clostridiales; f__Lachnospiraceae; g__Blautia; s__ rs30187 ERAP 187210 -0.092 7.89E-03 Firmicutes; c__Clostridia; o__Clostridiales; f__Lachnospiraceae; g__Blautia; s__ rs30187 ERAP 191734 -0.034 7.89E-03 Firmicutes; c__Clostridia; o__Clostridiales; f__Lachnospiraceae; g__Blautia; s__ rs30187 ERAP 193852 -0.079 7.89E-03 Firmicutes; c__Clostridia; o__Clostridiales; f__Lachnospiraceae; g__Blautia; s__ rs30187 ERAP 363029 -0.092 7.89E-03 Firmicutes; c__Clostridia; o__Clostridiales; f__Lachnospiraceae; g__Blautia; s__ rs30187 ERAP 181047 -0.098 7.89E-03 Firmicutes; c__Clostridia; o__Clostridiales; f__Lachnospiraceae; g__Coprococcus; s__ rs30187 ERAP 181918 -0.085 7.89E-03 Firmicutes; c__Clostridia; o__Clostridiales; f__Lachnospiraceae; g__Coprococcus; s__ rs30187 ERAP 193969 -0.142 7.89E-03 Firmicutes; c__Clostridia; o__Clostridiales; f__Lachnospiraceae; g__Coprococcus; s__ rs30187 ERAP 174752 -0.083 7.89E-03 Firmicutes; c__Clostridia; o__Clostridiales; f__Ruminococcaceae; g__; s__

142 rs30187 ERAP 177134 -0.06 7.89E-03 Firmicutes; c__Clostridia; o__Clostridiales; f__Ruminococcaceae; g__; s__ rs30187 ERAP 178485 -0.084 7.89E-03 Firmicutes; c__Clostridia; o__Clostridiales; f__Ruminococcaceae; g__; s__ rs30187 ERAP 181826 -0.087 7.89E-03 Firmicutes; c__Clostridia; o__Clostridiales; f__Ruminococcaceae; g__; s__ rs30187 ERAP 194372 -0.073 7.89E-03 Firmicutes; c__Clostridia; o__Clostridiales; f__Ruminococcaceae; g__; s__ rs30187 ERAP 194488 -0.061 7.89E-03 Firmicutes; c__Clostridia; o__Clostridiales; f__Ruminococcaceae; g__; s__ rs30187 ERAP 196518 -0.061 7.89E-03 Firmicutes; c__Clostridia; o__Clostridiales; f__Ruminococcaceae; g__; s__ rs30187 ERAP 198962 -0.047 7.89E-03 Firmicutes; c__Clostridia; o__Clostridiales; f__Ruminococcaceae; g__; s__ rs30187 ERAP 260414 -0.177 7.89E-03 Firmicutes; c__Clostridia; o__Clostridiales; f__Ruminococcaceae; g__; s__ rs30187 ERAP 287790 -0.104 7.89E-03 Firmicutes; c__Clostridia; o__Clostridiales; f__Ruminococcaceae; g__; s__ rs30187 ERAP 313524 -0.066 7.89E-03 Firmicutes; c__Clostridia; o__Clostridiales; f__Ruminococcaceae; g__; s__ rs30187 ERAP 324894 -0.069 7.89E-03 Firmicutes; c__Clostridia; o__Clostridiales; f__Ruminococcaceae; g__; s__ rs30187 ERAP 538322 -0.086 7.89E-03 Firmicutes; c__Clostridia; o__Clostridiales; f__Ruminococcaceae; g__; s__ rs30187 ERAP 571642 -0.106 7.89E-03 Firmicutes; c__Clostridia; o__Clostridiales; f__Ruminococcaceae; g__; s__ rs30187 ERAP 4412540 -0.26 7.89E-03 Firmicutes; c__Clostridia; o__Clostridiales; f__Ruminococcaceae; g__; s__ rs30187 ERAP 4450214 -0.176 7.89E-03 Firmicutes; c__Clostridia; o__Clostridiales; f__Ruminococcaceae; g__; s__ rs30187 ERAP 4480359 -0.091 7.89E-03 Firmicutes; c__Clostridia; o__Clostridiales; f__Ruminococcaceae; g__; s__ rs30187 ERAP 4437359 -0.212 7.89E-03 Firmicutes; c__Clostridia; o__Clostridiales; f__Ruminococcaceae; g__Oscillospira; s__ rs30187 ERAP 4428714 -0.105 7.89E-03 Tenericutes; c__Mollicutes; o__RF39; f__; g__; s__ rs10045403 ERAP1-ERAP2 12574 -0.085 6.33E-04 Actinobacteria; c__Actinobacteria; o__Actinomycetales; f__Actinomycetaceae; g__Actinomyces; s__ rs10045403 ERAP1-ERAP2 352747 -0.091 6.33E-04 Bacteroidetes; c__Bacteroidia; o__Bacteroidales; f__Bacteroidaceae; g__Bacteroides; s__ovatus

143 rs10045403 ERAP1-ERAP2 4424239 -0.112 6.33E-04 Firmicutes; c__Bacilli; o__Lactobacillales; f__Streptococcaceae; g__Streptococcus; s rs10045403 ERAP1-ERAP2 177207 -0.119 6.33E-04 Firmicutes; c__Clostridia; o__Clostridiales; f__; g__; s__ rs10045403 ERAP1-ERAP2 2256425 -0.1 6.33E-04 Firmicutes; c__Clostridia; o__Clostridiales; f__Christensenellaceae; g__; s__ rs10045403 ERAP1-ERAP2 194659 -0.145 6.33E-04 Firmicutes; c__Clostridia; o__Clostridiales; f__Clostridiaceae; g__; s__ rs10045403 ERAP1-ERAP2 178018 -0.14 6.33E-04 Firmicutes; c__Clostridia; o__Clostridiales; f__Lachnospiraceae; g__; s__ rs10045403 ERAP1-ERAP2 187196 -0.086 6.33E-04 Firmicutes; c__Clostridia; o__Clostridiales; f__Lachnospiraceae; g__; s__ rs10045403 ERAP1-ERAP2 2210028 -0.137 6.33E-04 Firmicutes; c__Clostridia; o__Clostridiales; f__Lachnospiraceae; g__[Ruminococcus]; s__ rs10045403 ERAP1-ERAP2 148279 -0.13 6.33E-04 Firmicutes; c__Clostridia; o__Clostridiales; f__Lachnospiraceae; g__Blautia; s__ rs10045403 ERAP1-ERAP2 193852 -0.089 6.33E-04 Firmicutes; c__Clostridia; o__Clostridiales; f__Lachnospiraceae; g__Blautia; s__ rs10045403 ERAP1-ERAP2 181826 -0.089 6.33E-04 Firmicutes; c__Clostridia; o__Clostridiales; f__Ruminococcaceae; g__; s__ rs10045403 ERAP1-ERAP2 185164 -0.135 6.33E-04 Firmicutes; c__Clostridia; o__Clostridiales; f__Ruminococcaceae; g__; s__ rs10045403 ERAP1-ERAP2 187404 -0.136 6.33E-04 Firmicutes; c__Clostridia; o__Clostridiales; f__Ruminococcaceae; g__; s__ rs10045403 ERAP1-ERAP2 110192 -0.099 6.33E-04 Firmicutes; c__Clostridia; o__Clostridiales; f__Ruminococcaceae; g__Oscillospira; s__ rs10045403 ERAP1-ERAP2 186133 -0.091 6.33E-04 Firmicutes; c__Clostridia; o__Clostridiales; f__Ruminococcaceae; g__Ruminococcus; s__ rs10045403 ERAP1-ERAP2 192383 -0.139 6.33E-04 Firmicutes; c__Clostridia; o__Clostridiales; f__Ruminococcaceae; g__Ruminococcus; s__ rs10045403 ERAP1-ERAP2 16054 -0.138 6.33E-04 Firmicutes; c__Clostridia; o__Clostridiales; f__Ruminococcaceae; g__Ruminococcus; s__callidus rs10045403 ERAP1-ERAP2 4374302 0.259 6.87E-04 Firmicutes; c__Clostridia; o__Clostridiales; f__Lachnospiraceae; g__Dorea; s__ rs10045403 ERAP1-ERAP2 348009 0.195 6.87E-04 Firmicutes; c__Clostridia; o__Clostridiales; f__Ruminococcaceae; g__Oscillospira; s__

144 rs10045403 ERAP1-ERAP2 4347159 -0.349 1.26E-03 Actinobacteria; c__Actinobacteria; o__Bifidobacteriales; f__Bifidobacteriaceae; g__Bifidobacterium; s__adolescentis rs10045403 ERAP1-ERAP2 535955 -0.209 1.26E-03 Firmicutes; c__Clostridia; o__Clostridiales; f__Clostridiaceae; g__SMB53; s__ rs10045403 ERAP1-ERAP2 681370 -0.139 1.40E-03 Actinobacteria; c__Actinobacteria; o__Bifidobacteriales; f__Bifidobacteriaceae; g__Bifidobacterium; s__pseudolongum rs10045403 ERAP1-ERAP2 185420 -0.309 1.40E-03 Bacteroidetes; c__Bacteroidia; o__Bacteroidales; f__Bacteroidaceae; g__Bacteroides; s__ rs10045403 ERAP1-ERAP2 4476780 -0.225 1.40E-03 Bacteroidetes; c__Bacteroidia; o__Bacteroidales; f__Rikenellaceae; g__; s__ rs10045403 ERAP1-ERAP2 173876 -0.186 1.40E-03 Firmicutes; c__Clostridia; o__Clostridiales; f__; g__; s__ rs10045403 ERAP1-ERAP2 192676 -0.09 1.40E-03 Firmicutes; c__Clostridia; o__Clostridiales; f__; g__; s__ rs10045403 ERAP1-ERAP2 192741 -0.21 1.40E-03 Firmicutes; c__Clostridia; o__Clostridiales; f__; g__; s__ rs10045403 ERAP1-ERAP2 193148 -0.077 1.40E-03 Firmicutes; c__Clostridia; o__Clostridiales; f__; g__; s__ rs10045403 ERAP1-ERAP2 292758 -0.151 1.40E-03 Firmicutes; c__Clostridia; o__Clostridiales; f__; g__; s__ rs10045403 ERAP1-ERAP2 4372528 -0.172 1.40E-03 Firmicutes; c__Clostridia; o__Clostridiales; f__; g__; s__ rs10045403 ERAP1-ERAP2 4377149 -0.164 1.40E-03 Firmicutes; c__Clostridia; o__Clostridiales; f__; g__; s__ rs10045403 ERAP1-ERAP2 4402903 -0.188 1.40E-03 Firmicutes; c__Clostridia; o__Clostridiales; f__; g__; s__ rs10045403 ERAP1-ERAP2 302932 -0.068 1.40E-03 Firmicutes; c__Clostridia; o__Clostridiales; f__Clostridiaceae; g__; s__ rs10045403 ERAP1-ERAP2 4434334 -0.325 1.40E-03 Firmicutes; c__Clostridia; o__Clostridiales; f__Clostridiaceae; g__; s__ rs10045403 ERAP1-ERAP2 4465124 -0.264 1.40E-03 Firmicutes; c__Clostridia; o__Clostridiales; f__Clostridiaceae; g__Clostridium; s__ rs10045403 ERAP1-ERAP2 183439 -0.2 1.40E-03 Firmicutes; c__Clostridia; o__Clostridiales; f__Lachnospiraceae; g__; s__ rs10045403 ERAP1-ERAP2 186416 -0.229 1.40E-03 Firmicutes; c__Clostridia; o__Clostridiales; f__Lachnospiraceae; g__; s__ rs10045403 ERAP1-ERAP2 348642 -0.155 1.40E-03 Firmicutes; c__Clostridia; o__Clostridiales; f__Lachnospiraceae; g__[Ruminococcus];

145 rs10045403 ERAP1-ERAP2 177037 -0.252 1.40E-03 Firmicutes; c__Clostridia; o__Clostridiales; f__Lachnospiraceae; g__Blautia; s__ rs10045403 ERAP1-ERAP2 177062 -0.155 1.40E-03 Firmicutes; c__Clostridia; o__Clostridiales; f__Lachnospiraceae; g__Blautia; s__ rs10045403 ERAP1-ERAP2 187868 -0.048 1.40E-03 Firmicutes; c__Clostridia; o__Clostridiales; f__Lachnospiraceae; g__Coprococcus; s__ rs10045403 ERAP1-ERAP2 258099 -0.073 1.40E-03 Firmicutes; c__Clostridia; o__Clostridiales; f__Ruminococcaceae; g__; s__ rs10045403 ERAP1-ERAP2 295258 -0.149 1.40E-03 Firmicutes; c__Clostridia; o__Clostridiales; f__Ruminococcaceae; g__; s__ rs10045403 ERAP1-ERAP2 312882 -0.176 1.40E-03 Firmicutes; c__Clostridia; o__Clostridiales; f__Ruminococcaceae; g__; s__ rs10045403 ERAP1-ERAP2 368236 -0.066 1.40E-03 Firmicutes; c__Clostridia; o__Clostridiales; f__Ruminococcaceae; g__; s__ rs10045403 ERAP1-ERAP2 2835813 -0.128 1.40E-03 Firmicutes; c__Clostridia; o__Clostridiales; f__Ruminococcaceae; g__; s__ rs10045403 ERAP1-ERAP2 4427290 -0.151 1.40E-03 Firmicutes; c__Clostridia; o__Clostridiales; f__Ruminococcaceae; g__; s__ rs10045403 ERAP1-ERAP2 4468466 -0.254 1.40E-03 Firmicutes; c__Clostridia; o__Clostridiales; f__Ruminococcaceae; g__; s__ rs10045403 ERAP1-ERAP2 300374 -0.095 1.40E-03 Firmicutes; c__Clostridia; o__Clostridiales; f__Ruminococcaceae; g__Oscillospira; s__ rs10045403 ERAP1-ERAP2 307238 -0.257 1.40E-03 Firmicutes; c__Clostridia; o__Clostridiales; f__Ruminococcaceae; g__Oscillospira; s__ rs10045403 ERAP1-ERAP2 4421273 -0.136 1.40E-03 Firmicutes; c__Clostridia; o__Clostridiales; f__Ruminococcaceae; g__Oscillospira; s__ rs10045403 ERAP1-ERAP2 368490 -0.178 1.91E-02 Firmicutes; c__Bacilli; o__Turicibacterales; f__Turicibacteraceae; g__Turicibacter; s__ rs2910686 ERAP1- 216599 -0.22 1.40E-03 Bacteroidetes; c__Bacteroidia; o__Bacteroidales; f__Rikenellaceae; g__; s__ ERAP2-LNPEP rs2910686 ERAP1- 368490 -0.274 1.40E-03 Firmicutes; c__Bacilli; o__Turicibacterales; f__Turicibacteraceae; g__Turicibacter; s__ ERAP2-LNPEP rs2910686 ERAP1- 174010 -0.111 1.40E-03 Firmicutes; c__Clostridia; o__Clostridiales; f__; g__; s__ ERAP2-LNPEP rs2910686 ERAP1- 4377149 -0.15 1.40E-03 Firmicutes; c__Clostridia; o__Clostridiales; f__; g__; s__ ERAP2-LNPEP

146 rs2910686 ERAP1- 555547 -0.109 1.40E-03 Firmicutes; c__Clostridia; o__Clostridiales; f__Christensenellaceae; g__; s__ ERAP2-LNPEP rs2910686 ERAP1- 177230 -0.096 1.40E-03 Firmicutes; c__Clostridia; o__Clostridiales; f__Lachnospiraceae; g__; s__ ERAP2-LNPEP rs2910686 ERAP1- 192983 -0.102 1.40E-03 Firmicutes; c__Clostridia; o__Clostridiales; f__Lachnospiraceae; g__; s__ ERAP2-LNPEP rs2910686 ERAP1- 4472399 -0.18 1.40E-03 Firmicutes; c__Clostridia; o__Clostridiales; f__Lachnospiraceae; g__; s__ ERAP2-LNPEP rs2910686 ERAP1- 2210028 -0.103 1.40E-03 Firmicutes; c__Clostridia; o__Clostridiales; f__Lachnospiraceae; g__[Ruminococcus]; ERAP2-LNPEP s__ rs2910686 ERAP1- 193666 -0.151 1.40E-03 Firmicutes; c__Clostridia; o__Clostridiales; f__Lachnospiraceae; g__Blautia; s__ ERAP2-LNPEP rs2910686 ERAP1- 358798 -0.13 1.40E-03 Firmicutes; c__Clostridia; o__Clostridiales; f__Lachnospiraceae; g__Blautia; s__ ERAP2-LNPEP rs2910686 ERAP1- 178238 -0.117 1.40E-03 Firmicutes; c__Clostridia; o__Clostridiales; f__Lachnospiraceae; g__Coprococcus; s__ ERAP2-LNPEP rs2910686 ERAP1- 184770 -0.127 1.40E-03 Firmicutes; c__Clostridia; o__Clostridiales; f__Lachnospiraceae; g__Coprococcus; s__ ERAP2-LNPEP rs2910686 ERAP1- 187924 -0.131 1.40E-03 Firmicutes; c__Clostridia; o__Clostridiales; f__Ruminococcaceae; g__; s__ ERAP2-LNPEP rs2910686 ERAP1- 312882 -0.26 1.40E-03 Firmicutes; c__Clostridia; o__Clostridiales; f__Ruminococcaceae; g__; s__ ERAP2-LNPEP rs2910686 ERAP1- 688923 -0.2 1.40E-03 Firmicutes; c__Clostridia; o__Clostridiales; f__Ruminococcaceae; g__; s__

147

ERAP2-LNPEP rs2910686 ERAP1- 3530697 -0.104 1.40E-03 Firmicutes; c__Clostridia; o__Clostridiales; f__Ruminococcaceae; g__; s__ ERAP2-LNPEP rs2910686 ERAP1- 4439469 -0.11 1.40E-03 Firmicutes; c__Clostridia; o__Clostridiales; f__Ruminococcaceae; g__; s__ ERAP2-LNPEP rs2910686 ERAP1- 146554 -0.277 1.40E-03 Firmicutes; c__Clostridia; o__Clostridiales; f__Ruminococcaceae; g__Ruminococcus; ERAP2-LNPEP s__ rs2910686 ERAP1- 180826 -0.137 1.40E-03 Firmicutes; c__Clostridia; o__Clostridiales; f__Ruminococcaceae; g__Ruminococcus; ERAP2-LNPEP s__ rs2910686 ERAP1- 356745 -0.178 1.40E-03 Firmicutes; c__Clostridia; o__Clostridiales; f__Ruminococcaceae; g__Ruminococcus; ERAP2-LNPEP s__ rs2910686 ERAP1- 798581 -0.163 1.40E-03 Firmicutes; c__Clostridia; o__Clostridiales; f__Ruminococcaceae; g__Ruminococcus; ERAP2-LNPEP s__bromii rs2910686 ERAP1- 4428714 -0.108 1.40E-03 Tenericutes; c__Mollicutes; o__RF39; f__; g__; s__ ERAP2-LNPEP rs2910686 ERAP1- 4425214 -0.168 1.91E-02 Firmicutes; c__Bacilli; o__Lactobacillales; f__Streptococcaceae; g__Streptococcus; ERAP2-LNPEP s__ rs2910686 ERAP1- 192684 -0.062 2.64E-02 Bacteroidetes; c__Bacteroidia; o__Bacteroidales; f__Bacteroidaceae; g__Bacteroides; ERAP2-LNPEP s__ rs2910686 ERAP1- 352747 -0.087 2.64E-02 Bacteroidetes; c__Bacteroidia; o__Bacteroidales; f__Bacteroidaceae; g__Bacteroides; ERAP2-LNPEP s__ovatus rs2910686 ERAP1- 772282 -0.08 2.64E-02 Bacteroidetes; c__Bacteroidia; o__Bacteroidales; f__Rikenellaceae; g__; s__ ERAP2-LNPEP

148 rs2910686 ERAP1- 4424239 -0.094 2.64E-02 Firmicutes; c__Bacilli; o__Lactobacillales; f__Streptococcaceae; g__Streptococcus; ERAP2-LNPEP rs2910686 ERAP1- 178420 -0.048 2.64E-02 Firmicutes; c__Clostridia; o__Clostridiales; f__; g__; s__ ERAP2-LNPEP rs2910686 ERAP1- 2996838 -0.088 2.64E-02 Firmicutes; c__Clostridia; o__Clostridiales; f__; g__; s__ ERAP2-LNPEP rs2910686 ERAP1- 178183 -0.089 2.64E-02 Firmicutes; c__Clostridia; o__Clostridiales; f__Clostridiaceae; g__; s__ ERAP2-LNPEP rs2910686 ERAP1- 194659 -0.077 2.64E-02 Firmicutes; c__Clostridia; o__Clostridiales; f__Clostridiaceae; g__; s__ ERAP2-LNPEP rs2910686 ERAP1- 178642 -0.052 2.64E-02 Firmicutes; c__Clostridia; o__Clostridiales; f__Lachnospiraceae; g__; s__ ERAP2-LNPEP rs2910686 ERAP1- 183147 -0.061 2.64E-02 Firmicutes; c__Clostridia; o__Clostridiales; f__Lachnospiraceae; g__; s__ ERAP2-LNPEP rs2910686 ERAP1- 192536 -0.072 2.64E-02 Firmicutes; c__Clostridia; o__Clostridiales; f__Lachnospiraceae; g__; s__ ERAP2-LNPEP rs2910686 ERAP1- 194733 -0.085 2.64E-02 Firmicutes; c__Clostridia; o__Clostridiales; f__Lachnospiraceae; g__; s__ ERAP2-LNPEP rs2910686 ERAP1- 198626 -0.08 2.64E-02 Firmicutes; c__Clostridia; o__Clostridiales; f__Lachnospiraceae; g__; s__ ERAP2-LNPEP rs2910686 ERAP1- 4331364 -0.079 2.64E-02 Firmicutes; c__Clostridia; o__Clostridiales; f__Lachnospiraceae; g__; s__ ERAP2-LNPEP rs2910686 ERAP1- 179583 -0.063 2.64E-02 Firmicutes; c__Clostridia; o__Clostridiales; f__Ruminococcaceae; g__; s__

149

ERAP2-LNPEP rs2910686 ERAP1- 181826 -0.065 2.64E-02 Firmicutes; c__Clostridia; o__Clostridiales; f__Ruminococcaceae; g__; s__ ERAP2-LNPEP rs2910686 ERAP1- 189924 -0.071 2.64E-02 Firmicutes; c__Clostridia; o__Clostridiales; f__Ruminococcaceae; g__; s__ ERAP2-LNPEP rs2910686 ERAP1- 258099 -0.06 2.64E-02 Firmicutes; c__Clostridia; o__Clostridiales; f__Ruminococcaceae; g__; s__ ERAP2-LNPEP rs2910686 ERAP1- 2066056 -0.073 2.64E-02 Firmicutes; c__Clostridia; o__Clostridiales; f__Ruminococcaceae; g__; s__ ERAP2-LNPEP rs2910686 ERAP1- 2835813 -0.083 2.64E-02 Firmicutes; c__Clostridia; o__Clostridiales; f__Ruminococcaceae; g__; s__ ERAP2-LNPEP

150

Table 5.6 OTUs reaching nominal significance (P<0.05) associated with the IL-23R SNP, rs11209026, predominantly Ruminococcaceae, Lachnospiraceae and Bacteroidaceae families. . Taxonomy is shown with full classification from phylum through class (c_), order (o_), family (f_), genus (g_) and species (s_).

SNP Gene OTU Beta Pvalue Taxonomy rs11209026 IL23R 4453609 -0.777 7.89E-03 Bacteroidetes; c__Bacteroidia; o__Bacteroidales; f__Rikenellaceae; g__; s__ rs11209026 IL23R 4424239 -0.214 7.89E-03 Firmicutes; c__Bacilli; o__Lactobacillales; f__Streptococcaceae; g__Streptococcus; s__ rs11209026 IL23R 179847 -0.117 7.89E-03 Firmicutes; c__Clostridia; o__Clostridiales; f__Lachnospiraceae; g__; s__ rs11209026 IL23R 183147 -0.12 7.89E-03 Firmicutes; c__Clostridia; o__Clostridiales; f__Lachnospiraceae; g__; s__ rs11209026 IL23R 186735 -0.218 7.89E-03 Firmicutes; c__Clostridia; o__Clostridiales; f__Lachnospiraceae; g__; s__ rs11209026 IL23R 192735 -0.167 7.89E-03 Firmicutes; c__Clostridia; o__Clostridiales; f__Lachnospiraceae; g__; s__ rs11209026 IL23R 196100 -0.113 7.89E-03 Firmicutes; c__Clostridia; o__Clostridiales; f__Lachnospiraceae; g__; s__ rs11209026 IL23R 191734 -0.085 7.89E-03 Firmicutes; c__Clostridia; o__Clostridiales; f__Lachnospiraceae; g__Blautia; s__ rs11209026 IL23R 177663 -0.327 7.89E-03 Firmicutes; c__Clostridia; o__Clostridiales; f__Ruminococcaceae; g__; s__ rs11209026 IL23R 730906 -0.133 7.89E-03 Firmicutes; c__Clostridia; o__Clostridiales; f__Ruminococcaceae; g__; s__ rs11209026 IL23R 4426298 0.301 1.71E-02 Actinobacteria; c__Actinobacteria; o__Bifidobacteriales; f__Bifidobacteriaceae; g__Bifidobacterium; s__animalis rs11209026 IL23R 336559 0.308 1.71E-02 Bacteroidetes; c__Bacteroidia; o__Bacteroidales; f__Bacteroidaceae; g__Bacteroides; s__ rs11209026 IL23R 3211875 0.225 1.71E-02 Bacteroidetes; c__Bacteroidia; o__Bacteroidales; f__Bacteroidaceae; g__Bacteroides; s__ rs11209026 IL23R 4256470 0.613 1.71E-02 Bacteroidetes; c__Bacteroidia; o__Bacteroidales; f__Bacteroidaceae; g__Bacteroides; s__ovatus rs11209026 IL23R 328617 0.574 1.71E-02 Bacteroidetes; c__Bacteroidia; o__Bacteroidales; f__Bacteroidaceae; g__Bacteroides; s__uniformis rs11209026 IL23R 157338 0.249 1.71E-02 Firmicutes; c__Clostridia; o__Clostridiales; f__; g__; s__ rs11209026 IL23R 173876 0.378 1.71E-02 Firmicutes; c__Clostridia; o__Clostridiales; f__; g__; s__ rs11209026 IL23R 173927 0.284 1.71E-02 Firmicutes; c__Clostridia; o__Clostridiales; f__; g__; s__ rs11209026 IL23R 181059 0.615 1.71E-02 Firmicutes; c__Clostridia; o__Clostridiales; f__; g__; s__ rs11209026 IL23R 192741 0.365 1.71E-02 Firmicutes; c__Clostridia; o__Clostridiales; f__; g__; s__ rs11209026 IL23R 192720 0.368 1.71E-02 Firmicutes; c__Clostridia; o__Clostridiales; f__Ruminococcaceae; g__; s__ rs11209026 IL23R 317677 0.168 1.71E-02 Firmicutes; c__Clostridia; o__Clostridiales; f__Ruminococcaceae; g__; s__ rs11209026 IL23R 571642 0.216 1.71E-02 Firmicutes; c__Clostridia; o__Clostridiales; f__Ruminococcaceae; g__; s__

151 rs11209026 IL23R 4408801 0.205 1.71E-02 Firmicutes; c__Clostridia; o__Clostridiales; f__Ruminococcaceae; g__Oscillospira; s__ rs11209026 IL23R 178117 0.193 1.71E-02 Firmicutes; c__Clostridia; o__Clostridiales; f__Ruminococcaceae; g__Ruminococcus;

152

Table 5.7 OTUs reaching nominal significance (P<0.05) associated with the HLA-B27 allele, predominantly Clostridiaceae, Ruminococcaceae and Lachnospiraceae families. Taxonomy is shown with full classification from phylum through class (c_), order (o_), family (f_), genus (g_) and species(s_).

SNP Gene OTU Beta Pvalue Taxonomy rs4349859 HLA-B27 4232045 -0.081 1.42E-02 Bacteroidetes; c__Bacteroidia; o__Bacteroidales; f__Bacteroidaceae; g__Bacteroides; s__ rs4349859 HLA-B27 2318497 -0.135 1.42E-02 Firmicutes; c__Clostridia; o__Clostridiales; f__Clostridiaceae; g__; s__ rs4349859 HLA-B27 4383953 -0.157 1.42E-02 Firmicutes; c__Clostridia; o__Clostridiales; f__Clostridiaceae; g__; s__ rs4349859 HLA-B27 304779 -0.061 1.42E-02 Firmicutes; c__Clostridia; o__Clostridiales; f__Clostridiaceae; g__Clostridium; s__perfringens rs4349859 HLA-B27 181485 -0.049 1.42E-02 Firmicutes; c__Clostridia; o__Clostridiales; f__Lachnospiraceae; g__[Ruminococcus]; s__ rs4349859 HLA-B27 193852 -0.054 1.42E-02 Firmicutes; c__Clostridia; o__Clostridiales; f__Lachnospiraceae; g__Blautia; s__ rs4349859 HLA-B27 28914 -0.054 1.42E-02 Firmicutes; c__Clostridia; o__Clostridiales; f__Lachnospiraceae; g__Roseburia; s__ rs4349859 HLA-B27 177515 -0.177 1.42E-02 Firmicutes; c__Clostridia; o__Clostridiales; f__Lachnospiraceae; g__Roseburia; s__ rs4349859 HLA-B27 3393191 -0.085 1.42E-02 Firmicutes; c__Clostridia; o__Clostridiales; f__Lachnospiraceae; g__Roseburia; s__ rs4349859 HLA-B27 3926480 -0.179 1.42E-02 Firmicutes; c__Clostridia; o__Clostridiales; f__Lachnospiraceae; g__Roseburia; s__ rs4349859 HLA-B27 193644 -0.081 1.42E-02 Firmicutes; c__Clostridia; o__Clostridiales; f__Ruminococcaceae; g__; s__ rs4349859 HLA-B27 157338 0.092 1.89E-02 Firmicutes; c__Clostridia; o__Clostridiales; f__; g__; s__ rs4349859 HLA-B27 176865 0.067 1.89E-02 Firmicutes; c__Clostridia; o__Clostridiales; f__; g__; s__ rs4349859 HLA-B27 185558 0.049 1.89E-02 Firmicutes; c__Clostridia; o__Clostridiales; f__; g__; s__ rs4349859 HLA-B27 317814 0.133 1.89E-02 Firmicutes; c__Clostridia; o__Clostridiales; f__; g__; s__ rs4349859 HLA-B27 3973322 0.11 1.89E-02 Firmicutes; c__Clostridia; o__Clostridiales; f__; g__; s__ rs4349859 HLA-B27 4425663 0.115 1.89E-02 Firmicutes; c__Clostridia; o__Clostridiales; f__; g__; s__

153 rs4349859 HLA-B27 217109 0.105 1.89E-02 Firmicutes; c__Clostridia; o__Clostridiales; f__Christensenellaceae; g__; s__ rs4349859 HLA-B27 231952 0.062 1.89E-02 Firmicutes; c__Clostridia; o__Clostridiales; f__Christensenellaceae; g__; s__ rs4349859 HLA-B27 2256425 0.081 1.89E-02 Firmicutes; c__Clostridia; o__Clostridiales; f__Christensenellaceae; g__; s__ rs4349859 HLA-B27 177047 0.129 1.89E-02 Firmicutes; c__Clostridia; o__Clostridiales; f__Lachnospiraceae; g__; s__ rs4349859 HLA-B27 192461 0.043 1.89E-02 Firmicutes; c__Clostridia; o__Clostridiales; f__Lachnospiraceae; g__; s__ rs4349859 HLA-B27 4349261 0.113 1.89E-02 Firmicutes; c__Clostridia; o__Clostridiales; f__Lachnospiraceae; g__; s__ rs4349859 HLA-B27 4398588 0.059 1.89E-02 Firmicutes; c__Clostridia; o__Clostridiales; f__Lachnospiraceae; g__[Ruminococcus]; s__ rs4349859 HLA-B27 193969 0.109 1.89E-02 Firmicutes; c__Clostridia; o__Clostridiales; f__Lachnospiraceae; g__Coprococcus; s__ rs4349859 HLA-B27 175148 0.064 1.89E-02 Firmicutes; c__Clostridia; o__Clostridiales; f__Ruminococcaceae; g__; s__ rs4349859 HLA-B27 194320 0.073 1.89E-02 Firmicutes; c__Clostridia; o__Clostridiales; f__Ruminococcaceae; g__; s__ rs4349859 HLA-B27 196982 0.068 1.89E-02 Firmicutes; c__Clostridia; o__Clostridiales; f__Ruminococcaceae; g__; s__ rs4349859 HLA-B27 198221 0.073 1.89E-02 Firmicutes; c__Clostridia; o__Clostridiales; f__Ruminococcaceae; g__; s__ rs4349859 HLA-B27 216710 0.123 1.89E-02 Firmicutes; c__Clostridia; o__Clostridiales; f__Ruminococcaceae; g__; s__ rs4349859 HLA-B27 288810 0.103 1.89E-02 Firmicutes; c__Clostridia; o__Clostridiales; f__Ruminococcaceae; g__; s__ rs4349859 HLA-B27 759816 0.084 1.89E-02 Firmicutes; c__Clostridia; o__Clostridiales; f__Ruminococcaceae; g__; s__ rs4349859 HLA-B27 4153054 0.153 1.89E-02 Firmicutes; c__Clostridia; o__Clostridiales; f__Ruminococcaceae; g__; s__ rs4349859 HLA-B27 4446669 0.053 1.89E-02 Firmicutes; c__Clostridia; o__Clostridiales; f__Ruminococcaceae; g__; s__ rs4349859 HLA-B27 2307779 0.093 1.89E-02 Firmicutes; c__Clostridia; o__Clostridiales; f__Ruminococcaceae; g__Oscillospira; s__ rs4349859 HLA-B27 147969 0.149 1.89E-02 Firmicutes; c__Clostridia; o__Clostridiales; f__Ruminococcaceae; g__Ruminococcus; s__ rs4349859 HLA-B27 180826 0.118 1.89E-02 Firmicutes; c__Clostridia; o__Clostridiales; f__Ruminococcaceae; g__Ruminococcus; s__ rs4349859 HLA-B27 186133 0.056 1.89E-02 Firmicutes; c__Clostridia; o__Clostridiales; f__Ruminococcaceae; g__Ruminococcus; s__ rs4349859 HLA-B27 4472091 0.172 1.89E-02 Firmicutes; c__Clostridia; o__Clostridiales; f__Ruminococcaceae; g__Ruminococcus; s__

154 rs4349859 HLA-B27 4306262 0.206 1.89E-02 Verrucomicrobia; c__Verrucomicrobiae; o__Verrucomicrobiales; f__Verrucomicrobiaceae;

155

156

5.4 Discussion

To better understand the possibilities of modifying the microbiome as a therapeutic strategy we need to better understand the factors that influence the microbiome, from diet to environment and the underlying host genetics. Understanding the effect of host genetics in the bacterial composition of the microbiome is important, because if the underlying genetics dictates the response of the immune system to certain microbes, or how the body tolerates or processes them, then no amount of microbiota modulation or probiotics will permanently shift the intestinal microbiome. However, if host genetics favours microbiota modulation, then the potential for the use of microbes as a treatment through faecal transplants or probiotics, as it is currently employed in clinical practice for antibiotic resistant Clostridium difficile infections, presents a new avenue for therapeutic developments for AS.

Studies comparing genotype and the microbiome, particularly in patients with IBD, have demonstrated evidence of gene-microbiota interactions (102, 240- 242). Recently, several studies showed that the Fucosyltransferase 2 (FUT2) gene, in patients with CD, was associated both with a shift of overall community composition as well as alterations of abundances of specific microbes within the community (241-244).The FUT2 gene is responsible for regulating the secretion of the blood group antigens in the digestive mucosa and from the secretory glands (243). The suggestion has been that individulas that do not secret blood group products in their mucosa due to a FUT2 mutation, may have a disrupted immunogenic/homeostatic equilibrium, due to changes in carbohydrate levels in the mucos, that shifts the community of bacteria leaving the patient more susceptible to the development of chronic mucosal inflammation (241-244).

These studies, however, did not address the central questions of whether the observed alterations were functional nor what their potential mechanisms of action risk for developing IBD is. The questions of function and mechanism are of particular relevance, because microbial composition has been shown to

157 exhibit large inter-individual variations when compared with function-based analyses, even in otherwise healthy individuals (233). A more general approach to this question has also linked genetic loci with alterations of bacterial abundances in the intestines of in mice (136), however in humans and outside of a specific disease setting, approaches using twins has failed to reveal significant genotype effects on microbiome composition and diversity (114, 118).

The investigation of underlying host genetics and microbiome composition has only recently begun to be explored; therefore we sort to better understand the role of AS susceptibility variants on microbiome composition. There are a number of genes involved peptide presentation, immunity, microbial sensing and response to infection associated with AS, and given the increased incidence of clinical and sub clinical gut disease in AS patients, we asked what impact does host genetics have on overall microbiome composition. The demonstration of individual genes and genetic profiles associated with risk of AS, also associated with microbes known to drive the AS microbial signature, particularly if found in both health and disease, would be supportive of model whereby susceptibility genes modulate the microbiome to influence disease risk. We have shown, in an otherwise healthy Twin cohort, that genes associated with developing AS were linked to families of bacteria known to be inflammatory and key indicator species in the AS microbiome (102-104). Given that AS is a complex genetic disease, an allelic risk score was constructed to test if the combined genetic profile, rather than single genes, was associated with changes in the intestinal microbiome (245). The allelic risk score linked an AS genetic profile with the families of Ruminococcaceae, Lachnospiraceae and Clostridia, which are keystone species of the AS microbiome and members of the core microbiome as detailed in Chapter 3. Further investigation of individual genes involved in microbial response, peptide processing, trimming and presentation were also correlated with these same families of bacteria. There is currently a lack of consensus with respect to measuring and dissecting associations between underlying host genetics and members of the intestinal microbiome. In this study, many approaches were attempted, including false discovery rate and Bonferroni correction,

158 however none of the variants survived multiple testing corrections; therefore, we opted for a nominal P-value of <0.05. Nevertheless, the associations seen between AS-associated SNPs and particular bacterial families, although not statically significant, intimates that host genetics play a role in not only overall microbiome composition, but also structure of the core microbiome.

However, whether the underlying host genetics influences the microbiome directly or indirectly via the immune system is still yet to be unravelled. In this study we were only able to assess the association of AS susceptibility variants with microbiome composition in the TwinsUK cohort and we were not able to interrogate the effect of genetic variants known not to affect disease risk or known to affect risk to other forms of immune-mediated diseases. A more extensive analysis would permit the validation of our approach undertaken, by providing the appropriate genetic loci to serve as negative controls and ascertain possible baseline influence of genetics influence on a healthy microbiome.

Whilst further research is required, here we show evidence that genetic variants that affect disease susceptibility were also associated with changes of the microbiome composition. These observations open a new avenue of research for our understanding of AS biology, but caution needs to be taken in the interpretations of the results, as the directionality of causation has not been explored and warrants further investigation.

159

160

5.5 Supplementary Tables

Supplementary table 5.8 OTUs reaching nominal significance (P<0.05) associated with BACH2. 50 OTUs associated with rs17765610 and 81 with rs639575; the majority of which belong to Ruminococcaceae, Lachnospiraceae and Bacteroidaceae families. Taxonomy is shown with full classification from phylum through class (c_), order (o_), family (f_), genus (g_) and species (s_).

SNP Gene OTU Beta Pvalue Taxonomy rs17765610 BACH2 681370 -0.171 1.87E-03 Actinobacteria; c__Actinobacteria; o__Bifidobacteriales; f__Bifidobacteriaceae; g__Bifidobacterium; s__pseudolongum rs17765610 BACH2 3439403 0.191 1.87E-03 Bacteroidetes; c__Bacteroidia; o__Bacteroidales; f__Bacteroidaceae; g__Bacteroides; s__ rs17765610 BACH2 3563235 0.343 1.87E-03 Bacteroidetes; c__Bacteroidia; o__Bacteroidales; f__Bacteroidaceae; g__Bacteroides; s__ rs17765610 BACH2 3588390 0.345 1.87E-03 Bacteroidetes; c__Bacteroidia; o__Bacteroidales; f__Bacteroidaceae; g__Bacteroides; s__ rs17765610 BACH2 3600504 0.454 1.87E-03 Bacteroidetes; c__Bacteroidia; o__Bacteroidales; f__Bacteroidaceae; g__Bacteroides; s__ rs17765610 BACH2 3931537 0.282 1.87E-03 Firmicutes; c__Clostridia; o__Clostridiales; f__Clostridiaceae; g__Clostridium; s__ rs17765610 BACH2 4469576 0.135 1.87E-03 Firmicutes; c__Clostridia; o__Clostridiales; f__Lachnospiraceae; g__; s__ rs17765610 BACH2 199761 0.224 1.87E-03 Firmicutes; c__Clostridia; o__Clostridiales; f__Ruminococcaceae; g__; s__ rs17765610 BACH2 180468 0.244 1.87E-03 Firmicutes; c__Clostridia; o__Clostridiales; f__Ruminococcaceae; g__Oscillospira; s__ rs17765610 BACH2 1504042 0.309 1.87E-03 Firmicutes; c__Clostridia; o__Clostridiales; f__Ruminococcaceae; g__Oscillospira; s__ rs17765610 BACH2 4217963 -0.089 3.49E-03 Firmicutes; c__Clostridia; o__Clostridiales; f__Lachnospiraceae; g__; s__ rs17765610 BACH2 189524 -0.111 3.49E-03 Firmicutes; c__Clostridia; o__Clostridiales; f__Ruminococcaceae; g__; s__ rs17765610 BACH2 216599 -0.298 7.89E-03 Bacteroidetes; c__Bacteroidia; o__Bacteroidales; f__Rikenellaceae; g__; s__ rs17765610 BACH2 4377149 -0.209 7.89E-03 Firmicutes; c__Clostridia; o__Clostridiales; f__; g__; s__ rs17765610 BACH2 182643 -0.186 7.89E-03 Firmicutes; c__Clostridia; o__Clostridiales; f__Clostridiaceae; g__; s__ rs17765610 BACH2 168071 -0.196 7.89E-03 Firmicutes; c__Clostridia; o__Clostridiales; f__Lachnospiraceae; g__; s__ rs17765610 BACH2 183439 -0.348 7.89E-03 Firmicutes; c__Clostridia; o__Clostridiales; f__Lachnospiraceae; g__; s__ rs17765610 BACH2 186416 -0.32 7.89E-03 Firmicutes; c__Clostridia; o__Clostridiales; f__Lachnospiraceae; g__; s__ rs17765610 BACH2 189176 -0.165 7.89E-03 Firmicutes; c__Clostridia; o__Clostridiales; f__Lachnospiraceae; g__[Ruminococcus]; s__gnavus

161 rs17765610 BACH2 193769 -0.199 7.89E-03 Firmicutes; c__Clostridia; o__Clostridiales; f__Lachnospiraceae; g__[Ruminococcus]; s__gnavus rs17765610 BACH2 193666 -0.25 7.89E-03 Firmicutes; c__Clostridia; o__Clostridiales; f__Lachnospiraceae; g__Blautia; s__ rs17765610 BACH2 363735 -0.209 7.89E-03 Firmicutes; c__Clostridia; o__Clostridiales; f__Lachnospiraceae; g__Blautia; s__ rs17765610 BACH2 1614788 -0.384 7.89E-03 Firmicutes; c__Clostridia; o__Clostridiales; f__Lachnospiraceae; g__Coprococcus; s__catus rs17765610 BACH2 176980 -0.376 7.89E-03 Firmicutes; c__Clostridia; o__Clostridiales; f__Lachnospiraceae; g__Dorea; s__ rs17765610 BACH2 181167 -0.161 7.89E-03 Firmicutes; c__Clostridia; o__Clostridiales; f__Lachnospiraceae; g__Dorea; s__ rs17765610 BACH2 184114 -0.243 7.89E-03 Firmicutes; c__Clostridia; o__Clostridiales; f__Ruminococcaceae; g__; s__ rs17765610 BACH2 185814 -0.182 7.89E-03 Firmicutes; c__Clostridia; o__Clostridiales; f__Ruminococcaceae; g__; s__ rs17765610 BACH2 192720 -0.269 7.89E-03 Firmicutes; c__Clostridia; o__Clostridiales; f__Ruminococcaceae; g__; s__ rs17765610 BACH2 295258 -0.22 7.89E-03 Firmicutes; c__Clostridia; o__Clostridiales; f__Ruminococcaceae; g__; s__ rs17765610 BACH2 312882 -0.231 7.89E-03 Firmicutes; c__Clostridia; o__Clostridiales; f__Ruminococcaceae; g__; s__ rs17765610 BACH2 368236 -0.096 7.89E-03 Firmicutes; c__Clostridia; o__Clostridiales; f__Ruminococcaceae; g__; s__ rs17765610 BACH2 4468466 -0.297 7.89E-03 Firmicutes; c__Clostridia; o__Clostridiales; f__Ruminococcaceae; g__; s__ rs17765610 BACH2 4421273 -0.168 7.89E-03 Firmicutes; c__Clostridia; o__Clostridiales; f__Ruminococcaceae; g__Oscillospira; s__ rs17765610 BACH2 163243 -0.228 7.89E-03 Firmicutes; c__Clostridia; o__Clostridiales; f__Ruminococcaceae; g__Ruminococcus; s__ rs17765610 BACH2 16054 -0.163 7.89E-03 Firmicutes; c__Clostridia; o__Clostridiales; f__Ruminococcaceae; g__Ruminococcus; s__callidus rs17765610 BACH2 157327 0.175 9.92E-03 Bacteroidetes; c__Bacteroidia; o__Bacteroidales; f__Bacteroidaceae; g__Bacteroides; s__ rs17765610 BACH2 3426658 0.215 9.92E-03 Bacteroidetes; c__Bacteroidia; o__Bacteroidales; f__Bacteroidaceae; g__Bacteroides; s__ rs17765610 BACH2 211706 0.077 3.17E-02 Bacteroidetes; c__Bacteroidia; o__Bacteroidales; f__Bacteroidaceae; g__Bacteroides; s__ rs17765610 BACH2 2875735 0.097 3.17E-02 Bacteroidetes; c__Bacteroidia; o__Bacteroidales; f__Bacteroidaceae; g__Bacteroides; s__ rs17765610 BACH2 4326080 0.085 3.17E-02 Firmicutes; c__Clostridia; o__Clostridiales; f__Clostridiaceae; g__Clostridium; s__ rs17765610 BACH2 184897 0.08 3.17E-02 Firmicutes; c__Clostridia; o__Clostridiales; f__Ruminococcaceae; g__Faecalibacterium; s__prausnitzii rs17765610 BACH2 182073 -0.127 4.34E-02 Firmicutes; c__Clostridia; o__Clostridiales; f__; g__; s__ rs17765610 BACH2 2996838 -0.123 4.34E-02 Firmicutes; c__Clostridia; o__Clostridiales; f__; g__; s__

162 rs17765610 BACH2 4419459 -0.144 4.34E-02 Firmicutes; c__Clostridia; o__Clostridiales; f__; g__; s__ rs17765610 BACH2 173996 -0.135 4.34E-02 Firmicutes; c__Clostridia; o__Clostridiales; f__Lachnospiraceae; g__; s__ rs17765610 BACH2 306499 -0.133 4.34E-02 Firmicutes; c__Clostridia; o__Clostridiales; f__Lachnospiraceae; g__; s__ rs17765610 BACH2 177758 -0.144 4.34E-02 Firmicutes; c__Clostridia; o__Clostridiales; f__Lachnospiraceae; g__Dorea; s__ rs17765610 BACH2 181826 -0.127 4.34E-02 Firmicutes; c__Clostridia; o__Clostridiales; f__Ruminococcaceae; g__; s__ rs17765610 BACH2 185164 -0.144 4.34E-02 Firmicutes; c__Clostridia; o__Clostridiales; f__Ruminococcaceae; g__; s__ rs17765610 BACH2 4428714 -0.161 4.34E-02 Tenericutes; c__Mollicutes; o__RF39; f__; g__; s__ rs639575 BACH2 193672 0.23 1.87E-03 Firmicutes; c__Clostridia; o__Clostridiales; f__Clostridiaceae; g__; s__ rs639575 BACH2 125624 0.22 1.87E-03 Firmicutes; c__Clostridia; o__Clostridiales; f__Lachnospiraceae; g__; s__ rs639575 BACH2 168071 0.182 1.87E-03 Firmicutes; c__Clostridia; o__Clostridiales; f__Lachnospiraceae; g__; s__ rs639575 BACH2 178018 0.161 1.87E-03 Firmicutes; c__Clostridia; o__Clostridiales; f__Lachnospiraceae; g__; s__ rs639575 BACH2 190961 0.151 1.87E-03 Firmicutes; c__Clostridia; o__Clostridiales; f__Lachnospiraceae; g__; s__ rs639575 BACH2 192127 0.175 1.87E-03 Firmicutes; c__Clostridia; o__Clostridiales; f__Lachnospiraceae; g__; s__ rs639575 BACH2 193477 0.105 1.87E-03 Firmicutes; c__Clostridia; o__Clostridiales; f__Lachnospiraceae; g__; s__ rs639575 BACH2 305318 0.205 1.87E-03 Firmicutes; c__Clostridia; o__Clostridiales; f__Lachnospiraceae; g__; s__ rs639575 BACH2 3275562 0.321 1.87E-03 Firmicutes; c__Clostridia; o__Clostridiales; f__Lachnospiraceae; g__; s__ rs639575 BACH2 4354235 0.341 1.87E-03 Firmicutes; c__Clostridia; o__Clostridiales; f__Lachnospiraceae; g__; s__ rs639575 BACH2 4464173 0.214 1.87E-03 Firmicutes; c__Clostridia; o__Clostridiales; f__Lachnospiraceae; g__; s__ rs639575 BACH2 4468384 0.179 1.87E-03 Firmicutes; c__Clostridia; o__Clostridiales; f__Lachnospiraceae; g__Blautia; s__ rs639575 BACH2 4374302 0.207 1.87E-03 Firmicutes; c__Clostridia; o__Clostridiales; f__Lachnospiraceae; g__Dorea; s__ rs639575 BACH2 184114 0.186 1.87E-03 Firmicutes; c__Clostridia; o__Clostridiales; f__Ruminococcaceae; g__; s__ rs639575 BACH2 185583 0.196 1.87E-03 Firmicutes; c__Clostridia; o__Clostridiales; f__Ruminococcaceae; g__; s__ rs639575 BACH2 185814 0.174 1.87E-03 Firmicutes; c__Clostridia; o__Clostridiales; f__Ruminococcaceae; g__; s__ rs639575 BACH2 187180 0.144 1.87E-03 Firmicutes; c__Clostridia; o__Clostridiales; f__Ruminococcaceae; g__; s__ rs639575 BACH2 187924 0.155 1.87E-03 Firmicutes; c__Clostridia; o__Clostridiales; f__Ruminococcaceae; g__; s__ rs639575 BACH2 332185 0.131 1.87E-03 Firmicutes; c__Clostridia; o__Clostridiales; f__Ruminococcaceae; g__; s__

163 rs639575 BACH2 3756485 0.091 1.87E-03 Firmicutes; c__Clostridia; o__Clostridiales; f__Ruminococcaceae; g__; s__ rs639575 BACH2 4437359 0.252 1.87E-03 Firmicutes; c__Clostridia; o__Clostridiales; f__Ruminococcaceae; g__Oscillospira; s__ rs639575 BACH2 216599 -0.305 3.49E-03 Bacteroidetes; c__Bacteroidia; o__Bacteroidales; f__Rikenellaceae; g__; s__ rs639575 BACH2 217109 -0.088 3.49E-03 Firmicutes; c__Clostridia; o__Clostridiales; f__Christensenellaceae; g__; s__ rs639575 BACH2 231952 -0.121 3.49E-03 Firmicutes; c__Clostridia; o__Clostridiales; f__Christensenellaceae; g__; s__ rs639575 BACH2 181452 -0.072 3.49E-03 Firmicutes; c__Clostridia; o__Clostridiales; f__Lachnospiraceae; g__; s__ rs639575 BACH2 195494 -0.066 3.49E-03 Firmicutes; c__Clostridia; o__Clostridiales; f__Ruminococcaceae; g__; s__ rs639575 BACH2 197624 -0.104 3.49E-03 Firmicutes; c__Clostridia; o__Clostridiales; f__Ruminococcaceae; g__; s__ rs639575 BACH2 718358 -0.056 3.49E-03 Firmicutes; c__Clostridia; o__Clostridiales; f__Ruminococcaceae; g__; s__ rs639575 BACH2 113003 -0.062 3.49E-03 Firmicutes; c__Clostridia; o__Clostridiales; f__Ruminococcaceae; g__Ruminococcus; s__ rs639575 BACH2 3709990 -0.103 3.49E-03 Firmicutes; c__Erysipelotrichi; o__Erysipelotrichales; f__Erysipelotrichaceae; g__[Eubacterium]; s__dolichum rs639575 BACH2 181074 -0.096 3.49E-03 Firmicutes; c__Erysipelotrichi; o__Erysipelotrichales; f__Erysipelotrichaceae; g__cc_115; s__ rs639575 BACH2 151870 -0.117 3.49E-03 Firmicutes; c__Erysipelotrichi; o__Erysipelotrichales; f__Erysipelotrichaceae; g__Coprobacillus; s__ rs639575 BACH2 656881 -0.099 3.49E-03 Proteobacteria; c__Gammaproteobacteria; o__Enterobacteriales; f__Enterobacteriaceae; g__; s__ rs639575 BACH2 4424239 0.136 3.76E-03 Firmicutes; c__Bacilli; o__Lactobacillales; f__Streptococcaceae; g__Streptococcus; s__ rs639575 BACH2 4347159 -0.355 7.89E-03 Actinobacteria; c__Actinobacteria; o__Bifidobacteriales; f__Bifidobacteriaceae; g__Bifidobacterium; s__adolescentis rs639575 BACH2 72820 -0.218 7.89E-03 Actinobacteria; c__Actinobacteria; o__Bifidobacteriales; f__Bifidobacteriaceae; g__Bifidobacterium; s__longum rs639575 BACH2 4449054 -0.165 7.89E-03 Bacteroidetes; c__Bacteroidia; o__Bacteroidales; f__Bacteroidaceae; g__Bacteroides; s__ rs639575 BACH2 193528 -0.266 7.89E-03 Bacteroidetes; c__Bacteroidia; o__Bacteroidales; f__Bacteroidaceae; g__Bacteroides; s__caccae rs639575 BACH2 688923 -0.184 7.89E-03 Firmicutes; c__Clostridia; o__Clostridiales; f__Ruminococcaceae; g__; s__ rs639575 BACH2 359872 -0.196 7.89E-03 Proteobacteria; c__Deltaproteobacteria; o__Desulfovibrionales; f__Desulfovibrionaceae; g__Bilophila; s__ rs639575 BACH2 782953 -0.204 7.89E-03 Proteobacteria; c__Gammaproteobacteria; o__Enterobacteriales; f__Enterobacteriaceae; g__; s__

164 rs639575 BACH2 4391262 -0.156 7.89E-03 Proteobacteria; c__Gammaproteobacteria; o__Enterobacteriales; f__Enterobacteriaceae; g__; s__ rs639575 BACH2 4425571 -0.154 7.89E-03 Proteobacteria; c__Gammaproteobacteria; o__Enterobacteriales; f__Enterobacteriaceae; g__; s__ rs639575 BACH2 4454531 -0.141 7.89E-03 Proteobacteria; c__Gammaproteobacteria; o__Enterobacteriales; f__Enterobacteriaceae; g__; s__ rs639575 BACH2 4425214 0.192 1.54E-02 Firmicutes; c__Bacilli; o__Lactobacillales; f__Streptococcaceae; g__Streptococcus; s__ rs639575 BACH2 157338 0.125 3.17E-02 Firmicutes; c__Clostridia; o__Clostridiales; f__; g__; s__ rs639575 BACH2 179744 0.063 3.17E-02 Firmicutes; c__Clostridia; o__Clostridiales; f__; g__; s__ rs639575 BACH2 180352 0.089 3.17E-02 Firmicutes; c__Clostridia; o__Clostridiales; f__; g__; s__ rs639575 BACH2 196371 0.075 3.17E-02 Firmicutes; c__Clostridia; o__Clostridiales; f__; g__; s__ rs639575 BACH2 4419459 0.125 3.17E-02 Firmicutes; c__Clostridia; o__Clostridiales; f__; g__; s__ rs639575 BACH2 193831 0.125 3.17E-02 Firmicutes; c__Clostridia; o__Clostridiales; f__Clostridiaceae; g__Clostridium; s__ rs639575 BACH2 2840201 0.08 3.17E-02 Firmicutes; c__Clostridia; o__Clostridiales; f__Clostridiaceae; g__Clostridium; s__ rs639575 BACH2 179512 0.068 3.17E-02 Firmicutes; c__Clostridia; o__Clostridiales; f__Clostridiaceae; g__SMB53; s__ rs639575 BACH2 179847 0.064 3.17E-02 Firmicutes; c__Clostridia; o__Clostridiales; f__Lachnospiraceae; g__; s__ rs639575 BACH2 186735 0.104 3.17E-02 Firmicutes; c__Clostridia; o__Clostridiales; f__Lachnospiraceae; g__; s__ rs639575 BACH2 191214 0.108 3.17E-02 Firmicutes; c__Clostridia; o__Clostridiales; f__Lachnospiraceae; g__; s__ rs639575 BACH2 192735 0.086 3.17E-02 Firmicutes; c__Clostridia; o__Clostridiales; f__Lachnospiraceae; g__; s__ rs639575 BACH2 193654 0.095 3.17E-02 Firmicutes; c__Clostridia; o__Clostridiales; f__Lachnospiraceae; g__; s__ rs639575 BACH2 2688035 0.099 3.17E-02 Firmicutes; c__Clostridia; o__Clostridiales; f__Lachnospiraceae; g__; s__ rs639575 BACH2 4331782 0.105 3.17E-02 Firmicutes; c__Clostridia; o__Clostridiales; f__Lachnospiraceae; g__; s__ rs639575 BACH2 4450010 0.082 3.17E-02 Firmicutes; c__Clostridia; o__Clostridiales; f__Lachnospiraceae; g__; s__ rs639575 BACH2 179557 0.05 3.17E-02 Firmicutes; c__Clostridia; o__Clostridiales; f__Lachnospiraceae; g__Blautia; s__ rs639575 BACH2 186022 0.069 3.17E-02 Firmicutes; c__Clostridia; o__Clostridiales; f__Lachnospiraceae; g__Blautia; s__ rs639575 BACH2 189067 0.057 3.17E-02 Firmicutes; c__Clostridia; o__Clostridiales; f__Lachnospiraceae; g__Blautia; s__ rs639575 BACH2 191779 0.077 3.17E-02 Firmicutes; c__Clostridia; o__Clostridiales; f__Lachnospiraceae; g__Blautia; s__

165 rs639575 BACH2 189459 0.054 3.17E-02 Firmicutes; c__Clostridia; o__Clostridiales; f__Lachnospiraceae; g__Coprococcus; s__ rs639575 BACH2 105287 0.06 3.17E-02 Firmicutes; c__Clostridia; o__Clostridiales; f__Ruminococcaceae; g__; s__ rs639575 BACH2 188044 0.058 3.17E-02 Firmicutes; c__Clostridia; o__Clostridiales; f__Ruminococcaceae; g__; s__ rs639575 BACH2 189924 0.084 3.17E-02 Firmicutes; c__Clostridia; o__Clostridiales; f__Ruminococcaceae; g__; s__ rs639575 BACH2 192438 0.084 3.17E-02 Firmicutes; c__Clostridia; o__Clostridiales; f__Ruminococcaceae; g__; s__ rs639575 BACH2 313524 0.109 3.17E-02 Firmicutes; c__Clostridia; o__Clostridiales; f__Ruminococcaceae; g__; s__ rs639575 BACH2 3422630 0.09 3.17E-02 Firmicutes; c__Clostridia; o__Clostridiales; f__Ruminococcaceae; g__; s__ rs639575 BACH2 174439 0.112 3.17E-02 Firmicutes; c__Clostridia; o__Clostridiales; f__Ruminococcaceae; g__Faecalibacterium; s__prausnitzii rs639575 BACH2 357261 0.098 3.17E-02 Firmicutes; c__Clostridia; o__Clostridiales; f__Ruminococcaceae; g__Ruminococcus; s__ rs639575 BACH2 681370 -0.134 4.34E-02 Actinobacteria; c__Actinobacteria; o__Bifidobacteriales; f__Bifidobacteriaceae; g__Bifidobacterium; s__pseudolongum rs639575 BACH2 4365130 -0.131 4.34E-02 Bacteroidetes; c__Bacteroidia; o__Bacteroidales; f__Porphyromonadaceae; g__Parabacteroides; s__distasonis rs639575 BACH2 4331760 -0.155 4.34E-02 Bacteroidetes; c__Bacteroidia; o__Bacteroidales; f__Rikenellaceae; g__; s__ rs639575 BACH2 2442706 -0.16 4.34E-02 Firmicutes; c__Clostridia; o__Clostridiales; f__Christensenellaceae; g__; s__ rs639575 BACH2 212532 -0.151 4.34E-02 Firmicutes; c__Clostridia; o__Clostridiales; f__Ruminococcaceae; g__; s__ rs639575 BACH2 4439469 -0.139 4.34E-02 Firmicutes; c__Clostridia; o__Clostridiales; f__Ruminococcaceae; g__; s__ rs639575 BACH2 2979308 -0.128 4.34E-02 Firmicutes; c__Clostridia; o__Clostridiales; f__Ruminococcaceae; g__Ruminococcus; s__

166

Supplementary table 5.9 OTUs reaching nominal significance (P<0.05) associated with NOS2. NOS2 rs2297518 showed 65 OTU associations and rs2531875 with 18 OTUs associations, all dominated by Ruminococcaceae, Lachnospiraceae and Bacteroidaceae families along with Rikenellaceae and Clostridiaceae Taxonomy is shown with full classification from phylum through class (c_), order (o_), family (f_), genus (g_) and species (s_).

SNP Gene OTU Beta Pvalue Taxonomy rs2297518 NOS2 182643 -0.159 1.67E-02 Firmicutes; c__Clostridia; o__Clostridiales; f__Clostridiaceae; g__; s__ rs2297518 NOS2 4383953 -0.268 1.67E-02 Firmicutes; c__Clostridia; o__Clostridiales; f__Clostridiaceae; g__; s__ rs2297518 NOS2 304779 -0.092 1.67E-02 Firmicutes; c__Clostridia; o__Clostridiales; f__Clostridiaceae; g__Clostridium; s__perfringens rs2297518 NOS2 2123717 -0.054 1.67E-02 Firmicutes; c__Clostridia; o__Clostridiales; f__Lachnospiraceae; g__; s__ rs2297518 NOS2 181826 -0.068 1.67E-02 Firmicutes; c__Clostridia; o__Clostridiales; f__Ruminococcaceae; g__; s__ rs2297518 NOS2 4020502 0.245 3.30E-02 Bacteroidetes; c__Bacteroidia; o__Bacteroidales; f__Bacteroidaceae; g__Bacteroides; s__ rs2297518 NOS2 107044 0.122 3.30E-02 Bacteroidetes; c__Bacteroidia; o__Bacteroidales; f__Rikenellaceae; g__; s__ rs2297518 NOS2 216599 0.177 3.30E-02 Bacteroidetes; c__Bacteroidia; o__Bacteroidales; f__Rikenellaceae; g__; s__ rs2297518 NOS2 2617854 0.222 3.30E-02 Bacteroidetes; c__Bacteroidia; o__Bacteroidales; f__Rikenellaceae; g__; s__ rs2297518 NOS2 4424239 0.078 3.30E-02 Firmicutes; c__Bacilli; o__Lactobacillales; f__Streptococcaceae; g__Streptococcus; s__ rs2297518 NOS2 4425214 0.156 3.30E-02 Firmicutes; c__Bacilli; o__Lactobacillales; f__Streptococcaceae; g__Streptococcus; s__ rs2297518 NOS2 4439603 0.101 3.30E-02 Firmicutes; c__Bacilli; o__Lactobacillales; f__Streptococcaceae; g__Streptococcus; s__ rs2297518 NOS2 4442130 0.052 3.30E-02 Firmicutes; c__Bacilli; o__Lactobacillales; f__Streptococcaceae; g__Streptococcus; s__ rs2297518 NOS2 157338 0.122 3.30E-02 Firmicutes; c__Clostridia; o__Clostridiales; f__; g__; s__ rs2297518 NOS2 176865 0.07 3.30E-02 Firmicutes; c__Clostridia; o__Clostridiales; f__; g__; s__ rs2297518 NOS2 186468 0.124 3.30E-02 Firmicutes; c__Clostridia; o__Clostridiales; f__; g__; s__ rs2297518 NOS2 190980 0.058 3.30E-02 Firmicutes; c__Clostridia; o__Clostridiales; f__; g__; s__ rs2297518 NOS2 192015 0.138 3.30E-02 Firmicutes; c__Clostridia; o__Clostridiales; f__; g__; s__ rs2297518 NOS2 309391 0.05 3.30E-02 Firmicutes; c__Clostridia; o__Clostridiales; f__; g__; s__

167 rs2297518 NOS2 1646171 0.093 3.30E-02 Firmicutes; c__Clostridia; o__Clostridiales; f__Clostridiaceae; g__; s__ rs2297518 NOS2 3931537 0.179 3.30E-02 Firmicutes; c__Clostridia; o__Clostridiales; f__Clostridiaceae; g__Clostridium; s__ rs2297518 NOS2 167373 0.164 3.30E-02 Firmicutes; c__Clostridia; o__Clostridiales; f__Lachnospiraceae; g__; s__ rs2297518 NOS2 184342 0.124 3.30E-02 Firmicutes; c__Clostridia; o__Clostridiales; f__Lachnospiraceae; g__; s__ rs2297518 NOS2 185607 0.11 3.30E-02 Firmicutes; c__Clostridia; o__Clostridiales; f__Lachnospiraceae; g__; s__ rs2297518 NOS2 192127 0.082 3.30E-02 Firmicutes; c__Clostridia; o__Clostridiales; f__Lachnospiraceae; g__; s__ rs2297518 NOS2 289734 0.275 3.30E-02 Firmicutes; c__Clostridia; o__Clostridiales; f__Lachnospiraceae; g__; s__ rs2297518 NOS2 2017729 0.063 3.30E-02 Firmicutes; c__Clostridia; o__Clostridiales; f__Lachnospiraceae; g__; s__ rs2297518 NOS2 175559 0.072 3.30E-02 Firmicutes; c__Clostridia; o__Clostridiales; f__Lachnospiraceae; g__Blautia; s__ rs2297518 NOS2 178462 0.132 3.30E-02 Firmicutes; c__Clostridia; o__Clostridiales; f__Lachnospiraceae; g__Blautia; s__ rs2297518 NOS2 184238 0.221 3.30E-02 Firmicutes; c__Clostridia; o__Clostridiales; f__Lachnospiraceae; g__Blautia; s__ rs2297518 NOS2 186022 0.079 3.30E-02 Firmicutes; c__Clostridia; o__Clostridiales; f__Lachnospiraceae; g__Blautia; s__ rs2297518 NOS2 187210 0.125 3.30E-02 Firmicutes; c__Clostridia; o__Clostridiales; f__Lachnospiraceae; g__Blautia; s__ rs2297518 NOS2 189067 0.062 3.30E-02 Firmicutes; c__Clostridia; o__Clostridiales; f__Lachnospiraceae; g__Blautia; s__ rs2297518 NOS2 194371 0.145 3.30E-02 Firmicutes; c__Clostridia; o__Clostridiales; f__Lachnospiraceae; g__Blautia; s__ rs2297518 NOS2 292735 0.16 3.30E-02 Firmicutes; c__Clostridia; o__Clostridiales; f__Lachnospiraceae; g__Blautia; s__ rs2297518 NOS2 302049 0.18 3.30E-02 Firmicutes; c__Clostridia; o__Clostridiales; f__Lachnospiraceae; g__Blautia; s__ rs2297518 NOS2 307113 0.102 3.30E-02 Firmicutes; c__Clostridia; o__Clostridiales; f__Lachnospiraceae; g__Blautia; s__ rs2297518 NOS2 318970 0.123 3.30E-02 Firmicutes; c__Clostridia; o__Clostridiales; f__Lachnospiraceae; g__Blautia; s__ rs2297518 NOS2 4465907 0.072 3.30E-02 Firmicutes; c__Clostridia; o__Clostridiales; f__Lachnospiraceae; g__Blautia; s__ rs2297518 NOS2 179267 0.248 3.30E-02 Firmicutes; c__Clostridia; o__Clostridiales; f__Lachnospiraceae; g__Coprococcus; s__ rs2297518 NOS2 182289 0.2 3.30E-02 Firmicutes; c__Clostridia; o__Clostridiales; f__Lachnospiraceae; g__Coprococcus; s__ rs2297518 NOS2 187569 0.147 3.30E-02 Firmicutes; c__Clostridia; o__Clostridiales; f__Lachnospiraceae; g__Coprococcus; s__ rs2297518 NOS2 188079 0.18 3.30E-02 Firmicutes; c__Clostridia; o__Clostridiales; f__Lachnospiraceae; g__Coprococcus; s__

168 rs2297518 NOS2 367889 0.116 3.30E-02 Firmicutes; c__Clostridia; o__Clostridiales; f__Lachnospiraceae; g__Coprococcus; s__ rs2297518 NOS2 180462 0.106 3.30E-02 Firmicutes; c__Clostridia; o__Clostridiales; f__Ruminococcaceae; g__; s__ rs2297518 NOS2 182911 0.153 3.30E-02 Firmicutes; c__Clostridia; o__Clostridiales; f__Ruminococcaceae; g__; s__ rs2297518 NOS2 195651 0.109 3.30E-02 Firmicutes; c__Clostridia; o__Clostridiales; f__Ruminococcaceae; g__; s__ rs2297518 NOS2 196518 0.079 3.30E-02 Firmicutes; c__Clostridia; o__Clostridiales; f__Ruminococcaceae; g__; s__ rs2297518 NOS2 197624 0.079 3.30E-02 Firmicutes; c__Clostridia; o__Clostridiales; f__Ruminococcaceae; g__; s__ rs2297518 NOS2 232828 0.074 3.30E-02 Firmicutes; c__Clostridia; o__Clostridiales; f__Ruminococcaceae; g__; s__ rs2297518 NOS2 308544 0.158 3.30E-02 Firmicutes; c__Clostridia; o__Clostridiales; f__Ruminococcaceae; g__; s__ rs2297518 NOS2 759816 0.062 3.30E-02 Firmicutes; c__Clostridia; o__Clostridiales; f__Ruminococcaceae; g__; s__ rs2297518 NOS2 199145 0.29 3.30E-02 Firmicutes; c__Clostridia; o__Clostridiales; f__Ruminococcaceae; g__Faecalibacterium; s__prausnitzii rs2297518 NOS2 199702 0.067 3.30E-02 Firmicutes; c__Clostridia; o__Clostridiales; f__Ruminococcaceae; g__Faecalibacterium; s__prausnitzii rs2297518 NOS2 146554 0.379 3.30E-02 Firmicutes; c__Clostridia; o__Clostridiales; f__Ruminococcaceae; g__Ruminococcus; s__ rs2297518 NOS2 178151 0.061 3.30E-02 Firmicutes; c__Clostridia; o__Clostridiales; f__Ruminococcaceae; g__Ruminococcus; s__ rs2297518 NOS2 180509 0.192 3.30E-02 Firmicutes; c__Clostridia; o__Clostridiales; f__Ruminococcaceae; g__Ruminococcus; s__ rs2297518 NOS2 180826 0.178 3.30E-02 Firmicutes; c__Clostridia; o__Clostridiales; f__Ruminococcaceae; g__Ruminococcus; s__ rs2297518 NOS2 189150 0.143 3.30E-02 Firmicutes; c__Clostridia; o__Clostridiales; f__Ruminococcaceae; g__Ruminococcus; s__ rs2297518 NOS2 191874 0.074 3.30E-02 Firmicutes; c__Clostridia; o__Clostridiales; f__Ruminococcaceae; g__Ruminococcus; s__ rs2297518 NOS2 356745 0.24 3.30E-02 Firmicutes; c__Clostridia; o__Clostridiales; f__Ruminococcaceae; g__Ruminococcus; s__ rs2297518 NOS2 798581 0.212 3.30E-02 Firmicutes; c__Clostridia; o__Clostridiales; f__Ruminococcaceae; g__Ruminococcus; s__bromii rs2297518 NOS2 191355 0.191 3.30E-02 Firmicutes; c__Erysipelotrichi; o__Erysipelotrichales; f__Erysipelotrichaceae; g__; s__ rs2297518 NOS2 360636 0.311 3.30E-02 Firmicutes; c__Erysipelotrichi; o__Erysipelotrichales; f__Erysipelotrichaceae; g__; s__ rs2297518 NOS2 529979 0.053 3.30E-02 Firmicutes; c__Erysipelotrichi; o__Erysipelotrichales; f__Erysipelotrichaceae; g__; s__ rs2531875 NOS2 161423 -0.131 2.07E-03 Bacteroidetes; c__Bacteroidia; o__Bacteroidales; f__Bacteroidaceae; g__Bacteroides; s__

169 rs2531875 NOS2 3141094 -0.062 2.07E-03 Bacteroidetes; c__Bacteroidia; o__Bacteroidales; f__Bacteroidaceae; g__Bacteroides; s__ rs2531875 NOS2 4449054 -0.137 2.07E-03 Bacteroidetes; c__Bacteroidia; o__Bacteroidales; f__Bacteroidaceae; g__Bacteroides; s__ rs2531875 NOS2 193528 -0.219 2.07E-03 Bacteroidetes; c__Bacteroidia; o__Bacteroidales; f__Bacteroidaceae; g__Bacteroides; s__caccae rs2531875 NOS2 368490 -0.139 2.07E-03 Firmicutes; c__Bacilli; o__Turicibacterales; f__Turicibacteraceae; g__Turicibacter; s__ rs2531875 NOS2 537219 -0.077 2.07E-03 Firmicutes; c__Clostridia; o__Clostridiales; f__; g__; s__ rs2531875 NOS2 4372528 -0.147 2.07E-03 Firmicutes; c__Clostridia; o__Clostridiales; f__; g__; s__ rs2531875 NOS2 182643 -0.147 2.07E-03 Firmicutes; c__Clostridia; o__Clostridiales; f__Clostridiaceae; g__; s__ rs2531875 NOS2 4383953 -0.237 2.07E-03 Firmicutes; c__Clostridia; o__Clostridiales; f__Clostridiaceae; g__; s__ rs2531875 NOS2 304779 -0.088 2.07E-03 Firmicutes; c__Clostridia; o__Clostridiales; f__Clostridiaceae; g__Clostridium; s__perfringens rs2531875 NOS2 125624 -0.229 2.07E-03 Firmicutes; c__Clostridia; o__Clostridiales; f__Lachnospiraceae; g__; s__ rs2531875 NOS2 191052 -0.089 2.07E-03 Firmicutes; c__Clostridia; o__Clostridiales; f__Lachnospiraceae; g__; s__ rs2531875 NOS2 2123717 -0.071 2.07E-03 Firmicutes; c__Clostridia; o__Clostridiales; f__Lachnospiraceae; g__; s__ rs2531875 NOS2 181826 -0.073 2.07E-03 Firmicutes; c__Clostridia; o__Clostridiales; f__Ruminococcaceae; g__; s__ rs2531875 NOS2 334336 -0.06 2.07E-03 Firmicutes; c__Clostridia; o__Clostridiales; f__Ruminococcaceae; g__; s__ rs2531875 NOS2 2835813 -0.063 2.07E-03 Firmicutes; c__Clostridia; o__Clostridiales; f__Ruminococcaceae; g__; s__ rs2531875 NOS2 16054 -0.106 2.07E-03 Firmicutes; c__Clostridia; o__Clostridiales; f__Ruminococcaceae; g__Ruminococcus; s__callidus rs2531875 NOS2 4453501 -0.092 2.07E-03 Firmicutes; c__Clostridia; o__Clostridiales; f__Veillonellaceae; g__Veillonella; s__dispar rs2531875 NOS2 4477696 -0.115 2.07E-03 Proteobacteria; c__Gammaproteobacteria; o__Pasteurellales; f__Pasteurellaceae; g__Haemophilus; s__parainfluenzae

170

Supplementary table 5.10 OTUs reaching nominal significance (P<0.05) associated with CARD9 SNP, rs1128905, predominately Lachnospiraceae, Veillonellaceae and Ruminococcaceae, including Faecalibacterium prausnitzii. Taxonomy is shown with full classification from phylum through class (c_), order (o_), family (f_), genus (g_) and species (s_).

SNP Gene OTU Beta Pvalue Taxonomy rs1128905 CARD9 186981 0.144 1.87E-03 Bacteroidetes; c__Bacteroidia; o__Bacteroidales; f__[Barnesiellaceae]; g__; s__ rs1128905 CARD9 157327 0.074 1.87E-03 Bacteroidetes; c__Bacteroidia; o__Bacteroidales; f__Bacteroidaceae; g__Bacteroides; s__ rs1128905 CARD9 198449 0.156 1.87E-03 Bacteroidetes; c__Bacteroidia; o__Bacteroidales; f__Bacteroidaceae; g__Bacteroides; s__ rs1128905 CARD9 3426658 0.087 1.87E-03 Bacteroidetes; c__Bacteroidia; o__Bacteroidales; f__Bacteroidaceae; g__Bacteroides; s__ rs1128905 CARD9 3600504 0.277 1.87E-03 Bacteroidetes; c__Bacteroidia; o__Bacteroidales; f__Bacteroidaceae; g__Bacteroides; s__ rs1128905 CARD9 107044 0.141 1.87E-03 Bacteroidetes; c__Bacteroidia; o__Bacteroidales; f__Rikenellaceae; g__; s__ rs1128905 CARD9 216599 0.165 1.87E-03 Bacteroidetes; c__Bacteroidia; o__Bacteroidales; f__Rikenellaceae; g__; s__ rs1128905 CARD9 4425663 0.122 1.87E-03 Firmicutes; c__Clostridia; o__Clostridiales; f__; g__; s__ rs1128905 CARD9 217109 0.081 1.87E-03 Firmicutes; c__Clostridia; o__Clostridiales; f__Christensenellaceae; g__; s__ rs1128905 CARD9 181452 0.056 1.87E-03 Firmicutes; c__Clostridia; o__Clostridiales; f__Lachnospiraceae; g__; s__ rs1128905 CARD9 193129 0.167 1.87E-03 Firmicutes; c__Clostridia; o__Clostridiales; f__Lachnospiraceae; g__; s__ rs1128905 CARD9 3856408 0.122 1.87E-03 Firmicutes; c__Clostridia; o__Clostridiales; f__Lachnospiraceae; g__; s__ rs1128905 CARD9 183686 0.114 1.87E-03 Firmicutes; c__Clostridia; o__Clostridiales; f__Ruminococcaceae; g__; s__ rs1128905 CARD9 195494 0.063 1.87E-03 Firmicutes; c__Clostridia; o__Clostridiales; f__Ruminococcaceae; g__; s__ rs1128905 CARD9 266274 0.1 1.87E-03 Firmicutes; c__Clostridia; o__Clostridiales; f__Ruminococcaceae; g__; s__ rs1128905 CARD9 295258 0.13 1.87E-03 Firmicutes; c__Clostridia; o__Clostridiales; f__Ruminococcaceae; g__; s__ rs1128905 CARD9 300620 0.139 1.87E-03 Firmicutes; c__Clostridia; o__Clostridiales; f__Ruminococcaceae; g__; s__ rs1128905 CARD9 312882 0.191 1.87E-03 Firmicutes; c__Clostridia; o__Clostridiales; f__Ruminococcaceae; g__; s__

171 rs1128905 CARD9 358781 0.117 1.87E-03 Firmicutes; c__Clostridia; o__Clostridiales; f__Ruminococcaceae; g__; s__ rs1128905 CARD9 688923 0.106 1.87E-03 Firmicutes; c__Clostridia; o__Clostridiales; f__Ruminococcaceae; g__; s__ rs1128905 CARD9 4342104 0.056 1.87E-03 Firmicutes; c__Clostridia; o__Clostridiales; f__Ruminococcaceae; g__Anaerotruncus; s__ rs1128905 CARD9 2979308 0.095 1.87E-03 Firmicutes; c__Clostridia; o__Clostridiales; f__Ruminococcaceae; g__Ruminococcus; s__ rs1128905 CARD9 3709990 0.081 1.87E-03 Firmicutes; c__Erysipelotrichi; o__Erysipelotrichales; f__Erysipelotrichaceae; g__[Eubacterium]; s__dolichum rs1128905 CARD9 4472721 0.232 7.45E-03 Bacteroidetes; c__Bacteroidia; o__Bacteroidales; f__[Odoribacteraceae]; g__Odoribacter; s__ rs1128905 CARD9 4357811 0.046 7.89E-03 Bacteroidetes; c__Bacteroidia; o__Bacteroidales; f__Bacteroidaceae; g__Bacteroides; s__ rs1128905 CARD9 176113 -0.092 7.89E-03 Firmicutes; c__Clostridia; o__Clostridiales; f__; g__; s__ rs1128905 CARD9 181008 -0.084 7.89E-03 Firmicutes; c__Clostridia; o__Clostridiales; f__; g__; s__ rs1128905 CARD9 188127 -0.072 7.89E-03 Firmicutes; c__Clostridia; o__Clostridiales; f__; g__; s__ rs1128905 CARD9 309391 -0.046 7.89E-03 Firmicutes; c__Clostridia; o__Clostridiales; f__; g__; s__ rs1128905 CARD9 4419459 -0.123 7.89E-03 Firmicutes; c__Clostridia; o__Clostridiales; f__; g__; s__ rs1128905 CARD9 3931537 -0.19 7.89E-03 Firmicutes; c__Clostridia; o__Clostridiales; f__Clostridiaceae; g__Clostridium; s__ rs1128905 CARD9 175704 -0.041 7.89E-03 Firmicutes; c__Clostridia; o__Clostridiales; f__Lachnospiraceae; g__; s__ rs1128905 CARD9 175729 -0.112 7.89E-03 Firmicutes; c__Clostridia; o__Clostridiales; f__Lachnospiraceae; g__; s__ rs1128905 CARD9 176604 -0.12 7.89E-03 Firmicutes; c__Clostridia; o__Clostridiales; f__Lachnospiraceae; g__; s__ rs1128905 CARD9 186997 -0.136 7.89E-03 Firmicutes; c__Clostridia; o__Clostridiales; f__Lachnospiraceae; g__; s__ rs1128905 CARD9 192461 -0.05 7.89E-03 Firmicutes; c__Clostridia; o__Clostridiales; f__Lachnospiraceae; g__; s__ rs1128905 CARD9 192536 -0.069 7.89E-03 Firmicutes; c__Clostridia; o__Clostridiales; f__Lachnospiraceae; g__; s__ rs1128905 CARD9 192625 -0.048 7.89E-03 Firmicutes; c__Clostridia; o__Clostridiales; f__Lachnospiraceae; g__; s__ rs1128905 CARD9 192735 -0.075 7.89E-03 Firmicutes; c__Clostridia; o__Clostridiales; f__Lachnospiraceae; g__; s__

17 2 rs1128905 CARD9 194177 -0.045 7.89E-03 Firmicutes; c__Clostridia; o__Clostridiales; f__Lachnospiraceae; g__; s__ rs1128905 CARD9 194733 -0.08 7.89E-03 Firmicutes; c__Clostridia; o__Clostridiales; f__Lachnospiraceae; g__; s__ rs1128905 CARD9 1602805 -0.057 7.89E-03 Firmicutes; c__Clostridia; o__Clostridiales; f__Lachnospiraceae; g__; s__ rs1128905 CARD9 2123717 -0.06 7.89E-03 Firmicutes; c__Clostridia; o__Clostridiales; f__Lachnospiraceae; g__; s__ rs1128905 CARD9 3134492 -0.184 7.89E-03 Firmicutes; c__Clostridia; o__Clostridiales; f__Lachnospiraceae; g__; s__ rs1128905 CARD9 3265161 -0.117 7.89E-03 Firmicutes; c__Clostridia; o__Clostridiales; f__Lachnospiraceae; g__; s__ rs1128905 CARD9 4331360 -0.118 7.89E-03 Firmicutes; c__Clostridia; o__Clostridiales; f__Lachnospiraceae; g__; s__ rs1128905 CARD9 4448492 -0.117 7.89E-03 Firmicutes; c__Clostridia; o__Clostridiales; f__Lachnospiraceae; g__; s__ rs1128905 CARD9 4473509 -0.116 7.89E-03 Firmicutes; c__Clostridia; o__Clostridiales; f__Lachnospiraceae; g__; s__ rs1128905 CARD9 4483337 -0.125 7.89E-03 Firmicutes; c__Clostridia; o__Clostridiales; f__Lachnospiraceae; g__; s__ rs1128905 CARD9 181754 -0.104 7.89E-03 Firmicutes; c__Clostridia; o__Clostridiales; f__Lachnospiraceae; g__[Ruminococcus]; s__ rs1128905 CARD9 176381 -0.053 7.89E-03 Firmicutes; c__Clostridia; o__Clostridiales; f__Lachnospiraceae; g__Blautia; s__ rs1128905 CARD9 180414 -0.105 7.89E-03 Firmicutes; c__Clostridia; o__Clostridiales; f__Lachnospiraceae; g__Blautia; s__ rs1128905 CARD9 187210 -0.085 7.89E-03 Firmicutes; c__Clostridia; o__Clostridiales; f__Lachnospiraceae; g__Blautia; s__ rs1128905 CARD9 190162 -0.075 7.89E-03 Firmicutes; c__Clostridia; o__Clostridiales; f__Lachnospiraceae; g__Blautia; s__ rs1128905 CARD9 191779 -0.052 7.89E-03 Firmicutes; c__Clostridia; o__Clostridiales; f__Lachnospiraceae; g__Blautia; s__ rs1128905 CARD9 193946 -0.08 7.89E-03 Firmicutes; c__Clostridia; o__Clostridiales; f__Lachnospiraceae; g__Blautia; s__ rs1128905 CARD9 28914 -0.061 7.89E-03 Firmicutes; c__Clostridia; o__Clostridiales; f__Lachnospiraceae; g__Roseburia; s__ rs1128905 CARD9 313593 -0.112 7.89E-03 Firmicutes; c__Clostridia; o__Clostridiales; f__Lachnospiraceae; g__Roseburia; s__ rs1128905 CARD9 1740283 -0.063 7.89E-03 Firmicutes; c__Clostridia; o__Clostridiales; f__Lachnospiraceae; g__Roseburia; s__ rs1128905 CARD9 4332082 -0.072 7.89E-03 Firmicutes; c__Clostridia; o__Clostridiales; f__Lachnospiraceae; g__Roseburia; s__ rs1128905 CARD9 105287 -0.037 7.89E-03 Firmicutes; c__Clostridia; o__Clostridiales; f__Ruminococcaceae; g__; s__

173 rs1128905 CARD9 174818 -0.128 7.89E-03 Firmicutes; c__Clostridia; o__Clostridiales; f__Ruminococcaceae; g__; s__ rs1128905 CARD9 174840 -0.128 7.89E-03 Firmicutes; c__Clostridia; o__Clostridiales; f__Ruminococcaceae; g__; s__ rs1128905 CARD9 175184 -0.051 7.89E-03 Firmicutes; c__Clostridia; o__Clostridiales; f__Ruminococcaceae; g__; s__ rs1128905 CARD9 176507 -0.117 7.89E-03 Firmicutes; c__Clostridia; o__Clostridiales; f__Ruminococcaceae; g__; s__ rs1128905 CARD9 180473 -0.097 7.89E-03 Firmicutes; c__Clostridia; o__Clostridiales; f__Ruminococcaceae; g__; s__ rs1128905 CARD9 182577 -0.098 7.89E-03 Firmicutes; c__Clostridia; o__Clostridiales; f__Ruminococcaceae; g__; s__ rs1128905 CARD9 185584 -0.138 7.89E-03 Firmicutes; c__Clostridia; o__Clostridiales; f__Ruminococcaceae; g__; s__ rs1128905 CARD9 186478 -0.041 7.89E-03 Firmicutes; c__Clostridia; o__Clostridiales; f__Ruminococcaceae; g__; s__ rs1128905 CARD9 188676 -0.179 7.89E-03 Firmicutes; c__Clostridia; o__Clostridiales; f__Ruminococcaceae; g__; s__ rs1128905 CARD9 189828 -0.142 7.89E-03 Firmicutes; c__Clostridia; o__Clostridiales; f__Ruminococcaceae; g__; s__ rs1128905 CARD9 190301 -0.051 7.89E-03 Firmicutes; c__Clostridia; o__Clostridiales; f__Ruminococcaceae; g__; s__ rs1128905 CARD9 191872 -0.059 7.89E-03 Firmicutes; c__Clostridia; o__Clostridiales; f__Ruminococcaceae; g__; s__ rs1128905 CARD9 192406 -0.224 7.89E-03 Firmicutes; c__Clostridia; o__Clostridiales; f__Ruminococcaceae; g__; s__ rs1128905 CARD9 192720 -0.186 7.89E-03 Firmicutes; c__Clostridia; o__Clostridiales; f__Ruminococcaceae; g__; s__ rs1128905 CARD9 193644 -0.084 7.89E-03 Firmicutes; c__Clostridia; o__Clostridiales; f__Ruminococcaceae; g__; s__ rs1128905 CARD9 193968 -0.14 7.89E-03 Firmicutes; c__Clostridia; o__Clostridiales; f__Ruminococcaceae; g__; s__ rs1128905 CARD9 194036 -0.092 7.89E-03 Firmicutes; c__Clostridia; o__Clostridiales; f__Ruminococcaceae; g__; s__ rs1128905 CARD9 194287 -0.08 7.89E-03 Firmicutes; c__Clostridia; o__Clostridiales; f__Ruminococcaceae; g__; s__ rs1128905 CARD9 195436 -0.093 7.89E-03 Firmicutes; c__Clostridia; o__Clostridiales; f__Ruminococcaceae; g__; s__ rs1128905 CARD9 195651 -0.101 7.89E-03 Firmicutes; c__Clostridia; o__Clostridiales; f__Ruminococcaceae; g__; s__ rs1128905 CARD9 196982 -0.047 7.89E-03 Firmicutes; c__Clostridia; o__Clostridiales; f__Ruminococcaceae; g__; s__ rs1128905 CARD9 197970 -0.144 7.89E-03 Firmicutes; c__Clostridia; o__Clostridiales; f__Ruminococcaceae; g__; s__

174 rs1128905 CARD9 198198 -0.094 7.89E-03 Firmicutes; c__Clostridia; o__Clostridiales; f__Ruminococcaceae; g__; s__ rs1128905 CARD9 199761 -0.128 7.89E-03 Firmicutes; c__Clostridia; o__Clostridiales; f__Ruminococcaceae; g__; s__ rs1128905 CARD9 211907 -0.084 7.89E-03 Firmicutes; c__Clostridia; o__Clostridiales; f__Ruminococcaceae; g__; s__ rs1128905 CARD9 317677 -0.068 7.89E-03 Firmicutes; c__Clostridia; o__Clostridiales; f__Ruminococcaceae; g__; s__ rs1128905 CARD9 334336 -0.064 7.89E-03 Firmicutes; c__Clostridia; o__Clostridiales; f__Ruminococcaceae; g__; s__ rs1128905 CARD9 730906 -0.066 7.89E-03 Firmicutes; c__Clostridia; o__Clostridiales; f__Ruminococcaceae; g__; s__ rs1128905 CARD9 2506486 -0.082 7.89E-03 Firmicutes; c__Clostridia; o__Clostridiales; f__Ruminococcaceae; g__; s__ rs1128905 CARD9 2700883 -0.147 7.89E-03 Firmicutes; c__Clostridia; o__Clostridiales; f__Ruminococcaceae; g__; s__ rs1128905 CARD9 4094259 -0.055 7.89E-03 Firmicutes; c__Clostridia; o__Clostridiales; f__Ruminococcaceae; g__; s__ rs1128905 CARD9 174611 -0.221 7.89E-03 Firmicutes; c__Clostridia; o__Clostridiales; f__Ruminococcaceae; g__Faecalibacterium; s__prausnitzii rs1128905 CARD9 181139 -0.106 7.89E-03 Firmicutes; c__Clostridia; o__Clostridiales; f__Ruminococcaceae; g__Faecalibacterium; s__prausnitzii rs1128905 CARD9 181422 -0.048 7.89E-03 Firmicutes; c__Clostridia; o__Clostridiales; f__Ruminococcaceae; g__Faecalibacterium; s__prausnitzii rs1128905 CARD9 189092 -0.167 7.89E-03 Firmicutes; c__Clostridia; o__Clostridiales; f__Ruminococcaceae; g__Faecalibacterium; s__prausnitzii rs1128905 CARD9 199145 -0.254 7.89E-03 Firmicutes; c__Clostridia; o__Clostridiales; f__Ruminococcaceae; g__Faecalibacterium; s__prausnitzii rs1128905 CARD9 208739 -0.116 7.89E-03 Firmicutes; c__Clostridia; o__Clostridiales; f__Ruminococcaceae; g__Faecalibacterium; s__prausnitzii rs1128905 CARD9 180468 -0.12 7.89E-03 Firmicutes; c__Clostridia; o__Clostridiales; f__Ruminococcaceae; g__Oscillospira; s__ rs1128905 CARD9 197890 -0.065 7.89E-03 Firmicutes; c__Clostridia; o__Clostridiales; f__Ruminococcaceae; g__Oscillospira; s__

175 rs1128905 CARD9 348009 -0.103 7.89E-03 Firmicutes; c__Clostridia; o__Clostridiales; f__Ruminococcaceae; g__Oscillospira; s__ rs1128905 CARD9 4458959 -0.071 7.89E-03 Firmicutes; c__Clostridia; o__Clostridiales; f__Veillonellaceae; g__Veillonella; s__ rs1128905 CARD9 4388775 -0.106 7.89E-03 Firmicutes; c__Clostridia; o__Clostridiales; f__Veillonellaceae; g__Veillonella; s__dispar rs1128905 CARD9 4453501 -0.105 7.89E-03 Firmicutes; c__Clostridia; o__Clostridiales; f__Veillonellaceae; g__Veillonella; s__dispar rs1128905 CARD9 4477696 -0.112 7.89E-03 Proteobacteria; c__Gammaproteobacteria; o__Pasteurellales; f__Pasteurellaceae; g__Haemophilus; s__parainfluenzae

176

Supplementary table 5.11 OTUs reaching nominal significance (P<0.05) for NKX2-3, ICOSLG and SH2B3. The families of Ruminococcaceae, Lachnospiraceae dominating the associations. Taxonomy is shown with full classification from phylum through class (c_), order (o_), family (f_), genus (g_) and species (s_).

SNP Gene OTU Beta Pvalue Taxonomy rs7282490 ICOSLG 191361 -0.449 7.89E-03 Firmicutes; c__Clostridia; o__Clostridiales; f__Lachnospiraceae; g__; s__ rs7282490 ICOSLG 289734 -0.352 7.89E-03 Firmicutes; c__Clostridia; o__Clostridiales; f__Lachnospiraceae; g__; s__ rs7282490 ICOSLG 195937 -0.397 7.89E-03 Firmicutes; c__Clostridia; o__Clostridiales; f__Lachnospiraceae; g__Blautia; s__ rs7282490 ICOSLG 182289 -0.415 7.89E-03 Firmicutes; c__Clostridia; o__Clostridiales; f__Lachnospiraceae; g__Coprococcus; s__ rs7282490 ICOSLG 4393532 -0.25 1.83E-02 Actinobacteria; c__Coriobacteriia; o__Coriobacteriales; f__Coriobacteriaceae; g__Eggerthella; s__lenta rs7282490 ICOSLG 176862 -0.139 1.83E-02 Firmicutes; c__Clostridia; o__Clostridiales; f__; g__; s__ rs7282490 ICOSLG 322580 -0.12 1.83E-02 Firmicutes; c__Clostridia; o__Clostridiales; f__; g__; s__ rs7282490 ICOSLG 1646171 -0.232 1.83E-02 Firmicutes; c__Clostridia; o__Clostridiales; f__Clostridiaceae; g__; s__ rs7282490 ICOSLG 199694 -0.136 1.83E-02 Firmicutes; c__Clostridia; o__Clostridiales; f__Clostridiaceae; g__Clostridium; s__ rs7282490 ICOSLG 175704 -0.086 1.83E-02 Firmicutes; c__Clostridia; o__Clostridiales; f__Lachnospiraceae; g__; s__ rs7282490 ICOSLG 178642 -0.086 1.83E-02 Firmicutes; c__Clostridia; o__Clostridiales; f__Lachnospiraceae; g__; s__ rs7282490 ICOSLG 184037 -0.216 1.83E-02 Firmicutes; c__Clostridia; o__Clostridiales; f__Lachnospiraceae; g__; s__ rs7282490 ICOSLG 185607 -0.281 1.83E-02 Firmicutes; c__Clostridia; o__Clostridiales; f__Lachnospiraceae; g__; s__ rs7282490 ICOSLG 186687 -0.203 1.83E-02 Firmicutes; c__Clostridia; o__Clostridiales; f__Lachnospiraceae; g__; s__ rs7282490 ICOSLG 186997 -0.222 1.83E-02 Firmicutes; c__Clostridia; o__Clostridiales; f__Lachnospiraceae; g__; s__ rs7282490 ICOSLG 196054 -0.284 1.83E-02 Firmicutes; c__Clostridia; o__Clostridiales; f__Lachnospiraceae; g__; s__ rs7282490 ICOSLG 198127 -0.168 1.83E-02 Firmicutes; c__Clostridia; o__Clostridiales; f__Lachnospiraceae; g__; s__

177 rs7282490 ICOSLG 182864 -0.111 1.83E-02 Firmicutes; c__Clostridia; o__Clostridiales; f__Lachnospiraceae; g__Blautia; s__ rs7282490 ICOSLG 186022 -0.136 1.83E-02 Firmicutes; c__Clostridia; o__Clostridiales; f__Lachnospiraceae; g__Blautia; s__ rs7282490 ICOSLG 190162 -0.189 1.83E-02 Firmicutes; c__Clostridia; o__Clostridiales; f__Lachnospiraceae; g__Blautia; s__ rs7282490 ICOSLG 176300 -0.121 1.83E-02 Firmicutes; c__Clostridia; o__Clostridiales; f__Lachnospiraceae; g__Coprococcus; s__ rs7282490 ICOSLG 177111 -0.174 1.83E-02 Firmicutes; c__Clostridia; o__Clostridiales; f__Lachnospiraceae; g__Coprococcus; s__ rs7282490 ICOSLG 177754 -0.232 1.83E-02 Firmicutes; c__Clostridia; o__Clostridiales; f__Lachnospiraceae; g__Coprococcus; s__ rs7282490 ICOSLG 187569 -0.312 1.83E-02 Firmicutes; c__Clostridia; o__Clostridiales; f__Lachnospiraceae; g__Coprococcus; s__ rs7282490 ICOSLG 191081 -0.216 1.83E-02 Firmicutes; c__Clostridia; o__Clostridiales; f__Lachnospiraceae; g__Coprococcus; s__ rs7282490 ICOSLG 174752 -0.199 1.83E-02 Firmicutes; c__Clostridia; o__Clostridiales; f__Ruminococcaceae; g__; s__ rs7282490 ICOSLG 184990 -0.223 1.83E-02 Firmicutes; c__Clostridia; o__Clostridiales; f__Ruminococcaceae; g__; s__ rs7282490 ICOSLG 258099 -0.123 1.83E-02 Firmicutes; c__Clostridia; o__Clostridiales; f__Ruminococcaceae; g__; s__ rs7282490 ICOSLG 2066056 -0.145 1.83E-02 Firmicutes; c__Clostridia; o__Clostridiales; f__Ruminococcaceae; g__; s__ rs7282490 ICOSLG 2979308 -0.189 1.83E-02 Firmicutes; c__Clostridia; o__Clostridiales; f__Ruminococcaceae; g__Ruminococcus; s__ rs11190133 NKX2-3 197004 -0.393 7.89E-03 Firmicutes; c__Clostridia; o__Clostridiales; f__Lachnospiraceae; g__; s__ rs11190133 NKX2-3 4343627 -0.302 1.67E-02 Bacteroidetes; c__Bacteroidia; o__Bacteroidales; f__Bacteroidaceae; g__Bacteroides; s__fragilis rs11190133 NKX2-3 197364 -0.249 1.67E-02 Firmicutes; c__Clostridia; o__Clostridiales; f__; g__; s__ rs11190133 NKX2-3 198183 -0.285 1.67E-02 Firmicutes; c__Clostridia; o__Clostridiales; f__; g__; s__ rs11190133 NKX2-3 199677 -0.103 1.67E-02 Firmicutes; c__Clostridia; o__Clostridiales; f__; g__; s__ rs11190133 NKX2-3 2840201 -0.118 1.67E-02 Firmicutes; c__Clostridia; o__Clostridiales; f__Clostridiaceae; g__Clostridium; s__ rs11190133 NKX2-3 174792 -0.142 1.67E-02 Firmicutes; c__Clostridia; o__Clostridiales; f__Lachnospiraceae; g__; s__ rs11190133 NKX2-3 180312 -0.123 1.67E-02 Firmicutes; c__Clostridia; o__Clostridiales; f__Lachnospiraceae; g__; s__

178 rs11190133 NKX2-3 180999 -0.329 1.67E-02 Firmicutes; c__Clostridia; o__Clostridiales; f__Lachnospiraceae; g__; s__ rs11190133 NKX2-3 197397 -0.114 1.67E-02 Firmicutes; c__Clostridia; o__Clostridiales; f__Lachnospiraceae; g__; s__ rs11190133 NKX2-3 199344 -0.138 1.67E-02 Firmicutes; c__Clostridia; o__Clostridiales; f__Lachnospiraceae; g__; s__ rs11190133 NKX2-3 311820 -0.138 1.67E-02 Firmicutes; c__Clostridia; o__Clostridiales; f__Lachnospiraceae; g__; s__ rs11190133 NKX2-3 3272764 -0.195 1.67E-02 Firmicutes; c__Clostridia; o__Clostridiales; f__Lachnospiraceae; g__; s__ rs11190133 NKX2-3 4281639 -0.202 1.67E-02 Firmicutes; c__Clostridia; o__Clostridiales; f__Lachnospiraceae; g__; s__ rs11190133 NKX2-3 2740950 -0.165 1.67E-02 Firmicutes; c__Clostridia; o__Clostridiales; f__Lachnospiraceae; g__Coprococcus; s__ rs11190133 NKX2-3 4374302 -0.333 1.67E-02 Firmicutes; c__Clostridia; o__Clostridiales; f__Lachnospiraceae; g__Dorea; s__ rs11190133 NKX2-3 162623 -0.131 1.67E-02 Firmicutes; c__Clostridia; o__Clostridiales; f__Lachnospiraceae; g__Roseburia; s__ rs11190133 NKX2-3 178485 -0.156 1.67E-02 Firmicutes; c__Clostridia; o__Clostridiales; f__Ruminococcaceae; g__; s__ rs11190133 NKX2-3 193915 -0.095 1.67E-02 Firmicutes; c__Clostridia; o__Clostridiales; f__Ruminococcaceae; g__; s__ rs11190133 NKX2-3 358781 -0.169 1.67E-02 Firmicutes; c__Clostridia; o__Clostridiales; f__Ruminococcaceae; g__; s__ rs11190133 NKX2-3 2700883 -0.26 1.67E-02 Firmicutes; c__Clostridia; o__Clostridiales; f__Ruminococcaceae; g__; s__ rs11190133 NKX2-3 4396292 -0.248 1.67E-02 Firmicutes; c__Clostridia; o__Clostridiales; f__Ruminococcaceae; g__; s__ rs11190133 NKX2-3 4414476 -0.297 1.67E-02 Firmicutes; c__Clostridia; o__Clostridiales; f__Ruminococcaceae; g__; s__ rs11190133 NKX2-3 4458959 -0.131 1.67E-02 Firmicutes; c__Clostridia; o__Clostridiales; f__Veillonellaceae; g__Veillonella; s__ rs11190133 NKX2-3 4453501 -0.194 1.67E-02 Firmicutes; c__Clostridia; o__Clostridiales; f__Veillonellaceae; g__Veillonella; s__dispar rs11190133 NKX2-3 4323124 0.229 3.30E-02 Bacteroidetes; c__Bacteroidia; o__Bacteroidales; f__[Barnesiellaceae]; g__; s__ rs11190133 NKX2-3 185420 0.418 3.30E-02 Bacteroidetes; c__Bacteroidia; o__Bacteroidales; f__Bacteroidaceae; g__Bacteroides; s__ rs11190133 NKX2-3 198449 0.219 3.30E-02 Bacteroidetes; c__Bacteroidia; o__Bacteroidales; f__Bacteroidaceae; g__Bacteroides; s__ rs11190133 NKX2-3 336559 0.277 3.30E-02 Bacteroidetes; c__Bacteroidia; o__Bacteroidales; f__Bacteroidaceae; g__Bacteroides; s__ rs11190133 NKX2-3 4401580 0.173 3.30E-02 Bacteroidetes; c__Bacteroidia; o__Bacteroidales; f__Bacteroidaceae; g__Bacteroides; s__

179 rs11190133 NKX2-3 344525 0.16 3.30E-02 Bacteroidetes; c__Bacteroidia; o__Bacteroidales; f__Bacteroidaceae; g__Bacteroides; s__eggerthii rs11190133 NKX2-3 4405146 0.124 3.30E-02 Firmicutes; c__Clostridia; o__Clostridiales; f__; g__; s__ rs11190133 NKX2-3 2318497 0.247 3.30E-02 Firmicutes; c__Clostridia; o__Clostridiales; f__Clostridiaceae; g__; s__ rs11190133 NKX2-3 232828 0.113 3.30E-02 Firmicutes; c__Clostridia; o__Clostridiales; f__Ruminococcaceae; g__; s__ rs11190133 NKX2-3 4427290 0.233 3.30E-02 Firmicutes; c__Clostridia; o__Clostridiales; f__Ruminococcaceae; g__; s__ rs11190133 NKX2-3 147969 0.259 3.30E-02 Firmicutes; c__Clostridia; o__Clostridiales; f__Ruminococcaceae; g__Ruminococcus; s__ rs11065898 SH2B3 332732 -0.172 3.26E-02 Bacteroidetes; c__Bacteroidia; o__Bacteroidales; f__Bacteroidaceae; g__Bacteroides; s__ rs11065898 SH2B3 193654 -0.1 3.26E-02 Firmicutes; c__Clostridia; o__Clostridiales; f__Lachnospiraceae; g__; s__ rs11065898 SH2B3 189459 -0.077 3.26E-02 Firmicutes; c__Clostridia; o__Clostridiales; f__Lachnospiraceae; g__Coprococcus; s__ rs11065898 SH2B3 1740283 -0.108 3.26E-02 Firmicutes; c__Clostridia; o__Clostridiales; f__Lachnospiraceae; g__Roseburia; s__ rs11065898 SH2B3 19611 -0.152 3.26E-02 Firmicutes; c__Clostridia; o__Clostridiales; f__Ruminococcaceae; g__; s__ rs11065898 SH2B3 191872 -0.101 3.26E-02 Firmicutes; c__Clostridia; o__Clostridiales; f__Ruminococcaceae; g__; s__ rs11065898 SH2B3 3236435 -0.296 3.26E-02 Firmicutes; c__Clostridia; o__Clostridiales; f__Ruminococcaceae; g__; s__ rs11065898 SH2B3 4427290 -0.277 3.26E-02 Firmicutes; c__Clostridia; o__Clostridiales; f__Ruminococcaceae; g__; s__ rs11065898 SH2B3 4381553 0.355 4.46E-02 Bacteroidetes; c__Bacteroidia; o__Bacteroidales; f__Bacteroidaceae; g__Bacteroides; s__ rs11065898 SH2B3 4436552 0.137 4.46E-02 Bacteroidetes; c__Bacteroidia; o__Bacteroidales; f__Prevotellaceae; g__Prevotella; s__copri rs11065898 SH2B3 194597 0.19 4.46E-02 Firmicutes; c__Clostridia; o__Clostridiales; f__; g__; s__ rs11065898 SH2B3 2840201 0.085 4.46E-02 Firmicutes; c__Clostridia; o__Clostridiales; f__Clostridiaceae; g__Clostridium; s__ rs11065898 SH2B3 181452 0.098 4.46E-02 Firmicutes; c__Clostridia; o__Clostridiales; f__Lachnospiraceae; g__; s__ rs11065898 SH2B3 193129 0.304 4.46E-02 Firmicutes; c__Clostridia; o__Clostridiales; f__Lachnospiraceae; g__; s__ rs11065898 SH2B3 3265161 0.121 4.46E-02 Firmicutes; c__Clostridia; o__Clostridiales; f__Lachnospiraceae; g__; s__

180 rs11065898 SH2B3 1667433 0.118 4.46E-02 Firmicutes; c__Clostridia; o__Clostridiales; f__Lachnospiraceae; g__Dorea; s__ rs11065898 SH2B3 182044 0.239 4.46E-02 Firmicutes; c__Clostridia; o__Clostridiales; f__Ruminococcaceae; g__; s__ rs11065898 SH2B3 350503 0.183 4.46E-02 Firmicutes; c__Clostridia; o__Clostridiales; f__Ruminococcaceae; g__; s__

181

6 Chapter 6 Final Discussion

6.1 Overview and Conclusions

The work presented in this thesis examines the role of the gut microbiome in AS by characterising the AS microbiome. It examines how changes in host genetics influences the composition of intestinal microbial composition as well as the association between AS susceptibility loci and overall microbiome composition. The key findings of this work, its limitations and future directions are summarised and discussed below.

Microbial involvement has been suggested in AS, however, a definitive link is yet to be established (17, 138, 142, 143, 202, 246, 247). To date there has been no comprehensive characterisation of the gut microbiome in patients with AS. I sought to determine if the TI of AS patients carries a distinct microbial signature in comparison to healthy controls. Microbial profiles from TI biopsies from subjects with recent-onset, TNF-antagonist naïve AS and HC, were determined using culture-independent 16S rRNA gene sequencing and analysis techniques developed in Chapter 2. Analysis showed evidence for a discrete microbial signature in the TI of cases with AS compared to HC. Distinct grouping of AS cases was discriminated from HC on PCoA with clustering shown to be driven by higher abundances of Lachnospiraceae, Ruminococcaceae, Rikenellaceae, Porphyromonadaceae, Bacteroidaceae and decreases in Prevotellaceae and Veillonellaceae. Interactions between these indicator species within the microbial community were shown to further shape the AS microbial community signature. No difference was noted between AS cases and controls in either bacteria known to be associated with reactive arthritis, or Klebsiella species, which have been proposed to play a role in triggering AS. Statistically, the relationship between disease status and microbial community composition was confirmed indicating that the differences between the microbial community compositions was driven by disease state and not due to heterogeneity in the biopsy or lack of technical reproducibility. These results are consistent with the hypothesis that genes associated with AS act at least in part through effects on the gut microbiome. While the data here shows a clear and distinct microbial signature in AS cases, the effect of TNF treatment on the microbiome composition and its contribution to disease pathogenesis is yet to be explored. Cause and affect interactions between the host genome, immune system and the gut microbiome also remains to be dissected.

183

With over 40 loci currently associated with ankylosing spondylitis, one of the strongest associations, along with HLA-B27, is ERAP1 (24, 62). Given that loss of function of ERAP1 is protective in AS, In Chapter 4 I asked how a lack or down regulation of ERAP influenced inherent gut microbiome composition, the antigen repertoire and the immune system. To investigate this hypothesis I explored if the loss of ERAP, in a mouse model, caused a shift in the intestinal flora towards an anti-inflammatory composition. Consistent with this hypothesis, 16S community profiling revealed that the gut microbiome in ERAP-/- mice did shift. However, it was towards a more inflammatory profile, with an increase of bacteria such as Rikenellaceae, Lachnospiraceae and Ruminococcus which are not only known to elicit a strong immune response, but are also key bacteria in the TI of AS patients (103, 248, 249). To minimise such potential confounders, the intestinal microbial composition of a single litter of ERAP mice comprising of ERAP+/+, ERAP+/- and ERAP -/- was explored. Analysis showed that ERAP+/-and ERAP -/- mice were more similar to each other and clustered away from the ERAP+/+ mice, suggesting that a change in genotype, from ERAP+/+ to ERAP+/-, is sufficient to influence the overall structure and composition of the gut microbiome. This is a small study with only one litter studied, and larger numbers are needed to better characterise the impact of genotype on the intestinal microbiome. Nonetheless, this is consistent with the hypothesis that AS-associated genes contribute to disease aetiopathogenesis through effects of the underlying host genetics on intestinal microbial composition.

The fifth chapter of this thesis further investigated the influence of host genetics, specifically AS risk loci, on microbiome composition by examining the association between AS SNPs and intestinal microbiome composition in a non-AS, otherwise healthy twin cohort. Given the number of genes involved in peptide presentation, immunity, microbial sensing and response to infection, and the increased incidence of clinical and sub clinical gut disease, I asked what impact does host genetics have on overall microbiome composition. Results showed that in an otherwise healthy cohort of twins, genes associated with developing AS were associated with families of bacteria known to be inflammatory and key indicator species in the AS microbiome. The allelic risk score linked the AS genetic profile with the families of Ruminococcaceae, Lachnospiraceae and the order Clostridiales, which are keystone species of the AS microbiome. Further investigation of individual genes involved in microbial response, peptide processing, trimming and presentation showed correlation with these same AS indicator species. This 184 suggests that host genetics plays a role in overall microbiome composition however whether the underlying host genetics influences the microbiome directly or indirectly via the immune system is still yet to be unravelled. In this study we were only able to assess the association of AS susceptibility variants with microbiome composition in the TwinsUK cohort and were unable to interrogate the effect of other genetic variants known not to affect disease risk or known to affect risk to other forms of immune-mediated diseases. A more extensive analysis would permit the validation of our approach undertaken by providing the appropriate genetic loci to serve as negative controls and ascertain possible baseline influence of genetics influence on a healthy microbiome. Caution needs to be taken in the interpretations of these results as directionality of causation has not been explored and requires further investigation.

6.2 Future directions

There is mounting evidence for a role of the gut microbiome in the aetiopathogenesis in AS, although exact mechanism remains unclear. To further explore the hypothesis that genes predisposing to AS do so by influencing the gut microbiome, either directly or indirectly via the immune system, will require substantially larger cohorts of several hundred patients and controls with matched genetic, clinical and microbiome data. Larger studies may identify specific disease triggering bacteria, and further define the AS microbiome profile and interactions, raising the possibility both the use of the microbiome as a biomarker as well as for novel therapeutic interventions.

The gastrointestinal tract is the primary site for interaction between microbes and the host immune system. Moreover, the microbes found in the gut help to shape host immune systems from an early age (210, 211), although little is understood regarding how individual and bacterial communities interact with the immune system creating an inflammatory environment. To date only a handful of bacteria-immune interactions have been described, such as Prevotella and the innate immune system (249). The mechanism of interaction between other AS indicator species of bacteria such as Ruminococcus, Rikenella and Clostridia and the immune system causing disease is yet to be investigated. Loss of members of the Bacteroidetes and Firmicutes phyla, especially Clostridia, have been associated with the risk alleles of CD genes NOD2 (250) and ATG16L1 (248). These two genes are associated with abnormal Paneth cell function suggesting alteration of secreted antimicrobials from intestinal Paneth cells perturbs the intestinal microbiome, 185 which leads to inflammation and disease. Unravelling the interactions between AS indicator species and the immune system will help us understand to role of the gut microbiota in AS pathogenesis and how immune system sculpts the microbiome, and in what way microbiome counters the immune response perpetuating the inflammatory environment.

The intestinal microbiome is a dynamic and constantly changing environment (127, 128, 251). Whist the microbiome ebbs and flows with daily living (251), overall the microbiota remains reasonably stable for months, and possibly even years (127, 252). However, most of what we know regarding the make-up of the human gut microbiome and its stability is based off stool studies and not mucosal biopsies (114, 118, 126, 127, 183, 252, 253). Several studies have shown that the microbiome profile from stool differs from the profile obtained from mucosal biopsy suggesting that stool, while the most accessible sample, is not optimal for detailing gut microbiome composition and may skew our understanding (152, 153). Moreover, studies across mice and humans suggest that common aspects of a modern Western lifestyle including antibiotic use (254, 255) and high fat and fibre poor diets (118, 119, 256), can persistently alter commensal microbial communities. In turn, these microbial disturbances may increase susceptibility to pathogens (257), obesity (114, 121, 197), and autoimmune disease (199, 224, 258). This makes dissecting cause and effect challenging given the significant environmental factors which can shape the microbiome as well as normal temporal variance; and we have only been focusing on the bacteria, not archea, viruses or fungi which also inhabit the intestinal tract. The successful use of the gut microbiome as a therapeutic target requires a deeper understanding of factors that shape the community composition including age, diet, geographical location, gender as well as underlying host genetics. Investigations to date have largely focused on the bacterial composition of the microbiome, and so know relatively little about the viruses and fungi that also inhabit the gut. Despite the considerable limitations in sequencing and functional annotation of the eukaryotic viruses present in the gastrointestinal tract, recent deep-sequencing efforts have revealed the existence of a complex enteric virome (259, 260). A recent study revealed the surprising beneficial role viruses may play a role in a healthy microbiome. Murine norovirus (MNV) infection of germ-free or antibiotic-treated mice rescued both intestinal morphology and lymphocyte function in the absence of overt inflammation and disease. This study demonstrated that eukaryotic viruses have the capacity to support intestinal homeostasis and shape mucosal immunity, similarly to commensal bacteria (261). This work demonstrates how little we currently known and truly 186 understand about the composition of a healthy intestinal microbiome and work along these lines will improve our knowledge of the intestinal microbiome as well as well microbiome host interactions critical for overall health. Particularly given that ReA is triggered by a bacterial infection and that it is looking likely that microbiome has a critical role in AS pathogenesis.

The gut microbiota is increasingly looked at as a target for novel therapies, including microbiome modulation with probiotics and faecal microbiota transplants (FMT). These novel therapies are being increasingly used to treat patients with debilitating Clostridium difficile infections, where conventional treatments and antibiotics have failed. The number and frequency of FMT is growing exponentially (262), and in response biobanks for stool have been created at Massachusetts General Hospital in Boston and Emory University Hospital in Atlanta, Georgia where ‘healthy’ stool is screened and stored for medical use in approved cases (263). A small trial comparing antibiotic treatment to FMT for C. difficile showed that FMT was more effective at resolving patient symptoms as antibiotics alone (264). The effectiveness of FMT caused the trial to be stopped early. Non-randomised studies of C. difficile treated with either antibiotics only or FMT have shown a typical success rate of about 90% (264-266). While the majority of testimonies surrounding FMT are positive, there are still many unknowns. Screening of the donor faeces is currently not standardised and can be very basic. We all carry different bacteria, viruses and parasites at any given moment (126), and while they not be harmful to donor, potential pathogens as well as the healthy flora may inadvertently be to transplanted. Little long-term safety data exists for FMT recipients (267), however transient abdominal pain and bloated have been observed. It may not be as simple as taking microbes from one person and giving them to another, just as other transplants like bone marrow and solid organ, require typing and cross matching prior to transplant. Research is underway in several laboratories, such as Openbiome (http://www.openbiome.org/) in the US, working on patient screening procedures as well as overall FMT regulation, similar to the model used by the Red Cross – except for faeces (263, 266). This is to ensure the microbes transplanted behave as we expect them to, as well as minimising unexpected consequences, with the goal of eventually not having to use a donor for the transplant at all, but tailoring the combination of synthetic microbes grown in the lab tailored to each individual patient (267, 268). FMT is becoming more accepted as a valid treatment option for C. difficile infections and is currently being trialed for patients with CD (227). Though, the hope that manipulating the

187 gut microbiome to treat diseases other than C. difficile infection is still speculative (262, 269).

The role of the host genetics in shaping intestinal microbial community composition is unclear. Recent animal studies have shown that host gene deletions resulted in shifts in microbiota composition as well as linking certain genetic polymorphisms with microbial abundances (135, 136). This highlights the importance of underlying host genetics in community composition and raises the question: if underlying host genetics influences microbiome composition, is FMT in complex genetic diseases futile because the transplant will never permanently shift the intestinal flora? In diseases such as AS and CD, the expectation is that since the microbiome influences the immune system, then transplanting the gut with healthy intestinal flora will lead to interactions with the immune system which create a less inflammatory environment reducing gut disease and providing overall improvement of symptoms and maybe even disease. The caveat here is that many genes associated with AS and CD are involved in mucosal immunology and microbial processing, so underlying host genetics may eventually override the transplant.

What is clear is the need to better understand our second genome and to do this we require better tools for comparison and analysis. Currently there is a lack of central open access database and repository for human microbial community data. There is the NIH Human microbiome project database (http://www.hmpdacc.org/) with 16S rRNA and annotated microbial genomes img/hmp where all the human microbiome project data are deposited, the MetaHit database (http://www.metahit.eu/), National Center for Biotechnology Information (NCBI) (http://www.ncbi.nlm.nih.gov/) as well as the European Bioinformatics Institute (http://www.ebi.ac.uk/) where the recent TwinsUK 16S rRNA sequence deposited (137). This demonstrates how spread out all the data is. Some of the data is annotated, meaning the individual whole microbial genome has been sequenced and function and biochemical pathways have been described, and some of the data is just the 16S rRNA sequence. What is needed is the microbial equivalent of HapMap or UK10K were there is access to samples from varying habitats (mouth, skin, gut) with well annotated taxonomy and function so that eventually microbial communities can be imputed in the way we currently impute human SNPs. American gut project (http://humanfoodproject.com/americangut/) is currently one of the largest open-source project endeavouring to understand the microbial diversity of the gut is leading the way however, we need reasonable metadata accompanying the microbial data such as age, 188 gender, diet, lifestyle as well as geographic location and ethnicity. Much time and development is being dedicated to improving the 16S tools we currently have as well as creating new ones such as QIIME (169), Mothur (270) and LotuS (271) . However moving beyond 16S rRNA taxonomy is imperative for furthering our understanding of the interaction between host and microbes to better understand disease. Whilst using the gut microbiome as a phenotype for association studies for the purpose of using Plink (272) and Merlin (239) to test for association testing is a start, these programs are not designed to be used for microbiome analysis and do not allow within microbiome interactions and network dynamics to be taken into account, such as co-occurrence (spa). In fact no programs currently do. The microbial network packages such as Spaa (176) and SparCC (273), which have been borrowed from environmental microbial ecology are still rather crude. There is of coarse OTU networks and cytoscape as enabled in QIIME however, this program is more big picture and does not lend its self to the within clade analysis and direction of correlation. There are currently tools for the profiling of communities and clades in metagenomic data, including bacteria, viruses and archea using MetaPhiAn (274); high dimensional biomarker discovery with LEfSe (275); determining the presence/absence and abundance of microbial pathways in a community from metagenomic data using HUMaN (276); prediction of function from 16S rRNA data using PICRUSt (115); as well as for multivariate analysis with linear models (MaAsLin) for testing associations between clinical metadata and the microbial community abundance or function (277). However, none of the above packages allow for the integration or correlation of human data, such as genotype information, with microbiome and metagenomic data and this is currently what is lacking.

16S taxonomy does not inform us about what microbes are active and what they are expressing (278), let alone if expression is in reaction to host gene transcription (135, 136). To better understand how host and commensal microbes interact, we also need better tools to examine interaction at the transcriptomic level (279). Metagenome analyses of the human intestine has previously identified genes and pathways involved in the transport and metabolism of simple carbohydrate substrates are enriched in the intestine microbiome (114, 278, 280) and that alterations in these pathways are linked to obesity, suggesting their importance for the appropriate functioning of this microbial ecosystem. However, elucidation of the actual activity and metabolic role of individual microbial members within the intestinal microbiome is still limited. Much of the microbial transcriptomic studies have used stool (114, 278, 280), as the use of intestinal biopsies is 189 still technically very difficult due to the shear amount of human DNA. There are microbial enrichment kits that can be used prior to sequencing in an effort to reduce the amount of human DNA, such as the New England Biosciences microbial enrichment kit (281) and MoBio Powersoil isolation kit (282), however the use of these kits prior to sequencing, and the impact on microbial community results, is still being explored. Examining host transcriptomics in the gut in combination with microbial transcriptions will further our understanding into how host genetics influences intestinal microbial community composition and function (283), which contributes to disease.

Finally, the pursuit to understand the pathogenesis of AS, enabling accurate and early diagnosis and effective treatment, requires better understanding of how underlying host genetics influences the immune system and the intestinal microbiome. In the next few years, it is expected that larger studies with matched genotype and microbiome data will elucidate how it is that genetic variants influence the immune response, which in turn shapes the gut microbiome, in such a way that causes disease. Microbial profiling of tissue or stool samples, either alone or in combination with genetic tests, may identify individuals at high risk of developing AS, enabling either preventative intervention or early diagnosis. It is with high hopes that the research presented in this thesis and further studies along these lines improves the understanding of AS pathogenesis, which in turn lead to improved diagnosis and treatment.

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REVIEW Microbes, the gut and ankylosing spondylitis

Mary-Ellen Costello1, Dirk Elewaut2, Tony J Kenna1 and Matthew A Brown*1

with Campylobacter, Salmonella, Shigella, or Yersinia [3]. Abstract Such cause-and-eff ect relationships are not established It is increasingly clear that the interaction between for other SpAs. host and microbiome profoundly aff ects health. There are 10 times more bacteria in and on our Genetic overlap between ankylosing spondylitis bodies than the total of our own cells, and the human and gut disease intestine contains approximately 100 trillion bacteria. Strong genetic overlap exists between AS and IBD, and Interrogation of microbial communities by using classic the two conditions commonly occur together in families microbiology techniques off ers a very restricted view [4]. Danoy and colleagues [5] (2010) studied genes known of these communities, allowing us to see only what to be associated with IBD in a large AS cohort. New loci we can grow in isolation. However, recent advances and genes were identifi ed, and of particular note were in sequencing technologies have greatly facilitated genes involved in the interleukin-23 (IL-23) pathway, systematic and comprehensive studies of the role such as STAT3, IL-23 receptor (IL23R), and IL12B (which of the microbiome in human health and disease. encodes IL-12p40, the share subunit of IL-23 and IL-12) Comprehensive understanding of our microbiome [5-7]. How these genetic lesions infl uence gut homeo- will enhance understanding of disease pathogenesis, stasis and function remains unclear. Major diff er ences which in turn may lead to rationally targeted therapy also exist between the genetics of IBD and AS, and AS for a number of conditions, including autoimmunity. has shown no association to date with NOD2/CARD15 or the autophagy gene ATG16L1, which are major susceptibility factors in IBD, whereas IBD shows no association Ankylosing spondylitis with HLA-B or ERAP1, which are the strongest AS Ankylosing spondylitis (AS) belongs to a common group susceptibility genes [8]. Although no association has been of arthritides called spondyloarthopathies (SpA). AS shown with NOD2/CARD15 and AS specifi cally, poly- targets primarily the spine and pelvis and is characterized morphisms in NOD2/CARD15 have been associated with histopathologically by entheseal infl am mation. Disease an increased risk of gut disease in patients with SpA [9]. progression in AS is characterized by excessive bone formation (ankylosis) that gradually bridges the gap The gut, barrier regulation, and intestinal between joints, eventually fusing joints and causing stiff - epithelial cells ness, pain, signifi cant morbidity, and increased mortality Homeostasis of the normal microbial fl ora in the gut is [1]. essential for intestinal health. Th e gastrointestinal tract is Th e coexistence of AS and intestinal infl ammation has heavily populated with microbes and is the primary site been known for some time [2]. Between 5% and 10% of for interaction between these microorganisms and the patients with AS develop clinically diagnosed infl amma- immune system [10,11]. Furthermore, microbes found in tory bowel disease (IBD), and a further 70% of patients the gut help to shape host immune systems from an early with AS develop subclinical gut infl ammation [1,2]. In age. Th e incomplete development of the immune system reactive arthritis, a member of the SpA family, infl am- in neonates and under germ-free (GF) conditions tells us matory arthritis develops following urogenital infection that microbiota sculpt the host immune system [12,13]. with Chlamydia trachomatis or gastrointestinal infection Maintenance of intestinal and microbial homeostasis is increasingly recognized as playing a pivotal role in overall *Correspondence: [email protected] health [14], and dysregulation of either gut or microbial 1The University of Queensland Diamantina Institute, Translational Research Institute, Level 7, 37 Kent Road, Princess Alexandra Hospital, Woolloongabba, homeostasis may play a role in autoimmunity. Physio- Brisbane QLD 4102, Australia logical processes in the host that maintain gut homeo stasis Full list of author information is available at the end of the article and respond to perturbance in the gut micro environ ment involve both the adaptive and innate immune system and © 2010 BioMed Central Ltd © 2013 BioMed Central Ltd the barrier function of the intestines themselves. Costello et al. Arthritis Research & Therapy 2013, 15:214 Page 2 of 10 http://arthritis-research.com/content/15/3/214

The physical barrier sensing with receptors such as Toll-like receptors (TLRs) Th e human gastrointestinal tract is not a complete and monitoring the bacteria on the mucosal surface [28]. barrier. It is composed of a single layer of intestinal Intestinal DCs orchestrate production of intestine- epithelial cells (IECs), which form a physical barrier specifi c IgA to limit bacterial contact with the intestinal separating the intestinal lumen from the lamina propria epithelial cell surface [29]. Activated DCs can secrete a (Figure 1). IECs secrete soluble factors that are crucial to number of cytokines and chemokines, including IL-23 intestinal homeostasis, such as mucins and anti-microbial and IL-6, that are involved in infl ammation and migration peptides, including lysozymes, defensins, cathelicidins, of DCs [30]. lipocalins, and C-type lectins such as RegIIIγ [15-17]. Macrophages patrol the gastrointestinal systems in Release of these molecules into luminal crypts is thought high numbers. Th ey frequently come in contact with to prevent microbial invasion into the crypt micro- ‘stray’ bacteria, including commensals that have breached environment as well as limit bacteria-epithelial cell the epithelial cell barrier. Macrophages phagocytose and contact [16,18]. Compared with healthy controls, Crohn’s rapidly kill such bacteria by using mechanisms that disease patients with active disease have pronounced include the production of antimicrobial proteins and decreases in the human α-defensins DEFA5 and DEFA6 reactive oxygen species [31]. Intestinal macrophages have in the ileum, resulting in altered mucosal function and several unique characteristics, including the expression overgrowth or dysregulation of commensal microbial of the anti-infl ammatory cytokine IL-10, both constitu- fl ora [18,19]. Conversely, overexpression of anti-micro- tively and after bacterial stimulation [32,33]. Th is makes bials, including α-defensins, are reported in sub-clinically intestinal macrophages non-infl ammatory cells that still infl amed ileum of AS patients compared with Crohn’s have the capacity to phagocytose. Th e importance of this disease patients and healthy controls [20]. However, it pathway is highlighted by the fact that loss-of-function remains unclear whether changes in innate mucosal mutations in IL10R lead to early-onset IBD [34]. How- defenses lead to alterations in gut-resident microbial fl ora ever, not all intestinal macrophages are non-infl amma- or whether early changes in the microbiome sculpt tory. In patients with Crohn’s disease, a population of intestinal host responses. Furthermore, depletion of the intestinal macrophages secrete infl ammatory cytokines mucin layer leads to an IBD-like phenotype and endo- such as IL-23, tumor necrosis factor-alpha (TNF-α), and plasmic reticulum stress, potentially driving IL-23 pro- IL-6. Th ese macrophages contribute to an infl ammatory duc tion [21]. IL-23 excess alone is suffi cient to induce microenvironment in patients with Crohn’s disease [35]. spondyloarthritis in mice [22], and genetic evidence, in Intestinal macrophages also play a role in the restoration particular, indicates that the cytokine plays a key role in of the physical integrity of the epithelial cell barrier the development of spondyloarthritis in humans. following injury or bacterial insult [24]. Re-establishing this barrier after injury is imperative to avoid bacterial Permeability and disease penetration and sepsis in such a microbe-laden environ- Th e dynamic crosstalk between IECs, microbes, and the ment [36]. local immune cells is fundamental to intestinal homeostasis but is also suggested to play a part in disease Adaptive immunity pathogenesis [23]. In IBD and celiac disease, tight junc- IL-23-responsive cells tions are dysregulated, causing increased permeability IL-23 is a key cytokine in the development of IL-17- and between IECs, resulting in a ‘leaky gut’ [24,25]. Studies IL-22-secreting cells. IL-23 signals through a receptor looking at fi rst-degree relatives of patients with AS consisting of the specifi c IL-23R subunit and IL-12Rβ1, showed that they too have increased gut permeability, so also shared with IL-12R [37]. Loss-of-function polymor- it is likely that there is an underlying genetic process phisms in IL23R are associated with protection from AS operating in the gut [26,27]. [7], psoriasis [38], and IBD [39], and many other genes in the IL-23 pathway are associated with these diseases. The immune barrier Under physiological conditions, IL-23-, IL-17-, and IL- Th e complexity of the intestinal immune system is the 22-producing cells are enriched in gut mucosa, and IL-17 subject of many reviews, but here we will focus on some and IL-22 are known to be important regulators of intestinal immune populations that are believed to be intestinal ‘health’. IL-17 plays important roles in intestinal important in rheumatic disorders. homoeostasis in several ways, including maintenance of epithelial barrier tight junctions [40] and induction of Innate immunity anti-microbial proteins such as β-defensins, S100 Dendritic cells (DCs) densely populate the intestinal proteins, and REG proteins. IL-22 induces secretion of lamina propria and form a widespread microbe-sensing anti-microbial peptides [41]. In the gut, innate-like network. DCs recognize a broad repertoire of bacteria, immune cells act as sentinels, responding very rapidly to Costello et al. Arthritis Research & Therapy 2013, 15:214 Page 3 of 10 http://arthritis-research.com/content/15/3/214

Figure 1. The physical barrier. Separating the intestinal lumen and its inhabiting commensal bacteria from the underlying lamina propria is a single layer of intestinal epithelial cells (IECs). These IECs are stitched together, creating a tight junction and regulating the paracellular fl ux. IECs also secrete soluble factors that are crucial to intestinal homeostasis, such as mucins and anti-microbial peptides (AMPs), including lysozymes, IgA, defensins, and C-type lectins such as RegIIIγ. Release of these molecules into luminal crypts is thought to prevent microbial invasion into the crypt microenvironment as well as limit bacteria-epithelial cell contact. Toll-like receptors (TLRs) are also expressed on IECs to sense a breach of barrier or bacterial invasion. Underneath the IECs, the lamina propria contains T cells, bacteria-sampling dendritic cells (DCs), and macrophages. alterations to the microbial composition of the gut with Th ey are pathogenic in the collagen-induced arthritis rapid secretion of IL-17. Key among these sentinels are model [47] and mouse models of colitis [48], and IL-17- γδ T cells, natural killer T (NKT) cells, mucosa- secreting γδ T cells are expanded in patients with AS associated invariant T (MAIT) cells, and lymphoid tissue [49]. Intraepithelial γδ T cells also play an important role inducer (LTi)-like cells (Figure 2). in modulating intestinal epithelial growth through secretion of fi broblast growth factor [50]. Alterations to γδ T cells γδ T-cell numbers or functions, therefore, may have γδ T cells are found in large numbers at epithelial surfaces profound eff ects on intestinal health. such as the gut and skin, where they can account for up to 50% of T cells. γδ T cells not only bear an antigen- Natural killer T cells specifi c T-cell receptor (TCR) but also have many pro- NKT cells are characterized by expression of an invariant perties of cells of the innate immune system, including TCR, Vα24Jα18 in humans and the orthologous expression of the major innate immunity receptors and Vα14Jα18 in mice. NKT cells recognize glycolipid struc- TLRs [42] and dectin-1 [43], which recognizes microbial tures presented to them by the non-classic antigen- β-glucans. Expression of these receptors supports a role presenting molecule CD1d. Like γδ T cells, NKT cells are for γδ T cells in early responses to microbes. Of further rapid responders to antigenic stimuli and are capable of relevance, we and others have recently confi rmed that producing a range of immunoregulatory cytokines CARD9, part of the dectin-1 response pathway, is a [51-54]. Within the gut, NKT cells are protective in T susceptibility gene for AS and IBD [44]. γδ T cells are helper 1 (TH1)-mediated models of IBD but are patho- potent producers of infl ammatory cytokines such as genic in TH2 models [55,56]. Recently, it has been shown interferon-gamma (IFN-γ), TNF-α, and IL-17 [45,46]. that microbial stimulation of NKT cells in the gut of mice Costello et al. Arthritis Research & Therapy 2013, 15:214 Page 4 of 10 http://arthritis-research.com/content/15/3/214

Figure 2. The immune barrier. Dendritic cells (DCs) and macrophages patrol the gastrointestinal tract in high numbers. They densely populate the intestinal lamina propria and form a widespread microbe-sensing network. Activated DCs can secrete a number of cytokines and chemokines, including interleukin-23 (IL-23), IL-6, and IL-1, activating IL-23-responsive cells. IL-23-, IL-17-, and IL-22-producing cells are enriched in gut mucosa, and IL-17 and IL-22 are known to be important regulators of intestinal ‘health’. IL-17 plays important roles in intestinal homoeostasis in several ways, including maintenance of epithelial barrier tight junctions. LTi, lymphoid tissue inducer; MAIT, mucosa-associated invariant T; NKT, natural killer T; T reg, regulatory T; TNF, tumor necrosis factor. aff ects NKT cell phenotypic and functional maturation MAIT cells react to a wide range of microbial stimuli, [57,58]. Given that NKT cells have protective roles in including Gram-positive and Gram-negative bacteria as models of arthritis [59] and SpA [60], their functional well as yeasts [61,62]. Although the precise role of MAIT maturation in the gut provides evidence for a role for cells in maintenance of the mucosal barrier remains mucosal T-cell priming in infl ammatory joint disease. unclear, the rapid postnatal expansion of MAIT cells and their acquisition of phenotypic markers of memory likely Mucosa-associated invariant T cells represent an interaction with developing commensal MAIT cells are a population of innate-like T cells that are microfl ora [67]. abundant in human gut, liver, and blood and secrete a range of infl ammatory cytokines such as IL-17 and IFN-γ Lymphoid tissue inducer-like cells and innate lymphoid cells in response to antigenic stimulation [61-63]. Like NKT LTi-like cells are found in spleen, lymph node, and gut cells, MAIT cells bear an invariant TCR (Vα7.2 in lamina propria. LTi-like cells constitutively express hall- humans) that recognizes antigen presented by the non- marks of IL-17-secreting cells, including IL-23R, RORγt, classic MHC-like molecule, MR1 [64]. In blood, MAIT AHR, and CCR6 [68]. Stimulation of LTi-like cells with cells display a memory phenotype [63] and express the IL-23 induces IL-17 secretion [68], whereas PMA and transcription factor ZBTB16 [65], allowing them to ionomycin stimulation invokes secretion of IL-22 [69]. rapidly secrete cytokine in response to antigenic stimuli. LTi cells appear to be related to an increasingly interest- Furthermore, they express high levels of IL-23R [66]. ing and heterogeneous population of innate lymphoid Costello et al. Arthritis Research & Therapy 2013, 15:214 Page 5 of 10 http://arthritis-research.com/content/15/3/214

cells (ILCs). ILCs have been linked to gut infl ammation metagenomics. Th e Roche 454 sequencing platform through colitis models in which IL-23-responsive ILCs (Roche, Basel, Switzerland) uses large-scale parallel secrete IL-17 and IFN-γ and promote intestinal infl am- pyrosequencing to produce reads of between 450 and mation [70]. NKp46+ ILCs are involved in host defense 1000 base pairs (bp) in length. Read lengths produced by against Citrobacter rodentium infection through secre- the 454 platform are well suited to 16S rRNA amplicon tion of IL-22 [71]. metagenomics studies as well as shotgun sequencing as they are easily aligned to reference bacterial genetic data The human microbiome revolution sets. Sequencing plat forms from Illumina (San Diego, Together, the multi-factorial components of the immune CA, USA), the HiSeq2000 and MiSeq, produce shorter system shape the microbial population that inhabits the reads than the Roche 454. Th e HiSeq was designed gut and (to an extent) vice versa, with each side pushing primarily for human genomic sequencing, and current to establish a stable state. Understanding the yin and chemistry produces 100-bp paired-end reads. Illumina yang of this relationship is at the heart of current micro- sequencing is best suited for shotgun sequencing or biome research. indexed amplicon sequencing of multiple samples. Microbiome refers to the totality of microbes, their Th is is a rapidly developing fi eld. Advances in chemistry genetic elements, and environmental interaction in a are predicted to increase both read lengths and output defi ned environment [72]. Projects such as the Human for both of these platforms over the next 12 to 24 months, Microbiome Project, run by the National Institutes of particularly for the Illumina platforms that are less Health, and the Metagenomics of the Human Intestinal mature than the Roche 454. New platforms coming to the Tract (MetaHit) consortium aim to determine what market are likely to have a major impact on meta- constitutes a ‘normal’ or healthy microbiome. Research in genomics study design. For example, the Pacifi c Bio- this fi eld has recently been greatly accelerated by the sciences sequencing technology (Pacifi c Biosciences, development of high throughput sequencing methods for Menlo Park, CA, USA) provides reads of approximately microbial profi ling, which can profi le microbial popula- 3,000 bases, which will make it particularly suited to this tions whether or not the microbes present can be cultured. fi eld, despite its lower overall output (approximately 100 Mb of sequence per run). Life Technologies Ion 16S rRNA sequencing Torrent technology (Life Technologies, Grand Island, NY, 16S rRNA is a section of prokaryotic DNA found in all USA) is reported to produce up to 400 base reads and up bacteria and archaea. 16S rRNA sequences are used to to 1 Gb of sequence per run. Th e relative positions of diff erentiate between organisms across all major phyla of these competing technologies in metagenomics have yet bacteria and to classify strains down to the species level to be established. [73]. 16S rRNA sequencing has dramatically changed our understanding of phylogeny and microbial diversity The normal human gut microbiome because it provides an unbiased assessment of the To date, only a handful of studies have examined in any microbiome and is not restricted by the ability to culture depth the function as well as the composition and the bacteria present. diversity of the human gut microbiome. Two large studies Further improvements in sequencing technologies have interrogating and cataloguing microbiomes from various reduced the need for targeted studies such as 16S sequen- regions of the body in health and disease have been cing and enabled high-throughput shotgun sequencing. undertaken by the National Institutes of Health Human Th is latter type of sequen cing randomly samples all genes Microbiome Project in the US and the European MetaHit present in a habitat rather than just 16S rRNA. Shotgun project [74-76]. Th e European MetaHit consortium sequencing provides more information about the micro- combined published data sets from around the world and biome than just the 16S characterization, which is of added 22 newly sequenced fecal metagenomes from four particular benefi t in determining the functional capacity of diff erent European countries. Th ey identifi ed three robust the microbial community rather than just its phylogeny. clusters, termed ‘enterotypes’, that were not nation- or However, shotgun sequencing is more complex to analyze, continent-specifi c. Th ese enterotypes characterized the owing in part to the challenge of distinguishing between microbial phylogenetic variation as well as the function host and bacterial genomic material, and requires far more variation of the clusters at gene and functional class levels sequencing to be performed. Th erefore, most studies to [74]. Each enterotype had a dominant bacterial genus: date have relied on 16S rRNA sequencing approaches. enterotype 1 was dominated by the genus Bacteriodes, enterotype 2 by Prevotella, and enterotype 3 by Rumino- Advances in tools for metagenomic studies coccus. Th e three enterotypes were also shown to be Several sequencing technologies have been developed for functionally diff erent. For example, enterotype 2, which human genetic studies and have since been adapted to is Prevotella-dominant, also contains Desfulfovibrio, Costello et al. Arthritis Research & Therapy 2013, 15:214 Page 6 of 10 http://arthritis-research.com/content/15/3/214

which may act in synergy with Prevotella to degrade between the host genetic factors determining risk of IBD mucin glycoproteins present in the mucosal layer of the and the gut microbiome. gut. It may be that diff erent enterotypes may be asso- Th e microbiome is thought to play a signifi cant role in ciated with diseases such as obesity and IBD rather than psoriasis, another AS-related condition. It has long been necessarily specifi c bacterial species, given their diff ering suggested that streptococcal infection, especially throat functional capacities. Further studies will be required to infections, may trigger psoriasis in a genetically suscep- determine whether enterotypes are consis tently found in tible individual [87]. Recent studies using 16S rRNA expanded data sets and in studies of intestinal biopsies as sequencing have found signifi cant diff erences between well as the fecal samples used in the MetaHit study. the cutaneous microbiota of psoriasis cases and controls and in involved and control skin in psoriasis cases, and The microbiome in immune-mediated arthritis less staphylococci and propionibacteria have been ob- Th e major fi ndings of microbial profi ling studies in major served in cases and in aff ected skin [88]. Again, further immune-mediated diseases associated with arthritis are studies will be required to determine whether there is a summarised in Table 1. To date, no study investigating particular microbial profi le or specifi c bacterial species the gut microbiome by using sequencing-based methods involved in psoriasis. in AS has been reported. Many studies largely using Th ese studies are thus consistent with the hypothesis antibody tests have suggested an increased carriage of that the microbiome contributes to the etiopathogenesis Klebsiella species in patients with AS, but this has not of immune-mediated arthritis or seronegative diseases been universally supported [77]. One study using like IBD and psoriasis. However, at this point, there is no denaturing gradient gel electrophoresis to profi le the defi nitive evidence of specifi c bacterial infections or microbiome by using fecal samples found no diff erences changes in microbial profi le that play a causative role in between AS cases and healthy controls [78]. these conditions (with the exception of reactive arthritis). Rheumatoid arthritis (RA) is the only infl ammatory arthritis for which modern metagenomic studies have Chicken and the egg been reported. Community profi ling studies of gut fl ora Whether the changes noted in these early metagenomic of patients with RA reveal diff erences in the composition studies of immune-mediated disease are a consequence of gut microbiota of patients with RA compared with of disease or are involved in its development or persis- those of healthy controls, and a lower abundance of tence is unclear. Th is distinction may prove impossible to Bifi dobacterium and Bacteroides bacteria was observed dissect in human studies, but considerable evidence in in RA cases [79,80]. However, these studies used fecal studies in mice supports a role for the microbiome in samples and not intestinal biopsies, possibly infl uencing driving immune-mediated diseases. the populations observed [81]. Also, microbial profi ling In the B27 rat model of AS, rats housed under GF data suggest that gingival infection may be important, conditions did not develop disease [89], demonstrating particularly in anti-citrullinated peptide antibody-posi- that microbes in this model are important for disease tive RA, although the studies suggesting this have penetrance. In contrast, in the New Zealand black model generally used antibody tests rather than sequencing- of systemic lupus erythematosus, mice maintained under based approaches (reviewed in [82]). GF conditions produced higher levels of antinuclear Several lines of evidence indicate that the gut micro- antibodies and developed worse disease [90], biome plays an important role in IBD, including the demonstrating a protective role for commensal microbes. association of genes involved in mucosal immunity with Given the IL-17/IL-22 cytokines, which are of relevance IBD (such as CARD15, CARD9, IL-23R, and ATG16L1), to AS, IBD, and psoriasis in particular, strong evidence the therapeutic eff ect of antibiotics on the condition, and from murine studies indicates that interaction between the benefi cial eff ect of fecal stream diversion in Crohn’s the gut microbiome and the host determines the overall disease. Previous studies of human IBD have been under- level of activation of the immune cells producing these taken by using standard culture techniques (for example, cytokines. Segmented fi lamentous bacteria (SFB) are [83]) or molecular analysis (for example, [84,85]). Th ese com mensal bacteria that induce IL-17 secretion. Mice studies noted alterations in intestinal microbiota when lacking SFB have low levels of intestinal IL-17 and are compared with non-IBD patients, a fi nding recently susceptible to infection with pathogenic Citrobacter spp. confi rmed by using 16S rRNA sequencing of intestinal Restoration of SFB in these mice increased the number of biopsies [86]. Th is sequencing study, however, did not gut-resident IL-17-producing cells and enhanced resis- identify an IBD-specifi c microbial profi le, perhaps because tance to infection [12]. Salzman and colleagues [91] of insuffi cient resolution of the sequencing performed or illustrated that α-defensins modulate mucosal T-cell small sample size (12 patients and 5 controls). Much larger response by regulating the composition, but not the total studies will be required to dissect the relationship numbers, of bacteria in the intestinal microbiome. Costello et al. Arthritis Research & Therapy 2013, 15:214 Page 7 of 10 http://arthritis-research.com/content/15/3/214

Table 1. Alterations in gut microbiota associated with immune-mediated diseases Associated microbes Microbiota changes References IBD – Crohn’s disease Reduction in microbial diversity when compared with controls [95] Gut microbiome Bacteroides ovatus  Bacteroides vulgatus  Bacteroides uniformis 

IBD Associated with overall community shift and dysbiosis [96] Gut microbiome Bacteroidetes  Lachnospiraceae  Actinobacteria  Proteobacteria  Clostridium  Firmicutes/Bacteroidetes ratio  Bifi dobacteria

Celiac disease Overall higher diversity of microbes in patients with celiac disease compared with controls [97] Gut microbiome Bacteroides vulgatus  Escherichia coli  Clostridium coccoides 

Psoriasis Overrepresentation of Firmicutes and an underrepresentation of Actinobacteria and [98,99] Skin microbiome Proteobacteria when compared with controls Firmicutes  Bacteroidetes  Actinobacteria  Proteobacteria 

Rheumatoid arthritis Dysbiosis and increased diversity [100,101] Oral microbiome Porphyromonas gingivalis 

IBD, infl ammatory bowel disease.

Examining the intestinal microbiota of mice expressing turn increasing IL-10+ CTLA4high Treg (regulatory T) the human α-defensin gene, they demonstrated signifi - activation [94]. Clostridial colonization of neonatal mice cant α-defensin-dependent alterations in commensal reduced severity of induced colitis by using dextran composition, leading to a loss of SFB and fewer IL-17- sulphate sodium or oxazolone and reduced serum IgE producing lamina propria T cells [91]. Furthermore, levels in adulthood. So it is likely that alterations to the using recolonization studies, investigators recently demon- gut microfl ora or invasion of the gut by pathogenic strated that, in neonatal mice, commensal microbes bacteria infl uences the balance of IL-17- and IL-22- infl uence invariant NKT (iNKT) cell intestinal infi ltration producing cells and other immune cells, infl uencing and activation, establishing mucosal iNKT cell tolerance susceptibility to local and systemic immune-mediated to later environmental exposures [92]. disease. Th e mechanism by which the microbiome infl uences Th e above studies highlight that changes in the micro- IL-17-producing cell activation is still being determined. biome can lead to infl ammation which may have far- Ivanov and colleagues [12] demonstrated that serum reaching eff ects and demonstrate experimental approaches amyloid A, produced in the terminal ileum, can induce by which fi ndings of metagenomic studies in mice and + TH17 diff erentiation of CD4 T lymphocytes. It has also humans can be explored to successfully dissect the role of been shown that development of TH17 lymphocytes in the microbiome in human immune-mediated diseases. the intestine is stimulated by microbiota-induced IL-1β (but not IL-6) production [93]. Colonization with Conclusions Clostridial species has been shown to stimulate intestinal Th e human gut microbiome is a dynamic and complex transforming growth factor-beta (TGF-β) production, in ecosystem that is only now beginning to be understood. Costello et al. Arthritis Research & Therapy 2013, 15:214 Page 8 of 10 http://arthritis-research.com/content/15/3/214

It is increasingly clear that the interaction between host Farrar C, Hadler J, Harvey D, Karaderi T, Mogg R, Pomeroy E, Pryce K, Taylor J, and microbiome profoundly aff ects health. It is still Savage L, Deloukas P, Kumanduri V, Peltonen L, Ring SM, Whittaker P, Glazov E, Thomas GP, Maksymowych WP, Inman RD, Ward MM, Stone MA, Weisman MH, unclear how interactions between host genes, microbes, Wordsworth BP, Brown MA: Genome-wide association study of ankylosing and environmental factors can predispose patients to the spondylitis identifi es non-MHC susceptibility loci. Nat Genet 2010, development autoimmune diseases such as AS. We are 42:123-127. 7. Wellcom e Trust Case Control Consortium; Australo-Anglo-American only beginning to grasp the infl uence the microbiome has Spondylitis Consortium (TASC), Burton PR, Clayton DG, Cardon LR, Craddock on health. 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Laukens D, Georges M, Libioulle C, Sandor C, Mni M, Vander Cruyssen B, Abbreviations Peeters H, Elewaut D, De Vos M: Evidence for signifi cant overlap between AS, ankylosing spondylitis; bp, base pair(s); DC, dendritic cell; GF, germ-free; common risk variants for Crohn’s disease and ankylosing spondylitis. IBD, infl ammatory bowel disease; IEC, intestinal epithelial cell; IFNγ, interferon- PLoS ONE 2010, 5:e13795. gamma; IL, interleukin; IL-23R, interleukin-23 receptor; ILC, innate lymphoid 10. Round JL, Mazmanian SK: The gut microbiota shapes intestinal immune cell; iNKT, invariant natural killer T; LTi, lymphoid tissue inducer; MAIT, mucosa- responses during health and disease. Nat Rev Immunol 2009, 9:313-323. associated invariant T; MetaHit, Metagenomics of the Human Intestinal Tract; 11. Clemen te JC, Ursell LK, Parfrey LW, Knight R: The impact of the gut NKT, natural killer T; RA, rheumatoid arthritis; SFB, segmented fi lamentous microbiota on human health: an integrative view. Cell 2012, 148:1258-1270. 12. Ivanov II, Atarashi K, Manel N, Brodie EL, Shima T, Karaoz U, Wei D, Goldfarb bacteria; SpA, spondyloarthopathies; TCR, T-cell receptor; TH, T helper; TLR, Toll- like receptor; TNF-α, tumor necrosis factor-alpha. KC, Santee CA, Lynch SV, Tanoue T, Imaoka A, Itoh K, Takeda K, Umesaki Y, Honda K, Littman DR: Induction of intestinal Th17 cells by segmented Competing interests fi lamentous bacteria. Cell 2009, 139:485-498. The authors declare that they have no competing interests. 13. Duan J , Chung H, Troy E, Kasper DL: Microbial colonization drives expansion of IL-1 receptor 1-expressing and IL-17-producing gamma/delta T cells. Acknowledgments Cell Host Microbe 2010, 7:140-150. The authors thank Murray Hargrave and Philip Robinson for their assistance 14. Cho I, Blaser MJ: The human microbiome: at the interface of health and in the preparation of this review. TJK is supported by an Arthritis Australia disease. Nat Rev Genet 2012, 13:10. Heald Fellowship. 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BRIEF REPORT

Intestinal Dysbiosis in Ankylosing Spondylitis

Mary-Ellen Costello,1 Francesco Ciccia,2 Dana Willner,3 Nicole Warrington,1 Philip C. Robinson,1 Brooke Gardiner,1 Mhairi Marshall,1 Tony J. Kenna,1 Giovanni Triolo,2 and Matthew A. Brown1

Ruminococcaceae ,[0.001 ؍ Objective. Ankylosing spondylitis (AS) is a com- bacteria (Lachnospiraceae [P -Porphyromon ,[0.004 ؍ Rikenellaceae [P ,[0.012 ؍ mon, highly heritable immune-mediated arthropathy [P ([0.001 ؍ and Bacteroidaceae [P ,[0.001 ؍ that occurs in genetically susceptible individuals ex- adaceae [P posed to an unknown but likely ubiquitous environmen- and a decrease in the abundance of 2 families of and Prevotellaceae [0.01 ؍ tal trigger. There is a close relationship between the gut bacteria (Veillonellaceae [P .([0.004 ؍ and spondyloarthritis, as exemplified in patients with [P reactivearthritis,inwhomatypicallyself-limitingarthro- Conclusion. We show evidence for a discrete pathy follows either a gastrointestinal or urogenital microbial signature in the terminal ileum of patients infection. Microbial involvement in AS has been sug- with AS compared with healthy control subjects. The gested; however, no definitive link has been established. microbial composition was demonstrated to correlate The aim of this study was to determine whether the gut with disease status, and greater differences were ob- in patients with AS carries a distinct microbial signa- served between disease groups than within disease ture compared with that in the gut of healthy control groups. These results are consistent with the hypothesis subjects. that genes associated with AS act, at least in part, Methods. Microbial profiles for terminal ileum through effects on the gut microbiome. biopsy specimens obtained from patients with recent- onset tumor necrosis factor antagonist–naive AS and Intestinal microbiome dysbiosis and microbial from healthy control subjects were generated using infections have been implicated in several immune- culture-independent 16S ribosomal RNA gene sequenc- mediated diseases, including multiple sclerosis, inflam- ing and analysis techniques. matory bowel disease (IBD), and type 1 diabetes. Results. Our results showed that the terminal Bacterial infection in the gut and urogenital tract is ileum microbial communities in patients with AS differ known to trigger episodes of reactive arthritis, a form of significantly (P < 0.001) from those in healthy control spondyloarthropathy (SpA) belonging to a group of subjects, driven by a higher abundance of 5 families of related inflammatory arthropathies, of which ankylosing Dr. Brown’s work was supported by a Senior Principal spondylitis (AS) is the prototypic disease. The close Research Fellowship from the National Health and Medical Research relationship between the gut and SpA is exemplified in Council of Australia. patients with reactive arthritis, in whom a typically 1Mary-Ellen Costello, MSc, Nicole Warrington, PhD, Philip C. Robinson, MBChB, FRACP, Brooke Gardiner, PhD, Mhairi Mar- self-limiting arthropathy follows either gastrointestinal shall, MSc, Tony J. Kenna, BSc, PhD, Matthew A. Brown, MBBS, MD, infection with Campylobacter, Salmonella, Shigella,or FRACP: University of Queensland Diamantina Institute, Transla- Yersinia, or urogenital infection with Chlamydia. Micro- tional Research Institute, and Princess Alexandra Hospital, Woolloon- gabba, Brisbane, Queensland, Australia; 2Francesco Ciccia, MD, PhD, bial involvement in AS has been suggested; however, no Giovanni Triolo, PhD: University of Palermo, Palermo, Italy; 3Dana definitive link has been established (1). Willner, PhD: Australian Centre for Ecogenomics and University of Queensland, Brisbane, Queensland, Australia. Up to 70% of patients with AS have subclinical Drs. Ciccia, Willner, Kenna, and Triolo contributed equally to gut inflammation, and 5–10% of these patients have this work. more severe intestinal inflammation that progresses to Address correspondence to Matthew A. Brown, MBBS, MD, FRACP, The University of Queensland, The University of Queens- clinically defined IBD resembling Crohn’s disease (CD) land Diamantina Institute, Translational Research Institute, Princess (2). The high heritability of AS, the global disease Alexandra Hospital, Brisbane, Queensland 4102, Australia. E-mail: distribution, and the absence of outbreaks of the disease [email protected]. Submitted for publication August 28, 2014; accepted in suggest that AS is triggered by a common environmental revised form November 18, 2014. agent in genetically susceptible individuals (3). Multiple

686 GUT MICROBIOME AND AS-RELATED GENES 687

Table 1. Characteristics of the patients with AS and the healthy control subjects at the time of biopsy* Subject Age, Disease duration, ESR, CRP, HLA–B27 NSAID Histologic ID years Sex years† mm/hour mg/liter BASDAI status treatment inflammation AS01 56 M 7 34 18 7 Positive Current Chronic AS02 33 M 4 22 1 5.5 Positive None Acute AS03 24 F 5 18 5 4.8 Positive None Normal AS04 22 M 3 33 3 5.5 Positive None Normal AS05 33 M 5 41 2.7 6 Positive None Chronic AS06 28 M 6 28 1.5 6.2 Positive None Normal AS08 36 M 8 34 1.5 5.6 Positive None Acute AS09 38 F 6 45 2.2 6.7 Positive None Chronic AS10 31 M 11 27 1.3 8 Positive None Normal HC01 58 F – – 0.3 – – – – HC02 43 M – – 0.5 – – – – HC03 38 F – – 0.05 – – – – HC04 65 M – – 0.8 – – – – HC05 48 F – – 0.02 – – – – HC06 34 F – – 0.03 – – – – HC07 45 M – – 0.5 – – – – HC08 44 F – – 0.8 – – – – HC09 56 F – – 0.02 – – – – * In all subjects, the ileum was the biopsy site. AS ϭ ankylosing spondylitis; ESR ϭ erythrocyte sedimentation rate; CRP ϭ C-reactive protein; BASDAI ϭ Bath AS Disease Activity Index; NSAID ϭ nonsteroidal antiinflammatory drug; HC ϭ healthy control. † Beginning at the onset of symptoms. genes associated with AS also play a role in gut immu- University of Palermo and University of Queensland research nity, such as genes involved in the interleukin-23 (IL-23) ethics committees. One patient with AS was receiving non- pathway (4), which are important regulators of intestinal steroidal antiinflammatory drugs (NSAIDs) at the time of biopsy. AS patients 3, 4, and 10 had reported occasional use of “health.” Marked overrepresentation of genes that are NSAIDs, which was interrupted due to gastrointestinal upset. associated with CD is also associated with AS (5); this Other patients with AS did not report use of NSAIDs but were suggests that the 2 diseases may have similar etiologic receiving either acetaminophen and/or tramadol. mechanisms, possibly involving gut dysbiosis. The terminal ileum microbial and mock communities Several studies have shown that patients with AS were profiled by high-throughput amplicon sequencing of the 16S ribosomal RNA (rRNA) gene (2ϫ 250-bp barcode) on an and their first-degree relatives have increased intestinal Illumina MiSeq sequencer using dual-indexed v4-region for- permeability relative to unrelated healthy control sub- ward primer 517F (5Ј-GCCAGCAGCCGCGGTAA-3Ј) and jects, which, again, is consistent with a role for the gut reverse primer 803R (5Ј-CTACCRGGGTATCTAATCC-3Ј). microbiome in AS (6). To date, no comprehensive In addition to exploring community-level differences between characterization of intestinal microbiota in patients with disease states, we evaluated technical and biologic replication. Biologic replication was examined by halving the biopsy spec- AS has been performed. We therefore performed imens and performing all subsequent studies in parallel to culture-independent microbial community profiling of assess the reproducibility of findings from the time of biopsy terminal ileum biopsy specimens to characterize and forward. Technical replication was performed by analyzing investigate differences in the gut microbiome between forward and reverse sequencing reads separately and then patients with AS and healthy control subjects. comparing them. The resulting sequence libraries were ana- lyzed using the Quantitative Insights Into Microbial Ecology (QIIME) pipeline (8). PATIENTS AND METHODS Alpha diversity metrics, which are used to examine the diversity within a sample, were generated using the QIIME Biopsy specimens from the terminal ileum were ob- workflow. Hierarchical clustering was performed, using the tained at the time of colonoscopy from 9 consecutively en- unweighted pair group method with arithmetic mean (includ- rolled tumor necrosis factor inhibitor–naive patients with ing both the weighted and unweighted UniFrac distance) to recent-onset (duration from symptom onset Յ48 months) AS detect significant differences within microbial communities (defined according to the modified New York classification between patients with AS and healthy controls. The weighted criteria for AS [7]) and 9 healthy control subjects (Table 1) UniFrac distance metric detects changes in the number of (see also Supplementary Table 1, available on the Arthritis & organisms present in a community, taking into account the Rheumatology web site at http://onlinelibrary.wiley.com/doi/ relative abundance of microbes present. The unweighted Uni- 10.1002/art.38967/abstract). All patients gave written informed Frac distance metric describes community membership. Uni- consent, and the study protocol was approved by the relevant Frac, enabled in QIIME, was used to generate sample distance 688 COSTELLO ET AL

A B C D

*** AS HC *** Proteobacteria *** 0.4 Fusobacteria

Firmicutes 0.3 Bacteroides Acinetobacteria 0.2 NS Observed Species 0.1 UniFrac Distance

0.0 PC2 - Percent variation explained 11.49% AS=9 Technical Biological Within Between AS HC HC=9 PC1 - Percent variation explained 65.07% replicates replicates disease status disease status Supervised Learning True/Predicted AS HC AS 9 0 HC 0 9 Figure 1. A, Difference in taxonomy at the phylum level between patients with ankylosing spondylitis (AS) and healthy control subjects (HCs), showing the increase in Bacteroides and the change in the Bacteroides-to-Firmicutes ratio in the AS samples. B, Distinct clustering of AS samples compared with control samples, as determined by weighted principal coordinate analysis (PCoA). Supervised learning showed that all 9 AS samples were predicted to be AS. C, Alpha diversity box plot showing increased microbial diversity (observed species) in AS samples compared with control samples. Data are presented as box plots, where the boxes represent the 25th to 75th percentiles, the lines within the boxes represent the median, and the lines outside the boxes represent the 10th and 90th percentiles. D, Comparison of variation within and between disease status, showing greater significance between and within disease status than between technical replicates and biologic replicates. Values are the mean Ϯ SD. .ϭ P Ͻ 0.001 by Mann-Whitney test. NS ϭ not significant ءءء

metrics as well as principal coordinate analysis (PCoA). Core 1B) (see also Supplementary Figure 1, available on the microbiome analysis as well as supervised learning were per- Arthritis & Rheumatology web site at http://onlinelibrary. formed to further characterize the intestinal microbial signa- wiley.com/doi/10.1002/art.38967/abstract). ture, using the workflow pipelines in QIIME (8). The function of bacteria and communities was predicted using PICRUSt (9). To further demonstrate the distinct groupings of To determine differences in the microbial load, the AS samples and control samples, supervised learning total microbial biomass in biopsy samples was quantified using was conducted to test the predictive capacity of the real-time quantitative polymerase chain reaction (PCR) ana- microbiome differences, and all 9 samples obtained from lysis of the 16S rRNA gene, as described by Willner et al (10). PERMANOVA analysis was conducted at the genus level patients with AS had been predicted to be AS samples using the vegan package in R to test the relationship between (Figure 1B). PERMANOVA analysis further confirmed the whole microbial community and disease. Indicator species the presence of a significant relationship between dis- analysis was performed using the labdsv package in R. Co- ease status and microbial community composition (P Ͻ occurrence network analysis was conducted using the spaa 0.001). This was not due to heterogeneity in biopsy package in R. samples, because biologic replicates showed no significant differences between each other. Quantitative PCR RESULTS analysis of biomass showed no significant differences, on The microbial communities in the terminal ileum average, in the 16S rRNA copy number between AS of patients with AS were significantly different (Figure samples and control samples, indicating that the ob- 1A) and more diverse (Figure 1C) compared with those served differences were not attributable to overgrowth in healthy control subjects (P Ͻ 0.001), as determined or dominance of bacteria driving community differences using PERMANOVA. PCoA showed that AS samples, (see Supplementary Figure 2, available on the Arthritis & including biologic replicates, grouped separately from Rheumatology web site at http://onlinelibrary.wiley.com/ control samples, indicating that disease is the primary doi/10.1002/art.38967/abstract). The average UniFrac factor influencing the community differences (Figure distances demonstrated that differences between both GUT MICROBIOME AND AS-RELATED GENES 689

biologic and technical replicates were significantly less than differences between individuals and disease states (P Ͻ 0.001, by Mann-Whitney test) (Figure 1D). Community profiling showed 51 genera across all biopsy specimens, with major differences observed at the phylum level between AS and control specimens (Figure 1A). Indicator species analysis was performed to determine whether alterations in specific species were driving the differences observed between communities in patients with AS and healthy control subjects. This analysis showed that, compared with the microbial communities in healthy control subjects, those in patients with AS were characterized by a higher abundance of Lachno- spiraceae, including Coprococcus species and Roseburia species (P ϭ 0.001), Ruminococcaceae (P ϭ 0.012), Rikenellaceae (P ϭ 0.004), Porphyromonadaceae including Parabacteroides species (P ϭ 0.001), and Bacte- roidaceae (P ϭ 0.001). Decreases in the abundance of Veillonellaceae (P ϭ 0.01) and Prevotellaceae (P ϭ 0.004) were observed (Figure 2A). Further drilling down into the AS microbiome signature using co-occurrence analysis, which examines the correlation between microbes, showed that bacterial interactions further shape the AS microbial community signature, with positive correlations observed between Figure 2. A, Differences in the abundance of bacterial families be- the indicator species Lachnospiraceae and Ruminococ- tween patients with ankylosing spondylitis (AS) and healthy control caceae and negative correlations observed between Veil- subjects. The first 7 families of bacteria (from left to right) are AS lonella and Prevotella (Figure 2B). Microbial functional indicator species, and the last 3 bacterial families are control indicator prediction using PICRUSt indicated 31 significant path- species. B, Co-occurrence network showing positive (blue lines) and ways with differential representation in patients with AS negative (red lines) correlations between AS indicator species includ- Ͻ ing members of the Lachnospiraceae family (Coprococcus and Rose- compared with healthy controls (P 0.02, with Bonfer- buria); Parabacteroides, which is a member of Porphyromonadaceae; roni correction) (see Supplementary Table 2, available and Faecalibacterium, which is a member of the Ruminococcaceae on the Arthritis & Rheumatology web site at http:// family. Increasing thickness of the lines indicates increasing strength of onlinelibrary.wiley.com/doi/10.1002/art.38967/abstract). (Pearson’s) correlation. These pathways included a decrease in bacterial invasion of epithelial cells (P ϭ 4.33 ϫ 10Ϫ5), an increase in antimicrobial production in the butirosin and neomycin tent components across complex microbial communities, biosynthesis pathway (P ϭ 0.002), which is consistent given the distinct AS microbial signature. The Core 100 with an increase in bacteria from the Bacteroidaceae species (microbes present in all samples) included the family, and an increase in the secondary bile acid Clostridium, Actinomycetaceae, Bacteroidaceae, Lachno- biosynthesis pathway (P ϭ 0.004), which is consistent spiraceae, Porphyromonadaceae, Rikenellaceae, Rumino- with an increase in Clostridia and Ruminococcaceae coccaceae, and Veillonellaceae families of bacteria. These species. families of bacteria are also AS indicator species, sug- We were interested in further interrogating the gesting that the core microbiome is driving the AS AS microbiome signature, which is the overall combina- microbial signature (Figure 2A). tion of microbes that distinguishes patients with AS from The Core 100 species belonged to 22 significant healthy control subjects, by investigating whether an pathways (P Ͻ 0.02, with Bonferroni correction) (see assemblage or “core” set of microbes was present in all Supplementary Table 3, available on the Arthritis & AS and control samples at varying levels, and to probe Rheumatology web site at http://onlinelibrary.wiley.com/ their functional capacity. Exploring the core microbiome doi/10.1002/art.38967/abstract) that again included bac- is imperative to better understand the stable and consis- terial invasion of epithelial cells (P ϭ 0.004), antimicro- 690 COSTELLO ET AL

bial production in the butirosin and neomycin abundance within the AS microbiome, Lachnospiraceae, biosynthesis pathway (P ϭ 0.007), and the secondary bile Ruminococcaceae, and Prevotellaceae are strongly asso- acid biosynthesis pathway (P ϭ 0.013). Members of the ciated with colitis and CD, with Prevotellaceae especially Bacteroides, Clostridium, and Ruminococcus groups are known to elicit a strong inflammatory response in the known to be involved in cholesterol metabolism and gut (13). Further investigations showed that correlations secondary bile synthesis; disordered bile acid synthesis between these families of bacteria further shape the AS due to intestinal dysbiosis has previously been impli- microbial signature, and that these microbes were pres- cated in the pathogenesis of IBD (11). ent in all AS samples studied, suggesting that they are As the threshold for core membership was re- not only driving the microbial signature but also are at duced to 90%, 75%, and 50%, the families of bacteria the core of the AS microbial signature. Increases in present remained the same; however, the number of Prevotellaceae and decreases in Rikenellaceae in the genus and species within these families increased, espe- intestinal microbiome have also recently been observed cially in Clostridia, Lachnospiraceae, and Ruminococcus. in the HLA–B27–transgenic rat model of spondylo- This demonstrates that the structure of the core micro- arthritis, suggesting that underlying host genetics may biome is robust, and that decreasing the threshold play a role in sculpting the gut microbiome in this animal expands the number of genera and species of bacteria model (14). only within these families. As the core threshold de- The increased diversity of the AS community creased, however, the number of significant pathways without an overall change in microbial load shows that also decreased, suggesting that as the threshold is re- no overgrowth or dominance of a particular microbe laxed, the expansion of genera and operational taxo- drives the signature. However, murine experiments dem- nomic units dilutes pathway signals. onstrate that both the overall composition of the intes- Indicator species analysis also determined that tinal microbiome and the presences and/or absence of healthy control subjects had an increased abundance of specific microbes can have a substantial impact on host Streptococcus and Actinomyces compared with patients response, regulation of inflammation, and development with AS. No difference was noted between patients with of intestinal cells. In the K/BxN mouse model of arthri- AS and control subjects in the presence of either bacte- tis, it was shown that the introduction of a single ria known to be associated with reactive arthritis or gut-residing species, segmented filamentous bacteria, Klebsiella species, which have been proposed to play a into germ-free mice was sufficient to reinstate Th17 role in triggering AS (12). cells, leading to the production of autoantibodies and arthritis. When IL-17 was neutralized in specific pathogen–free K/BxN mice, development of arthritis DISCUSSION was prevented. Thus, a single commensal microbe, via its Here, we present the first characterization and ability to promote a specific T helper cell subset, can identification of intestinal dysbiosis in the AS micro- drive immune-mediated disease (15). biome, using 16S rRNA community profiling of terminal Further studies are needed to investigate whether ileum biopsy specimens. We show evidence for a discrete the changes in intestinal microbial composition are due microbial signature in the terminal ileum of patients to host genetics and how this affects the overall function with AS compared with healthy control subjects. The of the gut microbiome in AS patients, including how the microbial profile differences are not attributable to microbiome then goes on to shape the immune response differences in overall bacterial quantity between patients and influence inflammation. In particular, given the with different diseases but are qualitative. PCoA was strong association of HLA–B27 with AS, it has been able to show the distinct grouping of patients with AS hypothesized that HLA–B27 induces AS by effects on versus healthy control subjects; larger studies will be the gut microbiome, in turn driving spondyloarthritis- required to define the individual bacterial species in- inducing immunologic processes such as IL-23 produc- volved. Statistically, the relationship between disease tion (16). Our data showing intestinal dysbiosis in pa- status and microbial community composition was con- tients with AS is consistent with this hypothesis, but firmed, indicating that the differences in composition of further studies are clearly required to distinguish cause- the microbial community were not due to heterogeneity and-effect interactions between the host genome and in the biopsy specimens or a lack of technical reproduc- immune system and the gut microbiome. These investi- ibility, but that the driving force is disease state. gations will provide new insights and help us to better Of the 7 families of microbes with differences in understand the pathogenesis of AS. GUT MICROBIOME AND AS-RELATED GENES 691

ACKNOWLEDGMENT 5. Parkes M, Cortes A, van Heel DA, Brown MA. Genetic insights into common pathways and complex relationships among immune- We thank the individuals who provided samples for mediated diseases. Nat Rev Genet 2013;14:661–73. this study. 6. Mielants H, De Vos M, Goemaere S, Schelstraete K, Cuvelier C, Goethals K, et al. Intestinal mucosal permeability in inflammatory rheumatic diseases. II. Role of disease. J Rheumatol 1991;18: AUTHOR CONTRIBUTIONS 394–400. All authors were involved in drafting the article or revising it 7. Moll JM, Wright V. New York clinical criteria for ankylosing critically for important intellectual content, and all authors approved spondylitis: a statistical evaluation. Ann Rheum Dis 1973;32: the final version to be published. Dr. Brown had full access to all of the 354–63. data in the study and takes responsibility for the integrity of the data 8. Caporaso JG, Kuczynski J, Stombaugh J, Bittinger K, Bushman and the accuracy of the data analysis. FD, Costello EK, et al. QIIME allows analysis of high-throughput Study conception and design. Costello, Ciccia, Willner, Kenna, Triolo, community sequencing data. Nat Methods 2010;7:335–6. Brown. 9. Langille MG, Zaneveld J, Caporaso JG, McDonald D, Knights D, Acquisition of data. Costello, Ciccia, Gardiner, Marshall, Triolo, Reyes JA, et al. Predictive functional profiling of microbial Brown. communities using 16S rRNA marker gene sequences. Nat Bio- Analysis and interpretation of data. Costello, Willner, Warrington, tech 2013;31:814–21. Robinson, Marshall, Kenna, Brown. 10. Willner DL, Hugenholtz P, Yerkovich ST, Tan ME, Daly JN, Lachner N, et al. Reestablishment of recipient-associated microbiota in the lung allograft is linked to reduced risk of bronchiolitis REFERENCES obliterans syndrome. Am J Respir Crit Care Med 2013;15:640–7. 11. Devkota S, Wang Y, Musch MW, Leone V, Fehlner-Peach H, 1. Stebbings S, Munro K, Simon MA, Tannock G, Highton J, Nadimpalli A, et al. Dietary-fat-induced taurocholic acid promotes Harmsen H, et al. Comparison of the faecal microflora of patients pathobiont expansion and colitis in Il102/2 mice. Nature 2012;487: with ankylosing spondylitis and controls using molecular methods 104–8. of analysis. Rheumatology (Oxford) 2002;41:1395–401. 12. Ebringer R, Cooke D, Cawdell DR, Cowling P, Ebringer A. 2. Mielants H, Veys EM, Cuvelier C, De Vos M, Botelberghe L. Ankylosing spondylitis: klebsiella and HL-A B27. Rheumatol HLA-B27 related arthritis and bowel inflammation. Part 2: ileo- Rehabil 1977;16:190–6. colonoscopy and bowel histology in patients with HLA-B27 related 13. Elinav E, Strowig T, Kau AL, Henao-Mejia J, Thaiss CA, Booth arthritis. J Rheumatol 1985;12:294–8. CJ, et al. NLRP6 inflammasome regulates colonic microbial 3. Brown MA, Kennedy LG, MacGregor AJ, Darke C, Duncan E, ecology and risk for colitis. Cell 2011;145:745–57. Shatford JL, et al. Susceptibility to ankylosing spondylitis in twins: 14. Lin P, Bach M, Asquith M, Lee AY, Akileswaran L, Stauffer P, et the role of genes, HLA, and the environment. Arthritis Rheum al. HLA-B27 and human ␤2-microglobulin affect the gut micro- 1997;40:1823–8. biota of transgenic rats. PLoS One 2014;9:e105684. 4. Wellcome Trust Case Control Consortium, Australo-Anglo- 15. Wu HJ, Ivanov II, Darce J, Hattori K, Shima T, Umesaki Y, et al. American Spondylitis Consortium (TASC), Burton PR, Clayton Gut-residing segmented filamentous bacteria drive autoimmune DG, Cardon LR, Craddock N, et al. Association scan of 14,500 arthritis via T helper 17 cells. Immunity 2010;32:815–27. nonsynonymous SNPs in four diseases identifies autoimmunity 16. Kenna TJ, Brown MA. Immunopathogenesis of ankylosing spon- variants. Nat Genet 2007;39:1329–37. dylitis. Int J Clin Rheumatol 2013;8:265–74. Clin Rheumatol (2014) 33:763–767 DOI 10.1007/s10067-014-2664-5

ORIGINAL ARTICLE

Does the microbiome play a causal role in spondyloarthritis?

James T. Rosenbaum & Phoebe Lin & Mark Asquith & Mary-Ellen Costello & Tony J. Kenna & Matthew A. Brown

Received: 31 January 2014 /Accepted: 24 March 2014 /Published online: 10 May 2014 # Clinical Rheumatology 2014

Abstract The purpose of this study is to review the potential cohabit our bodies [1]. Although this assortment of microbes causal role of the microbiome in the pathogenesis of includes viruses, fungi, and parasites, bacteria are the predom- spondyloarthritis. The method used for the study is literature inant form of life. In fact, bacterial cells outnumber mamma- review. The microbiome plays a major role in educating the lian cells in the human body by about 10 to 1 [2]. Bacterial immune response. The microbiome is strongly implicated in RNA transcripts expressed in a human outnumber mammalian inflammatory bowel disease which has clinical and genetic transcripts by greater than 100 to 1 [3]. The National Institutes overlap with spondyloarthritis. The microbiome also plays a of Health has recognized the importance of characterizing the causal role in bowel and joint disease in HLA B27/human beta microbiome through the initiation of the Human Microbiome 2 microglobulin transgenic rats. The mechanism(s) by which Project [2, 3]. Europe is home to a similar initiative known at HLA B27 could influence the microbiome is unknown but Meta Hit, the metagenomics of the human intestinal tract. theories include an immune response gene selectivity, an Because the majority of these organisms are strictly anaerobic, effect on dendritic cell function, or a mucosal immunodefi- they are difficult to culture. They can now be characterized by ciency. Bacteria are strongly implicated in the pathogenesis of sequencing techniques which have diminished in cost dramat- spondyloarthritis. Studies to understand how HLA B27 affects ically over the past decade. These techniques exploit differ- bacterial ecosystems should be encouraged. ences in the ribosomal sequence of bacteria compared to mammals, allowing primers to be designed such that primarily Keywords Inflammatory bowel disease . Microbiome . bacterial DNA is amplified. Some characterization of the Spondyloarthritis microbiome has also been done by mass sequencing that relies on databases to sort the sequences into bacterial or other origin [4]. The microbiome is a term coined by the Nobel Laureate Joshua In this review, we begin with an overview of selected recent Lederberg to describe the collection of microorganisms that publications that highlight the emerging recognition of the microbiome’s role in health and disease. We conclude by describing observations that implicate the microbiome strongly in the pathogenesis of ankylosing spondylitis. The reader is * : : J. T. Rosenbaum ( ) P. Lin M. Asquith also referred to two previous reviews on the topic from our Division of Arthritis and Rheumatic Diseases and Department of Ophthalmology, Oregon Health & Science University, 3181 SW Sam groups [5, 6]. Jackson Park Rd. L467Ad, Portland, OR 97239, USA One of the key functions of the microbiome is to educate e-mail: [email protected] the immune response. Many authorities argue that the intestinal tract is the largest organ in the immune system. When mice J. T. Rosenbaum Legacy Devers Eye Institute, 1040 NW 22nd Ave, Portland, are raised in a strictly germfree environment, the lymphoid OR 97210, USA organs do not develop normally and the immune response to a foreign antigenic challenge is blunted [7]. Our microbiome < : : M. E. Costello T. J. Kenna M. A. Brown also plays an essential role in the generation of vitamins such The University of Queensland Diamantina Institute, Princess Alexandra Hospital, University of Queensland, Brisbane 4102, as vitamin K. All food ingested is metabolized by the Australia microbiome which consequently has immense influence in 764 Clin Rheumatol (2014) 33:763–767 both starvation [8] and obesity [9]. Several dramatic examples As is the case for inflammatory bowel disease, the strongest of drug metabolism or efficacy have also been shown to be argument that the microbiome is important in ankylosing dependent on the microbiome including acetaminophen [10], spondylitis derives from animal data. Taurog, Hammer, and platinum-based chemotherapy [11], and cyclophosphamide colleagues derived transgenic rats which overexpress HLA- [12]. B27 and human beta 2 microglobulin [30]. These rats known The microbiome is now recognized to play a role in nu- as 33–3 on the Fischer background develop a spontaneous merous diseases. For example, the microbiome generates illness characterized by diarrhea and subsequent axial and metabolites called trimethylamine oxides which are highly peripheral arthritis. When raised in a germfree environment, atherogenic [13, 14]. The vegetarian diet alters the both the arthritis and diarrhea are largely eliminated [31]. microbiome such that these metabolites are not generated Furthermore, certain probiotics like Lactobacillus rhamnosus [14]. Primarily based on data from rodent models, the GG can maintain the remission [32], while colonization of the microbiome has been implicated in a vast array of diseases susceptible rats with other bacteria such as Bacteroides which include inflammatory bowel disease [15], diabetes [16], thetaiotamicron (B. theta)orBacteroides vulgatus will induce hypertension [17], fatty liver [18], several cancers [19], ulcers disease. In the SKG mouse model, which has features of [20], inflammatory arthritis [21], multiple sclerosis [22], and spondyloarthritis and inflammatory bowel disease, and like psoriasis [23]. Even personality characteristics may be influ- ankylosing spondylitis shows marked involvement of IL-23 enced by the microbiome [24, 25]. The two diseases that are dependent immunological pathways [33], mice raised in most clearly linked to the microbiome are ulcerative colitis germfree conditions are unaffected [34]. As with all animal and Crohn’s disease. A National Library of Medicine search models though, the relevance of these findings to health on the term inflammatory bowel disease and microbiome in and disease will require demonstration specifically in December 2013 yielded 941 references. The evidence that the humans. microbiome is critical in inflammatory bowel disease primar- A plausible hypothesis is that HLA molecules, either alone ily derives from rodents. In most but not all instances, the or in combination with other ankylosing spondylitis- murine model of bowel inflammation is markedly improved in associated genes, determine the microbiome. No alleles within a germfree setting [15]. In some models, cohousing a mouse the genome are as polymorphic as HLA. A teleological argu- with bowel inflammation with a normal wild-type mouse will ment is that the diversity of HLA evolved to protect the cause the germfree wild-type animal to develop aspects of survival of the race in case of epidemic infection. Accordingly, bowel inflammation such as in the example of the NLRP6 it is logical that the HLA type would affect the response to knock-out mouse [15]. NLRP6 is expressed by intestinal bacterial antigens such that different HLA types would deter- epithelial cells and contributes to innate immunity. The ab- mine the differential survival of specific organisms. Some sence of NLRP6 results in colitis and an alteration of the experimental evidence to support this has been published in intestinal microbiota. mice which are transgenic for the expression of the human Some of the evidence that ankylosing spondylitis may be a HLA type DR4 [35]. In HLA-B27 transgenic Lewis rats, microbiome-driven disease is based on the analogy between which have a high penetrance of arthritis resembling human inflammatory bowel disease and ankylosing spondylitis. ankylosing spondylitis without overt colitis (the 21-3×283-2 About 10 to 20 % of patients with inflammatory bowel disease Lewis strain) [36], we have found key differences in the gut develop sacroiliitis identical to ankylosing spondylitis [26]. microbiota compared to wild-type control animals by both Conversely, more than 50 % of patients with ankylosing 16S rDNA sequencing and short fragment mass sequencing spondylitis have microscopic colonic lesions that resemble techniques (unpublished data by Phoebe Lin, Mark Asquith, Crohn’s disease even if bowel symptoms are absent [27]. Joel Taurog, Russ Van Gelder, and James Rosenbaum). Peripheral arthritis and uveitis are characteristic of both anky- Several theories have been proposed as to how HLA-B27 losing spondylitis and inflammatory bowel disease. might predispose to disease [37]. These theories include the A large number of genes associated with inflammatory possibility that B27 dictates an immune response to an bowel disease are also associated with ankylosing spondylitis. autoantigen. While this theory fits with the concept that In nearly all cases, the genetic variant carries the same risk HLA molecules are immune response gene products, no direction for each disease [28]. A high proportion of those autoantigen has been identified to explain the pathogenesis genes are involved in mucosal immunity [29]. For example, of ankylosing spondylitis adequately. It has also been ob- ankylosing spondylitis is associated with variants in the tran- served that B27 is unique in that it dimerizes on the cell scription factors RUNX3, EOMES,andTBX21,aswellasthe surface and thus can trigger a natural killer cell response. cytokine receptors IL7R and IL23R [29], all of which are key HLA-B27 within the cytosol is relatively unstable and can regulators of the differentiation and activation of innate lymphoid trigger a series of intracellular events known as the unfolded cells, which are themselves critical components of mucosal protein response. None of these three observations adequately immune defenses. clarifies the clinical characteristics of the disease. In theory, Clin Rheumatol (2014) 33:763–767 765 each hypothesis might be true and the mechanism by which FOXP3. Implicated bacteria include Clostridia spp.[45, 46], each contributes to disease could be indirect through an alter- Helicobacter pylori [47], and Bacteroides fragilis [48]. Mu- ation of the microbiome. rine studies have also implicated segmented filamentous bac- It has been proposed that HLA-B27 leads to a state of teria in the generation of T cells that synthesize interleukin 17 mucosal immunodeficiency [38], and that genes associated [21]. Altered dendritic cell function has been noted in with ankylosing spondylitis either contribute to that immuno- B27-related disease models [49]. Mucus from the gut deficiency, or lead to heightened immunological reactions alters dendritic cell function [50]soitislikelythatthe driven by bacterial invasion permitted by HLA-B27. This microbiome will affect dendritic cell function directly may either be direct (due to reduced ability to drive immuno- and indirectly. logical responses to particular bacteria), indirect due to effects Although it seems plausible that HLA-B27 shapes the on intestinal permeability, or due to alterations in the gut microbiome and that HLA-B27-related disease is associated microbiome such as a loss of protective bacterial species. with altered gut permeability, it is unknown which comes first: Given the strong interaction of HLA-B27 and ERAP1 in the change in permeability or the change in the microbiome. causing ankylosing spondylitis, and the known function of The alteration in the immune response could alter gut perme- ERAP1 in preparing peptides prior to HLA Class I presenta- ability or the alteration in the immune response could affect tion, it seems likely that these genes operate in disease through the microbial population in the gut and this could result in a altered peptide handling. Two other aminopeptidases are change in permeability. We hypothesize that altered gut per- known to be associated with ankylosing spondylitis (ERAP2 meability allows the escape of microbial products which be- and NPEPPS). The disease-protective variants of ERAP1 and come the target of innate and adaptive immune responses. ERAP2 are known to be loss-of-function variants, leading to HLA-B27 might be critical in the response to the microbial lower HLA Class I cell surface expression. This would be product when it deposits in the synovium or uveal tract. most consistent with models in which HLA-B27 in individ- Alternatively, B27’s role might be essential in creating the uals carrying the risk, gain-of-function variants, alter the leaky gut but not critical in the immune response to these microbiome in a manner which promotes ankylosing products once they deposit in these tissues. spondylitis-relevant immunological activation. At the time of this presentation, several groups are actively If HLA molecules shape the microbiome, multiple hypoth- trying to confirm the preliminary observation that HLA-B27 eses could explain how this change could result in disease. For alters the microbiome either in rodents or in humans. The example, a heightened immune response to specific bacteria complexity of the microbiome and the uncertainty as to which could result in synovitis if these specific bacteria escape to the microbiome population (for example, cecum, skin, or ileum), synovium where they could trigger an immune response. is most important increases the difficulty of this quest. What is Specific bacterial antigens have been detected in the joint in certain is that the microbiome is integral to human health and reactive arthritis [39, 40]. Another reasonable hypothesis is the understanding of the microbiome will lead to novel in- that the change in microbiome changes gut permeability and sights into disease. Spondyloarthropathy seems highly likely allows leakage of multiple bacterial products which could to be one such disease. become arthritogenic. An increase in intestinal permeability has been shown in the HLA-B27 transgenic rat [41]andin Acknowledgements We are grateful for the financial support from the patients with ankylosing spondylitis [42, 43]. Another expla- National Eye Institute (Grants EY 021733 and KO8EY022948), the Stan nation is that the presence of the HLA-B27 transgene alters the and Madelle Rosenfeld Family Trust, the William and Mary Bauman transcriptional profiles of the bacteria present in the gut. Foundation, and Research to Prevent Blindness. PL is funded by a Research to Prevent Blindness Career development award. 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