New Results

HLA alleles associated with risk of ankylosing spondylitis and rheumatoid arthritis influence the gut microbiome

Mark Asquith, Peter R. Sternes, Mary-Ellen Costello, Lisa Karstens, Sarah Diamond, Tammy M. Martin, Timothy D. Spector, Kim-Anh le Cao, James T. Rosenbaum, Matthew A. Brown
doi: https://doi.org/10.1101/517813

ABSTRACT

Objectives HLA alleles affect susceptibility to more than 100 diseases, but the mechanisms to account for these genotype-disease associations are largely unknown. HLA-alleles strongly influence predisposition to ankylosing spondylitis (AS) and rheumatoid arthritis (RA). Both AS and RA patients have discrete intestinal and faecal microbiome signatures. Whether these changes are cause or consequence of the diseases themselves is unclear. To distinguish these possibilities, we examine the effect of HLA-B27 and HLA-DRB1 RA-risk alleles on the composition of the intestinal microbiome in healthy individuals.

Methods 568 samples from 6 intestinal sites were collected from 107 otherwise healthy unrelated subjects and stool samples from 696 twin pairs from the TwinsUK cohort. Microbiome profiling was performed using sequencing of the 16S rRNA bacterial marker gene. All patients were genotyped using the Illumina CoreExome SNP microarray, and HLA genotypes were imputed from these data.

Results Association was observed between HLA-B27 genotype, and RA-risk HLA-DRB1 alleles, and overall microbial composition (P=0.0002 and P=0.00001 respectively). These associations were replicated in the TwinsUK cohort stool samples (P=0.023 and P=0.033 respectively).

Conclusions This study shows that the changes in intestinal microbiome composition seen in AS and RA are at least partially due to effects of HLA-B27 and –DRB1 on the gut microbiome. These findings support the hypothesis that HLA alleles operate to cause or increase the risk of these diseases through interaction with the intestinal microbiome, and suggest that therapies targeting the microbiome may be effective in their prevention and/or treatment.

INTRODUCTION

HLA molecules affect susceptibility to many diseases, but in the majority of cases the mechanism by which HLA molecules predispose to disease remains a mystery. The risks of developing both ankylosing spondylitis (AS) and rheumatoid arthritis are primarily driven by genetic effects, with heritability >90% (1, 2) for AS, and 53-68% for RA (3, 4). In both diseases HLA alleles are the major susceptibility factors, with AS being strongly associated with HLA-B27, and RA with HLA-DRB1 ‘shared-epitope’ (SE) alleles.

Particularly in AS, there is strong evidence of a role for gut disease in disease pathogenesis. Up to an estimated 70% of AS patients have either clinical or subclinical gut disease, suggesting that intestinal inflammation may play a role in disease pathogenesis (5, 6). Increased gut permeability has been demonstrated in both AS patients and their first-degree relatives compared with unrelated healthy controls (7–11). Crohn’s disease (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 (12, 13). Strong co-familiality (14), and the extensive sharing of genetic factors between AS and inflammatory bowel disease (IBD) (15, 16) suggests that they have a shared aetiopathogenesis. This is consistent with the hypothesis that increased ‘leakiness’ of the gut, and potentially the capability of HLA-B27 to present relevant microbial antigens, may alter the gut microbial composition and increase gut mucosal microbial dissemination, and hence drive inflammation in the disease (17, 18). Reduced expression of the intestinal barrier protein zonulin and histopathological evidence of gut wall invasion by bacteria in AS further support this hypothesis (19).

Using 16S rRNA community profiling we have previously demonstrated that AS cases have a discrete intestinal microbial signature in the terminal ileum (TI) compared with healthy controls (HC) (P<0.001) (20), a finding that has subsequently been confirmed by other studies (21, 22). We have also demonstrated that dysbiosis is an early feature of disease in HLA-B27 transgenic rats, preceding the onset of clinical disease in the gut or joints (23). Similarly, RA cases have also been shown to have gut dysbiosis (24, 25), and animal models of RA such as collagen-induced arthritis have been shown to be influenced by the gut microbiome (26, 27). In these studies it is difficult to distinguish between effects of the immunological processes going on in the intestinal wall in cases, and the effects of treatments on the intestinal microbiome, from potential effects of the gut microbiome on the disease.

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 (28). In addition, a recent quantitative trait locus mapping study in an inter-cross murine model, linked specific genetic polymorphisms with microbial abundances (29). Large scale studies in twins (n=1126 twin pairs) have demonstrated that of 945 widely shared taxa, 8.8% showed significant heritability, with some taxa having heritability of >40% (e.g. family Christensenellaceae, heritability 42%) (30).

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 cases, including how the microbiome then goes on to shape the immune response and influence inflammation. In AS, given the strong association of HLA-B27, the hypothesis has been raised that HLA-B27 induces AS by effects on the gut microbiome, in turn driving spondyloarthritis and inducing immunological processes such as IL-23 production (31, 32). 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 HLA-B27 transgenic rats showing that HLA-B27 was associated with altered ileal, caecal, colonic and fecal microbiota (23, 33, 34). Similar theories have been proposed with regard to interaction between the gut microbiome and the immunological processes that drive RA (reviewed in (35)).

In this study we investigated if AS and RA-associated HLA alleles influence the gut microbiome in healthy individuals, to support the hypothesis that they influence the risk of developing AS and RA through effects on the gut microbiome.

METHODS

Human subjects

A total of 107 subjects (aged 40-75) who were undergoing routine colorectal cancer screening at Oregon Health & Science University’s Center for Health and Healing were included in this study. Individuals were excluded if they had a personal history of inflammatory bowel disease or colon cancer, prior bowel or intestinal surgery or were pregnant. All subjects underwent a standard polyethylene glycol bowel prep the day prior to their colonoscopy procedure. During the procedure, biopsies were collected for research purposes from the terminal ileum or other tissue sites as indicated. Subjects were instructed to collect a stool sample on a sterile swab at home, just prior to starting their bowel prep procedure. Stool samples were brought to the colonoscopy appointment at room temperature. All samples (biopsies and fecal swabs) were placed at 4°C in the clinic and transported to the lab within 2 hours of the colonoscopy procedure, where they were snap frozen and stored at −80°C prior to processing. Patient samples were obtained over a 24-month period.

Ethical approval for this study was obtained from the Oregon Health & Science University Institutional Review Board. Written informed consent was obtained from all subjects. This study was performed subject to all applicable U.S. Federal and State regulations.

TwinsUK

All work involving human subjects was approved by the Cornell University IRB (Protocol ID 1108002388). Matched genotyped and stool samples were collected from 1392 twins. Genotyping, 16S rRNA amplicon sequencing, filtering and analysis were performed as described in Goodrich et al., 2014 (36).

16S rRNA amplicon sequencing and analysis

568 stool and biopsy samples across 107 individuals were extracted and amplified for the bacterial marker gene 16S rRNA as previously described (20). Samples were demultiplexed and filtered for quality using the online platform BaseSpace (http://basespace.illumina.com). Paired end reads were joined, quality filtered and analysed using Quantitative Insights Into Microbial Ecology (QIIME) v1.9.1 (37). Operational taxonomy units (OTU) were picked against a closed reference and taxonomy was assigned using the Greengenes database (gg_13_8) (38), clustered at 97% similarity by UCLUST (39) and low abundance OTUs were removed (<0.01%).

Multidimensional data visualisation was conducted using a sparse partial least squares discriminant analysis (sPLSDA) on centered log ratio transformed data, as implemented in R as part of the MixOmics package v6.3.1 (40). Association of the microbial composition with metadata of interest was conducted using a PERMANOVA test as part of vegan v2.4-5 (41) on arcsine square root transformed data at species level, taking into account individual identity where multiple sites per individual were co-analysed, as well as the sources of covariation such as BMI and gender. Alpha diversity was calculated at species level using the rarefy function as implemented in vegan v2.4-5 and differences were evaluated using a Wilcoxon rank-sum test. The metagenome functional content was predicted using PICRUSt v1.1.3 (42) and the resulting predictions were mapped to KEGG pathways using HUMAnN2 v0.11.1 (43) Differential abundance of bacterial taxa and KEGG pathways were tested for significance using MaAsLin v0.0.5 (44).

Genotyping

DNA was extracted from mucosal biopsies and stool samples, and genotyped using Illumina CoreExome SNP microarrays according to standard protocols. Bead intensity data were processed and normalized for each sample, and genotypes called using Genome Studio (Illumina). We imputed HLA-B genotypes using SNP2HLA (45), as previously reported (46). The distribution of HLA-B27 and HLA-DRB1 RA-risk, -protective and –neutral subtypes is available in Supplementary Table 1.

View this table:
Table 1:

Number of samples and HLA-B27 and HLA-DRB1 shared epitope allele status by site. Note that different subjects had different numbers of samples obtained, and at no individual site did all subjects have samples obtained.

RESULTS

16S rRNA profiling and SNP array genotyping was successfully completed for 107 individuals (61 female, 46 male) involving a total of 564 biopsy samples (see Table 1).

We studied the effect of BMI, gender and sampling site on the gut microbiome to identify relevant covariates for analysis of AS-associated genes and their association with the gut microbiome. Considering sample site, striking differences were observed, particularly between the stool samples and mucosal samples (Figure 1A, P<0.0001). Excluding stool samples, marked difference was still observed between sites (P<0.0001), but it can be observed that this is mainly driven by differences of the ileal samples from the colonic mucosal samples (left and right colon, cecum, rectum), which largely clustered together (Figure 1B).

Figure 1:
Figure 1:

sPLSDA comparing the microbiome composition at various sample sites, showing A. marked difference of stool/luminal site compared with all other sites, which are mucosal, and B. in the absence of stool samples, the ileal site remains distinct from colonic sites.

Stool samples are much more convenient to obtain than ileal or colonic mucosal samples, which require an endoscopic procedure for collection. Given the prior evidence of primarily ileal inflammation in AS (5), we were interested in the relationship between the ileal and stool microbiome. In this comparison marked differences were observed between sites, though with some overlap seen on the sPLSDA plot (Supplementary Figure 1, P<0.0001).

Several studies have noted an increase (47), decrease (20, 21) or no change (48) in alpha diversity metrics for AS cases, and an increase (22) or decrease (49) in alpha diversity for RA cases. In the current study, calculation of rarefied species richness revealed that carriage of HLA-B27 and HLA-DRB1 alleles was not associated with differences in alpha diversity, except for stool samples for which carriage of HLA-DRB1 RA-risk alleles was associated an increased alpha diversity across both cohorts (Figure 2).

Figure 2:
Figure 2:

Alpha diversity across each sampling site, and in the TwinsUK cohort A. Alpha diversity according HLA-B27 status. B. Alpha diversity according to HLA-DRB1 status, revealing increased alpha diversity in stool samples of both cohorts.

Considering beta diversity via sPLSDA and PERMANOVA, significant association of BMI category was seen with microbiome composition (P=0.0022)(Supplementary Figure 2A). This appears to be driven particularly by the difference of underweight individuals (BMI<18.5) compared with other BMI categories. Removing underweight samples from the analysis, a non-significant trend of association of BMI category with microbiome composition is seen (P=0.078)(Supplementary Figure 2B), consistent with previous reports.

Given the marked gender biases in RA and AS, and evidence in mice that gender related hormonal differences are associated with differences in the intestinal microbiome (53, 54), we sought to evaluate the influence of gender on the microbiome in this cohort. Whilst substantial overlap between males and females was evident (Supplementary Figure 3), significant difference between genders in microbiome composition was observed (considering all sites, P=0.0004). Considering indicator species, a significant reduction in carriage of Prevotella genus in males was observed (P=0.005).

Figure 3:
Figure 3:

A. sPLSDA comparing the microbiome composition of HLA-B27 positive and negative individuals across each sampling site. Considering all sampling sites and accounting for repeated sampling, significant differentiation of the microbiome was observed (PERMANOVA P=0.002). B. sPLSDA comparing individuals carrying the HLA-DRB1 RA-risk and -neutral genotypes across each sampling site. Considering all sites and accounting for repeated sampling, significant differentiation of the microbiome was observed (PERMANOVA P=0.0001). C. sPLSDA plot comparing HLA-B27 positive and negative twins (one twin randomly selected from each twin pair, PERMANOVA P=0.023), and HLA-DRB1 risk and neutral genotypes (one twin randomly selected from each twin pair, PERMANOVA P=0.033).

We tested whether HLA-B alleles associated with AS were also associated with gut microbial profiles. The association of HLA-B alleles with AS is complex, with risk associations observed with HLA-B27, -B13, -B40, -B47 and –B51, and protective associations with HLA-B7 and –B57 (55). Of these, only HLA-B27 showed statistically significant association with microbiome profile across both cohorts. Differences in the microbiome composition were more pronounced when comparing risk-associated alleles to protective alleles. For example, when focusing on a subset of data (ileal samples), marginal differentiation for –B27 (P=0.16) and no differentiation for –B7 (P=0.61) was observed, potentially highlighting sample size constraints. However, direct comparison of –B27 to –B7 revealed significant differentiation (P=0.008).

HLA-B27-positive subjects exhibited reduced carriage (P<0.05) of Bacterioides ovatus across multiple sites (ileum, cecum, left colon, right colon and stool), as well as Blautia obeum (left colon and right colon) and Dorea formicigenerans (rectum and stool). Increased carriage of a Roseburia species was observed across multiple sites (left colon, right colon, rectum and stool) and family Neisseriaceae (cecum and ileum). For subjects with RA-risk HLA-DRB1 alleles, numerous taxonomic groups were enriched across multiple sites, notably a Lachnospiraceae species (ileum, cecum, left colon, right colon and rectum), a Clostridiaceae species (left colon, right colon, rectum and stool) Bifidobacterium longum (cecum, right colon and rectum), amongst many others. Enrichment of Ruminococcus gnavus was also observed in the ileum of subjects carrying risk alleles. A full list of differently abundant taxa according to HLA-B27 and HLA-DRB1 status are available in Supplementary Tables 2 and 3, respectively. Interestingly, when accounting for false discovery rate, no single taxa was significantly associated with the investigated genotypes, indicating that community-level differences detectable via PERMANOVA may be driven by subtle changes in a high number of taxa, as opposed to marked changes in a select few.

Considering the inferred metabolic profiles for HLA-B27 positive and negative subjects, we observed significant differences (P<0.05) across multiple sites for numerous KEGG pathways (Supplementary Table 4). Examples include flagellar assembly (ileum, cecum, left colon, right colon and rectum), alanine metabolism (cecum, ileum, left colon, and right colon), lysine biosynthesis (left and right colon) and degradation (ileum, rectum and stool), secondary bile acid biosynthesis (ileum and stool) and bacterial invasion of epithelial cells (ileum). For the RA-risk alleles (HLA-DRB1), numerous differences in KEGG pathways were observed (Supplementary Table 5). Examples include thiamine metabolism, the citric acid cycle, lipopolysaccharide biosynthesis, biosynthesis of ansamycins, and bacterial chemotaxis, all of which were differentially abundant across every intestinal site.

DISCUSSION

In this study we have demonstrated for the first time that in the absence of disease or treatment, HLA-B27 and HLA-DRB1 have significant effects on the gut microbiome in humans. This extends previous demonstrations that AS and RA are characterized by intestinal dysbiosis by confirming that this is at least in part due to the effects of the major genetic risk factors for AS and RA, HLA-B27 and HLA-DRB1 risk alleles, respectively.

We demonstrate a clear distinction in microbiome profile between luminal stool samples and mucosal samples, as well as between mucosal samples from different intestinal sites. Of particular note, marked difference was observed between ileal and stool samples. These findings contrast a previous smaller study, which may not have observed a difference between ileal and colonic biopsies due to sample size considerations (48). Many studies of the influence of gut microbiome focus on stool samples, as they are easier to obtain than mucosal samples. The existence of gut inflammation, particularly involving the ileum, in AS cases has been well documented. Therefore, our findings suggest that studies of the microbiome in AS and RA, particularly where the aim is to identify the key indicator species driving or protecting from the disease, should use samples that reflect the ileal microbiome. As the microbiome profile of stool samples do not closely correlate with the ileal microbiome, they would not appear to be an optimal sample to study, although studying IgA coated bacteria isolated from stool samples may prove more informative (56).

Following our initial study, three further studies have now reported on the difference in gut microbial composition in AS cases and controls. Tito et al (48) in a study of 27 spondyloarthritis patients (i.e. not necessarily AS) and 15 healthy controls using 16S rRNA profiling report association of carriage of Dialister in ileal or colonic mucosal biopsies with disease activity assessed by the self-reported questionnaire the Bath Ankylosing Spondylitis Disease Activity Index (BASDAI), and Ankylosing Spondylitis Disease Activity Score (ASDAS). We did not observe Dialister in our study and therefore cannot comment on whether it is associated with HLA-B27 carriage. Tito et al did not observe association of the gut microbiome with HLA-B27 carriage, but the sample size, particularly in healthy controls, was too small to exclude other than a large effect. Wen et al used shotgun sequencing of stool samples from in 97 Chinese AS cases and 114 healthy controls, and reported significant dysbiosis in the AS cases (21). Breban et al (22) used 16S rRNA profiling of the stool microbiome to study 87 +-patients with axial spondyloarthritis (42 with AS), 69 healthy controls and 28 rheumatoid arthritis patients. They also report evidence of intestinal dysbiosis in the spondyloarthritis patients, and report correlation of Ruminococcus gnavus carriage with BASDAI. Whilst we did not observe an association with the carriage of HLA-B27, Ruminococcus gnavus was noted to be enriched in the ileum of individuals carrying the HLA-DRB1 RA-risk alleles (Supplementary Table 3). In a comparison of HLA-B27 positive and negative siblings (n=22 and 21 respectively), no difference in microbial composition was noted overall, but HLA-B27 positive siblings had increased carriage of the Microcaccaceae family (including the species Rothia mucilaginosa within it), several Blautia and Ruminococcus species, and of Egerthella lenta. They also observed a reduced carriage of Bifidobacterium and Odoribacter species. Of these we also see reduction in Blautia obeum. Although we did not find dysbiotic changes that were shared with these specific taxa, we note the enrichment of genera within the Lachospiraceae-Ruminococcaceae grouping in HLA-B27 carriers was a shared feature of our studies; Roseburia and Ruminococcus by Breban et al (22) and Roseburia, Blautia, Dorea and Oscillospira in our current study. These bacteria are known to be enriched within the intestinal mucosa (57), and are plausibly more immunogenic as a result (58). The differences observed between these studies may relate to analytical differences such as handling of covariates, disease definition, sample site studied, ethnicity and diet, and the different methods employed to profile the microbiome. Our study also confirms the significant effect of gender and BMI category on gut microbial profiles, suggesting that future studies should control for these covariates.

HLA molecules affect susceptibility to many diseases, most of which are immunologically mediated. In almost all instances, the mechanism that accounts for that predisposition is not known. The microbiome has now been implicated in a long list of diseases, many of which are immunologically mediated. Our studies suggest that HLA molecules could be important factors that contribute to the heterogeneity of the microbiome and operate at least partially through this mechanism in the pathogenesis of many different diseases, not just AS and RA. Consistent with this hypothesis, in celiac disease carriage of HLA-DQ2 has been shown to influence the gut microbiome in infants as yet unaffected but at high risk through family history of the disease (59).

The hypothesized metabolic changes imbued by dysbiosis in our current work are of interest in light of a recent study by our group in the HLA-B27 transgenic rat model of spondyloarthritis (60). We observe a number of HLA-B27 dependent metabolic changes in this model that include enrichment of bile acid metabolism, lysine metabolism, fatty acid metabolism and tryptophan metabolism. All of these pathways were predicted to be enriched in HLA-B27 positive individuals in our current study. Importantly, HLA-B27-dependent dysbiosis can be observed prior to the onset of disease in this model. Thus, our human and rat studies support the hypothesis that HLA-B27 dependent dysbiosis is a preceding event in AS pathogenesis and may not merely be secondary to disease.

In conclusion, this study demonstrates that HLA-B27 and RA-associated HLA-DRB1 allele carriage in humans influences the gut microbiome. In association with the replicated demonstration of intestinal changes in microbiome in AS, this is consistent with disease models in which HLA molecules interact with the gut microbiome to cause disease. Different models as to how this may occur include effects of HLA-B27 to favour a more inflammatory gut microbiome, increased invasiveness of the gut mucosa in HLA-B27 carriers, and/or aberrant immunological responses to bacteria in HLA-B27 carriers. Similar hypotheses may explain the role of HLA-DRB1 in driving the immunopathogenesis of RA. Whichever of these models is correct, the data presented here support further research in this field, including into whether manipulation of the gut microbiome may be therapeutic in AS or RA, or even potentially capable of preventing disease in at risk subjects.

ACKNOWLEDGEMENTS

MAB is funded by a National Health and Medical Research Council (Australia) Senior Principal Research Fellowship. This work was funded by a National Health and Medical Research Council (Australia) project grant (reference APP1065509). MA and JTR receive funding from the Spondylitis Association of America and the Rheumatology Research Foundation and from NIH Grant RO1 EY029266. TDS is a National Institute for Health Research (NIHR) senior investigator. TwinsUK is funded by the Wellcome Trust, Medical Research Council, CDRF, Arthritis Research UK, European Union, the NIHR – funded Clinical Research Facility and Biomedical Research Centre based at Guy’s, and St. Thomas’ NHS Foundation Trust in partnership with King’s College London (Grant code WT081878MA). JTR receives funding from the William and Mary Bauman Foundation, the Stan and Madelle Family Trust, and Research to Prevent Blindness. This study was funded in part by the OHSU Foundation (Collins Medical Trust) to JTR. LK receives funding supported by the Eunice Kennedy Shriver National Institute Of Child Health & Human Development of the National Institutes of Health under Award Number K12HD043488. We wish to acknowledge Dr. Erica Roberson, MD for her valuable contribution to study design; Patrick Stauffer and Sean Davin for technical assistance; and all OHSU patients and staff whom helped contribute samples for this study.

References

  1. 1.↵
    Brown MA, Kennedy LG, MacGregor AJ, Darke C, Duncan E, Shatford JL, et al. Susceptibility to ankylosing spondylitis in twins: the role of genes, HLA, and the environment. Arthritis Rheum. 1997;40(10):1823–8.
    OpenUrlCrossRefPubMedWeb of Science
  2. 2.↵
    Pedersen OB, Svendsen AJ, Ejstrup L, Skytthe A, Harris JR, Junker P. Ankylosing spondylitis in Danish and Norwegian twins: occurrence and the relative importance of genetic vs. environmental effectors in disease causation. Scand J Rheumatol. 2008;37(2):120–6.
    OpenUrl
  3. 3.↵
    MacGregor AJ, Snieder H, Rigby AS, Koskenvuo M, Kaprio J, Aho K, et al. Characterizing the quantitative genetic contribution to rheumatoid arthritis using data from twins. Arthritis Rheum. 2000;43(1):30–7.
    OpenUrlCrossRefPubMedWeb of Science
  4. 4.↵
    van der Woude D, Houwing-Duistermaat JJ, Toes RE, Huizinga TW, Thomson W, Worthington J, et al. Quantitative heritability of anti-citrullinated protein antibody-positive and anti-citrullinated protein antibody-negative rheumatoid arthritis. Arthritis Rheum. 2009;60(4):916–23.
    OpenUrlCrossRefPubMedWeb of Science
  5. 5.↵
    Mielants H, Veys EM, Cuvelier C, De Vos M, Botelberghe L. HLA-B27 related arthritis and bowel inflammation. Part 2. Ileocolonoscopy and bowel histology in patients with HLA-B27 related arthritis. J Rheumatol. 1985;12(2):294–8.
    OpenUrl
  6. 6.↵
    Ciccia F, Accardo-Palumbo A, Alessandro R, Rizzo A, Principe S, Peralta S, et al. Interleukin-22 and interleukin-22-producing NKp44+ natural killer cells in subclinical gut inflammation in ankylosing spondylitis. Arthritis Rheum. 2012;64(6):1869–78.
    OpenUrlCrossRefPubMedWeb of Science
  7. 7.↵
    Martinez-Gonzalez O, Cantero-Hinojosa J, Paule-Sastre P, Gomez-Magan JC, Salvatierra-Rios D. Intestinal Permeability in Patients with Ankylosing Spondyllitis and their healthy relatives. Rheumatology. 1994;33(7):644–7.
    OpenUrlCrossRefPubMed
  8. 8.
    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(3):394–400.
    OpenUrl
  9. 9.
    Morris AJ, Howden CW, Robertson C, Duncan A, Torley H, Sturrock RD, et al. Increased intestinal permeability in ankylosing spondylitis--primary lesion or drug effect? Gut. 1991;32(12):1470–2.
    OpenUrlAbstract/FREE Full Text
  10. 10.
    Vaile J, Meddings J, Yacyshyn B, AS. R, Maksymowych W. Bowel permeability and CD45RO expression on circulating CD20+ B cells in patients with ankylosing spondylitis and their relatives. Journal of Rheumatology. 1999 26(1):128–35.
    OpenUrlPubMed
  11. 11.↵
    Bjarnason I, Helgason KO, Geirsson AJ, Sigthorsson G, Reynisdottir I, Gudbjartsson D, et al. Subclinical intestinal inflammation and sacroiliac changes in relatives of patients with ankylosing spondylitis. Gastroenterology. 2003;125(6):1598–605.
    OpenUrlCrossRefPubMedWeb of Science
  12. 12.↵
    Palm O, Moum B, Ongre A, Gran JT. Prevalence of ankylosing spondylitis and other spondyloarthropathies among patients with inflammatory bowel disease: a population study (the IBSEN study). J Rheumatol. 2002;29(3):511–5.
    OpenUrlAbstract/FREE Full Text
  13. 13.↵
    Orchard TR, Holt H, Bradbury L, Hammersma J, McNally E, Jewell DP, et al. The prevalence, clinical features and association of HLA-B27 in sacroiliitis associated with established Crohn’s disease. Alimentary Pharmacology & Therapeutics. 2009;29(2):193–7.
    OpenUrlCrossRefPubMedWeb of Science
  14. 14.↵
    Thjodleifsson B, Geirsson AJ, Bjornsson S, Bjarnason I. A common genetic background for inflammatory bowel disease and ankylosing spondylitis: a genealogic study in Iceland. Arthritis Rheum. 2007;56(8):2633–9.
    OpenUrlCrossRefPubMedWeb of Science
  15. 15.↵
    Ellinghaus D, Jostins L, Spain SL, Cortes A, Bethune J, Han B, et al. Analysis of five chronic inflammatory diseases identifies 27 new associations and highlights disease-specific patterns at shared loci. Nature genetics. 2016;48(5):510–8.
    OpenUrlCrossRefPubMed
  16. 16.↵
    Parkes M, Cortes A, van Heel DA, Brown MA. Genetic insights into common pathways and complex relationships among immune-mediated diseases. Nat Rev Genet. 2013;14(9):661–73.
    OpenUrlCrossRefPubMed
  17. 17.↵
    Cua DJ, Sherlock JP. Autoimmunity’s collateral damage: Gut microbiota strikes ‘back’. Nature medicine. 2011;17(9):1055–6.
    OpenUrlCrossRefPubMed
  18. 18.↵
    Costello ME, Elewaut D, Kenna TJ, Brown MA. Microbes, the gut and ankylosing spondylitis. Arthritis research & therapy. 2013;15(3):214.
    OpenUrl
  19. 19.↵
    Ciccia F, Guggino G, Rizzo A, Alessandro R, Luchetti MM, Milling S, et al. Dysbiosis and zonulin upregulation alter gut epithelial and vascular barriers in patients with ankylosing spondylitis. Ann Rheum Dis. 2017;76(6):1123–32.
    OpenUrlAbstract/FREE Full Text
  20. 20.↵
    Costello ME, 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.
    OpenUrl
  21. 21.↵
    Wen C, Zheng Z, Shao T, Liu L, Xie Z, Le Chatelier E, et al. Quantitative metagenomics reveals unique gut microbiome biomarkers in ankylosing spondylitis. Genome Biol. 2017;18(1):142.
    OpenUrlCrossRef
  22. 22.↵
    Breban M, Tap J, Leboime A, Said-Nahal R, Langella P, Chiocchia G, et al. Faecal microbiota study reveals specific dysbiosis in spondyloarthritis. Annals of the rheumatic diseases. 2017;76(9):1614–22.
    OpenUrlAbstract/FREE Full Text
  23. 23.↵
    Gill T, Asquith M, Brooks SR, Rosenbaum JT, Colbert RA. Effects of HLA-B27 on Gut Microbiota in Experimental Spondyloarthritis Implicate an Ecological Model of Dysbiosis. Arthritis Rheumatol. 2017.
  24. 24.↵
    Scher JU, Sczesnak A, Longman RS, Segata N, Ubeda C, Bielski C, et al. Expansion of intestinal Prevotella copri correlates with enhanced susceptibility to arthritis. Elife. 2013;2:e01202.
    OpenUrlCrossRefPubMed
  25. 25.↵
    Zhang X, Zhang D, Jia H, Feng Q, Wang D, Liang D, et al. The oral and gut microbiomes are perturbed in rheumatoid arthritis and partly normalized after treatment. Nature medicine. 2015;21(8):895–905.
    OpenUrlCrossRefPubMed
  26. 26.↵
    Jubair WK, Hendrickson JD, Severs EL, Schulz HM, Adhikari S, Ir D, et al. Modulation of Inflammatory Arthritis in Mice by Gut Microbiota Through Mucosal Inflammation and Autoantibody Generation. Arthritis Rheumatol. 2018;70(8):1220–33.
    OpenUrl
  27. 27.↵
    Rogier R, Evans-Marin H, Manasson J, van der Kraan PM, Walgreen B, Helsen MM, et al. Alteration of the intestinal microbiome characterizes preclinical inflammatory arthritis in mice and its modulation attenuates established arthritis. Sci Rep. 2017;7(1):15613.
    OpenUrl
  28. 28.↵
    Spor A, Koren O, Ley R. Unravelling the effects of the environment and host genotype on the gut microbiome. Nat Rev Micro. 2011;9(4):279–90.
    OpenUrl
  29. 29.↵
    Benson AK, Kelly SA, Legge R, Ma F, Low SJ, Kim J, et al. Individuality in gut microbiota composition is a complex polygenic trait shaped by multiple environmental and host genetic factors. Proceedings of the National Academy of Sciences. 2010;107(44):18933–8.
    OpenUrlAbstract/FREE Full Text
  30. 30.↵
    Goodrich JK, Davenport ER, Beaumont M, Jackson MA, Knight R, Ober C, et al. Genetic Determinants of the Gut Microbiome in UK Twins. Cell Host Microbe. 2016;19(5):731–43.
    OpenUrlCrossRefPubMed
  31. 31.↵
    Kenna TJ, Brown MA. Immunopathogenesis of ankylosing spondylitis. Int J Clin Rheumatol. 2013;8(2):265–74.
    OpenUrl
  32. 32.↵
    Rosenbaum JT, Davey MP. Time for a gut check: Evidence for the hypothesis that HLA–B27 predisposes to ankylosing spondylitis by altering the microbiome. Arthritis & Rheumatism. 2011;63(11):3195–8.
    OpenUrlCrossRefPubMed
  33. 33.↵
    Asquith MJ, Stauffer P, Davin S, Mitchell C, Lin P, Rosenbaum JT. Perturbed Mucosal Immunity and Dysbiosis Accompany Clinical Disease in a Rat Model of Spondyloarthritis. Arthritis & rheumatology. 2016;68(9):2151–62.
    OpenUrl
  34. 34.↵
    Lin P, Bach M, Asquith M, Lee AY, Akileswaran L, Stauffer P, et al. HLA-B27 and human beta2-microglobulin affect the gut microbiota of transgenic rats. PLoS One. 2014;9(8):e105684.
    OpenUrlCrossRefPubMed
  35. 35.↵
    Caminer AC, Haberman R, Scher JU. Human microbiome, infections, and rheumatic disease. Clin Rheumatol. 2017;36(12):2645–53.
    OpenUrl
  36. 36.↵
    Goodrich Julia K, Waters Jillian L, Poole Angela C, Sutter Jessica L, Koren O, Blekhman R, et al. Human Genetics Shape the Gut Microbiome. Cell. 2014;159(4):789–99.
    OpenUrlCrossRefPubMedWeb of Science
  37. 37.↵
    Caporaso JG, Kuczynski J, Stombaugh J, Bittinger K, Bushman FD, Costello EK, et al. QIIME allows analysis of high-throughput community sequencing data. Nat Meth. 2010;7(5):335–6.
    OpenUrl
  38. 38.↵
    DeSantis TZ, Hugenholtz P, Larsen N, Rojas M, Brodie EL, Keller K, et al. Greengenes, a chimera-checked 16S rRNA gene database and workbench compatible with ARB. Appl Environ Microbiol. 2006;72(7):5069–72.
    OpenUrlAbstract/FREE Full Text
  39. 39.↵
    Edgar RC. Search and clustering orders of magnitude faster than BLAST. Bioinformatics. 2010;26(19):2460–1.
    OpenUrlCrossRefPubMedWeb of Science
  40. 40.↵
    Lê Cao K-A, Costello M-E, Lakis VA, Bartolo F, Chua X-Y, Brazeilles R, et al. MixMC: a multivariate statistical framework to gain insight into microbial communities. PloS one. 2016;11(8):e0160169.
    OpenUrlCrossRef
  41. 41.↵
    Dixon PJJoVS. VEGAN, a package of R functions for community ecology. 2003;14(6):927–30.
    OpenUrl
  42. 42.↵
    Langille MG, Zaneveld J, Caporaso JG, McDonald D, Knights D, Reyes JA, et al. Predictive functional profiling of microbial communities using 16S rRNA marker gene sequences. Nat Biotechnol. 2013;31(9):814–21.
    OpenUrlCrossRefPubMed
  43. 43.↵
    Abubucker S, Segata N, Goll J, Schubert AM, Izard J, Cantarel BL, et al. Metabolic reconstruction for metagenomic data and its application to the human microbiome. PLoS Comput Biol. 2012;8(6):e1002358.
    OpenUrlCrossRefPubMed
  44. 44.↵
    Morgan XC, Tickle TL, Sokol H, Gevers D, Devaney KL, Ward DV, et al. Dysfunction of the intestinal microbiome in inflammatory bowel disease and treatment. Genome Biol. 2012;13(9):R79.
    OpenUrlCrossRefPubMed
  45. 45.↵
    Jia X, Han B, Onengut-Gumuscu S, Chen WM, Concannon PJ, Rich SS, et al. Imputing amino acid polymorphisms in human leukocyte antigens. PloS one. 2013;8(6):e64683.
    OpenUrlCrossRefPubMed
  46. 46.↵
    Cortes A, Pulit SL, Leo PJ, Pointon JJ, Robinson PC, Weisman MH, et al. Major histocompatibility complex associations of ankylosing spondylitis are complex and involve further epistasis with ERAP1. Nat Commun. 2015;6:7146.
    OpenUrlCrossRefPubMed
  47. 47.↵
    Chen Z, Qi J, Zheng X, Wu X, Li X, Gu J. AB0147 Faecal microbiota study identifies dysbiosis in ankylosing spondylitis patients. BMJ Publishing Group Ltd; 2018.
  48. 48.↵
    Tito RY, Cypers H, Joossens M, Varkas G, Van Praet L, Glorieus E, et al. Brief Report: Dialister as a Microbial Marker of Disease Activity in Spondyloarthritis. Arthritis & rheumatology. 2017;69(1):114–21.
    OpenUrl
  49. 49.↵
    Wu X, Liu J, Xiao L, Lu A, Zhang GJO, Cartilage. Alterations of Gut Microbiome in Rheumatoid Arthritis. 2017;25:S287–S8.
    OpenUrl
  50. 50.
    Turnbaugh PJ, Hamady M, Yatsunenko T, Cantarel BL, Duncan A, Ley RE, et al. A core gut microbiome in obese and lean twins. Nature. 2009;457(7228):480–4.
    OpenUrlCrossRefPubMedWeb of Science
  51. 51.
    Turnbaugh PJ, Bäckhed F, Fulton L, Gordon JI. Diet-Induced Obesity Is Linked to Marked but Reversible Alterations in the Mouse Distal Gut Microbiome. Cell Host & Microbe. 2008;3(4):213–23.
    OpenUrlCrossRefPubMedWeb of Science
  52. 52.
    Turnbaugh PJ. An obesity-associated gut microbiome with increased capacity for energy harvest. Nature. 2006;444:1027–31.
    OpenUrlCrossRefPubMedWeb of Science
  53. 53.↵
    Markle JGM, Frank DN, Mortin-Toth S, Robertson CE, Feazel LM, Rolle-Kampczyk U, et al. Sex Differences in the Gut Microbiome Drive Hormone-Dependent Regulation of Autoimmunity. Science. 2013;339(6123):1084–8.
    OpenUrlAbstract/FREE Full Text
  54. 54.↵
    Yurkovetskiy L, Burrows M, Khan Aly A, Graham L, Volchkov P, Becker L, et al. Gender Bias in Autoimmunity Is Influenced by Microbiota. Immunity. 2013;39(2):400–12.
    OpenUrlCrossRefPubMedWeb of Science
  55. 55.↵
    Cortes A, Maksymowych WP, Wordsworth BP, Inman RD, Danoy P, Rahman P, et al. Association study of genes related to bone formation and resorption and the extent of radiographic change in ankylosing spondylitis. Ann Rheum Dis. 2015;74(7):1387–93.
    OpenUrlAbstract/FREE Full Text
  56. 56.↵
    Palm NW, de Zoete MR, Cullen TW, Barry NA, Stefanowski J, Hao L, et al. Immunoglobulin A coating identifies colitogenic bacteria in inflammatory bowel disease. Cell. 2014;158(5):1000–10.
    OpenUrlCrossRefPubMedWeb of Science
  57. 57.↵
    Nava GM, Friedrichsen HJ, Stappenbeck TS. Spatial organization of intestinal microbiota in the mouse ascending colon. ISME J. 2011;5(4):627–38.
    OpenUrlCrossRefPubMedWeb of Science
  58. 58.↵
    Atarashi K, Tanoue T, Ando M, Kamada N, Nagano Y, Narushima S, et al. Th17 Cell Induction by Adhesion of Microbes to Intestinal Epithelial Cells. Cell. 2015;163(2):367–80.
    OpenUrlCrossRefPubMed
  59. 59.↵
    Olivares M, Neef A, Castillejo G, Palma GD, Varea V, Capilla A, et al. The HLA-DQ2 genotype selects for early intestinal microbiota composition in infants at high risk of developing coeliac disease. Gut. 2015;64(3):406–17.
    OpenUrlAbstract/FREE Full Text
  60. 60.↵
    Asquith M, Davin S, Stauffer P, Michell C, Janowitz C, Lin P, et al. Intestinal Metabolites Are Profoundly Altered in the Context of HLA-B27 Expression and Functionally Modulate Disease in a Rat Model of Spondyloarthritis. Arthritis Rheumatol. 2017;69(10):1984–95.
    OpenUrl
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Posted January 11, 2019.
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HLA alleles associated with risk of ankylosing spondylitis and rheumatoid arthritis influence the gut microbiome
Mark Asquith, Peter R. Sternes, Mary-Ellen Costello, Lisa Karstens, Sarah Diamond, Tammy M. Martin, Timothy D. Spector, Kim-Anh le Cao, James T. Rosenbaum, Matthew A. Brown
bioRxiv 517813; doi: https://doi.org/10.1101/517813
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