POS0330 DIFFERENCES IN GUT MICROBIOTA ASSOCIATED WITH LYMPHOCYTE SUBSETS, CYTOKINES AND DISEASE ACTIVITY IN ANKYLOSING SPONDYLITIS

Abstract

BackgroundAnkylosing spondylitis (AS), a common chronic inflammatory disease, is a prototype of spondyloarthritis affecting sacroiliac joints and spine with or without peripheral arthritis and other systemic symptoms[1]. Environmental factors, especially microorganisms have been suggested to implicate with AS pathogenesis[2].ObjectivesUtilizing 16S rRNA genes sequencing on the feces of untreated AS patients and healthy controls (HCs), our study aimed to provide an in-depth understanding of AS gut microbiota and identifying a feasible diagnostic strategy for AS.MethodsFecal samples were collected from 62 AS patients and 62 age-and-gender- matched HCs. Microbial genome was extracted from approximately 250mg fresh fecal samples from all participants using QIAamp PowerFecal DNA Kit (Qiagen). The V3-V4 variable regions of bacterial 16S rRNA genes were sequenced with the Illumina Miseq PE300 system. QIIME2 based pipeline was used to process the raw sequence data. Alpha and beta diversities were assessed using result from QIIME2, and comparisons of gut microbiome profile were performed using linear discriminant analysis (LDA) effect size (LEfSe) to examine differences between AS and HCs. R (version 4. 0.1) was used for comparative statistics, and pearson’s correlation was used to assess the correlations between the relative abundances of bacterial genera and clinical parameters; correlations with p<0.05 were considered significant.ResultsAS for alpha-diversity, ACE and Chao1 indices were lower in AS compared with those HCs(Figure 1A, p<0.05), though no significant differences observed in Shannon and Simpson index. Bray curtis distance-based beta-diversity analysis revealed significant differences in the microbial community between AS and HCs (Figure 1B, p=0.003, ANOSIM). Fecal microbial communities in AS differed significantly from those in HCs, driven by higher abundances of Escherichia-Shigella, Turicibacter, Enterococcus, et al. and a lower abundance of Agathobacter, Roseburia, Eubacterium_eligens_group, et al (Figure 1C, p<0.05). There was a significant positive correlation between ESR and Klebsiella, Butyricicoccus, Roseburia, CRP and Faecalibacterium, Muribaculaceae, ASDAS-CRP score and Faecalibacterium, Ruminococcus, total lymphocyte cells and Agathobacter, Ruminococcus, T cell and Agathobacter, CD4+T cell and Agathobacter, B cell and Agathobacter, Streptococcus, Th1 and Prevotella, CAG−352, Th2 and Agathobacter, Th17 and Prevotella, Agathobacter, IL-2 and Agathobacter, IL-4 and Agathobacter, IL-6 and Lachnospiraceae_UCG−004, Muribaculaceae, IL-17 and Eubacterium_hallii_group, IFN-gama and Phascolarctobacterium.There were negative correlations between total lymphocytes and Escherichia−Shigella, CD4+T cell and Enterobacteriaceae, Th2 cell and Escherichia−Shigella, IL-10 and CAG−352, Ruminococcus (Figure 2, p<0.05).Figure 1.Feature of gut microbiota in AS patients and HCs. (A) Alpha-diversity assessed by richness (Chao1, ACE) and diversity (Shannon, Simpson), Median estimates compared across cohorts. (B) PCoA plot based on the Bray curtis distance of gut microbiota samples from AS patients vs. HC group(p=0.003, ANOSIM). (C) Panel demonstrated the average relative abundance of different genus in AS and HCs. (D) Distribution of gut microbiota at genus level.Figure 2.Correlations between the relative abundance of significantly different bacteria and clinical variables. *p<0.05, **p < 0.01, ***p <0 .001, ****p < 0.0001.ConclusionHuman gut microbiome in patients with AS differed from that of the HCs. Characters of bacteria communities were associated with disease activity.