Background: Ankylosing spondylitis (AS) is an autoimmune disorder linked to the human leukocyte antigen B27 (HLA-B27) gene. While the abnormal composition of the gut microbiome and its related disturbed metabolites may co-contribute to AS, the relationship between HLA-B27, the gut microbiota, and their metabolites in AS patients is still largely uncharted.

Objectives: We sought to further investigate the relationship between HLA-B27, the gut microbiota, and their metabolites in AS patients.

Methods: We conducted an integrated analysis of the gut microbiome and metabolome, examining 88 fecal samples across three groups: HLA-B27-positive AS patients (n = 28), HLA-B27-positive healthy controls (n = 30), and HLA-B27-negative healthy controls (n = 30) (Table 1).

Results: Our analysis uncovered disturbances in the gut microbiome and fecal metabolites in HLA-B27-positive participants, particularly in those diagnosed with AS. We identified 40 species displaying gradual changes in relative abundance across the three groups. Metabolome profiling, when coupled with microbial functional analysis, revealed three compromised pathways of amino acid metabolism within AS, including tryptophan metabolism, cysteine metabolism, and biosynthesis of branched-chain amino acids, ornithine, and lysine. By employing correlation networks between differential bacterial species, fecal metabolites, and clinical parameters in AS, we identified several novel AS-associated species and metabolites, including Negativibacillus massiliensis, Bacteroidetes bacterium 41-46, Lachnospiraceae bacterium oral taxon 082, N -acetylornithine, and tridecanedioic acid (Figure 1). These AS-associated features showed a high degree of accuracy in differentiating AS from HLA-B27-positive and negative healthy controls.

Conclusion: Our results imply that HLA-B27 may contribute to the onset of AS through its impact on the abnormal gut microbiome, and the disturbance of microbiota-driven metabolites in the presence of HLA-B27 may influence the development of AS in this population. Ultimately, this study opens promising paths for creating innovative therapeutic strategies to address AS.

: Table 1. Demographic and clinical characteristics of the participants

Figure 1.

Correlations of the disease-related microbial species and metabolites with clinical parameters. ( A ): The heatmap depicted associations between the species and metabolites altered in AS group (Spearman rank correlation test). *P<0.05, **P<0.01. ( B ): Examples of individual species–metabolite correlations. For visualization, abundances of species and metabolites are plotted after log10-transformation, and 0 values were assigned 1e-08. Each dot represents one sample. ( C ) The integrative network of correlations revealed significant associations ((p<0.05 and |r|>0.35, Spearman rank correlation test) between differentially abundant taxa or metabolites and clinical indexes in AS group. Nodes represent features increased (in red) or decreased (in blue) in AS compared with B27(+) and B27(-), size of the nodes represents the abundance of these features. Edges connecting nodes indicate positive (in red) or negative (in blue) correlations, edge thickness indicates the size of Spearman’s rank correlation coefficient.

REFERENCES: NIL.

Acknowledgements: NIL.

Disclosure of Interests: None declared.