Dysbiosis of Gut Microbiota in Ankylosing Spondylitis Patients

Abstract

Purpose: Ankylosing spondylitis (AS) is a chronic inflammatory disease associated with genetic, immune, and microbial factors. The role of gut microbiota in the pathogenesis of AS is increasingly recognized, with studies suggesting that intestinal dysfunction may trigger systemic inflammation. This study aimed to investigate the gut microbiota profiles of AS patients from Southern China and explore the relationship between gut microbiota and the occurrence and development of AS.

Patients and methods: We enrolled 30 AS patients and 25 healthy controls from the Fifth Affiliated Hospital of Sun Yat-sen University. Fecal samples were collected, and DNA was extracted for 16S rDNA sequencing to analyze the V3-V4 variable regions. Bioinformatic processing and statistical analysis were performed to assess the microbial community structure, diversity, and function.

Results: The study revealed significant differences in gut microbiota composition between AS patients and healthy controls. AS patients exhibited a decrease in beneficial bacteria such as Firmicutes and Actinobacteria and an increase in harmful bacteria like Proteobacteria and Enterobacteriaceae. The functional prediction of gut microbiota indicated significant metabolic pathway alterations, particularly in energy metabolism, degradation metabolism, and nucleotide metabolism, which may be linked to the pathophysiology of AS.

Conclusion: This study demonstrates that the gut microbiota of Han Chinese AS patients in Guangdong Province is characterized by a decrease in beneficial bacterial communities and an increase in harmful ones, potentially contributing to AS progression through intestinal barrier disruption and intensified inflammatory responses. These findings provide a theoretical basis for developing new intervention strategies targeting the gut microbiota.

Keywords: 16S rDNA sequencing; abundance; inflammation; metabolic pathways.

Graphical abstract

Figure 1

Microbial community OUT analysis. (a) Venn diagram of shared and unique OTUs among different groups. (b) Rank-abundance curve diagram of AS and Healthy groups, the y-axis indicates the abundance of OTUs, while the x-axis represents the sequence number of corresponding abundance of OTUs.

Figure 2

α-diversity of the gut microbial community between AS and Healthy groups. (a) Comparison of the richness of microbiota in fecal samples between AS and Healthy groups based on Chao1 index. (b) Comparison of the richness between two groups based on the Observed OTUs index. (c) Comparison of the OTU coverage between two groups based on the Goods coverage index. (d) Diversity of the microbiota between two groups based on the Shannon index. (e) Diversity of the microbiota between two groups based on the Simpson index. Statistical analysis was performed with Wilcoxon signed rank test, and the p values are shown, * p < 0.05, ** p < 0.01.

Figure 3

β-diversity of the gut microbial community between AS and Healthy groups. (a) Comparison of the structural differences in AS and Healthy groups based on PCA. (b) Unweighted Principal Coordinate Analysis (PCoA) plot. (c) Unweighted Pair Group Method with Arithmetic Mean (UPGMA). (d) Unweighted Non-metric Multidimensional Scaling (NMDS) analysis.

Figure 4

Taxonomic assignments of the gut microbial community of participants. Presented at the (a) Phylum, (b) Class, (c) Order, (d) Family, and (e) Genus levels, respectively.

Figure 5

Linear discriminant analysis effect size (LEfSe) analysis of gut microbiota. (a) LEfSe analysis of evolutionary clades of the gut microbiota in the AS and Healthy groups. (b) LEfSe identified the taxa with the greatest differences in abundance between the AS and Healthy groups.

Figure 6

KEGG pathway analysis of the AS and Healthy groups. Mean proportions, difference in mean proportions of the AS and Healthy groups, and p values are shown.