Background: Systemic sclerosis (SSc) is a rare autoimmune-mediated chronic inflammatory disease characterized by vascular abnormalities and progressive fibrosis in affected organs such as skin, lung and gastrointestinal tract (GIT). While the exact causes of SSc remains unclear, recent several studies have shown that the GIT dysbiosis may be a pathological feature of SSc. Tax4Fun is a software package that predicts the functional capabilities of microbial communities based on16S rRNA datasets.

Objectives: This study was conducted to compare faecal microbial composition in SSc patients with controls and to get insight regarding the SSc pathophysiology through Tax4Fun software.

Methods: Forty-one patients with SSc and 28 healthy controls (HCs) participated in this study. All participants provided stool specimens for 16S rRNA sequencing, and clinical features such as autoantibody, and presence of interstitial lung disease (ILD) were collected simultaneously. Taxonomic differences at specific levels were examined using linear discriminant analysis effect size (LEfSe).We used Tax4Fun analysis through Kyoto Encyclopedia of Genes and Genomes to predict possible pathways of fecal microbiome involved in the SSc pathophysiology.

Results: The majority of SSc participants were female (85 %) with a median age of 58.0 (interquartile range: 45.5 to65.0). The mean age of onset for SSc was 48.1 (± 15.0), and the mean duration of SSc disease was 4.73 (± 6.4) years. Principal component analysis (PCA) found the different microbial taxa between feces of SSc individuals and HCs. Based on PCA results, there was no significant difference in the distribution of GIT microbiome with respect to ILD status, type of autoantibodies, disease duration, or SSc type (limited vs. diffuse). Microbiome analysis showed that the Firmicutes to Bacteroidetes ratio was significantly higher in the fecal samples of SSc patients compared to those of HCs. Additionally, the abundance of Christensenellaceae, Ruminococcaceae, and Faecalibacterium was significantly lower in the fecal samples of SSc patients compared to HCs, while Enterobacteriaceae family and Proteobacteria were significantly higher in SScpatients. Among lactate-producing bacteria, the Lactobacillus genus was found to be significantly higher in SSc patients, whereas the proportion of Bifidobacterium did not dffer between SSc and HCs. Predictive analysis of functional capacity suggested the possible involvement of several pathway such as lysosome, sugar and purine metabolism in SSc pathophysiology.

Conclusion: The present analysis revealed specific alterations in the gut microbiota of SSc patients compared to controls. The functional pathway inferred from the gut microbiome data can be further elucidated through additional research.

REFERENCES: [1] De Luca F, et al. The microbiome in autoimmune diseases. Clin Exp Immunol 2019 Jan;195(1):74-85.

[2] Russo E, et al. The differential crosstalk of the skin-gut microbiome axis as a new emerging actor in systemic sclerosis Rheumatology (Oxford) 2024 Jan 4;63(1):226-234.

[3] Tan TC, et al. Gut microbiome profiling in systemic sclerosis: a metagenomic approach. Clin Exp Rheumatol 2023 Aug;41(8):1578-1588.

Acknowledgements: NIL.

Disclosure of Interests: None declared.