Identification of Differentially Abundant Microorganisms Associated with Sjögren's Syndrome: A 16S rRNA Sequencing Data Mining Approach.

Manifestations of Sjögren's syndrome (SS) considerably affect the quality of life. Owing to the multifactorial nature of the syndrome, some individuals present alterations in oral microbiota profiles. This study analysed the oral cavity microbiota profiles of 242 samples -168 from pSS group and 74 from control group- using metabarcoding technique based on sequencing of 16S rRNA gene. Data processing, amplicon sequence variants, and taxonomic assignment were performed using DADA2. Statistical and microbial diversity analyses were performed using phyloseq in R. Alpha and beta diversity metrics were evaluated, as well as the identification of enriched taxa using Linear discriminant analysis Effect Size (LEfSe) analysis. Results revealed no significant differences in the composition and structure of the microbiota between groups. However, differential abundance analysis allowed the identification of 27 microbial taxa, including species Actinomyces dentocariosa; genera Streptococcus, Leptotrichia, Veillonella, Fusobacterium, and Alloprevotella; and phyla Actinobacteriota and Fusobacteriota, in pSS group. Although no direct associations have yet been established between oral microbiota and SS, some of the identified genera have been documented to possess pathogenic factors that induce immune responses. Accordingly, this study lays the groundwork for future analyses of oral microbiota profiles in SS to identify potential microbial biomarkers of disease.