Conclusion
The results of the study indicate significant changes in the phyla and genera in SS and NSS compared to healthy. Both discriminative analysis and network analysis indicated a possible association of predominant pro-inflammatory bacteria with SS and NSS.
Methods
Bacterial microbiome was generated from the deoxyribonucleic acid of tear film samples in healthy (n = 33), SS (n = 17), and NSS (n = 28) individuals. Sequencing of the V3-V4 region of the 16S rRNA gene was performed on the Illumina HiSeq2500 platform. Quantitative Insights Into Microbial Ecology (QIIME) pipeline was used to assign taxa to sequences. Statistical analysis was performed in R to assess the alpha diversity and beta diversity indices. Significant changes between the healthy, SS, and NSS cohorts were depicted by principal coordinate analysis (PCoA), differential abundance, and network analysis.
Purpose
To understand the bacterial microbiome changes associated with Sjogren's syndrome (SS) and non-Sjogren's syndrome (NSS) aqueous-deficient dry eyes compared to healthy eyes.
Results
Tear microbiome was generated in healthy, SS, and NSS samples. Phyla Actinobacteria, Firmicutes, and Bacteroidetes showed significant changes in SS and NSS compared to healthy. Genera Lactobacillus and Bacillus were predominantly present in all samples. PCoA and heat map analysis showed distinct clusters for SS and NSS from the healthy cohort. Genera Prevotella, Coriobacteriaceae UCG-003, Enterococcus, Streptomyces, Rhodobacter, Ezakiella, and Microbacterium significantly increased in abundance in SS and NSS compared to a healthy cohort. Bacteria-bacteria interaction in SS, NSS, and healthy cohorts was predicted by CoNet network analysis. This analysis predicted a major hub of interaction for the pro-inflammatory bacterium Prevotella in the SS and NSS cohorts.