ArticleIndian journal of ophthalmology2023
Tear film microbiome in Sjogren's and non-Sjogren's aqueous deficiency dry eye.
Article in Indian journal of ophthalmology, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
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Who cites it
9 citing papers in PubMed, 9 citations in OpenAlex.
- Microbiome immune crosstalk in Sjögren's syndrome: mechanistic insights and translational perspectives.Immunologic research · 2026Review
- Integrative analysis of the meibum microbiome in dry eye disease: from dysbiosis and diagnostic biomarkers to immunomodulation by Bradyrhizobium-derived outer membrane vesicles.Journal of translational medicine · 2026Article
- The Gut-Eye Axis and Microbiome in Ophthalmic Diseases: A Narrative Review.Journal of clinical medicine · 2026Review
- Gut-ocular surface axis in dry eye disease: phenotype-specific mechanisms, evidence, and microbiome-targeted interventions.Frontiers in cellular and infection microbiology · 2026Review
- Diagnostic methods for managing dry eyes.World journal of methodology · 2025Article
- Key findings from 15 years ofApplied and environmental microbiology · 2025Review
- The microbiome and the eye: a new era in ophthalmology.Eye (London, England) · 2025Review
- Lid Margin Microbiome in Stevens-Johnson Syndrome Patients With Lid Margin Keratinization and Severe Dry Eye Disease.Investigative ophthalmology & visual science · 2024Article
- Insights into Microbiota in Sjögren's Syndrome.Medicina (Kaunas, Lithuania) · 2023Review
Corrections and comments
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Authors and funding
5 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
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. 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. 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. 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.
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Registered trials
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