Role of microbiome in ocular surface disease: interpreting biology in a low-biomass environment

Growing use of sequencing technologies has accelerated investigation of the ocular surface microbiome, yet this environment is characterized by extremely low microbial biomass, complicating data interpretation. This review assesses current evidence linking microbial communities to ocular surface disease, discusses methodological and biological factors influencing interpretation of microbiome-disease associations, and proposes a framework in which microbial roles may be considered as drivers, modifiers, or markers.

Studies across multiple ocular surface diseases report alterations in microbial composition, including reduced α-diversity and shifts in dominant taxa. Genera such as Staphylococcus , Corynebacterium , and Cutibacterium are frequently reported as resident members of the ocular surface microbiome, although their abundance varies across individuals and sampling sites. Across diseases, microbial patterns often overlap and remain inconsistent between studies. Emerging mechanistic evidence has identified specific microbial products, such as lipoteichoic acid, that promote ocular surface inflammation through defined signaling pathways, providing initial support for a potential driver or modifier role. In low-biomass environments such as the ocular surface, contamination, host DNA predominance, and methodological variability can strongly influence detected microbial signals.

Interpretation of ocular surface microbiome data remains inherently challenging in this low-biomass context. However, the emergence of mechanistic studies suggests a transition from purely associative observations toward functional and translational investigation. Future studies should be designed to better define microbial roles by integrating standardized methodologies with multiomics approaches and detailed clinical phenotyping. Until such evidence emerges, microbiome research is best viewed as advancing biological insight rather than informing clinical decision-making.