Role of Microbiome in Ocular Surface Disease: Interpreting Biology in a Low-Biomass Environment

Abstract

Purpose of review:

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.

Recent findings:

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.

Summary:

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 multi-omics approaches and detailed clinical phenotyping. Until such evidence emerges, microbiome research is best viewed as advancing biological insight rather than informing clinical decision-making.

Introduction

Interest in the ocular surface microbiome has expanded rapidly with advances in sequencing technologies and analytical methods. These developments have enabled characterization of microbial communities inhabiting the ocular surface and have raised the possibility that microbial profiling could help explain variability in ocular surface health and disease.¹ Emerging studies have linked microbial composition to immune signaling, inflammatory phenotypes, and treatment response, generating interest in whether microbiome-derived insights could eventually inform diagnosis, risk stratification, or therapeutic strategies in ocular surface disorders.²

However, the ocular surface presents one of the most challenging environments in which to study host-microorganism interactions. Compared with other mucosal sites, microbial biomass is extremely low, while host DNA from epithelial cells predominates in collected samples.³ The ocular surface is also continuously influenced by blinking, tear flow, environmental exposure, and topical medications. These biological features amplify the impact of technical factors including sample collection technique, DNA extraction protocols, sequencing strategy, and bioinformatic processing.⁴ As a result, methodological variability can substantially influence which microbial signals are detected and how they are interpreted.

In low-biomass systems, detection or relative abundance of microbial DNA does not necessarily indicate biological relevance.⁵ Associations between microbial taxa and disease states may reflect transient exposure, contamination, or analytical bias rather than functional host-microbe interactions.⁶ This review examines recent advances in ocular surface microbiome research with a focus on biological plausibility, methodological constraints, and clinical interpretability. To interpret these findings, microbiome data are discussed within a proposed conceptual framework that distinguishes potential microbial roles as disease drivers, modifiers, or markers, aiming to outline what current evidence supports and what is required before responsible clinical translation.

Biological Rationale: Why the Microbiome Could Influence Ocular Surface Disease

The ocular surface functions as an integrated neuro-immune-epithelial barrier comprising the corneal and conjunctival epithelium, tissue-resident immune cells, sensory nerve fibers, and a tear film enriched with antimicrobial molecules.^7–10 Together these components coordinate barrier integrity, immune surveillance, and tissue repair.^11–13

Innate immune signaling at the ocular surface is mediated by epithelial and immune cell-expressed pattern recognition receptors such as Toll-like and NOD-like receptors, which detect microbial and damage-associated molecular patterns.^12,14 Activation of these pathways induces cytokines, chemokines, and antimicrobial peptides that regulate microbial exposure while maintaining epithelial homeostasis.¹⁵ Additional protective mechanisms include membrane-associated mucins that strengthen the glycocalyx barrier, and antimicrobial tear components such as secretory IgA, lysozyme, and lactoferrin.¹⁵

Emerging evidence suggests that resident microbial communities contribute to maintaining this immune balance. In experimental models, commensal organisms influence local immune responses; for example, Corynebacterium mastitidis stimulates conjunctival γδ T cells to produce IL-17, enhancing epithelial barrier defense against pathogens such as Candida albicans.^16,17 Subsequent work demonstrated that this γδ T cell IL-17A response requires TLR2-dependent metabolic reprogramming, indicating that commensal recognition involves coordinated metabolic and immune signaling.¹⁷ These findings indicate that even sparse microbial communities may shape immune tone at the ocular surface.

Disruption of this neuro-immune-epithelial balance is increasingly implicated in ocular surface diseases, including dry eye, contact lens-associated keratitis, and infectious keratitis susceptibility.^18–22 These observations provide a biological rationale for investigating whether alterations in microbial communities influence disease development and severity.

Local Versus Distant Microbiome Influence

The influence of microbial communities on ocular surface disease extends beyond organisms physically present on the eye. Microbial ecosystems at adjacent and distant anatomical sites can shape immune regulation across mucosal tissues, including the ocular surface, anterior chamber, and retina.^23–27

The gut microbiome plays a particularly important role in systemic immune modulation through microbial metabolites and immune signaling pathways that influence cytokine profiles, T-cell differentiation, and inflammatory thresholds.^28–29 Experimental studies demonstrate that germ-free mice exhibit reduced ocular surface regulatory T cells, impaired tear film stability, and increased susceptibility to ocular inflammation,³⁰ effects partially reversible with recolonization, supporting the concept of a gut-eye immune axis.^31–33

In parallel, microbial communities in periocular skin and the eyelid margin may form a continuous epithelial environment with the ocular surface, allowing microbial communities to influence lid margin inflammation, meibomian gland function, and tear film stability. For example, Demodex-associated blepharitis has been linked to shifts in lid margin and ocular surface microbiome, including increased Staphylococcus species that correlate with mite burden and symptom severity.^34–36 Additionally, Bacillus oleronius, a bacterium isolated from within Demodex mites, may act as a co-pathogen in severe blepharitis. ³⁷

Together, these observations suggest that microbiome-disease associations may arise through both systemic and local host-microorganism interactions at the ocular surface.

The Low-Biomass Constraint: Why Defining “Role” Is Uniquely Difficult

Interpreting ocular surface microbiome data requires careful distinction between true biological signal and background contamination. Unlike high-biomass environments such as the gut, skin, or oral cavity, the ocular surface harbors sparse microbial communities.^38,39 While the colon contains trillions of organisms, conjunctival samples typically contain only very small numbers of detectable bacterial genomes and are substantially outnumbered by host epithelial DNA.^3,6,40,41

Methodological choices at each stage of analysis influence detected microbial signals. DNA extraction protocols vary in their ability to lyse different organisms, favoring recovery of certain taxa while underrepresenting others.^5,42–48 Sequencing strategy further shapes results: culture-based methods detect only cultivable organisms, 16S rRNA profiling broadens bacterial detection but with limited taxonomic resolution, and shotgun metagenomics enables multi-kingdom and functional analysis but is particularly challenged by host DNA predominance in low-biomass samples. ^49–54 These technical variables interact with bioinformatic decisions regarding quality filtering, reference databases and normalization can substantially alter apparent community composition.^55–57

In this context, rigorous contamination control requires negative controls throughout sampling and sequencing, transparent reporting of collection procedures, and careful interpretation of low-abundance taxa.⁴³ However, methodological reporting in ocular surface microbiome studies remains inconsistent, with many studies lacking detailed descriptions of contamination mitigation, storage conditions, or sampling protocols, limiting reproducibility, complicating comparisons across studies, and making it difficult to distinguish biological signals from technical artifacts.^58,59 Table 1 summarizes key pre-analytical variables that may influence microbial detection and interpretation in ocular surface microbiome studies.

Table 1.

Key variables that may influence microbial detection and interpretation in ocular surface microbiome studies.

Stages	Category	Key Variables	Potential Impact
Pre-analytical	Environmental Factors	Sampling environment, operator consistency, sterile collection field, glove use	Environmental contamination can introduce exogenous microbial DNA, particularly in low-biomass samples
	Sampling Materials	Swab type (flock vs. cotton), collection buffer, use of topical anesthetic, wetting solution	Sampling materials may influence microbial recovery and introduce reagent-derived contaminants
	Subject Variables	Contact lens wear, topical medications, cosmetics, lid hygiene products	These exposures may alter ocular surface microbial composition and affect study comparability
	Sampling Protocol	Collection site (conjunctiva, lid margin, periocular skin), pressure and contact time	Different ocular niches may harbor distinct microbial communities; sampling technique influences microbial yield
	Transport and Storage	Time from collection to freezing, storage temperature, freeze-thaw cycles	Delayed processing or temperature fluctuations can degrade microbial DNA or introduce contamination
Analytical	DNA Extraction	Extraction kit, lysis chemistry, mechanical disruption (beads)	Extraction protocols may preferentially recover certain taxa and influence microbial profiles
	Contamination Controls	Negative extraction controls, no-template PCR controls, sequencing blanks, positive controls	Absence of controls prevents identification of reagent-derived or environmental contaminants, particularly critical in low-biomass samples
	Library Preparation	PCR amplification cycles, primer selection (e.g., 16S variable regions), indexing/barcoding strategy, index hopping, amplification bias, library input DNA quantity	Amplification bias and stochastic effects in low-input samples, while index hopping can result in misassignment of reads and distortion of microbial profiles.
	Sequencing Strategy	Culture-based methods, 16S rRNA sequencing, shotgun metagenomics	Different approaches vary in taxonomic resolution and detectable organisms
Post-analytical	Bioinformatic Processing	Quality filtering thresholds, host DNA removal, reference database selection, normalization methods	Analytical pipelines can significantly alter detected microbial composition

Beyond these methodological constraints, detection does not establish biological relevance. Microbial DNA does not distinguish between viable organisms from nonviable remnants, nor stable residents from transient exposures rapidly cleared by the tear film.⁶⁰ Even when microorganisms are present, current approaches rarely capture metabolic activity or host-microorganism interactions, leaving it unclear whether detected organisms contribute to disease, respond to environmental changes, or represent incidental bystanders. Addressing these limitations will likely require integrative approaches that combine microbiome profiling with host-focused data, including tear proteomics, metabolomics, and detailed clinical phenotyping, to better define functional host-microorganism interactions rather than relying on taxonomic presence alone.^61,62

Defining a Core Ocular Surface Microbiome

Advances in sequencing technologies have prompted efforts to define a “core” ocular surface microbiome. Many investigations rely on prevalence-based criteria, identifying taxa that are consistently detected across individuals as potential core members.^38,63–65 Genera such as Staphylococcus, Corynebacterium, and Cutibacterium are frequently reported in ocular surface samples, although their relative abundance varies widely among individuals and sampling sites.⁶⁶

However, the identification of a core microbiome in this setting remains challenging. Detection of microbial DNA does not necessarily indicate stable colonization, as taxa identified across individuals may represent transient environmental exposure, stochastic detection in low-biomass samples, or background contamination rather than persistent microbial residents.⁶

Recent whole-metagenome shotgun sequencing has refined core microbiome definitions to species-level resolution, identifying Cutibacterium acnes, Malassezia restricta, and Staphylococcus epidermidis as consistently present across individuals, with Corynebacterium segmentosum additionally detected on the lid margin; notably, microbial profiles were more similar within individuals over time than between individuals, suggesting person-specific signatures.⁶⁷ A complementary multi-cohort analysis identified eight bacterial genera showing shared, non-disease-specific alterations across dry eye, keratitis, and trachoma, reinforcing the concern that many reported shifts may reflect generalized responses to ocular surface compromise rather than disease-specific signatures – a theme explored further in the disease-specific evidence below.³⁵ Prevalence alone may not reliably distinguish true core members from repeatedly detected but biologically irrelevant signals.

In addition, microbial composition varies across ocular surface sites, including the lid margin, conjunctiva, and periocular skin, complicating attempts to define a universal microbial baseline.⁶⁴ Longitudinal studies incorporating complementary methods, such as culture, RNA-based analyses, or imaging techniques are needed to assess organism viability and determine whether detected taxa represent stable colonization or transient signals. ⁶⁸

Evidence from Ocular Surface Diseases: What Roles Are Supported by Current Data?

Ocular surface microbiome studies across multiple diseases have reported alterations in microbial composition; however, findings remain inconsistent and often nonspecific to individual diseases. Reported patterns include reduced α-diversity, shifts in Gram-positive and Gram-negative taxa, and enrichment of opportunistic organisms in more severe disease states.^69–71 These observations vary substantially across studies, likely reflecting differences in methodology, patient populations, and sampling approaches.

As an example, meibomian gland dysfunction (MGD) studies using 16S sequencing reported increased abundance of Staphylococcus that correlated with meiboscore (gland dropout), while findings for Corynebacterium have been inconsistent across studies.⁷² In contrast, culture-based studies identified similar bacterial profiles (Staphylococcus epidermidis, Cutibacterium acnes) across disease severities, highlighting the influence of methodological variability.⁷³ Beyond compositional associations, a 2026 study demonstrated that lipoteichoic acid (LTA), a Gram-positive bacterial cell wall component, promotes dry eye by inducing corneal inflammation through TLR2 and platelet-activating factor receptor signaling via p38 MAPK and NF-κB pathways.⁷⁴ This represents one of the clearest mechanistic links to date between a specific microbial product and dry eye pathogenesis, providing the strongest evidence to date for a potential driver or modifier role within the framework proposed in this review. Comparable findings have been reported in other ocular surface diseases, including Stevens-Johnson syndrome, ocular graft-versus-host disease, and Sjögren’s disease, where microbial alterations often overlap with patterns observed in non-autoimmune dry eye.^{19,69,75–80} Taken together, these findings suggest that shared ocular surface stress may drive convergent microbial patterns across clinically distinct diseases.

These overlapping microbial profiles suggest that observed microbiome changes may reflect downstream effects of epithelial disruption, tear film instability, or inflammatory stress rather than disease-specific microbial signatures.⁸⁰ Interpreting these findings may benefit from a conceptual framework in which microbial changes are categorized into three potential roles: drivers, modifiers, and markers.

A driver role may imply that microbial taxa precede disease onset and contribute directly to pathogenesis. A modifier role may be supported when microbial shifts correlate with disease severity or inflammatory activity but do not precede disease development. A marker role may describe microbial changes that accompany epithelial barrier disruption or surface stress without contributing directly to disease mechanisms.

Representative findings across ocular surface diseases are summarized in Table 2. Overall, current evidence more consistently supports microbiome changes as modifiers or markers rather than primary drivers.^4,64

Table 2.

Reported Microbiome Findings Across Ocular Surface Diseases and Proposed Biological Roles

Disease	Reported Microbiome Findings	Detection Method*	Possible Microbiome Role**
Dry Eye Disease 69,74,96–99	Reduced microbial diversity; increased relative abundance of Staphylococcus, Corynebacterium, Cutibacterium acnes, Streptococcus, Bacillus, and occasional Gram-negative taxa such as Klebsiella. Findings vary across cohorts and methods. Lipoteichoic acid (LTA) from Gram-positive bacteria shown to promote corneal inflammation through TLR2/PAFr signaling.	Culture based methods,16S rRNA sequencing, whole genome sequencing	Modifier, Marker, and mechanistic evidence support potential driver role
Meibomian Gland Dysfunction 35,36,72,73,98	Increased Corynebacterium in moderate disease; increased Staphylococcus in severe disease with meibomian gland dropout; mixed findings across studies	16S rDNA sequencing, culture-based studies	Modifier and Marker
Ocular Rosacea 81–83	Increased Demodex folliculorum and associated bacteria such as Bacillus oleronius; samples from eyelid margin/eyelashes	Direct microscopy, culture, sequencing	Modifier
Sjogren’s Disease 80,90	Altered microbial composition with reduced diversity; overlap with non-autoimmune dry eye; reduced α-diversity correlating with disease severity; ocular microbiome may be similar to controls in some studies	16S rRNA sequencing, shotgun whole genome metagenomics	Marker
Stevens-Johnson Syndrome/Toxic Epidermal Necrolysis 69,75,80	Increased abundance of Pseudomonas, Acinetobacter, Streptococcus; decreased Staphylococcus; higher proportion of pathogenic organisms; MRSA reported in microbial keratitis cases	Culture; sequencing studies	Modifier and Marker
Ocular Graft-versus-Host Disease 19,69,76	Reduced microbial diversity; enrichment of Staphylococcus sp., Streptococcus, Corynebacterium sp., and other gram positive organisms; dysbiosis has been associated with chronic ocular surface inflammation	Culture, 16S rDNA sequencing	Modifier and Marker
Contact Lens-Associated Disease 19,99	Increased Gram-negative organisms including Pseudomonas species; altered microbial ecology with lens wear	Culture, 16S rRNA sequencing	Modifier

^*Detection methods vary across studies and influence reported microbial composition.

^**Proposed microbiome roles are based on current evidence and may evolve as additional longitudinal and mechanistic studies emerge. In this framework, a driver implies microbial taxa that precede disease onset and contribute to pathogenesis; a modifier refers to microbial changes associated with disease severity or inflammatory activity; and a marker reflects microbial alterations secondary to epithelial disruption or surface stress.

Ocular Rosacea: A Case Study in Theoretical Role Versus Practical Limitation

Ocular rosacea, characterized by chronic lid margin inflammation, MGD, and tear film instability, provides a useful case study given its proposed pathogenesis involving immune dysregulation, neurovascular mechanisms, environmental triggers, and microbial factors.⁸¹

Microbial involvement has long been proposed in rosacea. Increased prevalence of Demodex folliculorum at the eyelid margin has been consistently reported in affected patients, and the mite has been associated with bacterial species such as Bacillus oleronius, which may stimulate inflammatory responses at the lid margin.^82,83 These observations provide a biologically plausible mechanism through which microbial communities could influence ocular surface inflammation and MGD.

Despite this plausibility, direct evidence remains limited, as most studies have relied on culture-based approaches or small cross-sectional cohorts rather than sequencing or longitudinal designs.^64,79 It therefore remains unclear whether observed microbial changes precede disease or occur secondarily.

Within the driver-modifier-marker framework, current evidence mostly supports a modifier role for the microbiome in ocular rosacea, with microbial shifts potentially influencing disease severity rather than acting as primary disease drivers. Notably, the type of mechanistic investigation recently applied to dry eye, such as identifying specific microbial products that activate defined inflammatory pathways, has not been undertaken in ocular rosacea, representing an important gap.

Functional and Multi-Omics Approaches to Clarifying Microbiome Role

Taxonomic profiling has provided initial insights into ocular surface microbial composition, but identifying microbial DNA alone does not establish biological function or pathogenic relevance. In low-biomass environments such as the ocular surface, microbial sequences may represent transient exposure or nonviable genetic material rather than metabolically active organisms. As a result, taxonomic studies alone are often insufficient to distinguish whether microbial changes act as drivers, modifiers, or markers of disease.

Functional approaches are therefore necessary to clarify host-microbe interactions. Multi-omics strategies, including metagenomics, transcriptomics, proteomics, and metabolomics, can integrate microbial profiles with host immune signaling, epithelial responses, and tear film biochemistry.⁸⁴ These approaches may help identify pathways through which microbes influence inflammation, barrier integrity, and tissue repair.

However, comparable multi-omics investigations remain limited in ocular surface disease. Studies combining microbial and host molecular analyses will be essential to move beyond associative observations and define mechanistic roles for the microbiome.

Clinical Implications: What Can Be Translated Today and What Cannot?

Interest in the ocular surface microbiome has raised the possibility that microbial profiling could inform diagnosis or treatment selection in ocular surface disease.^85,86 However, current evidence does not support routine clinical application. Most studies are cross-sectional, involve small cohorts, and use heterogeneous methodologies, limiting comparability across datasets.^4,58,87–90

Nonetheless, institutional recognition is evolving: the 2024 AAO Dry Eye Preferred Practice Pattern incorporated the concept of the “ocular surface microenvironment,” acknowledging that microbial disruption may contribute to loss of homeostasis.⁹¹ However, the guidelines do not recommend microbiome-based diagnostics or therapeutics, consistent with the position that biological plausibility does not yet support clinical application.

Premature clinical translation of microbiome findings into clinical decision-making carries important risks. Apparent microbial associations may reflect contamination, methodological artifacts, or secondary changes related to epithelial disruption rather than causal disease mechanisms.^5,6 Interpreting such findings as pathogenic drivers could lead to unnecessary antimicrobial treatment or misdirected therapeutic strategies.

At present, microbiome research should be viewed primarily as a tool for biological discovery rather than a guide for clinical decision-making. When patients inquire about microbiome-targeted therapies such as oral probiotics for dry eye, clinicians can acknowledge the biological rationale while noting that no controlled clinical evidence currently supports their use for ocular surface disease.

Future Directions: Defining the Microbiome’s Role Responsibly

Clarifying the role of the microbiome in ocular surface disease will require improvements in both study design and methodology. Standardization of sampling protocols, contamination controls, sequencing strategies, and bioinformatic pipelines is essential to improve reproducibility and enable meaningful comparisons across studies.^4,6

Future investigations should prioritize longitudinal designs that evaluate microbial dynamics over time and assess how microbial profiles change with disease progression or treatment.^58,92 Such approaches, combined with detailed phenotyping and integration of host-derived data, may help distinguish whether microbial changes act as drivers, modifiers, or markers of disease. Interventional studies examining whether modulation of microbial communities alters disease outcomes will be particularly important for distinguishing causal mechanisms from secondary associations.^93,94 Novel delivery strategies, such as bioprinting of structured bacterial communities, represent emerging concepts that may eventually enable controlled testing of microbiome modulation as a therapeutic intervention.⁹⁵

Disease-focused approaches may provide clearer insights than broad comparisons across heterogeneous ocular surface conditions. Conditions such as ocular rosacea, where microbial involvement is biologically plausible and closely linked to lid margin inflammation, may represent particularly informative models for investigating host-microbe interactions at the ocular surface.

Conclusion

The microbiome may contribute to ocular surface homeostasis and disease through interactions with epithelial, immune, and neural pathways. While current evidence has primarily supported associative relationships, recent mechanistic studies identifying specific microbial products that drive ocular surface inflammation represent an important shift toward establishing functional relevance. In low-biomass environments such as the ocular surface, methodological variability can strongly influence detected microbial signals and complicate interpretation. Defining the microbiome’s role will therefore require rigorous experimental design, standardized methodologies, and mechanistic studies that move beyond taxonomic profiling. As the field evolves, methodological discipline must precede clinical translation to ensure that microbiome discoveries ultimately lead to meaningful advances in patient care.

Key points:

1. The ocular surface microbiome is characterized by a low-biomass environment, making it highly susceptible to contamination and methodological variability.

2. Emerging mechanistic evidence has begun to clarify potential driver and modifier roles of ocular surface microbiome, although most data remain associative.

3. Microbiome alterations reported in disease states may represent true biological shifts or reflect technical and sampling variability.

4. Integration with host immune responses, cellular activity, and clinical findings is essential to interpret microbiome-disease associations.

5. Future studies should adopt standardized, multi-omics, and longitudinal designs to better define microbial roles.