Characterization of the microbial profile in tears of patients with primary open-angle glaucoma: results from a pilot study

Abstract

This pilot study characterized the tear microbiota profile in patients with primary open-angle glaucoma (POAG) and compared it with healthy controls. Tear samples from 22 participants (10 POAG patients and 12 matched controls) were analyzed using 16 S rRNA gene sequencing. While no significant differences were found in alpha diversity, beta diversity analyses revealed distinct microbial community structures between groups, with POAG patients exhibiting a more homogeneous and less diverse microbiota. Three phyla—Fusobacteriota, Planctomycetota, and Synergistota—were significantly more abundant in the glaucoma group (p < 0.0001), and 23 bacterial genera showed differential abundance. Additionally, preserved eye drops appeared to modulate microbial composition, with specific alterations observed in several genera. These findings support the hypothesis of ocular dysbiosis associated with POAG, potentially linked to inflammatory and immunological mechanisms. The study suggests that microbiota-based interventions, such as probiotics or postbiotics, may offer complementary therapeutic strategies, and highlights the need for longitudinal studies with larger sample sizes to validate these preliminary results.

Supplementary Information

The online version contains supplementary material available at 10.1038/s41598-025-22536-8.

Introduction

Glaucoma is a leading cause of irreversible blindness worldwide, affecting approximately 76 million people in 2020, and this figure is expected to increase to 111.8 million by 2040¹. Within the different forms of glaucoma, primary open-angle glaucoma (POAG) is the most common type and represents a significant public health challenge due to its asymptomatic progression and late detection². Although elevated intraocular pressure (IOP) is a well-established risk factor, the pathogenesis of POAG is multifactorial and not fully understood³.

A possible risk factor for the onset and/or progression of glaucoma is intestinal dysbiosis⁴ since, through the gut-eye axis, biological signals released by intestinal microbes could trigger an immune response at the ocular level⁵. This axis can be described as a network of biological interactions that link the intestinal microbiota with ocular health. This axis is based on immunomodulatory and metabolic mechanisms, where short-chain fatty acids (SCFAs), produced by intestinal bacteria (e.g. Firmicutes and Bacteroidetes), play a key role in the regulation of systemic inflammation and the integrity of the intestinal barrier⁶. Some conditions, such as intestinal hyperpermeability, compromise this barrier, allowing endotoxins such as lipopolysaccharides (LPS) to enter the bloodstream, reach distant tissues such as the eye, and trigger chronic inflammatory responses⁷. Furthermore, intestinal dysbiosis can alter the proportion of T lymphocytes, affecting the immune balance and promoting autoimmune or neurodegenerative processes that could be involved in the progression of glaucoma⁸.

In recent years, there has been a growing interest in the study of the ocular microbiome and its potential involvement in various ophthalmological diseases, including glaucoma⁹. It has been suggested that interactions between the ocular surface microbiota and proteins present in tears may be related to the etiopathogenic mechanisms of glaucoma¹⁰. The ocular microbiome refers to the community of microorganisms that inhabit the eye and its adnexa and has been shown to play a crucial role in ocular homeostasis and immune response¹¹. However, the relationship between the ocular microbiome and POAG has been little explored, and there are gaps in our knowledge about how these microbial communities may influence disease progression.

The study of the ocular microbiome not only provides valuable information on the pathogenetic mechanisms of POAG but also opens the door to the development of biotic compounds (prebiotics, probiotics, and postbiotics) that could have therapeutic applications. Prebiotics are substances that promote the growth of beneficial microorganisms; probiotics are live microorganisms that confer health benefits to the host; and postbiotics are metabolites or cellular components derived from microorganisms that have positive biological effects¹².

These biotic compounds have significant potential as adjuvants in the treatment of POAG, as they could help restore and maintain a balanced ocular microbiome, improve the immune response and reduce inflammation, factors that are implicated in the progression of glaucoma (8). Research in this area could lead to the development of new therapeutic strategies that, in combination with current treatments, contribute to the prevention of glaucomatous blindness.

However, the relationship between the ocular microbiome and POAG has been poorly investigated, and most studies have focused on infectious or inflammatory pathologies of the ocular surface, leaving its involvement in neurodegenerative diseases such as glaucoma largely unexplored. Despite advances in the characterization of the ocular microbiome using next-generation sequencing techniques, a profound gap remains regarding how these specific microbial communities might influence the ocular microenvironment, local inflammatory processes, or immune modulation associated with POAG. This lack of evidence limits our understanding of the potential etiopathogenic mechanisms that could link ocular dysbiosis with glaucoma progression and underscores the urgent need for studies that systematically analyze the ocular surface microbial profile in these patients. Our work aims to contribute to closing this gap by providing an initial characterization of the tear microbiome in patients with POAG, thereby laying the groundwork for future studies and innovative therapeutic strategies.

Method

Study design

A pilot, analytical, observational, case-control study was carried out, selecting a total of 24 participants from the Centro de Especialidades Monteolivete and the Clínica Vila Innova Ocular in Valencia (Spain).

Participants

The case group consisted of patients with POAG (GG, n = 10), of both sexes and aged between 40 and 80 years.

The control group (CG, n = 12) consisted of subjects without POAG, of both sexes, aged between 40 and 80 years, and matched by age (± 2 years) and sex with the case group.

The following exclusion criteria were applied: (1) viral, bacterial, or allergic conjunctivitis in the last 6 months; (2) treatment with antibiotics in the last 6 months; (3) eye surgery in the last year; and (4) suffering from other neurodegenerative diseases (Alzheimer’s, Parkinson’s, or sclerosis).

Sample collection and Dna extraction

Ophthalmologists from the research team performed an ocular examination on the participants to confirm their correct classification into the study groups (glaucoma or control). They then collected a tear sample from both eyes of each participant. To do this, the eyelashes and the inner and outer margins of the eyelids were carefully dried with a soft wipe to remove as much basal tear fluid as possible. The tear sample was then collected from the inferior meniscus using a 75 mm capillary tube. Using a rubber nozzle, the tear samples from both eyes (right and left) were transferred to a single, properly labeled cryotube. This increased the tear volume, ensuring a sufficient quantity for laboratory procedures.

These samples were stored at −80 °C until shipment to Microomics Systems SL (Barcelona, ​​Spain), which was responsible for the ocular microbiota analysis.

The extraction and purification of genomic DNA from the tear samples was carried out using the QIAamp DNA Microbiome Kit (ref: 51704; QIAGEN, Hilden, Germany) and its quantification was performed using an Agilent 2100 Bioanalyzer (Waldbronn, Germany).

16 S RRNA GENE SEQUENCE FOR MICROBIOTA ANALYSIS.

DNA from samples was extracted using a standardized protocol that includes automated physical disruption. Samples were amplified using 16 S rRNA V3-V4 regions specific primers (V3-V4), resulting in sequencing ready libraries with approximately 450 bp insert sizes.

Sequencing was performed in an Illumina MiSeq with 2 × 300 bp reads using v3 chemistry with a loading concentration of 10 pM. In all cases, 15% of PhIX control libraries was spiked in to increase the diversity of the sequenced sample. Negative controls of the sample collection buffer, DNA extraction, and PCR amplification steps were routinely performed in parallel, as well as positive controls, using the same conditions and reagents.

Bioinformatics analysis

Raw demultiplexed forward and reverse reads were processed using the following methods and pipelines as implemented in QIIME2 version 2020.11 with default parameters unless stated¹³. DADA2 was used for quality filtering, denoising, pair-end merging and amplicon sequence variant calling (ASV, i.e. phylotypes) using qiime dada2 denoise-paired method¹⁴. Quality threshold was established to define read sizes for trimming before merging. Reads were truncated at the position when the 75th percentile Phred score felt below the established threshold (< Q20).

After applying quality filtering with DADA2, a total of 31,809 reads remained for analysis across all samples. To normalize sequencing effort, rarefaction was performed to 2,000 reads per sample, corresponding to the lowest retained read count. This depth was sufficient to capture microbial diversity, as indicated by saturation in rarefaction curves.

ASVs were aligned using the qiime alignment mafft method¹⁵. The alignment was used to create a tree and to calculate phylogenetic relations between ASVs using qiime phylogeny fasttree method¹⁶. ASV tables were subsampled without replacement in order to even sample sizes for diversity analysis using qiime diversity core-metrics-phylogenetic pipeline. Phylotype data was used to calculate the following Alpha diversity metrics: Richness and Pielou’s evenness. The phylotype and phylogenetic data were used to calculate Beta diversity unweighted and weighted Unifrac, Jaccard and Bray Curtis distances¹⁷. Taxonomic assignment of ASVs was performed using a Bayesian Classifier trained with SILVA v138 database (i.e. 99% OTUs database) using the qiime feature-classifier classify-sklearn method. Lactobacillus genus was reassigned using BLAST v2.12 against in-house filtered SILVA database.

Alpha diversity comparisons were performed using a Generalized Linear Mixed Model (GLMM), the R package NBZIMM v.1.0 was used for richness and the R package betareg v.3.1–4.1 for evenness. Beta diversity distances matrix and ASV tables were used to calculate principal coordinates using BiodiversityR v2.11-1, and construct ordination plots using ggplot2 v2.2.1. The significance of groups in community structure was tested using Permanova of R package vegan v2.5-5. Differential abundance of taxa and functions were tested using Negative Binomial GLMM. Enrichment analysis of KEGG and COG categories were done using the one-sided version of the Fisher’s exact test. Significant threshold was set at 0.05.

Ethics statement

Informed consent was obtained from participants, and approval was obtained from the Comité de Ethics Committee for Research Involving Human Subjects (CEISH) of the Valencian International University-VIU (CEID2023_04). All study tests and procedures were conducted in accordance with the Declaration of Helsinki on human experimentation (Helsinki 1964, updated version 2004) and with the current regulations for this type of study in the European Union.

Results

A total of 24 participants were recruited: 12 with POAG and 12 controls. Two of the POAG patients were excluded because they had undergone trabeculectomy surgery in the previous year. Thus, the final sample consisted of 22 participants: 10 patients with POAG (GG) and 12 controls (CG).

Sociodemographic, lifestyle, and clinical characteristics of the participants are shown in Table 1. There were no significant differences in the median age of both groups (68.4 ± 1.3 years in GG and 69.3 ± 4.3 years in CG, p = 0.527). In the GG, 60% were men (n = 6) and 40% were women (n = 4), while in the CG, 58% were men (n = 7) and 42% were women (n = 5). These differences were not statistically significant (p = 0.911). There was a noticeably higher percentage of smokers in the glaucoma group compared to the control group (60% vs. 25%, respectively), although the differences did not reach statistical significance (p = 0.096).

Table 1.

Sociodemographic, lifestyle and clinical data of participants.

Variable	GG (n = 10)	CG (n = 12)	p
Age (years)	68.4 ± 1.3	69.3 ± 4.3	0.527
Women (n (%))	4 (40)	5 (42)	0.911
Smokers (%)	60	25	0.096
IOP (mm Hg)	21.2 ± 3.4	14.8 ± 2.0	0.00002*

GG: glaucoma group; CG: control group; IOP: intraocular pressure.

Data from quantitative variables are presented as median ± interquartile range.

* Significance level p < 0.05.

Negative controls included throughout sample processing did not yield detectable amplification or sequencing reads, confirming the absence of contamination.

Rarefaction curves based on observed ASVs showed that species richness began to saturate at approximately 1000 reads and reached a plateau by roughly 2000 reads per sample, indicating that this sequencing depth was sufficient to capture the majority of microbial diversity.

Alpha diversity was assessed using two complementary metrics: the evenness and the number of observed OTUs. The evenness of tear microbial communities was assessed by means of the Pielou’s Evenness Index, used to determine whether the species present in the tear samples of each group are evenly distributed (Fig. 1A). The boxplot showed that the GG had a narrower dispersion in the Evenness scores (median = 0.75, IQR = 0.09) compared to the CG, which exhibited greater interindividual variability. This lower variability suggests a more homogeneous microbial structure in glaucoma, while the controls reflect a potentially more flexible and diverse community in terms of species balance. However, statistical analysis showed no significant differences between the two groups (p = 0.459).

Fig. 1.

Alpha Diversity and Beta Diversity Comparison Between Control and Glaucoma Groups. (A) Box plot illustrating the evenness of microbial communities, measured by Pielou’s evenness index. (B) Box plot showing the richness of microbial communities, represented by the number of observed OTUs (Operational Taxonomic Units). (C) Principal coordinates analysis (PCoA) of beta diversity using Jaccard distance, which considers only the presence or absence of taxa. (D) Principal coordinates analysis (PCoA) of beta diversity using Bray-Curtis distance, which incorporates relative abundance information. (C) and (D) axes represent the first two principal coordinates (CP1 and CP2), with the percentage of explained variance indicated.

Observed OTU richness was compared between groups using a negative-binomial regression model with the glm.nb function from the MASS package (v.7.3–54), as this approach handles overdispersion in count data. Boxplots revealeda narrower distribution in the GG (median = 30.5 OTUs, IQR = 22), while greater interindividual dispersion was observed in controls (median = 43 OTUs, IQR = 41.8) (Fig. 1B). This pattern suggests that the tear microbiota of glaucoma patients has a more homogeneous composition among individuals, while diversity in controls seems to be more variable at the individual level. However, this difference did not reach statistical significance (p = 0.380), likely due to the small sample size.

To analyze the differences in ocular microbial composition between GG and CG, beta diversity was determined using the Jaccard and Bray-Curtis indices, which are based on the presence/absence and relative abundance of taxa, respectively. The significance of groups was tested using PERMANOVA and ANOSIM tests. Permdisp test was used to identify location vs. dispersion effects.

The Jaccard index (presence/absence) showed statistically significant differences between groups in all three tests: PERMANOVA confirmed significant differences in centroids (p = 0.004) and ANOSIM confirmed this difference (p = 0.021). PERMDISP test was also significant (p = 0.00002), suggesting unequal dispersions between groups. Our results suggest a differentiation in ocular microbial composition based on the presence or absence of bacterial species (Fig. 1C). However, it is important to note that this dispersion, caused by CG, may have contributed to these results. The Bray–Curtis distance method was used to evaluate differences in community structure based on the relative abundance of taxa. PERMANOVA revealed a modest but statistically significant separation between GG and CG (p = 0.049), indicating possible differences in the relative abundance of taxa (Fig. 1D). PERMDISP (p = 0.038) was also significant, suggesting that, although taxon abundances vary, the overall community structure is not as clearly divided by disease status.

In the taxonomic analysis at the phylum level, a total of 17 phyla were identified across all tear samples. Of these, 15 were detected in control subjects and 10 were found in glaucoma patients. Notably, 8 phyla were shared between both groups (Fig. 2), suggesting a shift in microbial composition associated with the disease. The barplot (Fig. 3A) illustrates the mean relative abundance of each phylum by group (GG vs. CG), highlighting compositional differences. Differential abundance analysis using the negative binomial model (Table 2) revealed statistically significant differences in three phyla: Fusobacteriota (p < 0.0001), Planctomycetota (p < 0.0001), and Synergistota (p < 0.0001). In all three cases, the mean relative abundance was higher in the GG. This, together with the presence of 2 phyla exclusive to the GG, Verrucomicrobiota and Crenarchaeota could indicate an association between these taxa and the presence of this optic neuropathy.

Fig. 2.

Venn diagram for microbial diversity comparison at phylum and genus levels between control and glaucoma groups. Left panel: comparison at the phylum level shows 7 phyla exclusive to controls, 2 exclusive to glaucoma, and 8 common to both. Right panel: comparison at the genus level reveals 67 genera exclusive to controls, 21 exclusive to glaucoma, and 49 shared.

Fig. 3.

Taxonomic Composition of Microbial Communities in Control and Glaucoma Groups. (A) Barplot showing the relative abundance of bacterial taxa at the phylum level. (B) Barplot displaying the relative abundance at the genus level. Each bar represents the average microbial composition within each group.

Table 2.

Relative abundance at the phylum level.

Phylum	GG (n = 10)	CG (n = 12)	p
Actinobacteriota	27.26	25.58	0.149
Bacteroidota	0.86	2.03	0.273
Chloroflexi	0.42	0.33	0.246
Firmicutes	55.08	60.53	0.503
Fusobacteriota	1.31	0.54	< 0.0001*
Planctomycetota	0,74	0,09	< 0.0001*
Proteobacteria	12.13	10.68	0,868
Synergistota	0.41	0.03	< 0.0001*

Regarding the bacterial genus, a total of 137 genera were identified. Of these, 116 were identified in tears of CG and 70 in tears of glaucomatous patients, with 49 found in both groups (supplemental Table 1). The mean relative abundance of each genus by group is illustrated in Fig. 3B. Several taxa could not be classified at the genus level and were grouped under the category “Unclassified genera”. These entries represent sequences that were confidently assigned to higher taxonomic ranks (e.g., phylum or class), but lacked sufficient resolution for genus-level identification. Several genera (Actinomyces, Serratia, Aeromonas, Brochothrix, Chryseobacterium and Bacteroides) were detected in both groups and included in the relative abundance tables, but they were not retained in the differential abundance analysis using the negative binomial model. This was due to low prevalence, low abundance, or insufficient variability across samples, which are common criteria for filtering taxa prior to statistical modeling. After filtering, 42 genera were tested for differential abundance using a negative-binomial regression model, finding statistically significant differences in 23 of them (Table 3).

Table 3.

Relative abundance at the genus level.

Taxa	GG (n = 10)	CG (n = 12)	p	Taxa	GG (n = 10)	CG (n = 12)	p
Acinetobacter	0.256	0.45583	0.00004*	Lactobacillus	21.843	33.6575	0.16837
Aggregatibacter	0.136	0.0275	< 0.0001*	Lawsonella	0.587	0.09917	< 0.0001*
Alloiococcus	0.019	0.34833	< 0.0001*	Leptotrichia	0.036	0.47583	< 0.0001*
Anaerococcus	0.096	3.35583	0.90434	Leuconostoc	1.373	0.08417	0.03028*
Bacillus	0.028	0.05667	0.40091	Luteimonas	0.071	0.01583	0.01956*
Bifidobacterium	0.324	0.2175	0.81699	Mesorhizobium	0.296	0.1125	0.00123*
Blautia	0.242	0.05167	0.00973*	Micrococcus	0.225	0.4925	0.56255
Collinsella	0.163	0.155	0.8346	Moraxella	4.678	0.01083	< 0.0001*
Corynebacterium	17.697	16.04583	0.02641*	Neisseria	0.022	0.38	< 0.0001*
Cutibacterium	2.193	1.5025	0.23153	Paracoccus	0.015	0.45917	0.01883*
Dolosigranulum	3.25	0.17333	0.01376*	Peptoniphilus	0.035	0.16917	0.73368
Enterococcus	0.037	0.04333	0.75402	Porphyromonas	0.059	0.13583	0.38443
Escherichia-Shigella	0.239	0.91083	< 0.0001*	Prevotella	0.398	0.89333	0.66846
Finegoldia	0.015	0.125	< 0.0001*	Pseudomonas	0.644	0.60833	0.98366
Fretibacterium	0.411	0.02917	< 0.0001*	Pseudonocardia	0.087	0.01917	0.11391
Fusobacterium	0.386	0.06167	< 0.0001*	Rothia	0.247	0.0925	< 0.0001*
Gardnerella	4.034	3.28833	0.13222	Selenomonas	0.2	0.0675	0.00001*
Gemella	0.1	0.55583	< 0.0001*	Staphylococcus	19.63	14.41833	0.31051
Granulicatella	0.033	0.03917	< 0.0001*	Streptococcus	4.16	2.93667	0.59916
Haemophilus	0.193	0.18167	0.84737	Veillonella	0.435	0.43583	< 0.0001*
Komagataeibacter	0.007	0.0025	0.23496	Vibrio	0.074	0.02	0.97538

To better visualize the direction and magnitude of taxonomic differences between groups, a barplot was generated showing the genera with statistically significant differences in relative abundance (p < 0.05) according to the negative binomial model (Fig. 4). The genera are ordered by their log2 fold change, calculated as the logarithm base 2 of the ratio between mean relative abundance in the GG and the CG. Positive values indicate enrichment in glaucoma (GG) and negative values enrichment in controls (CG). This visualization highlights the most discriminative taxa between conditions and supports the interpretation of microbial shifts associated with glaucoma.

Fig. 4.

Differential abundance of bacterial genera between glaucoma and control groups. Barplot showing the log₂ fold change in relative abundance of bacterial genera in glaucoma patients compared to control subjects. Blue bars indicate genera with increased abundance in the glaucoma group, while red bars represent genera with decreased abundance. Only genera with significant differences in abundance between the study groups are shown.

To explore patterns of microbial composition across samples, a heatmap was generated at the genus level (Fig. 5). This visualization clusters samples based on the centered log-ratio (clr) transformed abundances of bacterial genera, allowing for the identification of similarities and differences in microbial profiles. The color gradient represents standardized abundance values, with red indicating higher abundance and blue indicating lower abundance of a given genus. Samples were grouped using a hierarchical clustering dendrogram based on their microbial similarity, revealing distinct clustering patterns between the GG and CG, reinforcing the evidence of glaucoma-associated changes in the ocular microbiota.

Fig. 5.

Heatmap of bacterial genera abundance across samples. Heatmap showing the clr-transformed abundance of bacterial genera in tear samples from control and glaucoma subjects. Rows represent bacterial genera (grouped by phylum), and columns represent individual samples. The color scale indicates standardized abundance values, with dark red representing higher abundance and light red representing lower abundance. A hierarchical clustering dendrogram groups samples based on microbial similarity. Color-coded bars above the heatmap indicate experimental group (blue for control, red for glaucoma).

We then segmented the GG according to medication type: preservative-free vs. preserved formulations. Of the 10 POAG patients, 7 received preservative-free formulations (2 with beta-blockers, 4 with prostaglandin analogues, and 1 with a carbonic anhydrase inhibitor), while 3 received treatment with preserved medications (1 with a prostaglandin analogue and 2 with a combination of prostaglandins and beta-blockers). All preserved formulations contained benzalkonium chloride (BAK), and one combination also included cetrimide.

No significant differences were observed at the phylum level between the preservative-free and preservative-treated subgroups (supplementary Table 2). However, when comparing the genus level, significant differences were found in the relative abundance of 14 of them (supplementary Table 3). The most significant were Blautia (more abundant in the preservative-treated subgroup) and Moraxella (more abundant in the preservative-free subgroup). These results demonstrate the effect of preservative formulations on the bacterial composition of tears.

When comparing the relative abundance at the phylum level between the subgroup without preservatives and the control group, significant differences were observed in the phylum Firmicutes (28.3% vs. 41.7%, p = 0.041) and in the phylum Proteobacteria (26.7% vs. 12.9, p = 0.048) (Supplementary Table 4). Similarly, significant differences were observed in the relative abundance of several genera, although with p-values ​​at the limit of statistical significance, probably due to the small sample size (Supplementary Table 5).

Discussion

The results of this pilot study suggest that patients with POAG exhibit significant alterations in the composition of their tear microbiota compared to control subjects, including a reduction in taxonomic diversity and the altered representation of specific bacterial genera. Although no significant differences were observed in alpha diversity, beta diversity analyses revealed a distinct shift in microbial community structure rather than a global loss of richness. However, the PERMDISP test indicated that these differences may be influenced by unequal dispersion rather than true separation of microbial community centroids. This suggests that the observed variation could reflect greater heterogeneity within one group rather than distinct compositional profiles. Therefore, our findings should be interpreted with caution, and future studies with larger cohorts are needed to disentangle dispersion effects from true biological differences.

Growing evidence supports the existence of a gut–retina axis, suggesting that alterations in the intestinal microbiota may be implicated in the pathogenesis of various ocular diseases, including retinal and neurodegenerative disorders¹⁸. Other studies have broadened the scope of this interaction by proposing the gut–eye axis as a conceptual framework linking intestinal dysbiosis to the onset and progression of various ocular diseases (including glaucoma, age-related macular degeneration, and uveitis) through immune, metabolic, and inflammatory pathways¹⁹. These results agree with that hypothesis, providing evidence of an ocular dysbiosis associated with glaucoma, where certain bacteria (such as Moraxella, Finegoldia, Lawsonella, or Neisseria) are overrepresented. Interestingly, several of these genera have previously been linked to chronic inflammatory processes or immune modulation of mucosal surfaces^20–23, suggesting potential mechanisms by which these microorganisms could influence ocular homeostasis and contribute to glaucoma progression.

The results of this study are consistent with recent evidence highlighting the role of the ocular surface microbiota in glaucoma. Spörri et al.¹⁰ reported compositional shifts in bacterial genera and tear proteins in glaucoma patients, supporting the hypothesis of a microbiota–immune axis in disease pathogenesis. Similarly, Kamdougha et al.²⁴ found that glaucoma, particularly when combined with dry eye, is associated with reduced microbial diversity and altered community structure. In another study, authors found a genetic correlation that suggests a broader systemic microbial influence, although they did not establish a causal link between gut microbiota and glaucoma²⁵. Together, these studies support the notion that glaucoma is associated with measurable changes in the ocular microbiota, which may be shaped by both disease mechanisms and therapeutic interventions. Our findings reinforce these observations, showing differences in both taxonomic richness and the relative abundance of specific genera between glaucoma and control groups. Moreover, Chang et al.²⁶ demonstrated that topical glaucoma medications—especially preserved eyedrops—can directly alter the ocular surface microbiome, increasing the abundance of gram-negative bacteria and reducing tear film stability. In line with this, our stratified analysis revealed significant differences in the abundance of bacterial genera in glaucoma patients treated with preservative-containing eye drops compared with those receiving preservative-free formulations. Staphylococcus, Veillonella, Blautia, and Fusobacterium, among others, were significantly higher in the first group of patients. This is especially relevant considering that some species of Staphylococcus possess toxins and enzymes that can damage tissue and promote eye inflammation²⁷. In addition, the preservatives in some glaucoma eyedrops, such as benzalkonium chloride (BAC), can lead to the selection of BAC-resistant Staphylococcus strains, contributing to antimicrobial resistance on the ocular surface²⁸. This finding highlights the potential modulatory role of preservatives on the ocular microbiota and, therefore, as a confounding factor in the study. Indeed, preservatives such as benzalkonium chloride have previously been shown to alter ocular microbial communities even in healthy individuals²⁹. Thus, our findings suggest that both disease and its treatment modulate the ocular surface microbial.

A subanalysis comparing the preservative-free glaucoma subgroup and CG revealed several statistically significant differences in microbial relative abundance at the phylum level (Firmicutes, p = 0.041; and Proteobacteria, p = 0.048). Furthermore, multiple genera including Staphylococcus, Corynebacterium, Acinetobacter, Gardnerella, Anaerococcus, Fusobacterium, Escherichia-Shigella, Haemophilus, and Moraxella were also significantly altered (all p < 0.05). Our findings suggest that glaucoma itself may be associated with a distinct tear microbiota profile, independent of preservative exposure. However, these results should be interpreted as exploratory and require validation in larger cohorts due to the small sample size (n = 7 vs. n = 12).

In addition to topical treatment, another potential confounding factor to consider is smoking, which has been shown to alter microbial communities across various mucosal surfaces. Tobacco exposure may influence both local and systemic immune responses, potentially affecting the composition and diversity of the ocular microbiota. However, we did not include smoking status as a variable in our analysis due to the limited sample size. Stratifying the cohort into subgroups—smokers, ex-smokers, and non-smokers—would have resulted in groups too small to yield meaningful statistical comparisons. We acknowledge this limitation and recommend that future studies with larger cohorts incorporate smoking history as a covariate to better assess its impact on ocular microbial profiles.

The findings of this study also align with growing evidence that alterations in the ocular microbiota are associated with a range of chronic ocular and systemic conditions. Previous observational studies have reported compositional shifts in the conjunctival microbiota in HIV-infected individuals, with changes influenced by antiretroviral therapy but not by CD4 + T cell count³⁰ Similarly, patients with primary Sjögren’s syndrome exhibited distinct microbial profiles in crevicular fluid, with increased abundance of genera such as Prevotella and Fusobacterium³¹. In severe ocular surface diseases, Staphylococcus and Corynebacterium were dominant in specific conditions, and microbial diversity was reduced compared to healthy controls³². Other studies have shown that the ocular microbiome remains relatively stable across different severities of meibomian gland dysfunction³³, while patients with keratoconus displayed significant differences in corneal microbial composition, including increased Aquabacterium³⁴. Notably, even antiseptic treatments such as povidone-iodine eye drops have been shown to modulate ocular surface symptoms without disrupting microbial homeostasis³⁵. Furthermore, systemic inflammatory conditions like HLA-B27-associated uveitis have demonstrated rapid, stage-dependent shifts in gut microbiota composition, suggesting a potential link between microbial dynamics and ocular inflammation³⁶. Taken together, these studies support the hypothesis that ocular and systemic microbiota may play a role in the pathophysiology of ocular diseases. The present findings, which reveal differences in taxonomic composition and specific genera associated with glaucoma, contribute to this emerging field and underscore the need for further research into the functional implications of these microbial shifts.

This expanding body of literature supports the idea that microbial dysbiosis—whether local or systemic—may play a broader role in ocular pathophysiology. Our data contribute to this emerging field by identifying specific taxonomic shifts associated with POAG and reinforcing the need for further investigation into their potential functional and immunological consequences.

The observation of lower interindividual variability in the tear microbiota of POAG patients is also noteworthy, as it may reflect a homogenization of the ocular ecosystem under pathological conditions—an effect similarly described in gut dysbiosis associated with chronic diseases³⁷.

From a pathophysiological perspective, the gut-eye axis hypothesis is reinforced by our data, as microbially induced immune dysregulation may extend beyond the intestinal environment. Of note, the Verrucomicrobiota phylum was only present in the glaucoma group and Akkermansia genus, a mucin-degrading bacteria within this phylum, was present in this group. Zhang et al. showed that this genus was dramatically increased in a glaucomatous rat model compared with the control, and this alteration was negatively correlated with the retinal ganglion cell count³⁸. While the authors focused on the gut microbiota, the presence of these Gram-negative bacteria in the eye would increase LPS levels, triggering a proinflammatory response through the activation of microglia via Foxp3 modulation. Excess nitric oxide (NO) and proinflammatory cytokines would generate oxidative stress in retinal ganglion neurons, causing their progressive death.

Given the exploratory nature of this pilot study, the findings should be interpreted with caution. While they do not allow for definitive conclusions, they provide strong indications of alterations in the tear microbiota associated with glaucoma. These preliminary findings provide compelling hypotheses that merit validation through longitudinal studies with larger, more diverse cohorts. Identifying potential microbial biomarkers or dysbiosis patterns could ultimately enhance our understanding of POAG pathogenesis and pave the way for new therapeutic strategies.

In this regard, the genera with the most pronounced differences in relative abundance between groups, according to our results, may offer promising targets for microbiome-based interventions. For instance, the increased abundance of Fusobacterium and Moraxella in the glaucoma group may reflect a dysbiotic state that promotes inflammation or epithelial disruption. Fusobacterium nucleatum has been shown to impair epithelial integrity, activate pro-inflammatory pathways such as STAT3 and Wnt/β-catenin, and contribute to tumorigenesis and immune evasion^39–42. Moraxella catarrhalis, a known mucosal pathogen, adheres to epithelial cells and induces inflammatory responses via outer membrane vesicles and virulence factors such as MID and UspA1^43,44. The Fusobacterium genera, which are overrepresented in the glaucoma group, have also been identified in oral microbiome as risk factors for age-related macular degeneration⁴⁵. Their abundance would increase the presence of PAMPs and pro-inflammatory cytokines, such as IL-1β and TNF-α, in the eye. This would induce chronic inflammation, activate macrophages in the retina and damage the retinal microvasculature, thereby promoting the onset and progression of retinopathy. Our results also show genus that appear exclusively in glaucoma, such as Klebsiella, Actinobacillus, Eubacterium, Peptostreptococcus, and Bergeyella, which have been linked to various eye diseases such as endophthalmitis, keratitis and orbital cellulitis^46–50.

Similarly, the presence of certain butyrate-producing bacterial genera, such as Faecalibacterium, Roseburia, Anaerostipes, Ruminococcus and Subdoligranulum, which were only found in the control group, could promote the prevention or attenuation of inflammatory eye diseases through butyrate production. It has recently been demonstrated in mice undergoing desiccating stress that the butyrate transporter SLC5A8 is expressed in vivo in the conjunctival and corneal epithelia, and that butyrate downregulates inflammatory type I IFN signalling, thereby protecting against corneal barrier disruption and loss of conjunctival goblet cells⁵¹. Given that cGAS–STING signaling in microglia contributes to glaucoma pathogenesis via type I IFN production⁵², it can be postulated that the presence of these genera in healthy individuals may confer protection against retinal ganglion cell degeneration, enhance epithelial barrier integrity, and modulate proinflammatory cytokines, thereby supporting ocular surface homeostasis. The loss of these beneficial bacteria in the glaucoma group could promote a more proinflammatory microenvironment and therefore aggravate the disease.

These findings support the potential of using specific microbial signatures not only as biomarkers but also as therapeutic levers in the management of glaucoma. Although these applications remain in early stages, our data provide a foundation for future clinical research.

In addition, this study presents certain methodological limitations that should be considered. First is the small sample size. Our study was designed as an exploratory pilot research to characterize the ocular microbial profile in POAG and identify microbiological signatures that need further validation for consistency with larger studies to develop a longitudinal picture. Similar studies in dry eye disease patients⁵³ or diabetic retinopathy⁵⁴ have also used similar sample sizes to identify ocular dysbiosis. Given the exploratory nature of the study and the inherent sparsity of microbiome datasets, no formal correction for multiple comparisons was applied. This decision reflects a balance between minimizing false negatives and preserving sensitivity to biologically relevant signals, as recommended in early-phase microbiome research^55–57. The trends observed between the CG and the glaucoma subgroup treated with preservative-free eye drops are biologically plausible and consistent with the previous literature, highlighting the value of the ocular microbiota, which for years was thought to be sterile, and its role in the pathogenesis of this glaucomatous optic neuropathy.

Another potentially relevant factor not explored in depth was the use of contact lenses. Although this variable was recorded in the initial questionnaire, the low number of users prevented a stratified analysis with sufficient validity. Some evidence suggests that contact lens wear can alter the ocular microbiota, making it more similar to the skin microbiota, with increased abundance of genera such as Methylobacterium, Lactobacillus, Acinetobacter, and Pseudomonas⁵⁸. However, the clinical significance of these changes remains uncertain. Moreover, although certain lenses incorporate antimicrobial coatings (such as Mel4 peptide-coated lenses) recent studies have shown that their extended wear does not significantly alter the conjunctival microbiota or reduce microbial contamination compared to uncoated lenses⁵⁹. Therefore, future studies with larger sample sizes should consider this variable as a possible confounding factor and analyze its specific impact.

Most existing data on this topic are derived from cross-sectional studies, which limit causal inferences. Although the present work followed a case-control design, its retrospective nature still underscores the need for longitudinal approaches to assess microbiota dynamics over time.

To date, only three studies have investigated the relationship between the ocular microbiota and glaucoma^10,24,25, and just one of them has analyzed tear samples²⁴. In this context, the present work stands as one of the first to characterize the tear microbial profile in patients with POAG, providing pioneering evidence of its potential involvement in the pathophysiology of this optic neuropathy.

Overall, our data support the hypothesis of a significant alteration in the tear microbiota associated with POAG, while highlighting the modulating effect of treatments containing preservatives. These preliminary observations have a potential implication for the pathophysiology, diagnosis, and treatment of this optic neuropathy. The identification of differentially represented bacterial genera and altered diversity patterns opens new avenues for exploring the gut-eye axis and developing microbiota-based therapeutic strategies. Longitudinal studies with larger cohorts are needed to confirm these findings and elucidate the immunological or metabolic mechanisms involved.

Supplementary Information

Below is the link to the electronic supplementary material.

Author contributions

ENT: Conceptualization, Investigation, Methodology, Formal Analysis, Writing – Review & Editing, Visualization, Project Administration. JTL: Formal Analysis, Data Curation, Writing – Review & Editing. EBT: Investigation, Writing – Review & Editing, Visualization. IAB: Investigation, Methodology, Writing – Review & Editing, Visualization. MDPD: Conceptualization, Investigation, Resources. ENT: Investigation, Visualization. VAF: Conceptualization, Writing – Review & Editing, Visualization.VZM: Conceptualization, Methodology, Investigation, Resources, Writing – Original Draft, Writing – Review & Editing, Visualization, Supervision, Project Administration, Funding Acquisition.

Funding

This study was supported by the Valencian International University-VIU (PII2023/36). In addition, RICORS-REI (RD21/0002/0032) funded by Instituto de Salud Carlos III (ISCIII), and Next Generation EU funds, which finance the actions of the Recovery and Resilience Mechanism (RRM), have funded part of this study through the financial support given to the research activities of the members pertaining to this Network.

Data availability

The datasets generated during and/or analysed during the current study are available in the NCBI Sequence Read Archive (SRA) repository under BioProject accession number PRJNA1297142. The data are publicly available at: [https://www.ncbi.nlm.nih.gov/bioproject/PRJNA1297142](https:/www.ncbi.nlm.nih.gov/bioproject/PRJNA1297142).

Declarations

Competing interests

The authors declare no competing interests.