Characterization of the Microbial Profile in Tears of Patients with Primary Open-Angle Glaucoma: Results from a Pilot Study

Characterization of the Microbial Profile in Tears of Patients with Primary Open-Angle Glaucoma: Results from a Pilot Study | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article Characterization of the Microbial Profile in Tears of Patients with Primary Open-Angle Glaucoma: Results from a Pilot Study E Navarro-Tapia, J Tronchoni-León, E Bendala-Tufanisco, I Andrés-Blasco, and 4 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7250015/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 05 Nov, 2025 Read the published version in Scientific Reports → Version 1 posted 9 You are reading this latest preprint version Abstract This pilot study aimed to characterize the tear microbiota in patients with primary open-angle glaucoma (POAG) and compare to healthy controls. Tear samples from 22 participants (10 with POAG, 12 controls), matched by age and sex, were analyzed using 16S rRNA sequencing to assess microbial diversity and taxonomic composition. While alpha diversity showed no significant differences, beta diversity analyses revealed distinct microbial community structures between groups. Patients with POAG exhibited a more uniform microbiota and fewer bacterial genera overall. Notably, three phyla—Fusobacteriota, Planctomycetota, and Synergistota—were significantly more abundant in the glaucoma group (p < 0.0001). At the genus level, 23 genera displayed significant differences in relative abundance: 10 genera were significantly less abundance, and 13 genera were significantly more abundant in POAG compared to controls. These findings support the hypothesis of ocular dysbiosis associated with POAG and highlight specific microbial shifts potentially linked to inflammatory or immune mechanisms involved in disease progression. The study suggests that microbiota-based therapies such as probiotics or postbiotics might represent novel adjunctive strategies for managing glaucoma and calls for larger, longitudinal studies to validate these preliminary results and explore clinical applications. Health sciences/Diseases Biological sciences/Microbiology primary open-angle glaucoma ocular microbiota tear fluid neurodegeneration inflammation Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 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 2040 [ 1 ]. 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 detection [ 2 ]. Although elevated intraocular pressure (IOP) is a well-established risk factor, the pathogenesis of POAG is multifactorial and not fully understood [ 3 ]. A possible risk factor for the onset and/or progression of glaucoma is intestinal dysbiosis [ 4 ] since, through the gut-eye axis, biological signals released by intestinal microbes could trigger an immune response at the ocular level [ 5 ]. 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 barrier [ 6 ]. 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 responses [ 7 ]. 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 glaucoma [ 8 ]. 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 glaucoma [ 9 ]. It has been suggested that interactions between the ocular surface microbiota and proteins present in tears may be related to the etiopathogenic mechanisms of glaucoma [ 10 ]. 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 response [ 11 ]. 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 effects [ 12 ]. 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 = 12), 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). 16S RRNA GENE SEQUENCE FOR MICROBIOTA ANALYSIS DNA from samples was extracted using a standardized protocol that includes automated physical disruption. Samples were amplified using 16S 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 stated [ 13 ]. DADA2 was used for quality filtering, denoising, pair-end merging and amplicon sequence variant calling (ASV, i.e. phylotypes) using qiime dada2 denoise-paired method [ 14 ]. 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). ASVs were aligned using the qiime alignment mafft method [ 15 ]. The alignment was used to create a tree and to calculate phylogenetic relations between ASVs using qiime phylogeny fasttree method [ 16 ]. 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 distances [ 17 ]. 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 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 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. 1 A). 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). 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. 1 B). 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. 1 C). 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. 1 D). 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. 3 A) 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. 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* GG: glaucoma group; CG: control group Data show the relative abundance in percentage. * Significance level p < 0.05 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. 3 B. 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 GG: glaucoma group; CG: control group Data show the relative abundance in percentage. * Significance level p < 0.05 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. 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. 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. 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 disorders [ 18 ]. 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 pathways [ 19 ]. 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. 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. [ 25 ] 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. Given that most patients in our cohort were under long-term topical treatment, it is plausible that some of the microbial shifts we observed may reflect not only disease-related dysbiosis but also treatment-induced alterations. 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 glaucoma [ 26 ]. 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. 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 count [ 27 ] Similarly, patients with primary Sjögren’s syndrome exhibited distinct microbial profiles in crevicular fluid, with increased abundance of genera such as Prevotella and Fusobacterium [ 28 ]. In severe ocular surface diseases, Staphylococcus and Corynebacterium were dominant in specific conditions, and microbial diversity was reduced compared to healthy controls [ 29 ]. Other studies have shown that the ocular microbiome remains relatively stable across different severities of meibomian gland dysfunction [ 30 ], while patients with keratoconus displayed significant differences in corneal microbial composition, including increased Aquabacterium [ 31 ]. Notably, even antiseptic treatments such as povidone-iodine eye drops have been shown to modulate ocular surface symptoms without disrupting microbial homeostasis [ 32 ]. 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 inflammation [ 33 ]. 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 diseases [ 34 ]. 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. In this context, the differential presence of phyla such as Fusobacteriota or Synergistota in the glaucoma group may have implications for the modulation of local or systemic inflammatory pathways. Remarkably, Fusobacterium nucleatum , a representative of Fusobacteriota, has been shown to exacerbate colitis by disrupting epithelial integrity and activating pro-inflammatory STAT3 signaling via acetyl-CoA accumulation, highlighting its potential role in systemic immune modulation [ 35 ]. Furthermore, a murine study demonstrated that reducing Synergistota abundance through riboflavin-enriched soymilk supplementation alleviated intestinal inflammation and oxidative stress, suggesting a possible link between this phylum and gut-derived inflammatory responses [ 36 ]. Of particular 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 count [ 37 ]. 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. Paracoccus , for instance, has been studied for its metabolic versatility and redox regulation capacity, including the modulation of nitrate respiration through systems such as DksA, ppGpp, and RegAB, which may contribute to mucosal homeostasis [ 38 ]. Although Neisseria includes pathogenic species such as N. meningitidis , some commensal strains like N. lactamica are known to compete with pathogens and may play a role in mucosal immune balance [ 39 ]. Alloiococcus otitidis , while traditionally associated with otitis media, has also been isolated in other mucosal infections, but its role remains unclear and likely context-dependent [ 40 ]. These genera, particularly those reduced in glaucoma patients, warrant further investigation as potential components of probiotic or postbiotic formulations aimed at restoring ocular microbial equilibrium. Conversely, 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 [ 41 – 44 ]. 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 [ 45 – 46 ]. The Rothia and Fusobacterium genera, which are overrepresented in the glaucoma group, have also been identified in oral microbiome as risk factors for age-related macular degeneration [ 47 ]. 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 [ 48 – 52 ]. 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 cells [ 53 ]. "Given that cGAS–STING signaling in microglia contributes to glaucoma pathogenesis via type I IFN production [ 54 ], 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, the small sample size may limit the generalizability of the findings, as well as the ability to detect differences in microbial richness and composition. This study was designed as an exploratory pilot 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 patients [ 55 ] or diabetic retinopathy [ 56 ] have also used similar sample sizes to identify ocular dysbiosis. The results of our study are sufficiently concrete to demonstrate the value of the microbiota established in the eye, which for years was thought to be sterile, in understanding 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 Pseudomonas [ 57 ]. 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 lenses [ 58 ]. 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 , 26 ], and just one of them has analyzed tear samples [ 24 ]. 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. In summary, our results support the hypothesis of a significant alteration in the tear microbiota associated with POAG, with potential implications 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. Declarations COMPETING INTERESTS STATEMENT The authors declare no competing interests. FUNDING DECLARATION This study was supported by the Valencian International University-VIU (PII2023/36). Author Contribution 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. 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. 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Sci Rep. 10, 3862; 10.1038/s41598-020-60674-3 (2020) Sridhar J, Flynn HW Jr, Kuriyan AE, Dubovy S, Miller D. Endophthalmitis caused by Klebsiella species. Retina. 34, 1875-1881; 10.1097/IAE.0000000000000162 (2014) Hong J, Xu J, Cao W, Ji J, Sun X. Actinobacillus actinomycetemcomitans Keratitis After Glaucoma Infiltration Surgery: A Clinical Report and Literature Review. Medicine (Baltimore). 95, e2608; 10.1097/MD.0000000000002608 (2016) Petrela RB, Lieberman JA, Swan RT. An Unnamed Human Oral Bergeyella sp. as the Cause of an Unusual Bacterial Keratitis. Case Rep Ophthalmol Med. 2023, 3288984; 10.1155/2023/3288984 (2023) Chung CY, Wong ES, Liu CCH, Wong MOM, Li KKW. Clinical features and prognostic factors of Klebsiella endophthalmitis-10-year experience in an endemic region. Eye (Lond). 31, 1569-1575; 10.1038/eye.2017.92 (2017) Malik NN, Goh D, McLean C, Huchzermeyer P. Orbital cellulitis caused by Peptostreptococcus. Eye (Lond). 18, 643-644; 10.1038/sj.eye.6700657 (2004) Schaefer L, et al. Gut-derived butyrate suppresses ocular surface inflammation. Sci Rep. 12, 4512; 10.1038/s41598-022-08442-3 (2022) Liu Y, et al. Microglial cGAS-STING signaling underlies glaucoma pathogenesis. Proc Natl Acad Sci U S A. 121, e2409493121; 10.1073/pnas.2409493121 (2024) Gupta N, et al. Ocular conjunctival microbiome profiling in dry eye disease: A case control pilot study. Indian J Ophthalmol. 71, 1574-1581; 10.4103/ijo.IJO_1756_22 (2023) Das T, Padakandla SR, Shivaji S, Jayasudha R, Takkar B. Intraocular Microbiome in Diabetes and Diabetic Retinopathy: A Pilot Study. Ophthalmol Ther. 12, 1109-1126; 10.1007/s40123-023-00660-w (2023) Shin H, et al. Changes in the Eye Microbiota Associated with Contact Lens Wearing. mBio. 7, e00198; 10.1128/mBio.00198-16 (2016) Kalaiselvan P, et al. Ocular microbiota and lens contamination following Mel4 peptide-coated antimicrobial contact lens (MACL) extended wear. Cont Lens Anterior Eye. 45 , 101431; 10.1016/j.clae.2021.02.017 (2022) Additional Declarations No competing interests reported. Supplementary Files Supplementaltable1.docx Cite Share Download PDF Status: Published Journal Publication published 05 Nov, 2025 Read the published version in Scientific Reports → Version 1 posted Editorial decision: Revision requested 25 Aug, 2025 Reviews received at journal 23 Aug, 2025 Reviews received at journal 23 Aug, 2025 Reviewers agreed at journal 20 Aug, 2025 Reviewers agreed at journal 15 Aug, 2025 Reviewers invited by journal 07 Aug, 2025 Editor assigned by journal 06 Aug, 2025 Submission checks completed at journal 01 Aug, 2025 First submitted to journal 01 Aug, 2025 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-7250015","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":496951282,"identity":"4247e3e5-0bfa-45f3-9435-c9120bd2017a","order_by":0,"name":"E Navarro-Tapia","email":"","orcid":"","institution":"Valencian International University - VIU","correspondingAuthor":false,"prefix":"","firstName":"E","middleName":"","lastName":"Navarro-Tapia","suffix":""},{"id":496951283,"identity":"687cfb96-6a7e-406b-8dd2-47e639e5ad01","order_by":1,"name":"J Tronchoni-León","email":"","orcid":"","institution":"Valencian International University - VIU","correspondingAuthor":false,"prefix":"","firstName":"J","middleName":"","lastName":"Tronchoni-León","suffix":""},{"id":496951284,"identity":"3bbf5453-d892-4701-ba3f-6021528ea49f","order_by":2,"name":"E Bendala-Tufanisco","email":"","orcid":"","institution":"University Cardenal Herrera CEU","correspondingAuthor":false,"prefix":"","firstName":"E","middleName":"","lastName":"Bendala-Tufanisco","suffix":""},{"id":496951285,"identity":"187e4f3a-99ec-40db-9d16-f5904458b335","order_by":3,"name":"I Andrés-Blasco","email":"","orcid":"","institution":"University of Valencia","correspondingAuthor":false,"prefix":"","firstName":"I","middleName":"","lastName":"Andrés-Blasco","suffix":""},{"id":496951286,"identity":"e3325ea4-6b36-4654-8e7a-6b16b6c700e5","order_by":4,"name":"MD Pinazo-Durán","email":"","orcid":"","institution":"University of Valencia","correspondingAuthor":false,"prefix":"","firstName":"MD","middleName":"","lastName":"Pinazo-Durán","suffix":""},{"id":496951287,"identity":"c5fdf6bc-4d2d-4281-ac51-c598d22e316f","order_by":5,"name":"E Navarro-Hernández","email":"","orcid":"","institution":"Hospital Clínico Universitario","correspondingAuthor":false,"prefix":"","firstName":"E","middleName":"","lastName":"Navarro-Hernández","suffix":""},{"id":496951288,"identity":"2ee55eff-2dc1-4afc-9072-a748c148f909","order_by":6,"name":"V Andreu-Fernández","email":"","orcid":"","institution":"Valencian International University - VIU","correspondingAuthor":false,"prefix":"","firstName":"V","middleName":"","lastName":"Andreu-Fernández","suffix":""},{"id":496951289,"identity":"8005550b-7630-476e-ae16-ca07b8913b96","order_by":7,"name":"V Zanón-Moreno","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAABB0lEQVRIiWNgGAWjYDACZqyiHwhqSUATZJxB0Cp0Lcw8eBQbHGd++Ljwhw2DfPvZg48LGLbJmbf3Hnxs23aYgZ//AHYth9mMjWckpDEw9uQlG89guG0sc+ZcsnEuUIvkDHTLIUCymYdNmifhMNBPOWbSvP9uJ86QADJy29IYDG5gdxhQC/tvnoT/DGz8b8ykeRiAWuSBDEugFvvz2B3Gz8zDxsyTcICBB2Q4WIsEj5k0Y5sNgwFGqMC0sBlL86Ql80hIvDE2BmoxluDJMTbsOWfDI3EDuxY2/sMPP/PY2MnJ9+cYPgZqkZNgP2P44EeZhBx/P3aHwQBmROCLmlEwCkbBKBgFBAAANtdIGDEvgvcAAAAASUVORK5CYII=","orcid":"","institution":"University of Valencia","correspondingAuthor":true,"prefix":"","firstName":"V","middleName":"","lastName":"Zanón-Moreno","suffix":""}],"badges":[],"createdAt":"2025-07-30 07:53:14","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-7250015/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-7250015/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1038/s41598-025-22536-8","type":"published","date":"2025-11-05T15:57:38+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":88895739,"identity":"13e95226-31d8-4296-9e3f-d2c59556d2cf","added_by":"auto","created_at":"2025-08-12 13:04:38","extension":"jpg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":178176,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eAlpha Diversity and Beta Diversity Comparison Between Control and Glaucoma Groups.\u003c/strong\u003e (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. \u0026nbsp;(C) and (D) axes represent the first two principal coordinates (CP1 and CP2), with the percentage of explained variance indicated.\u003c/p\u003e","description":"","filename":"figure1todo.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7250015/v1/3665d54a099c3eeb57dcdde3.jpg"},{"id":88895732,"identity":"aa696053-4cdc-4990-95b7-e9c6ecb7df56","added_by":"auto","created_at":"2025-08-12 13:04:38","extension":"jpg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":39390,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eVenn diagram for microbial diversity comparison at phylum and genus levels between control and glaucoma groups.\u003c/strong\u003e 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.\u003c/p\u003e","description":"","filename":"figure2diagramaVenn.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7250015/v1/cd8b75a7fa1a7af83920f834.jpg"},{"id":88899365,"identity":"3f87b0df-5e4c-43e6-b3fe-5850c6af162c","added_by":"auto","created_at":"2025-08-12 13:28:38","extension":"jpg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":148330,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eTaxonomic Composition of Microbial Communities in Control and Glaucoma Groups. \u003c/strong\u003e(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.\u003c/p\u003e","description":"","filename":"figure3phylumygenus.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7250015/v1/1c4bee8917baeb70460a4eff.jpg"},{"id":88899362,"identity":"e044d36b-facf-4d6a-bfaf-0f44b739d536","added_by":"auto","created_at":"2025-08-12 13:28:38","extension":"jpg","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":83104,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eDifferential abundance of bacterial genera between glaucoma and control groups.\u003c/strong\u003e Barplot showing the log\u003csub\u003e2\u003c/sub\u003e 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.\u003c/p\u003e","description":"","filename":"figure4foldchange.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7250015/v1/5fa0f1cd8f6f03bba804b2f1.jpg"},{"id":88897530,"identity":"c0a8ad32-d147-446d-b0b0-a384fec85538","added_by":"auto","created_at":"2025-08-12 13:12:38","extension":"jpeg","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":217424,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eHeatmap of bacterial genera abundance across samples.\u003c/strong\u003e 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).\u003c/p\u003e","description":"","filename":"figure5heatmap.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-7250015/v1/af6ca70d5ba2b110d980ed29.jpeg"},{"id":95564151,"identity":"4ace106b-2396-452a-b413-9a2def2685e2","added_by":"auto","created_at":"2025-11-10 16:08:25","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1687918,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7250015/v1/50864b83-54d4-4b19-9cff-3f57213f8c98.pdf"},{"id":88897521,"identity":"a9584de6-b6a6-4f4c-9553-18a51cf03d2e","added_by":"auto","created_at":"2025-08-12 13:12:38","extension":"docx","order_by":0,"title":"","display":"","copyAsset":false,"role":"supplement","size":19539,"visible":true,"origin":"","legend":"","description":"","filename":"Supplementaltable1.docx","url":"https://assets-eu.researchsquare.com/files/rs-7250015/v1/f479d76e06e0438f1e701096.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Characterization of the Microbial Profile in Tears of Patients with Primary Open-Angle Glaucoma: Results from a Pilot Study","fulltext":[{"header":"INTRODUCTION","content":"\u003cp\u003eGlaucoma is a leading cause of irreversible blindness worldwide, affecting approximately 76\u0026nbsp;million people in 2020, and this figure is expected to increase to 111.8\u0026nbsp;million by 2040 [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. 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 detection [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. Although elevated intraocular pressure (IOP) is a well-established risk factor, the pathogenesis of POAG is multifactorial and not fully understood [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eA possible risk factor for the onset and/or progression of glaucoma is intestinal dysbiosis [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e] since, through the gut-eye axis, biological signals released by intestinal microbes could trigger an immune response at the ocular level [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. 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. \u003cem\u003eFirmicutes\u003c/em\u003e and \u003cem\u003eBacteroidetes\u003c/em\u003e), play a key role in the regulation of systemic inflammation and the integrity of the intestinal barrier [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. 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 responses [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. 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 glaucoma [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eIn recent years, there has been a growing interest in the study of the ocular microbiome and its potential involvement in various ophthalmological diseases, including glaucoma [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. It has been suggested that interactions between the ocular surface microbiota and proteins present in tears may be related to the etiopathogenic mechanisms of glaucoma [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. 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 response [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. 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.\u003c/p\u003e\u003cp\u003eThe 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 effects [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eThese 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.\u003c/p\u003e\u003cp\u003eHowever, 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.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e"},{"header":"METHOD","content":"\u003cp\u003eSTUDY DESIGN\u003c/p\u003e\u003cp\u003eA 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).\u003c/p\u003e\u003cp\u003ePARTICIPANTS\u003c/p\u003e\u003cp\u003eThe case group consisted of patients with POAG (GG, n = 12), of both sexes and aged between 40 and 80 years.\u003c/p\u003e\u003cp\u003eThe 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.\u003c/p\u003e\u003cp\u003eThe 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).\u003c/p\u003e\u003cp\u003eSAMPLE COLLECTION AND DNA EXTRACTION\u003c/p\u003e\u003cp\u003eOphthalmologists 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.\u003c/p\u003e\u003cp\u003eThese samples were stored at -80°C until shipment to Microomics Systems SL (Barcelona, ​​Spain), which was responsible for the ocular microbiota analysis.\u003c/p\u003e\u003cp\u003eThe 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).\u003c/p\u003e\u003cp\u003e16S RRNA GENE SEQUENCE FOR MICROBIOTA ANALYSIS\u003c/p\u003e\u003cp\u003eDNA from samples was extracted using a standardized protocol that includes automated physical disruption. Samples were amplified using 16S rRNA V3-V4 regions specific primers (V3-V4), resulting in sequencing ready libraries with approximately 450 bp insert sizes.\u003c/p\u003e\u003cp\u003eSequencing 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.\u003c/p\u003e\u003cp\u003eBIOINFORMATICS ANALYSIS\u003c/p\u003e\u003cp\u003eRaw demultiplexed forward and reverse reads were processed using the following methods and pipelines as implemented in QIIME2 version 2020.11 with default parameters unless stated [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. DADA2 was used for quality filtering, denoising, pair-end merging and amplicon sequence variant calling (ASV, i.e. phylotypes) using qiime dada2 denoise-paired method [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. 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 (\u0026lt; Q20).\u003c/p\u003e\u003cp\u003eASVs were aligned using the qiime alignment mafft method [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. The alignment was used to create a tree and to calculate phylogenetic relations between ASVs using qiime phylogeny fasttree method [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. 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 distances [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]. 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.\u003c/p\u003e\u003cp\u003eAlpha 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 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.\u003c/p\u003e\u003cp\u003eETHICS STATEMENT\u003c/p\u003e\u003cp\u003eInformed 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.\u003c/p\u003e"},{"header":"RESULTS","content":"\u003cp\u003eA 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).\u003c/p\u003e\u003cp\u003eSociodemographic, lifestyle, and clinical characteristics of the participants are shown in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. There were no significant differences in the median age of both groups (68.4\u0026thinsp;\u0026plusmn;\u0026thinsp;1.3 years in GG and 69.3\u0026thinsp;\u0026plusmn;\u0026thinsp;4.3 years in CG, p\u0026thinsp;=\u0026thinsp;0.527). In the GG, 60% were men (n\u0026thinsp;=\u0026thinsp;6) and 40% were women (n\u0026thinsp;=\u0026thinsp;4), while in the CG, 58% were men (n\u0026thinsp;=\u0026thinsp;7) and 42% were women (n\u0026thinsp;=\u0026thinsp;5). These differences were not statistically significant (p\u0026thinsp;=\u0026thinsp;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\u0026thinsp;=\u0026thinsp;0.096).\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eSociodemographic, lifestyle and clinical data of participants\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"4\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eVariable\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eGG\u003c/p\u003e\u003cp\u003e(n\u0026thinsp;=\u0026thinsp;10)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eCG\u003c/p\u003e\u003cp\u003e(n\u0026thinsp;=\u0026thinsp;12)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003ep\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAge (years)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e68.4\u0026thinsp;\u0026plusmn;\u0026thinsp;1.3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e69.3\u0026thinsp;\u0026plusmn;\u0026thinsp;4.3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.527\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eWomen (n (%))\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e4 (40)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e5 (42)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.911\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSmokers (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e60\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e25\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.096\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eIOP (mm Hg)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e21.2\u0026thinsp;\u0026plusmn;\u0026thinsp;3.4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e14.8\u0026thinsp;\u0026plusmn;\u0026thinsp;2.0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cb\u003e0.00002*\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"4\" nameend=\"c4\" namest=\"c1\"\u003e\u003cp\u003eGG: glaucoma group; CG: control group; IOP: intraocular pressure\u003c/p\u003e\u003cp\u003eData from quantitative variables are presented as median\u0026thinsp;\u0026plusmn;\u0026thinsp;interquartile range.\u003c/p\u003e\u003cp\u003e* Significance level p\u0026thinsp;\u0026lt;\u0026thinsp;0.05\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eRarefaction 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.\u003c/p\u003e\u003cp\u003eAlpha 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.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eA). The boxplot showed that the GG had a narrower dispersion in the Evenness scores (median\u0026thinsp;=\u0026thinsp;0.75, IQR\u0026thinsp;=\u0026thinsp;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\u0026thinsp;=\u0026thinsp;0.459).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eObserved OTU richness was compared between groups using a negative-binomial regression model with the glm.nb function from the MASS package (v.7.3\u0026ndash;54), as this approach handles overdispersion in count data. Boxplots revealeda narrower distribution in the GG (median\u0026thinsp;=\u0026thinsp;30.5 OTUs, IQR\u0026thinsp;=\u0026thinsp;22), while greater interindividual dispersion was observed in controls (median\u0026thinsp;=\u0026thinsp;43 OTUs, IQR\u0026thinsp;=\u0026thinsp;41.8) (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eB). 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\u0026thinsp;=\u0026thinsp;0.380), likely due to the small sample size.\u003c/p\u003e\u003cp\u003eTo 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.\u003c/p\u003e\u003cp\u003eThe Jaccard index (presence/absence) showed statistically significant differences between groups in all three tests: PERMANOVA confirmed significant differences in centroids (p\u0026thinsp;=\u0026thinsp;0.004) and ANOSIM confirmed this difference (p\u0026thinsp;=\u0026thinsp;0.021). PERMDISP test was also significant (p\u0026thinsp;=\u0026thinsp;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.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eC). However, it is important to note that this dispersion, caused by CG, may have contributed to these results. The Bray\u0026ndash;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\u0026thinsp;=\u0026thinsp;0.049), indicating possible differences in the relative abundance of taxa (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eD). PERMDISP (p\u0026thinsp;=\u0026thinsp;0.038) was also significant, suggesting that, although taxon abundances vary, the overall community structure is not as clearly divided by disease status.\u003c/p\u003e\u003cp\u003eIn 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.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e), suggesting a shift in microbial composition associated with the disease. The barplot (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eA) 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\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e) revealed statistically significant differences in three phyla: Fusobacteriota (p\u0026thinsp;\u0026lt;\u0026thinsp;0.0001), Planctomycetota (p\u0026thinsp;\u0026lt;\u0026thinsp;0.0001), and Synergistota (p\u0026thinsp;\u0026lt;\u0026thinsp;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.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eRelative abundance at the phylum level\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"4\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePhylum\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eGG\u003c/p\u003e\u003cp\u003e(n\u0026thinsp;=\u0026thinsp;10)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eCG\u003c/p\u003e\u003cp\u003e(n\u0026thinsp;=\u0026thinsp;12)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003ep\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eActinobacteriota\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e27.26\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e25.58\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.149\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eBacteroidota\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.86\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e2.03\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.273\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eChloroflexi\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.42\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.33\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.246\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFirmicutes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e55.08\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e60.53\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.503\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFusobacteriota\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.31\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.54\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.0001*\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePlanctomycetota\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0,74\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0,09\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.0001*\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eProteobacteria\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e12.13\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e10.68\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0,868\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSynergistota\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.41\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.03\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.0001*\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"4\" nameend=\"c4\" namest=\"c1\"\u003e\u003cp\u003eGG: glaucoma group; CG: control group\u003c/p\u003e\u003cp\u003eData show the relative abundance in percentage.\u003c/p\u003e\u003cp\u003e* Significance level p\u0026thinsp;\u0026lt;\u0026thinsp;0.05\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eRegarding 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 \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). The mean relative abundance of each genus by group is illustrated in Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eB. Several taxa could not be classified at the genus level and were grouped under the category \u0026ldquo;Unclassified genera\u0026rdquo;. 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 (\u003cem\u003eActinomyces\u003c/em\u003e, \u003cem\u003eSerratia\u003c/em\u003e, \u003cem\u003eAeromonas\u003c/em\u003e, \u003cem\u003eBrochothrix\u003c/em\u003e, \u003cem\u003eChryseobacterium\u003c/em\u003e and \u003cem\u003eBacteroides\u003c/em\u003e) 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 \u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e).\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eRelative abundance at the Genus level\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"8\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTaxa\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eGG\u003c/p\u003e\u003cp\u003e(n\u0026thinsp;=\u0026thinsp;10)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eCG\u003c/p\u003e\u003cp\u003e(n\u0026thinsp;=\u0026thinsp;12)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003ep\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eTaxa\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003eGG\u003c/p\u003e\u003cp\u003e(n\u0026thinsp;=\u0026thinsp;10)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c7\"\u003e\u003cp\u003eCG\u003c/p\u003e\u003cp\u003e(n\u0026thinsp;=\u0026thinsp;12)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c8\"\u003e\u003cp\u003ep\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eAcinetobacter\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.256\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.45583\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cb\u003e0.00004*\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u003cem\u003eLactobacillus\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e21.843\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e33.6575\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.16837\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eAggregatibacter\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.136\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.0275\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.0001*\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u003cem\u003eLawsonella\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.587\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.09917\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.0001*\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eAlloiococcus\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.019\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.34833\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.0001*\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u003cem\u003eLeptotrichia\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.036\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.47583\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.0001*\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eAnaerococcus\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.096\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e3.35583\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.90434\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u003cem\u003eLeuconostoc\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1.373\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.08417\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e\u003cb\u003e0.03028*\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eBacillus\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.028\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.05667\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.40091\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u003cem\u003eLuteimonas\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.071\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.01583\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e\u003cb\u003e0.01956*\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eBifidobacterium\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.324\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.2175\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.81699\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u003cem\u003eMesorhizobium\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.296\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.1125\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e\u003cb\u003e0.00123*\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eBlautia\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.242\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.05167\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cb\u003e0.00973*\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u003cem\u003eMicrococcus\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.225\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.4925\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.56255\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eCollinsella\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.163\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.155\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.8346\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u003cem\u003eMoraxella\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e4.678\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.01083\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.0001*\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eCorynebacterium\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e17.697\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e16.04583\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cb\u003e0.02641*\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u003cem\u003eNeisseria\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.022\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.38\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.0001*\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eCutibacterium\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e2.193\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.5025\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.23153\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u003cem\u003eParacoccus\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.015\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.45917\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e\u003cb\u003e0.01883*\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eDolosigranulum\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e3.25\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.17333\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cb\u003e0.01376*\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u003cem\u003ePeptoniphilus\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.035\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.16917\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.73368\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eEnterococcus\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.037\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.04333\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.75402\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u003cem\u003ePorphyromonas\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.059\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.13583\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.38443\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eEscherichia-Shigella\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.239\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.91083\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.0001*\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u003cem\u003ePrevotella\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.398\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.89333\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.66846\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eFinegoldia\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.015\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.125\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.0001*\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u003cem\u003ePseudomonas\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.644\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.60833\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.98366\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eFretibacterium\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.411\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.02917\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.0001*\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u003cem\u003ePseudonocardia\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.087\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.01917\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.11391\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eFusobacterium\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.386\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.06167\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.0001*\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u003cem\u003eRothia\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.247\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.0925\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.0001*\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eGardnerella\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e4.034\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e3.28833\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.13222\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u003cem\u003eSelenomonas\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.0675\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e\u003cb\u003e0.00001*\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eGemella\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.55583\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.0001*\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u003cem\u003eStaphylococcus\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e19.63\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e14.41833\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.31051\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eGranulicatella\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.033\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.03917\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.0001*\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u003cem\u003eStreptococcus\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e4.16\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e2.93667\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.59916\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eHaemophilus\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.193\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.18167\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.84737\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u003cem\u003eVeillonella\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.435\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.43583\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.0001*\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eKomagataeibacter\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.007\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.0025\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.23496\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u003cem\u003eVibrio\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.074\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.02\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.97538\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"8\" nameend=\"c8\" namest=\"c1\"\u003e\u003cp\u003eGG: glaucoma group; CG: control group\u003c/p\u003e\u003cp\u003eData show the relative abundance in percentage.\u003c/p\u003e\u003cp\u003e* Significance level p\u0026thinsp;\u0026lt;\u0026thinsp;0.05\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eTo 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\u0026thinsp;\u0026lt;\u0026thinsp;0.05) according to the negative binomial model (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e). 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.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eTo explore patterns of microbial composition across samples, a heatmap was generated at the genus level (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e). 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.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e"},{"header":"DISCUSSION","content":"\u003cp\u003eThe 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.\u003c/p\u003e\u003cp\u003eGrowing evidence supports the existence of a gut\u0026ndash;retina axis, suggesting that alterations in the intestinal microbiota may be implicated in the pathogenesis of various ocular diseases, including retinal and neurodegenerative disorders [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. Other studies have broadened the scope of this interaction by proposing the gut\u0026ndash;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 pathways [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]. These results agree with that hypothesis, providing evidence of an ocular dysbiosis associated with glaucoma, where certain bacteria (such as \u003cem\u003eMoraxella\u003c/em\u003e, \u003cem\u003eFinegoldia\u003c/em\u003e, \u003cem\u003eLawsonella\u003c/em\u003e, or \u003cem\u003eNeisseria\u003c/em\u003e) are overrepresented. Interestingly, several of these genera have previously been linked to chronic inflammatory processes or immune modulation of mucosal surfaces [\u003cspan additionalcitationids=\"CR21 CR22\" citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e], suggesting potential mechanisms by which these microorganisms could influence ocular homeostasis and contribute to glaucoma progression.\u003c/p\u003e\u003cp\u003eThe results of this study are consistent with recent evidence highlighting the role of the ocular surface microbiota in glaucoma. Sp\u0026ouml;rri et al. [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e] reported compositional shifts in bacterial genera and tear proteins in glaucoma patients, supporting the hypothesis of a microbiota\u0026ndash;immune axis in disease pathogenesis. Similarly, Kamdougha et al. [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e] found that glaucoma, particularly when combined with dry eye, is associated with reduced microbial diversity and altered community structure. 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. [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e] demonstrated that topical glaucoma medications\u0026mdash;especially preserved eyedrops\u0026mdash;can directly alter the ocular surface microbiome, increasing the abundance of gram-negative bacteria and reducing tear film stability. Given that most patients in our cohort were under long-term topical treatment, it is plausible that some of the microbial shifts we observed may reflect not only disease-related dysbiosis but also treatment-induced alterations. 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 glaucoma [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]. 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.\u003c/p\u003e\u003cp\u003eThe 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\u0026thinsp;+\u0026thinsp;T cell count [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e] Similarly, patients with primary Sj\u0026ouml;gren\u0026rsquo;s syndrome exhibited distinct microbial profiles in crevicular fluid, with increased abundance of genera such as \u003cem\u003ePrevotella\u003c/em\u003e and \u003cem\u003eFusobacterium\u003c/em\u003e [\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e]. In severe ocular surface diseases, \u003cem\u003eStaphylococcus\u003c/em\u003e and \u003cem\u003eCorynebacterium\u003c/em\u003e were dominant in specific conditions, and microbial diversity was reduced compared to healthy controls [\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e]. Other studies have shown that the ocular microbiome remains relatively stable across different severities of meibomian gland dysfunction [\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e], while patients with keratoconus displayed significant differences in corneal microbial composition, including increased \u003cem\u003eAquabacterium\u003c/em\u003e [\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e]. Notably, even antiseptic treatments such as povidone-iodine eye drops have been shown to modulate ocular surface symptoms without disrupting microbial homeostasis [\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e]. 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 inflammation [\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e]. 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.\u003c/p\u003e\u003cp\u003eThis expanding body of literature supports the idea that microbial dysbiosis\u0026mdash;whether local or systemic\u0026mdash;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.\u003c/p\u003e\u003cp\u003eThe 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\u0026mdash;an effect similarly described in gut dysbiosis associated with chronic diseases [\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eFrom a pathophysiological perspective, the gut-eye axis hypothesis is reinforced by our data, as microbially induced immune dysregulation may extend beyond the intestinal environment. In this context, the differential presence of phyla such as Fusobacteriota or Synergistota in the glaucoma group may have implications for the modulation of local or systemic inflammatory pathways. Remarkably, \u003cem\u003eFusobacterium nucleatum\u003c/em\u003e, a representative of Fusobacteriota, has been shown to exacerbate colitis by disrupting epithelial integrity and activating pro-inflammatory STAT3 signaling via acetyl-CoA accumulation, highlighting its potential role in systemic immune modulation [\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e]. Furthermore, a murine study demonstrated that reducing Synergistota abundance through riboflavin-enriched soymilk supplementation alleviated intestinal inflammation and oxidative stress, suggesting a possible link between this phylum and gut-derived inflammatory responses [\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e]. Of particular note, the Verrucomicrobiota phylum was only present in the glaucoma group and \u003cem\u003eAkkermansia\u003c/em\u003e 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 count [\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e]. 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.\u003c/p\u003e\u003cp\u003eGiven 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.\u003c/p\u003e\u003cp\u003eIn 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. \u003cem\u003eParacoccus\u003c/em\u003e, for instance, has been studied for its metabolic versatility and redox regulation capacity, including the modulation of nitrate respiration through systems such as DksA, ppGpp, and RegAB, which may contribute to mucosal homeostasis [\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e]. Although \u003cem\u003eNeisseria\u003c/em\u003e includes pathogenic species such as \u003cem\u003eN. meningitidis\u003c/em\u003e, some commensal strains like \u003cem\u003eN. lactamica\u003c/em\u003e are known to compete with pathogens and may play a role in mucosal immune balance [\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e]. \u003cem\u003eAlloiococcus otitidis\u003c/em\u003e, while traditionally associated with otitis media, has also been isolated in other mucosal infections, but its role remains unclear and likely context-dependent [\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e]. These genera, particularly those reduced in glaucoma patients, warrant further investigation as potential components of probiotic or postbiotic formulations aimed at restoring ocular microbial equilibrium. Conversely, the increased abundance of \u003cem\u003eFusobacterium\u003c/em\u003e and \u003cem\u003eMoraxella\u003c/em\u003e in the glaucoma group may reflect a dysbiotic state that promotes inflammation or epithelial disruption. \u003cem\u003eFusobacterium nucleatum\u003c/em\u003e 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 [\u003cspan additionalcitationids=\"CR42 CR43\" citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e]. \u003cem\u003eMoraxella catarrhalis\u003c/em\u003e, a known mucosal pathogen, adheres to epithelial cells and induces inflammatory responses via outer membrane vesicles and virulence factors such as MID and UspA1 [\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e]. The \u003cem\u003eRothia\u003c/em\u003e and \u003cem\u003eFusobacterium\u003c/em\u003e genera, which are overrepresented in the glaucoma group, have also been identified in oral microbiome as risk factors for age-related macular degeneration [\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e]. 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 \u003cem\u003eKlebsiella\u003c/em\u003e, \u003cem\u003eActinobacillus\u003c/em\u003e, \u003cem\u003eEubacterium\u003c/em\u003e, \u003cem\u003ePeptostreptococcus\u003c/em\u003e, and \u003cem\u003eBergeyella\u003c/em\u003e, which have been linked to various eye diseases such as endophthalmitis, keratitis and orbital cellulitis [\u003cspan additionalcitationids=\"CR49 CR50 CR51\" citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e52\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eSimilarly, the presence of certain butyrate-producing bacterial genera, such as \u003cem\u003eFaecalibacterium\u003c/em\u003e, \u003cem\u003eRoseburia\u003c/em\u003e, \u003cem\u003eAnaerostipes\u003c/em\u003e, \u003cem\u003eRuminococcus\u003c/em\u003e and \u003cem\u003eSubdoligranulum\u003c/em\u003e, 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 cells [\u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e53\u003c/span\u003e]. \"Given that cGAS\u0026ndash;STING signaling in microglia contributes to glaucoma pathogenesis via type I IFN production [\u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e54\u003c/span\u003e], 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.\u003c/p\u003e\u003cp\u003eThese 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.\u003c/p\u003e\u003cp\u003eIn addition, this study presents certain methodological limitations that should be considered. First, the small sample size may limit the generalizability of the findings, as well as the ability to detect differences in microbial richness and composition. This study was designed as an exploratory pilot 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 patients [\u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e55\u003c/span\u003e] or diabetic retinopathy [\u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e56\u003c/span\u003e] have also used similar sample sizes to identify ocular dysbiosis. The results of our study are sufficiently concrete to demonstrate the value of the microbiota established in the eye, which for years was thought to be sterile, in understanding the pathogenesis of this glaucomatous optic neuropathy.\u003c/p\u003e\u003cp\u003eAnother 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 \u003cem\u003eMethylobacterium\u003c/em\u003e, \u003cem\u003eLactobacillus\u003c/em\u003e, \u003cem\u003eAcinetobacter\u003c/em\u003e, and \u003cem\u003ePseudomonas\u003c/em\u003e [\u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e57\u003c/span\u003e]. 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 lenses [\u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e58\u003c/span\u003e]. Therefore, future studies with larger sample sizes should consider this variable as a possible confounding factor and analyze its specific impact.\u003c/p\u003e\u003cp\u003eMost 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.\u003c/p\u003e\u003cp\u003eTo date, only three studies have investigated the relationship between the ocular microbiota and glaucoma [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e, \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e, \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e], and just one of them has analyzed tear samples [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]. 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.\u003c/p\u003e\u003cp\u003eIn summary, our results support the hypothesis of a significant alteration in the tear microbiota associated with POAG, with potential implications 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.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003ch2\u003eCOMPETING INTERESTS STATEMENT\u003c/h2\u003e\u003cp\u003eThe authors declare no competing interests.\u003c/p\u003e\u003c/p\u003e\u003ch2\u003eFUNDING DECLARATION\u003c/h2\u003e\u003cp\u003eThis study was supported by the Valencian International University-VIU (PII2023/36).\u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eENT: Conceptualization, Investigation, Methodology, Formal Analysis, Writing \u0026ndash; Review \u0026amp; Editing, Visualization, Project Administration. JTL: Formal Analysis, Data Curation, Writing \u0026ndash; Review \u0026amp; Editing. EBT: Investigation, Writing \u0026ndash; Review \u0026amp; Editing, Visualization. IAB: Investigation, Methodology, Writing \u0026ndash; Review \u0026amp; Editing, Visualization. MDPD: Conceptualization, Investigation, Resources. ENT: Investigation, Visualization. VAF: Conceptualization, Writing \u0026ndash; Review \u0026amp; Editing, Visualization.VZM: Conceptualization, Methodology, Investigation, Resources, Writing \u0026ndash; Original Draft, Writing \u0026ndash; Review \u0026amp; Editing, Visualization, Supervision, Project Administration, Funding Acquisition.\u003c/p\u003e\u003ch2\u003eData Availability\u003c/h2\u003e\u003cp\u003eThe 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\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eTham YC, Li X, Wong TY, Quigley HA, Aung T, Cheng CY. Global prevalence of glaucoma and projections of glaucoma burden through 2040: a systematic review and meta-analysis. 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Cont Lens Anterior Eye. \u003cstrong\u003e45\u003c/strong\u003e, 101431; 10.1016/j.clae.2021.02.017 (2022)\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"primary open-angle glaucoma, ocular microbiota, tear fluid, neurodegeneration, inflammation","lastPublishedDoi":"10.21203/rs.3.rs-7250015/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7250015/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThis pilot study aimed to characterize the tear microbiota in patients with primary open-angle glaucoma (POAG) and compare to healthy controls. Tear samples from 22 participants (10 with POAG, 12 controls), matched by age and sex, were analyzed using 16S rRNA sequencing to assess microbial diversity and taxonomic composition. While alpha diversity showed no significant differences, beta diversity analyses revealed distinct microbial community structures between groups. Patients with POAG exhibited a more uniform microbiota and fewer bacterial genera overall. Notably, three phyla\u0026mdash;Fusobacteriota, Planctomycetota, and Synergistota\u0026mdash;were significantly more abundant in the glaucoma group (p\u0026thinsp;\u0026lt;\u0026thinsp;0.0001). At the genus level, 23 genera displayed significant differences in relative abundance: 10 genera were significantly less abundance, and 13 genera were significantly more abundant in POAG compared to controls. These findings support the hypothesis of ocular dysbiosis associated with POAG and highlight specific microbial shifts potentially linked to inflammatory or immune mechanisms involved in disease progression. The study suggests that microbiota-based therapies such as probiotics or postbiotics might represent novel adjunctive strategies for managing glaucoma and calls for larger, longitudinal studies to validate these preliminary results and explore clinical applications.\u003c/p\u003e","manuscriptTitle":"Characterization of the Microbial Profile in Tears of Patients with Primary Open-Angle Glaucoma: Results from a Pilot Study","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-08-12 13:04:25","doi":"10.21203/rs.3.rs-7250015/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2025-08-25T10:41:23+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-08-23T10:02:01+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-08-23T08:57:09+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"9849135985827255708773263246101854084","date":"2025-08-20T05:15:36+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"221460252979935057008328356551350231613","date":"2025-08-15T07:28:22+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-08-07T06:06:32+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-08-06T09:09:59+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-08-01T09:25:17+00:00","index":"","fulltext":""},{"type":"submitted","content":"Scientific Reports","date":"2025-08-01T09:21:38+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"3f9a0d84-5e8b-46f3-9d37-aaf2f41fb1ee","owner":[],"postedDate":"August 12th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[{"id":52787947,"name":"Health sciences/Diseases"},{"id":52787948,"name":"Biological sciences/Microbiology"}],"tags":[],"updatedAt":"2025-11-10T16:04:43+00:00","versionOfRecord":{"articleIdentity":"rs-7250015","link":"https://doi.org/10.1038/s41598-025-22536-8","journal":{"identity":"scientific-reports","isVorOnly":false,"title":"Scientific Reports"},"publishedOn":"2025-11-05 15:57:38","publishedOnDateReadable":"November 5th, 2025"},"versionCreatedAt":"2025-08-12 13:04:25","video":"","vorDoi":"10.1038/s41598-025-22536-8","vorDoiUrl":"https://doi.org/10.1038/s41598-025-22536-8","workflowStages":[]},"version":"v1","identity":"rs-7250015","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7250015","identity":"rs-7250015","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}