Comparative portrayal of ocular surface microbe with and without dry eye

Unique composition of ocular surface microbiome in the old patients with dry eye and diabetes mellitus in a community from Shanghai, China

BACKGROUND

This study investigates the variations in microbiome abundance and diversity on the ocular surfaces of diabetic patients suffering from dry eye within a community setting. The goal is to offer theoretical insights for the community-level prevention and treatment of dry eye in diabetic cohorts.

METHODS

Dry eye screening was performed in the Shanghai Cohort Study of Diabetic Eye Disease (SCODE) from July 15, 2021, to August 15, 2021, in the Xingjing community; this study included both a population with diabetes and a normal population. The population with diabetes included a dry eye group (DM-DE, n = 40) and a non-dry eye group (DM-NoDE, n = 39). The normal population included a dry eye group (NoDM-DE, n = 40) and a control group (control, n = 39). High-throughput sequencing of the 16 S rRNA V3-V4 region was performed on conjunctival swab from both eyes of each subject, and the composition of microbiome on the ocular surface of each group was analyzed.

RESULTS

Significant statistical differences were observed in both α and β diversity of the ocular surface microbiome among the diabetic dry eye, diabetic non-dry eye, non-diabetic dry eye, and normal control groups (P < 0.05).

CONCLUSIONS

The study revealed distinct microecological compositions on the ocular surfaces between the diabetic dry eye group and other studied groups. Firmicutes and Anoxybacillus were unique bacterial phyla and genera in the dry eye with DM group, while Actinobacteria and Corynebacterium were unique bacterial phyla and genera in the normal control group.

Longitudinal Changes of Ocular Surface Microbiome in Patients Undergoing Hemopoietic Stem Cell Transplant (HSCT)

PURPOSE

To evaluate changes in the ocular surface microbiome (OSM) between pre- and post-haemopoietic stem cell transplant (HSCT) in the same patient, and to assess the potential impact of these changes in ocular graft-versus-host disease (o)GVHD development.

METHODS

Lower fornix conjunctival swabs of 24 patients were obtained before and after HSCT and subjected to DNA extraction for amplification and sequencing of the V3-V4 regions of the bacterial 16S rRNA gene. The obtained reads were reconstructed, filtered, and clustered into zero-radius operational taxonomic units (zOTUs) at 97% identity level before taxonomic assignment, and biodiversity indexes were calculated. Transplant characteristics were recorded, and dry eye was diagnosed and staged 1-4 according to the Dry Eye WorkShop (DEWS) score.

RESULTS

No significant difference in OSM alpha diversity between pre- and post-transplant was found. A significant difference in beta diversity was observed between patients with a DEWS score of 1 versus 3 (p = 0.035). Increased corneal damage between pre- and post-HSCT was significantly associated with a decrease in alpha diversity. The changes in OSM were not associated with oGVHD, nor with any transplant parameter.

CONCLUSIONS

This preliminary study is the first study to analyse changes in the OSM before and after HSCT longitudinally. No trend in OSM biodiversity, microbial profile, or overall composition changes before and after HSCT was significant or associated with oGVHD onset. The great variability in the observed OSM profiles seems to suggest the absence of a patient-specific OSM "signature".

Composition and diversity of meibum microbiota in meibomian gland dysfunction and the correlation with tear cytokine levels

Meibomian gland dysfunction (MGD) leads to meibum stasis and pathogenic bacteria proliferation. We determined meibum microbiota via next-generation sequencing (NGS) and examined their association with tear cytokine levels in patients with MGD. This cross-sectional study included 44 moderate-severe patients with MGD and 44 healthy controls (HCs). All volunteers underwent assessment with the ocular surface disease index questionnaire, Schirmer without anesthesia, tear break-up time, Oxford grading of ocular surface staining, and lid and meibum features. Sample collection included tears for cytokine detection and meibum for 16S rRNA NGS. No significant differences were observed in the α-diversity of patients with MGD compared with that in HCs. However, Simpson's index showed significantly decreased α-diversity for severe MGD than for moderate MGD (p = 0.045). Principal coordinate analysis showed no significant differences in β-diversity in meibum samples from patients with MGD and HCs. Patients with MGD had significantly higher relative abundances of Bacteroides (8.54% vs. 6.00%, p = 0.015) and Novosphingobium (0.14% vs. 0.004%, p = 0.012) than the HCs. Significantly higher interleukin (IL)-17A was detected in the MGD group than in the HC group, particularly for severe MGD (p = 0.008). Although Bacteroides was more abundant in the MGD group than in the HC group, it was not positively correlated with IL-17A. The relationship between core meibum microbiota and tear cytokine levels remains unclear. However, increased Bacteroides and Novosphingobium abundance may be critical in MGD pathophysiology.

The Microbiome, Ocular Surface, and Corneal Disorders

The ocular surface microbiome is an emerging field of study that seeks to understand how the community of microorganisms found on the ocular surface may help maintain homeostasis or can potentially lead to disease and dysbiosis. Initial questions include whether the organisms detected on the ocular surface inhabit that ecological niche and, if so, whether there exists a core microbiome found in most or all healthy eyes. Many questions have emerged around whether novel organisms and/or a redistribution of organisms play a role in disease pathogenesis, response to therapies, or convalescence. Although there is much enthusiasm about this topic, the ocular surface microbiome is a new field with many technical challenges. These challenges are discussed in this review as well as a need for standardization to adequately compare studies and advance the field. In addition, this review summarizes the current research on the microbiome of various ocular surface diseases and how these findings may impact treatments and clinical decision-making.

Ocular surface microbiota dysbiosis contributes to the high prevalence of dry eye disease in diabetic patients

People with diabetes mellitus (DM) are at an increased risk for developing dry eye disease (DED). However, the mechanisms underlying this phenomenon remain unclear. Recent studies have found that the ocular surface microbiota (OSM) differs significantly between patients with DED and healthy people, suggesting that OSM dysbiosis may contribute to the pathogenesis of DED. This hypothesis provides a new possible explanation for why diabetic patients have a higher prevalence of DED than healthy people. The high-glucose environment and the subsequent pathological changes on the ocular surface can cause OSM dysbiosis. The unbalanced microbiota then promotes ocular surface inflammation and alters tear composition, which disturbs the homeostasis of the ocular surface. This "high glucose-OSM dysbiosis" pathway in the pathogenesis of DED with DM (DM-DED) is discussed in this review.

Metagenomic analysis of microbiological changes on the ocular surface of diabetic children and adolescents with a dry eye

BACKGROUND

Microbiome changes on the ocular surface may cause dry eyes. A metagenome assay was used to compare the microbiome composition and function of the ocular surface between diabetic children and adolescents with dry eye, diabetic children and adolescents without dry eye, and normal children.

MATERIALS AND METHODS

Twenty children and adolescents aged 8 to 16 with diabetes were selected from the Shanghai Children and Adolescent Diabetes Eye Study. Ten healthy children and adolescents belonging to the same age group were selected from the outpatient clinic during the same period. The participants were classified into the dry eye group (DM-DE group, n = 10), the non-dry eye group (DM-NDE group, n = 10) and the normal group (NDM group, n = 10). A conjunctival sac swab was collected for metagenomic sequencing, and the relationship between the microbiome composition and functional gene differences on the ocular surface with dry eye was studied.

RESULTS

The classification composition and metabolic function of the microorganisms on the ocular surface of children in the 3 groups were analyzed. It was found that children's ocular microbiota was composed of bacteria, viruses and fungi. There were significant differences in α diversity and β diversity of microbial composition of ocular surface between DM-DE group and NDM group(P<0.05). There were significant differences in α and β diversity of metabolic pathways between the two groups(P<0.05). The functional pathways of ocular surface microorganisms in diabetic children with dry eyes were mainly derived from human disease, antibiotic resistance genes, carbohydrate, coenzyme and lipid transport and metabolism-related functional genes; In normal children, the functional pathways were mainly derived from replication, recombination, repair, signal transduction and defense-related functional genes.

CONCLUSION

The DM-DE group have unique microbial composition and functional metabolic pathways. The dominant species and unique metabolic pathways of the ocular surface in the DM-DE group may be involved in the pathogenesis of dry eye in diabetic children.

Contact lenses and ocular dysbiosis, from the transitory to the pathological

Normal ocular microbiota is composed of different Gram-negative and positive bacterial communities that act as commensals on the ocular surface. An imbalance in the homeostasis of the native species or dysbiosis triggers functional alterations that can eventually lead to ocular conditions, indicating the use of contact lenses as the most relevant predisposing factor. Through a bibliographic review that added scientific articles published between 2018 and 2022, the relationship between healthy ocular microbiota and dysbiosis associated with the use of contact lenses that trigger ocular conditions was analyzed. The ocular microbiota in healthy individuals is mainly composed of bacteria from the phyla: Proteobacteria, Actinobacteria and Firmicutes. These bacterial communities associated with the use of contact lenses develop dysbiosis, observing an increase in certain genera such as Staphylococcus spp. and Pseudomonas spp., which under normal conditions are commensals of the ocular surface, but as their abundance is increased, they condition the appearance of various ocular conditions such as corneal infiltrative events, bacterial keratitis and corneal ulcer. These pathologies tend to evolve rapidly, which, added to late detection and treatment, can lead to a poor visual prognosis. It is suggested that professionals in the ophthalmology area learn about the composition of the communities of microorganisms that make up this ocular microbiota, in order to correctly distinguish and identify the causative agent, thereby providing a adequate and effective treatment to the user.

Ocular microbiome changes in dry eye disease and meibomian gland dysfunction

The most common and chronic ocular problem of aging is dry eye disease (DED) and the associated condition of meibomian gland dysfunction (MGD). The resident ocular surface bacteria may have a role in maintaining homeostasis and perturbation may contribute to disease development. The aim of this study was to compare the microbiomes of the conjunctiva and eyelid margin in humans with mild and moderate DED and controls using 16 S rRNA gene sequencing. The conjunctiva and lid margin of three cohorts (N = 60; MGD, MGD with lacrimal dysfunction [MGD + LD] and controls) were swabbed bilaterally three times over three months. Microbial communities were analysed by extracting DNA and sequencing the V3-V4 region of the 16 S ribosomal RNA gene using the Illumina MiSeq platform. Sequences were quality filtered, clustered into amplicon sequence variants (ASVs) using UNOISE algorithm and taxonomically classified using a Bayesian Last Common Ancestor (BCLA) algorithm against the GTDB 2207 database. The overall microbial communities of the MGD, MGD + LD and control groups were significantly different from each other (P = 0.001). The MGD and MGD + LD dry eye groups showed greater variability between individuals compared to the control (PERMDISP, P < 0.01). There was decreased richness and diversity in females compared to males for the conjunctiva (P < 0.04) and eyelid margin (P < 0.018). The conjunctiva in the MGD + LD group had more abundant Pseudomonas azotoformans, P. oleovorans and Caballeronia zhejiangensis compared to MGD and control (P < 0.05), while the MGD group had more abundant Corynebacterium macginleyi and C. kroppenstedtii compared to control (P < 0.05). The lid margin in MGD was more abundant in C. macginleyi, C. accolens, and C. simulans compared to the MGD + LD and control (P < 0.05). There were differences in the overall microbial community composition and certain taxa, including increased levels of lipophilic bacteria, on the conjunctiva and eyelid margin in mild to moderate DED/MGD compared to controls. DED/MGD was also associated with a reduced bacterial richness and diversity in females.

Challenges and insights in the exploration of the low abundance human ocular surface microbiome

Purpose

The low microbial abundance on the ocular surface results in challenges in the characterization of its microbiome. The purpose of this study was to reveal factors introducing bias in the pipeline from sample collection to data analysis of low-abundant microbiomes.

Methods

Lower conjunctiva and lower lid swabs were collected from six participants using either standard cotton or flocked nylon swabs. Microbial DNA was isolated with two different kits (with or without prior host DNA depletion and mechanical lysis), followed by whole-metagenome shotgun sequencing with a high sequencing depth set at 60 million reads per sample. The relative microbial compositions were generated using the two different tools MetaPhlan3 and Kraken2.

Results

The total amount of extracted DNA was increased by using nylon flocked swabs on the lower conjunctiva. In total, 269 microbial species were detected. The most abundant bacterial phyla were Actinobacteria, Firmicutes and Proteobacteria. Depending on the DNA extraction kit and tool used for profiling, the microbial composition and the relative abundance of viruses varied.

Conclusion

The microbial composition on the ocular surface is not dependent on the swab type, but on the DNA extraction method and profiling tool. These factors have to be considered in further studies about the ocular surface microbiome and other sparsely colonized microbiomes in order to improve data reproducibility. Understanding challenges and biases in the characterization of the ocular surface microbiome may set the basis for microbiome-altering interventions for treatment of ocular surface associated diseases.

Targeting the Gut-Eye Axis: An Emerging Strategy to Face Ocular Diseases

The human microbiota refers to a large variety of microorganisms (bacteria, viruses, and fungi) that live in different human body sites, including the gut, oral cavity, skin, and eyes. In particular, the presence of an ocular surface microbiota with a crucial role in maintaining ocular surface homeostasis by preventing colonization from pathogen species has been recently demonstrated. Moreover, recent studies underline a potential association between gut microbiota (GM) and ocular health. In this respect, some evidence supports the existence of a gut-eye axis involved in the pathogenesis of several ocular diseases, including age-related macular degeneration, uveitis, diabetic retinopathy, dry eye, and glaucoma. Therefore, understanding the link between the GM and these ocular disorders might be useful for the development of new therapeutic approaches, such as probiotics, prebiotics, symbiotics, or faecal microbiota transplantation through which the GM could be modulated, thus allowing better management of these diseases.

Effects of Carboxymethylcellulose Artificial Tears on Ocular Surface Microbiome Diversity and Composition, A Randomized Controlled Trial

Purpose

Carboxymethylcellulose is an artificial tear ingredient known to decrease gut microbiome diversity when ingested. This study examines the effect of carboxymethylcellulose on ocular surface microbiome diversity and composition.

Methods

Healthy adult participants without significant ophthalmic disease or concurrent carboxymethylcellulose artificial tear use were allocated randomly to take carboxymethylcellulose or control polyethylene glycol artificial tears for seven days. Conjunctival swabs were collected before and after artificial tear treatment. This trial is registered at clinicaltrials.gov (NCT05292755). Primary outcomes included abundance of bacterial taxa and microbiome diversity as measured by the Chao-1 richness estimate, Shannon's phylogenetic diversity index, and UniFrac analysis. Secondary outcomes included Ocular Surface Disease Index scores and artificial tear compliance.

Results

Of the 80 enrolled participants, 66 completed the trial. Neither intervention affected Chao-1 richness (analysis of variance [ANOVA], P = 0.231) or Shannon's diversity index (ANOVA, P = 0.224). Microbiome samples did not separate by time point (permutation multivariate analysis of variance [PERMANOVA], P = 0.223) or intervention group (PERMANOVA, P = 0.668). LEfSe taxonomic analysis revealed that carboxymethylcellulose depleted several taxa including Bacteroides and Lachnoclostridium, but enriched Enterobacteriaceae, Citrobacter, and Gordonia. Both interventions decreased OSDI scores (Wilcoxon signed rank test, P < 0.05), but there was no significant difference between interventions (Mann-Whitney U, P = 0.54).

Conclusions

Carboxymethylcellulose artificial tears increased Actinobacteriota but decreased Bacteroides and Firmicutes bacteria. Carboxymethylcellulose artificial tears do not affect ocular surface microbiome diversity and are not significantly more effective than polyethylene glycol artificial tears for dry eye treatment.

Translational Relevance

The 16S microbiome analysis has revealed small changes in the ocular surface microbiome associated with artificial tear use.

Impact of Exposomes on Ocular Surface Diseases

Ocular surface diseases (OSDs) are significant causes of ocular morbidity, and are often associated with chronic inflammation, redness, irritation, discomfort, and pain. In severe OSDs, loss of vision can result from ocular surface failure, characterised by limbal stem cell deficiencies, corneal vascularisation, corneal opacification, and surface keratinisation. External and internal exposomes are measures of environmental factors that individuals are exposed to, and have been increasingly studied for their impact on ocular surface diseases. External exposomes consist of external environmental factors such as dust, pollution, and stress; internal exposomes consist of the surface microbiome, gut microflora, and oxidative stress. Concerning internal exposomes, alterations in the commensal ocular surface microbiome of patients with OSDs are increasingly reported due to advancements in metagenomics using next-generation sequencing. Changes in the microbiome may be a consequence of the underlying disease processes or may have a role in the pathogenesis of OSDs. Understanding the changes in the ocular surface microbiome and the impact of various other exposomes may also help to establish the causative factors underlying ocular surface inflammation and scarring, the hallmarks of OSDs. This review provides a summary of the current evidence on exposomes in various OSDs.

Comparison of Conjunctival Sac Microbiome between Low and High Myopic Eyes

Microbial communities played a vital role in maintaining homeostasis of ocular surface. However, no studies explored the myopia-associated conjunctiva microbiota changes until now. In this study, conjunctival sac swab specimens were collected from 12 eyes of low myopia (LM), and 14 eyes of high myopia (HM) patients. The V3-V4 region of the 16S rRNA gene was amplified and then sequenced. Statistical analysis was performed to investigate differences in the taxonomy and diversity between two groups. Compared to LM, higher Ocular Surface Disease Index (OSDI) scores were observed in HM group. The Shannon index of the HM was lower than that of the LM group (P = 0.017). Principle coordinate analysis and Partial Least Squares Discrimination Analysis showed distinct microbiome composition between two groups. At the phylum level, there were higher relative abundances of Proteobacteria (68.27% vs 38.51%) and lower abundances of Actinobacteria (3.71% vs 9.19%) in HM, compared to LM group (P = 0.031, 0.010, respectively). At the genus level, the abundances of Acinetobacter in HM (18.16%) were significantly higher than the LM (6.52%) group (P = 0.011). Actinobacteria levels were negatively correlated with the myopic spherical equivalent and OSDI scores. Moreover, positive correlations were found between Proteobacteria levels and OSDI scores, Acinetobacter levels were positively correlated with myopic spherical equivalent and OSDI scores. In conclusion, HM Patients have bacterial microbiota imbalance in the conjunctival sac, compared with LM patients. Proteobacteria, Actinobacteria, Acinetobacter may play roles in the HM associated ocular surface irritation.

An insight on the eye bacterial microbiota and its role on dry eye disease

The human ocular surface hosts a bacterial assemblage that integrates a diverse and complex microbiome. This bacterial microbiota is part of a healthy eye and plays a protective role in it. However, this ocular bacterial assemblage may alter the ocular surface inflammation response and can influence the development and progression of dry eye disease. For this reason, the present review describes the changes generated on the ocular surface by bacterial assemblages during the development of dry eye disease. Likewise, the interaction of this microbiota with the other inflammatory factors that influence the development of this disease is analyzed, as well as the use of treatments focused on modifying the bacteria on the ocular surface.

Significantly different results in the ocular surface microbiome detected by tear paper and conjunctival swab

BACKGROUND

Great variation has been observed in the composition of the normal microbiota of the ocular surface, and therefore, in addition to differences in detection techniques, the method of collecting ocular surface specimens has a significant impact on the test results.The goal of this study is to ascertain whether the eye surface microbial communities detected by two different sampling methods are consistent and hence explore the feasibility of using tear test paper instead of conjunctival swabs to collect eye surface samples for microbial investigation.

MATERIALS AND METHODS

From July 15, 2021, to July 30, 2021, nonirritating tear test strips and conjunctival swabs of both eyes were used in 158 elderly people (> 60 years old) (79 diabetic and 79 nondiabetic adults) in Xinjing Community for high-throughput sequencing of the V3-V4 region of the 16S rRNA gene. The composition of the microbial communities in tear test paper and conjunctival swab samples was analyzed.

RESULTS

There was no statistically significant difference in Alpha diversity of ocular surface microorganisms represented by tear strip and conjunctival swab in diabetic group (P > 0.05), but there was statistically significant difference in Alpha diversity of ocular surface microorganisms detected by tear strip and conjunctival swab in nondiabetic group (P < 0.05). There were statistically significant differences in Beta diversity of ocular surface microorganisms detected by two sampling methods between diabetic group and nondiabetic group (P < 0.05). There were statistically significant differences in ocular surface microorganisms detected by tear strip method between diabetic group and nondiabetic group (P < 0.05), but there was no statistically significant difference in conjunctival swab method (P > 0.05).

CONCLUSIONS

Tear test paper and conjunctival swabs detect different compositions of microbes through two different techniques of eye surface microbe sampling. Tear test paper cannot completely replace conjunctival swab specimens for the study of microbes related to eye surface diseases.

Investigating the Ocular Surface Microbiome: What Can It Tell Us?

While pathogens of the eye have been studied for a very long time, the existence of resident microbes on the surface of healthy eyes has gained interest only recently. It appears that commensal microbes are a normal feature of the healthy eye, whose role and properties are currently the subject of extensive research. This review provides an overview of studies that have used 16s rRNA gene sequencing and whole metagenome shotgun sequencing to characterize microbial communities associated with the healthy ocular surface from kingdom to genus level. Bacteria are the primary colonizers of the healthy ocular surface, with three predominant phyla: Proteobacteria, Actinobacteria, and Firmicutes, regardless of the host, environment, and method used. Refining the microbial classification to the genus level reveals a highly variable distribution from one individual and study to another. Factors accounting for this variability are intriguing - it is currently unknown to what extent this is attributable to the individuals and their environment and how much is artifactual. Clearly, it is technically challenging to accurately describe the microorganisms of the ocular surface because their abundance is relatively low, thus, permitting substantial contaminations. More research is needed, including better experimental standards to prevent biases, and the exploration of the ocular surface microbiome's role in a spectrum of healthy to pathological states. Outcomes from such research include the opportunity for therapeutic interventions targeting the microbiome.

DNA extraction protocol impacts ocular surface microbiome profile

Purpose

The aim of this study is to provide a reference frame to allow the comparison and interpretation of currently published studies on 16S ribosomal ribonucleic acid amplicon sequencing of ocular microbiome samples using different DNA extraction protocols. Alongside, the quantitative and qualitative yield and the reproducibility of different protocols has been assessed.

Methods

Both eyes of 7 eligible volunteers were sampled. Five commercially available DNA extraction protocols were selected based on previous publications in the field of the ocular surface microbiome and 2 host DNA depletion protocols were added based on their reported effective host DNA depletion without significant reduction in bacterial DNA concentration. The V3-V4 region of the 16S rRNA gene was targeted using Illumina MiSeq sequencing. The DADA2 pipeline in R was used to perform the bio-informatic processing and taxonomical assignment was done using the SILVA v132 database. The Vegdist function was used to calculate Bray-Curtis distances and the Galaxy web application was used to identify potential metagenomic biomarkers via linear discriminant analysis Effect Size (LEfSe). The R package Decontam was applied to control for potential contaminants.

Results

Samples analysed with PowerSoil, RNeasy and NucleoSpin had the highest DNA yield. The host DNA depletion kits showed a very low microbial DNA yield; and these samples were pooled per kit before sequencing. Despite pooling, 1 of both failed to construct a library.Looking at the beta-diversity, clear microbial compositional differences - dependent on the extraction protocol used - were observed and remained present after decontamination. Eighteen genera were consistently retrieved from the ocular surface of every volunteer by all non-pooled extraction kits and a comprehensive list of differentially abundant bacteria per extraction method was generated using LefSe analysis.

Conclusion

High-quality papers have been published in the field of the ocular surface microbiome but consensus on the importance of the extraction protocol used are lacking. Potential contaminants and discriminative genera per extraction protocol used, were introduced and a reference frame was built to facilitate both the interpretation of currently published papers and to ease future choice - making based on the research question at hand.

Microbiological Characteristics of Ocular Surface Associated With Dry Eye in Children and Adolescents With Diabetes Mellitus

Purpose

To analyze the characteristics of ocular surface microbial composition in children and adolescents with diabetes mellitus and dry eye (DE) by tear analysis.

Methods

We selected 65 children and adolescents aged 8 to 16 years with DE and non-DE diabetes mellitus and 33 healthy children in the same age group from the Shanghai Children and Adolescent Diabetes Eye Study. Tears were collected for high-throughput sequencing of the V3 and V4 region of 16S rRNA. The ocular surface microbiota in diabetic DE (DM-DE; n = 31), diabetic with non-DE (DM-NDE; n = 34), and healthy (NDM; n = 33) groups were studied. QIIME2 software was used to analyze the microbiota of each group.

Results

The DM-DE group had the highest amplicon sequence variants, and the differences in α-diversity and β-diversity of micro-organisms in the ocular surfaces of DM-DE, diabetic with non-DE, and healthy eyes were statistically significant (P < 0.05). Bacteroidetes (15.6%), Tenericutes (9.3%), Firmicutes (21.8%), and Lactococcus (7.9%), Bacteroides (7.8%), Acinetobacter (3.9%), Clostridium (0.8%), Lactobacillus (0.8%) and Streptococcus (0.2%) were the specific phyla and genera, respectively, in the DM-DE group.

Conclusions

Compared with the patients with non-DE and healthy children, the microbial diversity of the ocular surface in children and adolescents with diabetes mellitus and DE was higher with unique bacterial phyla and genera composition.

Shotgun metagenomic sequencing analysis of ocular surface microbiome in Singapore residents with mild dry eye

The ocular surface microbiome has implications for ocular surface inflammation and immunology. Previous shotgun metagenomics analyses were performed in China, showing results that differed according to environment and age. Patients with Sjogren's syndrome were reported to have altered conjunctival microbiome, but such studies have not been done in milder dry eye. The aim of this study is to describe the conjunctival microbiome in people with mild dry eye in Singapore. Samples were collected from 14 participants with mild dry eye and 10 age-matched comparison participants recruited from Singapore National Eye Centre (SNEC) clinics. Shotgun metagenomic sequencing analysis was employed to evaluate the conjunctival microbiome composition. Proteobacteria formed the predominant phylum in the conjunctiva. As in a study from a coastal city in China, Achromobacter spp. was numerically most abundant. Compared to age-matched controls, the conjunctival microbial composition in mild dry eye was similar. Several microorganisms, including Streptococcus spp. increased in representation with age, and the abundance of Staphylococcus correlated with Schirmer readings. In addition, when cultured corneal epithelial cells were exposed to three strains of Achromobacter xylosoxidans, cytokines such as TNF-α and IL-6 were upregulated in the cell lysates and supernatants. Ourresults suggest that age is an important factor that affects composition of the conjunctival microbiome, and relative abundance of specific microorganism may vary according to the environment of the human host.

The species-level microbiota of healthy eyes revealed by the integration of metataxonomics with culturomics and genome analysis

Objectives

To characterize the healthy ocular surface microbiota at the species level, including cultured and uncultured taxa.

Methods

We integrated the metataxonomic method with culturomics and genome sequencing analysis of selected isolated strains to better illustrate the taxonomic structure of the ocular surface microbiota. The metataxonomics used the full-length 16S rRNA gene sequences and the operational phylogenetic unit strategy, which can precisely identify the cultured and uncultured or potentially new taxa to species level based on the phylogenetic tree constructed.

Results

We detected 1,731 operational phylogenetic units (OPUs) in 196 healthy eyes from 128 people, affiliated to 796 cultured species, 784 potentially new species, and 151 potentially new higher taxa. The microbiota for each eye had 49.17 ± 35.66 OPUs. Of the 796 cultured species, 170 (21.36%) had previously caused clinical infections. Based on where they were initially isolated, the ocular surface microbiota mainly came from human body sites (34.55%), the environment (36.93%), plants (9.05%), animals (4.90%), and others; 428 strains were isolated from 20 eyes, affiliated to 42 species, and had come from the environment (33.33%) and the skin (16.67%). Of these, 47.62% had previously caused clinical infections. Genome analysis of 73 isolators revealed that 68.5% of them carried antibiotic resistance genes. The most frequently isolated genera, namely Staphylococcus, Streptococcus, and Moraxella, had an average of 5.30, four, and three resistance genes per strain, respectively.

Discussion

The study found that the ocular surface microbiota mainly came from the environment, plants, animals, food, and human body sites such as the skin, oral cavity, upper respiratory tract, etc. No core member of ocular surface microbiota was detected at the species level. The human eyes were invaded and colonized by bacteria from the exposed environment, some of which were capable of causing infections in humans and carried antibiotic resistance genes. Preventive measures should be developed to protect our eyes from danger.

Ocular surface microbiota: Ophthalmic infectious disease and probiotics

Recently, increasing studies have emphasized the importance of commensal bacteria in humans, including microbiota in the oral cavity, gut, vagina, or skin. Ocular surface microbiota (OSM) is gaining great importance as new methodologies for bacteria DNA sequencing have been published. This review outlines the current understanding and investigation of OSM and introduces the new concept of the gut–eye axis. Moreover, we have collected current studies that focus on the relationship between ophthalmic infectious disease and alterations in the OSM or human gut microbiota. Finally, we discuss the current application of probiotics in ophthalmic infectious disease, its limitations to date, and futural directions.

Updates in diagnostics, treatments, and correlations between oral and ocular manifestations of Sjogren's syndrome

Sjogren's syndrome (SS) is characterized as an autoimmune disorder targeting secretory glands, including the lacrimal and salivary glands, causing dry eye and dry mouth predominantly in women over the age of 40. In this review, we summarize recent advancements in SS diagnostics, treatments, and our understanding of correlations between oral and ocular manifestations of SS. Google Scholar and PubMed databases were utilized to search peer-reviewed papers since 2016 on SS diagnosis, treatment, and correlations between oral and ocular manifestations. For diagnostics, we discuss the updated SS classification criteria by the American College of Rheumatology/European League Against Rheumatism (ACR/EULAR), new biomarkers, and compare studies of current diagnostic methods with alternative technologies. For treatments, we discuss topical, systemic, and surgical treatment developments in the management of oral and ocular symptoms of SS as there is still no cure for the disorder. Finally, we report studies that directly suggest correlations between the ocular surface disease and oral disease in SS, as well as shared abnormalities in the microbiome and cytokine expression that may be correlated. We conclude by stating limitations to our review as well as paths moving forward. Elucidating correlations between oral and ocular manifestations may be the key to furthering our understanding of SS pathogenesis as well as defining new standards for diagnosis and treatment.

Comparison of the ocular surface microbiota between thyroid-associated ophthalmopathy patients and healthy subjects

Purpose

Thyroid-associated ophthalmopathy (TAO) is a chronic autoimmune disease. In this study, high-throughput sequencing was used to investigate the diversity and composition of the ocular microbiota in patients with TAO.

Methods

Patients with TAO did not receive treatment for the disease and did not have exposed keratitis. Patients with TAO (TAO group) and healthy individuals (control group) were compared. All samples were swabbed at the conjunctival vault of the lower eyelid. The V3 to V4 region of the 16S rDNA was amplified using polymerase chain reaction and sequenced on the Illumina HiSeq 2500 Sequencing Platform. Statistical analysis was performed to analyze the differences between the groups and the correlation between ocular surface microbiota and the disease. The ocular surface microbiota of patients and healthy individuals were cultured.

Results

The ocular surface microbiota structure of TAO patients changed significantly. The average relative abundance of Bacillus and Brevundimonas increased significantly in the TAO group. Corynebacterium had a significantly decreased relative abundance (P<0.05). Paracoccus, Haemophilus, Lactobacillus, and Bifidobacterium were positively correlated with the severity of clinical manifestations or disease activity (P<0.05). Bacillus cereus and other opportunistic pathogens were obtained by culture from TAO patients.

Conclusions

This study found that the composition of ocular microbiota in patients with TAO was significantly different from that in healthy individuals. The ocular surface opportunistic pathogens, such as Bacillus, Brevundimonas, Paracoccus, and Haemophilus in TAO patients, increase the potential risk of ocular surface infection. The findings of this study provide a new avenue of research into the mechanism of ocular surface in TAO patients.

Metagenomic profiling of ocular surface microbiome changes in Demodex blepharitis patients

Purpose

To compare the ocular surface and meibum microbial communities of humans with Demodex Blepharitis (DB) and healthy controls.

Methods

Conjunctival sac and meibum samples from 25 DB patients and 11 healthy controls were analyzed using metagenomic next-generation sequencing (mNGS).

Results

The alpha-diversity of the conjunctival sac microbiome of the DB group (observed, Chao1, ACE) was lower than that of the control group, whereas all meibum diversity indicators were similar. In conjunctival samples, the relative abundance (RA) of the phylum Proteobacteria was significantly higher (p=0.023), and the RA of both phyla Actinobacteria and Firmicutes was significantly lower (p=0.002, 0.025, respectively) in the DB group than that in the control group. In meibum samples, the RA of the phyla Proteobacteria and Actinobacteria were similar, whereas that of the phylum Firmicutes was significantly lower in the DB group (p=0.019) than that in the control group. Linear discriminant analysis with effect size measurement of the conjunctival and meibum microbiomes showed that Sphingobium sp. YG1 and Acinetobacter guillouiae were enriched in the DB group. Sphingobium sp. YG1, Acinetobacter guillouiae and Pseudomonas putida in the DB group were related to more severe ocular surface clinical parameters. Discriminative genera’s principal coordinate analysis separated all control and DB microbiomes into two distinct clusters.

Conclusions

Proteobacteria’s increased prevalence may indicate ocular microbial community instability. The species Sphingobium sp. YG1 and Acinetobacter guillouiae are potentially pathogenic bacterial biomarkers in DB. Demodex infection mainly affects the ocular surface microbiome rather than penetrating deeper into the meibomian gland.

Bacteria and Dry Eye: A Narrative Review

(1) Background: Dry eye is a multifactorial disease of the ocular surface, the incidence of which has been increasing sharply. The pathogenesis of dry eye, especially in terms of the bacterial flora, has drawn great attention. Additionally, the potential treatment methods need to be explored. (2) Methods: We reviewed more than 100 studies and summarized them briefly in a review. (3) Results: We summarized the bacterial communities found on the ocular surface in the general population and patients with dry eye and found a relationship between dry eye and antibiotic therapy. We identified the possible mechanisms of bacteria in the development of dry eye by discussing factors such as the destruction of the antibacterial barrier, infectious diseases, microbiome homeostasis, inflammatory factors on the ocular surface and vitamin deficiency. (4) Conclusion: We systematically reviewed the recent studies to summarize the bacterial differences between patients with dry eye and the general population and brought up several possible mechanisms and possible treatment targets.

Temporal impacts of topical ceftazidime and tobramycin-vancomycin mixtures on the ocular surface microbiota in rabbits

Antibiotics are one of the important factors that can alter the diversity and composition of ocular surface microbiota. At present, there are a few studies about the antibiotic effect on ocular surface microbiota, including its time-dependent changes. However, these limited studies have revealed various results, and more experiments are required. In this study, we used 16 S rRNA sequencing method to investigate the effects of topical ceftazidime and tobramycin-vancomycin mixtures on the ocular surface microbiota and the temporal changes of the microbiota after discontinuing antibiotic treatment in rabbits. Seventeen healthy rabbits were treated with 5% ceftazidime and a mixture of 0.3% tobramycin-5% vancomycin (CTV) eye drops on one eye four times a day for 7 days. Swab samples of conjunctiva sacs were collected before antibiotic treatment (D0), 12 h after the last antibiotic treatment (D8) and two further time points on Day 15 (D15) and Day 30 (D30). We found that the species diversity of the ocular surface microbiota increased significantly at D8 and was restored at D15, namely, one week after antibiotic cessation. The community structure of the ocular surface microbiota changed after treatment with CTV but recovered at D30. At D8, the relative abundances of 13 bacterial phyla of the initial top 20 phyla and 11 bacterial genera of the initial top 20 genera were significantly different from the relative abundances of the phyla and genera at D0. Furthermore, the relative abundance of the dominant phylum Epsilonbacteraeota obviously decreased, while Proteobacteria and Bacteroidetes markedly increased. For dominant genera, the relative abundance of Helicobacter notably decreased, while Acinetobacter and Pasteurella greatly increased. Thirteen altered bacterial phyla and 7 of 11 altered bacterial genera recovered to preantibiotic levels at D30. In addition, there was a group of nondominant and rare bacteria enriched at D8, and most of them were restored at D30. In conclusion, the species diversity, community structure and composition of the ocular surface microbiota changed greatly after exposure to CTV, but they tended to be restored within weeks after discontinuing antibiotic treatment.

Analysis of Conjunctival Sac Microbiome in Dry Eye Patients With and Without Sjögren's Syndrome

Purpose

To analyze the conjunctival sac microbial communities in patients with Sjögren's syndrome-associated dry eyes (SSDE) and non-Sjögren's syndrome-associated dry eyes (NSSDE), compared with normal controls (NC).

Methods

Conjunctival sac swab samples from 23 eyes of SSDE, 36 eyes of NSSDE, and 39 eyes of NC were collected. The V3–V4 region of the 16S ribosomal RNA (rRNA) gene high-throughput sequencing was performed on an Illumina MiSeq platform and analyzed using Quantitative Insights Into Microbial Ecology (QIIME). Alpha diversity was employed to analyze microbiome diversity through Chao1 and Shannon indexes. Beta diversity was demonstrated by the principal coordinates analysis (PCoA) and Partial Least Squares Discrimination Analysis (PLS-DA). The relative abundance was bioinformatically analyzed at the phylum and genus levels.

Results

The alpha diversity was lower in patients with dry eye disease (Shannon index: NC vs. SSDE: P = 0.020, NC vs. NSSDE: P = 0.029). The beta diversity showed divergent microbiome composition in different groups (NC vs. SSDE: P = 0.001, NC vs. NSSDE: P = 0.001, NSSDE vs. SSDE: P = 0.005). The top 5 abundant phyla were Firmicutes, Proteobacteria, Actinobacteriota, Bacteroidota, and Cyanobacteria in all three groups. The top five abundant genera included Acinetobacter, Staphylococcus, Bacillus, Corynebacterium, and Clostridium_sensu_stricto_1. The relative microbiome abundance was different between groups. The Firmicutes/Bacteroidetes (F/B) ratio was 6.42, 7.31, and 9.71 in the NC, NSSDE, and SSDE groups, respectively (NC vs. SSDE: P = 0.038, NC vs. NSSDE: P = 0.991, SSDE vs. NSSDE: P = 0.048).

Conclusion

The diversity of conjunctival sac microbiome in patients with NSSDE and SSDE was diminished compared with NC. The main microbiome at the phylum and genus level were similar between groups, but the relative abundance had variations. The Firmicutes/Bacteroidetes ratio was higher in the SSDE group.

The Effect of Topical Anesthetics on 16S Ribosomal Ribonucleic Acid Amplicon Sequencing Results in Ocular Surface Microbiome Research

PURPOSE

To clarify the short-term effect of topical anesthetics on 16S ribosomal ribonucleic acid amplicon sequencing results in ocular surface microbiome research.

METHODS

Both eyes of 24 eligible volunteers undergoing general anesthesia were sampled. Before sampling, a drop of artificial tears or a drop of topical anesthetic was applied in a randomized way. By using artificial tears as a control, we assured blinding of the executer and took a potential diluting effect into account. Bacterial DNA was extracted using the QIAGEN RNeasy PowerMicrobiome Kit with specific adaptations. Amplified DNA was sequenced with the Illumina MiSeq sequencing platform.

RESULTS

Four sample pairs were excluded due to low yield of bacterial DNA. In the remaining 20 sample pairs, no differences were observed with topical anesthetics at the levels of amplicon sequence variants (ASVs), phylum, genera, or alpha and beta diversity. Weighted UniFrac distance confirmed that the intraindividual distance between the right and left eye was smaller than the effect of the topical anesthetic. Interestingly, however, we identified Cutibacterium as a potential discriminative biomarker for topical anesthetic use. Overall, a significantly higher number of observed reads were assigned to genera with Gram-positive characteristics.

CONCLUSIONS

Based on our targeted, double-blinded, within-subject study, topical anesthetics do not affect the overall sequencing results but display a specific effect on Cutibacterium. When comparing research results, the impact of topical anesthetics on prevalence and abundance of Cutibacterium should be considered.

TRANSLATIONAL RELEVANCE

Understanding and standardization of sampling techniques are indispensable to properly execute clinical microbiome research.

Characterization of Conjunctival Sac Microbiome from Patients with Allergic Conjunctivitis

Conjunctival sac microbiome alterations have been reported to be closely associated with many ocular diseases. However, the characteristic of conjunctival sac microbiome in allergic conjunctivitis (AC) was scarcely described. In this study, we aimed to identify the differences of the conjunctival sac microbiome composition in AC patients compared with normal controls (NCs) using high-throughput 16S rDNA sequencing metagenomic analysis. The conjunctival sac microbiome samples from 28 AC patients and 39 NC patients were collected. The V3-V4 region of 16S rRNA gene high-throughput sequencing was performed on the illumina MiSeq platform. Alpha diversity, beta diversity and the relative abundance at the phylum and genus levels were analyzed using QIIME. Alpha diversity demonstrated by Chao1, Observed_species and PD_whole_tree indexes did not show significant difference between the AC and NC groups, while the Shannon index was higher in the AC group. Beta diversity showed divergent microbiome composition in different groups (p < 0.005). The top five abundant phyla were Firmicutes, Proteobacteria, Actinobacteriota, Bacteroidota and Cyanobacteria in both groups. The top five abundant genera were Bacillus, Staphylococcus, Corynebacterium, Acinetobacter and Ralstonia in the AC group and Acinetobacter, Staphylococcus, Bacillus, Clostridium_sensu_stricto_1, Corynebacterium and Geobacillus in the NC group. The Firmicutes/Bacteroidetes (F/B) ratio at the phylum level was similar between groups (p = 0.144). The Bacillus/Acinetobacter (B/A) ratio at the genus level was higher in the AC group (p = 0.021). The dysbiosis detected in this study might provide further evidence to investigate the mechanism and treatment methods for allergic conjunctivitis.

Effects of Sodium Hyaluronate Eye Drops With or Without Preservatives on Ocular Surface Bacterial Microbiota

Purpose

This study aimed to determine the composition and diversity of bacterial communities on the ocular surface before and after the intervention with sodium hyaluronate eye drops (with or without preservatives) using 16S rRNA gene amplicon sequencing.

Methods

Sixteen healthy adults were randomly divided into two groups and treated with sodium hyaluronate eye drops with or without preservatives for 2 weeks. The individuals used the same artificial tears in both eyes. The microbial samples from the conjunctival sac of each participant were collected at baseline and 2 weeks after intervention. The diversity and taxonomic differences among different groups before and after intervention were compared by sequencing the V3–V4 region of the 16S rRNA gene.

Results

The similarity in the binocular microbial community was high in 1 of the 16 volunteers (Bray-Curtis dissimilarity score < 0.3). At the genus level, 11 bacteria were detected in all samples with an average relative abundance of more than 1%. The bacterial community changed significantly after the use of sodium hyaluronate eye drops (with or without preservatives), whether within individuals or between individuals in different groups (P < 0.05, PERMANOVA). Different dosage forms of sodium hyaluronate eye drops significantly decreased the relative abundance of Flavobacterium caeni and Deinococcus antarcticus, respectively (P < 0.05).

Conclusions

Healthy people had a rich diversity of the bacterial microbiota on the ocular surface, but the bacterial communities between the eyes were not completely similar. Irrespective of containing benzalkonium chloride (BAC), sodium hyaluronate eye drops can change the bacterial community on the ocular surface.

[Visualization of normal ocular surface microflora via impression cytology sample using scanning electron microscopy with lanthanide contrasting]

PURPOSE

To determine the possibilities of impression cytology (IC) with subsequent visualization of the sample on a scanning electron microscope in assessment of normal microflora of the ocular surface.

MATERIALS AND METHODS

The article presents a visual characteristic of the microorganisms of the ocular surface (OS) captured during impression cytology (IC) in individuals without signs of inflammatory and degenerative eye diseases. The original method of staining the sample with heavy metal salts made it possible to identify the individual signs of the microorganisms in their subsequent visualization by scanning electron microscopy (SEM).

RESULTS

The paper presents photomicrographs of the microorganisms most common for the OS obtained with the help of SEM, confirming and supplementing the data of non-visual methods of studying the ocular microflora. It was shown that the detection frequency of the microbial component of the OS by the visual method presented in this study is comparable with the detection frequency when using the microbial cultivation method (<80%). Coccoid and rod-shaped microorganisms were detected with relatively equal frequency, with the coccoid organisms mainly represented in association with epithelial cells. The morphological diversity of rod-shaped microorganisms is shown.

CONCLUSION

The results of the study can be used as a visual reference for the normal microbiome of the eye.

Comparison of the Ocular Microbiomes of Dry Eye Patients With and Without Autoimmune Disease

Purpose

The pathogenesis of dry eye concomitant with autoimmune disease is different from that of dry eye without autoimmune disease. The aim of this study was to explore differences in the microbiota diversity and composition in dry eye with and without autoimmune disease.

Methods

Swab samples from the inferior fornix of the conjunctival sac were obtained from dry eye patients without autoimmune disease (n = 49, dry eye group) and from those with autoimmune disease (n = 38, immdry eye group). Isolated bacterial DNAs from swabs were analyzed with 16S rRNA amplicon sequencing.

Results

Analysis of the alpha diversity revealed no significant differences between subjects in the dry eye and immdry eye groups. Those in the immdry eye group had a distinct microbial composition compared with those in the dry eye group. The combination of the genera Corynebacterium and Pelomonas distinguished subjects in the immdry eye group from those in the dry eye group, with an area under the curve of 0.73 (95% CI = 0.62-0.84). For the same bacteria, the correlations between microbe abundance and the ocular surface parameters were different in the two groups. In addition, the functions of the microbial communities were altered in the two groups.

Conclusions

Our study demonstrates changes in the composition and function of the ocular microbiome between subjects in the immdry eye and dry eye groups, which suggests that the potential pathogenesis is different.

Ocular Surface Microbiota in Diabetic Patients With Dry Eye Disease

Purpose

To investigate the ocular surface (OS) commensal bacteria profiles of patients with diabetes mellitus (DM) and dry eye disease (DED).

Methods

In the present study, subjects were assigned to four groups: 37 to the diabetic mellitus with dry eye disease (DM with DED) group, 22 to the diabetes mellitus (DM)-only group, 34 to the dry eye disease (DED)-only group, and 22 to the control group. Tear fluid was collected using Schirmer's tear secretion test paper. 16S ribosomal ribonucleic acid (rRNA) gene sequencing was used to analyze the bacterial microbiota.

Results

The DM with DED group showed the highest operational taxonomic unit (OTU) numbers and alpha diversity and the most different beta diversity. The groups shared the four most abundant phyla, accounting for over 96% of the total abundance. At the genus level, there were 10 types of overlap in the core microbiota in the groups. They showed significant differences between the groups. Additionally, the DM with DED group and the control group showed four unique core genera, respectively. Unclassified Clostridiales and Lactobacillus were the core microbiota members of the DM with DED group, the DM-only group, and the DED-only group, but not the control group.

Conclusions

In the present study, our results showed that the patients in the DM with DED group had a more complex and comprehensive ocular surface microbial composition. To the best of our knowledge, this is the first study to reveal the microbial profile of dry eye disease in patients with diabetes mellitus.

Eye Make-up Products and Dry Eye Disease: A Mini Review

Dry eye disease (DED), a multifactorial condition of the tear film and ocular surface, is one of the leading reasons for patients seeking eye care. Despite the multiple toxic ingredients of eye make-up products and their long-term application close to the ocular surface, few studies have analyzed their role in initiating and worsening DED. Females and the elderly experience the highest prevalence of DED and may be particularly vulnerable to the effects of eye make-up. The multifactorial nature of DED and common mechanisms behind several ocular surface diseases make it difficult to link a particular ingredient-driven mechanism to DED. Therefore, here, we list potential responses to eye cosmetics that may be involved in DED development. The first part of this review introduces the anatomy of the eye and DED, the second section explains the classification of eye cosmetic products, and the final part discusses the undesired effects under physical, pathogenic, and chemical insults.

Demodex Infection Changes Ocular Surface Microbial Communities, in Which Meibomian Gland Dysfunction May Play a Role

INTRODUCTION

Demodex and bacteria are both components of the ocular surface micro-ecology, constituting a complex interaction. This study aims to explore how ocular surface Demodex infestation (DI) affects ocular surface microbial communities and diversity.

METHODS

We recruited 255 subjects, and examined the correlation between ocular surface mite infestation and clinical indicators such as age, blood glucose level, dry eye symptoms, and blood pressure. 16S rRNA sequencing was performed on the conjunctival swab samples of 14 patients with ocular DI (P group) and 17 healthy people (N group). For further analysis, the subjects were divided into four subgroups, i.e. N-NMGD (n = 11), N-MGD (n = 6), P-NMGD (n = 6), and P-MGD (n = 8), according to meibomian gland dysfunction (MGD) or no MGD (NMGD).

RESULTS

There was no difference in the α-diversity of ocular surface microbial communities between the DI and healthy control groups. In linear discriminant analysis effect size (LEfSe), there were more Acinetobacter, Novosphingobium, and Anoxybacillus in the DI group and fewer Novosphingobium, Lactobacillus, and Candidatus Microthrix in the healthy control group. P-NMGD had more Thermaceae and fewer Pseudomonas than P-MGD. There were more Bacteroidetes in N-NMGD than in N-MGD. The α-diversity of P-NMGD was lower than that of N-NMGD (Shannon index, P = 0.027). At the same time, the α-diversity of N-MGD was lower than that of N-NMGD (Shannon, Simpson, and dominance index, P = 0.048). There was no significant difference in β-diversity or in the primary flora at the phylum and genus levels between groups and subgroups.

CONCLUSION

DI had no significant effect on the diversity of ocular surface microbial communities. DI primarily changed the dominant flora and relative abundance of ocular surface microbial communities. MGD may play an important role in this process.

Ocular Surface Microbiota in Contact Lens Users and Contact-Lens-Associated Bacterial Keratitis

Our objectives were to investigate whether the conjunctival microbiota is altered by contact lens wear and/or bacterial keratitis and to explore the hypothesis that commensals of conjunctival microbiota contribute to bacterial keratitis. Swab samples from both eyes were collected separately from the inferior fornix of the conjunctiva of non-contact-lens users (n_participants = 28) and contact lens users (n_participants = 26) and from patients with contact-lens-associated bacterial keratitis (n_participants = 9). DNA from conjunctival swab samples was analyzed with 16S rRNA gene amplicon sequencing. Pathogens from the corneal infiltrates were identified by cultivation. In total, we identified 19 phyla and 283 genera; the four most abundant genera were Pseudomonas, Enhydrobacter, Staphylococcus, and Cutibacterium. Several pathogens related to bacterial keratitis were identified in the conjunctival microbiota of the whole study population, and the same bacteria were identified by both methods in the conjunctiva and cornea for four patients with contact-lens-associated bacterial keratitis. The overall conjunctival microbiota profile was not altered by contact lens wear or bacterial keratitis; thus, it does not appear to contribute to the development of bacterial keratitis in contact lens users. However, in some individuals, conjunctival microbiota may harbor opportunistic pathogens causing contact-lens-associated bacterial keratitis.

Differences in the eyelid and buccal microbiome of glaucoma patients receiving long-term administration of prostaglandin analog drops

PURPOSE

To investigate the differences in the eyelid and buccal microbiomes between patients receiving long-term prostaglandin analogs for open-angle glaucoma (PG-OAG) and naïve-OAG patients by using metagenomics.

METHODS

Eyelid and buccal samples were collected from 30 PG-OAG and 32 naïve-OAG patients. The taxonomic composition of the microbiome was obtained via 16S rRNA gene sequencing, operational taxonomic unit analysis, and diversity analysis. Differential gene expression analysis (DEG) and Bland-Altman (MA) plots were used to determine taxon differences between the microbiomes of PG-OAG and naïve-OAG patients.

RESULTS

The eyelid microbiome showed marginally significant differences, while the alpha-diversity of the buccal microbiome showed significant differences between PG-OAG and naïve-OAG patients. However, the beta-diversity of both eyelid and buccal microbiomes was higher in PG-OAG patients than in naïve-OAG patients. The MA plot showed cluster differences in the eyelid microbiome. DEG analysis of the eyelid microbiome revealed various taxa differences, including enrichment of Azomonas, Pseudomonas, and Granulicatella in PG-OAG patients over naïve-OAG patients, as well as significant depletion of Delftia and Rothia. In the buccal microbiome in PG-OAG patients, taxa such as Rikenella and Stenotrophomonas were significantly enriched.

CONCLUSION

Our findings suggest that the eyelid microbiome differs between PG-OAG and naïve-OAG patients, raising concerns regarding the eyelid environment in patients receiving these drugs. The overexpressed microbiome in the eyelid area suggests that microbiota may change after the administration of glaucoma medications in OAG.

The ocular microbiome and microbiota and their effects on ocular surface pathophysiology and disorders

The ocular surface flora perform an important role in the defense mechanisms of the ocular surface system. Its regulation of the immunological activity and the barrier effect against pathogen invasion are remarkable. Composition of the flora differs according to the methods of investigation, because the microbiome, composed of the genetic material of bacteria, fungi, viruses, protozoa, and eukaryotes on the ocular surface, differs from the microbiota, which are the community of microorganisms that colonize the ocular surface. The observed composition of the ocular surface flora depends on harvesting and examining methods, whether with traditional culture or with more refined genetic analysis based on rRNA and DNA sequencing. Environment, diet, sex, and age influence the microbial flora composition, thus complicating the analysis of the baseline status. Moreover, potentially pathogenic organisms can affect its composition, as do various disorders, including chronic inflammation, and therapies applied to the ocular surface. A better understanding of the composition and function of microbial communities at the ocular surface could bring new insights and clarify the epidemiology and pathology of ocular surface dynamics in health and disease. The purpose of this review is to provide an up-to-date overview of knowledge about this topic.

The ocular surface immune system through the eyes of aging

Since the last century, advances in healthcare, housing, and education have led to an increase in life expectancy. Longevity is accompanied by a higher prevalence of age-related diseases, such as cancer, autoimmunity, diabetes, and infection, and part of this increase in disease incidence relates to the significant changes that aging brings about in the immune system. The eye is not spared by aging either, presenting with age-related disorders of its own, and interestingly, many of these diseases have immune pathophysiology. Being delicate organs that must be exposed to the environment in order to capture light, the eyes are endowed with a mucosal environment that protects them, the so-called ocular surface. As in other mucosal sites, immune responses at the ocular surface need to be swift and potent to eliminate threats but are at the same time tightly controlled to prevent excessive inflammation and bystander damage. This review will detail how aging affects the mucosal immune response of the ocular surface as a whole and how this process relates to the higher incidence of ocular surface disease in the elderly.

Ocular surface response of two preservative-free cylcosporine A emulsion eye drops in a mouse model of dry eye

PURPOSE/AIM

Dry eye (DE) disease is a multifactorial disease in which uncontrolled inflammation can lead to corneal epithelium lesions and symptoms of discomfort. The aim of the present study was to evaluate the efficacy of two cyclosporine emulsions in a mouse model of DE with corneal epithelium lesions.

MATERIALS AND METHODS

Six- to 9-week-old female C57BL/6 N mice were housed in a controlled-environment room to induce DE. Following DE development, mice were instilled with: QD 0.1%CsA cationic emulsion (CaEm), BID 0.05%CsA anionic emulsion (AEm), or left untreated. Aqueous tear production and corneal epithelium lesions were assessed throughout the experiment. At the end of the treatment period, left eyes were sampled, fixed, and stained for histology, while the cornea, conjunctiva, and lacrimal gland of right eyes were sampled for transcriptomic analysis.

RESULTS

Corneal lesion scores were reduced by 10.4%, 18.4%, and 10.9% at day 6, 10, and 14, respectively, with CaEm (QD), and by 2.6%, 3.0%, and 5.5% at day 6, 10, and 14, respectively, with AEm (BID). Histology demonstrated that 7 out of 10 DE mice presented moderate to severe ocular lesions, while only 2 and 5 out of 10 mice presented slight to moderate ocular lesions when treated with the CaEm (QD) and AEm (BID), respectively. The transcriptomic profile analysis suggests that a different set of inflammatory genes are modulated in the cornea, conjunctiva, and lacrimal gland upon DE development. In addition, the two emulsions distinctively modulate the gene expression profile.

CONCLUSIONS

This study demonstrates that both emulsions were effective at reducing corneal lesions, with the CaEm (QD) being slightly better than the AEm (BID). Interestingly, this study suggests that ocular tissues may not respond similarly to a dry environment and that a different set of genes is modulated by the two formulations in the ocular tissues.

Current knowledge on the human eye microbiome: a systematic review of available amplicon and metagenomic sequencing data

Insights in the ocular surface microbiome are still at an early stage and many more questions remain unanswered compared with other human-associated microbial communities. The current knowledge on the human microbiome changed our viewpoint on bacteria and human health and significantly enhanced our understanding of human pathophysiology. Also in ocular medicine, microbiome research might impact treatment. Here, we summarize the current knowledge on ocular microbiome research with a particular focus on potential confounding factors and their effects on microbiome composition. Moreover, we present the ocular surface core microbiome based on current available data and defined it as genera present in almost half of the published control cohorts with a relative abundance of at least 1%.

Safety and Tolerability of an Eye Drop Based on 0.6% Povidone-Iodine Nanoemulsion in Dry Eye Patients

Purpose: To evaluate safety and tolerability on the ocular surface of an anti-septic formulation containing 0.6% povidone–iodine (0.6% PVI) for a 4 week period.

Methods: An observational, prospective study included 20 mild-moderate dry eye disease (DED) patients who enrolled at the Ocular Surface Disease Unit of the University of Messina, receiving 0.6% PVI eye drops for 28 days, 2 drops twice daily (BID). The assessment included the Ocular Surface Disease Index questionnaire; symptoms score (0 = absent to 3 = severe) for burning, ocular dryness, foreign body sensation, watery eyes, tearing, photophobia, and ocular pain; fluorescein tear break-up time (TBUT); and corneal-conjunctival staining, performed at baseline (T0), after 7 (T7) and 28 (T28). Schirmer I-test, corneal endothelial cell count, intraocular pressure, and fundus examination were performed at T0 and T28. The main outcome measures were TBUT and corneal-conjunctival staining as markers of ocular surface homeostasis. For statistical analysis, Student's T-test and Wilcoxon test were used as appropriate.

Results: No significant alterations of the safety parameters were found throughout the study. Further, at T28 a significant improvement of burning, ocular dryness, foreign body sensation, and watery eyes (T0 vs. T28 P < 0.03) were observed; corneal-conjunctival staining improved at T28 (T0 vs. T28 P < 0.0001), and TBUT improved already at T7 (T0 vs. T7 P = 0.0008) lasting so till the end of the study. The only adverse event was mild burning at instillation for the first 3 days of treatment in most of the patients.

Conclusions: The treatment with 0.6% PVI was safe and well tolerated in a group of patients with a damaged ocular surface.

Ocular Microbiota and Intraocular Inflammation

The term ocular microbiota refers to all types of commensal and pathogenic microorganisms present on or in the eye. The ocular surface is continuously exposed to the environment and harbors various commensals. Commensal microbes have been demonstrated to regulate host metabolism, development of immune system, and host defense against pathogen invasion. An unbalanced microbiota could lead to pathogenic microbial overgrowth and cause local or systemic inflammation. The specific antigens that irritate the deleterious immune responses in various inflammatory eye diseases remain obscure, while recent evidence implies a microbial etiology of these illnesses. The purpose of this review is to provide an overview of the literature on ocular microbiota and the role of commensal microbes in several eye diseases. In addition, this review will also discuss the interaction between microbial pathogens and host factors involved in intraocular inflammation, and evaluate therapeutic potential of targeting ocular microbiota to treat intraocular inflammation.

Ocular surface microbiota in patients with aqueous tear-deficient dry eye

PURPOSE

An altered ocular surface microbiota may contribute to the pathophysiology of dry eye disease. The aim of the study was to explore potential differences in microbiota diversity and composition in aqueous tear-deficient dry eye (with and without ocular graft-versus-host disease) compared with controls.

METHODS

Swab samples from the inferior fornix of the conjunctiva were obtained from patients with aqueous tear-deficient dry eye with and without ocular graft-versus-host disease (n = 18, n = 21, respectively) and controls (n = 28). Isolated bacterial DNA from swabs were analyzed with 16S rRNA gene amplicon sequencing.

RESULTS

Decreased microbiota diversity was observed in patients with aqueous tear-deficient dry eye (p ≤ 0.003) who also showed a difference in microbiota composition compared with controls (p = 0.001). Although several genera were less abundant in aqueous tear-deficient dry eye, a minimal core ocular surface microbiota comprising five genera was shared by >75% of the study participants: Enhydrobacter, Brevibacterium, Staphylococcus, Streptococcus and Cutibacterium. Pseudomonas was identified as a bacterial biomarker for controls and Bacilli for patients with aqueous tear-deficient dry eye.

CONCLUSIONS

Ocular surface microbiota in patients with aqueous tear-deficient dry eye was characterized by an aberrant microbiota composition in comparison to controls, with decreased diversity and reduced relative abundances of several genera. Additionally, a few genera were present in most of the study population, indicating that a minimal core ocular surface microbiota may exist.