The ocular surface microbiome of rhesus macaques

The ocular surface microbiome of rhesus macaques | 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 Research Article The ocular surface microbiome of rhesus macaques Joelle K Hass, Arthur G Fernandes, Mike J Montague, Armando Burgos-Rodriguez, and 7 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6205866/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 20 Aug, 2025 Read the published version in Animal Microbiome → Version 1 posted 4 You are reading this latest preprint version Abstract Background : The ocular surface microbiome (OSM) is important for eye health, and variations in OSM composition have been associated with multiple diseases in humans. Studies of OSM-disease dynamics in humans are confounded by lifestyle factors. Animal models provide a complementary approach to understanding biological systems, free from many confounds of human studies. Here, we provide the first study of the OSM of rhesus macaques, a premier animal model for eye health and disease. We describe the taxonomy of the rhesus macaque OSM, and explore compositional correlations with age, sex, and living condition. Methods : We analyzed eyelid and conjunctival microbiota swabs from 132 individual rhesus macaques ( Macaca mulatta ) (57 males, 75 females, 1-26 years old) from one captive and one free-ranging group using 16S V3/V4 MiSeq sequencing. We investigated alpha diversity, beta diversity, and differential abundance. Results : We found several similarities between the top Phyla and Genera of the rhesus macaque OSM and those reported in human literature. Significantly higher alpha diversity, which may reflect age-related ocular surface mucous membrane integrity and immune function, was present in younger individuals compared to older ones. Higher alpha diversity was also present in free-ranging rhesus macaques compared to ones in captivity, possibly related to differences in diet, exercise, and medical exposures between macaques in different living conditions. Beta diversity was most strongly influenced by individual identity, followed by living conditions. Sex did not correlate with any OSM variation. Conclusions : In this study we describe the taxonomic composition of the rhesus macaque OSM, and identify significant differences in alpha and beta diversity according to individual nonhuman primate host variables and the surrounding environment. Our findings suggest composition of the nonhuman primate OSM is shaped by age-related physiology, individual identity, and external living conditions. Our results offer novel insights into an underexplored region of the primate microbiome and highlight the utility of rhesus macaques as a model system for investigating the links between the OSM, ocular health, and disease. eye microbiome nonhuman primates MiSeq 16S amplicon sequencing Figures Figure 1 Figure 2 Figure 3 Figure 4 Background The ocular surface microbiome (OSM) –the community of microorganisms living on the surface of the eye– plays an important role in regulating eye health, and OSM composition has been correlated with a number of diseases [1, 2]. The OSM was first described using traditional culture techniques in 1930 by Keilty [3]. However, research on the OSM has been slow to develop, largely due to its low biomass nature [4]. For example, the OSM was not included in the original Human Microbiome Project [5]. Despite several mechanisms which limit bacterial growth on the eye surface, including the mechanical clearance caused by the movement of the eyelids and the antibacterial nature of the tears, there is now a strong consensus that human eyes harbour a diverse community of microbiota [1, 2]. In recent years, studies using next generation sequencing techniques have shed light on the taxonomy of the OSM in healthy research participants [6–11], while other studies have begun to investigate how these microbiota may contribute to eye health through metabolic and immunologic mechanisms [12–14]. Correlations between OSM composition and a number of eye diseases have been identified, including dry eye disease, Sjogren syndrome, meibomian gland dysfunction, and glaucoma [12, 15–17]. Variation in OSM composition has also been correlated with several systemic conditions, such as diabetes and graft-versus-host disease [18–20]. Understanding the typical and pathological OSM is an important step for improving our knowledge of the pathogenesis of these diseases, and may shed light on both systemic and local interactions between eye microbiota and the host. Recent OSM studies in healthy human participants have revealed conflicting results on the relationship between demographic factors and OSM composition. For example, there are mixed results regarding the relationship between OSM diversity and age, with some studies showing higher diversity with increased age [9, 21], others showing lower diversity with age [22], and yet others showing no relationship at all [10, 23]. Similarly, evidence of sex differences have been reported [10, 21] or not [9] depending on the study. Individual identity, or inter-individual variation resulting from a complex combination of genetics, exposures, and experiences throughout an organism’s lifetime, has also been identified as an important factor in shaping the diversity of the OSM in humans [7]. Since OSM composition may be influenced by diverse individual host lifestyle factors including diet, exercise, use of medications and contact lenses, and overall host health, [1, 2, 11] it is difficult to draw conclusions about age and sex dynamics with the OSM. Additionally, OSM studies have varied in methodology used to determine alpha and beta diversity, sequencing technologies, and filtering and decontamination parameters. Overall, there are extensive sociocultural and individual factors that contribute to variation in human eye microbiota composition, suggesting that the use of an appropriate animal model might prove to be highly valuable. The study of non-human primates (NHPs) as model organisms for understanding human health is complementary to human research and offers many advantages and insights. Studies of NHPs are able to avoid many sources of variation common in human OSM studies, such as cultural differences in diet, alcohol consumption, and medical treatments. Rhesus macaques ( Macaca mulatta ) are a premier animal model for human health as they show a high similarity of genomic sequences (around 90%) encoding for highly conserved protein sequences with humans. NHPs also demonstrate comparable eye anatomy and physiology to that of humans [24, 25]. Rhesus macaques are already considered an excellent model of human aging and eye diseases, including myopia, cataracts, glaucoma, and macular degeneration [24–28]. Furthermore, recent studies extensively characterizing the microbiota on and in rhesus macaques have made valuable contributions to human health research [29]. However, there are currently no studies available on the rhesus macaque OSM either in healthy individuals, or as it relates to ocular disease. We endeavor to provide such a resource for the rhesus macaque eye microbiome, and establish a foundational understanding of rhesus macaque OSM composition. In this study we (1) describe the taxonomy of the rhesus macaque OSM at two sampling sites commonly assessed in humans, the eyelid (EL) and the conjunctiva (CJ); and (2) examine OSM composition related to internal and external variables, including age and sex, and captive versus free-ranging environments. Since individual host characteristics and external environmental exposures can influence the relationships among microbiota and the host biological systems they interact with [30–32], we expected that OSM composition may vary with age, sex, and external living conditions. The results reported here will set the stage for future research on the rhesus macaque OSM as it relates to a variety of ocular diseases, and help to fill gaps of human OSM literature by describing OSM composition and relationships with host factors in an animal model with reduced variation in individual lifestyle, but still with high physiological and genetic relevance. Methods Study Population We studied 132 rhesus macaques ( Macaca mulatta ) from a colony managed by the Caribbean Primate Research Center (CPRC) through the University of Puerto Rico. 54 macaques (14 males, 40 females, 8-26 years old) were located in the Sabana Seca facility and 78 macaques (43 males, 35 females, 1-25 years old) were from the free-ranging population on the island of Cayo Santiago, Puerto Rico. The Cayo Santiago rhesus macaque population was established in 1938 with a group of 409 individuals imported from India [33], and the population now includes around 2000 individuals. A number of individuals have been transferred from Cayo Santiago to the Sabana Seca Field Station since 1984, where animals now live in captivity in outdoor corrals. The CPRC maintains a detailed census of both populations, recording the birth, death, and kinship information for all individuals; monkeys are recognized and tracked by tattoos, ear notches, and facial features. The CPRC provisions both of these primate populations with monkey chow and water. However, the free-ranging group on Cayo Santiago occasionally supplement their diet with native species of plants and invertebrates. The Cayo population also experiences natural weather conditions and individuals freely associate into, and move between, different social groups. Comparatively, the Sabana Seca individuals are housed in outdoor corrals, so while they experience some natural weather fluctuations, they have consistent shade and cover and individuals are not free to move between social groups. The Cayo population receives a tetanus inoculation at approximately one year of age, and certain individuals are trapped-and-released annually as part of longitudinal research studies [24]. The Sabana Seca group interacts regularly with caregivers during feeding and medical assessments. Sample Collection/Shipping/Transportation We collected eye microbiome swabs during animal exams that were scheduled as part of ongoing research studies. Prior to sample collection all monkeys were anesthetised by a trained CPRC veterinarian with intramuscular ketamine, and given 1-2 drops of topical anesthetic solution in each eye (tetracaine hydrochloride 0.5% or proparacaine hydrochloride 0.5% sterile ophthalmic solution). We collected two types of samples using individual sterile swabs ( BBL CultureSwab, Ottawa, ON): (1) EL swabs were collected from the lower eyelid margin by applying light friction and rubbing the swab from the medial to the lateral edge of the eyelid along the base of the eyelashes, avoiding contact with the conjunctiva; (2) CJ surface swabs were collected by rolling the swab back and forth 4-6 times along the bulbar conjunctival surface, while avoiding contact with the eyelid, eyelashes, or skin. Following sampling, we cut the swab tips into sterile 2mL cryogenic vials ( fisherscientific , Pittsburgh, PA) for storage and transport. Swabs were placed on ice while in the field, and frozen at -80°C within 5 hours of collection. Samples were transported from Puerto Rico to New York University (NYU) on dry ice and immediately stored at -80°C upon arrival. DNA extractions were performed in a BSL-2 laboratory at NYU, and extracted DNA was shipped on dry ice to the University of Calgary for library preparation and sequencing. DNA Extraction & Sequencing As a low biomass microbial niche, OSM samples are at high risk for contamination [4], and we followed strict protocols to minimize contamination during this study. DNA extractions were performed in a BioSafety Cabinet, with all equipment sterilized using 10% bleach, 70% ethanol, and 60 minutes of UV radiation prior to use. All disposable equipment was sterile, PCR-grade, DNA, and DNAse/RNAse free. The researcher performing the DNA extractions wore a sterile hair net, sleeve covers, face mask, and gloves throughout the procedure. DNA extractions were performed using the Macherey-Nagel DNeasy-96-PowerSoil Pro kit (QIAGEN, Hilden, Germany) with a modified protocol. The CD2 inhibitor removal step was not performed, as per the manufacturer's suggestion for low biomass samples. Negative controls were included in the form of extraction/reagent blanks: sample wells with sterile swabs were included in the extraction process as reagent controls, and processed along with the samples for all remaining steps. We selected the sequencing parameters for this study based on recent human OSM studies in order to maximize the comparability of our results to current knowledge of the human OSM; many OSM studies have used Illumina MiSeq sequencing of the V3/V4 gene region [6, 8, 34]. Library preparation and sequencing were performed at the University of Calgary Centre for Health Genomics and Informatics (CHGI) core lab. Samples were processed into NGS libraries using Illumina 16S Metagenomic Sequencing Library Preparation using an adjusted low biomass protocol. 11.5ul of stock sample was used as input into the first stage PCR. Adjustments to the library prep protocol to account for the low DNA input included: a reduction in PCR bead clean up elution volumes to 30ul for PCR clean up 1 and to 20ul for PCR clean up 2; adjustments to the indexing reaction include 3x volume increase in amplicon used along with a removal of PCR grade water. Libraries were sequenced on two MiSeq 600 cycle v3 sequencing runs, PE300 with 25% phiX spike in due to the low diversity nature of the libraries. Run 1 included an equal molar pool of 288 libraries and Run 2 included an equal molar pool of 258 libraries. Fifty-nine libraries were below the minimum concentration of 2ng/uL and were used ‘as is’. A total of 23,723,252 raw reads were produced with an average of 41,186 reads per sample for Run 1, and a total of 28,197,388 raw reads were produced with an average of 54,646 reads per sample for Run 2. Data Analysis We assessed the quality of the sequences using FastQC and MultiQC. We removed primers and adapters and performed quality trimming using Trimmomatic with the parameters fa:2:30:10:2:True LEADING:5 TRAILING:5 SLIDINGWINDOW:5:20 MINLEN:50. This means leading and trailing bases were trimmed immediately if they had a quality of 5 or less, and nucleotide bases were trimmed in a sliding window (5 bases long) if the average quality within that window fell below 20. Trimmed reads for Run 1 and Run 2 were then processed separately through the dada2 [35] pipeline in R 4.3.1, and the resulting taxonomy and sequence tables were merged for further downstream analysis. This resulted in 11,374,429 reads (average of 20,832 per sample), which clustered into 42,059 unique amplicon sequence variants (ASVs). Taxonomy was assigned using the SILVA v.138 database. All downstream analyses were carried out in R using the packages phyloseq, decontam, vegan, lme4, lmerTest, fantaxtic, microbiome, microbiomeMarker, ANCOMBC, with visualizations using ggplot2, ggpubr , and sjPlot . We filtered the reads for contaminants using the decontam package at a threshold of 0.5. 627 ASVs were identified as contaminants based on the reagent blanks included in sequencing, and subsequently pruned from the dataset. We also removed ASVs uncharacterized at the Phyla level, chloroplasts and mitochondria, ASV singletons, and ASVs present in less than 2% of samples. Dong et al. (2022) [9] found that 85% of ASVs from the human OSM were present in less than 5% of samples. We chose two percent as a threshold to capture the diversity of this low biomass microbial community, where many ASVs might be expected to occur in less than 5% of samples but still represent meaningful biological taxa. After all quality control preprocessing steps, there were 1431 unique ASVs belonging to 7 Phyla and 63 Genera. Rarefaction (the process of randomly subsampling all samples down to a uniform sequencing depth) has been debated in the literature and critiqued for discarding meaningful biological information [36, 37], although recent work has combated this critique [38]. In OSM studies it is common to use alternative normalization strategies instead of rarefaction, especially due to the low biomass nature of this microbial niche [21, 34]. We have normalized our samples for read count as described below. We measured alpha diversity using three standard metrics: the Shannon Diversity Index, Simpson Index, and Chao1 richness [39]. We visualized read count (number of reads per sample) against these metrics and identified a strong positive relationship for all three. To normalize the data for read count we plotted linear models with these metrics according to read count and extracted the residuals for use in all further analyses. We used Wilcoxon-signed rank tests to compare Shannon Index residuals, Simpson Index residuals, and Chao1 richness residuals between categorical groups of interest, including age (young versus old), sex (male versus female), living condition (captive versus free-ranging), sampled site (EL versus CJ), and eye laterality (left versus right). We also constructed linear mixed models to explore relationships between our variables of interest and alpha diversity variation; in all models age, sex, living condition, sequencing run, eye laterality, and sampled site were included as fixed effects with individual identity (represented by each macaque’s unique identification number) as a random effect. Including individual identity accounts for non-independent sampling, as each monkey was represented by multiple swabs (right and left eyes, EL and CJ). The inclusion of individual identity also allowed us to parse the influences of individual characteristics such as age and sex, versus the influence of overall host identity. Current understandings of human ocular physiology suggest that older adults can experience lower ocular mucous membrane integrity, decreased tear production, and changes in immune function which may affect eye health [40]. Here we defined ‘old age’ as individuals in the top 75% quartile, which for this dataset was >17.75 years of age (17.75-26.56 years old), and ‘younger’ monkeys -including young and middle-aged animals- (1-17.59 years old) below this threshold (Supplementary Material 1). Based on life history traits, rhesus macaques are sometimes separated into further age groups containing pre-weaning individuals and pre-sexual maturity [41], but this dataset lacked sufficient numbers of these pre-adult individuals for a more granular comparison. Therefore, we have focussed our comparison on the effects of old age on microbiome composition. We measured beta diversity using two metrics, Bray Curtis Dissimilarity and Aitchison Distance. We chose Bray Curtis due to its common use in the OSM literature to enable comparison of our results to existing studies, while Aitchison Distance was chosen due to recent papers recommending it for the highly compositional and sparse nature of microbiome datasets [42, 43]. We visualized structural relationships using Principal Coordinate Analysis (PCoA, Bray Curtis) and Principal Component Analysis (PCA, Aitchison Distance). We used PERMANOVA models to explore the significance of variables of interest in shaping beta diversity, including age, sex, individual identity, living condition, and sampled site, with eye laterality and sequencing run included to control for possible confounding effects. We measured differential abundance (DA) using ANCOM-BC. ANCOM-BC is considered a conservative test and has been recommended for microbiome studies due to its ability to account for the compositional and sparse nature of microbiome datasets [44–46]. We filtered ANCOM-BC results to include Genera that were identified as differentially abundant according to the function (diff_ == TRUE), resistant to differential treatment of zeroes (ss_ == TRUE), and had a q -value of less than 0.05, with a log fold change of greater than |0.25|. This log fold change threshold was chosen for ANCOM-BC due to the highly conservative nature of the other filtering parameters. In addition, we provide alternative results using the DESeq2 test as supplemental material. DESeq2 is commonly used in microbiome literature, but was adapted from RNAseq purposes and has been criticized as less appropriate for the high sparsity nature of microbiome datasets [44, 46]. To enable comparison of our results with human literature, and to explore differences produced by analyzing the same dataset with different DA techniques, we provide the DESeq2 results in Supplementary Material 2. Results After trimming and filtering, final analyses included samples from 131 individuals and a total of 251 eyes. The free-ranging group included samples from 77 individuals and 147 eyes (43 males, 34 females, 1-25 years old) and the captive group included samples from 54 individuals and 104 eyes (14 males, 40 females, ages 8-26 years old). The taxonomy of the Rhesus macaque OSM Top Taxa The final 1431 ASVs were assigned to 7 Phyla across the entire dataset of captive and free-ranging individuals, including Firmicutes (58.03%), Bacteroidota (21.39%), Actinobacteriota (14.23%), Proteobacteria (4.72%), Campylobacterota (0.85%), Spirochaetota (0.42%), and Fusobacteriota (0.37%). There were 63 total Genera, with the top 10 being Prevotella_9 (12.05%), Streptococcus (10.51%), Lactobacillus (8.91%), Staphylococcus (7.84%), HT002 (7.74%), Corynebacterium (5.79%), Prevotella (3.89%), Faecalibacterium (3.70%), Ligilactobacillus (3.17%), and Brevibacterium (2.60%). Prevotella_9 is distinguished in the SILVA database from Prevotella as a genetically distinct sub-lineage within the greater Prevotella genus. Within the captive group (n= 54) the top 4 Phyla were Firmicutes (60.94%), Actinobacteriota (21.42%), Bacteroidota (13.41%), and Proteobacteria (3.33%); within the free-ranging group (n= 77) the top Phyla were Firmicutes (55.98%), Bacteroidota (27.01%), Actinobacteriota (9.16%), and Proteobacteria (5.70%)(Figure 1A). The top ten Genera of the captive group were Streptococcus (14.55%), Staphylococcus (12.47%), Prevotella_9 (8.35%), Lactobacillus (7.55%), HT002 (6.75%), Corynebacterium (6.05%), Brevibacterium (5.18%), Dietzia (4.11%), Ligilactobacillus (3.77%), and Brachybacterium (2.67%); the top ten Genera of the free-ranging group were Prevotella_9 (14.65%), Lactobacillus (9.87%), HT002 (8.45%), Streptococcus (7.66%), Corynebacterium (5.61%), Prevotella (5.31%), Faecalibacterium (4.77%), Staphylococcus (4.58%), Ligilactobacillus (2.74%), and Ruminococcus (1.91%)(Figure 1B). Alpha Diversity & Sampled Site Differential Abundance For metrics of alpha diversity, sampled site did not differ significantly in Shannon Index or Simpson Index, but Chao1 was significantly different between CJ and EL samples (Wilcoxon test, p= 0.0006, CI 95%= 2.492 to 8.499) with CJ samples demonstrating a higher average Chao1 richness. Alpha diversity did not differ significantly between eye laterality by any metrics. Using ANCOM-BC to assess the DA of specific taxa, we identified that EL samples were enriched in Peptoniphilus compared to CJ samples, and CJ samples were comparatively enriched in Dolosigranulum . OSM Composition Related to Age, Sex, and Living Conditions Alpha Diversity The following results exploring associations between alpha diversity and the host variables of age, sex, and living condition did not differ when subset into CJ versus EL sampling locations, and we therefore combined all samples for our analyses. First, we directly compared alpha diversity metrics between the categorical groups of interest (age, sex, and living condition) using Wilcoxon-signed rank tests. The old and young groups differed significantly in Shannon Index (Wilcoxon test, p= 0.013, CI 95%= 0.017 to 0.144) and Chao1 richness (Wilcoxon test, p= 0.034, CI 95%= 0.265 to 7.165), but not in Simpson Index (Figure 2). The young group demonstrated higher Shannon Index diversity and higher Chao1 richness compared to the older group. Males and females did not differ significantly in any alpha diversity metrics. Shannon Index (Wilcoxon test, p= 0.00020, CI 95%= 0.058 to 0.179) and Simpson Index (Wilcoxon test, p= 1.985e-06, CI 95%= 0.003 to 0.008) differed significantly between the captive and free-ranging groups, with the free-ranging group demonstrating higher alpha diversity by both metrics, but Chao1 richness did not differ significantly (Figure 3). Next, we used linear modelling with the lme4 package in R to explore the influence of age, sex, and living condition while controlling for individual identity in a combined model of alpha diversity variation. Because the data included both CJ and EL samples, we included sampled site as a variable in the models. In both Shannon Index and Simpson Index models, living condition was the only significant predictor of alpha diversity (p= 0.0189 and p= 0.0016 respectively), with captive individuals demonstrating lower alpha diversity by both metrics. In the Chao1 richness model none of the variables of interest (age, sex, and living condition) were significant predictors of richness. This suggests that living condition has the strongest influence on alpha diversity. Beta Diversity In a combined PERMANOVA model using Bray Curtis Dissimilarity normalized through a relative abundance transformation, all variables came back as a statistically significant predictor of beta diversity: age (p= 0.007, R2= 0.0027), sex (p= 0.012, R2= 0.0024), living condition (p= 0.001, R2= 0.0124), sampled site (p= 0.012, R2= 0.0024), and individual identity (p= 0.001, R2= 0.3291). However, only living condition and individual identity had R2 effect sizes demonstrating that they accounted for greater than one percent of variance in beta diversity, with living condition accounting for approximately 1.24% and individual identity for approximately 32.90%. The combined PERMANOVA model using Aitchison distance demonstrated similar results to the Bray Curtis model: age (p= 0.006, R2= 0.0026), sex (p= 0.014, R2= 0.0025), living condition (p= 0.001, R2= 0.0133), sampled site (p= 0.025, R2= 0.0024), and individual identity (p= 0.001, R2= 0.3380). No clear clustering for the variables of interest based on either model was present in the PCoA or PCA visualizations. Differential Abundance Using ANCOM-BC we identified that young macaques were enriched with the genera Aerococcus , Ruminococcus , Micrococcus , and L achnospiraceae NK4A136 group , and less abundant in Dolosigranulum compared to older individuals (Figure 4A). Regarding sex differences, we found that males were significantly enriched in Ezakiella and Peptoniphilus , and significantly lower in Sneathia (Figure 4B). We also identified 5 genera as being significantly enriched in the captive group compared to the free-ranging group: Actinobacillus , Staphylococcus , Nocardioides , Rothia , and Brevibacterium ; and 4 genera as being significantly lower in abundance in the captive group: [Eubacterium] siraeum group , Prevotellaceae UCG-003 , Prevotella , and Ruminococcus (Figure 4C). Discussion In this study we aimed to identify the taxonomy of the rhesus macaque OSM and explore OSM composition as it relates to internal and external variables, including age, sex, and living condition. Overall, we identified 1431 ASVs and found that the top taxa in the rhesus macaque OSM included 7 phyla and 63 genera. We identified significantly different alpha diversity between older and younger rhesus macaques and between captive and free-ranging macaques, whereas sex did not correlate with alpha diversity by any measure. We also found that living condition and individual identity played a substantial role in shaping beta diversity, while age and sex did not. We identified specific taxa that differed in relative abundance between age groups, sex classes, and captive versus free-ranging populations. Taxonomy of the Rhesus Macaque OSM The total number of unique microbial taxa reported in human OSM literature varies somewhat between studies. Patra et al. found a total of 3370 unique ASVs [47], Dong et al. reported 1731 OPUs (operational phylogenetic units)[9], and Ozkan et al. described 2465 zOTUs (zero radius operational taxonomic units)[11]. Although it can be difficult to directly compare between different taxonomic clustering units such as ASVs, OPUs, and zOTUs [48], our result of 1431 ASVs is in a similar range to human OSM findings. We identified Firmicutes , Bacteroidota , Actinobacteriota , and Proteobacteria as the top four phyla, and Prevotella_9 , Streptococcus , Lactobacillus , Staphylococcus , HT002 , Corynebacterium , Prevotella , Faecalibacterium , Ligilactobacillus , and Brevibacterium as the top ten genera. Relative to humans, we demonstrate that rhesus macaques share the same top four phyla, and four of the same top ten genera including Brevibacterium , Corynebacterium , Staphylococcus , and Streptococcus . Of the four genera, Corynebacterium , Staphylococcus , and Streptococcus were identified in a recent review as being in the top genera found across all current human OSM literature [49]. Several other taxa identified as part of the top ten genera in human studies were also present in our study, including Brevibacterium, Micrococcus , and Moraxella which were not in the top ten genera of the rhesus macaque OSM, but were taxa of interest during DA analysis between age groups and living condition groups. We also found a few key differences between the rhesus macaque OSM and human literature. Primarily, in humans, Proteobacteria is often identified as the most prevalent phylum by relative abundance [11, 50], whereas our results demonstrate Firmicutes as the top phylum for rhesus macaques. We also found that a genus commonly identified across human OSM studies, Pseudomonas , was completely absent from the rhesus macaque OSM. Pseudomonas has been found embedded in human conjunctival tissue, suggesting it is a core member of the human OSM and not a transient environmental taxa [51]; this may stand as an example of the influence of phylogeny on the OSM, as host evolutionary relationships can strongly shape microbial features [52]. OSM Composition Related to Age, Sex, and Living Conditions Older rhesus macaques demonstrated significantly lower alpha diversity compared to younger individuals, whereas sex did not correlate with any metric of alpha diversity. Differences in OSM composition with age may be associated with age-related changes in ocular surface mucous membrane integrity and immune function [40], which could influence microbiota growth on the ocular surface. Similar to our results, some human studies have found lower alpha diversity in older individuals [22], while others have found higher alpha diversity in aged groups [9, 21] or no effect of age on OSM alpha diversity [10, 23]. Beta diversity results also differ across the literature, with some studies similar to ours demonstrating little beta diversity variation with age [10, 23], and others suggesting the opposite [21, 22]. Regarding sex, some studies have found an influence of sex on alpha or beta diversity [10, 21], while others have not [11]. This variation may be attributed to a number of factors. First, the ocular surface is exposed to constant environmental fluctuations and perturbations, and therefore the OSM may be highly individual in nature. Indeed, we found that the variable accounting for the most variation in OSM composition was individual identity, which is inter-individual variation coming from a complex combination of genetics, exposures, and experiences throughout an organism’s lifetime. Our PERMANOVA results suggest that ASVs are much less likely to be shared between individuals; each monkey demonstrates a relatively unique OSM composition, which underscores how microbial community composition in a low biomass niche like the eye is heavily individualized. In support of our results, several human studies have also identified strong differences in OSM composition based on individual identity and discussed the apparent lack of a core human OSM [7, 51]. There were substantial differences between free-ranging and captive rhesus macaque OSM composition; numerous genera were differentially abundant between groups and living condition was the second strongest predictor of variation in OSM composition in our PERMANOVA models. Our findings are supported by several human studies which have found that the physical environment is a strong predictor of OSM composition, such as geographic location and altitude [7, 53]. There are also similarities between our findings and the findings of two studies which investigated fecal microbiome composition between the Cayo and Sabana Seca macaque populations. Kuthyar et al. found that location (equivalent to living condition) explained the greatest amount of variation in fecal microbiome composition [54]. However, the authors reported that there were no specific taxa differing between the captive and free-ranging groups. It is possible that the OSM, as a low biomass microbial niche uniquely exposed to constant environmental perturbations, is more strongly influenced by external living conditions than a high biomass internal biogeographic niche such as the gut. Similar to our findings and those of Kuthyar et al. [54], Compo et al. [55] also reported that housing site (equivalent to living condition) accounted for the most variation in gut microbiome composition. In contrast, however, they found that captive individuals demonstrated higher microbial diversity and richness compared to the free-ranging group [55]. This may be further evidence that a superficial and low biomass microbial niche like the OSM responds differently to environmental influences than a niche such as the gut microbiome, which is internal and known to be heavily shaped by diet. Overall, our results suggest that OSM diversity and composition are shaped strongly by individual identity and living condition, supporting the conclusions of human OSM literature while providing novel insights regarding an understudied NHP microbial niche. Conclusions Our results suggest many similarities exist between the human and rhesus macaque OSM and support the use of rhesus macaques as a model for studying OSM-host health interactions in diseases of interest to human health. In humans, unique variation of the OSM has been associated with various local and systemic conditions, several of which rhesus macaques are already used as a medical model for, including diabetes, graft-versus-host disease, glaucoma, and dry eye disease [56–59]. Studies of these diseases in rhesus macaques may benefit from the inclusion of OSM sampling and analysis to expand our understanding of OSM-disease dynamics from a cross-species perspective. This study establishes a foundational understanding of rhesus macaque OSM composition, its similarities and differences compared to the known human OSM, and its relationship with individual variables and the living environment of the NHP host. We highlight the utility of rhesus macaques as a closely related species which may serve to fill in the gaps of human OSM literature. Due to the prevalence of MiSeq sequencing in OSM literature thus far [6, 8, 34], we selected the same method for this study; however, the integration of deeper sequencing technologies may offer better alternatives for identifying low abundance taxa in future studies. Given the low biomass and highly individual natures of the OSM [4, 9], technologies which capture rare taxa at a higher resolution will likely offer important insights to our understanding of OSM composition. If microbial changes are studied as potential biomarkers for disease, it will be crucial to understand and control for individual variation. Moreover, as the field of OSM-host health dynamics expands, it will be useful to employ techniques such as whole genome shotgun sequencing, which allow for functional analysis of the OSM. We recommend future studies that collect clinical data in rhesus macaques regarding eye diseases with known OSM composition associations prioritize the functional gene analysis of eye microbiota to offer insight on OSM-host physiology relationships. This will enable us to build our understanding of how the eye microbiota interacts with the host to affect health and disease through possible metabolic and immunoregulatory pathways, and elucidate whether antibiotics or probiotics should be considered in the treatment of some OSM-associated conditions. Abbreviations ASV- amplicon sequence variant CJ- conjunctiva CPRC- Caribbean Primate Research Center DA- differential abundance EL- eyelid NHP- nonhuman primate OSM- ocular surface microbiome PCA- principal component analysis PCoA- principal coordinate analysis Declarations Ethics Approval and Consent to Participate Permission for this research was granted through animal welfare and ethics committees of the IACUC of the University of Puerto Rico (#A150121), and the ACC, University of Calgary (#AC23-0114). Permits to import samples to Canada were granted by the CFIA (Canadian Food Inspection Agency, A-2023-05630-1). Consent for Publication Not applicable. Availability of Data and Material The code for this study is available in Supplementary Material 3. The datasets generated and/or analysed during the current study are available in the [NAME] repository, [PERSISTENT WEB LINK TO DATASETS]. Competing Interests The authors declare that they have no competing interests. (?) Funding We gratefully acknowledge funding provided by the Canada Research Chairs program (950-231257, ADM), Social Sciences and Humanities Research Council CGS-M (JKH), Michael Smith Foreign Study Supplement SSHRC (JKH), New Frontiers Grant NFRFE-2018-02159 (ADM, JPH), National Eye Institute NIH Grant no 035174 (GW), National Institute on Aging R01AG060931, R56AG071023, R01AG084706 (JPH, NSM, LJNB), and Bright Focus Foundation G2020047 (ADM, JPH, JD, GW). The CPRC is supported by an Animal and Biological Material Resource Center Grant (P40OD012217), which was awarded to the University of Puerto Rico from the Office of Research Infrastructure Programs (ORIP). A Research Facilities Construction Grant [C06OD026690] was awarded for the renovation of CPRC facilities after Hurricane Maria. Authors' Contributions JKH, AGF, JD, GW, JPH, and ADM conceptualized the study design. AGF collected microbial swabs and associated metadata, with support from ABR and MIM. JKH performed DNA extractions for the microbial swabs, facilitated by JPH. JKH analyzed and interpreted the genetic data and metadata, conducted computational analyses, and wrote the manuscript. MJM, LJNB, NSM, JD, GW, JPH, and ADM secured funding, obtained regulatory permissions, and provided project oversight. All authors reviewed, revised, and approved the manuscript for publication. Full membership of the CBRU: S. C. Antón, L. J. N. Brent, J. P. Higham, M. I. Martínez, A. D. Melin, M. J. Montague, M. L. Platt, J. Sallet and N. Snyder-Mackler. Acknowledgements We thank Dr. Gwen Duytschaever, the University of Calgary CHGI lab, and the entire teams of the Higham Lab, Melin lab, and the CPRC for their support and contributions to this project. References Peter VG, Morandi SC, Herzog EL, Zinkernagel MS, Zysset-Burri DC. Investigating the ocular surface microbiome: What can it tell us? Clin Ophthalmol Auckl NZ. 2023;17:259–71. Zilliox MJ, Bouchard CS. 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Persistent neutrophil infiltration and unique ocular surface microbiome typify dupilumab–associated conjunctivitis in patients with atopic dermatitis. Ophthalmol Sci. 2023;4:100340. Callahan BJ, McMurdie PJ, Holmes SP. Exact sequence variants should replace operational taxonomic units in marker-gene data analysis. ISME J. 2017;11:2639–43. Delbeke H, Younas S, Casteels I, Joossens M. Current knowledge on the human eye microbiome: a systematic review of available amplicon and metagenomic sequencing data. Acta Ophthalmol (Copenh). 2021;99:16–25. Huang Y, Yang B, Li W. Defining the normal core microbiome of conjunctival microbial communities. Clin Microbiol Infect. 2016;22:643.e7-643.e12. Ozkan J, Coroneo M, Willcox M, Wemheuer B, Thomas T. Identification and visualization of a distinct microbiome in ocular surface conjunctival tissue. Invest Ophthalmol Vis Sci. 2018;59:4268–76. Björk JR, Dasari M, Grieneisen L, Archie EA. Primate microbiomes over time: Longitudinal answers to standing questions in microbiome research. Am J Primatol. 2019;81:e22970. Li Z, Xiang Y, Wang Y, Wan W, Ye Z, Zheng S, et al. Ocular microbial diversity, community structure, and function at high altitude. Microb Pathog. 2021;161:105253. Kuthyar S, Watson K, Huang S, Brent LJN, Platt M, Horvath J, et al. Limited microbiome differences in captive and semi-wild primate populations consuming similar diets. FEMS Microbiol Ecol. 2022;98:fiac098. Compo NR, Mieles-Rodriguez L, Gomez DE. Fecal bacterial microbiota of healthy free-ranging, healthy corralled, and chronic diarrheic corralled rhesus macaques (Macaca mulatta). Comp Med. 2021;71:152–65. Gong L, Guan Y, Cho W, Li B, Pan L, Yang Z, et al. A new non-human primate model of desiccating stress-induced dry eye disease. Sci Rep. 2022;12:7957. Jiang C, Pan X, Luo J, Liu X, Zhang L, Liu Y, et al. Alterations in microbiota and metabolites related to spontaneous diabetes and pre-diabetes in rhesus macaques. Genes. 2022;13:1513. Raghunathan V, Eaton JS, Christian BJ, Morgan JT, Ver Hoeve JN, Yang C-YC, et al. Biomechanical, ultrastructural, and electrophysiological characterization of the non-human primate experimental glaucoma model. Sci Rep. 2017;7:14329. Tkachev V, Kaminski J, Potter EL, Furlan SN, Yu A, Hunt DJ, et al. Spatiotemporal single-cell profiling reveals that invasive and tissue-resident memory donor CD8+ T cells drive gastrointestinal acute graft-versus-host disease. Sci Transl Med. 2021;13:eabc0227. Additional Declarations No competing interests reported. Supplementary Files SupplementaryMaterial1.csv SupplementaryMaterial2.docx SupplementaryMaterial3.rmd Cite Share Download PDF Status: Published Journal Publication published 20 Aug, 2025 Read the published version in Animal Microbiome → Version 1 posted Editorial decision: Revision requested 26 Mar, 2025 Editor assigned by journal 23 Mar, 2025 Submission checks completed at journal 12 Mar, 2025 First submitted to journal 11 Mar, 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. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. 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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-6205866","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":427942436,"identity":"4bac38c0-e0a0-4677-a479-b0082afb5961","order_by":0,"name":"Joelle K 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Archaeology)","correspondingAuthor":false,"prefix":"","firstName":"Amanda","middleName":"D","lastName":"Melin","suffix":""}],"badges":[],"createdAt":"2025-03-11 18:23:11","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6205866/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6205866/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1186/s42523-025-00454-4","type":"published","date":"2025-08-20T16:29:17+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":78510441,"identity":"d8064aed-3bc3-49e8-8ec7-a6bbf3a6e1ed","added_by":"auto","created_at":"2025-03-14 09:28:55","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":372919,"visible":true,"origin":"","legend":"\u003cp\u003eTaxa of the rhesus macaque ocular surface microbiota. A. Relative abundance of phyla in free-ranging versus captive rhesus macaques. B. Relative abundance of genera in free-ranging versus captive rhesus macaques; genera marked with a red dot were identified as significantly different between the captive and free-ranging groups using ANCOM-BC differential abundance analysis (see \u003cem\u003eDifferential Abundance\u003c/em\u003esection).\u003c/p\u003e","description":"","filename":"floatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-6205866/v1/6acc42a04ec1b839fe511f25.png"},{"id":78511343,"identity":"c316eed4-29f2-4cc9-aa86-6f7095720cfd","added_by":"auto","created_at":"2025-03-14 09:44:55","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":255296,"visible":true,"origin":"","legend":"\u003cp\u003eYounger animals have higher ocular microbial Shannon Index and Chao1 richness compared to older animals. Significant results are denoted with an asterisk. A. Shannon Index (p= 0.013). B. Simpson Index (p= 0.07398). C. Chao1 richness (p= 0.034).\u003c/p\u003e","description":"","filename":"floatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-6205866/v1/e847d9cc4a5996bec7db4878.png"},{"id":78510444,"identity":"593d4c6b-8bb9-46db-8d06-91ad0571f052","added_by":"auto","created_at":"2025-03-14 09:28:55","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":256440,"visible":true,"origin":"","legend":"\u003cp\u003eCaptive macaques have lower ocular microbial Shannon Index and Simpson Index compared to free-ranging macaques. Significant results are denoted with an asterisk. A. Shannon Index (p= 0.00020). B. Simpson Index (p= 1.985e-06). C. Chao1 richness (p= 0.7555).\u003c/p\u003e","description":"","filename":"floatimage3.png","url":"https://assets-eu.researchsquare.com/files/rs-6205866/v1/a1954e790b1f1a1880f26c6f.png"},{"id":78510752,"identity":"876b3568-a404-4d20-8f46-fc7c1c6227bf","added_by":"auto","created_at":"2025-03-14 09:36:55","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":193165,"visible":true,"origin":"","legend":"\u003cp\u003eDifferentially abundant microbial taxa identified using the statistical test ANCOM-BC. Effect size is represented by log fold change, where a value further from zero in either direction represents a stronger relationship. A. Taxa identified as enriched in young rhesus macaques have a positive direction log fold change, whereas those identified as less abundant in young macaques have a negative log fold change. B. Taxa identified as enriched in male rhesus macaques have a positive direction log fold change, whereas those identified as less abundant in male macaques have a negative log fold change. C. Taxa identified as enriched in captive rhesus macaques have a positive direction log fold change, whereas those identified as less abundant in captive macaques have a negative log fold change.\u003c/p\u003e","description":"","filename":"floatimage4.png","url":"https://assets-eu.researchsquare.com/files/rs-6205866/v1/802a120be9e5c84fafd3e566.png"},{"id":89847258,"identity":"1ba0f9ac-05dd-4239-90c0-93fbf95a834c","added_by":"auto","created_at":"2025-08-25 16:42:40","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1471868,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6205866/v1/50d0b404-f8ae-4946-9c43-631402a103b0.pdf"},{"id":78510440,"identity":"3d8cec64-3b61-4549-b711-da8da6c3d5b8","added_by":"auto","created_at":"2025-03-14 09:28:55","extension":"csv","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":65957,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryMaterial1.csv","url":"https://assets-eu.researchsquare.com/files/rs-6205866/v1/0972721b69349b5a44e170a2.csv"},{"id":78510447,"identity":"7d51bb55-c0e3-4109-9c7e-dc34364d7bed","added_by":"auto","created_at":"2025-03-14 09:28:55","extension":"docx","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":10539,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryMaterial2.docx","url":"https://assets-eu.researchsquare.com/files/rs-6205866/v1/9c60aeabe7fd31791779ceea.docx"},{"id":78510442,"identity":"e44be6ef-4407-4b39-aa0d-dc8bd52455cf","added_by":"auto","created_at":"2025-03-14 09:28:55","extension":"rmd","order_by":3,"title":"","display":"","copyAsset":false,"role":"supplement","size":47068,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryMaterial3.rmd","url":"https://assets-eu.researchsquare.com/files/rs-6205866/v1/60c47d1998ee06f9f4974580.rmd"}],"financialInterests":"No competing interests reported.","formattedTitle":"The ocular surface microbiome of rhesus macaques","fulltext":[{"header":"Background","content":"\u003cp\u003eThe ocular surface microbiome (OSM) \u0026ndash;the community of microorganisms living on the surface of the eye\u0026ndash; plays an important role in regulating eye health, and OSM composition has been correlated with a number of diseases [1, 2]. The OSM was first described using traditional culture techniques in 1930 by Keilty [3]. However, research on the OSM has been slow to develop, largely due to its low biomass nature [4]. For example, the OSM was not included in the original Human Microbiome Project [5]. Despite several mechanisms which limit bacterial growth on the eye surface, including the mechanical clearance caused by the movement of the eyelids and the antibacterial nature of the tears, there is now a strong consensus that human eyes harbour a diverse community of microbiota [1, 2]. In recent years, studies using next generation sequencing techniques have shed light on the taxonomy of the OSM in healthy research participants [6\u0026ndash;11], while other studies have begun to investigate how these microbiota may contribute to eye health through metabolic and immunologic mechanisms [12\u0026ndash;14]. Correlations between OSM composition and a number of eye diseases have been identified, including dry eye disease, Sjogren syndrome, meibomian gland dysfunction, and glaucoma [12, 15\u0026ndash;17]. Variation in OSM composition has also been correlated with several systemic conditions, such as diabetes and graft-versus-host disease [18\u0026ndash;20]. Understanding the typical and pathological OSM is an important step for improving our knowledge of the pathogenesis of these diseases, and may shed light on both systemic and local interactions between eye microbiota and the host.\u003c/p\u003e \u003cp\u003eRecent OSM studies in healthy human participants have revealed conflicting results on the relationship between demographic factors and OSM composition. For example, there are mixed results regarding the relationship between OSM diversity and age, with some studies showing higher diversity with increased age [9, 21], others showing lower diversity with age [22], and yet others showing no relationship at all [10, 23]. Similarly, evidence of sex differences have been reported [10, 21] or not [9] depending on the study. Individual identity, or inter-individual variation resulting from a complex combination of genetics, exposures, and experiences throughout an organism\u0026rsquo;s lifetime, has also been identified as an important factor in shaping the diversity of the OSM in humans [7]. Since OSM composition may be influenced by diverse individual host lifestyle factors including diet, exercise, use of medications and contact lenses, and overall host health, [1, 2, 11] it is difficult to draw conclusions about age and sex dynamics with the OSM. Additionally, OSM studies have varied in methodology used to determine alpha and beta diversity, sequencing technologies, and filtering and decontamination parameters. Overall, there are extensive sociocultural and individual factors that contribute to variation in human eye microbiota composition, suggesting that the use of an appropriate animal model might prove to be highly valuable.\u003c/p\u003e \u003cp\u003eThe study of non-human primates (NHPs) as model organisms for understanding human health is complementary to human research and offers many advantages and insights. Studies of NHPs are able to avoid many sources of variation common in human OSM studies, such as cultural differences in diet, alcohol consumption, and medical treatments. Rhesus macaques (\u003cem\u003eMacaca mulatta\u003c/em\u003e) are a premier animal model for human health as they show a high similarity of genomic sequences (around 90%) encoding for highly conserved protein sequences with humans. NHPs also demonstrate comparable eye anatomy and physiology to that of humans [24, 25]. Rhesus macaques are already considered an excellent model of human aging and eye diseases, including myopia, cataracts, glaucoma, and macular degeneration [24\u0026ndash;28]. Furthermore, recent studies extensively characterizing the microbiota on and in rhesus macaques have made valuable contributions to human health research [29]. However, there are currently no studies available on the rhesus macaque OSM either in healthy individuals, or as it relates to ocular disease.\u003c/p\u003e \u003cp\u003eWe endeavor to provide such a resource for the rhesus macaque eye microbiome, and establish a foundational understanding of rhesus macaque OSM composition. In this study we (1) describe the taxonomy of the rhesus macaque OSM at two sampling sites commonly assessed in humans, the eyelid (EL) and the conjunctiva (CJ); and (2) examine OSM composition related to internal and external variables, including age and sex, and captive versus free-ranging environments. Since individual host characteristics and external environmental exposures can influence the relationships among microbiota and the host biological systems they interact with [30\u0026ndash;32], we expected that OSM composition may vary with age, sex, and external living conditions. The results reported here will set the stage for future research on the rhesus macaque OSM as it relates to a variety of ocular diseases, and help to fill gaps of human OSM literature by describing OSM composition and relationships with host factors in an animal model with reduced variation in individual lifestyle, but still with high physiological and genetic relevance.\u003c/p\u003e"},{"header":"Methods","content":"\u003cp\u003e\u003cem\u003eStudy Population\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eWe studied 132 rhesus macaques (\u003cem\u003eMacaca mulatta\u003c/em\u003e) from a colony managed by the Caribbean Primate Research Center (CPRC) through the University of Puerto Rico. 54 macaques (14 males, 40 females, 8-26 years old) were located in the Sabana Seca facility and 78 macaques (43 males, 35 females, 1-25 years old) were from the free-ranging population on the island of Cayo Santiago, Puerto Rico. The Cayo Santiago rhesus macaque population was established in 1938 with a group of 409 individuals imported from India [33], and the population now includes around 2000 individuals. A number of individuals have been transferred from Cayo Santiago to the Sabana Seca Field Station since 1984, where animals now live in captivity in outdoor corrals. The CPRC maintains a detailed census of both populations, recording the birth, death, and kinship information for all individuals; monkeys are recognized and tracked by tattoos, ear notches, and facial features. The CPRC provisions both of these primate populations with monkey chow and water. However, the free-ranging group on Cayo Santiago occasionally supplement their diet with native species of plants and invertebrates. The Cayo population also experiences natural weather conditions and individuals freely associate into, and move between, different social groups. Comparatively, the Sabana Seca individuals are housed in outdoor corrals, so while they experience some natural weather fluctuations, they have consistent shade and cover and individuals are not free to move between social groups. The Cayo population receives a tetanus inoculation at approximately one year of age, and certain individuals are trapped-and-released annually as part of longitudinal research studies [24]. The Sabana Seca group interacts regularly with caregivers during feeding and medical assessments.\u003cem\u003e\u0026nbsp;\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eSample Collection/Shipping/Transportation\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eWe collected eye microbiome swabs during animal exams that were scheduled as part of ongoing research studies. Prior to sample collection all monkeys were anesthetised by a trained CPRC veterinarian with intramuscular ketamine, and given 1-2 drops of topical anesthetic solution in each eye (tetracaine hydrochloride 0.5% or proparacaine hydrochloride 0.5% sterile ophthalmic solution).\u003c/p\u003e\n\u003cp\u003eWe collected two types of samples using individual sterile swabs (\u003cem\u003eBBL CultureSwab,\u0026nbsp;\u003c/em\u003eOttawa, ON): (1) EL swabs were collected from the lower eyelid margin by applying light friction and rubbing the swab from the medial to the lateral edge of the eyelid along the base of the eyelashes, avoiding contact with the conjunctiva; (2) CJ surface swabs were collected by rolling the swab back and forth 4-6 times along the bulbar conjunctival surface, while avoiding contact with the eyelid, eyelashes, or skin. Following sampling, we cut the swab tips into sterile 2mL cryogenic vials (\u003cem\u003efisherscientific\u003c/em\u003e, Pittsburgh, PA) for storage and transport. Swabs were placed on ice while in the field, and frozen at -80\u0026deg;C within 5 hours of collection.\u003c/p\u003e\n\u003cp\u003eSamples were transported from Puerto Rico to New York University (NYU) on dry ice and immediately stored at -80\u0026deg;C upon arrival. DNA extractions were performed in a BSL-2 laboratory at NYU, and extracted DNA was shipped on dry ice to the University of Calgary for library preparation and sequencing.\u003cem\u003e\u0026nbsp;\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eDNA Extraction \u0026amp; Sequencing\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eAs a low biomass microbial niche, OSM samples are at high risk for contamination [4], and we followed strict protocols to minimize contamination during this study. DNA extractions were performed in a BioSafety Cabinet, with all equipment sterilized using 10% bleach, 70% ethanol, and 60 minutes of UV radiation prior to use. All disposable equipment was sterile, PCR-grade, DNA, and DNAse/RNAse free. The researcher performing the DNA extractions wore a sterile hair net, sleeve covers, face mask, and gloves throughout the procedure.\u003c/p\u003e\n\u003cp\u003eDNA extractions were performed using the Macherey-Nagel DNeasy-96-PowerSoil Pro kit (QIAGEN, Hilden, Germany) with a modified protocol. The CD2 inhibitor removal step was not performed, as per the manufacturer\u0026apos;s suggestion for low biomass samples. Negative controls were included in the form of extraction/reagent blanks: sample wells with sterile swabs were included in the extraction process as reagent controls, and processed along with the samples for all remaining steps.\u003c/p\u003e\n\u003cp\u003eWe selected the sequencing parameters for this study based on recent human OSM studies in order to maximize the comparability of our results to current knowledge of the human OSM; many OSM studies have used Illumina MiSeq sequencing of the V3/V4 gene region [6, 8, 34]. Library preparation and sequencing were performed at the University of Calgary Centre for Health Genomics and Informatics (CHGI) core lab. Samples were processed into NGS libraries using Illumina 16S Metagenomic Sequencing Library Preparation using an adjusted low biomass protocol. 11.5ul of stock sample was used as input into the first stage PCR. Adjustments to the library prep protocol to account for the low DNA input included: a reduction in PCR bead clean up elution volumes to 30ul for PCR clean up 1 and to 20ul for PCR clean up 2; adjustments to the indexing reaction include 3x volume increase in amplicon used along with a removal of PCR grade water. Libraries were sequenced on two MiSeq 600 cycle v3 sequencing runs, PE300 with 25% phiX spike in due to the low diversity nature of the libraries. Run 1 included an equal molar pool of 288 libraries and Run 2 included an equal molar pool of 258 libraries. Fifty-nine libraries were below the minimum concentration of 2ng/uL and were used \u0026lsquo;as is\u0026rsquo;. A total of 23,723,252 raw reads were produced with an average of 41,186 reads per sample for Run 1, and a total of 28,197,388 raw reads were produced with an average of 54,646 reads per sample for Run 2.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eData Analysis\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eWe assessed the quality of the sequences using FastQC and MultiQC. We removed primers and adapters and performed quality trimming using Trimmomatic with the parameters fa:2:30:10:2:True LEADING:5 TRAILING:5 SLIDINGWINDOW:5:20 MINLEN:50. This means leading and trailing bases were trimmed immediately if they had a quality of 5 or less, and nucleotide bases were trimmed in a sliding window (5 bases long) if the average quality within that window fell below 20. Trimmed reads for Run 1 and Run 2 were then processed separately through the dada2 [35] pipeline in R 4.3.1, and the resulting taxonomy and sequence tables were merged for further downstream analysis. This resulted in 11,374,429 reads (average of 20,832 per sample), which clustered into 42,059 unique amplicon sequence variants (ASVs). Taxonomy was assigned using the SILVA v.138 database.\u003c/p\u003e\n\u003cp\u003eAll downstream analyses were carried out in R using the packages \u003cem\u003ephyloseq, decontam, vegan, lme4, lmerTest, fantaxtic, microbiome, microbiomeMarker, ANCOMBC,\u003c/em\u003e with visualizations using \u003cem\u003eggplot2, ggpubr\u003c/em\u003e, and\u003cem\u003e\u0026nbsp;sjPlot\u003c/em\u003e. We filtered the reads for contaminants using the \u003cem\u003edecontam\u003c/em\u003e package at a threshold of 0.5. 627 ASVs were identified as contaminants based on the reagent blanks included in sequencing, and subsequently pruned from the dataset. We also removed ASVs uncharacterized at the Phyla level, chloroplasts and mitochondria, ASV singletons, and ASVs present in less than 2% of samples. Dong et al. (2022) [9] found that 85% of ASVs from the human OSM were present in less than 5% of samples. We chose two percent as a threshold to capture the diversity of this low biomass microbial community, where many ASVs might be expected to occur in less than 5% of samples but still represent meaningful biological taxa. After all quality control preprocessing steps, there were 1431 unique ASVs belonging to 7 Phyla and 63 Genera. Rarefaction (the process of randomly subsampling all samples down to a uniform sequencing depth) has been debated in the literature and critiqued for discarding meaningful biological information [36, 37], although recent work has combated this critique [38]. In OSM studies it is common to use alternative normalization strategies instead of rarefaction, especially due to the low biomass nature of this microbial niche [21, 34]. We have normalized our samples for read count as described below.\u003c/p\u003e\n\u003cp\u003eWe measured alpha diversity using three standard metrics: the Shannon Diversity Index, Simpson Index, and Chao1 richness [39]. We visualized read count (number of reads per sample) against these metrics and identified a strong positive relationship for all three. To normalize the data for read count we plotted linear models with these metrics according to read count and extracted the residuals for use in all further analyses. We used Wilcoxon-signed rank tests to compare Shannon Index residuals, Simpson Index residuals, and Chao1 richness residuals between categorical groups of interest, including age (young versus old), sex (male versus female), living condition (captive versus free-ranging), sampled site (EL versus CJ), and eye laterality (left versus right). We also constructed linear mixed models to explore relationships between our variables of interest and alpha diversity variation; in all models age, sex, living condition, sequencing run, eye laterality, and sampled site were included as fixed effects with individual identity (represented by each macaque\u0026rsquo;s unique identification number) as a random effect. Including individual identity accounts for non-independent sampling, as each monkey was represented by multiple swabs (right and left eyes, EL and CJ). The inclusion of individual identity also allowed us to parse the influences of individual characteristics such as age and sex, versus the influence of overall host identity.\u003c/p\u003e\n\u003cp\u003eCurrent understandings of human ocular physiology suggest that older adults can experience lower ocular mucous membrane integrity, decreased tear production, and changes in immune function which may affect eye health [40]. Here we defined \u0026lsquo;old age\u0026rsquo; as individuals in the top 75% quartile, which for this dataset was \u0026gt;17.75 years of age (17.75-26.56 years old), and \u0026lsquo;younger\u0026rsquo; monkeys -including young and middle-aged animals- (1-17.59 years old) below this threshold (Supplementary Material 1). Based on life history traits, rhesus macaques are sometimes separated into further age groups containing pre-weaning individuals and pre-sexual maturity [41], but this dataset lacked sufficient numbers of these pre-adult individuals for a more granular comparison. Therefore, we have focussed our comparison on the effects of old age on microbiome composition.\u003c/p\u003e\n\u003cp\u003eWe measured beta diversity using two metrics, Bray Curtis Dissimilarity and Aitchison Distance. We chose Bray Curtis due to its common use in the OSM literature to enable comparison of our results to existing studies, while Aitchison Distance was chosen due to recent papers recommending it for the highly compositional and sparse nature of microbiome datasets [42, 43]. We visualized structural relationships using Principal Coordinate Analysis (PCoA, Bray Curtis) and Principal Component Analysis (PCA, Aitchison Distance). We used PERMANOVA models to explore the significance of variables of interest in shaping beta diversity, including age, sex, individual identity, living condition, and sampled site, with eye laterality and sequencing run included to control for possible confounding effects.\u003c/p\u003e\n\u003cp\u003eWe measured differential abundance (DA) using ANCOM-BC. ANCOM-BC is considered a conservative test and has been recommended for microbiome studies due to its ability to account for the compositional and sparse nature of microbiome datasets [44\u0026ndash;46]. We filtered ANCOM-BC results to include Genera that were identified as differentially abundant according to the function (diff_ == TRUE), resistant to differential treatment of zeroes (ss_ == TRUE), and had a \u003cem\u003eq\u003c/em\u003e-value of less than 0.05, with a log fold change of greater than |0.25|. This log fold change threshold was chosen for ANCOM-BC due to the highly conservative nature of the other filtering parameters. In addition, we provide alternative results using the DESeq2 test as supplemental material. DESeq2 is commonly used in microbiome literature, but was adapted from RNAseq purposes and has been criticized as less appropriate for the high sparsity nature of microbiome datasets [44, 46]. To enable comparison of our results with human literature, and to explore differences produced by analyzing the same dataset with different DA techniques, we provide the DESeq2 results in Supplementary Material 2.\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003eAfter trimming and filtering, final analyses included samples from 131 individuals and a total of 251 eyes. The free-ranging group included samples from 77 individuals and 147 eyes (43 males, 34 females, 1-25 years old) and the captive group included samples from 54 individuals and 104 eyes (14 males, 40 females, ages 8-26 years old).\u003cem\u003e\u0026nbsp;\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eThe taxonomy of the Rhesus macaque OSM\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eTop Taxa\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eThe final 1431 ASVs were assigned to 7 Phyla across the entire dataset of captive and free-ranging individuals, including \u003cem\u003eFirmicutes\u003c/em\u003e (58.03%), \u003cem\u003eBacteroidota\u003c/em\u003e (21.39%), \u003cem\u003eActinobacteriota\u003c/em\u003e (14.23%), \u003cem\u003eProteobacteria\u003c/em\u003e (4.72%), \u003cem\u003eCampylobacterota\u003c/em\u003e (0.85%), \u003cem\u003eSpirochaetota\u003c/em\u003e (0.42%), and \u003cem\u003eFusobacteriota\u003c/em\u003e (0.37%). There were 63 total Genera, with the top 10 being \u003cem\u003ePrevotella_9\u003c/em\u003e (12.05%),\u003cem\u003e\u0026nbsp;Streptococcus\u003c/em\u003e (10.51%),\u003cem\u003e\u0026nbsp;Lactobacillus\u003c/em\u003e (8.91%), \u003cem\u003eStaphylococcus\u003c/em\u003e (7.84%), \u003cem\u003eHT002\u003c/em\u003e (7.74%), \u003cem\u003eCorynebacterium\u003c/em\u003e (5.79%), \u003cem\u003ePrevotella\u003c/em\u003e (3.89%), \u003cem\u003eFaecalibacterium\u003c/em\u003e (3.70%), \u003cem\u003eLigilactobacillus\u003c/em\u003e (3.17%), and \u003cem\u003eBrevibacterium\u003c/em\u003e (2.60%). \u003cem\u003ePrevotella_9\u003c/em\u003e is distinguished in the SILVA database from \u003cem\u003ePrevotella\u003c/em\u003e as a genetically distinct sub-lineage within the greater \u003cem\u003ePrevotella\u003c/em\u003e genus. Within the captive group (n= 54) the top 4 Phyla were \u003cem\u003eFirmicutes\u003c/em\u003e (60.94%), \u003cem\u003eActinobacteriota\u003c/em\u003e (21.42%), \u003cem\u003eBacteroidota\u003c/em\u003e (13.41%), and \u003cem\u003eProteobacteria\u003c/em\u003e (3.33%); within the free-ranging group (n= 77) the top Phyla were \u003cem\u003eFirmicutes\u003c/em\u003e (55.98%), \u003cem\u003eBacteroidota\u003c/em\u003e (27.01%), \u003cem\u003eActinobacteriota\u003c/em\u003e (9.16%), and \u003cem\u003eProteobacteria\u003c/em\u003e (5.70%)(Figure 1A). The top ten Genera of the captive group were \u003cem\u003eStreptococcus\u003c/em\u003e (14.55%), \u003cem\u003eStaphylococcus\u003c/em\u003e (12.47%), \u003cem\u003ePrevotella_9\u003c/em\u003e (8.35%), \u003cem\u003eLactobacillus\u003c/em\u003e (7.55%), \u003cem\u003eHT002\u003c/em\u003e (6.75%), \u003cem\u003eCorynebacterium\u003c/em\u003e (6.05%), \u003cem\u003eBrevibacterium\u0026nbsp;\u003c/em\u003e(5.18%), \u003cem\u003eDietzia\u003c/em\u003e (4.11%), \u003cem\u003eLigilactobacillus\u003c/em\u003e (3.77%), and \u003cem\u003eBrachybacterium\u003c/em\u003e (2.67%); the top ten Genera of the free-ranging group were \u003cem\u003ePrevotella_9\u003c/em\u003e (14.65%), \u003cem\u003eLactobacillus\u003c/em\u003e (9.87%), \u003cem\u003eHT002\u003c/em\u003e (8.45%),\u003cem\u003e\u0026nbsp;Streptococcus\u0026nbsp;\u003c/em\u003e(7.66%), \u003cem\u003eCorynebacterium\u003c/em\u003e (5.61%), \u003cem\u003ePrevotella\u003c/em\u003e (5.31%), \u003cem\u003eFaecalibacterium\u003c/em\u003e (4.77%), \u003cem\u003eStaphylococcus\u003c/em\u003e (4.58%), \u003cem\u003eLigilactobacillus\u003c/em\u003e (2.74%), and \u003cem\u003eRuminococcus\u003c/em\u003e (1.91%)(Figure 1B).\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eAlpha Diversity \u0026amp; Sampled Site Differential Abundance\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eFor metrics of alpha diversity, sampled site did not differ significantly in Shannon Index or Simpson Index, but Chao1 was significantly different between CJ and EL samples (Wilcoxon test, p= 0.0006, CI 95%= 2.492 to 8.499) with CJ samples demonstrating a higher average Chao1 richness. Alpha diversity did not differ significantly between eye laterality by any metrics. Using ANCOM-BC to assess the DA of specific taxa, we identified that EL samples were enriched in \u003cem\u003ePeptoniphilus\u003c/em\u003e compared to CJ samples, and CJ samples were comparatively enriched in \u003cem\u003eDolosigranulum\u003c/em\u003e.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eOSM Composition Related to Age, Sex, and Living Conditions\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eAlpha Diversity\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eThe following results exploring associations between alpha diversity and the host variables of age, sex, and living condition did not differ when subset into CJ versus EL sampling locations, and we therefore combined all samples for our analyses. First, we directly compared alpha diversity metrics between the categorical groups of interest (age, sex, and living condition) using Wilcoxon-signed rank tests. The old and young groups differed significantly in Shannon Index (Wilcoxon test, p= 0.013, CI 95%= 0.017 to 0.144) and Chao1 richness (Wilcoxon test, p= 0.034, CI 95%= 0.265 to 7.165), but not in Simpson Index (Figure 2). The young group demonstrated higher Shannon Index diversity and higher Chao1 richness compared to the older group. Males and females did not differ significantly in any alpha diversity metrics. Shannon Index (Wilcoxon test, p= 0.00020, CI 95%= 0.058 to 0.179) and Simpson Index (Wilcoxon test, p= 1.985e-06, CI 95%= 0.003 to 0.008) differed significantly between the captive and free-ranging groups, with the free-ranging group demonstrating higher alpha diversity by both metrics, but Chao1 richness did not differ significantly (Figure 3).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eNext, we used linear modelling with the \u003cem\u003elme4\u003c/em\u003e package in R to explore the influence of age, sex, and living condition while controlling for individual identity in a combined model of alpha diversity variation. Because the data included both CJ and EL samples, we included sampled site as a variable in the models. In both Shannon Index and Simpson Index models, living condition was the only significant predictor of alpha diversity (p= 0.0189 and p= 0.0016 respectively), with captive individuals demonstrating lower alpha diversity by both metrics. In the Chao1 richness model none of the variables of interest (age, sex, and living condition) were significant predictors of richness. This suggests that living condition has the strongest influence on alpha diversity.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eBeta Diversity\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;In a combined PERMANOVA model using Bray Curtis Dissimilarity normalized through a relative abundance transformation, all variables came back as a statistically significant predictor of beta diversity: age (p= 0.007, R2= 0.0027), sex (p= 0.012, R2= 0.0024), living condition (p= 0.001, R2= 0.0124), sampled site (p= 0.012, R2= 0.0024), and individual identity (p= 0.001, R2= 0.3291). However, only living condition and individual identity had R2 effect sizes demonstrating that they accounted for greater than one percent of variance in beta diversity, with living condition accounting for approximately 1.24% and individual identity for approximately 32.90%. The combined PERMANOVA model using Aitchison distance demonstrated similar results to the Bray Curtis model: age (p= 0.006, R2= 0.0026), sex (p= 0.014, R2= 0.0025), living condition (p= 0.001, R2= 0.0133), sampled site (p= 0.025, R2= 0.0024), and individual identity (p= 0.001, R2= 0.3380). No clear clustering for the variables of interest based on either model was present in the PCoA or PCA visualizations. \u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eDifferential Abundance\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eUsing ANCOM-BC we identified that young macaques were enriched with the genera \u003cem\u003eAerococcus\u003c/em\u003e, \u003cem\u003eRuminococcus\u003c/em\u003e, \u003cem\u003eMicrococcus\u003c/em\u003e, and L\u003cem\u003eachnospiraceae NK4A136 group\u003c/em\u003e, and less abundant in \u003cem\u003eDolosigranulum\u003c/em\u003e compared to older individuals (Figure 4A). Regarding sex differences, we found that males were significantly enriched in \u003cem\u003eEzakiella\u003c/em\u003e and \u003cem\u003ePeptoniphilus\u003c/em\u003e, and significantly lower in \u003cem\u003eSneathia\u003c/em\u003e (Figure 4B). We also identified 5 genera as being significantly enriched in the captive group compared to the free-ranging group: \u003cem\u003eActinobacillus\u003c/em\u003e, \u003cem\u003eStaphylococcus\u003c/em\u003e, \u003cem\u003eNocardioides\u003c/em\u003e, \u003cem\u003eRothia\u003c/em\u003e, and \u003cem\u003eBrevibacterium\u003c/em\u003e; and 4 genera as being significantly lower in abundance in the captive group: \u003cem\u003e[Eubacterium] siraeum group\u003c/em\u003e, \u003cem\u003ePrevotellaceae UCG-003\u003c/em\u003e, \u003cem\u003ePrevotella\u003c/em\u003e, and \u003cem\u003eRuminococcus\u003c/em\u003e (Figure 4C).\u003c/p\u003e"},{"header":"Discussion ","content":"\u003cp\u003eIn this study we aimed to identify the taxonomy of the rhesus macaque OSM and explore OSM composition as it relates to internal and external variables, including age, sex, and living condition. Overall, we identified 1431 ASVs and found that the top taxa in the rhesus macaque OSM included 7 phyla and 63 genera. We identified significantly different alpha diversity between older and younger rhesus macaques and between captive and free-ranging macaques, whereas sex did not correlate with alpha diversity by any measure. We also found that living condition and individual identity played a substantial role in shaping beta diversity, while age and sex did not. We identified specific taxa that differed in relative abundance between age groups, sex classes, and captive versus free-ranging populations. \u003cem\u003e\u0026nbsp;\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eTaxonomy of the Rhesus Macaque OSM\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eThe total number of unique microbial taxa reported in human OSM literature varies somewhat between studies. Patra et al. found a total of 3370 unique ASVs [47], Dong et al. reported 1731 OPUs (operational phylogenetic units)[9], and Ozkan et al. described 2465 zOTUs (zero radius operational taxonomic units)[11]. Although it can be difficult to directly compare between different taxonomic clustering units such as ASVs, OPUs, and zOTUs [48], our result of 1431 ASVs \u0026nbsp;is in a similar range to human OSM findings. We identified \u003cem\u003eFirmicutes\u003c/em\u003e, \u003cem\u003eBacteroidota\u003c/em\u003e, \u003cem\u003eActinobacteriota\u003c/em\u003e, and \u003cem\u003eProteobacteria\u003c/em\u003e as the top four phyla, and \u003cem\u003ePrevotella_9\u003c/em\u003e,\u003cem\u003e\u0026nbsp;Streptococcus\u003c/em\u003e,\u003cem\u003e\u0026nbsp;Lactobacillus\u003c/em\u003e, \u003cem\u003eStaphylococcus\u003c/em\u003e, \u003cem\u003eHT002\u003c/em\u003e, \u003cem\u003eCorynebacterium\u003c/em\u003e, \u003cem\u003ePrevotella\u003c/em\u003e, \u003cem\u003eFaecalibacterium\u003c/em\u003e, \u003cem\u003eLigilactobacillus\u003c/em\u003e, and \u003cem\u003eBrevibacterium\u003c/em\u003e as the top ten genera. Relative to humans, we demonstrate that rhesus macaques share the same top four phyla, and four of the same top ten genera including \u003cem\u003eBrevibacterium\u003c/em\u003e, \u003cem\u003eCorynebacterium\u003c/em\u003e, \u003cem\u003eStaphylococcus\u003c/em\u003e, and \u003cem\u003eStreptococcus\u003c/em\u003e. Of the four genera, \u003cem\u003eCorynebacterium\u003c/em\u003e, \u003cem\u003eStaphylococcus\u003c/em\u003e, and \u003cem\u003eStreptococcus\u003c/em\u003e were identified in a recent review as being in the top genera found across all current human OSM literature [49]. Several other taxa identified as part of the top ten genera in human studies were also present in our study, including \u003cem\u003eBrevibacterium, Micrococcus\u003c/em\u003e, and \u003cem\u003eMoraxella\u003c/em\u003e which were not in the top ten genera of the rhesus macaque OSM, but were taxa of interest during DA analysis between age groups and living condition groups. We also found a few key differences between the rhesus macaque OSM and human literature. Primarily, in humans, \u003cem\u003eProteobacteria\u003c/em\u003e is often identified as the most prevalent phylum by relative abundance [11, 50], whereas our results demonstrate \u003cem\u003eFirmicutes\u003c/em\u003e as the top phylum for rhesus macaques. We also found that a genus commonly identified across human OSM studies, \u003cem\u003ePseudomonas\u003c/em\u003e, was completely absent from the rhesus macaque OSM. \u003cem\u003ePseudomonas\u003c/em\u003e has been found embedded in human conjunctival tissue, suggesting it is a core member of the human OSM and not a transient environmental taxa [51]; this may stand as an example of the influence of phylogeny on the OSM, as host evolutionary relationships can strongly shape microbial features [52].\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eOSM Composition Related to Age, Sex, and Living Conditions\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eOlder rhesus macaques demonstrated significantly lower alpha diversity compared to younger individuals, whereas sex did not correlate with any metric of alpha diversity. Differences in OSM composition with age may be associated with age-related changes in ocular surface mucous membrane integrity and immune function [40], which could influence microbiota growth on the ocular surface. Similar to our results, some human studies have found lower alpha diversity in older individuals [22], while others have found higher alpha diversity in aged groups [9, 21]\u003cem\u003e\u0026nbsp;\u003c/em\u003eor no effect of age on OSM alpha diversity [10, 23]. Beta diversity results also differ across the literature, with some studies similar to ours demonstrating little beta diversity variation with age [10, 23], and others suggesting the opposite [21, 22]. Regarding sex, some studies have found an influence of sex on alpha or beta diversity [10, 21], while others have not [11]. This variation may be attributed to a number of factors. First, the ocular surface is exposed to constant environmental fluctuations and perturbations, and therefore the OSM may be highly individual in nature. Indeed, we found that the variable accounting for the most variation in OSM composition was individual identity, which is inter-individual variation coming from a complex combination of genetics, exposures, and experiences throughout an organism\u0026rsquo;s lifetime. Our PERMANOVA results suggest that ASVs are much less likely to be shared between individuals; each monkey demonstrates a relatively unique OSM composition, which underscores how microbial community composition in a low biomass niche like the eye is heavily individualized. In support of our results, several human studies have also identified strong differences in OSM composition based on individual identity and discussed the apparent lack of a core human OSM [7, 51].\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThere were substantial differences between free-ranging and captive rhesus macaque OSM composition; numerous genera were differentially abundant between groups and living condition was the second strongest predictor of variation in OSM composition in our PERMANOVA models. Our findings are supported by several human studies which have found that the physical environment is a strong predictor of OSM composition, such as geographic location and altitude [7, 53]. There are also similarities between our findings and the findings of two studies which investigated fecal microbiome composition between the Cayo and Sabana Seca macaque populations. Kuthyar et al. found that location (equivalent to living condition) explained the greatest amount of variation in fecal microbiome composition [54]. However, the authors reported that there were no specific taxa differing between the captive and free-ranging groups. It is possible that the OSM, as a low biomass microbial niche uniquely exposed to constant environmental perturbations, is more strongly influenced by external living conditions than a high biomass internal biogeographic niche such as the gut. Similar to our findings and those of Kuthyar et al. [54], Compo et al. [55] also reported that housing site (equivalent to living condition) accounted for the most variation in \u0026nbsp; gut microbiome composition. In contrast, however, they found that captive individuals demonstrated higher microbial diversity and richness compared to the free-ranging group [55]. This may be further evidence that a superficial and low biomass microbial niche like the OSM responds differently to environmental influences than a niche such as the gut microbiome, which is internal and known to be heavily shaped by diet. Overall, our results suggest that OSM diversity and composition are shaped strongly by individual identity and living condition, supporting the conclusions of human OSM literature while providing novel insights regarding an understudied NHP microbial niche.\u003c/p\u003e"},{"header":"Conclusions ","content":"\u003cp\u003eOur results suggest many similarities exist between the human and rhesus macaque OSM and support the use of rhesus macaques as a model for studying OSM-host health interactions in diseases of interest to human health. In humans, unique variation of the OSM has been associated with various local and systemic conditions, several of which rhesus macaques are already used as a medical model for, including diabetes, graft-versus-host disease, glaucoma, and dry eye disease [56\u0026ndash;59]. Studies of these diseases in rhesus macaques may benefit from the inclusion of OSM sampling and analysis to expand our understanding of OSM-disease dynamics from a cross-species perspective. \u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThis study establishes a foundational understanding of rhesus macaque OSM composition, its similarities and differences compared to the known human OSM, and its relationship with individual variables and the living environment of the NHP host. We highlight the utility of rhesus macaques as a closely related species which may serve to fill in the gaps of human OSM literature. Due to the prevalence of MiSeq sequencing in OSM literature thus far [6, 8, 34], we selected the same method for this study; however, the integration of deeper sequencing technologies may offer better alternatives for identifying low abundance taxa in future studies. Given the low biomass and highly individual natures of the OSM [4, 9], technologies which capture rare taxa at a higher resolution will likely offer important insights to our understanding of OSM composition. If microbial changes are studied as potential biomarkers for disease, it will be crucial to understand and control for individual variation. Moreover, as the field of OSM-host health dynamics expands, it will be useful to employ techniques such as whole genome shotgun sequencing, which allow for functional analysis of the OSM. We recommend future studies that collect clinical data in rhesus macaques regarding eye diseases with known OSM composition associations prioritize the functional gene analysis of eye microbiota to offer insight on OSM-host physiology relationships. This will enable us to build our understanding of how the eye microbiota interacts with the host to affect health and disease through possible metabolic and immunoregulatory pathways, and elucidate whether antibiotics or probiotics should be considered in the treatment of some OSM-associated conditions.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cp\u003eASV- amplicon sequence variant\u003c/p\u003e\n\u003cp\u003eCJ- conjunctiva\u003c/p\u003e\n\u003cp\u003eCPRC- Caribbean Primate Research Center\u003c/p\u003e\n\u003cp\u003eDA- differential abundance\u003c/p\u003e\n\u003cp\u003eEL- eyelid\u003c/p\u003e\n\u003cp\u003eNHP- nonhuman primate\u003c/p\u003e\n\u003cp\u003eOSM- ocular surface microbiome\u003c/p\u003e\n\u003cp\u003ePCA- principal component analysis\u0026nbsp;\u003c/p\u003e\n\u003cp\u003ePCoA- principal coordinate analysis\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cem\u003eEthics Approval and Consent to Participate\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003ePermission for this research was granted through animal welfare and ethics committees of the IACUC of the University of Puerto Rico \u0026nbsp;(#A150121), and the ACC, University of Calgary (#AC23-0114). Permits to import samples to Canada were granted by the CFIA (Canadian Food Inspection Agency, A-2023-05630-1).\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eConsent for Publication\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eAvailability of Data and Material\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eThe code for this study is available in \u0026nbsp;Supplementary Material 3. The datasets generated and/or analysed during the current study are available in the [NAME] repository, [PERSISTENT WEB LINK TO DATASETS].\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eCompeting Interests\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that they have no competing interests. (?)\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eFunding\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eWe gratefully acknowledge funding provided by the Canada Research Chairs program (950-231257, ADM), Social Sciences and Humanities Research Council CGS-M (JKH), Michael Smith Foreign Study Supplement SSHRC (JKH), New Frontiers Grant NFRFE-2018-02159 (ADM, JPH), National Eye Institute NIH Grant no 035174 (GW), \u0026nbsp;National Institute on Aging R01AG060931, R56AG071023, R01AG084706 (JPH, NSM, LJNB), and Bright Focus Foundation G2020047 (ADM, JPH, JD, GW). The CPRC is supported by an Animal and\u003c/p\u003e\n\u003cp\u003eBiological Material Resource Center Grant (P40OD012217), which was awarded to the University of Puerto Rico from the Office of Research Infrastructure Programs (ORIP). A Research Facilities Construction Grant [C06OD026690] was awarded for the renovation of CPRC facilities after Hurricane Maria.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eAuthors\u0026apos; Contributions\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eJKH, AGF, JD, GW, JPH, and ADM conceptualized the study design. AGF collected microbial swabs and associated metadata, with support from ABR and MIM. JKH performed DNA extractions for the microbial swabs, facilitated by JPH. JKH analyzed and interpreted the genetic data and metadata, conducted computational analyses, and wrote the manuscript. MJM, LJNB, NSM, JD, GW, JPH, and ADM secured funding, obtained regulatory permissions, and provided project oversight. All authors reviewed, revised, and approved the manuscript for publication. Full membership of the CBRU: S. C. Ant\u0026oacute;n, L. J. N. Brent, J. P. Higham, M. I. Mart\u0026iacute;nez, A. D. Melin, M. J. Montague, M. L. Platt, J. Sallet and N. Snyder-Mackler.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eAcknowledgements\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eWe thank Dr. Gwen Duytschaever, the University of Calgary CHGI lab, and the entire teams of the Higham Lab, Melin lab, and the CPRC for their support and contributions to this project.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003ePeter VG, Morandi SC, Herzog EL, Zinkernagel MS, Zysset-Burri DC. Investigating the ocular surface microbiome: What can it tell us? Clin Ophthalmol Auckl NZ. 2023;17:259\u0026ndash;71.\u003c/li\u003e\n\u003cli\u003eZilliox MJ, Bouchard CS. The microbiome, ocular surface, and corneal disorders. Am J Pathol. 2023. https://doi.org/10.1016/j.ajpath.2023.05.004.\u003c/li\u003e\n\u003cli\u003eKeilty RA. The bacterial flora of the normal conjunctiva with comparative nasal culture study. Am J Ophthalmol. 1930;13:876\u0026ndash;9.\u003c/li\u003e\n\u003cli\u003eOzkan J, Willcox MD. 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Acta Ophthalmol (Copenh). 2022;100:e770\u0026ndash;8.\u003c/li\u003e\n\u003cli\u003eCallahan BJ, McMurdie PJ, Rosen MJ, Han AW, Johnson AJA, Holmes SP. DADA2: High-resolution sample inference from Illumina amplicon data. Nat Methods. 2016;13:581\u0026ndash;3.\u003c/li\u003e\n\u003cli\u003eMcMurdie PJ, Holmes S. Waste not, want not: Why rarefying microbiome data is inadmissible. PLOS Comput Biol. 2014;10:e1003531.\u003c/li\u003e\n\u003cli\u003eWillis AD. Rarefaction, alpha diversity, and statistics. Front Microbiol. 2019;10.\u003c/li\u003e\n\u003cli\u003eSchloss PD. Waste not, want not: revisiting the analysis that called into question the practice of rarefaction. mSphere. 2023;9:e00355-23.\u003c/li\u003e\n\u003cli\u003eLucas R, Groeneveld J, Harms H, Johst K, Frank K, Kleinsteuber S. A critical evaluation of ecological indices for the comparative analysis of microbial communities based on molecular datasets. 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Limited microbiome differences in captive and semi-wild primate populations consuming similar diets. FEMS Microbiol Ecol. 2022;98:fiac098.\u003c/li\u003e\n\u003cli\u003eCompo NR, Mieles-Rodriguez L, Gomez DE. Fecal bacterial microbiota of healthy free-ranging, healthy corralled, and chronic diarrheic corralled rhesus macaques (Macaca mulatta). Comp Med. 2021;71:152\u0026ndash;65.\u003c/li\u003e\n\u003cli\u003eGong L, Guan Y, Cho W, Li B, Pan L, Yang Z, et al. A new non-human primate model of desiccating stress-induced dry eye disease. Sci Rep. 2022;12:7957.\u003c/li\u003e\n\u003cli\u003eJiang C, Pan X, Luo J, Liu X, Zhang L, Liu Y, et al. Alterations in microbiota and metabolites related to spontaneous diabetes and pre-diabetes in rhesus macaques. Genes. 2022;13:1513.\u003c/li\u003e\n\u003cli\u003eRaghunathan V, Eaton JS, Christian BJ, Morgan JT, Ver Hoeve JN, Yang C-YC, et al. Biomechanical, ultrastructural, and electrophysiological characterization of the non-human primate experimental glaucoma model. Sci Rep. 2017;7:14329.\u003c/li\u003e\n\u003cli\u003eTkachev V, Kaminski J, Potter EL, Furlan SN, Yu A, Hunt DJ, et al. Spatiotemporal single-cell profiling reveals that invasive and tissue-resident memory donor CD8+ T cells drive gastrointestinal acute graft-versus-host disease. Sci Transl Med. 2021;13:eabc0227.\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":"animal-microbiome","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"amic","sideBox":"Learn more about [Animal Microbiome](http://animalmicrobiome.biomedcentral.com)","snPcode":"42523","submissionUrl":"https://submission.nature.com/new-submission/42523/3","title":"Animal Microbiome","twitterHandle":"@bmc","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"BMC/SO AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"eye microbiome, nonhuman primates, MiSeq, 16S, amplicon sequencing","lastPublishedDoi":"10.21203/rs.3.rs-6205866/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6205866/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cem\u003e\u003cstrong\u003eBackground\u003c/strong\u003e\u003c/em\u003e\u003cem\u003e:\u003c/em\u003e The ocular surface microbiome (OSM) is important for eye health, and variations in OSM composition have been associated with multiple diseases in humans. Studies of OSM-disease dynamics in humans are confounded by lifestyle factors. Animal models provide a complementary approach to understanding biological systems, free from many confounds of human studies. Here, we provide the first study of the OSM of rhesus macaques, a premier animal model for eye health and disease. We describe the taxonomy of the rhesus macaque OSM, and explore compositional correlations with age, sex, and living condition.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003e\u003cstrong\u003eMethods\u003c/strong\u003e\u003c/em\u003e\u003cem\u003e:\u003c/em\u003e We analyzed eyelid and conjunctival microbiota swabs from 132 individual rhesus macaques (\u003cem\u003eMacaca mulatta\u003c/em\u003e) (57 males, 75 females, 1-26 years old) from one captive and one free-ranging group using 16S V3/V4 MiSeq sequencing. We investigated alpha diversity, beta diversity, and differential abundance.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003e\u003cstrong\u003eResults\u003c/strong\u003e\u003c/em\u003e: We found several similarities between the top Phyla and Genera of the rhesus macaque OSM and those reported in human literature. Significantly higher alpha diversity, which may reflect age-related ocular surface mucous membrane integrity and immune function, was present in younger individuals compared to older ones. Higher alpha diversity was also present in free-ranging rhesus macaques compared to ones in captivity, possibly related to differences in diet, exercise, and medical exposures between macaques in different living conditions. Beta diversity was most strongly influenced by individual identity, followed by living conditions. Sex did not correlate with any OSM variation.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003e\u003cstrong\u003eConclusions\u003c/strong\u003e\u003c/em\u003e\u003cem\u003e:\u003c/em\u003e In this study we describe the taxonomic composition of the rhesus macaque OSM, and identify significant differences in alpha and beta diversity according to individual nonhuman primate host variables and the surrounding environment. Our findings suggest composition of the nonhuman primate OSM is shaped by age-related physiology, individual identity, and external living conditions. Our results offer novel insights into an underexplored region of the primate microbiome and highlight the utility of rhesus macaques as a model system for investigating the links between the OSM, ocular health, and disease.\u003c/p\u003e","manuscriptTitle":"The ocular surface microbiome of rhesus macaques","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-03-14 09:28:50","doi":"10.21203/rs.3.rs-6205866/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2025-03-26T07:48:10+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-03-23T10:44:05+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-03-12T06:46:35+00:00","index":"","fulltext":""},{"type":"submitted","content":"Animal Microbiome","date":"2025-03-11T18:13:04+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"animal-microbiome","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"amic","sideBox":"Learn more about [Animal Microbiome](http://animalmicrobiome.biomedcentral.com)","snPcode":"42523","submissionUrl":"https://submission.nature.com/new-submission/42523/3","title":"Animal Microbiome","twitterHandle":"@bmc","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"BMC/SO AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"00e60f9c-be82-4c03-96bf-98ab729073c5","owner":[],"postedDate":"March 14th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2025-08-25T16:34:14+00:00","versionOfRecord":{"articleIdentity":"rs-6205866","link":"https://doi.org/10.1186/s42523-025-00454-4","journal":{"identity":"animal-microbiome","isVorOnly":false,"title":"Animal Microbiome"},"publishedOn":"2025-08-20 16:29:17","publishedOnDateReadable":"August 20th, 2025"},"versionCreatedAt":"2025-03-14 09:28:50","video":"","vorDoi":"10.1186/s42523-025-00454-4","vorDoiUrl":"https://doi.org/10.1186/s42523-025-00454-4","workflowStages":[]},"version":"v1","identity":"rs-6205866","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6205866","identity":"rs-6205866","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}