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

Although glaucoma is a leading cause of irreversible blindness worldwide, its pathogenesis is incompletely understood, and intraocular pressure (IOP) is the only modifiable risk factor to target the disease. Several associations between the gut microbiome and glaucoma, including the IOP, have been suggested. There is growing evidence that interactions between microbes on the ocular surface, termed the ocular surface microbiome (OSM), and tear proteins, collectively called the tear proteome, may also play a role in ocular diseases such as glaucoma. This study aimed to find characteristic features of the OSM and tear proteins in patients with glaucoma. The whole-metagenome shotgun sequencing of 32 conjunctival swabs identified Actinobacteria, Firmicutes, and Proteobacteria as the dominant phyla in the cohort. The species Corynebacterium mastitidis was only found in healthy controls, and their conjunctival microbiomes may be enriched in genes of the phospholipase pathway compared to glaucoma patients. Despite these minor differences in the OSM, patients showed an enrichment of many tear proteins associated with the immune system compared to controls. In contrast to the OSM, this emphasizes the role of the proteome, with a potential involvement of immunological processes in glaucoma. These findings may contribute to the design of new therapeutic approaches targeting glaucoma and other associated diseases.

Although dry eye disease (DED) is one of the most common ocular surface diseases worldwide, its pathogenesis is incompletely understood, and treatment options are limited. There is growing evidence that complex interactions between the ocular surface microbiome (OSM) and tear fluid constituents, potentially leading to inflammatory processes, are associated with ocular surface diseases such as DED. In this study, we aimed to find unique compositional and functional features of the OSM associated with human and microbial tear proteins in patients with DED. Applying whole-metagenome shotgun sequencing of forty lid and conjunctival swabs, we identified 229 taxa, with Actinobacteria and Proteobacteria being the most abundant phyla and Propionibacterium acnes the dominating species in the cohort. When DED patients were compared to controls, the species Corynebacterium tuberculostearicum was more abundant in conjunctival samples, whereas the family Propionibacteriaceae was more abundant in lid samples. Functional analysis showed that genes of L-lysine biosynthesis, tetrapyrrole biosynthesis, 5-aminoimidazole ribonucleotide biosynthesis, and the super pathway of L-threonine biosynthesis were enriched in conjunctival samples of controls. The relative abundances of Acinetobacter johnsonii correlated with seven human tear proteins, including mucin-16. The three most abundant microbial tear proteins were the chaperone protein DnaK, the arsenical resistance protein ArsH, and helicase. Compositional and functional features of the OSM and the tear proteome are altered in patients with DED. Ultimately, this may help to design novel interventional therapeutics to target DED.

Deep continental subsurface hosts diverse microbial ecosystems that are primarily driven by chemolithoautotrophy. In this study, we investigated deep continental igneous rock-hosted bacterial populations enriched under microoxic (ME) and anoxic (AE) conditions. Metataxonomic, metagenomics and metabolomics approaches, along with physiological analyses, were performed to elucidate community composition, CO 2 utilization and possible bioconversion potential of subsurface rock enrichment cultures under chemolithoautotrophic conditions. Following prolonged incubation, ME enrichments resulted in higher microbial growth with greater species diversity than the AE cultures. Ralstonia and unclassified Comamonadaceae were predominant in both the enrichment conditions. On the other hand, Cellulomonas , Phenylobacterium , Deinococcus , Desulfurispora , etc. were relatively abundant in ME, and Solimonas , Curvibacter , Caulobacter , Novosphingobium , Anaeromyxobacter , unclassified Clostridia, etc. were abundant in AE communities. CO 2 /H 2 utilization and organic acids production were greater in ME enrichments. Shotgun metagenomics and predictive metabolic profiling revealed CBB cycle as the predominant carbon fixation pathway in ME, whereas WL pathway was prominent in AE. Genes for hydrogen, sulfur, and nitrogen metabolisms were observed in both the enrichment cultures. HRLC-MS based untargeted metabolomics indicated the presence of valuable metabolites (organic acids, osmolytes, lipids/amides) in rock cultures, reflecting the potential of deep subsurface microorganisms for CO 2 utilization and possible bioconversion to valuable biomolecules.

Idiopathic inflammatory myopathies (IIMs) are systemic autoimmune disorders with unknown etiology. Despite the established link between gut microbes and immunity, the roles of gut bacteriome, mycobiome, and virome in IIM are unexplored. We performed shotgun metagenomic sequencing on fecal samples from 34 IIM patients and 37 healthy controls to profile gut microbiota. Taxonomic, functional, network, and machine-learning analyses revealed microbial dysbiosis and its potential for discriminating IIM. All three microbial kingdoms were significantly altered in IIM. Several inflammation-associated bacterial taxa (e.g., Rothia mucilaginosa , Streptococcus parasanguinis , Trueperella pyogenes ) and opportunistic fungi (e.g., Aspergillus spp.) were enriched in IIM, while SCFA-producing bacteria and fungi were depleted. Virome analysis revealed substantial shifts, with higher abundance of Siphoviridae in IIM. Altered viral functional gene profiles suggesting enhanced phage-mediated genome integration, recombination, and bacterial stress adaptation. Multi-kingdom network analysis showed extensive rewiring in IIM, characterized by increased network connectivity and a shift toward fungi-centered ecological hubs, contrasting with bacteria/virus-dominated networks in controls. In machine-learning models, the virome demonstrated the strongest discriminatory power, and viral signatures dominated the combined multi-kingdom classifier (AUC = 0.997). This first comprehensive multi-kingdom gut microbiota analysis in IIM provides a foundation for developing diagnostic and therapeutic strategies.

Background The human body harbors diverse microbial communities that play essential roles in health and disease. While the gut microbiome (GM) has been extensively studied and linked to numerous systemic conditions, the ocular surface microbiome (OSM) remains less well understood due to its low microbial biomass and technical challenges in sequencing. Previous work has suggested possible connections between gut dysbiosis and ocular disease, but whether the OSM and GM are directly related remains unclear. Results This study investigated the relationship between the ocular surface microbiome (OSM) and the gut microbiome (GM) by analysing matched samples from both niches in the same individuals. Using high-throughput sequencing, we characterised microbial composition and diversity at each niche, followed by statistical analysis to show common taxa. A Mantel test was applied to assess potential correlations between the two microbial communities. Our results revealed no significant correlation between the composition of the OSM and GM, neither in structure nor in diversity. Conclusion This study suggests that the two niches are shaped independently, likely due to their distinct environmental conditions and microbial densities. These findings highlight the uniqueness of each microbiome and underscore the need for niche specific approaches when investigating host microbiome interactions. Trial registration ClinicalTrials.gov, TRN: NCT02438111, Registration date: 28 April 2015, and TRN: NCT 04658238, Registration date: 01 December 2020.

Background The human gut microbiome (GM) is involved in inflammation and immune response regulation. Dysbiosis, an imbalance in this ecosystem, facilitates pathogenic invasion, disrupts immune equilibrium, and potentially triggers diseases including various human leucocyte antigen (HLA)-B27-associated autoinflammatory and autoimmune diseases such as inflammatory bowel disease (IBD) and spondyloarthropathy (SpA). This study assesses compositional and functional alterations of the GM in patients with HLA-B27-associated non-infectious anterior uveitis (AU) compared to healthy controls. Methods The gut metagenomes of 20 patients with HLA-B27-associated non-infectious AU, 21 age- and sex-matched HLA-B27-negative controls, and 6 HLA-B27-positive healthy controls without a history of AU were sequenced using the Illumina NovaSeq 6000 platform for whole metagenome shotgun sequencing. To identify taxonomic and functional features with significantly different relative abundances between groups and to identify associations with clinical metadata, the multivariate association by linear models (MaAsLin) R package was applied. Results Significantly higher levels of the Eubacterium ramulus species were found in HLA-B27-negative controls ( p  = 0.0085, Mann-Whitney U-test). No significant differences in microbial composition were observed at all other taxonomic levels. Functionally, the lipid IV A biosynthesis pathway was upregulated in patients ( p  < 0.0001, Mann-Whitney U-test). A subgroup analysis comparing patients with an active non-infectious AU to their age- and sex-matched HLA-B27-negative controls, showed an increase of the species Phocaeicola vulgatus in active AU ( p  = 0.0530, Mann-Whitney U-test). An additional analysis comparing AU patients to age- and sex-matched HLA-B27-positive controls, showed an increase of the species Bacteroides caccae in controls ( p  = 0.0022, Mann-Whitney U-test). Conclusion In our cohort, non-infectious AU development is associated with compositional and functional alterations of the GM. Further research is needed to assess the causality of these associations, offering potentially novel therapeutic strategies.

Background The contribution of gut microbiota to human high-altitude adaptation remains inadequately understood. Methods Here a comparative analysis of gut microbiota was conducted between healthy individuals living at sea level and high altitude using deep whole-metagenome shotgun sequencing, to investigate the adaptive mechanisms of gut microbiota in plateau inhabitants. Results The results showed the gut bacteriomes in high-altitude individuals exhibited greater within-sample diversity and significant alterations in both bacterial compositional and functional profiles when compared to those of sea-level individuals, indicating the potential selection of unique bacteria associated with high-altitude environments. The strain-level investigation revealed enrichment of Collinsella aerofaciens and Akkermansia muciniphila in high-altitude populations. The characteristics of gut virome and gut mycobiome were also investigated. Compared to sea-level subjects, high-altitude subjects exhibited a greater diversity in their gut virome, with an increased number of viral operational taxonomic units (vOTUs) and unique annotated genes. Finally, correlation analyses revealed 819 significant correlations between 42 bacterial species and 375 vOTUs, while no significant correlations were observed between bacteria and fungi or between fungi and viruses. Conclusion The findings have significantly contributed to an enhanced comprehension of the mechanisms underlying the high-altitude geographic adaptation of the human gut microbiota.

Background Colorectal cancer (CRC) is influenced by host factors and environmental exposures that shape gut microbial ecosystems. Although bacterial and viral alterations in CRC have been widely investigated, the role of gut fungi remains underexplored, partly because of their low biomass and the limited availability of well-curated fungal reference genomes. Methods We conducted a large-scale metagenomic analysis across 9 publicly available cohorts comprising 1,433 fecal samples to characterize CRC-associated fungal alterations and fungal-bacterial co-abundance patterns. The predictive value of microbial signatures was assessed using LASSO and random forest models, with external validation performed in 6 independent cohorts comprising 272 samples. Results Multi-cohort analysis revealed CRC-associated alterations in gut fungal community structure and selected diversity measures. Differential abundance analysis identified 15 fungal species with recurrent changes across cohorts. Among them, Saccharomyces cerevisiae c86 and Trichophyton rubrum c61 showed predominant enrichment in healthy controls, whereas Barnettozyma c122 and Pseudopithomyces c302 showed predominant enrichment in CRC. Fungal-only models exhibited limited standalone predictive capacity. However, integrating fungal features with bacterial biomarkers modestly improved CRC prediction performance compared with bacterial-only models. In external validation, the random forest-based fungal-bacterial model increased the mean AUC from 0.722 to 0.762, with improved AUCs in 5 of the 6 validation cohorts. Conclusions This study suggests that CRC is associated with gut fungal dysbiosis and supports the exploratory value of gut fungal signatures as adjunctive features in microbiome-based CRC prediction models. These findings highlight the importance of incorporating fungal communities into CRC microbiome research while emphasizing the need for prospective and mechanistic validation.

Background Inflammatory bowel disease (IBD) is a multifactorial chronic inflammatory disease resulting from dysregulation of the mucosal immune response and gut microbiota. Crohn's disease (CD) and ulcerative colitis (UC) are difficult to distinguish, and differential diagnosis is essential for establishing a long-term treatment plan for patients. Furthermore, the abundance of mucosal bacteria is associated with the severity of the disease. This study aimed to differentiate and diagnose these two diseases using the microbiome and identify specific biomarkers associated with disease activity. Results Differences in the abundance and composition of the microbiome between IBD patients and healthy controls (HC) were observed. Compared to HC, the diversity of the gut microbiome in patients with IBD decreased; the diversity of the gut microbiome in patients with CD was significantly lower. Sixty-eight microbiota members (28 for CD and 40 for UC) associated with these diseases were identified. Additionally, as the disease progressed through different stages, the diversity of the bacteria decreased. The abundances of Alistipes shahii and Pseudodesulfovibrio aespoeensis were negatively correlated with the severity of CD, whereas the abundance of Polynucleobacter wianus was positively correlated. The severity of UC was negatively correlated with the abundance of A. shahii , Porphyromonas asaccharolytica and Akkermansia muciniphilla, while it was positively correlated with the abundance of Pantoea candidatus pantoea carbekii . A regularized logistic regression model was used for the differential diagnosis of the two diseases. The area under the curve (AUC) was used to examine the performance of the model. The model discriminated UC and CD at an AUC of 0.873 (train set), 0.778 (test set), and 0.633 (validation set) and an area under the precision-recall curve (PRAUC) of 0.888 (train set), 0.806 (test set), and 0.474 (validation set). Conclusions Based on fecal whole-metagenome shotgun (WMS) sequencing, CD and UC were diagnosed using a machine-learning predictive model. Microbiome biomarkers associated with disease activity (UC and CD) are also proposed.