CRB1-associated retinal degeneration is dependent on bacterial translocation from the gut.

The Crumbs homolog 1 (CRB1) gene is associated with retinal degeneration, most commonly Leber congenital amaurosis (LCA) and retinitis pigmentosa (RP). Here, we demonstrate that murine retinas bearing the Rd8 mutation of Crb1 are characterized by the presence of intralesional bacteria. While normal CRB1 expression was enriched in the apical junctional complexes of retinal pigment epithelium and colonic enterocytes, Crb1 mutations dampened its expression at both sites. Consequent impairment of the outer blood retinal barrier and colonic intestinal epithelial barrier in Rd8 mice led to the translocation of intestinal bacteria from the lower gastrointestinal (GI) tract to the retina, resulting in secondary retinal degeneration. Either the depletion of bacteria systemically or the reintroduction of normal Crb1 expression colonically rescued Rd8-mutation-associated retinal degeneration without reversing the retinal barrier breach. Our data elucidate the pathogenesis of Crb1-mutation-associated retinal degenerations and suggest that antimicrobial agents have the potential to treat this devastating blinding disease.

Metagenomic Sequencing Analysis Identifies Cross-Cohort Gut Microbial Signatures Associated With Age-Related Macular Degeneration.

Purpose: Alterations in the gut microbiota have been associated with age-related macular degeneration (AMD). However, the dysbiosis shared by different ethnicity and geographic groups, which may associate with the disease pathogenesis, remain underexplored. Here, we characterized dysbiosis of the gut microbiota in patients with AMD from Chinese and Swiss cohorts and identified cross-cohort signatures associated with AMD. Methods: Shotgun metagenomic sequencing was performed on fecal samples from 30 patients with AMD and 30 healthy subjects. Published datasets with 138 samples from Swiss patients with AMD and healthy subjects were re-analyzed. Comprehensive taxonomic profiling was conducted by matching to the RefSeq genome database, metagenome-assembled genome (MAG) database, and Gut Virome Database (GVD). Functional profiling was performed by reconstruction of the MetaCyc pathways. Results: The α-diversity of the gut microbiota was decreased in patients with AMD according to taxonomic profiles generated using MAG but not RefSeq database as reference. The Firmicutes/Bacteroidetes ratio was also decreased in patients with AMD. Among AMD-associated bacteria shared between Chinese and Swiss cohorts, Ruminococcus callidus, Lactobacillus gasseri, and Prevotellaceae (f) uSGB 2135 were enriched in patients with AMD, whereas Bacteroidaceae (f) uSGB 1825 was depleted in patients with AMD and was negatively associated with hemorrhage size. Bacteroidaceae was one of the major hosts of phages associated with AMD. Three degradation pathways were reduced in AMD. Conclusions: These results demonstrated that dysbiosis of the gut microbiota was associated with AMD. We identified cross-cohort gut microbial signatures involving bacteria, viruses, and metabolic pathways, which potentially serve as promising targets for the prevention or treatment of AMD.

A multi-omics analysis for the prediction of neurocognitive disorders risk among the elderly in Macao.

BACKGROUND: Due to the increasing ageing population, neurocognitive disorders (NCDs) have been a global public health issue, and its prevention and early diagnosis are crucial. Our previous study demonstrated that there is a significant correlation between specific populations and NCDs, but the biological characteristics of the vulnerable group predispose to NCDs are unclear. The purpose of this study is to investigate the predictors for the vulnerable group by a multi-omics analysis. METHODS: Multi-omics approaches, including metagenomics, metabolomic and proteomic, were used to detect gut microbiota, faecal metabolites and urine exosome of 8 normal controls and 13 vulnerable elders after a rigorous screening of 400 elders in Macao. The multi-omics data were analysed using R and Bioconductor. The two-sided Wilcoxon's rank-sum test, Kruskal-Wallis rank sum test and the linear discriminant analysis effective size were applied to investigate characterized features. Moreover, a 2-year follow-up was conducted to evaluate cognitive function change of the elderly. RESULTS: Compared with the control elders, the metagenomics of gut microbiota showed that Ruminococcus gnavus, Lachnospira eligens, Escherichia coli and Desulfovibrio piger were increased significantly in the vulnerable group. Carboxylates, like alpha-ketoglutaric acid and d-saccharic acid, and levels of vitamins had obvious differences in the faecal metabolites. There was a distinct decrease in the expression of eukaryotic translation initiation factor 2 subunit 1 (eIF2α) and amine oxidase A (MAO-A) according to the proteomic results of the urine exosomes. Moreover, the compound annual growth rate of neurocognitive scores was notably decreased in vulnerable elders. CONCLUSIONS: The multi-omics characteristics of disturbed glyoxylate and dicarboxylate metabolism (bacteria), vitamin digestion and absorption and tricarboxylic acid cycle in vulnerable elders can serve as predictors of NCDs risk among the elderly of Macao. Intervention with them may be effective therapeutic approaches for NCDs, and the underlying mechanisms merit further exploration.

Bacteroides ovatus-mediated CD27- MAIT cell activation is associated with obesity-related T2D progression.

Type 2 diabetes (T2D) is highly associated with obesity. However, the factors that drive the transition from excessive weight gain to glucose metabolism disruption are still uncertain and seem to revolve around systemic immune disorder. Mucosal-associated invariant T (MAIT) cells, which are innate-like T cells that recognize bacterial metabolites, have been reported to be altered in obese people and to lead to metabolic dysfunction during obesity. By studying the immunophenotypes of blood MAIT cells from a cross-sectional cohort of obese participants with/without T2D, we found an elevation in CD27-negative (CD27-) MAIT cells producing a high level of IL-17 under T2D obese conditions, which could be positively correlated with impaired glucose metabolism in obese people. We further explored microbial translocation caused by gut barrier dysfunction in obese people as a triggering factor of MAIT cell abnormalities. Specifically, accumulation of the bacterial strain Bacteroides ovatus in the peripheral blood drove IL-17-producing CD27- MAIT cell expansion and could be associated with T2D risk in obese individuals. Overall, these results suggest that an aberrant gut microbiota-immune axis in obese people may drive or exacerbate T2D. Importantly, CD27- MAIT cell subsets and Bacteroides ovatus could represent targets for novel interventional strategies. Our findings extend current knowledge regarding the clinical relevance of body mass index (BMI)-associated variation in circulating MAIT cells to reveal the role of these cells in obesity-related T2D progression and the underlying cellular mechanisms.

Metagenomic Profiling of the Ocular Surface Microbiome in Patients After Allogeneic Hematopoietic Stem Cell Transplantation.

PURPOSE: To investigate the characteristics of the ocular surface microbiome in patients after allogeneic hematopoietic stem cell transplantation (allo-HSCT) and the associations between the microbial dysbiosis and chronic ocular graft-versus-host disease (oGVHD). DESIGN: Prospective cohort study. METHODS: Ocular surface samples from 48 healthy subjects and 76 patients after allo-HSCT, including 50 patients with chronic oGVHD and 26 patients without oGVHD, were collected. Species-level composition of the ocular surface microbiome was surveyed via metagenomic shotgun sequencing. OGVHD was diagnosed and graded according to the International Chronic Ocular GVHD Consensus Group criteria. RESULTS: The α-diversity of the microbiota was significantly decreased in patients after allo-HSCT. Nevertheless, we detected more types of viral species in the allo-HSCT group than the healthy group, especially anelloviruses. The mismatch of donor-recipient sex was only negatively associated with the α-diversity in male but not female recipients. Moreover, the microbiome of patients with oGVHD was distinct from patients without oGVHD. Gordonia bronchialis and Pseudomonas parafulva were enriched in patients with oGVHD and positively associated with International Chronic Ocular GVHD score. CONCLUSIONS: This study suggests that the ocular surface microbiome after allo-HSCT is characterized by a loss of diversity. Furthermore, the microbial dysbiosis at the ocular surface is associated with the status and severity of chronic oGVHD. These results lay the groundwork for future investigations of the potential microbial mechanism for oGVHD.

A New Way to Trace SARS-CoV-2 Variants Through Weighted Network Analysis of Frequency Trajectories of Mutations.

Early detection of SARS-CoV-2 variants enables timely tracking of clinically important strains in order to inform the public health response. Current subtype-based variant surveillance depending on prior subtype assignment according to lag features and their continuous risk assessment may delay this process. We proposed a weighted network framework to model the frequency trajectories of mutations (FTMs) for SARS-CoV-2 variant tracing, without requiring prior subtype assignment. This framework modularizes the FTMs and conglomerates synchronous FTMs together to represent the variants. It also generates module clusters to unveil the epidemic stages and their contemporaneous variants. Eventually, the module-based variants are assessed by phylogenetic tree through sub-sampling to facilitate communication and control of the epidemic. This process was benchmarked using worldwide GISAID data, which not only demonstrated all the methodology features but also showed the module-based variant identification had highly specific and sensitive mapping with the global phylogenetic tree. When applying this process to regional data like India and South Africa for SARS-CoV-2 variant surveillance, the approach clearly elucidated the national dispersal history of the viral variants and their co-circulation pattern, and provided much earlier warning of Beta (B.1.351), Delta (B.1.617.2), and Omicron (B.1.1.529). In summary, our work showed that the weighted network modeling of FTMs enables us to rapidly and easily track down SARS-CoV-2 variants overcoming prior viral subtyping with lag features, accelerating the understanding and surveillance of COVID-19.

Metagenomic Analysis Reveals the Heterogeneity of Conjunctival Microbiota Dysbiosis in Dry Eye Disease.

Background: Dry eye disease (DED) is a multifactorial inflammatory disease of the ocular surface. It is hypothesized that dysbiosis of the conjunctival microbiota contributes to the development of DED. However, species-level compositions of the conjunctival microbiota in DED and the potential dysbiosis involving microorganisms other than bacteria remain largely uncharacterized. Methods: We collected conjunctival impression samples from a cohort of 95 individuals, including 47 patients with DED and 48 healthy subjects. We examined the conjunctival microbiota of these samples using shotgun metagenomic sequencing and analyzed microbial dysbiosis in DED at the species level. Results: The conjunctival microbiota in DED exhibited a decreased α-diversity and an increased inter-individual variation. The α-diversity of female patients with DED was higher than that of male patients. Despite a decreased prevalence in DED, 23 microbial species were identified to show abnormally high abundance in DED samples positive for the species. Among these species, a fungal species Malassezia globosa was enriched female patients. In addition, distinct patterns of associations with disease status were observed for different species of the same genus. For DED subtypes, Staphylococcus aureus and S. capitis were associated with meibomian gland dysfunction (MGD), whereas S. hominis was enriched in patients solely with aqueous tear deficiency (ATD). The microbiota of patients with a mixed type of diagnosis was more similar to MGD patients than ATD patients. Conclusion: We demonstrated that the conjunctival microbiota dysbiosis in DED is characterized by significant heterogeneity. Microbial signatures may offer novel insights into the complicated etiology of DED and potentially promote the development of personalized treatment for DED in the future.

Identification of an intraocular microbiota.

The current dogma in ophthalmology and vision research presumes the intraocular environment to be sterile. However, recent evidence of intestinal bacterial translocation into the bloodstream and many other internal organs including the eyes, found in healthy and diseased animal models, suggests that the intraocular cavity may also be inhabited by a microbial community. Here, we tested intraocular samples from over 1000 human eyes. Using quantitative PCR, negative staining transmission electron microscopy, direct culture, and high-throughput sequencing technologies, we demonstrated the presence of intraocular bacteria. The possibility that the microbiome from these low-biomass communities could be a contamination from other tissues and reagents was carefully evaluated and excluded. We also provide preliminary evidence that a disease-specific microbial signature characterized the intraocular environment of patients with age-related macular degeneration and glaucoma, suggesting that either spontaneous or pathogenic bacterial translocation may be associated with these common sight-threatening conditions. Furthermore, we revealed the presence of an intraocular microbiome in normal eyes from non-human mammals and demonstrated that this varied across species (rat, rabbit, pig, and macaque) and was established after birth. These findings represent the first-ever evidence of intraocular microbiota in humans.

CSMD: a computational subtraction-based microbiome discovery pipeline for species-level characterization of clinical metagenomic samples.

MOTIVATION: Microbiome analyses of clinical samples with low microbial biomass are challenging because of the very small quantities of microbial DNA relative to the human host, ubiquitous contaminating DNA in sequencing experiments and the large and rapidly growing microbial reference databases. RESULTS: We present computational subtraction-based microbiome discovery (CSMD), a bioinformatics pipeline specifically developed to generate accurate species-level microbiome profiles for clinical samples with low microbial loads. CSMD applies strategies for the maximal elimination of host sequences with minimal loss of microbial signal and effectively detects microorganisms present in the sample with minimal false positives using a stepwise convergent solution. CSMD was benchmarked in a comparative evaluation with other classic tools on previously published well-characterized datasets. It showed higher sensitivity and specificity in host sequence removal and higher specificity in microbial identification, which led to more accurate abundance estimation. All these features are integrated into a free and easy-to-use tool. Additionally, CSMD applied to cell-free plasma DNA showed that microbial diversity within these samples is substantially broader than previously believed. AVAILABILITY AND IMPLEMENTATION: CSMD is freely available at https://github.com/liuyu8721/csmd. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

DeepMicrobes: taxonomic classification for metagenomics with deep learning.

Large-scale metagenomic assemblies have uncovered thousands of new species greatly expanding the known diversity of microbiomes in specific habitats. To investigate the roles of these uncultured species in human health or the environment, researchers need to incorporate their genome assemblies into a reference database for taxonomic classification. However, this procedure is hindered by the lack of a well-curated taxonomic tree for newly discovered species, which is required by current metagenomics tools. Here we report DeepMicrobes, a deep learning-based computational framework for taxonomic classification that allows researchers to bypass this limitation. We show the advantage of DeepMicrobes over state-of-the-art tools in species and genus identification and comparable accuracy in abundance estimation. We trained DeepMicrobes on genomes reconstructed from gut microbiomes and discovered potential novel signatures in inflammatory bowel diseases. DeepMicrobes facilitates effective investigations into the uncharacterized roles of metagenomic species.