Mendelian randomization analysis reveals a causal effect of Streptococcus salivarius on diabetic retinopathy through regulating host fasting glucose.

Diabetic retinopathy (DR) is one of leading causes of vision loss in adults with increasing prevalence worldwide. Increasing evidence has emphasized the importance of gut microbiome in the aetiology and development of DR. However, the causal relationship between gut microbes and DR remains largely unknown. To investigate the causal associations of DR with gut microbes and DR risk factors, we employed two-sample Mendelian Randomization (MR) analyses to estimate the causal effects of 207 gut microbes on DR outcomes. Inputs for MR included Genome-wide Association Study (GWAS) summary statistics of 207 taxa of gut microbes (the Dutch Microbiome Project) and 21 risk factors for DR. The GWAS summary statistics data of DR was from the FinnGen Research Project. Data analysis was performed in May 2023. We identified eight bacterial taxa that exhibited significant causal associations with DR (FDR < 0.05). Among them, genus Collinsella and species Collinsella aerofaciens were associated with increased risk of DR, while the species Bacteroides faecis, Burkholderiales bacterium_1_1_47, Ruminococcus torques, Streptococcus salivarius, genus Burkholderiales_noname and family Burkholderiales_noname showed protective effects against DR. Notably, we found that the causal effect of species Streptococcus salivarius on DR was mediated through the level of host fasting glucose, a well-established risk factor for DR. Our results reveal that specific gut microbes may be causally linked to DR via mediating host metabolic risk factors, highlighting potential novel therapeutic or preventive targets for DR.

Integration of human organoids single-cell transcriptomic profiles and human genetics repurposes critical cell type-specific drug targets for severe COVID-19.

Human organoids recapitulate the cell type diversity and function of their primary organs holding tremendous potentials for basic and translational research. Advances in single-cell RNA sequencing (scRNA-seq) technology and genome-wide association study (GWAS) have accelerated the biological and therapeutic interpretation of trait-relevant cell types or states. Here, we constructed a computational framework to integrate atlas-level organoid scRNA-seq data, GWAS summary statistics, expression quantitative trait loci, and gene-drug interaction data for distinguishing critical cell populations and drug targets relevant to coronavirus disease 2019 (COVID-19) severity. We found that 39 cell types across eight kinds of organoids were significantly associated with COVID-19 outcomes. Notably, subset of lung mesenchymal stem cells increased proximity with fibroblasts predisposed to repair COVID-19-damaged lung tissue. Brain endothelial cell subset exhibited significant associations with severe COVID-19, and this cell subset showed a notable increase in cell-to-cell interactions with other brain cell types, including microglia. We repurposed 33 druggable genes, including IFNAR2, TYK2, and VIPR2, and their interacting drugs for COVID-19 in a cell-type-specific manner. Overall, our results showcase that host genetic determinants have cellular-specific contribution to COVID-19 severity, and identification of cell type-specific drug targets may facilitate to develop effective therapeutics for treating severe COVID-19 and its complications.

Automatic detection and differential diagnosis of age-related macular degeneration from color fundus photographs using deep learning with hierarchical vision transformer.

Age-related macular degeneration (AMD) is a leading cause of vision loss in the elderly, highlighting the need for early and accurate detection. In this study, we proposed DeepDrAMD, a hierarchical vision transformer-based deep learning model that integrates data augmentation techniques and SwinTransformer, to detect AMD and distinguish between different subtypes using color fundus photographs (CFPs). The DeepDrAMD was trained on the in-house WMUEH training set and achieved high performance in AMD detection with an AUC of 98.76% in the WMUEH testing set and 96.47% in the independent external Ichallenge-AMD cohort. Furthermore, the DeepDrAMD effectively classified dryAMD and wetAMD, achieving AUCs of 93.46% and 91.55%, respectively, in the WMUEH cohort and another independent external ODIR cohort. Notably, DeepDrAMD excelled at distinguishing between wetAMD subtypes, achieving an AUC of 99.36% in the WMUEH cohort. Comparative analysis revealed that the DeepDrAMD outperformed conventional deep-learning models and expert-level diagnosis. The cost-benefit analysis demonstrated that the DeepDrAMD offers substantial cost savings and efficiency improvements compared to manual reading approaches. Overall, the DeepDrAMD represents a significant advancement in AMD detection and differential diagnosis using CFPs, and has the potential to assist healthcare professionals in informed decision-making, early intervention, and treatment optimization.

Polygenic regression uncovers trait-relevant cellular contexts through pathway activation transformation of single-cell RNA sequencing data.

Advances in single-cell RNA sequencing (scRNA-seq) techniques have accelerated functional interpretation of disease-associated variants discovered from genome-wide association studies (GWASs). However, identification of trait-relevant cell populations is often impeded by inherent technical noise and high sparsity in scRNA-seq data. Here, we developed scPagwas, a computational approach that uncovers trait-relevant cellular context by integrating pathway activation transformation of scRNA-seq data and GWAS summary statistics. scPagwas effectively prioritizes trait-relevant genes, which facilitates identification of trait-relevant cell types/populations with high accuracy in extensive simulated and real datasets. Cellular-level association results identified a novel subpopulation of naive CD8+ T cells related to COVID-19 severity and oligodendrocyte progenitor cell and microglia subsets with critical pathways by which genetic variants influence Alzheimer's disease. Overall, our approach provides new insights for the discovery of trait-relevant cell types and improves the mechanistic understanding of disease variants from a pathway perspective.