COLOCdb: a comprehensive resource for multi-model colocalization of complex traits.

Large-scale genome-wide association studies (GWAS) have provided profound insights into complex traits and diseases. Yet, deciphering the fine-scale molecular mechanisms of how genetic variants manifest to cause the phenotypes remains a daunting task. Here, we present COLOCdb (https://ngdc.cncb.ac.cn/colocdb), a comprehensive genetic colocalization database by integrating more than 3000 GWAS summary statistics and 13 types of xQTL to date. By employing two representative approaches for the colocalization analysis, COLOCdb deposits results from three key components: (i) GWAS-xQTL, pair-wise colocalization between GWAS loci and different types of xQTL, (ii) GWAS-GWAS, pair-wise colocalization between the trait-associated genetic loci from GWASs and (iii) xQTL-xQTL, pair-wise colocalization between the genetic loci associated with molecular phenotypes in xQTLs. These results together represent the most comprehensive colocalization analysis, which also greatly expands the list of shared variants with genetic pleiotropy. We expect that COLOCdb can serve as a unique and useful resource in advancing the discovery of new biological mechanisms and benefit future functional studies.

TargetGene: a comprehensive database of cell-type-specific target genes for genetic variants.

Annotating genetic variants to their target genes is of great importance in unraveling the causal variants and genetic mechanisms that underlie complex diseases. However, disease-associated genetic variants are often located in non-coding regions and manifest context-specific effects, making it challenging to accurately identify the target genes and regulatory mechanisms. Here, we present TargetGene (https://ngdc.cncb.ac.cn/targetgene/), a comprehensive database reporting target genes for human genetic variants from various aspects. Specifically, we collected a comprehensive catalog of multi-omics data at the single-cell and bulk levels and from various human tissues, cell types and developmental stages. To facilitate the identification of Single Nucleotide Polymorphism (SNP)-to-gene connections, we have implemented multiple analytical tools based on chromatin co-accessibility, 3D interaction, enhancer activities and quantitative trait loci, among others. We applied the pipeline to evaluate variants from nearly 1300 Genome-wide association studies (GWAS) and assembled a comprehensive atlas of multiscale regulation of genetic variants. TargetGene is equipped with user-friendly web interfaces that enable intuitive searching, navigation and browsing through the results. Overall, TargetGene provides a unique resource to empower researchers to study the regulatory mechanisms of genetic variants in complex human traits.

PharmGWAS: a GWAS-based knowledgebase for drug repurposing.

Leveraging genetics insights to promote drug repurposing has become a promising and active strategy in pharmacology. Indeed, among the 50 drugs approved by FDA in 2021, two-thirds have genetically supported evidence. In this regard, the increasing amount of widely available genome-wide association studies (GWAS) datasets have provided substantial opportunities for drug repurposing based on genetics discoveries. Here, we developed PharmGWAS, a comprehensive knowledgebase designed to identify candidate drugs through the integration of GWAS data. PharmGWAS focuses on novel connections between diseases and small-molecule compounds derived using a reverse relationship between the genetically-regulated expression signature and the drug-induced signature. Specifically, we collected and processed 1929 GWAS datasets across a diverse spectrum of diseases and 724 485 perturbation signatures pertaining to a substantial 33609 molecular compounds. To obtain reliable and robust predictions for the reverse connections, we implemented six distinct connectivity methods. In the current version, PharmGWAS deposits a total of 740 227 genetically-informed disease-drug pairs derived from drug-perturbation signatures, presenting a valuable and comprehensive catalog. Further equipped with its user-friendly web design, PharmGWAS is expected to greatly aid the discovery of novel drugs, the exploration of drug combination therapies and the identification of drug resistance or side effects. PharmGWAS is available at https://ngdc.cncb.ac.cn/pharmgwas.

Deciphering mechanisms of cardiomyocytes and non-cardiomyocyte transformation in myocardial remodeling of permanent atrial fibrillation.

INTRODUCTION: Atrial fibrillation (AF) is the most prevalent cardiac arrhythmia, and it significantly increases the risk of cardiovascular complications and morbidity, even with appropriate treatment. Tissue remodeling has been a significant topic, while its systematic transcriptional signature remains unclear in AF. OBJECTIVES: Our study aims to systematically investigate the molecular characteristics of AF at the cellular-level. METHODS: We conducted single-nuclei RNA-sequencig (snRNA-seq) analysis using nuclei isolated from the left atrial appendage (LAA) of AF patients and sinus rhythm. Pathological staining was performed to validate the key findings of snRNA-seq. RESULTS: A total of 30 cell subtypes were identified among 80, 592 nuclei. Within the LAA of AF, we observed a specific subtype of dedifferentiated cardiomyocytes (CMs) characterized by reduced expression of cardiac contractile proteins (TTN and TRDN) and heightened expression of extracellular-matrix related genes (COL1A2 and FBN1). Transcription factor prediction analysis revealed that gene expression patterns in dedifferentiated CMs were primarily regulated by CEBPG and GISLI. Additionally, we identified a distinct subtype of endothelial progenitor cells (EPCs) demonstrating elevated expression of PROM1 and KDR, a population decreased within the LAA of AF. Epicardial adipocytes disclosed a reduced release of the anti-inflammatory and anti-fibrotic factor PRG4, and an augmented secretion of VEGF signals targeting CMs. Additionally, we noted accumulation of M2-like macrophages and CD8+ T cells with high pro-inflammatory score in LAA of AF. Furthermore, the analysis of intercellular communication revealed specific pathways related to AF, such as inflammation, extracellular matrix, and vascular remodeling signals. CONCLUSIONS: This study has discovered the presence of dedifferentiated CMs, a decrease in endothelial progenitor cells, a shift in the secretion profile of adipocytes, and an amplified inflammatory response in AF. These findings could offer crucial insights for future research on AF and serve as valuable references for investigating novel therapeutic approaches for AF.

A Comprehensive Benchmark of Transcriptomic Biomarkers for Immune Checkpoint Blockades.

Immune checkpoint blockades (ICBs) have revolutionized cancer therapy by inducing durable clinical responses, but only a small percentage of patients can benefit from ICB treatments. Many studies have established various biomarkers to predict ICB responses. However, different biomarkers were found with diverse performances in practice, and a timely and unbiased assessment has yet to be conducted due to the complexity of ICB-related studies and trials. In this study, we manually curated 29 published datasets with matched transcriptome and clinical data from more than 1400 patients, and uniformly preprocessed these datasets for further analyses. In addition, we collected 39 sets of transcriptomic biomarkers, and based on the nature of the corresponding computational methods, we categorized them into the gene-set-like group (with the self-contained design and the competitive design, respectively) and the deconvolution-like group. Next, we investigated the correlations and patterns of these biomarkers and utilized a standardized workflow to systematically evaluate their performance in predicting ICB responses and survival statuses across different datasets, cancer types, antibodies, biopsy times, and combinatory treatments. In our benchmark, most biomarkers showed poor performance in terms of stability and robustness across different datasets. Two scores (TIDE and CYT) had a competitive performance for ICB response prediction, and two others (PASS-ON and EIGS_ssGSEA) showed the best association with clinical outcome. Finally, we developed ICB-Portal to host the datasets, biomarkers, and benchmark results and to implement the computational methods for researchers to test their custom biomarkers. Our work provided valuable resources and a one-stop solution to facilitate ICB-related research.

Dynamic cellular changes in acute kidney injury caused by different ischemia time.

Ischemia reperfusion injury (IRI), often related to surgical procedures, is one of the important causes of acute kidney injury (AKI). To decipher the dynamic process of AKI caused by IRI (with prolonged ischemia phase), we performed single-cell RNA sequencing (scRNA-seq) of clinically relevant IRI murine model with different ischemic intervals. We discovered that Slc5a2hi proximal tubular cells were susceptible to AKI and highly expressed neutral amino acid transporter gene Slc6a19, which was dramatically decreased over the time course. With the usage of mass spectrometry-based metabolomic analysis, we detected that the level of neutral amino acid isoleucine dropped off in AKI mouse plasma metabolites. And the reduction of plasma isoleucine was also verified in patients with cardiac surgery-associated acute kidney injury (CSA-AKI). The findings advanced the understanding of dynamic process of AKI and introduced reduction of isoleucine as a potential biomarker for CSA-AKI.

Single-Cell Transcriptome Sequencing Reveals Molecular Mechanisms of Renal Injury in Essential Hypertension.

INTRODUCTION: Hypertensive nephropathy is characterized by glomerular and tubulointerstitial damage, but we know little about changes in cell-specific gene expression in the early stages of hypertensive kidney injury, which usually has no obvious pathological changes. METHODS: We performed unbiased single-cell RNA sequencing of rat kidney samples from hypertensive kidney injury to generate 10,602 single-cell transcriptomes from 2 control and 2 early stage hypertensive kidney injury samples. RESULTS: All major cell types of the kidney were represented in the final dataset. Side-by-side comparisons showed that cell type-specific changes in gene expression are critical for functional impairment of glomeruli and tubules and activation of immune cells. In particular, we found a significantly reduced gene expression profile of maintaining vascular integrity in glomerular cells of hypertensive kidney injury. Meanwhile, the expression of genes associated with oxidative stress injury and fibrosis in the renal tubules and collecting ducts was elevated, but the degree of tubular cells response to injury differed between parts. We also found a signature of immune cell infiltration in hypertensive kidney injury. CONCLUSION: Exploring the changes of gene expression in hypertension-injured kidneys may be helpful to identify the early biomarkers and signal pathways of this disease. Our data provide rich resources for understanding the pathogenesis of hypertensive renal injury and formulating effective treatment strategies.

Brain Catalog: a comprehensive resource for the genetic landscape of brain-related traits.

A broad range of complex phenotypes are related to dysfunctions in brain (hereafter referred to as brain-related traits), including various mental and behavioral disorders and diseases of the nervous system. These traits in general share overlapping symptoms, pathogenesis, and genetic components. Here, we present Brain Catalog (https://ngdc.cncb.ac.cn/braincatalog), a comprehensive database aiming to delineate the genetic components of more than 500 GWAS summary statistics datasets for brain-related traits from multiple aspects. First, Brain Catalog provides results of candidate causal variants, causal genes, and functional tissues and cell types for each trait identified by multiple methods using comprehensive annotation datasets (58 QTL datasets spanning 6 types of QTLs). Second, Brain Catalog estimates the SNP-based heritability, the partitioning heritability based on functional annotations, and genetic correlations among traits. Finally, through bidirectional Mendelian randomization analyses, Brain Catalog presents inference of risk factors that are likely causal to each trait. In conclusion, Brain Catalog presents a one-stop shop for the genetic components of brain-related traits, potentially serving as a valuable resource for worldwide researchers to advance the understanding of how GWAS signals may contribute to the biological etiology of brain-related traits.

CeDR Atlas: a knowledgebase of cellular drug response.

Drug response to many diseases varies dramatically due to the complex genomics and functional features and contexts. Cellular diversity of human tissues, especially tumors, is one of the major contributing factors to the different drug response in different samples. With the accumulation of single-cell RNA sequencing (scRNA-seq) data, it is now possible to study the drug response to different treatments at the single cell resolution. Here, we present CeDR Atlas (available at https://ngdc.cncb.ac.cn/cedr), a knowledgebase reporting computational inference of cellular drug response for hundreds of cell types from various tissues. We took advantage of the high-throughput profiling of drug-induced gene expression available through the Connectivity Map resource (CMap) as well as hundreds of scRNA-seq data covering cells from a wide variety of organs/tissues, diseases, and conditions. Currently, CeDR maintains the results for more than 582 single cell data objects for human, mouse and cell lines, including about 140 phenotypes and 1250 tissue-cell combination types. All the results can be explored and searched by keywords for drugs, cell types, tissues, diseases, and signature genes. Overall, CeDR fine maps drug response at cellular resolution and sheds lights on the design of combinatorial treatments, drug resistance and even drug side effects.

scMethBank: a database for single-cell whole genome DNA methylation maps.

Single-cell bisulfite sequencing methods are widely used to assess epigenomic heterogeneity in cell states. Over the past few years, large amounts of data have been generated and facilitated deeper understanding of the epigenetic regulation of many key biological processes including early embryonic development, cell differentiation and tumor progression. It is an urgent need to build a functional resource platform with the massive amount of data. Here, we present scMethBank, the first open access and comprehensive database dedicated to the collection, integration, analysis and visualization of single-cell DNA methylation data and metadata. Current release of scMethBank includes processed single-cell bisulfite sequencing data and curated metadata of 8328 samples derived from 15 public single-cell datasets, involving two species (human and mouse), 29 cell types and two diseases. In summary, scMethBank aims to assist researchers who are interested in cell heterogeneity to explore and utilize whole genome methylation data at single-cell level by providing browse, search, visualization, download functions and user-friendly online tools. The database is accessible at: https://ngdc.cncb.ac.cn/methbank/scm/.

EWAS Open Platform: integrated data, knowledge and toolkit for epigenome-wide association study.

Epigenome-Wide Association Study (EWAS) has become a standard strategy to discover DNA methylation variation of different phenotypes. Since 2018, we have developed EWAS Atlas and EWAS Data Hub to integrate a growing volume of EWAS knowledge and data, respectively. Here, we present EWAS Open Platform (https://ngdc.cncb.ac.cn/ewas) that includes EWAS Atlas, EWAS Data Hub and the newly developed EWAS Toolkit. In the current implementation, EWAS Open Platform integrates 617 018 high-quality EWAS associations from 910 publications, covering 51 phenotypes, 275 diseases and 104 environmental factors. It also provides well-normalized DNA methylation array data and the corresponding metadata from 115 852 samples, which involve 707 tissues, 218 cell lines and 528 diseases. Taking advantage of integrated knowledge and data in EWAS Atlas and EWAS Data Hub, EWAS Open Platform equips with EWAS Toolkit, a powerful one-stop site for EWAS enrichment, annotation, and knowledge network construction and visualization. Collectively, EWAS Open Platform provides open access to EWAS knowledge, data and toolkit and thus bears great utility for a broader range of relevant research.

Genome Warehouse: A Public Repository Housing Genome-scale Data.

The Genome Warehouse (GWH) is a public repository housing genome assembly data for a wide range of species and delivering a series of web services for genome data submission, storage, release, and sharing. As one of the core resources in the National Genomics Data Center (NGDC), part of the China National Center for Bioinformation (CNCB; https://ngdc.cncb.ac.cn), GWH accepts both full and partial (chloroplast, mitochondrion, and plasmid) genome sequences with different assembly levels, as well as an update of existing genome assemblies. For each assembly, GWH collects detailed genome-related metadata of biological project, biological sample, and genome assembly, in addition to genome sequence and annotation. To archive high-quality genome sequences and annotations, GWH is equipped with a uniform and standardized procedure for quality control. Besides basic browse and search functionalities, all released genome sequences and annotations can be visualized with JBrowse. By May 21, 2021, GWH has received 19,124 direct submissions covering a diversity of 1108 species and has released 8772 of them. Collectively, GWH serves as an important resource for genome-scale data management and provides free and publicly accessible data to support research activities throughout the world. GWH is publicly accessible at https://ngdc.cncb.ac.cn/gwh.

Molecular evolutionary analysis of human primary microcephaly genes.

BACKGROUND: There has been a rapid increase in the brain size relative to body size during mammalian evolutionary history. In particular, the enlarged and globular brain is the most distinctive anatomical feature of modern humans that set us apart from other extinct and extant primate species. Genetic basis of large brain size in modern humans has largely remained enigmatic. Genes associated with the pathological reduction of brain size (primary microcephaly-MCPH) have the characteristics and functions to be considered ideal candidates to unravel the genetic basis of evolutionary enlargement of human brain size. For instance, the brain size of microcephaly patients is similar to the brain size of Pan troglodyte and the very early hominids like the Sahelanthropus tchadensis and Australopithecus afarensis. RESULTS: The present study investigates the molecular evolutionary history of subset of autosomal recessive primary microcephaly (MCPH) genes; CEP135, ZNF335, PHC1, SASS6, CDK6, MFSD2A, CIT, and KIF14 across 48 mammalian species. Codon based substitutions site analysis indicated that ZNF335, SASS6, CIT, and KIF14 have experienced positive selection in eutherian evolutionary history. Estimation of divergent selection pressure revealed that almost all of the MCPH genes analyzed in the present study have maintained their functions throughout the history of placental mammals. Contrary to our expectations, human-specific adoptive evolution was not detected for any of the MCPH genes analyzed in the present study. CONCLUSION: Based on these data it can be inferred that protein-coding sequence of MCPH genes might not be the sole determinant of increase in relative brain size during primate evolutionary history.