ChIP-Atlas (https://chip-atlas.org) is a web service providing both GUI- and API-based data-mining tools to reveal the architecture of the transcription regulatory landscape. ChIP-Atlas is powered by comprehensively integrating all data sets from high-throughput ChIP-seq and DNase-seq, a method for profiling chromatin regions accessible to DNase. In this update, we further collected all the ATAC-seq and whole-genome bisulfite-seq data for six model organisms (human, mouse, rat, fruit fly, nematode, and budding yeast) with the latest genome assemblies. These together with ChIP-seq data can be visualized with the Peak Browser tool and a genome browser to explore the epigenomic landscape of a query genomic locus, such as its chromatin accessibility, DNA methylation status, and protein–genome interactions. This epigenomic landscape can also be characterized for multiple genes and genomic loci by querying with the Enrichment Analysis tool, which, for example, revealed that inflammatory bowel disease-associated SNPs are the most significantly hypo-methylated in neutrophils. Therefore, ChIP-Atlas provides a panoramic view of the whole epigenomic landscape. All datasets are free to download via either a simple button on the web page or an API.

Atrial fibrillation (AF) is a common cardiac arrhythmia resulting in increased risk of stroke. Despite highly heritable etiology, our understanding of the genetic architecture of AF remains incomplete. Here we performed a genome-wide association study in the Japanese population comprising 9,826 cases among 150,272 individuals and identified East Asian-specific rare variants associated with AF. A cross-ancestry meta-analysis of >1 million individuals, including 77,690 cases, identified 35 new susceptibility loci. Transcriptome-wide association analysis identified IL6R as a putative causal gene, suggesting the involvement of immune responses. Integrative analysis with ChIP-seq data and functional assessment using human induced pluripotent stem cell-derived cardiomyocytes demonstrated ERRg as having a key role in the transcriptional regulation of AF-associated genes. A polygenic risk score derived from the cross-ancestry meta-analysis predicted increased risks of cardiovascular and stroke mortalities and segregated individuals with cardioembolic stroke in undiagnosed AF patients. Our results provide new biological and clinical insights into AF genetics and suggest their potential for clinical applications.

Background Elucidating the modes of action (MoAs) of drugs and drug candidate compounds is critical for guiding translation from drug discovery to clinical application. Despite the development of several data-driven approaches for predicting chemical–disease associations, the molecular cues that organize the epigenetic landscape of drug responses remain poorly understood. Results With the use of a computational method, we attempted to elucidate the epigenetic landscape of drug responses, in terms of transcription factors (TFs), through large-scale ChIP-seq data analyses. In the algorithm, we systematically identified TFs that regulate the expression of chemically induced genes by integrating transcriptome data from chemical induction experiments and almost all publicly available ChIP-seq data (consisting of 13,558 experiments). By relating the resultant chemical–TF associations to a repository of associated proteins for a wide range of diseases, we made a comprehensive prediction of chemical–TF–disease associations, which could then be used to account for drug MoAs. Using this approach, we predicted that: (1) cisplatin promotes the anti-tumor activity of TP53 family members but suppresses the cancer-inducing function of MYCs; (2) inhibition of RELA and E2F1 is pivotal for leflunomide to exhibit antiproliferative activity; and (3) CHD8 mediates valproic acid-induced autism. Conclusions Our proposed approach has the potential to elucidate the MoAs for both approved drugs and candidate compounds from an epigenetic perspective, thereby revealing new therapeutic targets, and to guide the discovery of unexpected therapeutic effects, side effects, and novel targets and actions.

To understand the genetic underpinnings of atrial fibrillation (AF) in the Japanese population, we performed a large-scale genome-wide association study comprising 9,826 cases of AF among 150,272 individuals and identified five new susceptibility loci, including East Asian-specific rare variants. A trans-ancestry meta-analysis of >1 million individuals, including 77,690 cases, identified 35 novel loci. Leveraging gene expression and epigenomic datasets to prioritize putative causal genes and their transcription factors revealed the involvement of IL6R gene and transcription factor ERG besides the known ones. Further, we constructed a polygenic risk score (PRS) for AF, using the trans-ancestry meta-analysis. PRS was associated with an increased risk of long-term cardiovascular and stroke mortality, and segregated individuals with cardioembolic stroke in undiagnosed AF patients. Our results provide novel biological and clinical insights into AF genetics and suggest their potential for clinical applications.

Despite well-documented effects on human health, the action modes of environmental pollutants are incompletely understood. Transcriptome-based approaches are widely used to predict associations between chemicals and disorders. However, the molecular cues regulating gene expression remain unclear. To elucidate the action modes of pollutants, we proposed a data-mining approach, termed "DAR-ChIPEA," combining epigenome (ATAC-Seq) and large-scale public ChIP-Seq data (human,n= 15,155; mouse,n= 13,156) to identify transcription factors (TFs) that are enriched not only in gene-adjacent domains but also across differentially accessible genomic regions, thereby integratively regulating gene expression upon pollutant exposure. The resultant pollutant–TF matrices are then cross-referenced to a repository of TF–disorder associations to account for pollutant modes of action. For example, TFs that regulate Th1/2 cell homeostasis are integral in the pathophysiology of tributyltin-induced allergic disorders; fine particulates (PM2.5) inhibit the binding of C/EBPs, Rela, and Spi1 to the genome, thereby perturbing normal blood cell differentiation and leading to immune dysfunction; and lead induces fatty liver by disrupting the normal regulation of lipid metabolism by altering hepatic circadian rhythms. Thus, our approach has the potential to reveal pivotal TFs that mediate adverse effects of pollutants, thereby facilitating the development of strategies to mitigate environmental pollution damage.