Integrative analysis of bulk and single-cell RNA-seq data for cardiometabolic disease

项目总结

PROJECT SUMMARY Cardiometabolic diseases (CMD), including obesity, type 2 diabetes, heart attack, stroke, and atherosclerosis, are caused by the effects and complex interplay of genetic and lifestyle factors. In the past decade, there has been a dramatic increase of CMD, which represents important causes of morbidity and mortality worldwide. Therefore, there is a great interest to understand the etiology and pathophysiology of CMD. Recent genome- wide association studies (GWAS) have provided increased insight into the genetic basis for CMD and related traits. Although GWAS have identified strong and highly replicated association of genetic loci for CMD and their related traits, GWAS findings can only suggest locations of associated variants and not directly link any one gene within a region to disease. Since most GWAS-identified single nucleotide polymorphisms (SNPs) are located in non-coding regions of the genome, their influence on disease is likely to be on modulating RNA expression by acting as expression quantitative trait loci (eQTL). Excess adipose tissue, especially in central abdominal depots, is associated with increased risk of CMD. Subcutaneous adipose tissue stores additional lipids and acts as a buffering system for lipid energy balance, thus providing a protective role for CMD. Previous eQTL studies in subcutaneous adipose tissue have implicated genes involved in obesity and metabolic traits. However, the role of candidate genes identified in GWAS is still not yet clear because published eQTL studies were based on bulk tissue gene expression in adipose. Adipose tissue is a loose connective tissue that is composed mostly of adipocytes. In addition to adipocytes, adipose tissue also contains adipocyte progenitor cells, endothelial cells, fibroblasts, and various immune cells such as macrophages. The resulting heterogeneity between samples can confound the analysis of bulk tissue data. To overcome these limitations, we propose to perform integrative secondary data analysis of publically available bulk RNA sequencing (RNA-seq) and single-cell RNA-seq (scRNA-seq) data from human adipose. We will test the hypothesis that measurable molecular deficits that include cell types and gene expression occur in adipose for CMD. We will further integrate with publically available GWAS data on CMD to advance post-GWAS interpretation of CMD genetic results. By detailed characterization of cell-type composition and cell-type- specific gene expression changes in human adipose, our results will elucidate the functional roles of GWAS findings that are still poorly understood and can power precision therapeutic targeting of CMD.