Project description:Myocardial infarction (MI), the most severe manifestation of coronary artery disease, is a multifactorial pathophysiologic process. Here, we constructed a MI mouse model through ligation of the proximal left anterior descending coronary artery. Then we detected and analysed multi-omics (transcriptome and proteome) at different time points (Control, 10 mininte, 1 hour, 6 hour, 24 hour and 72 hour) after MI. Immune-related pathway, pyroptosis pathway, and autophagy pathway r were significantly increased after MI.
Project description:This study used a trans-omics approach—combining genome-wide SNP analysis and metabolomics—to distinguish coronary artery disease (CAD) patients from high-risk and healthy individuals. It identified declining plasma phospholipids as potential biomarkers, linked key SNPs and genes (notably LPCAT1) to lipid changes, and developed a machine-learning model that accurately predicts CAD (AUC = 0.917). The results highlight the role of phospholipid metabolism and genetic variation in CAD progression.
Project description:The human LncRNA microarray analysis of the 6 monocytes samples from Coronary Artery Disease patients and non Coronary Artery Disease 3 Coronary Artery Disease patients and 3 non-Coronary Artery Disease donors
Project description:The human LncRNA microarray analysis of the 6 monocytes samples from Coronary Artery Disease patients and non Coronary Artery Disease
Project description:Genome-wide association studies (GWAS) have identified hundreds of genetic risk loci for coronary artery disease (CAD). However, non-European populations are underrepresented in coronary artery disease (CAD). However, non-European populations are underrepresented in GWAS and the causal gene-regulatory mechanisms of these risk loci during atherosclerosis remain unclear. We incorporated local ancestry and haplotype information to identify quantitative trait loci (QTL) for gene expression and splicing in coronary arteries obtained from 138 ancestrally diverse Americans.