ABSTRACT
Coronary heart disease (CHD) is the most common cause of death worldwide. The burden of CHD increases with risk factors such as smoking, hypertension, obesity and diabetes. Several studies have demonstrated the association of these classical risk factors with CHD. However, the mechanisms of these associations remain largely unclear due to the complexity of disease pathophysiology and the lack of an integrative approach that fails to provide a definite understanding of molecular linkage. To overcome these problems, we propose a novel systems biology approach that relates causative genes, interactomes and pathways to elucidate the risk factors mediating the molecular mechanisms and biomarkers for feasible diagnosis. The literature was mined to retrieve the causative genes of each risk factor and CHD to construct protein interactomes. The interactomes were examined to identify 298 common molecular signatures. The common signatures were mapped to the tissue network to synthesize a sub-network consisting of 82 proteins. Further, the dissection of the sub-network provides functional modules representing a diverse range of molecular functions, including the AKT/p13k, MAPK and wnt pathways. Also, the prioritization of functional modules identifies SRC, VEGFA and HIF1A as potential candidate markers. Further, we validate these candidates with the existing markers CRP, NOS3 and VCAM1 in the serum of 63 individuals, 33 with CHD and 30 controls, using ELISA. SRC, VEGFA, H1F1A, CRP and NOS3 were significantly altered in patients compared to controls. These results support the utility of these candidate markers for the diagnosis of CHD. Overall, our molecular observations indicate the influence of risk factors in the pathophysiology of CHD and identify serum markers for diagnosis.
Subject(s)
Coronary Artery Disease/metabolism , Proteomics , Systems Biology , Vascular Endothelial Growth Factor A/metabolism , src-Family Kinases/metabolism , Algorithms , Biomarkers , Cluster Analysis , Comorbidity , Coronary Artery Disease/etiology , Databases, Genetic , Gene Expression , Gene Regulatory Networks , Humans , Markov Chains , Protein Interaction Mapping , Protein Interaction Maps , Proteome , Proteomics/methods , Reproducibility of Results , Risk Factors , Signal Transduction , Systems Biology/methodsABSTRACT
Cardiovascular diseases (CVDs) account for high morbidity and mortality worldwide. Both, genetic and epigenetic factors are involved in the enumeration of various cardiovascular diseases. In recent years, a vast amount of multi-omics data are accumulated in the field of cardiovascular research, yet the understanding of key mechanistic aspects of CVDs remain uncovered. Hence, a comprehensive online resource tool is required to comprehend previous research findings and to draw novel methodology for understanding disease pathophysiology. Here, we have developed a literature-based database, CardioGenBase, collecting gene-disease association from Pubmed and MEDLINE. The database covers major cardiovascular diseases such as cerebrovascular disease, coronary artery disease (CAD), hypertensive heart disease, inflammatory heart disease, ischemic heart disease and rheumatic heart disease. It contains ~1,500 cardiovascular disease genes from ~2,4000 research articles. For each gene, literature evidence, ontology, pathways, single nucleotide polymorphism, protein-protein interaction network, normal gene expression, protein expressions in various body fluids and tissues are provided. In addition, tools like gene-disease association finder and gene expression finder are made available for the users with figures, tables, maps and venn diagram to fit their needs. To our knowledge, CardioGenBase is the only database to provide gene-disease association for above mentioned major cardiovascular diseases in a single portal. CardioGenBase is a vital online resource to support genome-wide analysis, genetic, epigenetic and pharmacological studies.