Journal of Proteomics & Bioinformatics
Open Access
ISSN: 0974-276X
ISSN: 0974-276X
Zhengyuan Xia, Zipeng Liu and Junwen Wang
University of Hong Kong, Hong Kong
Posters & Accepted Abstracts: J Proteomics Bioinform
Myocardial infarction (MI) is a major cause of sudden death and one of the most common perioperative complications prevalent in diabetes mellitus. The underlying biological process is different from that in non-diabetes partially due to increased oxidative stress in diabetes. Cardioprotective interventions that are effective in non-diabetic patients lose their effectiveness in diabetic patients, which exacerbates the susceptibility of diabetic hearts to myocardial ischemia reperfusion injury (IRI). However, the mechanism is still largely unclear. The rapid evolution of genomic and genetic approaches, such as microarray and genome-wide association study (GWAS), provides additional insights into complex disease studies. Here, by combining gene co-expression network analysis from a set of microarray profiling and MI/type 2 diabetes (T2D) associated gene sets from GWAS, we built a transcription factor (TF) based regulatory network to explore the pathological behavior. The resulting network using this combination method was validated by high enrichment in several well-documented pathways of diabetic cardiac pathology (e.g. PI3K/Akt and Jak/Stat3 signaling pathway) and was also significantly improved than that using only genomic or genetic data individually. This TF-based network also revealed numbers of previously unreported protein interactions linking distinct pathways, among which we verified a relation between Stat3 and Hif-1�?± in diabetic myocardial IRI model. Thus, our study showed potency of combining knowledge from genomic and genetic studies in discovering the hidden mechanism in diabetic cardiac dysfunction.
Email: zyxia@hku.hk