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Background: Based on global gene expression profile, therapeutic effects of Qishenyiqi (QSYQ) on acute myocardial infarction (AMI) were investigated by integrated analysis at multiple levels including gene expression, pathways involved and functional group. Methods: Sprague-Dawley (SD) rats were randomly divided into 3 groups: Sham-operated, AMI model (left anterior descending coronary artery ligation) and QSYQ-treated group. Cardiac tissues were obtained for analysing digital gene expression. Sequencing and transcriptome analyses were performed collaboratively, including analyses of differential gene expression, gene co-expression network, targeted attack on network and functional grouping. In this study, a new strategy known as keystone gene-based group significance analysis was also developed. Results: Analysis of top keystone QSYQ-regulated genes indicated that QSYQ ameliorated ventricular remodeling (VR), which is an irreversible process in the pathophysiology of AMI. At pathway level, both well-known cardiovascular diseases and cardiac signaling pathways were enriched. The most remarkable finding was the novel therapeutic effects identified from functional group analysis. This included anti-inflammatory effects mediated via suppression of arachidonic acid lipoxygenase (LOX) pathway and elevation of nitric oxide (NO); and amelioration of dyslipidaemia mediated via fatty acid oxidation. The regulatory patterns of QSYQ on key genes were confirmed by western blot, immunohistochemistry analysis and measurement of plasma lipids, which further validated the therapeutic effects of QSYQ proposed in this study. Conclusions: QSYQ exerts multipronged therapeutic effects on AMI, by concurrently alleviating VR progression, attenuating inflammation induced by arachidonic acid LOX pathway and NO production; and ameliorating dyslipidaemia.
Pubmed ID: 28303103
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View all literature mentionsSoftware package for differential gene expression analysis based on the negative binomial distribution. Used for analyzing RNA-seq data for differential analysis of count data, using shrinkage estimation for dispersions and fold changes to improve stability and interpretability of estimates.
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