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Pathogenesis of AMI Revealed by Integrative Global Transcriptomics and Proteomics Analysis
Author(s) -
Wang Yong,
Lin Weili,
Nugent Colleen A.,
Gao Sheng,
Ma Zhiyuan,
Zhu Ruixin,
Li Chun,
Zhu Lixin,
Wang Wei
Publication year - 2017
Publication title -
the faseb journal
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.709
H-Index - 277
eISSN - 1530-6860
pISSN - 0892-6638
DOI - 10.1096/fasebj.31.1_supplement.768.11
Subject(s) - transcriptome , gene , proteome , biology , gene expression , gene expression profiling , bioinformatics , genetics , computational biology , pathogenesis , candidate gene , immunology
Background Acute myocardial infarction (AMI) is a common cardiovascular event which becomes an important cause of mortality and morbidity worldwide. The molecular pathological mechanism of AMI can exhibit across multiple layers of gene regulation including gene expression and protein translation. Integrative analysis of multiple layers (transcriptome and proteome) of genetics information provides new insights into pathogenesis of AMI. Methods Sprague‐Dawley (SD) rats were randomly divided into 2 groups (3 rats in each group): Sham‐operated group and AMI model group. AMI model was induced by ligation of left anterior descending coronary artery. Twenty eight days after surgery, cardiac tissue was harvested for digital gene expression (DGE) sequencing and two‐dimensional electrophoresis coupled mass spectrometric (MS) analysis. To integrate transcriptome and proteome data, we performed multiple co‐inertia analysis (MCIA) to detect feature genes of AMI on both mRNA and protein levels. Feature genes were ranked with PageRank score in gene co‐expression network (GCN). Pathway enrichment analysis and functional module analysis were further performed on feature genes. Moreover, significance of functional module was evaluated with priority of feature genes. Results At the gene level, MCIA integrative analysis revealed 2313 feature genes of AMI, with 2264 mRNAs and 63 proteins. Among them, 14 feature genes were detected on both mRNA and protein levels, and most of them were the top‐ranked genes in GCN (NDUFA10, SIGLEC1, SUCLA2, ACAA2, NNT, KLF15, ALDOART2, CKMT2, ECIL1, HADH, GAPDHS, MMP17 and HADHB). These genes can be candidate biomarkers of AMI. At pathway level, pathway enrichment analysis indicated dysregulation of fatty acid degradation and its upstream signalling pathways, including both PPAR and AMPK signalling pathways. Additionally some other pathways related to cardiovascular diseases were enriched, including hypertrophic cardiomyopathy, dilated cardiomyopathy and renin secretion. At functional module level, significant functional modules were relevant to upstream signalling pathways of important metabolism, including AMPK and glucagon signalling pathways. These significant functional modules play pivotal roles in the control of energy metabolism, including fatty acid oxidation and glycolysis. Meanwhile, significant functional modules were involved in cardiac function pathways, including adrenergic signalling in cardiomyocytes and calcium signalling pathway. Conclusion Integrative analysis of transcriptome and proteome provides new insights into pathogenesis of AMI. Top‐ranked feature genes can be candidate biomarkers of AMI. Our study identified significant functional modules that regulate important energy metabolism and cardiac function in AMI development. Support or Funding Information This work was financially supported, in part, by the Grants from the National Natural Science Foundation of China (No. 81302908, 81530100, 81473456, 81503379 and 81470191), Beijing Natural Science Foundation (No.7142099), Fok Ying Tung Education Foundation (No. 151044), and excellent young scientist foundation of BUCM (No.2016‐JYB‐XJ003, 2015‐JYB‐QNJSZX001 and 2015‐JYB‐XYQ001).