MicroRNA Expression Analysis: Clinical Advantage of Propranolol Reveals Key MicroRNAs in Myocardial Infarction
Author(s) -
Wenliang Zhu,
Lei Yang,
Hongli Shan,
Yong Zhang,
Rui Zhou,
Zhe Su,
Zhimin Du
Publication year - 2011
Publication title -
plos one
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.99
H-Index - 332
ISSN - 1932-6203
DOI - 10.1371/journal.pone.0014736
Subject(s) - microrna , microarray , microarray analysis techniques , propranolol , myocardial infarction , gene regulatory network , gene expression profiling , bioinformatics , gene expression , biology , dna microarray , myocardial fibrosis , regulation of gene expression , medicine , computational biology , fibrosis , gene , genetics
Background As playing important roles in gene regulation, microRNAs (miRNAs) are believed as indispensable involvers in the pathogenesis of myocardial infarction (MI) that causes significant morbidity and mortality. Working on a hypothesis that modulation of only some key members in the miRNA superfamily could benefit ischemic heart, we proposed a microarray based network biology approach to identify them with the recognized clinical effect of propranolol as a prompt. Methods A long-term MI model of rat was established in this study. The microarray technology was applied to determine the global miRNA expression change intervened by propranolol. Multiple network analyses were sequentially applied to evaluate the regulatory capacity, efficiency and emphasis of the miRNAs which dysexpression in MI were significantly reversed by propranolol. Results Microarray data analysis indicated that long-term propranolol administration caused 18 of the 31 dysregulated miRNAs in MI undergoing reversed expression, implying that intentional modulation of miRNA expression might show favorable effects for ischemic heart. Our network analysis identified that, among these miRNAs, the prime players in MI were miR-1, miR-29b and miR-98. Further finding revealed that miR-1 focused on regulation of myocyte growth, yet miR-29b and miR-98 stressed on fibrosis and inflammation, respectively. Conclusion Our study illustrates how a combination of microarray technology and functional protein network analysis can be used to identify disease-related key miRNAs.
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