Large-scale gene analysis of rabbit atherosclerosis to discover new biomarkers for coronary artery disease
Author(s) -
Xiaolan Yu,
Wen Guan,
Yang Zhang,
Qing Deng,
Jingjing Li,
Ye Hao,
Shaorong Deng,
Wei Han,
Yan Yu
Publication year - 2019
Publication title -
open biology
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 3.078
H-Index - 53
ISSN - 2046-2441
DOI - 10.1098/rsob.180238
Subject(s) - biology , coronary artery disease , coronary atherosclerosis , candidate gene , bioinformatics , medicine , gene , genetics
Atherosclerosis is the pathological basis of coronary artery disease (CAD) and causes high mortality. Thus, early detection is thought to be crucial in reducing the risk of CAD. Uncovering the mechanisms of the progression and regression of atherosclerosis will provide insights into discovering novel biomarkers to identify subjects at risk for CAD and improve prevention. We established atherosclerosis progression and regression in a rabbit model. Then, we extracted mRNA of the abdominal aorta from control, model and recovery groups to perform gene chip analysis. Candidate biomarkers were screened by large-scale gene analysis and validated in patients with CAD or with CAD recovery by ELISA. The differentially expressed genes in the progression and regression of atherosclerosis were mainly enriched in four clusters. Genes associated with inflammation and extracellular matrix were returned to normal or close-to-normal levels much earlier than genes associated with metabolism and sarcoplasmic proliferation, and they were maintained downregulated or upregulated after feeding a normal diet. We then selected four candidate biomarkers and found that lipoprotein lipase (LPL), bone morphogenetic protein 7 and somatostatin concentrations could indicate CAD diagnosis. In addition, LPL and macrophage cationic peptide 2 can be indicators of the prognosis of CAD. Molecular changes during the progression and regression of atherosclerosis in rabbits were revealed, and candidate regulators were identified. The identified factors could be used as novel biomarkers and targets for improving the diagnosis and prognosis of human CAD in the future.
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