Thidathip Wongsurawat1, Chin Cheng Woo2, Antonis Giannakakis3, Xiao Yun Lin4, Esther Sok Hwee Cheow5, Chuen Neng Lee6, Mark Richards7, Siu Kwan Sze5, Intawat Nookaew8, Vladimir A Kuznetsov9, Vitaly Sorokin10. 1. Department of Genome and Gene Expression Data Analysis, Bioinformatics Institute, Agency for Science, Technology and Research (A*STAR), 138671, Singapore; Department of Biomedical Informatics, College of Medicine, University of Arkansas for Medical Sciences, Little Rock, AR, 72205, USA. 2. Department of Surgery, Yong Loo Lin School of Medicine, National University of Singapore, 119228, Singapore. 3. Department of Genome and Gene Expression Data Analysis, Bioinformatics Institute, Agency for Science, Technology and Research (A*STAR), 138671, Singapore. 4. Department of Cardiac, Thoracic and Vascular Surgery, National University Heart Centre, Singapore, National University Health System, 119228, Singapore. 5. School of Biological Sciences, Nanyang Technological University, 639798, Singapore. 6. Department of Surgery, Yong Loo Lin School of Medicine, National University of Singapore, 119228, Singapore; Department of Cardiac, Thoracic and Vascular Surgery, National University Heart Centre, Singapore, National University Health System, 119228, Singapore. 7. Cardiovascular Research Institute, National University Heart Centre, 119228, Singapore; Department of Medicine, Yong Loo Lin School of Medicine, National University of Singapore, 119228, Singapore. 8. Department of Biomedical Informatics, College of Medicine, University of Arkansas for Medical Sciences, Little Rock, AR, 72205, USA. 9. Department of Genome and Gene Expression Data Analysis, Bioinformatics Institute, Agency for Science, Technology and Research (A*STAR), 138671, Singapore; School of Computer Science and Engineering, Nanyang Technological University, 639798, Singapore. Electronic address: vladimirk@bii.a-star.edu.sg. 10. Department of Surgery, Yong Loo Lin School of Medicine, National University of Singapore, 119228, Singapore; Department of Cardiac, Thoracic and Vascular Surgery, National University Heart Centre, Singapore, National University Health System, 119228, Singapore. Electronic address: vitaly_sorokin@nuhs.edu.sg.
Abstract
BACKGROUND AND AIMS: We aim to identify significant transcriptome alterations of vascular smooth muscle cells (VSMCs) in the aortic wall of myocardial infarction (MI) patients. Providing a robust transcriptomic signature, we aim to highlight the most likely aberrant pathway(s) in MI VSMCs. METHODS AND RESULTS: Laser-captured microdissection (LCM) was used to obtain VSMCs from aortic wall tissues harvested during coronary artery bypass surgery. Microarray gene analysis was applied to analyse VSMCs from 17 MI and 19 non-MI patients. Prediction Analysis of Microarray (PAM) identified 370 genes that significantly discriminated MI and non-MI samples and were enriched in genes responsible for muscle development, differentiation and phenotype regulation. Incorporation of gene ontology (GO) led to the identification of a 21-gene VSMCs-associated classifier that discriminated between MI and non-MI patients with 92% accuracy. The mass spectrometry-based iTRAQ analysis of the MI and non-MI samples revealed 94 proteins significantly differentiating these tissues. Ingenuity Pathway Analysis (IPA) of 370 genes revealed top pathways associated with hypoxia signaling in the cardiovascular system. Enrichment analysis of these proteins suggested an activation of the superoxide radical degradation pathway. An integrated transcriptome-proteome pathway analysis revealed that superoxide radical degradation pathway remained the most implicated pathway. The intersection of the top candidate molecules from the transcriptome and proteome highlighted superoxide dismutase (SOD1) overexpression. CONCLUSIONS: We provided a novel 21-gene VSMCs-associated MI classifier in reference to significant VSMCs transcriptome alterations that, in combination with proteomics data, suggests the activation of superoxide radical degradation pathway in VSMCs of MI patients.
BACKGROUND AND AIMS: We aim to identify significant transcriptome alterations of vascular smooth muscle cells (VSMCs) in the aortic wall of myocardial infarction (MI) patients. Providing a robust transcriptomic signature, we aim to highlight the most likely aberrant pathway(s) in MI VSMCs. METHODS AND RESULTS: Laser-captured microdissection (LCM) was used to obtain VSMCs from aortic wall tissues harvested during coronary artery bypass surgery. Microarray gene analysis was applied to analyse VSMCs from 17 MI and 19 non-MI patients. Prediction Analysis of Microarray (PAM) identified 370 genes that significantly discriminated MI and non-MI samples and were enriched in genes responsible for muscle development, differentiation and phenotype regulation. Incorporation of gene ontology (GO) led to the identification of a 21-gene VSMCs-associated classifier that discriminated between MI and non-MI patients with 92% accuracy. The mass spectrometry-based iTRAQ analysis of the MI and non-MI samples revealed 94 proteins significantly differentiating these tissues. Ingenuity Pathway Analysis (IPA) of 370 genes revealed top pathways associated with hypoxia signaling in the cardiovascular system. Enrichment analysis of these proteins suggested an activation of the superoxide radical degradation pathway. An integrated transcriptome-proteome pathway analysis revealed that superoxide radical degradation pathway remained the most implicated pathway. The intersection of the top candidate molecules from the transcriptome and proteome highlighted superoxide dismutase (SOD1) overexpression. CONCLUSIONS: We provided a novel 21-gene VSMCs-associated MI classifier in reference to significant VSMCs transcriptome alterations that, in combination with proteomics data, suggests the activation of superoxide radical degradation pathway in VSMCs of MI patients.
Authors: Komal Sodhi; James Denvir; Jiang Liu; Juan R Sanabria; Yiliang Chen; Roy Silverstein; Zijian Xie; Nader G Abraham; Joseph I Shapiro Journal: Int J Mol Sci Date: 2020-08-18 Impact factor: 5.923