Rahul Suresh1, Xing Li2, Anca Chiriac3, Kashish Goel4, Andre Terzic5, Carmen Perez-Terzic6, Timothy J Nelson7. 1. Mayo Medical School, College of Medicine, Mayo Clinic, 200 First Street SW, Rochester 55905 MN, USA. 2. Division of Biomedical Statistics and Informatics, Department of Health Sciences Research, Mayo Clinic, 200 First Street SW, Rochester 55905 MN, USA. 3. Department of Molecular Pharmacology and Experimental Therapeutics, Mayo Clinic, 200 First Street SW, Rochester 55905 MN, USA. 4. Division of Cardiovascular Diseases, Department of Medicine, Mayo Clinic, 200 First Street SW, Rochester 55905 MN, USA. 5. Department of Molecular Pharmacology and Experimental Therapeutics, Mayo Clinic, 200 First Street SW, Rochester 55905 MN, USA; Division of Cardiovascular Diseases, Department of Medicine, Mayo Clinic, 200 First Street SW, Rochester 55905 MN, USA; Center for Regenerative Medicine, Mayo Clinic, 200 First Street SW, Rochester 55905 MN, USA. 6. Division of Cardiovascular Diseases, Department of Medicine, Mayo Clinic, 200 First Street SW, Rochester 55905 MN, USA; Department of Physical Medicine and Rehabilitation, Mayo Clinic, 200 First Street SW, Rochester 55905 MN, USA. 7. Department of Molecular Pharmacology and Experimental Therapeutics, Mayo Clinic, 200 First Street SW, Rochester 55905 MN, USA; Center for Regenerative Medicine, Mayo Clinic, 200 First Street SW, Rochester 55905 MN, USA; Division of General Internal Medicine and Transplant Center, Department of Medicine, Mayo Clinic, 200 First Street SW, Rochester 55905 MN, USA. Electronic address: nelson.timothy@mayo.edu.
Abstract
BACKGROUND: Whole-genome gene expression analysis has been successfully utilized to diagnose, prognosticate, and identify potential therapeutic targets for high-risk cardiovascular diseases. However, the feasibility of this approach to identify outcome-related genes and dysregulated pathways following first-time myocardial infarction (AMI) remains unknown and may offer a novel strategy to detect affected expressome networks that predict long-term outcome. METHODS AND RESULTS: Whole-genome expression microarray on blood samples from normal cardiac function controls (n=21) and first-time AMI patients (n=31) within 48-hours post-MI revealed expected differential gene expression profiles enriched for inflammation and immune-response pathways. To determine molecular signatures at the time of AMI associated with long-term outcomes, transcriptional profiles from sub-groups of AMI patients with (n=5) or without (n=22) any recurrent events over an 18-month follow-up were compared. This analysis identified 559 differentially-expressed genes. Bioinformatic analysis of this differential gene-set for associated pathways revealed 1) increasing disease severity in AMI patients is associated with a decreased expression of genes involved in the developmental epithelial-to-mesenchymal transition pathway, and 2) modulation of cholesterol transport genes that include ABCA1, CETP, APOA1, and LDLR is associated with clinical outcome. CONCLUSION: Differentially regulated genes and modulated pathways were identified that were associated with recurrent cardiovascular outcomes in first-time AMI patients. This cell-based approach for risk stratification in AMI could represent a novel, non-invasive platform to anticipate modifiable pathways and therapeutic targets to optimize long-term outcome for AMI patients and warrants further study to determine the role of metabolic remodeling and regenerative processes required for optimal outcomes.
BACKGROUND: Whole-genome gene expression analysis has been successfully utilized to diagnose, prognosticate, and identify potential therapeutic targets for high-risk cardiovascular diseases. However, the feasibility of this approach to identify outcome-related genes and dysregulated pathways following first-time myocardial infarction (AMI) remains unknown and may offer a novel strategy to detect affected expressome networks that predict long-term outcome. METHODS AND RESULTS: Whole-genome expression microarray on blood samples from normal cardiac function controls (n=21) and first-time AMI patients (n=31) within 48-hours post-MI revealed expected differential gene expression profiles enriched for inflammation and immune-response pathways. To determine molecular signatures at the time of AMI associated with long-term outcomes, transcriptional profiles from sub-groups of AMI patients with (n=5) or without (n=22) any recurrent events over an 18-month follow-up were compared. This analysis identified 559 differentially-expressed genes. Bioinformatic analysis of this differential gene-set for associated pathways revealed 1) increasing disease severity in AMI patients is associated with a decreased expression of genes involved in the developmental epithelial-to-mesenchymal transition pathway, and 2) modulation of cholesterol transport genes that include ABCA1, CETP, APOA1, and LDLR is associated with clinical outcome. CONCLUSION: Differentially regulated genes and modulated pathways were identified that were associated with recurrent cardiovascular outcomes in first-time AMI patients. This cell-based approach for risk stratification in AMI could represent a novel, non-invasive platform to anticipate modifiable pathways and therapeutic targets to optimize long-term outcome for AMI patients and warrants further study to determine the role of metabolic remodeling and regenerative processes required for optimal outcomes.
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