Abstract
Although metabolic syndrome (MS) is a significant risk of cardiovascular disease (CVD), the cardiac response (MR) to MS remains unclear due to traditional MS models’ narrow scope around a limited number of cell-cycle regulation biomarkers and drawbacks of limited human tissue samples. To date, we developed the most comprehensive platform studying MR to MS in a pig model tightly related to human MS criteria. By incorporating comparative metabolomic, transcriptomic, functional analyses, and unsupervised machine learning (UML), we can discover unknown metabolic pathways connections and links on numerous biomarkers across the MS-associated issues in the heart. For the first time, we show severely diminished availability of glycolytic and citric acid cycle (CAC) pathways metabolites, altered expression, GlcNAcylation, and activity of involved enzymes. A notable exception, however, is the excessive succinate accumulation despite reduced succinate dehydrogenase complex iron-sulfur subunit b (SDHB) expression and decreased content of precursor metabolites. Finally, the expression of metabolites and enzymes from the GABA-glutamate, GABA-putrescine, and the glyoxylate pathways significantly increase, suggesting an alternative cardiac means to replenish succinate and malate in MS. Our platform discovers potential therapeutic targets for MS-associated CVD within pathways that were previously unknown to corelate with the disease.
Highlights
Metabolic syndrome (MS) is a significant risk of cardiovascular disease (CVD), the cardiac response (MR) to metabolic syndrome (MS) remains unclear due to traditional MS models’ narrow scope around a limited number of cell-cycle regulation biomarkers and drawbacks of limited human tissue samples
We discovered that the heart adopts a strategy to defend against MS by severely altering glycolysis, the availability of several Krebs’s cycle (Citric Acid Cycle, citric acid cycle (CAC)) intermediates, and changes in CAC related enzyme expression, GlcNAcylation, and activity
Our studies suggest that increased activity of the gamma-aminobutyric acid (GABA) and glyoxylate pathways replenish CAC intermediates in MS
Summary
Metabolic syndrome (MS) is a significant risk of cardiovascular disease (CVD), the cardiac response (MR) to MS remains unclear due to traditional MS models’ narrow scope around a limited number of cell-cycle regulation biomarkers and drawbacks of limited human tissue samples. Transcriptomic, functional analyses, and unsupervised machine learning (UML), we can discover unknown metabolic pathways connections and links on numerous biomarkers across the MS-associated issues in the heart. MS is a state of simultaneously appearing at least three of the medical conditions elevated triglycerides, low-density lipoprotein (LDL), blood pressure, hyperglycemia, and obesity[1,2] Each of these factors independently and synergistically increase the risk of developing CVD1. Because of the pleiotropic nature of CVD and MS, it is unlikely that single metabolites or metabolic pathways underlie the development of CVD in MS To this end, understanding the global metabolic state of the MS heart, using high throughput metabolomics, transcriptomics, and proteomics, may provide novel insights. We apply unsupervised machine learning (UML) to differentiate metabolite brown.edu www.nature.com/scientificreports/
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