The cardiac molecular setting of metabolic syndrome in pigs reveals disease susceptibility and suggests mechanisms that exacerbate COVID-19 outcomes in patients

Abstract

Although metabolic syndrome (MetS) is linked to an elevated risk of cardiovascular disease (CVD), the cardiac-specific risk mechanism is unknown. Obesity, hypertension, and diabetes (all MetS components) are the most common form of CVD and represent risk factors for worse COVID-19 outcomes compared to their non MetS peers. Here, we use obese Yorkshire pigs as a highly relevant animal model of human MetS, where pigs develop the hallmarks of human MetS and reproducibly mimics the myocardial pathophysiology in patients. Myocardium-specific mass spectroscopy-derived metabolomics, proteomics, and transcriptomics enabled the identity and quality of proteins and metabolites to be investigated in the myocardium to greater depth. Myocardium-specific deregulation of pro-inflammatory markers, propensity for arterial thrombosis, and platelet aggregation was revealed by computational analysis of differentially enriched pathways between MetS and control animals. While key components of the complement pathway and the immune response to viruses are under expressed, key N6-methyladenosin RNA methylation enzymes are largely overexpressed in MetS. Blood tests do not capture the entirety of metabolic changes that the myocardium undergoes, making this analysis of greater value than blood component analysis alone. Our findings create data associations to further characterize the MetS myocardium and disease vulnerability, emphasize the need for a multimodal therapeutic approach, and suggests a mechanism for observed worse outcomes in MetS patients with COVID-19 comorbidity.

Impaired cardiac glycolysis and glycogen depletion are linked to poor myocardial outcomes in juvenile male swine with metabolic syndrome and ischemia

“…Because of this, we used an unsupervised learning method based on NMF to compare variations in the metabolite profiles in MetS versus. LD based on the specific proportion of the 283 metabolites in each swine (Cichocki et al, 2009; Ziegler et al, 2021). The differentially represented processes underlying the metabolomics signature in the MetS swine (Figure 2) are related to sugar metabolism ( p = 4e‐5), Warburg effect ( p = 3.4e‐5), glycolysis, and butyrate metabolism ( p = 1.1e‐4).…”

“…NMF was applied to search for metabolic signatures in the LC/MS–MS data set of 305 metabolites in a total of 12 swine (obesogenic diet n = 6, LD n = 6). Hierarchical clustering was performed (Cichocki et al, 2009; Ziegler et al, 2021). All simulations were run on Linux clusters at the Los Alamos National Laboratory.…”

“…The relative label‐free quantitative and comparative among the samples were performed using the Minora algorithm and the adjoining bioinformatics tools of the Proteome Discoverer 2.3 software. To select proteins that show a statistically significant change in abundance between two groups, a threshold of 1.5‐fold change with p ‐value (0.05) were selected (Karimi et al, 2020; Ziegler et al, 2021). Total mRNA was used for RNA‐seq at Next Generation Sequencing (Azenta US, Inc).…”

“…Metabolites were extracted from 100 mg flash‐frozen tissue or 0.2 mL blood with 1 mL of ice‐cold 80% (v/v) methanol and 0.6 mL acetonitrile. Samples were analyzed using a 5500 QTRAP hybrid triple quadrupole mass spectrometer (AB/SCIEX) coupled to a Prominence UFLC HPLC system (Shimadzu) with SRM, as previously reported (Karimi et al, 2020; Yuan et al, 2012; Ziegler et al, 2021). LC/MS–MS was conducted for the individuals pig samples.…”

“…Analyses of blood or blood components are unlikely to capture all of the metabolic alterations that the myocardium undergoes in MetS due to the myocardium's specific gene expression pattern and metabolic characteristics (Karimi et al, 2020; Ziegler et al, 2021). The difficulty in obtaining human cardiac tissue, as well as the significant differences in MetS between humans and small animal models, complicate the identification of myocardium‐specific risk factors in MetS (Abel, 2021; Ahluwalia et al, 2015).…”