The role of glycolytic metabolic pathways in cardiovascular disease and potential therapeutic approaches

The attenuated hepatic clearance of propionate increases cardiac oxidative stress in propionic acidemia

Propionic acidemia (PA), arising from PCCA or PCCB variants, manifests as life-threatening cardiomyopathy and arrhythmias, with unclear pathophysiology. In this work, propionyl-CoA metabolism in rodent hearts and human pluripotent stem cell-derived cardiomyocytes was investigated with stable isotope tracing analysis. Surprisingly, gut microbiome-derived propionate rather than the propiogenic amino acids (valine, isoleucine, threonine, and methionine) or odd-chain fatty acids was found to be the primary cardiac propionyl-CoA source. In a Pcca-/-(A138T) mouse model and PA patients, accumulated propionyl-CoA and diminished acyl-CoA synthetase short-chain family member 3 impede hepatic propionate disposal, elevating circulating propionate. Prolonged propionate exposure induced significant oxidative stress in PCCA knockdown HL-1 cells and the hearts of Pcca-/-(A138T) mice. Additionally, Pcca-/-(A138T) mice exhibited mild diastolic dysfunction after the propionate challenge. These findings suggest that elevated circulating propionate may cause oxidative damage and functional impairment in the hearts of patients with PA.

Identification of hub glycolysis-related genes in acute myocardial infarction and their correlation with immune infiltration using bioinformatics analysis

PURPOSE

Glycolysis and immune metabolism play important roles in acute myocardial infarction (AMI). Therefore, this study aimed to identify and experimentally validate the glycolysis-related hub genes in AMI as diagnostic biomarkers, and further explore the association between hub genes and immune infiltration.

METHODS

Differentially expressed genes (DEGs) from AMI peripheral blood mononuclear cells (PBMCs) were analyzed using R software. Glycolysis-related DEGs (GRDEGs) were identified and analyzed using the Database for Annotation, Visualization, and Integrated Discovery (DAVID) for functional enrichment. A protein-protein interaction network was constructed using the STRING database and visualized using Cytoscape software. Immune infiltration analysis between patients with AMI and stable coronary artery disease (SCAD) controls was performed using CIBERSORT, and correlation analysis between GRDEGs and immune cell infiltration was performed. We also plotted nomograms and receiver operating characteristic (ROC) curves to assess the predictive accuracy of GRDEGs for AMI occurrence. Finally, key genes were experimentally validated using reverse transcription-quantitative polymerase chain reaction (RT-qPCR) and western blotting using PBMCs.

RESULTS

A total of 132 GRDEGs and 56 GRDEGs were identified on the first day and 4-6 days after AMI, respectively. Enrichment analysis indicated that these GRDEGs were mainly clustered in the glycolysis/gluconeogenesis and metabolic pathways. Five hub genes (HK2, PFKL, PKM, G6PD, and ALDOA) were selected using the cytoHubba plugin. The link between immune cells and hub genes indicated that HK2, PFKL, PKM, and ALDOA were significantly positively correlated with monocytes and neutrophils, whereas G6PD was significantly positively correlated with neutrophils. The calibration curve, decision curve analysis, and ROC curves indicated that the five hub GRDEGs exhibited high predictive value for AMI. Furthermore, the five hub GRDEGs were validated by RT-qPCR and western blotting.

CONCLUSION

We concluded that HK2, PFKL, PKM, G6PD, and ALDOA are hub GRDEGs in AMI and play important roles in AMI progression. This study provides a novel potential immunotherapeutic method for the treatment of AMI.

Exploring the mechanisms of glycolytic genes involvement in pulmonary arterial hypertension through integrative bioinformatics analysis

The purpose of this study was to identify the mechanisms underlying the involvement of glycolytic genes in pulmonary arterial hypertension (PAH). This study involved downloading 3 datasets from the GEO database at the National Center for Biotechnology Information. The datasets were processed to obtain expression matrices for analysis. Genes involved in glycolysis-related pathways were obtained, and genes related to glycolysis were selected based on significant differences in expression. Gene Ontology functional annotation analysis, Kyoto Encyclopedia of Genes and Genomes pathway enrichment analysis, and GSEA enrichment analysis were performed on the DEGs. Combining LASSO regression with SVM-RFE machine learning technology, a PAH risk prediction model based on glycolysis related gene expression was constructed, and CIBERSORTx technology was used to analyse the immune cell composition of PAH patients. Gene enrichment analysis revealed that the DEGs work synergistically across multiple biological pathways. A total of 6 key glycolysis-related genes were selected using LASSO regression and SVM. A bar plot was constructed to evaluate the weights of the key genes and predict the risk of PAH. The clinical application value and predictive accuracy of the model were assessed. Immunological feature analysis revealed significant correlations between key glycolysis-related genes and the abundances of different immune cell types. The glycolysis genes (ACSS2, ALAS2, ALDH3A1, ADOC3, NT5E, and TALDO1) identified in this study play important roles in the development of pulmonary arterial hypertension, providing new evidence for the involvement of glycolysis in PAH.

Extracellular Vesicles in Diabetic Cardiomyopathy-State of the Art and Future Perspectives

Cardiovascular complications are the most deadly and cost-driving effects of diabetes mellitus (DM). One of them, which is steadily attracting attention among scientists, is diabetes-induced heart failure, also known as diabetic cardiomyopathy (DCM). Despite significant progress in the research concerning the disease, a universally accepted definition is still lacking. The pathophysiology of the processes accelerating heart insufficiency in diabetic patients on molecular and cellular levels also remains elusive. However, the recent interest concerning extracellular vesicles (EVs) has brought promise to further clarifying the pathological events that lead to DCM. In this review, we sum up recent investigations on the involvement of EVs in DCM and show their therapeutic and indicatory potential.

Gut microbiome-based noninvasive diagnostic model to predict acute coronary syndromes

Background

Previous studies have shown that alterations in the gut microbiota are closely associated with Acute Coronary Syndrome (ACS) development. However, the value of gut microbiota for early diagnosis of ACS remains understudied.

Methods

We recruited 66 volunteers, including 29 patients with a first diagnosis of ACS and 37 healthy volunteers during the same period, collected their fecal samples, and sequenced the V4 region of the 16S rRNA gene. Functional prediction of the microbiota was performed using PICRUSt2. Subsequently, we constructed a nomogram and corresponding webpage based on microbial markers to assist in the diagnosis of ACS. The diagnostic performance and usefulness of the model were analyzed using boostrap internal validation, calibration curves, and decision curve analysis (DCA).

Results

Compared to that of healthy controls, the diversity and composition of microbial community of patients with ACS was markedly abnormal. Potentially pathogenic genera such as Streptococcus and Acinetobacter were significantly increased in the ACS group, whereas certain SCFA-producing genera such as Blautia and Agathobacter were depleted. In addition, in the correlation analysis with clinical indicators, the microbiota was observed to be associated with the level of inflammation and severity of coronary atherosclerosis. Finally, a diagnostic model for ACS based on gut microbiota and clinical variables was developed with an area under the receiver operating characteristic (ROC) curve (AUC) of 0.963 (95% CI: 0.925-1) and an AUC value of 0.948 (95% CI: 0.549-0.641) for bootstrap internal validation. The calibration curves of the model show good consistency between the actual and predicted probabilities. The DCA showed that the model had a high net clinical benefit for clinical applications.

Conclusion

Our study is the first to characterize the composition and function of the gut microbiota in patients with ACS and healthy populations in Southwest China and demonstrates the potential effect of the microbiota as a non-invasive marker for the early diagnosis of ACS.