Screening of Lipid Metabolism-Related Gene Diagnostic Signature for Patients With Dilated Cardiomyopathy

Expression characteristics of lipid metabolism-related genes and correlative immune infiltration landscape in acute myocardial infarction

Lipid metabolism is an important part of the heart's energy supply. The expression pattern and molecular mechanism of lipid metabolism-related genes (LMRGs) in acute myocardial infarction (AMI) are still unclear, and the link between lipid metabolism and immunity is far from being elucidated. In this study, 23 Common differentially expressed LMRGs were discovered in the AMI-related mRNA microarray datasets GSE61144 and GSE60993. These genes were mainly related to "leukotriene production involved in inflammatory response", "lipoxygenase pathway", "metabolic pathways", and "regulation of lipolysis in adipocytes" pathways. 12 LMRGs (ACSL1, ADCY4, ALOX5, ALOX5AP, CCL5, CEBPB, CEBPD, CREB5, GAB2, PISD, RARRES3, and ZNF467) were significantly differentially expressed in the validation dataset GSE62646 with their AUC > 0.7 except for ALOX5AP (AUC = 0.699). Immune infiltration analysis and Pearson correlation analysis explored the immune characteristics of AMI, as well as the relationship between these identified LMRGs and immune response. Lastly, the up-regulation of ACSL1, ALOX5AP, CEBPB, and GAB2 was confirmed in the mouse AMI model. Taken together, LMRGs ACSL1, ALOX5AP, CEBPB, and GAB2 are significantly upregulated in AMI patients' blood, peripheral blood of AMI mice, myocardial tissue of AMI mice, and therefore might be new potential biomarkers for AMI.

Transcriptomic analysis reveals the lipid metabolism-related gene regulatory characteristics and potential therapeutic agents for myocardial ischemia-reperfusion injury

Background

Impaired energy balance caused by lipid metabolism dysregulation is an essential mechanism of myocardial ischemia-reperfusion injury (MI/RI). This study aims to explore the lipid metabolism-related gene (LMRG) expression patterns in MI/RI and to find potential therapeutic agents.

Methods

Differential expression analysis was performed to screen the differentially expressed genes (DEGs) and LMRGs in the MI/RI-related dataset GSE61592. Enrichment and protein-protein interaction (PPI) analyses were performed to identify the key signaling pathways and genes. The expression trends of key LMRGs were validated by external datasets GSE160516 and GSE4105. The corresponding online databases predicted miRNAs, transcription factors (TFs), and potential therapeutic agents targeting key LMRGs. Finally, the identified LMRGs were confirmed in the H9C2 cell hypoxia-reoxygenation (H/R) model and the mouse MI/RI model.

Results

Enrichment analysis suggested that the "lipid metabolic process" was one of the critical pathways in MI/RI. Further differential expression analysis and PPI analysis identified 120 differentially expressed LMRGs and 15 key LMRGs. 126 miRNAs, 55 TFs, and 51 therapeutic agents were identified targeting these key LMRGs. Lastly, the expression trends of Acadm, Acadvl, and Suclg1 were confirmed by the external datasets, the H/R model and the MI/RI model.

Conclusion

Acadm, Acadvl, and Suclg1 may be the key genes involved in the MI/RI-related lipid metabolism dysregulation; and acting upon these factors may serve as a potential therapeutic strategy.

Shengmai injection inhibits palmitic acid-induced myocardial cell inflammatory death via regulating NLRP3 inflammasome activation

Objective

To determine the protective effect of Shengmai injection (SMI) on myocardial injury in diabetic rats and its mechanism based on NLRP3/Caspase1 signaling pathway.

Materials and methods

Rat H9c2 cardiomyocytes were cultured in vitro, and the cell survival rate of different concentrations of palmitate acid (PA) and different concentrations of SMI were detected by CCK-8. The myocardial injury cell model was induced with PA, treated with SMI, and combined with NLRP3 specific inhibitor (MCC950) to interfere with the high-fat-induced rat H9c2 myocardial cell injury model. The cell changes were observed by Hoechst/PI staining and the expression levels of MDA, SOD, and ROS in each group were detected. The protein and gene changes of the NLRP3/Caspase-1 signaling pathway were detected by Western blot and RT-qPCR, respectively.

Results

200 μmol/L of PA were selected to induce the myocardial injury cell model and 25 μL/mL of SMI was selected for intervention concentration. SMI could significantly reduce MDA expression, increase SOD level, and decrease ROS production. SMI could decrease the gene expression levels of NLRP3, ASC, Caspase-1, and GSDMD, and the protein expressions of NLRP3, ASC, Cleaved Caspase-1, GSDMD, and GSDMD-N.

Conclusion

SMI can inhibit the high-fat-induced activation of the NLRP3/Caspase-1 signaling pathway, intervene in cardiomyocyte pyroptosis, and prevent diabetic cardiomyopathy.

Selection of key genes for dilated cardiomyopathy based on machine learning algorithms and assessment of diagnostic accuracy

Background

The mechanisms of the occurrence and progression of dilated cardiomyopathy are still unclear and further exploration is needed. The upgrading of programming languages and the improvement of biological databases have created conditions for us to explore the structural and functional information of biological molecules at the nucleic acid and protein levels, screen key pathogenic genes, and elucidate pathogenic mechanisms. This study aimed to screen key pathogenic genes using machine learning algorithms and explore the correlation between key genes and immune microenvironment through transcriptome sequencing data sets of myocardial samples from patients with dilated cardiomyopathy, providing new ideas for elucidating the pathogenesis of the disease.

Methods

The transcriptome sequencing data sets of heart tissue from patients with dilated cardiomyopathy were downloaded from the Gene Expression Omnibus (GEO) database (GSE29819 and GSE21610). Differentially expressed genes (DEGs) were screened between pathological and normal tissues. The key genes were screened using least absolute shrinkage and selection operator (LASSO) regression analysis and random forest tree algorithms. The diagnostic efficiency of the key genes for the disease was evaluated using the receiver operating characteristic (ROC) curve.

Results

Compared with the normal heart tissue (control group) samples, there were 213 DEGs in the heart tissue samples of patients with dilated cardiomyopathy (treat group), including 101 upregulated and 102 downregulated genes. CCL5 and CTGF were highly expressed in the treat group compared to the control group. The ROC curve showed that the areas under the curve (AUCs) of CCL5 and CTGF were 0.821 and 0.902, respectively (P<0.05). In the treat group samples, CCL5 was positively correlated with the infiltration content of most immune cell subtypes.

Conclusions

CCL5 and CTGF are key disease-causing genes in dilated cardiomyopathy and have good diagnostic efficiency for the disease. CCL5 and CTGF may be related to immune cell enrichment and myocardial fibrosis, respectively.

5mC modification patterns provide novel direction for early acute myocardial infarction detection and personalized therapy

Background

Most deaths from coronary artery disease (CAD) are due to acute myocardial infarction (AMI). There is an urgent need for early AMI detection, particularly in patients with stable CAD. 5-methylcytosine (5mC) regulatory genes have been demonstrated to involve in the progression and prognosis of cardiovascular diseases, while little research examined 5mC regulators in CAD to AMI progression.

Method

Two datasets (GSE59867 and GSE62646) were downloaded from Gene Expression Omnibus (GEO) database, and 21 m5C regulators were extracted from previous literature. Dysregulated 5mC regulators were screened out by "limma." The least absolute shrinkage and selection operator (LASSO) and support vector machine recursive feature elimination (SVM-RFE) algorithm were employed to identify hub 5mC regulators in CAD to AMI progression, and 43 clinical samples (Quantitative real-time PCR) were performed for expression validation. Then a logistic model was built to construct 5mC regulator signatures, and a series of bioinformatics algorithms were performed for model validation. Besides, 5mC-associated molecular clusters were studied via unsupervised clustering analysis, and correlation analysis between immunocyte and 5mC regulators in each cluster was conducted.

Results

Nine hub 5mC regulators were identified. A robust model was constructed, and its prominent classification accuracy was verified via ROC curve analysis (area under the curve [AUC] = 0.936 in the training cohort and AUC = 0.888 in the external validation cohort). Besides, the clinical effect of the model was validated by decision curve analysis. Then, 5mC modification clusters in AMI patients were identified, along with the immunocyte infiltration levels of each cluster. The correlation analysis found the strongest correlations were TET3-Mast cell in cluster-1 and TET3-MDSC in cluster-2.

Conclusion

Nine hub 5mC regulators (DNMT3B, MBD3, UHRF1, UHRF2, NTHL1, SMUG1, ZBTB33, TET1, and TET3) formed a diagnostic model, and concomitant results unraveled the critical impact of 5mC regulators, providing interesting epigenetics findings in AMI population vs. stable CAD.