Toll-Like Receptors/TNF-α Pathway Crosstalk and Impact on Different Sites of Recurrent Myocardial Infarction in Elderly Patients

Construction of the ceRNA network in the progression of acute myocardial infarction

Acute myocardial infarction (AMI) is a common disease that induced by sudden occlusion of a coronary artery and myocardial necrosis, which causes a great medical burden worldwide. Noncoding RNAs, such as circRNA, lncRNA and miRNA, play crucial roles in the progression of cardiovascular diseases. However, the circRNA-miRNA-mRNA network in the occurrence and development of AMI needs further investigation. In this study, we downloaded three AMI datasets, including circRNA (GSE160717), miRNA (GSE24591), and mRNA (GSE66360) from GEO database. The differentially expressed candidates, and GO and KEGG functions were analyzed by RStudio, and subsequently import to PPI and Cytoscape to obtain the hub genes. By using the starbase target prediction database, we further screen the ceRNA network of circRNA-miRNA-mRNA based on the selected differentially expressed candidates. We found 46 differential expressed mRNAs, 65 miRNAs, and five circRNAs. GO functions and KEGG enrichment of the 46 mRNAs focused on immune response and functions, involving IL-17 signaling pathway, Toll-like receptor signaling pathway, cytokine-cytokine receptor interaction, TNF signaling pathway, chemokine signaling pathway, and NF-kappaB signaling pathway, which may aggravate the pathologies of AMI. PPI and Cytoscape analysis showed 10 hub genes, including TLR2, IL1B, CCL4, CCL3, CCR5, TREM1, CXCL2, NLRP3, CSF3, and CCL20. By using starbase and circinteractome databases, ceRNA network construction showed that circRNA_023461 and circRNA_400027 regulate several miRNA-mRNA axes in AMI. In summary, this study uncovered the circRNA-miRNA-mRNA network based on three AMI datasets. The differentially expressed genes, including CCL20, CCL4, CSF3, and IL1B, focus on immune functions and pathways. Furthermore, circRNA_023461 and circRNA_400027 regulate several miRNA-mRNA axes, exerting important roles in AMI progression. Our founding provides new insights into AMI and improve the therapeutic strategies for AMI.

Levels of soluble tumor necrosis factor receptor 1 and 2 are associated with survival after ST segment elevation myocardial infarction

The soluble tumor necrosis factor receptors (sTNFR1 and sTNFR2) are suggested to play dual roles on physiological and pathophysiological actions of TNF-α. The aim of this study was to investigate the dynamic changes of these biomarkers in patients with ST-segment elevation myocardial infarction (STEMI). Blood was collected from 165 STEMI patients at admission, 1–3 days and 3 months after percutaneous coronary intervention (PCI) and from 40 healthy blood donors. sTNFR1 and sTNFR2 were measured with ELISA. The plasma levels of both sTNFR1 and sTNFR2 were significantly higher than in healthy donors at all three time points. We found no significant differences in sTNFR1 or sTNFR2 when comparing patients with patent versus occluded culprit vessels, or between patients having a thrombus aspiration or not. Survival analysis was performed comparing patients with levels of biomarkers above and below the median values at that time point. We found significant differences in survival for sTNFR2 in acute samples (p = 0.0151) and for both sTNFR1 and sTNFR2 in samples 1–3 days after PCI (p = 0.0054 and p = 0.0003, respectively). Survival analyses suggest that sTNFR1 or sTNFR2 could be promising markers to predict mortality in STEMI patients after PCI.

Identification of hub biomarkers of myocardial infarction by single-cell sequencing, bioinformatics, and machine learning

Background

Myocardial infarction (MI) is one of the first cardiovascular diseases endangering human health. Inflammatory response plays a significant role in the pathophysiological process of MI. Messenger RNA (mRNA) has been proven to play a key role in cardiovascular diseases. Single-cell sequencing (SCS) technology is a new technology for high-throughput sequencing analysis of genome, transcriptome, and epigenome at the single-cell level, and it also plays an important role in the diagnosis and treatment of cardiovascular diseases. Machine learning algorithms have a wide scope of utilization in biomedicine and have demonstrated superior efficiency in clinical trials. However, few studies integrate these three methods to investigate the role of mRNA in MI. The aim of this study was to screen the expression of mRNA, investigate the function of mRNA, and provide an underlying scientific basis for the diagnosis of MI.

Methods

In total, four RNA microarray datasets of MI, namely, GSE66360, GSE97320, GSE60993, and GSE48060, were downloaded from the Gene Expression Omnibus database. The function analysis was carried out by Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG), and Disease Ontology (DO) enrichment analysis. At the same time, inflammation-related genes (IRGs) were acquired from the GeneCards database. Then, 52 co-DEGs were acquired from differentially expressed genes (DEGs) in differential analysis, IRGs, and genes from SCS, and they were used to construct a protein-protein interaction (PPI) network. Two machine learning algorithms, namely, (1) least absolute shrinkage and selection operator and (2) support vector machine recursive feature elimination, were used to filter the co-DEGs. Gene set enrichment analysis (GSEA) was performed to screen the hub-modulating signaling pathways associated with the hub genes. The results were validated in GSE97320, GSE60993, and GSE48060 datasets. The CIBERSORT algorithm was used to analyze 22 infiltrating immune cells in the MI and healthy control (CON) groups and to analyze the correlation between these immune cells. The Pymol software was used for molecular docking of hub DEGs and for potential treatment of MI drugs acquired from the COREMINE.

Results

A total of 126 DEGs were in the MI and CON groups. After screening two machine learning algorithms and key co-DEGs from a PPI network, two hub DEGs (i.e., IL1B and TLR2) were obtained. The diagnostic efficiency of IL1B, TLR2, and IL1B + TLR2 showed good discrimination in the four cohorts. GSEA showed that KEGG enriched by DEGs were mainly related to inflammation-mediated signaling pathways, and GO biological processes enriched by DEGs were linked to biological effects of various inflammatory cells. Immune analysis indicated that IL1B and TLR2 were correlated with various immune cells. Dan shen, san qi, feng mi, yuan can e, can sha, san qi ye, san qi hua, and cha shu gen were identified as the potential traditional Chinese medicine (TCM) for the treatment of MI. 7-hydroxyflavone (HF) had stable combinations with IL1B and TLR2, respectively.

Conclusion

This study identified two hub DEGs (IL1B and TLR2) and illustrated its potential role in the diagnosis of MI to enhance our knowledge of the underlying molecular mechanism. Infiltrating immune cells played an important role in MI. TCM, especially HF, was a potential drug for the treatment of MI.

Potential therapeutic strategies for myocardial infarction: the role of Toll-like receptors

Myocardial infarction (MI) is a life-threatening condition among patients with cardiovascular diseases. MI increases the risk of stroke and heart failure and is a leading cause of morbidity and mortality worldwide. Several genetic and epigenetic factors contribute to the development of MI, suggesting that further understanding of the pathomechanism of MI might help in the early management and treatment of this disease. Toll-like receptors (TLRs) are well-known members of the pattern recognition receptor (PRR) family and contribute to both adaptive and innate immunity. Collectively, studies suggest that TLRs have a cardioprotective effect. However, prolonged TLR activation in the response to signals generated by damage-associated molecular patterns (DAMPs) results in the release of inflammatory cytokines and contributes to the development and exacerbation of myocardial inflammation, MI, ischemia-reperfusion injury, myocarditis, and heart failure. The objective of this review is to discuss and summarize the association of TLRs with MI, highlighting their therapeutic potential for the development of advanced TLR-targeted therapies for MI.