m6A regulator-mediated RNA methylation modification patterns are involved in immune microenvironment regulation of coronary heart disease

Expression patterns and clinical value of key m6A RNA modification regulators in smoking patients with coronary artery disease

Even though N6-methyladenosine (m6A) RNA modifications are increasingly being implicated in human disease, their mechanisms are not fully understood in smokers with coronary artery disease (CAD). Thirty m6A-related regulators' expression (MRRE) in CAD individuals (smokers and non-smokers) were analyzed from GEO. Support Vector Machine, random forest, and nomogram models were constructed to assess its clinical value. Consensus clustering, principal component analysis, and ssGSEA were used to construct a full picture of m6A-related regulators in smokers with CAD. Oxygen-glucose deprivation (OGD) and qRT-PCR were used to validate hypoxia's effect on MRRE. A comparison between smokers with CAD and controls revealed lower expression levels of RBM15B, YTHDC2, and ZC3H13. Based on three key MRREs, all models showed good clinical value, and smokers with CAD were divided into two distinct molecular subgroups. The correlations were found between key MRRE and the degree of immune infiltration. Three key MRREs in HUVECs and FMC84 mouse cardiomyocytes were reduced in the OGD group. Through hypoxia, smoking might reduce the expression levels of RBM15B, YTHDC2, and ZC3H13 in smokers with CAD. Our findings provide an important theoretical basis for the treatment of smokers with CAD.

Unraveling the landscape of m6A RNA methylation in wound healing and scars

Wound healing is a complex process involving sequential stages of hemostasis, inflammation, proliferation, and remodeling. Multiple cell types and factors, including underlying conditions like diabetes and bacterial colonization, can influence healing outcomes and scar formation. N6-methyladenosine (m6A), a predominant RNA modification, plays crucial roles in gene expression regulation, impacting various biological processes and diseases. m6A regulates embryonic skin morphogenesis, wound repair, and pathophysiological processes like inflammation and angiogenesis. Recent studies have highlighted the role of m6A in wound healing, scar formation, and tissue remodeling. Additionally, m6A presents a unique expression pattern in pathological wounds and scars, potentially influencing wound healing and scar formation through modulating gene expression and cellular signaling, thereby serving as potential biomarkers or therapeutic targets. Targeting m6A modifications are potential strategies to enhance wound healing and reduce scar formation. This review aims to explore the roles and mechanisms of m6A RNA methylation in wound healing and scars, and discuss current challenges and perspectives. Continued research in this field will provide significant value for optimal wound repair and scar treatment.

Single-cell transcriptomics reveals writers of RNA modification-mediated immune microenvironment and cardiac resident Macro-MYL2 macrophages in heart failure

BACKGROUND

Heart failure (HF), which is caused by cardiac overload and injury, is linked to significant mortality. Writers of RNA modification (WRMs) play a crucial role in the regulation of epigenetic processes involved in immune response and cardiovascular disease. However, the potential roles of these writers in the immunological milieu of HF remain unknown.

METHODS

We comprehensively characterized the expressions of 28 WRMs using datasets GSE145154 and GSE141910 to map the cardiac immunological microenvironment in HF patients. Based on the expression of WRMs, the immunological cells in the datasets were scored.

RESULTS

Single-cell transcriptomics analysis (GSE145154) revealed immunological dysregulation in HF as well as differential expression of WRMs in immunological cells from HF and non-HF (NHF) samples. WRM-scored immunological cells were positively correlated with the immunological response, and the high WRM score group exhibited elevated immunological cell infiltration. WRMs are involved in the differentiation of T cells and myeloid cells. WRM scores of T cell and myeloid cell subtypes were significantly reduced in the HF group compared to the NHF group. We identified a myogenesis-related resident macrophage population in the heart, Macro-MYL2, that was characterized by an increased expression of cardiomyocyte structural genes (MYL2, TNNI3, TNNC1, TCAP, and TNNT2) and was regulated by TRMT10C. Based on the WRM expression pattern, the transcriptomics data (GSE141910) identified two distinct clusters of HF samples, each with distinct functional enrichments and immunological characteristics.

CONCLUSION

Our study demonstrated a significant relationship between the WRMs and immunological microenvironment in HF, as well as a novel resident macrophage population, Macro-MYL2, characterized by myogenesis. These results provide a novel perspective on the underlying mechanisms and therapeutic targets for HF. Further experiments are required to validate the regulation of WRMs and Macro-MYL2 macrophage subtype in the cardiac immunological milieu.

The potential of RNA methylation in the treatment of cardiovascular diseases

RNA methylation has emerged as a dynamic regulatory mechanism that impacts gene expression and protein synthesis. Among the known RNA methylation modifications, N6-methyladenosine (m6A), 5-methylcytosine (m5C), 3-methylcytosine (m3C), and N7-methylguanosine (m7G) have been studied extensively. In particular, m6A is the most abundant RNA modification and has attracted significant attention due to its potential effect on multiple biological processes. Recent studies have demonstrated that RNA methylation plays an important role in the development and progression of cardiovascular disease (CVD). To identify key pathogenic genes of CVD and potential therapeutic targets, we reviewed several common RNA methylation and summarized the research progress of RNA methylation in diverse CVDs, intending to inspire effective treatment strategies.

Characterization of the m6A regulators' landscape highlights the clinical significance of acute myocardial infarction

Objective

Acute myocardial infarction (AMI) is a severe cardiovascular disease that threatens human life and health globally. N6-methyladenosine (m6A) governs the fate of RNAs via m6A regulators. Nevertheless, how m6A regulators affect AMI remains to be deciphered. To solve this issue, an integrative analysis of m6A regulators in AMI was conducted.

Methods

We acquired transcriptome profiles (GSE59867, GSE48060) of peripheral blood samples from AMI patients and healthy controls. Key m6A regulators were used for LASSO, and consensus clustering was conducted. Next, the m6A score was also computed. Immune cell infiltration, ferroptosis, and oxidative stress were evaluated. In-vitro and in-vivo experiments were conducted to verify the role of the m6A regulator ALKBH5 in AMI.

Results

Most m6A regulators presented notable expression alterations in circulating cells of AMI patients versus those of controls. Based on key m6A regulators, we established a gene signature and a nomogram for AMI diagnosis and risk prediction. AMI patients were classified into three m6A clusters or gene clusters, respectively, and each cluster possessed the unique properties of m6A modification, immune cell infiltration, ferroptosis, and oxidative stress. Finally, the m6A score was utilized to quantify m6A modification patterns. Therapeutic targeting of ALKBH5 greatly alleviated apoptosis and intracellular ROS in H/R-induced H9C2 cells and NRCMs.

Conclusion

Altogether, our findings highlight the clinical significance of m6A regulators in the diagnosis and risk prediction of AMI and indicate the critical roles of m6A modification in the regulation of immune cell infiltration, ferroptosis, and oxidative stress.

Identification of m6A-associated autophagy genes in non-alcoholic fatty liver

Background

Studies had shown that autophagy was closely related to nonalcoholic fat liver disease (NAFLD), while N6-methyladenosine (m6A) was involved in the regulation of autophagy. However, the mechanism of m6A related autophagy in NAFLD was unclear.

Methods

The NAFLD related datasets were gained via the Gene Expression Omnibus (GEO) database, and we also extracted 232 autophagy-related genes (ARGs) and 37 m6A. First, differentially expressed ARGs (DE-ARGs) and differentially expressed m6A (DE-m6A) were screened out by differential expression analysis. DE-ARGs associated with m6A were sifted out by Pearson correlation analysis, and the m6A-ARGs relationship pairs were acquired. Then, autophagic genes in m6A-ARGs pairs were analyzed for machine learning algorithms to obtain feature genes. Further, we validated the relationship between feature genes and NAFLD through quantitative real-time polymerase chain reaction (qRT-PCR), Western blot (WB). Finally, the immuno-infiltration analysis was implement, and we also constructed the TF-mRNA and drug-gene networks.

Results

There were 19 DE-ARGs and four DE-m6A between NAFLD and normal samples. The three m6A genes and five AGRs formed the m6A-ARGs relationship pairs. Afterwards, genes obtained from machine learning algorithms were intersected to yield three feature genes (TBK1, RAB1A, and GOPC), which showed significant positive correlation with astrocytes, macrophages, smooth muscle, and showed significant negative correlation with epithelial cells, and endothelial cells. Besides, qRT-PCR and WB indicate that TBK1, RAB1A and GOPC significantly upregulated in NAFLD. Ultimately, we found that the TF-mRNA network included FOXP1-GOPC, ATF1-RAB1A and other relationship pairs, and eight therapeutic agents such as R-406 and adavosertib were predicted based on the TBK1.

Conclusion

The study investigated the potential molecular mechanisms of m6A related autophagy feature genes (TBK1, RAB1A, and GOPC) in NAFLD through bioinformatic analyses and animal model validation. However, it is critical to note that these findings, although consequential, demonstrate correlations rather than cause-and-effect relationships. As such, more research is required to fully elucidate the underlying mechanisms and validate the clinical relevance of these feature genes.

Immune-related potential biomarkers and therapeutic targets in coronary artery disease

Background

Coronary artery disease (CAD) is a complex illness with unknown pathophysiology. Peripheral biomarkers are a non-invasive method required to track the onset and progression of CAD and have unbeatable benefits in terms of early identification, prognostic assessment, and categorization of the diagnosis. This study aimed to identify and validate the diagnostic and therapeutic potential of differentially expressed immune-related genes (DE-IRGs) in CAD, which will aid in improving our knowledge on the etiology of CAD and in forming genetic predictions.

Methods

First, we searched coronary heart disease in the Gene Expression Omnibus (GEO) database and identified GSE20680 (CAD = 87, Normal = 52) as the trial set and GSE20681 (CAD = 99, Normal = 99) as the validation set. Functional enrichment analysis using protein-protein interactions (PPIs), Gene Ontology (GO), and Kyoto Encyclopedia of Genes and Genomes (KEGG) was carried out on the identified differentially expressed genes. Optimal feature genes (OFGs) were generated using the support vector machine recursive feature elimination algorithm and the least absolute shrinkage and selection operator (LASSO) algorithm. Furthermore, immune infiltration in CAD patients and healthy controls was compared using CIBERSORT, and the relationship between immune cells and OFGs was examined. In addition, we constructed potential targeted drugs for this model through the Drug-Gene Interaction database (DGIdb) database. Finally, we verify the expression of S100A8-dominated OFGs in the GSE20681 dataset to confirm the universality of our study.

Results

We identified the ten best OFGs for CAD from the DE-IRGs. Functional enrichment analysis showed that these marker genes are crucial for receptor-ligand activity, signaling receptor activator activity, and positive control of the response to stimuli from the outside world. Additionally, CIBERSORT revealed that S100A8 could be connected to alterations in the immune microenvironment in CAD patients. Furthermore, with the help of DGIdb and Cytoscape, a total of 64 medicines that target five marker genes were subsequently discovered. Finally, we verified the expression of the OFGs genes in the GSE20681 dataset between CAD patients and normal patients and found that there was also a significant difference in the expression of S100A8.

Conclusion

We created a 10-gene immune-related prognostic model for CAD and confirmed its validity. The model can identify potential biomarkers for CAD prediction and more accurately gauge the progression of the disease.