Integrated Strategies of Diverse Feature Selection Methods Identify Aging-Based Reliable Gene Signatures for Ischemic Cardiomyopathy

Exploring the underlying mechanisms of Danshen-Shanzha Decoction on coronary heart disease: An integrated analysis combining pharmacoinformatics and experimental validation

ETHNOPHARMACOLOGICAL RELEVANCE

The Danshen-Shanzha Decoction (DSD) is a renowned herbal combination consisting of the root of Salvia miltiorrhiza Bunge (known as Danshen in Chinese) and the fruits of Crataegus pinnatifida Bunge (known as Shanzha in Chinese), which has exhibited remarkable clinical efficacy in the treatment of coronary heart disease (CHD) in traditional Chinese medicine, with its earliest recorded application dating to around 202 BCE during the Han Dynasty. Despite significant advancements in the fundamental research and clinical applications of DSD over the past few decades, the precise bioactive components as well as the underlying mechanisms responsible for its protective effect on CHD remain unelucidated.

AIM OF THE STUDY

The present study was designed to elucidate the bioactive components and potential mechanism of DSD in the treatment of CHD using in silico technologies integrated with pharmacoinformatic methods and experimental validation.

MATERIALS AND METHODS

The chemical components of DSD were analyzed and identified using UPLC-Q-TOF-MS. Pharmacoinformatic-based methods were employed to comprehensively investigate the principal active components and targets of DSD for treating CHD. GO and KEGG pathway analyses were utilized to elucidate the underlying mechanism responsible for DSD's efficacy against CHD. Molecular docking and molecular dynamics simulation were performed to assess the binding affinity between active components and putative targets. Furthermore, surface plasmon resonance (SPR) was carried out to verify the affinity and kinetic characteristics of major components to STAT3 protein. Subsequently, a series of in vitro experiments, including cell viability test, flow cytometric analysis, ELISA and western blotting, were conducted to validate the predicted results in an oxygen-glucose deprivation (OGD)-stimulated H9c2 model.

RESULTS

A total of 96 compounds were characterized by UPLC-Q-TOF-MS, and 281 overlapping targets were identified through pharmacoinformatic-based methods. Among these, ten critical compounds were determined as the core active components of DSD. The core targets associated with the development of CHD included STAT3, SRC, TP53, JUN, and AKT1. Notably, Dihydrotanshinone I and (+)-Epicatechin exhibited strong binding affinity towards STAT3. The potential mechanisms by which DSD modulates the pathological progression of CHD were predicted to involve inflammation, oxidative stress, and apoptosis. Importantly, the cytoprotective effect of DSD against apoptosis was confirmed in OGD-stimulated H9c2 cells, as evidenced by the upregulation of Bcl-2 expression and downregulation of both Bax and cleaved caspase-3 expressions upon DSD treatment. Furthermore, DSD significantly enhanced the phosphorylated protein expressions of JAK2 and STAT3 compared to the OGD group, suggesting its potential role in modulating related signaling pathways.

CONCLUSIONS

The current study successfully fills the gap in the understanding of the chemical profiles of DSD, predicting its active components, potential targets, and molecular mechanisms in the treatment of CHD. These findings not only provide a valuable strategy but also robust data support for future investigations into DSD, thereby facilitating the identification of novel therapeutic targets for traditional Chinese medicines in the battle against CHD.

Combining Bulk and Single Cell RNA-Sequencing Data to Identify Hub Genes of Fibroblasts in Dilated Cardiomyopathy

Background

Dilated cardiomyopathy (DCM) is the second leading cause of heart failure, with intricate pathophysiological underpinnings. In order to shed fresh light on the mechanistic research of DCM, we combined bulk RNA-seq and single-cell RNA-seq (scRNA-seq) data to examine significant cells and genes implicated in the disease.

Methods

This analysis employed publicly accessible bulk RNA-seq and scRNA-seq DCM datasets. The scRNA-seq data underwent normalization, principal component, and t-distribution stochastic neighbor embedding analysis. Cell-to-cell communication networks and activity analysis were conducted using CellChat. Utilizing enrichment analysis, the marker genes' role in the active cells was evaluated. After screening by limma software and weighted gene co-expression network analysis, the differentially expressed genes (DEGs) served as hub genes. Furthermore, these hub genes were subjected to immunological studies, transcription factor expression, and gene set enrichment. Lastly, the expression of the four hub genes and their connection to DCM were verified using the rat models.

Results

Fibroblasts and monocytes were chosen as hub cells from among the eight identified cell clusters; their marker genes intersected with DEGs to yield six hub genes. In addition, the six hub genes and the essential module genes intersected to yield four essential genes (ASPN, SFRP4, LUM, and FRZB) that were connected to the Wnt signaling pathway and highly expressed in fibroblast. The four hub DEGs had an expression pattern in the DCM rat model experiment results that was in line with the findings of the bioinformatics study. Additionally, there was a strong correlation between decreased cardiac function and the up-regulation of ASPN, SFRP4, LUM, and FRZB.

Conclusion

Ultimately, bulk RNA-seq and scRNA-seq data identified fibroblasts and monocytes as the main cell types implicated in DCM. The highly expressed genes ASPN, FRZB, LUM, and SFRP4 in fibroblasts may aid in the mechanistic investigation of DCM.

Methylation modifications in tRNA and associated disorders: Current research and potential therapeutic targets

High-throughput sequencing has sparked increased research interest in RNA modifications, particularly tRNA methylation, and its connection to various diseases. However, the precise mechanisms underpinning the development of these diseases remain largely elusive. This review sheds light on the roles of several tRNA methylations (m1A, m3C, m5C, m1G, m2G, m7G, m5U, and Nm) in diverse biological functions, including metabolic processing, stability, protein interactions, and mitochondrial activities. It further outlines diseases linked to aberrant tRNA modifications, related enzymes, and potential underlying mechanisms. Moreover, disruptions in tRNA regulation and abnormalities in tRNA-derived small RNAs (tsRNAs) contribute to disease pathogenesis, highlighting their potential as biomarkers for disease diagnosis. The review also delves into the exploration of drugs development targeting tRNA methylation enzymes, emphasizing the therapeutic prospects of modulating these processes. Continued research is imperative for a comprehensive comprehension and integration of these molecular mechanisms in disease diagnosis and treatment.

Prediction of diagnostic gene biomarkers for hypertrophic cardiomyopathy by integrated machine learning

OBJECTIVES

Hypertrophic cardiomyopathy (HCM), a leading cause of heart failure and sudden death, requires early diagnosis and treatment. This study investigated the underlying pathogenesis and explored potential diagnostic gene biomarkers for HCM.

METHODS

Transcriptional profiles of myocardial tissues from patients with HCM (dataset GSE36961) were downloaded from the Gene Expression Omnibus database and subjected to bioinformatics analyses, including differentially expressed gene (DEG) identification, enrichment analyses, and protein-protein interaction (PPI) network analysis. Least absolute shrinkage and selection operator (LASSO) regression and support vector machine recursive feature elimination were performed to identify candidate diagnostic gene biomarkers. mRNA expression levels of candidate biomarkers were tested in an external dataset (GSE141910); area under the receiver operating characteristic curve (AUC) values were obtained to validate diagnostic efficacy.

RESULTS

Overall, 156 DEGs (109 downregulated, 47 upregulated) were identified. Enrichment and PPI network analyses indicated that the DEGs were involved in biological functions and molecular pathways including inflammatory response, platelet activity, complement and coagulation cascades, extracellular matrix organization, phagosome, apoptosis, and VEGFA-VEGFR2 signaling. RASD1, CDC42EP4, MYH6, and FCN3 were identified as diagnostic biomarkers for HCM.

CONCLUSIONS

RASD1, CDC42EP4, MYH6, and FCN3 might be diagnostic gene biomarkers for HCM and can provide insights concerning HCM pathogenesis.

Identification MNS1, FRZB, OGN, LUM, SERP1NA3 and FCN3 as the potential immune-related key genes involved in ischaemic cardiomyopathy by random forest and nomogram

The immune molecular mechanisms involved in ischaemic cardiomyopathy (ICM) have not been fully elucidated. The current study aimed to elucidate the immune cell infiltration pattern of the ICM and identify key immune-related genes that participate in the pathologic process of the ICM. The differentially expressed genes (DEGs) were identified from two datasets (GSE42955 combined with GSE57338) and the top 8 key DEGs related to ICM were screened using random forest and used to construct the nomogram model. Moreover, the "CIBERSORT" software package was used to determine the proportion of infiltrating immune cells in the ICM. A total of 39 DEGs (18 upregulated and 21 downregulated) were identified in the current study. Four upregulated DEGs, including MNS1, FRZB, OGN, and LUM, and four downregulated DEGs, SERP1NA3, RNASE2, FCN3 and SLCO4A1, were identified by the random forest model. The nomogram constructed based on the above 8 key genes suggested a diagnostic value of up to 99% to distinguish the ICM from healthy participants. Meanwhile, most of the key DEGs presented prominent interactions with immune cell infiltrates. The RT-qPCR results suggested that the expression levels of MNS1, FRZB, OGN, LUM, SERP1NA3 and FCN3 between the ICM and control groups were consistent with the bioinformatic analysis results. These results suggested that immune cell infiltration plays a critical role in the occurrence and progression of ICM. Several key immune-related genes, including the MNS1, FRZB, OGN, LUM, SERP1NA3 and FCN3 genes, are expected to be reliable serum markers for the diagnosis of ICM and potential molecular targets for ICM immunotherapy.

GATA binding protein 1 recruits histone deacetylase 2 to the promoter region of nuclear receptor binding protein 2 to affect the tumor microenvironment and malignancy of thyroid carcinoma

The tumor microenvironment (TME) and activated angiogenesis in thyroid carcinoma (TC) are critical for tumor growth and metastasis. Nuclear receptor binding protein 2 (NRBP2) has been suggested as a tumor suppressor. This study examines the function of NRBP2 in the progression of TC and the regulatory mechanism. By analyzing bioinformatic tools including GSE165724 dataset and the Cancer Genome Atlas system, we predicted NRBP2 as a poorly expressed gene in TC. Decreased NRBP2 expression was detected in TC tumor tissues and cells. Poor expression of NRBP2 was linked to unfavorable prognosis of patients. GATA binding protein 1 (GATA1) was found as a negative regulator of NRBP2. It recruited histone deacetylase2 (HDAC2) to the NRBP2 promoter to trigger histone deacetylation. NRBP2 overexpression suppressed growth of TC cells, and it reduced expression of TME markers, M2 polarization of macrophages, and angiogenesis in TC. Similar results were reproduced in vivo in nude mice. However, the anti-oncogenic roles of NRBP2 were blocked after further overexpression of GATA1 or HDAC2. In summary, this study demonstrates that GATA1 recruits HDAC2 to the NRBP2 promoter and enhances the TME and angiogenesis in TC cells.