Identification of the distinctive role of DPT in dilated cardiomyopathy: a study based on bulk and single-cell transcriptomic analysis

Recent advances of the Ephrin and Eph family in cardiovascular development and pathologies

Erythropoietin-producing hepatoma (Eph) receptors, comprising the largest family of receptor tyrosine kinases (RTKs), exert profound influence on diverse biological processes and pathological conditions such as cancer. Interacting with their corresponding ligands, erythropoietin-producing hepatoma receptor interacting proteins (Ephrins), Eph receptors regulate crucial events like embryonic development, tissue boundary formation, and tumor cell survival. In addition to their well-established roles in embryonic development and cancers, emerging evidence highlights the pivotal contribution of the Ephrin/Eph family to cardiovascular physiology and pathology. Studies have elucidated their involvement in cardiovascular development, atherosclerosis, postnatal angiogenesis, and, more recently, cardiac fibrosis and calcification, suggesting a promising avenue for therapeutic interventions in cardiovascular diseases. There remains a need for a comprehensive synthesis of their collective impact in the cardiovascular context. By exploring the intricate interactions between Eph receptors, ephrins, and cardiovascular system, this review aims to provide a holistic understanding of their roles and therapeutic potential in cardiovascular health and diseases.

Identification of HTRA1, DPT and MXRA5 as potential biomarkers associated with osteoarthritis progression and immune infiltration

BACKGROUND

Our study aimed to identify potential specific biomarkers for osteoarthritis (OA) and assess their relationship with immune infiltration.

METHODS

We utilized data from GSE117999, GSE51588, and GSE57218 as training sets, while GSE114007 served as a validation set, all obtained from the GEO database. First, weighted gene co-expression network analysis (WGCNA) and functional enrichment analysis were performed to identify hub modules and potential functions of genes. We subsequently screened for potential OA biomarkers within the differentially expressed genes (DEGs) of the hub module using machine learning methods. The diagnostic accuracy of the candidate genes was validated. Additionally, single gene analysis and ssGSEA was performed. Then, we explored the relationship between biomarkers and immune cells. Lastly, we employed RT-PCR to validate our results.

RESULTS

WGCNA results suggested that the blue module was the most associated with OA and was functionally associated with extracellular matrix (ECM)-related terms. Our analysis identified ALB, HTRA1, DPT, MXRA5, CILP, MPO, and PLAT as potential biomarkers. Notably, HTRA1, DPT, and MXRA5 consistently exhibited increased expression in OA across both training and validation cohorts, demonstrating robust diagnostic potential. The ssGSEA results revealed that abnormal infiltration of DCs, NK cells, Tfh, Th2, and Treg cells might contribute to OA progression. HTRA1, DPT, and MXRA5 showed significant correlation with immune cell infiltration. The RT-PCR results also confirmed these findings.

CONCLUSIONS

HTRA1, DPT, and MXRA5 are promising biomarkers for OA. Their overexpression strongly correlates with OA progression and immune cell infiltration.

Deciphering the role of SMARCA4 in cardiac disorders: Insights from single-cell studies on dilated cardiomyopathy and coronary heart disease

BACKGROUND

Dilated cardiomyopathy (DCM) and coronary heart disease (CHD) stand as two of the foremost causes of mortality. However, the comprehensive comprehension of the regulatory mechanisms governing DCM and CHD remains limited, particularly from the vantage point of single-cell transcriptional analysis.

METHOD

We used the GSE121893 dataset from the GEO database, analyzing single-cell expressions with tools like DropletUtils, Seurat, and Monocle. We also utilized the GSVA package for comparing gene roles in DCM and CHD, Finally, we conducted qRT-PCR and Western blot analyses to measure the expression levels of SMARCA4, Col1A1, Col3A1 and α-SMA, and the role of SMARCA4 on fibroblasts were explored by EdU and Transwell assay.

RESULTS

Our analysis identified six cell types in heart tissue, with fibroblasts showing the most interaction with other cells. DEGs in fibroblasts were linked to muscle development and morphogenesis. Pseudotime analysis revealed the dynamics of fibroblast changes in both the normal and disease groups and many transcription factors (TFs) potentially involved in this process. Among these TFs, SMARCA4 which was translated into protein BRG1, showed the most significantly difference. In vivo experiments have demonstrated that SMARCA4 indeed promoted fibroblasts proliferation and migration.

CONCLUSION

This study provides a clearer understanding of cell-type dynamics in heart diseases, emphasizing the role of fibroblasts and the significance of SMARCA4 in their function. Our results offer insights into the cellular mechanisms underlying DCM and CHD, potentially guiding future therapeutic strategies.

Integration of machine learning to identify diagnostic genes in leukocytes for acute myocardial infarction patients

BACKGROUND

Acute myocardial infarction (AMI) has two clinical characteristics: high missed diagnosis and dysfunction of leukocytes. Transcriptional RNA on leukocytes is closely related to the course evolution of AMI patients. We hypothesized that transcriptional RNA in leukocytes might provide potential diagnostic value for AMI. Integration machine learning (IML) was first used to explore AMI discrimination genes. The following clinical study was performed to validate the results.

METHODS

A total of four AMI microarrays (derived from the Gene Expression Omnibus) were included in bioanalysis (220 sample size). Then, the clinical validation was finished with 20 AMI and 20 stable coronary artery disease patients (SCAD). At a ratio of 5:2, GSE59867 was included in the training set, while GSE60993, GSE62646, and GSE48060 were included in the testing set. IML was explicitly proposed in this research, which is composed of six machine learning algorithms, including support vector machine (SVM), neural network (NN), random forest (RF), gradient boosting machine (GBM), decision trees (DT), and least absolute shrinkage and selection operator (LASSO). IML had two functions in this research: filtered optimized variables and predicted the categorized value. Finally, The RNA of the recruited patients was analyzed to verify the results of IML.

RESULTS

Thirty-nine differentially expressed genes (DEGs) were identified between controls and AMI individuals from the training sets. Among the thirty-nine DEGs, IML was used to process the predicted classification model and identify potential candidate genes with overall normalized weights > 1. Finally, two genes (AQP9 and SOCS3) show their diagnosis value with the area under the curve (AUC) > 0.9 in both the training and testing sets. The clinical study verified the significance of AQP9 and SOCS3. Notably, more stenotic coronary arteries or severe Killip classification indicated higher levels of these two genes, especially SOCS3. These two genes correlated with two immune cell types, monocytes and neutrophils.

CONCLUSION

AQP9 and SOCS3 in leukocytes may be conducive to identifying AMI patients with SCAD patients. AQP9 and SOCS3 are closely associated with monocytes and neutrophils, which might contribute to advancing AMI diagnosis and shed light on novel genetic markers. Multiple clinical characteristics, multicenter, and large-sample relevant trials are still needed to confirm its clinical value.

m6A regulator-mediated RNA methylation modification remodels immune microenvironment in dilated cardiomyopathy

The latest evidence suggested that the onset of dilated cardiomyopathy (DCM) is closely associated with immune microenvironment disturbance. Since N6 -methyladenosine (m6A) RNA methylation impacts on immunocyte function and antitumor immunity, it is predictable that m6A RNA methylation may result in immune microenvironment disorder. Here, we attempted to verify this hypothesis. We used single-sample gene set enrichment analysis (ssGSEA) to investigate the infiltration abundance of immunocytes, single-cell RNA-Seq to identify key m6A regulator, and a doxorubicin (Dox)-induced DCM mouse model to confirm our findings. ssGSEA revealed a higher infiltration abundance of CD8+ T lymphocytes, NK cells, monocytes, and B+ lymphocytes in DCM myocardium tissue. Single-cell RNA-Seq indicated a critical role of IGFBP2 in DCM. Cross-checking analysis hinted an interaction between IGFBP2 and NSUN5, ALYREF, RRP8, and ALKBH3. Mechanically, IGFBP2-mediated RNA methylation deteriorated the immune microenvironment and thus increased the risk of DCM by enhancing CD8+ T lymphocyte, NK cell, monocyte, B+ lymphocyte infiltration and activating check-point, MHC-I, and T cell co-stimulation signaling pathways. In the DCM mouse model, echocardiography indicated a significant reduction in ejection fraction (EF) and fractional shortening (FS) and an increase in left ventricular internal dimensions at systole (LVIDs) and diastole (LVIDd). MASSON staining indicated an increased fibrosis in myocardium tissue. qPCR and immunofluorescence staining indicated a significant increase in mRNA and protein levels of IGFBP2. The present study indicated that IGFBP2-mediated RNA methylation remodeled the immune microenvironment and increased the risk of DCM. IGFBP2 may serve as potential therapeutic target for DCM.

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.

Identification of the Potential Molecular Mechanism of TGFBI Gene in Persistent Atrial Fibrillation

Background

Transforming growth factor beta-induced protein (TGFBI, encoded by TGFBI gene), is an extracellular matrix protein, widely expressed in variety of tissues. It binds to collagens type I, II, and IV and plays important roles in the interactions of cell with cell, collagen, and matrix. It has been reported to be associated with myocardial fibrosis, and the latter is an important pathophysiologyical basis of atrial fibrillation (AF). However, the mechanism of TGFBI in AF remains unclear. We aimed to detect the potential mechanism of TGFBI in AF via bioinformatics analysis.

Methods

The microarray dataset of GSE115574 was examined to detect the genes coexpressed with TGFBI from 14 left atrial tissue samples of AF patients. TGFBI coexpression genes were then screened using the R package. Using online analytical tools, we determined the Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis, Gene Ontology (GO) annotation, and protein-protein interaction (PPI) network of TGFBI and its coexpression genes. The modules and hub genes of the PPI-network were then identified. Another dataset, GSE79768 was examined to verify the hub genes. DrugBank was used to detect the potential target drugs.

Results

In GSE115574 dataset, a total of 1818 coexpression genes (769 positive and 1049 negative) were identified, enriched in 120 biological processes (BP), 38 cellular components (CC), 36 molecular functions (MF), and 39 KEGG pathways. A PPI-network with average 12.2-degree nodes was constructed. The genes clustered in the top module constructed from this network mainly play a role in PI3K-Akt signaling pathway, viral myocarditis, inflammatory bowel disease, and platelet activation. CXCL12, C3, FN1, COL1A2, ACTB, VCAM1, and MMP2 were identified and finally verified as the hub genes, mainly enriched in pathways like leukocyte transendothelial migration, PI3K-Akt signaling pathway, viral myocarditis, rheumatoid arthritis, and platelet activation. Pegcetacoplan, ocriplasmin, and carvedilol were the potential target drugs.

Conclusions

We used microdataset to identify the potential functions and mechanisms of the TGFBI and its coexpression genes in AF patients. Our findings suggest that CXCL12, C3, FN1, COL1A2, ACTB, VCAM1, and MMP2 may be the hub genes.

Silencing of TLR4 Inhibits Atrial Fibrosis and Susceptibility to Atrial Fibrillation via Downregulation of NLRP3-TGF-β in Spontaneously Hypertensive Rats

Introduction

This study was aimed at exploring whether silencing of TLR4 could inhibit atrial fibrosis and susceptibility to atrial fibrillation (AF) by regulating NLRP3-TGF-β in hypertensive rats.

Methods

Spontaneously hypertensive rats (SHRs) were transfected with either a virus containing TLR4-shRNA to downregulate TLR4 or an empty virus (vehicle) at the age of 14 weeks. Fibrosis of left atrium and susceptibility to AF were detected, and expression of NLRP3-TGF-β in left atrial tissue at 22 weeks of age was measured. Primary cardiac fibroblasts were transfected with TLR4-shRNA or scrambled vehicle and stimulated with angiotensin (Ang) II. Proliferation of cardiac fibroblasts and expression of NLRP3-TGF-β were detected.

Results

Silencing of TLR4 reduced left atrial fibrosis and susceptibility to AF in SHRs and downregulated expression of NLRP3, TGF-β, and collagen I. In vitro, TLR4 silencing reduced proliferation of cardiac fibroblasts induced by Ang II as well as expression of NLRP3, TGF-β, and collagen I.

Conclusion

Silencing of TLR4 can downregulate NLRP3-TGF-β to reduce atrial fibrosis and susceptibility to AF in SHRs.