Predicting the Key Genes Involved in Aortic Valve Calcification Through Integrated Bioinformatics Analysis

The Mediating Role of Plasma Inflammatory Proteins in Gut Microbiota-Driven Valvular Heart Disease: A Mendelian Randomization Study

This study investigates the causal relationships between gut microbiota (GM), plasma inflammatory proteins (PIPs), and valvular heart disease (VHD) using two-sample Mendelian Randomization (MR) analysis. We also assess whether PIPs mediate the link between GM and VHD. We conducted bidirectional MR analyses to explore causal associations between GM, PIPs, and VHD, and used multivariable MR to test the independence of associations. Genome-wide association study (GWAS) data on 196 GM taxa, 91 PIPs, and VHD were analyzed. MR methods including inverse-variance weighted (IVW), MR-Egger regression, and weighted median approaches were applied. Sensitivity analyses ensured robustness. Actinobacteria and Defluviitaleaceae were associated with lower VHD risk, while Oxalobacteraceae increased risk. At the genus level, Intestinibacter, Lachnospiraceae NC2004 group, Oscillospira, and Ruminococcaceae UCG004 were protective, whereas Oscillibacter increased risk. Among PIPs, Interleukin-10, Interleukin-17C, Leukemia inhibitory factor receptor (LIFR), and monocyte chemoattractant protein 2 were protective, while TNF-beta elevated risk. Multivariable MR confirmed the independent roles of TNF-beta, LIFR, and MCP-2. Actinobacteria's protective effect appeared partially mediated through increased LIFR expression, accounting for 14% of the effect. Our findings suggest that modulating gut microbiota, particularly enhancing Actinobacteria, may serve as a novel strategy for VHD prevention and treatment.

Causal association between triglycerides and cholesterol-lowering medication with non-rheumatic valve disease: A 2-sample Mendelian randomization study

Previous studies have found a possible causal relationship between triglycerides and lipid-lowering drugs and valvular disease. The aim of this study was to explore the potential causal relationship between triglycerides and lipid-lowering drugs and valvular disease using Mendelian randomization (MR) analysis. Data sets associated with triglycerides (441,016 participants and 12,321,875 single nucleotide polymorphisms [SNPs]) and cholesterol-lowering drugs (209,638 participants and 9851,867 SNPs) were retrieved from the Genome-Wide Association Study (GWAS) database. A total of 297 and 49 SNPs significantly associated with triglycerides and cholesterol-lowering drugs, respectively (P < 5 × 10-8), were identified. Similarly, data sets for non-rheumatic valve diseases (NVDs) (361,194 participants and 10,080,950 SNPs) were obtained from the GWAS database. Inverse variance weighting was used as the primary method for calculating the odds ratio (OR) and 95% confidence intervals (CI). The MR-Egger, weighted median, and weighted mode analyses were also used to test the robustness of the main results. The MR-Egger intercept test and the MR-PRESSO test were used to evaluate horizontal pleiotropy. Inverse variance weighted (IVW) results showed that both triglyceride and cholesterol-lowering medication were positively associated with NVDs (OR = 1.001, 95% CI 1.000-1.0012, P = 0.006; OR = 1.007, 95% CI 1.003-1.010; P = 0.002). This study suggests that both triglyceride and cholesterol-lowering medications are positively associated with NVDs, suggesting that lowering triglyceride levels or the use of cholesterol-lowering medications may reduce the incidence of NVDs. However, larger samples are required for further validation.

Identification of Diagnostic Genes of Aortic Stenosis That Progresses from Aortic Valve Sclerosis

Background

Aortic valve sclerosis (AVS) is a pathological state that can progress to aortic stenosis (AS), which is a high-mortality valvular disease. However, effective medical therapies are not available to prevent this progression. This study aimed to explore potential biomarkers of AVS-AS advancement.

Methods

A microarray dataset and an RNA-sequencing dataset were obtained from the Gene Expression Omnibus (GEO) database. Differentially expressed genes (DEGs) were screened from AS and AVS samples. Functional enrichment analysis, protein-protein interaction (PPI) network construction, and machine learning model construction were conducted to identify diagnostic genes. A receiver operating characteristic (ROC) curve was generated to evaluate diagnostic value. Immune cell infiltration was then used to analyze differences in immune cell proportion between tissues. Finally, immunohistochemistry was applied to further verify protein concentration of diagnostic factors.

Results

A total of 330 DEGs were identified, including 92 downregulated and 238 upregulated genes. The top 5% of DEGs (n = 17) were screened following construction of a PPI network. IL-7 and VCAM-1 were identified as the most significant candidate genes via least absolute shrinkage and selection operator (LASSO) regression. The diagnostic value of the model and each gene were above 0.75. Proportion of anti-inflammatory M2 macrophages was lower, but the fraction of pro-inflammatory gamma-delta T cells was elevated in AS samples. Finally, levels of IL-7 and VCAM-1 were validated to be higher in AS tissue than in AVS tissue using immunohistochemistry.

Conclusion

IL-7 and VCAM-1 were identified as biomarkers during the disease progression. This is the first study to analyze gene expression differences between AVS and AS and could open novel sights for future studies on alleviating or preventing the disease progression.

CircRNA/lncRNA-miRNA-mRNA network and gene landscape in calcific aortic valve disease

BACKGROUND

Calcific aortic valve disease (CAVD) is a common valve disease with an increasing incidence, but no effective drugs as of yet. With the development of sequencing technology, non-coding RNAs have been found to play roles in many diseases as well as CAVD, but no circRNA/lncRNA-miRNA-mRNA interaction axis has been established. Moreover, valve interstitial cells (VICs) and valvular endothelial cells (VECs) play important roles in CAVD, and CAVD differed between leaflet phenotypes and genders. This work aims to explore the mechanism of circRNA/lncRNA-miRNA-mRNA network in CAVD, and perform subgroup analysis on the important characteristics of CAVD, such as key cells, leaflet phenotypes and genders.

RESULTS

We identified 158 differentially expressed circRNAs (DEcircRNAs), 397 DElncRNAs, 45 DEmiRNAs and 167 DEmRNAs, and constructed a hsa-circ-0073813/hsa-circ-0027587-hsa-miR-525-5p-SPP1/HMOX1/CD28 network in CAVD after qRT-PCR verification. Additionally, 17 differentially expressed genes (DEGs) in VICs, 9 DEGs in VECs, 7 DEGs between different leaflet phenotypes and 24 DEGs between different genders were identified. Enrichment analysis suggested the potentially important pathways in inflammation and fibro-calcification during the pathogenesis of CAVD, and immune cell patterns in CAVD suggest that M0 macrophages and memory B cells memory were significantly increased, and many genes in immune cells were also differently expressed.

CONCLUSIONS

The circRNA/lncRNA-miRNA-mRNA interaction axis constructed in this work and the DEGs identified between different characteristics of CAVD provide a direction for a deeper understanding of CAVD and provide possible diagnostic markers and treatment targets for CAVD in the future.

Macrophage DCLK1 promotes obesity-induced cardiomyopathy via activating RIP2/TAK1 signaling pathway

Obesity increases the risk for cardiovascular diseases and induces cardiomyopathy. Chronic inflammation plays a significant role in obesity-induced cardiomyopathy and may provide new therapeutic targets for this disease. Doublecortin-like kinase 1 (DCLK1) is an important target for cancer therapy and the role of DCLK1 in obesity and cardiovascular diseases is unclear. Herein, we showed that DCLK1 was overexpressed in the cardiac tissue of obese mice and investigated the role of DCLK1 in obesity-induced cardiomyopathy. We generated DCLK1-deleted mice and showed that macrophage-specific DCLK1 knockout, rather than cardiomyocyte-specific DCLK1 knockout, prevented high-fat diet (HFD)-induced heart dysfunction, cardiac hypertrophy, and fibrosis. RNA sequencing analysis showed that DCLK1 deficiency exerted cardioprotective effects by suppressing RIP2/TAK1 activation and inflammatory responses in macrophages. Upon HFD/palmitate (PA) challenge, macrophage DCLK1 mediates RIP2/TAK1 phosphorylation and subsequent inflammatory cytokine release, which further promotes hypertrophy in cardiomyocytes and fibrogenesis in fibroblasts. Finally, a pharmacological inhibitor of DCLK1 significantly protects hearts in HFD-fed mice. Our study demonstrates a novel role and a pro-inflammatory mechanism of macrophage DCLK1 in obesity-induced cardiomyopathy and identifies DCLK1 as a new therapeutic target for the treatment of this disease. Upon HFD/PA challenge, DCLK1 induces RIP2/TAK1-mediated inflammatory response in macrophages, which subsequently promotes cardiac hypertrophy and fibrosis. Macrophage-specific DCLK1 deletion or pharmacological inhibition of DCLK1 protects hearts in HFD-fed mice.

Exploration and validation of the influence of angiogenesis-related factors in aortic valve calcification

Over the years, bioinformatics tools have been used to identify functional genes. In the present study, bioinformatics analyses were conducted to explore the underlying molecular mechanisms of angiogenic factors in calcific aortic valve disease (CAVD). The raw gene expression profiles were from datasets GSE153555, GSE83453, and GSE51472, and the angiogenesis-related gene set was from the Gene Set Enrichment Analysis database (GSEA). In this study, R was used to screen for differentially expressed genes (DEGs) and co-expressed genes. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genome (KEGG) Pathway enrichment analysis were performed on DEGs and validated in clinical samples. DEGs in CAVD were significantly enriched in numerous immune response pathways, inflammatory response pathways and angiogenesis-related pathways. Nine highly expressed angiogenesis-related genes were identified, of which secretogranin II (SCG2) was the most critical gene. MiRNA and transcription factors (TFs) networks were established centered on five DEGs, and zinc finger E-box binding homeobox 1 (ZEB1) was the most important transcription factor, verified by PCR, immunohistochemical staining and western blotting experiments. Overall, this study identified key genes and TFs that may be involved in the pathogenesis of CAVD and may have promising applications in the treatment of CAVD.

Development and analysis of a comprehensive diagnostic model for aortic valve calcification using machine learning methods and artificial neural networks

BACKGROUND

Although advanced surgical and interventional treatments are available for advanced aortic valve calcification (AVC) with severe clinical symptoms, early diagnosis, and intervention is critical in order to reduce calcification progression and improve patient prognosis. The aim of this study was to develop therapeutic targets for improving outcomes for patients with AVC.

MATERIALS AND METHODS

We used the public expression profiles of individuals with AVC (GSE12644 and GSE51472) to identify potential diagnostic markers. First, the R software was used to identify differentially expressed genes (DEGs) and perform functional enrichment analysis. Next, we combined bioinformatics techniques with machine learning methodologies such as random forest algorithms and support vector machines to screen for and identify diagnostic markers of AVC. Subsequently, artificial neural networks were employed to filter and model the diagnostic characteristics for AVC incidence. The diagnostic values were determined using the receiver operating characteristic (ROC) curves. Furthermore, CIBERSORT immune infiltration analysis was used to determine the expression of different immune cells in the AVC. Finally, the CMap database was used to predict candidate small compounds as prospective AVC therapeutics.

RESULTS

A total of 78 strong DEGs were identified. The leukocyte migration and pid integrin 1 pathways were highly enriched for AVC-specific DEGs. CXCL16, GPM6A, BEX2, S100A9, and SCARA5 genes were all regarded diagnostic markers for AVC. The model was effectively constructed using a molecular diagnostic score system with significant diagnostic value (AUC = 0.987) and verified using the independent dataset GSE83453 (AUC = 0.986). Immune cell infiltration research revealed that B cell naive, B cell memory, plasma cells, NK cell activated, monocytes, and macrophage M0 may be involved in the development of AVC. Additionally, all diagnostic characteristics may have varying degrees of correlation with immune cells. The most promising small molecule medicines for reversing AVC gene expression are Doxazosin and Terfenadine.

CONCLUSION

It was identified that CXCL16, GPM6A, BEX2, S100A9, and SCARA5 are potentially beneficial for diagnosing and treating AVC. A diagnostic model was constructed based on a molecular prognostic score system using machine learning. The aforementioned immune cell infiltration may have a significant influence on the development and incidence of AVC.

Identification of HIBCH as a Fatty Acid Metabolism-Related Biomarker in Aortic Valve Calcification Using Bioinformatics

Objective. To identify fatty acid metabolism-related biomarkers of aortic valve calcification (AVC) using bioinformatics and to research the role of immune cell infiltration for AVC. Methods. The AVC dataset was retrieved from the Gene Expression Omnibus database. R package is used for differential expression genes analysis and weighted gene coexpression analysis. The differentially coexpressed genes were identified by the Venn diagram, followed by Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses of differentially coexpressed genes. Functions closely related to AVC were identified by GO and KEGG enrichment analyses of differentially coexpressed genes. Genes related to fatty acid metabolism were retrieved from the Molecular Signatures Database (MSigDB) database. After removing duplicate genes, least absolute shrinkage and selection operator (LASSO) regression analysis, support vector machine recursive feature elimination (SVM-RFE), and random forest were applied to recognize biomarkers related to fatty acid metabolism in AVC. The CIBERSORT tool was used to analyze infiltration of immune cells in normal and AVC samples. Correlations between biomarkers and immune cells were calculated. Finally, HIBCH-related pathway was predicted by single-gene gene set enrichment analysis (GSEA). Results. 2416 differentially expressed genes and one coexpression module were identified. A total of 1473 differentially coexpressed genes were acquired. GO and KEGG enrichment analyses demonstrated that differentially coexpressed genes were closely related to fatty acid metabolism. LASSO regression analysis, SVM-REF, and random forest revealed that 3-hydroxyisobutyryl-CoA hydrolase (HIBCH) was a biomarker of fatty acid metabolism-related genes in AVC. Significant high levels of memory B cells were found in AVC than normal samples, while activated natural killer (NK) cells were significantly low in AVC than normal samples. A significantly positive relevance was observed between HIBCH and activated NK cells, regulatory T cells, monocytes, naïve B cells, activated dendritic cells, resting memory CD4 T cells, resting NK cells, and CD8 T cells. A significantly negative relevance was observed between HIBCH and activated memory CD4 T cells, memory B cells, neutrophils, gamma delta T cells, M0 macrophages, and plasma cells. The single-gene GSEA results suggest that HIBCH may work through the inhibition of multiple immune-related pathways. Conclusion. HIBCH is closely relevant to immune cell infiltration in AVC and could be applied as a diagnostic marker for AVC.

The landscape of immune cell infiltration in the glomerulus of diabetic nephropathy: evidence based on bioinformatics

Background

Increasing evidence suggests that immune cell infiltration contributes to the pathogenesis and progression of diabetic nephropathy (DN). We aim to unveil the immune infiltration pattern in the glomerulus of DN and provide potential targets for immunotherapy.

Methods

Infiltrating percentage of 22 types of immune cell in the glomerulus tissues were estimated by the CIBERSORT algorithm based on three transcriptome datasets mined from the GEO database. Differentially expressed genes (DEGs) were identified by the “limma” package. Then immune-related DEGs were identified by intersecting DEGs with immune-related genes (downloaded from Immport database). The protein–protein interactions of Immune-related DEGs were explored using the STRING database and visualized by Cytoscape. The enrichment analyses for KEGG pathways and GO terms were carried out by the gene set enrichment analysis (GSEA) method.

Results

11 types of immune cell were revealed to be significantly altered in the glomerulus tissues of DN (Up: B cells memory, T cells gamma delta, NK cells activated, Macrophages.M1, Macrophages M2, Dendritic cells resting, Mast cells resting; Down: B cells naive, NK cells resting, Mast cells activated, Neutrophils). Several pathways related to immune, autophagy and metabolic process were significantly activated. Moreover, 6 hub genes with a medium to strong correlation with renal function (eGFR) were identified (SERPINA3, LTF, C3, PTGDS, EGF and ALB).

Conclusion

In the glomerulus of DN, the immune infiltration pattern changed significantly. A complicated and tightly regulated network of immune cells exists in the pathological of DN. The hub genes identified here will facilitate the development of immunotherapy.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12882-022-02906-4.

Integrated bioinformatics analysis reveals marker genes and immune infiltration for pulmonary arterial hypertension

Pulmonary arterial hypertension (PAH) is a chronic cardiopulmonary syndrome with high pulmonary vascular load and eventually causing RV heart failure even death. However, the mechanism of pulmonary hypertension remains unclear. The purpose of this research is to detect the underlying key genes and potential mechanism of PAH using several bioinformatic methods. The microarrays GSE22356, GSE131793 and GSE168905 were acquired from the GEO. Subsequently, a host of bioinformatics techniques such as DAVID, STRING, R language and Cytoscape were utilized to investigate DEGs between PAH and healthy controls and conduct GO annotation, KEGG enrichment analysis and PPI network construction etc. Additionally, we predicted the transcription factors regulating DEGs through iRegulon plugin of Cytoscape and CIBERSORT was used to conduct immune infiltration analysis. One thousand two hundred and seventy-seven DEGs (403 up-regulated and 874 down-regulated) were identified from peripheral blood samples of 32 PAH patients and 29 controls, among which SLC4A1, AHSP, ALAS2, CA1, HBD, SNCA, HBM, SELENBP1, SERPINE1 and ITGA2B were detected as hub genes. The functional enrichment changes of DEGs were mainly enriched in protein binding, extracellular exosome, extracellular space, extracellular region and integral component of plasma membrane. The hub genes are chiefly enriched at extracellular exosome, hemoglobin complex, blood microparticle, oxygen transporter activity. Among TF-DEGs network, 42 target DEGs and 6 TFs were predicted with an NES > 4 (TEAD4, TGIF2LY, GATA5, GATA1, GATA2, FOS). Immune infiltration analysis showed that monocytes occupied the largest proportion of immune cells. The trend analysis results of infiltration immune cells illustrated that PAH patients had higher infiltration of NK cell activation, monocyte, T cell CD4 memory activation, and mast cell than healthy controls and lower infiltration of T cell CD4 naive. We detected SLC4A1, AHSP, ALAS2, CA1, HBD, SNCA, HBM, SELENBP1, SERPINE1 and ITGA2B as the most significant markers of PAH. The PAH patients had higher infiltration of NK cell activation, monocyte, T cell CD4 memory activation, and mast cell than healthy controls and lower infiltration of T cell CD4 naive. These identified genes and these immune cells probably have precise regulatory relationships in the development of PAH.

Bioinformatics and Machine Learning Methods to Identify FN1 as a Novel Biomarker of Aortic Valve Calcification

Aim

The purpose of this study was to identify potential diagnostic markers for aortic valve calcification (AVC) and to investigate the function of immune cell infiltration in this disease.

Methods

The AVC data sets were obtained from the Gene Expression Omnibus. The identification of differentially expressed genes (DEGs) and the performance of functional correlation analysis were carried out using the R software. To explore hub genes related to AVC, a protein–protein interaction network was created. Diagnostic markers for AVC were then screened and verified using the least absolute shrinkage and selection operator, logistic regression, support vector machine-recursive feature elimination algorithms, and hub genes. The infiltration of immune cells into AVC tissues was evaluated using CIBERSORT, and the correlation between diagnostic markers and infiltrating immune cells was analyzed. Finally, the Connectivity Map database was used to forecast the candidate small molecule drugs that might be used as prospective medications to treat AVC.

Results

A total of 337 DEGs were screened. The DEGs that were discovered were mostly related with atherosclerosis and arteriosclerotic cardiovascular disease, according to the analyses. Gene sets involved in the chemokine signaling pathway and cytokine–cytokine receptor interaction were differently active in AVC compared with control. As the diagnostic marker for AVC, fibronectin 1 (FN1) (area the curve = 0.958) was discovered. Immune cell infiltration analysis revealed that the AVC process may be mediated by naïve B cells, memory B cells, plasma cells, activated natural killer cells, monocytes, and macrophages M0. Additionally, FN1 expression was associated with memory B cells, M0 macrophages, activated mast cells, resting mast cells, monocytes, and activated natural killer cells. AVC may be reversed with the use of yohimbic acid, the most promising small molecule discovered so far.

Conclusion

FN1 can be used as a diagnostic marker for AVC. It has been shown that immune cell infiltration is important in the onset and progression of AVC, which may benefit in the improvement of AVC diagnosis and treatment.