Identification of key immune-related genes and immune infiltration in atrial fibrillation with valvular heart disease based on bioinformatics analysis

Genetically predicted systemic inflammation and the risk of atrial fibrillation: A bidirectional two-sample Mendelian randomization study

Background

Systemic inflammation has been proposed to be associated with the incidence of atrial fibrillation (AF), but whether it is a cause or a consequence of AF remains uncertain. We sought to explore the causal associations between systemic inflammation and AF using bidirectional Mendelian randomization (MR) analysis.

Methods

Independent genetic variants strongly associated with AF were selected as instrumental variables from the largest genome-wide association study (GWAS) with up to 1,030,836 individuals. Regarding inflammation traits, genetic associations with 41 inflammatory cytokines and 5 inflammatory biomarkers were obtained from their corresponding GWASs databases. Effect estimates were primarily evaluated using the inverse-variance weighted (IVW) method, supplemented by sensitivity analyses using MR-Egger, weighted median, and MR-PRESSO methods.

Results

In our initial MR analyses, we observed suggestive associations of genetically predicted interleukin-17 (IL-17), interleukin-2 receptor subunit alpha (IL-2rα), and procalcitonin (PCT) with AF. One standard deviation (SD) increase in IL-17, IL-2rα, and PCT caused an increase in AF risk by 6.3 % (OR 1.063, 95 %CI 1.011---1.118, p = 0.018), 4.9 % (OR 1.049, 95 %CI 1.007---1.094, p = 0.023) and 3.4 % (OR 1.034, 95 %CI 1.005---1.064, p = 0.022), respectively. Furthermore, our reverse MR analyses indicated that genetically predicted AF contributed to a suggestive increase in the levels of macrophage inflammatory protein-1β (MIP1β) (β 0.055, 95 %CI 0.006 to 0.103, p = 0.028), while a decrease in the levels of fibrinogen (Fbg) (β -0.091, 95 %CI -0.140 to -0.041, p < 0.001), which remained significant after multiple test correction.

Conclusions

Our MR study identified several inflammatory biomarkers with suggestive causal associations regarding the upstream and downstream regulation of AF occurrence, offering new insights for therapeutic exploitation of AF. Further research is required to validate the underlying link between systemic inflammation and AF in larger cohorts.

CDK5R1, GSE1, HSPG2 and WDFY3 as indirect epigenetic-sensitive genes in atrial fibrillation

BACKGROUND

Although mounting evidence supports that aberrant DNA methylation occurs in the hearts of patients with atrial fibrillation (AF), noninvasive epigenetic characterization of AF has not yet been defined.

METHODS

We investigated DNA methylome changes in peripheral blood CD4+ T cells isolated from 10 patients with AF relative to 11 healthy subjects (HS) who were enrolled in the DIANA clinical trial (NCT04371809) via reduced-representation bisulfite sequencing (RRBS).

RESULTS

An atrial-specific PPI network revealed 18 hub differentially methylated genes (DMGs), wherein ROC curve analysis revealed reasonable diagnostic performance of DNA methylation levels found within CDK5R1 (AUC = 0.76; p = 0.049), HSPG2 (AUC = 0.77; p = 0.038), WDFY3 (AUC = 0.78; p = 0.029), USP49 (AUC = 0.76; p = 0.049), GSE1 (AUC = 0.76; p = 0.049), AIFM1 (AUC = 0.76; p = 0.041), CDK5RAP2 (AUC = 0.81; p = 0.017), COL4A1 (AUC = 0.86; p < 0.001), SEPT8 (AUC = 0.90; p < 0.001), PFDN1 (AUC = 0.90; p < 0.01) and ACOT7 (AUC = 0.78; p = 0.032). Transcriptional profiling of the hub DMGs provided a significant overexpression of PSDM6 (p = 0.004), TFRC (p = 0.01), CDK5R1 (p < 0.001), HSPG2 (p = 0.01), WDFY3 (p < 0.001), USP49 (p = 0.004) and GSE1 (p = 0.021) in AF patients vs HS.

CONCLUSIONS

CDK5R1, GSE1, HSPG2 and WDFY3 resulted the best discriminatory genes both at methylation and gene expression level. Our results provide several candidate diagnostic biomarkers with the potential to advance precision medicine in AF.

Identification of the Key Genes of Immune Infiltration in Dilated Cardiomyopathy

Dilated cardiomyopathy (DCM) is a common cause of heart failure. In this study, we screened the immune infiltration-related genes associated with DCM to explore the potential molecular mechanisms and provide a basis for the early diagnosis and development of new immunotherapeutic targets. A dataset related to DCM was downloaded from the Gene Expression Omnibus (GEO) database. R software was applied to the genetic differential analysis of patients with DCM and healthy individuals, and the obtained differential expressed genes (DEGs) were screened for differentially expressed immune-related genes (DEIRGs) after comparison with the immune microsatellite database. Gene functional analysis established a protein interaction network (PPI). The immune infiltration in patients with DCM versus normal controls was assessed using the CIBERSORT algorithm, the hub genes were screened using the MOCDE app, and the hubs were validated in multiple datasets. A total of 246 DEGs were screened (adj. P < 0.05 and |logFC| > 0.3), and a total of 170 DEIRGs were compared. Gene Ontology analysis showed significant (adj. P < 0.05) Biological Process entries of 591, Cellular Component of 10, and Molecular Function of 39; Kyoto Encyclopedia of Genes and Genomes showed 20 significant entries, mainly focused on cytokines involved in immune-related response, etc. A protein interaction network comprising 28 hub DEGs was constructed in combination with the PPI network interactions. DEIRG was mainly distributed in the T-cell receptor pathway by immune infiltration detection analysis, and significant changes in central memory T-cells were found by analyzing T-cell-related subpathways, where INSR, HLA-B, IFITM1, and HBEGF were significantly differentially expressed. We selected 632 hospitalized patients for validation and found that INSR and HLA-B expression were associated with DCM development by Nomogram. The expression of HLA-B in peripheral blood T-cells was higher in DCM patients than in the normal group, as verified by qRT-PCR. However, the detailed mechanism needs to be further explored.

Exploring immune related gene signatures and mechanisms linking non alcoholic fatty liver disease to atrial fibrillation through transcriptome data analysis

Atrial fibrillation (AF) and related cardiovascular complications pose a heavy burden to patients and society. Mounting evidence suggests a close association between nonalcoholic fatty liver disease (NAFLD) and AF. NAFLD and AF transcriptomic datasets were obtained from GEO database and analyzed using several bioinformatics approaches. We established a NAFLD-AF associated gene diagnostic signature (NAGDS) using protein-protein interaction analysis and machine learning, which was further quantified through RT-qPCR. Potential miRNA targeting NAGDS were predicted. Gene modules highly correlated with NAFLD liver pathology or AF occurrence were identified by WGCNA. Enrichment analysis of the overlapped genes from key module revealed that T-cell activation plays essential roles in NAFLD and AF, which was further confirmed by immune infiltration. Furthermore, an integrated SVM-RFE and LASSO algorithm was used to identify CCL4, CD48, ITGB2, and RNASE6 as NAGDS, all of which were found to be upregulated in NAFLD and AF mouse tissues. Patients with higher NAGDS showed augmented T cell and macrophage immunity, more advanced liver pathological characteristics, and prolonged AF duration. Additionally, hsa-miR-26a-5p played a central role in the regulation of NAGDS. Our findings highlight the central role of T-cell immune response in linking NAFLD to AF, and established an accurate NAGDS diagnostic model, which could serve as potential targets for immunoregulatory therapy.

Analyses of m6A regulatory genes and subtype classification in atrial fibrillation

Objective

To explore the role of m6A regulatory genes in atrial fibrillation (AF), we classified atrial fibrillation patients into subtypes by two genotyping methods associated with m6A regulatory genes and explored their clinical significance.

Methods

We downloaded datasets from the Gene Expression Omnibus (GEO) database. The m6A regulatory gene expression levels were extracted. We constructed and compared random forest (RF) and support vector machine (SVM) models. Feature genes were selected to develop a nomogram model with the superior model. We identified m6A subtypes based on significantly differentially expressed m6A regulatory genes and identified m6A gene subtypes based on m6A-related differentially expressed genes (DEGs). Comprehensive evaluation of the two m6A modification patterns was performed.

Results

The data of 107 samples from three datasets, GSE115574, GSE14975 and GSE41177, were acquired from the GEO database for training models, comprising 65 AF samples and 42 sinus rhythm (SR) samples. The data of 26 samples from dataset GSE79768 comprising 14 AF samples and 12 SR samples were acquired from the GEO database for external validation. The expression levels of 23 regulatory genes of m6A were extracted. There were correlations among the m6A readers, erasers, and writers. Five feature m6A regulatory genes, ZC3H13, YTHDF1, HNRNPA2B1, IGFBP2, and IGFBP3, were determined (p < 0.05) to establish a nomogram model that can predict the incidence of atrial fibrillation with the RF model. We identified two m6A subtypes based on the five significant m6A regulatory genes (p < 0.05). Cluster B had a lower immune infiltration of immature dendritic cells than cluster A (p < 0.05). On the basis of six m6A-related DEGs between m6A subtypes (p < 0.05), two m6A gene subtypes were identified. Both cluster A and gene cluster A scored higher than the other clusters in terms of m6A score computed by principal component analysis (PCA) algorithms (p < 0.05). The m6A subtypes and m6A gene subtypes were highly consistent.

Conclusion

The m6A regulatory genes play non-negligible roles in atrial fibrillation. A nomogram model developed by five feature m6A regulatory genes could be used to predict the incidence of atrial fibrillation. Two m6A modification patterns were identified and evaluated comprehensively, which may provide insights into the classification of atrial fibrillation patients and guide treatment.

Identification of m6A regulator-mediated RNA methylation modification patterns and key immune-related genes involved in atrial fibrillation

The role of m6A in the regulation of the immune microenvironment in atrial fibrillation (AF) remains unclear. This study systematically evaluated the RNA modification patterns mediated by differential m6A regulators in 62 AF samples, identified the pattern of immune cell infiltration in AF and identified several immune-related genes associated with AF. A total of six key differential m6A regulators between healthy subjects and AF patients were identified by the random forest classifier. Three distinct RNA modification patterns (m6A cluster-A, -B and -C) among AF samples were identified based on the expression of 6 key m6A regulators. Differential infiltrating immune cells and HALLMARKS signaling pathways between normal and AF samples as well as among samples with three distinct m6A modification patterns were identified. A total of 16 overlapping key genes were identified by weighted gene coexpression network analysis (WGCNA) combined with two machine learning methods. The expression levels of the NCF2 and HCST genes were different between controls and AF patient samples as well as among samples with the distinct m6A modification patterns. RT-qPCR also proved that the expression of NCF2 and HCST was significantly increased in AF patients compared with control participants. These results suggested that m6A modification plays a key role in the complexity and diversity of the immune microenvironment of AF. Immunotyping of patients with AF will help to develop more accurate immunotherapy strategies for those with a significant immune response. The NCF2 and HCST genes may be novel biomarkers for the accurate diagnosis and immunotherapy of AF.

Inflammatory proteomics profiling for prediction of incident atrial fibrillation

OBJECTIVE

Atrial fibrillation (AF) has emerged as a common condition in older adults. Cardiovascular risk factors only explain about 50% of AF cases. Inflammatory biomarkers may help close this gap as inflammation can alter atrial electrophysiology and structure. This study aimed to determine a cytokine biomarker profile for this condition in the community using a proteomics approach.

METHODS

This study uses cytokine proteomics in participants of the Finnish population-based FINRISK cohort studies 1997/2002. Risk models for 46 cytokines were developed to predict incident AF using Cox regressions. Furthermore, the association of participants' C reactive protein (CRP) and N-terminal pro B-type natriuretic peptide (NT-proBNP) concentrations with incident AF was examined.

RESULTS

In 10 744 participants (mean age of 50.9 years, 51.3% women), 1246 cases of incident AF were observed (40.5% women). The main analyses, adjusted for participants' sex and age, suggested that higher concentrations of macrophage inflammatory protein-1β (HR=1.11; 95% CI 1.04, 1.17), hepatocyte growth factor (HR=1.12; 95% CI 1.05, 1.19), CRP (HR=1.17; 95% CI 1.10, 1.24) and NT-proBNP (HR=1.58; 95% CI 1.45, 1.71) were associated with increased risk of incident AF. In further clinical variable-adjusted models, only NT-proBNP remained statistically significant.

CONCLUSION

Our study confirmed NT-proBNP as a strong predictor for AF. Observed associations of circulating inflammatory cytokines were primarily explained by clinical risk factors and did not improve risk prediction. The potential mechanistic role of inflammatory cytokines measured in a proteomics approach remains to be further elucidated.

The Potential Importance of CXCL1 in the Physiological State and in Noncancer Diseases of the Cardiovascular System, Respiratory System and Skin

In this paper, we present a literature review of the role of CXC motif chemokine ligand 1 (CXCL1) in physiology, and in selected major non-cancer diseases of the cardiovascular system, respiratory system and skin. CXCL1, a cytokine belonging to the CXC sub-family of chemokines with CXC motif chemokine receptor 2 (CXCR2) as its main receptor, causes the migration and infiltration of neutrophils to the sites of high expression. This implicates CXCL1 in many adverse conditions associated with inflammation and the accumulation of neutrophils. The aim of this study was to describe the significance of CXCL1 in selected diseases of the cardiovascular system (atherosclerosis, atrial fibrillation, chronic ischemic heart disease, hypertension, sepsis including sepsis-associated encephalopathy and sepsis-associated acute kidney injury), the respiratory system (asthma, chronic obstructive pulmonary disease (COPD), chronic rhinosinusitis, coronavirus disease 2019 (COVID-19), influenza, lung transplantation and ischemic-reperfusion injury and tuberculosis) and the skin (wound healing, psoriasis, sunburn and xeroderma pigmentosum). Additionally, the significance of CXCL1 is described in vascular physiology, such as the effects of CXCL1 on angiogenesis and arteriogenesis.

Immune remodeling and atrial fibrillation

Atrial fibrillation (AF) is a highly prevalent arrhythmia that causes high morbidity and mortality. However, the underlying mechanism of AF has not been fully elucidated. Recent research has suggested that, during AF, the immune system changes considerably and interacts with the environment and cells involved in the initiation and maintenance of AF. This may provide a new direction for research and therapeutic strategies for AF. In this review, we elaborate the concept of immune remodeling based on available data in AF. Then, we highlight the complex relationships between immune remodeling and atrial electrical, structural and neural remodeling while also pointing out some research gaps in these field. Finally, we discuss several potential immunomodulatory treatments for AF. Although the heterogeneity of existing evidence makes it ambiguous to extrapolate immunomodulatory treatments for AF into the clinical practice, immune remodeling is still an evolving concept in AF pathophysiology and further studies within this field are likely to provide effective therapies for AF.

Identification and Verification of Feature Biomarkers Associated With Immune Cells in Dilated Cardiomyopathy by Bioinformatics Analysis

Objective: To explore immune-related feature genes in patients with dilated cardiomyopathy (DCM).

Methods: Expression profiles from three datasets (GSE1145, GSE21610 and GSE21819) of human cardiac tissues of DCM and healthy controls were downloaded from the GEO database. After data preprocessing, differentially expressed genes (DEGs) were identified by the ‘limma’ package in R software. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses were then performed to identify biological functions of the DEGs. The compositional patterns of stromal and immune cells were estimated using xCell. Hub genes and functional modules were identified based on protein-protein interaction (PPI) network analysis by STRING webtool and Cytoscape application. Correlation analysis was performed between immune cell subtypes and hub genes. Hub genes with |correlation coefficient| > 0.5 and p value <0.05 were selected as feature biomarkers. A logistic regression model was constructed based on the selected biomarkers and validated in datasets GSE5406 and GSE57338.

Results: A total of 1,005 DEGs were identified. Functional enrichment analyses indicated that extracellular matrix remodeling and immune and inflammation disorder played important roles in the pathogenesis of DCM. Immune cells, including CD8⁺ T-cells, macrophages M1 and Th1 cells, were proved to be significantly changed in DCM patients by immune cell infiltration analysis. In the PPI network analysis, STAT3, IL6, CCL2, PIK3R1, ESR1, CCL5, IL17A, TLR2, BUB1B and MYC were identified as hub genes, among which CCL2, CCL5 and TLR2 were further screened as feature biomarkers by using hub genes and immune cells correlation analysis. A diagnosis model was successfully constructed by using the three biomarkers with area under the curve (AUC) scores 0.981, 0.867 and 0.946 in merged dataset, GSE5406 and GSE57338, respectively.

Conclusion: The present study identified three immune-related genes as diagnostic biomarkers for DCM, providing a novel perspective of immune and inflammatory response for the exploration of DCM molecular mechanisms.

Uncovering potential novel biomarkers and immune infiltration characteristics in persistent atrial fibrillation using integrated bioinformatics analysis

Atrial fibrillation (AF) is the most common cardiac arrhythmia. This study aimed to identify potential novel biomarkers for persistent AF (pAF) using integrated analyses and explore the immune cell infiltration in this pathological process. Three pAF datasets (GSE31821, GSE41177, and GSE79768) from the Gene Expression Omnibus (GEO) database were integrated with the elimination of batch effects. 264 differentially expressed genes (DEGs) were identified using Linear models for microarray data (LIMMA), 12 modules were screened out by weighted gene co-expression network analysis (WGCNA) in pAF compared with normal controls. Subsequently, common genes (CGs) were identified as the intersection of DEGs and genes in the most significant module. Functional enrichment analysis showed that CGs were mainly enriched in the "Calcineurin-NFAT (nuclear factor of activated T-cells)" signaling pathway, particularly regulator of calcineurin 1 (RCAN1), and protein phosphatase 3 regulatory subunit B, alpha (PPP3R1). Ulteriorly, the microRNA-transcription factor-mRNA network revealed that microRNA-34a-5p could target both RCAN1 and PPP3R1 in the pAF pathogenesis. Finally, immune infiltration analysis by CIBERSORT, a versatile computational method, displayed a higher level of monocytes, dendritic cells and neutrophils, as well as a lower level of CD8+ T cells and T cells regulatory (Tregs) in pAF compared with the control group. In conclusion, our present study revealed several novel pAF-associated genes, miRNAs, and pathways, including microRNA-34a-5p, which might target RCAN1 and PPP3R1 to regulate pAF through the calcineurin-NFAT signaling pathway. In addition, there was a difference in immune infiltration between patients with pAF and normal groups and immune cells might interact with specific genes in pAF.