Differential left-to-right atria gene expression ratio in human sinus rhythm and atrial fibrillation: Implications for arrhythmogenesis and thrombogenesis

The transcriptional landscape of atrial fibrillation: A systematic review and meta-analysis

BACKGROUND

Despite advances in understanding atrial fibrillation (AF) pathophysiology, there is limited agreement on the key genes driving its pathophysiology. To understand the genome-wide transcriptomic landscape, we performed a meta-analysis from studies reporting gene expression patterns in atrial heart tissue from patients with AF and controls in sinus rhythm (SR).

METHODS

Bibliographic databases and data repositories were systematically searched for studies reporting gene expression patterns in atrial heart auricle tissue from patients with AF and controls in sinus rhythm. We calculated the pooled differences in individual gene expression from fourteen studies comprising 534 samples (353 AF and 181 SR) to create a consensus signature (CS), from which we identified differentially regulated pathways, estimated transcription factor activity, and evaluated its performance in classifying validation samples as AF or SR.

RESULTS

Despite heterogeneity in the top differentially expressed genes across studies, the AF-CS in both chambers were robust, showing a better performance in classifying AF status than individual study signatures. Functional analysis revealed commonality in the dysregulated cellular processes between chambers, including extracellular matrix remodeling (highlighting epithelial mesenchymal transition, actin filament organization, and actin binding hallmark pathways), cardiac conduction (including cardiac muscle cell action potential, gated channel activity, and cation channel activity pathways), metabolic derangements (highlighting oxidative phosphorylation and asparagine n linked glycosylation), and innate immune system activity (mainly neutrophil degranulation, and TNFα signaling pathways). Finally, the AF-CS showed a good performance differentiating AF from controls in three validation datasets (two from peripheral blood and one from left ventricle samples).

CONCLUSIONS

Despite variability in individual studies, this meta-analysis elucidated conserved molecular pathways involved in AF pathophysiology across its phenotypes and the potential of a transcriptomic signature in identifying AF from peripheral blood samples. Our work highlights the value of integrating published transcriptomics data in AF and the need for better data deposition practices.

Long Non-coding RNA Involved in the Pathophysiology of Atrial Fibrillation

BACKGROUND

Atrial fibrillation (AF) is a prevalent and chronic cardiovascular disorder associated with various pathophysiological alterations, including atrial electrical and structural remodeling, disrupted calcium handling, autonomic nervous system dysfunction, aberrant energy metabolism, and immune dysregulation. Emerging evidence suggests that long non-coding RNAs (lncRNAs) play a significant role in the pathogenesis of AF.

OBJECTIVE

This discussion aims to elucidate the involvement of AF-related lncRNAs, with a specific focus on their role as miRNA sponges that modulate crucial signaling pathways, contributing to the progression of AF. We also address current limitations in AF-related lncRNA research and explore potential future directions in this field. Additionally, we summarize feasible strategies and promising delivery systems for targeting lncRNAs in AF therapy.

CONCLUSION

In conclusion, targeting AF-related lncRNAs holds substantial promise for future investigations and represents a potential therapeutic avenue for managing AF.

Apolipoprotein and sphingolipid measurements: Can be used in the clinical practice of atrial fibrillation diagnosing and evaluating the cryoablation effectiveness?

Atrial fibrillation (AF) has become the most common arrhythmia of clinical importance. A well-established and recommended therapeutic option for AF is the balloon-based cryoablation (CBA) method. There are still no sensitive biomarkers for AF prediction and cryoablation effectiveness assessment, therefore in our prospective study, we examined the plasma content of apolipoproteins (Apo) and sphingolipids, as well as the distribution of selected sphingolipids among lipoprotein fractions. The study included 33 patients with AF on admission and 24 h after cryoablation therapy, while 20 healthy volunteers were recruited to the control group. Plasma Apo concentrations were determined using a multiplex assay kit measuring fluorescence signal, whereas the high-performance liquid chromatography (HPLC) method was applied to assess the total plasma sphingolipid levels as well as their content in isolated lipoprotein fractions. Our results showed that cryoballoon ablation in AF patients markedly reduced the level of almost all Apo compared to the individuals from the control and Pre-CBA groups (Apo-A1: -25.9% and -20.0%, Apo-A2: -19.9% and -17.3%, Apo-B: -26.8% and -14.4%, Apo-C1: -20.3% and -13.4%, Apo-D: -15.9% and -22.2%, Apo-E: -18.3% and -14.3%, and Apo-J: -36.4% and -21.5%, p < 0.05, respectively). Importantly, the area under the curve of Apo-J (AUC 0.81; 95% CI, 0.71-0.92) indicates that it might be a useful biomarker of cryotherapy success in AF patients. Moreover, we also observed a pronounced increase in sphinganine (Sa; +33.5%), sphingosine (So; +24.6%), sphinganine-1-phosphate (Sa1P; +34.3%), and sphingosine-1-phosphate (So1P; +22.3%) concentrations in the Pre-CBA group in comparison with controls. This is the first study that evaluates such a broad panel of Apo and sphingolipids in patients with AF undergoing the CBA procedure, however, to confirm whether any of these parameters could be a clinically useful biomarker for predicting AF or assessing the effectiveness of treatment, further research will be necessary due to limitations of the study.

Sex Differences in Atrial Fibrillation: Evidence from Circulating Metabolites

Background: Significant sex differences exist in atrial fibrillation (AF). Better understanding of its underlying mechanism would help AF management. This study aimed to investigate the contribution of circulating metabolites to sex differences in AF and the association between them. Methods: A total of 108 patients with AF were enrolled. Untargeted metabolomics were performed in plasma samples of male and female patients. Correlation analysis with clinical characteristics and Mendelian randomization were used to identify sex-specific metabolites associated with AF, which was further validated in additional patients. Transcriptomics data of the left atrium were used to investigate the molecular alteration of the left atrium responding to identified sex-specific circulating metabolites. The effect of selected sex-specific metabolites on cardiomyocytes was further investigated. Results: A total of 60 annotated metabolites were found with different levels between male and female patients. Among these sex-specific metabolites, three metabolites, 7-Methylguanosine, succinic acid, and N-Undecylbenzenesulfonic acid, were positively related to the left atrial remodeling. Additionally, succinic acid was significantly associated with increased risk of AF (OR = 1.26; 95% CI: 1.13 to 1.40; p < 0.001). And, SUCLA2, the gene of succinic acid metabolism, was significantly increased in the left atrium of male patients (fold change = 1.53; p = 0.008). Treatment with succinic acid led to cardiomyocyte hypertrophy and mitochondrial dysfunction. Conclusions: This study highlights sex differences in circulating metabolites in patients with AF and identifies the associations between sex-specific metabolites and AF. succinic acid, which is much higher in male patients, contributes to the process of AF.

Atrial fibrillation risk model based on LASSO and SVM algorithms and immune infiltration of key mitochondrial energy metabolism genes

Atrial fibrillation (AF) is a predominant cardiac arrhythmia with unclear etiology. This study used bioinformatics and machine learning to explore the relationship between mitochondrial energy metabolism-related genes (MEMRGs) and immune infiltration in AF. The datasets GSE31821, GSE41177, and GSE79768 were retrieved from the Gene Expression Omnibus (GEO) database, and differential expression analysis identified 59 mitochondrial energy metabolism-related differentially expressed genes (MEMRDEGs) associated with AF. Key MEMRDEGs were selected using the least absolute shrinkage and selection operator (LASSO) and support vector machine (SVM) methods, and a diagnostic model was developed. Immune infiltration was assessed using single-sample gene set enrichment analysis (ssGSEA) and the microenvironment cell population counter (MCPcounter). The diagnostic model, based on the key genes ACAT1, ALDH1L2, HTT, OGDH, and SLC25A3, achieved an area under the curve (AUC) of 0.903. Significant differences in immune cell composition were observed between the AF and control groups. ALDH1L2 was positively correlated with most immune cells, while SLC25A3 showed a negatively correlated with the monocytic lineage. The findings indicate that MEMRGs interact with immune responses in AF, offering insights into the potential molecular mechanisms and therapeutic targets for AF.

Transcriptomics, Proteomics and Bioinformatics in Atrial Fibrillation: A Descriptive Review

Atrial fibrillation (AF) is the most frequent cardiac arrhythmia, with an estimated five million cases globally. This condition increases the likelihood of developing cardiovascular complications such as thromboembolic events, with a fivefold increase in risk of both heart failure and stroke. Contemporary challenges include a better understanding AF pathophysiology and optimizing therapeutical options due to the current lack of efficacy and adverse effects of antiarrhythmic drug therapy. Hence, the identification of novel biomarkers in biological samples would greatly impact the diagnostic and therapeutic opportunities offered to AF patients. Long noncoding RNAs, micro RNAs, circular RNAs, and genes involved in heart cell differentiation are particularly relevant to understanding gene regulatory effects on AF pathophysiology. Proteomic remodeling may also play an important role in the structural, electrical, ion channel, and interactome dysfunctions associated with AF pathogenesis. Different devices for processing RNA and proteomic samples vary from RNA sequencing and microarray to a wide range of mass spectrometry techniques such as Orbitrap, Quadrupole, LC-MS, and hybrid systems. Since AF atrial tissue samples require a more invasive approach to be retrieved and analyzed, blood plasma biomarkers were also considered. A range of different sample preprocessing techniques and bioinformatic methods across studies were examined. The objective of this descriptive review is to examine the most recent developments of transcriptomics, proteomics, and bioinformatics in atrial fibrillation.

Identification of senescence-related genes for potential therapeutic biomarkers of atrial fibrillation by bioinformatics, human histological validation, and molecular docking

Background

Cellular senescence is pivotal in the occurrence and progression of atrial fibrillation (AF). This study aimed to identify senescence-related genes that could be potential therapeutic biomarkers for AF.

Methods

AF-related differentially expressed genes (DEGs) were identified using the Gene Expression Omnibus dataset. Weighted gene co-expression network analysis (WGCNA) was used to analyze important modules and potential hub genes. Integrating senescence-related genes, potential biomarkers were identified. Their differential expression levels were then validated in human atrial tissue, HL-1 cells, and Angiotensin II-infused mice. Finally, molecular docking analysis was conducted to predict potential interactions between potential biomarkers and the senolytic drug Navitoclax.

Results

We identified seven genes common to AF-related DEGs and senescence-related genes. Three significant modules were selected from WGCNA analysis. Taken together, three senescence-related genes (ETS1, SP1, and WT1) were found to be significantly associated with AF. Protein-protein interaction network analysis revealed biological connections among the predicted target genes of ETS1, SP1, and WT1. Notably, ETS1, SP1, and WT1 exhibited significant differential expression in clinical samples as well as in vitro and in vivo models. Molecular docking revealed favorable binding affinity between senolytic Navitoclax and these potential biomarkers.

Conclusions

This study highlights ETS1, SP1, and WT1 as crucial senescence-related genes associated with AF, offering potential therapeutic targets, with supportive evidence of binding affinity with senolytic Navitoclax. These findings provide novel insights into AF pathogenesis from a senescence perspective.

Exploring the shared biomarkers between cardioembolic stroke and atrial fibrillation by WGCNA and machine learning

Background

Cardioembolic Stroke (CS) and Atrial Fibrillation (AF) are prevalent diseases that significantly impact the quality of life and impose considerable financial burdens on society. Despite increasing evidence of a significant association between the two diseases, their complex interactions remain inadequately understood. We conducted bioinformatics analysis and employed machine learning techniques to investigate potential shared biomarkers between CS and AF.

Methods

We retrieved the CS and AF datasets from the Gene Expression Omnibus (GEO) database and applied Weighted Gene Co-Expression Network Analysis (WGCNA) to develop co-expression networks aimed at identifying pivotal modules. Next, we performed Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis on the shared genes within the modules related to CS and AF. The STRING database was used to build a protein-protein interaction (PPI) network, facilitating the discovery of hub genes within the network. Finally, several common used machine learning approaches were applied to construct the clinical predictive model of CS and AF. ROC curve analysis to evaluate the diagnostic value of the identified biomarkers for AF and CS.

Results

Functional enrichment analysis indicated that pathways intrinsic to the immune response may be significantly involved in CS and AF. PPI network analysis identified a potential association of 4 key genes with both CS and AF, specifically PIK3R1, ITGAM, FOS, and TLR4.

Conclusion

In our study, we utilized WGCNA, PPI network analysis, and machine learning to identify four hub genes significantly associated with CS and AF. Functional annotation outcomes revealed that inherent pathways related to the immune response connected to the recognized genes might could pave the way for further research on the etiological mechanisms and therapeutic targets for CS and AF.

Identification of key differentially expressed immune related genes in patients with persistent atrial fibrillation: an integrated bioinformation analysis

OBJECTIVE

We aimed to investigate key differentially expressed immune related genes in persistent atrial fibrillation.

METHODS

Gene expression profiles were downloaded from Gene Expression Omnibus (GEO) using "GEO query" package. "limma" package and "sva" package were used to conduct normalization and eliminate batch effects, respectively. We screened out differentially expressed genes (DEGs) based on "limma" package with the standard of |log fold change (FC)| ≥ 1.5 and false discovery rate (FDR) < 0.05. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses of DEGs were performed by "clusterProfler" package. We further applied LASSO to select key DEGs, and intersected key DEGs with immune related genes from ImmPort database. The ROC curve of each DEIRG was constructed to evaluate its diagnostic efficiency for AF.

RESULTS

A total of 103 DEGs we were screened out, of them, 48 genes were down-regulated and 55 genes were up-regulated. Result of functional enrichment analysis show that, most of DEGs were related to immune response, inflammation, and oxidative stress. Ultimately, CYBB, RORB, S100A12, and CHGB were determined as key DEIRGs, each of which displayed a favor efficiency for diagnosing persistent AF.

CONCLUSION

CYBB, RORB, S100A12, and CHGB were identified as key DEIRGs in persistent AF, and future studies are needed to further explore the underlying roles of CYBB, RORB, S100A12, and CHGB in persistent AF.

Evaluation of Toll-like Receptor 4 (TLR4) Involvement in Human Atrial Fibrillation: A Computational Study

In the present study, we have explored the involvement of Toll-like Receptor 4 (TLR4) in atrial fibrillation (AF), by using a meta-analysis of publicly available human transcriptomic data. The meta-analysis revealed 565 upregulated and 267 downregulated differentially expressed genes associated with AF. Pathway enrichment analysis highlighted a significant overrepresentation in immune-related pathways for the upregulated genes. A significant overlap between AF differentially expressed genes and TLR4-modulated genes was also identified, suggesting the potential role of TLR4 in AF-related transcriptional changes. Additionally, the analysis of other Toll-like receptors (TLRs) revealed a significant association with TLR2 and TLR3 in AF-related gene expression patterns. The examination of MYD88 and TICAM1, genes associated with TLR4 signalling pathways, indicated a significant yet nonspecific enrichment of AF differentially expressed genes. In summary, this study offers novel insights into the molecular aspects of AF, suggesting a pathophysiological role of TLR4 and other TLRs. By targeting these specific receptors, new treatments might be designed to better manage AF, offering hope for improved outcomes in affected patients.

Soluble PILRα: A novel plasma biomarker for atrial fibrillation progression and recurrence after catheter ablation

BACKGROUND

We aimed to identify plasma biomarkers of atrial fibrillation (AF) progression and recurrence after catheter ablation.

METHODS

Using AF gene profiling data from GEO database, a weighted gene co-expression network analysis (WGCNA) was conducted to determine the most significant module and hub genes associated with AF. Subsequently, 318 consecutively admitted patients who had undergone radiofrequency catheter ablation were enrolled in this study.

RESULTS

WGCNA results revealed that paired immunoglobulin-like type 2 receptor alpha (PILRA) was the only black module gene highly correlated with clinical traits. Plasma soluble PILRα (sPILRα) levels were elevated in patients with AF and significantly elevated in patients with persistent versus paroxysmal AF (4.64 ± 2.74 vs. 3.04 ± 1.56 ng/mL, p < 0.001). Elevated sPILRα level was an independent risk factor for AF progression even after adjusting for traditional factors (adjusted odds ratio: 3.06, 95 % confidence interval [CI]: 1.88-5.27, p < 0.001) and AF recurrence after catheter ablation in patients with persistent AF (adjusted hazards ratio: 4.41, 95 % CI: 1.22-15.92, p = 0.023).

CONCLUSIONS

WGCNA screening of GEO microarray gene profiling data showed PILRA expression levels to be correlated with AF progression and recurrence after catheter ablation in patients with persistent AF.

Identification and validation of aging-related genes in atrial fibrillation

Atrial fibrillation (AF) is the most common sustained cardiac arrhythmia in the clinic. Aging plays an essential role in the occurrence and development of AF. Herein, we aimed to identify the aging-related genes associated with AF using bioinformatics analysis. Transcriptome profiles of AF were obtained from the GEO database. Differential expression analysis was performed to identify AF-specific aging-related genes. GO and KEGG enrichment analyses were performed. Subsequently, the LASSO, SVM-RFE, and MCC algorithms were applied to screen aging-related genes. The mRNA expression of the screened genes was validated in the left atrial samples of aged rapid atrial pacing-induced AF canine models and their counterparts. The ROC curves of them were drawn to evaluate their diagnostic potential. Moreover, CIBERSORT was used to estimate immune infiltration. A correlation analysis between screened aging-related genes and infiltrating immune cells was performed. A total of 24 aging-related genes were identified, which were found to be mainly involved in the FoxO signaling pathway, PI3K-Akt signaling pathway, longevity regulating pathway, and peroxisome according to functional enrichment analysis. LASSO, SVM-RFE, and MCC algorithms identified three genes (HSPA9, SOD2, TXN). Furthermore, the expression levels of HSPA9 and SOD2 were validated in aged rapid atrial pacing-induced AF canine models. HSPA9 and SOD2 could be potential diagnostic biomarkers for AF, as evidenced by the ROC curves. Immune infiltration and correlation analysis revealed that HSPA9 and SOD2 were related to immune cell infiltrates. Collectively, these findings provide novel insights into the potential aging-related genes associated with AF. HSPA9 and SOD2 may play a significant role in the occurrence and development of AF.

Establishment of Risk Model and Analysis of Immunoinfiltration Based on Mitophagy-Related Associated Genes in Atrial Fibrillation

Objective

Atrial fibrillation (AF) is a common tachyarrhythmia whose pathogenesis remains elusive. In the present study, we aimed to investigate the pathological mechanism of mitophagy and immunoinfiltration in AF.

Methods

First, we identified differentially expressed mitophagy-related genes (DEMRGs) based on the GSE79768 and GSE115574 datasets, subjecting them to functional enrichment analysis. STRING, TRRUST, miRNet, miRwalk, and Cytoscape were used to explore the potential regulatory roles of downstream signaling pathways. Subsequently, the random forest method was used to construct the AF risk model, and the DEMRGs most correlated with AF risk were determined by combining the Gini index. ssGSEA algorithm, NMF algorithm, and unsupervised clustering were used to subdivide AF molecular types. We then studied the characteristics of mitophagy- and immune infiltration-related genes in AF. Ultimately, we detected the expression of key genes in canine atrial tissues and HL-1 cells by immunofluorescence and Western blot.

Results

Mitophagy and immune infiltration were significantly enriched and activated in AF samples. Thirty-seven DEMRGs were screened, of which MAPK1, VDAC1, MAPK14, and MTERF3 were most associated with AF risk. The risk model based on these could identify patients at a high risk of AF. The infiltration of immunocells such as mast cells and neutrophils was significantly different among AF types. Finally, expression verification indicated that the expression trend of four key genes in canine atrial muscle tissue and HL-1 cells was consistent.

Conclusion

We found that mitophagy may participate in AF progression through immune activation. In addition, the AF risk prediction model composed of VDAC1, MAPK1, MAPK14, and MTERF3 has a good AF prediction performance, which provides new ideas for the study of AF pathogenesis and potential therapeutic targets.

PILRA is associated with immune cells infiltration in atrial fibrillation based on bioinformatics and experiment validation

Background and aims

inflammation plays an important role in atrial fibrillation (AF). In this study, we investigated the significance of immune cell infiltration in AF and identified the potential Hub genes involved in the regulation of immune cell infiltration in AF.

Methods

we obtained AF datasets from the GEO database and analyzed them for obtaining differentially expressed genes (DEGs) by R software. Then, we performed GO, KEGG, and GSEA enrichment analyses of DEGs. The Hub genes of AF were determined by least absolute shrinkage selection operator (LASSO) regression analysis and weighted gene co-expression network analysis (WGCNA). Their validation was verified by using quantitative polymerase chain reaction (qPCR) in the AF rat model. Finally, we used a single sample GSEA (ssGSEA) to analyze immune cell infiltration and its relationship with hub genes.

Results

We obtained 298 DGEs from the heatmap and found that DGEs were closely related to inflammation, immunity, and cytokine interactions by enrichment analyses. We obtained 10 co-expression modules by WGCNA. Among them, the module including CLEC4A, COTL1, EVI2B, FCER1G, GAPT, HCST, NCF2, PILRA, TLR8, and TYROBP had the highest correlation with AF. Four Hub genes (PILRA, NCF2, EVI2B, GAPT) were obtained further by LASSO analysis. The results suggested that the expression level of PILRA was significantly elevated in the rats with AF by qPCR, compared to the rats without AF. The results revealed that the infiltration of neutrophils, macrophages, monocytes, mast cells, immature B cells, myeloid-derived suppressor cell (MDSC), dendritic cell, and T cells and their partial subpopulations were closely related to AF by ssGSEA analysis, and PILRA was positively correlated with immature B cell, monocyte, macrophage, mast cell, dendritic cell, and T cells and their partial subpopulations by Spearman correlation analysis.

Conclusions

PILRA was closely related to multiple types of immune cell infiltration, which may be associated with AF. PILRA may be a novel target of intervention for AF.

Transcriptome Discovery of Genes in the Three Phases of Autophagy That Are Upregulated During Atrial Fibrillation

Background: Autophagy may contribute to the maintenance of atrial fibrillation (AF), but no previous study has concurrently surveyed all 3 phases of autophagy, namely autophagosome formation, lysosome formation, and autophagosome-lysosome fusion. Here we aimed to identify disorders involving various phases of autophagy during AF. Methods and Results: We used bioinformatic techniques to analyze publicly available DNA microarray datasets from the left atrium (LA) and right atrium (RA) of 7 patients with AF and 6 patients with normal sinus rhythm who underwent valvular surgeries. We compared gene expression levels in the LA (AF-LA) and RA of patients with AF with those in the LA and RA of patients with normal sinus rhythm. Several differentially expressed genes in the AF-LA sample were significantly associated with the Gene Ontogeny term 'Autophagy', indicating that the expression of autophagic genes was specifically altered in this dataset. In particular, the expression of genes known or suspected to be involved in autophagosome formation (autophagy related 5 [ATG5], autophagy related 10 [ATG10], autophagy related 12 [ATG12], and light chain 3B [LC3B]), lysosome formation (lysosomal associated membrane protein 1 [LAMP1] and lysosomal associated membrane protein 2 [LAMP2]), and autophagosome-lysosome fusion (synaptosome associated protein 29 [SNAP29], SNAP associated protein [SNAPIN], and syntaxin 17 [STX17]) was significantly upregulated in the LA-AF dataset. Conclusions: Autophagy is activated excessively in, and may perpetuate, AF.

Combining bioinformatics and machine learning to identify common mechanisms and biomarkers of chronic obstructive pulmonary disease and atrial fibrillation

Background

Patients with chronic obstructive pulmonary disease (COPD) often present with atrial fibrillation (AF), but the common pathophysiological mechanisms between the two are unclear. This study aimed to investigate the common biological mechanisms of COPD and AF and to search for important biomarkers through bioinformatic analysis of public RNA sequencing databases.

Methods

Four datasets of COPD and AF were downloaded from the Gene Expression Omnibus (GEO) database. The overlapping genes common to both diseases were screened by WGCNA analysis, followed by protein-protein interaction network construction and functional enrichment analysis to elucidate the common mechanisms of COPD and AF. Machine learning algorithms were also used to identify key biomarkers. Co-expression analysis, “transcription factor (TF)-mRNA-microRNA (miRNA)” regulatory networks and drug prediction were performed for key biomarkers. Finally, immune cell infiltration analysis was performed to evaluate further the immune cell changes in the COPD dataset and the correlation between key biomarkers and immune cells.

Results

A total of 133 overlapping genes for COPD and AF were obtained, and the enrichment was mainly focused on pathways associated with the inflammatory immune response. A key biomarker, cyclin dependent kinase 8 (CDK8), was identified through screening by machine learning algorithms and validated in the validation dataset. Twenty potential drugs capable of targeting CDK8 were obtained. Immune cell infiltration analysis revealed the presence of multiple immune cell dysregulation in COPD. Correlation analysis showed that CDK8 expression was significantly associated with CD8+ T cells, resting dendritic cell, macrophage M2, and monocytes.

Conclusions

This study highlights the role of the inflammatory immune response in COPD combined with AF. The prominent link between CDK8 and the inflammatory immune response and its characteristic of not affecting the basal expression level of nuclear factor kappa B (NF-kB) make it a possible promising therapeutic target for COPD combined with AF.

The Nine RNA Methylation Regulatory Gene Signature Is Associated with the Pathogenesis of Atrial Fibrillation by Modulating the Immune Microenvironment in the Atrial Tissues

Background. Atrial fibrillation (AF) is the most common type of cardiac arrhythmias and a major cause of cardiovascular disease (CVD)-related deaths globally. RNA methylation is the most frequent posttranscriptional modification in the eukaryotic RNAs. Previous studies have demonstrated close associations between the status of RNA methylation and CVD. Methods. We comprehensively evaluated the relationship between RNA methylation and AF. Least absolute shrinkage and selection operator (LASSO) logistic regression analysis was used to establish a risk score model in AF. Biological functional analysis was used to explore the relationship between RNA methylation related signatures and immune microenvironment characteristics. Machine learning was used to recognize the outstanding RNA methylation regulators in AF. Results. There was a significant variant of the mRNA expression of RNA methylation regulators in AF. RNA methylation related risk score could predict the onset of AF and closely associated with immune microenvironment features. XG-Boost algorithm and SHAP recognized that NSUN3 and DCPS might play a key role in the development of AF. Meanwhile, NSUN3 and DCPS had potential diagnostic value in AF. Conclusion. RNA methylation regulatory genes are associated with the onset of AF by modulating the immune microenvironment. The nine AF risk-related RNA methylation regulatory gene signature is a potential diagnostic biomarker and therapeutic target for AF.

Identification and Verification of Biomarkers and Immune Infiltration in Obesity-Related Atrial Fibrillation

Obesity is an independent risk factor for atrial fibrillation (AF). However, the mechanisms underlying this crosstalk are still being uncovered. Co-differentially expressed genes (co-DEGs) of AF and obesity microarrays were identified by bioinformatics analysis. Subsequently, functional enrichment, cell-type enrichment, and protein-protein interaction network analyses of co-DEGs were carried out. Then, we validated the hub genes by qRT-PCR of patients' blood samples. Finally, CIBERSORT was utilized to evaluate the AF microarray to determine immune infiltration and the correlation between validated hub genes and immune cells. A total of 23 co-up-regulated DEGs in AF and obesity microarrays were identified, and these genes were enriched in inflammation- and immune-related function. The enriched cells were whole blood, CD33+ myeloid, and CD14+ monocytes. The hub genes were identified as MNDA, CYBB, CD86, FCGR2C, NCF2, LCP2, TLR8, HLA-DRA, LCP1, and PTPN22. All hub genes were only elevated in blood samples of obese-AF patients. The CIBERSORT analysis revealed that the AF patients' left atrial appendage had increased infiltration of naïve B cells and decreased infiltration of memory B cells. The hub genes were related positively to naïve B cells and negatively to memory B cells. Ten hub genes may serve as biomarkers for obesity-related AF. These findings may also aid in comprehending pathophysiological mechanisms for obesity-related AF.

Analysis of infiltrated immune cells in left atriums from patients with atrial fibrillation and identification of circRNA biomarkers for postoperative atrial fibrillation

Background: Atrial fibrillation (AF) increases the risk of stroke and heart failure. Postoperative AF (POAF) increases the risk of mortality after cardiac surgery. This study aims to explore mechanisms underlying AF, analyze infiltration of immune cells in left atrium (LA) from patients with AF, and identify potential circular RNA (circRNA) biomarkers for POAF. Methods: Raw data of GSE797689, GSE115574, and GSE97455 were downloaded and processed. AF-related gene co-expression network was constructed using weighted gene correlation network analysis and enrichment analysis of genes in relevant module was conducted. Gene set enrichment analysis (GSEA) and gene set variation analysis (GSVA) were applied to investigate pathways significantly enriched in AF group. Infiltration of immune cells was analyzed using single-sample GSEA. Differentially expressed genes (DEGs) between patients with or without AF were identified and competing endogenous RNA (ceRNA) networks of DEGs were constructed. To screen biomarkers for POAF, differentially expressed circRNAs (DEcircRNAs) between patients with or without POAF were identified. Intersection between DEcircRNAs and circRNAs in ceRNA networks of DEGs were extracted and circRNAs in the intersection were further screened using support vector machine, random forest, and neural network to identify biomarkers for POAF. Results: Three modules were found to be relevant with AF and enrichment analysis indicated that genes in these modules were enriched in synthesis of extracellular matrix and inflammatory response. The results of GSEA and GSVA suggested that inflammatory response-related pathways were significantly enriched in AF group. Immune cells like macrophages, mast cells, and neutrophils were significantly infiltrated in LA tissues from patients with AF. The expression levels of immune genes such as CHGB, HLA-DRA, LYZ, IGKV1-17 and TYROBP were significantly upregulated in patients with AF, which were correlated with infiltration of immune cells. ceRNA networks of DEGs were constructed and has_circ_0006314 and hsa_circ_0055387 were found to have potential predictive values for POAF. Conclusion: Synthesis of extracellular matrix and inflammatory response were main processes involved in development and progression of AF. Infiltration of immune cells was significantly different between patients with or without AF. Has_circ_0006314 and hsa_circ_0055387 were found to have potential predictive values for POAF.

Cardiac gene activation varies between young and adult cats and in the presence of hypertrophic cardiomyopathy

Little is known about the difference of myocardial gene transcription in young and adult cats and how transcription is further modified in cats with hypertrophic cardiomyopathy (HCM) and with left atrial (LA) thrombus formation. We hypothesized that selected factors for coagulation, endothelial activation, inflammation, and remodelling are modified with age and are activated in the hearts of cats with HCM. Left atrial and ventricular (LV) samples from 12 cats with HCM (seven without (HCMwoAT] and five with LA thrombi [HCMwAT]), and six young (YC) and six adult (AC) control cats without cardiac disease were investigated for relative expression of the following genes using quantitative polymerase chain reaction: von Willebrand factor, a disintegrin and metalloproteinase with a thrombospondin type 1 motif member 13, platelet activating factor, E- and P-selectin, intercellular and vascular adhesion molecules-1, ß2-integrin, vascular endothelial growth factor, interleukin-6 (IL-6), monocyte chemoattractant protein-1 (MCP-1), heat shock protein-70, and myocyte-specific enhancer factor 2C. Significant differences in gene activation were found between YC and AC, and YC and cats with HCM. Compared to AC, MCP-1 and IL-6 were significantly higher in cats with HCM. The presence of an LA thrombus was associated with higher IL-6 expression. These results illustrate the relevance of age and/or lifestyle on gene expression in the feline heart. The gene transcription pattern found in AC hearts might predispose cats to their characteristic cardiac remodelling processes and thrombus formation if disease occurs. It further supports the involvement of inflammation, but not coagulation and endothelial activation, in HCM.

Hub Genes Identification, Small Molecule Compounds Prediction for Atrial Fibrillation and Diagnostic Model Construction Based on XGBoost Algorithm

Background

Atrial fibrillation (AF) is the most common sustained cardiac arrhythmia and engenders significant global health care burden. The underlying mechanisms of AF is remained to be revealed and current treatment options for AF have limitations. Besides, a detection system can help identify those at risk of developing AF and will enable personalized management.

Materials and Methods

In this study, we utilized the robust rank aggregation method to integrate six AF microarray datasets from the Gene Expression Omnibus database, and identified a set of differentially expressed genes between patients with AF and controls. Potential compounds were identified by mining the Connectivity Map database. Functional modules and closely-interacted clusters were identified using weighted gene co-expression network analysis and protein–protein interaction network, respectively. The overlapped hub genes were further filtered. Subsequent analyses were performed to analyze the function, biological features, and regulatory networks. Moreover, a reliable Machine Learning-based diagnostic model was constructed and visualized to clarify the diagnostic features of these genes.

Results

A total of 156 upregulated and 34 downregulated genes were identified, some of which had not been previously investigated. We showed that mitogen-activated protein kinase and epidermal growth factor receptor inhibitors were likely to mitigate AF based on Connectivity Map analysis. Four genes, including CXCL12, LTBP1, LOXL1, and IGFBP3, were identified as hub genes. CXCL12 was shown to play an important role in regulation of local inflammatory response and immune cell infiltration. Regulation of CXCL12 expression in AF was analyzed by constructing a transcription factor-miRNA-mRNA network. The Machine Learning-based diagnostic model generated in this study showed good efficacy and reliability.

Conclusion

Key genes involving in the pathogenesis of AF and potential therapeutic compounds for AF were identified. The biological features of CXCL12 in AF were investigated using integrative bioinformatics tools. The results suggested that CXCL12 might be a biomarker that could be used for distinguishing subsets of AF, and indicated that CXCL12 might be an important intermediate in the development of AF. A reliable Machine Learning-based diagnostic model was constructed. Our work improved understanding of the mechanisms of AF predisposition and progression, and identified potential therapeutic avenues for treatment of AF.

FAM201A, a long noncoding RNA potentially associated with atrial fibrillation identified by ceRNA network analyses and WGCNA

Background

Being the most common arrhythmia in clinic, atrial fibrillation (AF) causes various comorbidities to patients such as heart failure and stroke. LncRNAs were reported involved in pathogenesis of AF, yet, little is known about AF-associated lncRNAs. The present study aims to explore lncRNAs associated with AF susceptibility based on competing endogenous RNA (ceRNA) network analysis and weighted gene co-expression network analysis (WGCNA).

Methods

GSE41177 and GSE79768 datasets were obtained from the Gene Expression Omnibus (GEO) database. Competing endogenous RNA (ceRNA) network analysis was performed using GSE41177. Differentially expressed lncRNAs (DElncRNAs), mRNAs (DEmRNAs) between AF patients and patients with sinus rhythm (SR) were identified from GSE41177 using R software. Then, the ceRNA network was constructed based on DElncRNAs, the predicted target miRNAs and DEmRNAs. Weighted gene co-expression network analysis (WGCNA) was performed using GSE79768 to validate the AF-related lncRNAs identified from GSE41177. LncRNA modules and crucial lncRNAs relevant to AF and were identified.

Results

In summary, 18 DElncRNAs and 350 DEmRNAs were found between AF patients and SR patients. A total of 5 lncRNAs, 10 miRNAs, and 21 mRNAs were contained in the final ceRNA network. Taking into consideration both the ceRNA theory and inference scores from the comparative toxicogenomics database (CTD) database, the ceRNA axis FAM201A-miR-33a-3p-RAC3 was identified as mostly relevant to AF susceptibility. FAM201A (Gene significance, GS = − 0.62; Module membership, MM = 0.75) was also proved in the blue module, which was identified most highly relevant with AF by WGCNA.

Conclusions

These results demonstrated that decreased expression of FAM201A might be associated with susceptibility of AF. Working as the ceRNA to regulate RAC3 might be one function of FAM201A in AF susceptibility, which requires further exploration in future research.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12920-022-01232-w.

Analysis of potential genetic biomarkers using machine learning methods and immune infiltration regulatory mechanisms underlying atrial fibrillation

Objective

We aimed to screen out biomarkers for atrial fibrillation (AF) based on machine learning methods and evaluate the degree of immune infiltration in AF patients in detail.

Methods

Two datasets (GSE41177 and GSE79768) related to AF were downloaded from Gene expression omnibus (GEO) database and merged for further analysis. Differentially expressed genes (DEGs) were screened out using “limma” package in R software. Candidate biomarkers for AF were identified using machine learning methods of the LASSO regression algorithm and SVM-RFE algorithm. Receiver operating characteristic (ROC) curve was employed to assess the diagnostic effectiveness of biomarkers, which was further validated in another independent validation dataset of GSE14975. Moreover, we used CIBERSORT to study the proportion of infiltrating immune cells in each sample, and the Spearman method was used to explore the correlation between biomarkers and immune cells.

Results

129 DEGs were identified, and CYBB, CXCR2, and S100A4 were identified as key biomarkers of AF using LASSO regression and SVM-RFE algorithm. Both in the training dataset and the validation dataset, CYBB, CXCR2, and S100A4 showed favorable diagnostic effectiveness. Immune infiltration analysis indicated that, compared with sinus rhythm (SR), the atrial samples of patients with AF contained a higher T cells gamma delta, neutrophils and mast cells resting, whereas T cells follicular helper were relatively lower. Correlation analysis demonstrated that CYBB, CXCR2, and S100A4 were significantly correlated with the infiltrating immune cells.

Conclusions

In conclusion, this study suggested that CYBB, CXCR2, and S100A4 are key biomarkers of AF correlated with infiltrating immune cells, and infiltrating immune cells play pivotal roles in AF.

Cardiac chamber-specific genetic alterations suggest candidate genes and pathways implicating the left ventricle in the pathogenesis of atrial fibrillation

It is believed that the atria play a predominant role in the initiation and maintenance of atrial fibrillation (AF), while the role of left ventricular dysfunction in the pathophysiology remains enigmatic. We sought to dissect chamber specificity of AF-associated transcriptional changes using RNA-sequencing. We performed intra- and inter-chamber differential expression analyses comparing AF against sinus rhythm to identify genes specifically dysregulated in human left atria, right atria, and left ventricle (LV), and integrated known AF genetic associations with expression quantitative trait loci datasets to inform the potential for disease causal contributions within each chamber. Inter-chamber patterns changed drastically. Vast AF-associated transcriptional changes specific to LV, enriched for biological pathway terms implicating mitochondrial function, developmental processes and immunity, were supported at the genetic level, but no major enrichments for candidate genes specific to the atria were found. Our observations suggest an active role of the LV in the pathogenesis of AF.

The Construction and Comprehensive Analysis of a ceRNA Immunoregulatory Network and Tissue-Infiltrating Immune Cells in Atrial Fibrillation

Background

At present, the mechanisms behind atrial fibrillation (AF) pathogenesis are still unclear. We construct a ceRNA immunoregulatory network to further understand the mechanism of AF.

Methods

Four AF mRNA datasets from the Gene Expression Omnibus (GEO) database were integrated by SVA method. AF-related immune genes (AF-IRGs) were selected via combining ImmPort database with the genes in the module most associated with AF obtained by a weighted gene coexpression network analysis (WGCNA). Then, circRNA and miRNA expressions from the GEO database were extracted and mapped with related databases. Next, an immune-related circRNA-miRNA-mRNA ceRNA network was constructed and hub genes were filtered from a protein–protein interaction (PPI) network, and the differentially expressed (DE) hub genes in AF were further screened. Additionally, immune infiltration was investigated in AF by using CIBERSORT. Subsequently, the relationships between DE hub genes and AF-related infiltrating immune cells were performed by using Pearson correlation coefficients. Ulteriorly, the immune-cells-related ceRNA subnetwork in AF was built.

Results

A total of 95 AF-IRGs were detected, and an immune-related ceRNA network in AF was constructed with 12 circRNAs, 7 miRNAs and 50 mRNAs. The immune infiltration analysis indicated that a higher level of neutrophils, as well as a lower level of T cells regulatory (Tregs) and NK cells activated in AF. Four DE hub genes (CXCL12, IL7R, TNFSF13B, CD8A) were associated with Tregs or NK cells activated immune cells (P < 0.05). Tregs or NK cells activated immune cells-related ceRNA subnetwork including 5 circRNAs (has_circ_0001190, has_circ_0006725, has_circ_0079284, has_circ_0005299, and has_circ_0002103), 4 miRNAs (has-miR-198, has-miR-623, has-miR-1246, and has-miR-339-3p) and 4 DE hub genes was eventually constructed in AF.

Conclusion

Our results provide new insights into the molecular mechanisms governing AF progression from the perspective of immune-related ceRNA network.

Hub microRNAs and genes in the development of atrial fibrillation identified by weighted gene co-expression network analysis

Co-expression network may contribute to better understanding molecular interaction patterns underlying cellular processes. To explore microRNAs (miRNAs) expression patterns correlated with AF, we performed weighted gene co-expression network analysis (WGCNA) based on the dataset GSE28954. Thereafter, we predicted target genes using experimentally verified databases (ENOCRI, miRTarBase, and Tarbase), and overlapped genes with differentially expressed genes (DEGs) from GSE79768 were identified as key genes. Integrated analysis of association between hub miRNAs and key genes was conducted to screen hub genes. In general, we identified 3 differentially expressed miRNAs (DEMs) and 320 DEGs, predominantly enriched in inflammation-related functional items. Two significant modules (red and blue) and hub miRNAs (hsa-miR-146b-5p and hsa-miR-378a-5p), which highly correlated with AF-related phenotype, were detected by WGCNA. By overlapping the DEGs and predicted target genes, 38 genes were screened out. Finally, 9 genes (i.e. ATP13A3, BMP2, CXCL1, GABPA, LIF, MAP3K8, NPY1R, S100A12, SLC16A2) located at the core region in the miRNA-gene interaction network were identified as hub genes. In conclusion, our study identified 2 hub miRNAs and 9 hub genes, which may improve the understanding of molecular mechanisms and help to reveal potential therapeutic targets against AF.

Molecular Signatures of Human Chronic Atrial Fibrillation in Primary Mitral Regurgitation

Objectives

Transcriptomics of atrial fibrillation (AFib) in the setting of chronic primary mitral regurgitation (MR) remains to be characterized. We aimed to compare the gene expression profiles of patients with degenerative MR in AFib and sinus rhythm (SR) for a clearer picture of AFib pathophysiology.

Methods

After transcriptomic analysis and bioinformatics (n = 59), differentially expressed genes were defined using 1.5-fold change as the threshold. Additionally, independent datasets from GEO were included as meta-analyses.

Results

QRT-PCR analysis confirmed that AFib persistence was associated with increased expression molecular changes underlying a transition to heart failure (NPPB, P = 0.002; ANGPTL2, P = 0.002; IGFBP2, P = 0.010), structural remodeling including changes in the extracellular matrix and cellular stress response (COLQ, P = 0.003; COMP, P = 0.028; DHRS9, P = 0.038; CHGB, P = 0.038), and cellular stress response (DNAJA4, P = 0.038). Furthermore, AFib persistence was associated with decreased expression of the targets of structural remodeling (BMP7, P = 0.021) and electrical remodeling (CACNB2, P = 0.035; MCOLN3, P = 0.035) in both left and right atrial samples. The transmission electron microscopic analysis confirmed ultrastructural atrial remodeling and autophagy in human AFib atrial samples.

Conclusions

Atrial cardiomyocyte remodeling in persistent AFib is closely linked to alterations in gene expression profiles compared to SR in patients with primary MR. Study findings may lead to novel therapeutic targets. This trial is registered with ClinicalTrials.gov identifier: NCT00970034.

Association between the APOE gene polymorphism and lipid profile and the risk of atrial fibrillation

Background

The relationship between the APOE gene polymorphism and lipid profiles and atrial fibrillation (AF) remains controversial. The current study purposed to investigate how the APOE gene SNPs (rs429358 and rs7412) and lipid profile are associated with the risk for AF among the Hakka population in southern China.

Methods

Finally, 1367 patients were enrolled in this study, including 706 participants with AF (41 ~ 98 years old, 58.64 % male) and 661 non-AF subjects (28 ~ 95 years old, 59.46 % male). The collected data included baseline characteristics, medical history, laboratory tests and echocardiography parameters. A general linear model (two-way analysis of variance (ANOVA)) and Tukey post-hoc tests were applied to identify an APOE allele, AF group, and interaction effect on lipid profiles. Logistic regression analysis was performed to identify risk factors for AF.

Results

For AF group, the most common genotype was E3/E3 (53.82 %), followed by E3/E4 (28.19 %), E2/E3 (13.60 %), E4/E4 (1.98 %), E2/E4 (1.84 %) and E2/E2 (0.57 %). The two-way ANOVA followed by the Tukey procedure showed the following: the lipid levels depended significantly on AF and APOE allele groups for TG, TC, LDL-C and Apo-B (all P < 0.001), and statistically significant interactions between AF and APOE allele were observed in the above 4 variables (all P < 0.05). Multivariate regression analysis indicated that age ≥ 65years (P < 0.001), high diastolic blood pressure (DBP ≥ 90mm Hg, P = 0.018), a high levels of total cholesterol (TC ≥ 5.2mmol/L, P < 0.001) and triglyceride (TG ≥ 1.7mmol/L, P = 0.028), but not the two SNPs of the APOE gene (rs7412 and rs429358) (OR 1.079, P = 0.683), were significant independent risk factors for AF in the study population.

Conclusions

The principal findings of this study showed that individuals at high risk for AF were those over 65 years of age, higher DBP as well as high levels of TC and TG among the southern China Hakka population. The levels of TG, TC, LDL-C and Apo-B depended significantly on AF and APOE allele groups, and statistically significant interactions between AF and APOE allele were observed in the above 4 variables, although the APOE gene SNPs (rs429358 and rs7412) were no significant risk for AF incidence. Further investigation is needed to elucidate whether other SNPs of the APOE gene have a bearing on AF incidents.

CXCL12/CXCR4 axis as a key mediator in atrial fibrillation via bioinformatics analysis and functional identification

Atrial fibrillation (AF) is an increasingly prevalent arrhythmia with significant health and socioeconomic impact. The underlying mechanism of AF is still not well understood. In this study, we sought to identify hub genes involved in AF, and explored their functions and underlying mechanisms based on bioinformatics analysis. Five microarray datasets in GEO were used to identify the differentially expressed genes (DEGs) by Robust Rank Aggregation (RRA), and hub genes were screened out using protein-protein interaction (PPI) network. AF model was established using a mixture of acetylcholine and calcium chloride (Ach-CaCl2) by tail vein injection. We totally got 35 robust DEGs that mainly involve in extracellular matrix formation, leukocyte transendothelial migration, and chemokine signaling pathway. Among these DEGs, we identified three hub genes involved in AF, of which CXCL12/CXCR4 axis significantly upregulated in AF patients stands out as one of the most potent targets for AF prevention, and its effect on AF pathogenesis and underlying mechanisms were investigated in vivo subsequently with the specific CXCR4 antagonist AMD3100 (6 mg/kg). Our results demonstrated an elevated transcription and translation of CXCL12/CXCR4 axis in AF patients and mice, accompanied with the anabatic atrial inflammation and fibrosis, thereby providing the substrate for AF maintenance. Blocking its signaling via AMD3100 administration in AF model mice reduced AF inducibility and duration, partly ascribed to decreased atrial inflammation and structural remodeling. Mechanistically, these effects were achieved by reducing the recruitment of CD3+ T lymphocytes and F4/80+ macrophages, and suppressing the hyperactivation of ERK1/2 and AKT/mTOR signaling in atria of AF model mice. In conclusion, this study provides new evidence that antagonizing CXCR4 prevents the development of AF, and suggests that CXCL12/CXCR4 axis may be a potential therapeutic target for AF.

Evidence for the expression of TRPM6 and TRPM7 in cardiomyocytes from all four chamber walls of the human heart

The expression of the channels-enzymes TRPM6 and TRPM7 in the human heart remains poorly defined, and TRPM6 is generally considered not to be expressed in cardiomyocytes. We examined their expression at protein and mRNA levels using right atrial samples resected from patients (n = 72) with or without ischemic heart disease (IHD) and samples from all chamber walls of explanted human hearts (n = 9). TRPM6 and TRPM7 proteins were detected using immunofluorescence on isolated cardiomyocytes, ELISA on tissue homogenates, and immunostaining of cardiac tissue, whereas their mRNAs were detected by RT-qPCR. Both TRPM6 and TRPM7 were present in all chamber walls, with TRPM7 being more abundant. TRPM6 was co-expressed with TRPM7. The expression levels were dependent on cell incubation conditions (presence or absence of divalent cations, pH of the extracellular milieu, presence of TRP channel inhibitors 2-aminoethoxydiphenyl-borate and carvacrol). These drugs reduced TRPM7 immunofluorescence but increased that of TRPM6. TRPM6 and TRPM7 expression was increased in tissues from IHD patients. This is the first demonstration of the presence and co-expression of TRPM6 and TRPM7 in cardiomyocytes from all chamber walls of the human heart. The increased TRPM6 and TRPM7 expression in IHD suggests that the chanzymes are involved in the pathophysiology of the disease.

Functional genomics and epigenomics of atrial fibrillation

Atrial fibrillation is a progressive cardiac arrhythmia that increases the risk of hospitalization and adverse cardiovascular events. Despite years of study, we still do not have a full comprehension of the molecular mechanism responsible for the disease. The recent implementation of large-scale approaches in both patient samples, population studies and animal models has helped us to broaden our knowledge on the molecular drivers responsible for AF and on the mechanisms behind disease progression. Understanding genomic and epigenomic changes that take place during chronification of AF will prove essential to design novel treatments leading to improved patient care.

Multiomics Analysis of Genetics and Epigenetics Reveals Pathogenesis and Therapeutic Targets for Atrial Fibrillation

Objective

This study is aimed at understanding the molecular mechanisms and exploring potential therapeutic targets for atrial fibrillation (AF) by multiomics analysis.

Methods

Transcriptomics and methylation data of AF patients were retrieved from the Gene Expression Omnibus (GEO). Differentially expressed genes (DEGs) and differentially methylated sites between AF and normal samples were screened. Then, highly expressed and hypomethylated and lowly expressed and hypermethylated genes were identified for AF. Weighted gene coexpression network analysis (WGCNA) was presented to construct AF-related coexpression networks. 52 AF blood samples were used for whole exome sequence. The mutation was visualized by the maftools package in R. Key genes were validated in AF using independent datasets.

Results

DEGs were identified between AF and controls, which were enriched in neutrophil activation and regulation of actin cytoskeleton. RHOA, CCR2, CASP8, and SYNPO2L exhibited abnormal expression and methylation, which have been confirmed to be related to AF. PCDHA family genes had high methylation and low expression in AF. We constructed two AF-related coexpression modules. Single-nucleotide polymorphism (SNP) was the most common mutation type in AF, especially T > C. MUC4 was the most frequent mutation gene, followed by PHLDA1, AHNAK2, and MAML3. There was no statistical difference in expression of AHNAK2 and MAML3, for AF. PHLDA1 and MUC4 were confirmed to be abnormally expressed in AF.

Conclusion

Our findings identified DEGs related to DNA methylation and mutation for AF, which may offer possible therapeutic targets and a new insight into the pathogenesis of AF from a multiomics perspective.

Epigenetic regulation of cardiac electrophysiology in atrial fibrillation: HDAC2 determines action potential duration and suppresses NRSF in cardiomyocytes

Atrial fibrillation (AF) is associated with electrical remodeling, leading to cellular electrophysiological dysfunction and arrhythmia perpetuation. Emerging evidence suggests a key role for epigenetic mechanisms in the regulation of ion channel expression. Histone deacetylases (HDACs) control gene expression through deacetylation of histone proteins. We hypothesized that class I HDACs in complex with neuron-restrictive silencer factor (NRSF) determine atrial K⁺ channel expression. AF was characterized by reduced atrial HDAC2 mRNA levels and upregulation of NRSF in humans and in a pig model, with regional differences between right and left atrium. In vitro studies revealed inverse regulation of Hdac2 and Nrsf in HL-1 atrial myocytes. A direct association of HDAC2 with active regulatory elements of cardiac K⁺ channels was revealed by chromatin immunoprecipitation. Specific knock-down of Hdac2 and Nrsf induced alterations of K⁺ channel expression. Hdac2 knock-down resulted in prolongation of action potential duration (APD) in neonatal rat cardiomyocytes, whereas inactivation of Nrsf induced APD shortening. Potential AF-related triggers were recapitulated by experimental tachypacing and mechanical stretch, respectively, and exerted differential effects on the expression of class I HDACs and K⁺ channels in cardiomyocytes. In conclusion, HDAC2 and NRSF contribute to AF-associated remodeling of APD and K⁺ channel expression in cardiomyocytes via direct interaction with regulatory chromatin regions. Specific modulation of these factors may provide a starting point for the development of more individualized treatment options for atrial fibrillation.

Meta-analysis of Transcriptomic Data Reveals Pathophysiological Modules Involved with Atrial Fibrillation

BACKGROUND

Atrial fibrillation (AF) is a complex disease and affects millions of people around the world. The biological mechanisms that are involved with AF are complex and still need to be fully elucidated. Therefore, we performed a meta-analysis of transcriptome data related to AF to explore these mechanisms aiming at more sensitive and reliable results.

METHODS

Ten public transcriptomic datasets were downloaded, analyzed for quality control, and individually pre-processed. Differential expression analysis was carried out for each dataset, and the results were meta-analytically aggregated using the rth ordered p value method. We analyzed the final list of differentially expressed genes through network analysis, namely topological and modularity analysis, and functional enrichment analysis.

RESULTS

The meta-analysis of transcriptomes resulted in 1197 differentially expressed genes, whose protein-protein interaction network presented 39 hubs-bottlenecks and four main identified functional modules. These modules were enriched for 39, 20, 64, and 10 biological pathways involved with the pathophysiology of AF, especially with the disease's structural and electrical remodeling processes. The stress of the endoplasmic reticulum, protein catabolism, oxidative stress, and inflammation are some of the enriched processes. Among hub-bottlenecks genes, which are highly connected and probably have a key role in regulating these processes, HSPA5, ANK2, CTNNB1, and MAPK1 were identified.

CONCLUSION

Our approach based on transcriptome meta-analysis revealed a set of key genes that demonstrated consistent overall changes in expression patterns associated with AF despite data heterogeneity related, among others, to type of tissue. Further experimental investigation of our findings may shed light on the pathophysiology of the disease and contribute to the identification of new therapeutic targets.

Comprehensive Analysis of Differential Immunocyte Infiltration and Potential ceRNA Networks Involved in the Development of Atrial Fibrillation

This study is aimed at identifying potential molecular mechanisms and candidate biomarkers in the left atrial regions for the diagnosis and treatment of valvular atrial fibrillation (VAF). Multibioinformatics methods, including linear models for microarray analysis (LIMMA), an SVA algorithm, CIBERSORT immune infiltration, and DNA methylation analysis, were employed. In addition, the protein-protein interaction (PPI) network, Gene Ontology (GO), and molecular pathways of differentially expressed genes (DEGs) or differential methylation regions were constructed. In all, compared with the normal rhythm group, 243 different mRNAs (29 downregulated and 214 upregulated) and 26 different lncRNAs (3 downregulated and 23 upregulated) were detected in the left atrium (LA) of atrial fibrillation (AF) patients, and the neutrophil and CD8⁺ T cell were infiltrated. Additionally, 199 different methylation sites (107 downregulated and 92 upregulated) were also identified based on DNA methylation analysis. After integration, ELOVL2, CCR2, and WEE1 were detected for differentially methylated and differentially transcribed genes. Among them, WEE1 was also a core gene identified by the competing endogenous RNA (ceRNA) network that included WEE1-KRBOX1-AS1-hsa-miR-17-5p, in VAF left atrial tissue. We combined the DNA methylation and transcriptional expression differential analysis and found that WEE1 (cg13365543) may well be a candidate gene regulated by DNA methylation modification. Moreover, KRBOX1-AS1 and WEE1 can compete endogenously and may mediate myocardial tissue infiltration into CD8⁺ T cells and participate in the AF process.

Chamber-specific transcriptional responses in atrial fibrillation

Atrial fibrillation (AF) is the most common cardiac arrhythmia, yet the molecular signature of the vulnerable atrial substrate is not well understood. Here, we delineated a distinct transcriptional signature in right versus left atrial cardiomyocytes (CMs) at baseline and identified chamber-specific gene expression changes in patients with a history of AF in the setting of end-stage heart failure (AF+HF) that are not present in heart failure alone (HF). We observed that human left atrial (LA) CMs exhibited Notch pathway activation and increased ploidy in AF+HF but not in HF alone. Transient activation of Notch signaling within adult CMs in a murine genetic model is sufficient to increase ploidy in both atrial chambers. Notch activation within LA CMs generated a transcriptomic fingerprint resembling AF, with dysregulation of transcription factor and ion channel genes, including Pitx2, Tbx5, Kcnh2, Kcnq1, and Kcnip2. Notch activation also produced distinct cellular electrophysiologic responses in LA versus right atrial CMs, prolonging the action potential duration (APD) without altering the upstroke velocity in the left atrium and reducing the maximal upstroke velocity without altering the APD in the right atrium. Our results support a shared human/murine model of increased Notch pathway activity predisposing to AF.

Distinct transcriptional changes occur in human left versus right atrial cardiomyocytes in atrial fibrillation, including Notch pathway activation, which alters electric properties and ploidy in murine models.

Ubiquitin Pathway Is Associated with Worsening Left Ventricle Function after Mitral Valve Repair: A Global Gene Expression Study

The molecular mechanism for worsening left ventricular (LV) function after mitral valve (MV) repair for chronic mitral regurgitation remains unknown. We wished to assess the LV transcriptome and identify determinants associated with worsening LV function post-MV repair. A total of 13 patients who underwent MV repair for chronic primary mitral regurgitation were divided into two groups, preserved LV function (N = 8) and worsening LV function (N = 5), for the study. Specimens of LV from the patients taken during surgery were used for the gene microarray study. Cardiomyocyte cell line HL-1 cells were transfected with gene-containing plasmids and further evaluated for mRNA and protein expression, apoptosis, and contractile protein degradation. Of 67,258 expressed sequence tags, microarrays identified 718 genes to be differentially expressed between preserved-LVF and worsening-LVF, including genes related to the protein ubiquitination pathway, bone morphogenetic protein (BMP) receptors, and regulation of eIF4 and p70S6K signaling. In addition, worsening-LVF was associated with altered expressions of genes pathologically relevant to heart failure, such asdownregulated apelin receptors and upregulated peroxisome proliferator-activated receptor gamma coactivator 1-alpha (PPARGC1A). HL-1 cardiomyocyte cells transfected with ubiquitination-related genes demonstrated activation of the protein ubiquitination pathwaywith an increase in the ubiquitin activating enzyme E1 (UAE-E1). It also led to increased apoptosis, downregulated and ubiquitinated X-linked inhibitor of apoptosis protein (XIAP), and reduced cell viability. Overexpression of ubiquitination-related genes also resulted in degradation and increased ubiquitination of α-smooth muscle actin (SMA). In conclusion, worsening-LVF presented differential gene expression profiles from preserved-LVF after MV repair. Upregulation of protein ubiquitination-related genes associated with worsening-LVF after MV repair may exert adverse effects on LV through increased apoptosis and contractile protein degradation.

Insight into atrial fibrillation through analysis of the coding transcriptome in humans

Atrial fibrillation is the most common sustained cardiac arrhythmia in humans, and its prevalence continues to increase because of the aging of the world population. Much still needs to be learned about the molecular pathways involved in the development and the persistence of the disease. Analysis of the transcriptome of cardiac tissue has provided valuable insight into diverse aspects of atrial remodeling, in particular concerning electrical remodeling-related to ion channels-and structural remodeling identified by dysregulation of processes linked to inflammation, fibrosis, oxidative stress, and thrombogenesis. The huge amount of data produced by these studies now represents a valuable source for the identification of novel potential therapeutic targets. In addition, the shift from cardiac tissue to peripheral blood as a substrate for transcriptome analysis revealed this strategy as a promising tool for improved diagnosis and therefore better patient care.

Olesen M, Hjorth Bentzen B, Schmitt N. Investigating gene-microRNA networks in atrial fibrillation patients with mitral valve regurgitation

BACKGROUND

Atrial fibrillation (AF) is predicted to affect around 17.9 million individuals in Europe by 2060. The disease is associated with severe electrical and structural remodelling of the heart, and increased the risk of stroke and heart failure. In order to improve treatment and find new drug targets, the field needs to better comprehend the exact molecular mechanisms in these remodelling processes.

OBJECTIVES

This study aims to identify gene and miRNA networks involved in the remodelling of AF hearts in AF patients with mitral valve regurgitation (MVR).

METHODS

Total RNA was extracted from right atrial biopsies from patients undergoing surgery for mitral valve replacement or repair with AF and without history of AF to test for differentially expressed genes and miRNAs using RNA-sequencing and miRNA microarray. In silico predictions were used to construct a mRNA-miRNA network including differentially expressed mRNAs and miRNAs. Gene and chromosome enrichment analysis were used to identify molecular pathways and high-density AF loci.

RESULTS

We found 644 genes and 43 miRNAs differentially expressed in AF patients compared to controls. From these lists, we identified 905 pairs of putative miRNA-mRNA interactions, including 37 miRNAs and 295 genes. Of particular note, AF-associated miR-130b-3p, miR-338-5p and miR-208a-3p were differentially expressed in our AF tissue samples. These miRNAs are predicted regulators of several differentially expressed genes associated with cardiac conduction and fibrosis. We identified two high-density AF loci in chromosomes 14q11.2 and 6p21.3.

CONCLUSIONS

AF in MVR patients is associated with down-regulation of ion channel genes and up-regulation of extracellular matrix genes. Other AF related genes are dysregulated and several are predicted to be targeted by miRNAs. Our novel miRNA-mRNA regulatory network provides new insights into the mechanisms of AF.

Weighted gene co‑expression network analysis to identify key modules and hub genes associated with atrial fibrillation

Atrial fibrillation (AF) is the most common form of cardiac arrhythmia and significantly increases the risks of morbidity, mortality and health care expenditure; however, treatment for AF remains unsatisfactory due to the complicated and incompletely understood underlying mechanisms. In the present study, weighted gene co‑expression network analysis (WGCNA) was conducted to identify key modules and hub genes to determine their potential associations with AF. WGCNA was performed in an AF dataset GSE79768 obtained from the Gene Expression Omnibus, which contained data from paired left and right atria in cardiac patients with persistent AF or sinus rhythm. Differentially expressed gene (DEG) analysis was used to supplement and validate the results of WGCNA. Gene Ontology and Kyoto Encyclopedia of Genes and Genomes enrichment analyses were also performed. Green and magenta modules were identified as the most critical modules associated with AF, from which 6 hub genes, acetyl‑CoA Acetyltransferase 1, death domain‑containing protein CRADD, gypsy retrotransposon integrase 1, FTX transcript, XIST regulator, transcription elongation factor A like 2 and minichromosome maintenance complex component 3 associated protein, were hypothesized to serve key roles in the pathophysiology of AF due to their increased intramodular connectivity. Functional enrichment analysis results demonstrated that the green module was associated with energy metabolism, and the magenta module may be associated with the Hippo pathway and contain multiple interactive pathways associated with apoptosis and inflammation. In addition, the blue module was identified to be an important regulatory module in AF with a higher specificity for the left atria, the genes of which were primarily correlated with complement, coagulation and extracellular matrix formation. These results suggest that may improve understanding of the underlying mechanisms of AF, and assist in identifying biomarkers and potential therapeutic targets for treating patients with AF.

Analysis of potential roles of combinatorial microRNA regulation in occurrence of valvular heart disease with atrial fibrillation based on computational evidences

Background

Atrial fibrillation (AF) is the most common arrhythmia. Patients with valvular heart disease (VHD) frequently have AF. Growing evidence demonstrates that a specifically altered pattern of microRNA (miRNA) expression is related to valvular heart disease with atrial fibrillation (AF-VHD) processes. However, the combinatorial regulation by multiple miRNAs in inducing AF-VHD remains largely unknown.

Methods

The work identified AF-VHD-specific miRNAs and their combinations through mapping miRNA expression profile into differential co-expression network. The expressions of some dysregulated miRNAs were measured by quantitative reverse transcription polymerase chain reaction (qRT-PCR). The regulations of signaling pathways by the combinatorial miRNAs were predicted by enrichment analysis tools.

Results

Thirty-two differentially expressed (DE) miRNAs were identified to be AF-VHD-specific, some of which were new findings. These miRNAs interacted to form 5 combinations. qRT-PCR confirmed the different expression of several identified miRNAs, which illustrated the reliability and biomarker potentials of 32 dysregulation miRNAs. The biological characteristics of combinatorial miRNAs related to AF-VHD were highlighted. Twelve signaling pathways regulated by combinatorial miRNAs were predicted to be possibly associated with AF-VHD.

Conclusions

The AF-VHD-related signaling pathways regulated by combinatorial miRNAs may play an important role in the occurrence of AF-VHD. The work brings new insights into biomarkers and miRNA combination regulation mechanism in AF-VHD as well as further biological experiments.

Differentially expressed genes for atrial fibrillation identified by RNA sequencing from paired human left and right atrial appendages

Atrial fibrillation is a significant worldwide contributor to cardiovascular morbidity and mortality. Few studies have investigated the differences in gene expression between the left and right atrial appendages, leaving their characterization largely unexplored. In this study, differential gene expression was investigated in atrial fibrillation and sinus rhythm using left and right atrial appendages from the same patients. RNA sequencing was performed on the left and right atrial appendages from five sinus rhythm (SR) control patients and five permanent AF case patients. Differential gene expression in both the left and right atrial appendages was analyzed using the Bioconductor package edgeR. A selection of differentially expressed genes, with relevance to atrial fibrillation, were further validated using quantitative RT-PCR. The distribution of the samples assessed through principal component analysis showed distinct grouping between left and right atrial appendages and between SR controls and AF cases. Overall 157 differentially expressed genes were identified to be downregulated and 90 genes upregulated in AF. Pathway enrichment analysis indicated a greater involvement of left atrial genes in the Wnt signaling pathway whereas right atrial genes were involved in clathrin-coated vesicle and collagen formation. The differing expression of genes in both left and right atrial appendages indicate that there are different mechanisms for development, support and remodeling of AF within the left and right atria.

Combined metabolic, phenomic and genomic data to prioritize atrial fibrillation-related metabolites

Metabolites in atrial fibrillation (AF) were characterized to further explore the molecular mechanisms of AF by integrating metabolic, phenomic and genomic data. Gene expression data on AF (E-GEOD-79768) were downloaded from the EMBL-EBI database, followed by identification of differentially expressed genes (DEGs) which were used to construct gene-gene network. Then, multi-omics composite networks were constructed. Subsequently, random walk with restart was expanded to a multi-omics composite network to identify and prioritize the metabolites according to the AF-related seed genes deposited in the OMIM database, the whole metabolome as candidates and the phenotype of AF. Using the interaction score among metabolites, we extracted the top 50 metabolites, and identified the top 100 co-expressed genes interacted with the top 50 metabolites. Based on the FDR <0.05, 622 DEGs were extracted. In order to demonstrate the intrinsic mode of this method, we sorted the metabolites of the composite network in descending order based on the interaction scores. The top 5 metabolites were respectively weighed potassium, sodium ion, chitin, benzo[a]pyrene-7,8-dihydrodiol-9,10-oxide, and celebrex (TN). Potassium and sodium ion possessed higher degrees in the subnetwork of the entire composite network and the co-expressed network. Metabolites such as potassium and sodium ion may provide valuable clues for early diagnostic and therapeutic targets for AF.

Regional Differences in mRNA and lncRNA Expression Profiles in Non-Failing Human Atria and Ventricles

The four chambers of the human heart play distinct roles in the maintenance of normal cardiac function, and are differentially affected by inherited/acquired cardiovascular disease. To probe the molecular determinants of these functional differences, we examined mRNA and lncRNA expression profiles in the left (LA) and right (RA) atria, the left (LV) and right (RV) ventricles, and the interventricular septum (IVS) of non-failing human hearts (N = 8). Analysis of paired atrial and ventricular samples (n = 40) identified 5,747 mRNAs and 2,794 lncRNAs that were differentially (>1.5 fold; FDR < 0.05) expressed. The largest differences were observed in comparisons between the atrial (RA/LA) and ventricular (RV/LV/IVS) samples. In every case (e.g., LA vs LV, LA vs RV, etc.), >2,300 mRNAs and >1,200 lncRNAs, corresponding to 17-28% of the total transcripts, were differentially expressed. Heterogeneities in mRNA/lncRNA expression profiles in the LA and RA, as well as in the LV, RV and IVS, were also revealed, although the numbers of differentially expressed transcripts were substantially smaller. Gender differences in mRNA and lncRNA expression profiles were also evident in non-failing human atria and ventricles. Gene ontology classification of differentially expressed gene sets revealed chamber-specific enrichment of numerous signaling pathways.