The role of immune cells in atrial fibrillation

Deciphering mechanisms of cardiomyocytes and non-cardiomyocyte transformation in myocardial remodeling of permanent atrial fibrillation

INTRODUCTION

Atrial fibrillation (AF) is the most prevalent cardiac arrhythmia, and it significantly increases the risk of cardiovascular complications and morbidity, even with appropriate treatment. Tissue remodeling has been a significant topic, while its systematic transcriptional signature remains unclear in AF.

OBJECTIVES

Our study aims to systematically investigate the molecular characteristics of AF at the cellular-level.

METHODS

We conducted single-nuclei RNA-sequencig (snRNA-seq) analysis using nuclei isolated from the left atrial appendage (LAA) of AF patients and sinus rhythm. Pathological staining was performed to validate the key findings of snRNA-seq.

RESULTS

A total of 30 cell subtypes were identified among 80, 592 nuclei. Within the LAA of AF, we observed a specific subtype of dedifferentiated cardiomyocytes (CMs) characterized by reduced expression of cardiac contractile proteins (TTN and TRDN) and heightened expression of extracellular-matrix related genes (COL1A2 and FBN1). Transcription factor prediction analysis revealed that gene expression patterns in dedifferentiated CMs were primarily regulated by CEBPG and GISLI. Additionally, we identified a distinct subtype of endothelial progenitor cells (EPCs) demonstrating elevated expression of PROM1 and KDR, a population decreased within the LAA of AF. Epicardial adipocytes disclosed a reduced release of the anti-inflammatory and anti-fibrotic factor PRG4, and an augmented secretion of VEGF signals targeting CMs. Additionally, we noted accumulation of M2-like macrophages and CD8+ T cells with high pro-inflammatory score in LAA of AF. Furthermore, the analysis of intercellular communication revealed specific pathways related to AF, such as inflammation, extracellular matrix, and vascular remodeling signals.

CONCLUSIONS

This study has discovered the presence of dedifferentiated CMs, a decrease in endothelial progenitor cells, a shift in the secretion profile of adipocytes, and an amplified inflammatory response in AF. These findings could offer crucial insights for future research on AF and serve as valuable references for investigating novel therapeutic approaches for AF.

Targeting the NLRP3 inflammasome signalling for the management of atrial fibrillation

Inflammatory signalling via the nod-like receptor (NLR) family pyrin domain-containing protein-3 (NLRP3) inflammasome has recently been implicated in the pathophysiology of atrial fibrillation (AF). However, the precise role of the NLRP3 inflammasome in various cardiac cell types is poorly understood. Targeting components or products of the inflammasome and preventing their proinflammatory consequences may constitute novel therapeutic treatment strategies for AF. In this review, we summarise the current understanding of the role of the inflammasome in AF pathogenesis. We first review the NLRP3 inflammasome pathway and inflammatory signalling in cardiomyocytes, (myo)fibroblasts and immune cells, such as neutrophils, macrophages and monocytes. Because numerous compounds targeting NLRP3 signalling are currently in preclinical development, or undergoing clinical evaluation for other indications than AF, we subsequently review known therapeutics, such as colchicine and canakinumab, targeting the NLRP3 inflammasome and evaluate their potential for treating AF.

Pathophysiology and clinical relevance of atrial myopathy

Atrial myopathy is a condition that consists of electrical, structural, contractile, and autonomic remodeling of the atria and is the substrate for development of atrial fibrillation, the most common arrhythmia. Pathophysiologic mechanisms driving atrial myopathy are inflammation, oxidative stress, atrial stretch, and neurohormonal signals, e.g., angiotensin-II and aldosterone. These mechanisms initiate the structural and functional remodeling of the atrial myocardium. Novel therapeutic strategies are being developed that target the pathophysiologic mechanisms of atrial myopathy. In this review, we will discuss the pathophysiology of atrial myopathy, as well as diagnostic and therapeutic strategies.

Inhibition of the P2X7 receptor prevents atrial proarrhythmic remodeling in experimental post-operative atrial fibrillation

BACKGROUND

Post-operative atrial fibrillation (POAF) is a common complication in patients undergoing cardiac surgery. The purinergic receptor P2X7 (P2X7R) is involved in some cardiovascular diseases, whereas its effects on atrial fibrillation (AF) are unclear.

OBJECTIVE

This study was to assess the effect of P2X7R on atrial arrhythmogenic remodeling in the rat model of sterile pericarditis (SP).

METHODS

Male Sprague-Dawley (SD) rats were used to induce the SP model. Electrocardiogram, atrial electrophysiological protocol, histology, mRNA sequencing, real-time quantitative PCR, western blot, and Elisa assay were performed.

RESULTS

SP significantly up-regulated P2X7R expression; increased AF susceptibility; reduced the protein expression of ion channels including Nav1.5, Cav1.2, Kv4.2, Kv4.3, and Kv1.5; caused atrial fibrosis; increased norepinephrine (NE) level in plasma; promoted the production of inflammatory cytokines such as TNF-α, IL-1β, and IL-6; increased the accumulation of immune cells (CD68- and MPO- positive cells); and activated NLRP3 inflammasome signaling pathway. P2X7R antagonist Brilliant Blue G (BBG) mitigated SP-induced alterations. The mRNA sequencing demonstrated that BBG prevented POAF mainly by regulating the immune system. In addition, another selective P2X7R antagonist A740003, and IL-1R antagonist anakinra also reduced AF inducibility in the SP model.

CONCLUSIONS

P2X7R inhibition prevents SP-induced atrial proarrhythmic remodeling, which is closely associated with the improvement of inflammatory changes, ion channel expression, atrial fibrosis, and sympathetic activation. The findings point to P2X7R inhibition as a promising target for AF (particularly POAF) and perhaps other conditions.

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.

The Role of Immune Cells Driving Electropathology and Atrial Fibrillation

Atrial fibrillation (AF) is the most common progressive cardiac arrhythmia worldwide and entails serious complications including stroke and heart failure. Despite decades of clinical research, the current treatment of AF is suboptimal. This is due to a lack of knowledge on the mechanistic root causes of AF. Prevailing theories indicate a key role for molecular and structural changes in driving electrical conduction abnormalities in the atria and as such triggering AF. Emerging evidence indicates the role of the altered atrial and systemic immune landscape in driving this so-called electropathology. Immune cells and immune markers play a central role in immune remodeling by exhibiting dual facets. While the activation and recruitment of immune cells contribute to maintaining atrial stability, the excessive activation and pronounced expression of immune markers can foster AF. This review delineates shifts in cardiac composition and the distribution of immune cells in the context of cardiac health and disease, especially AF. A comprehensive exploration of the functions of diverse immune cell types in AF and other cardiac diseases is essential to unravel the intricacies of immune remodeling. Usltimately, we delve into clinical evidence showcasing immune modifications in both the atrial and systemic domains among AF patients, aiming to elucidate immune markers for therapy and diagnostics.

Opioid prescription and risk of atrial fibrillation in younger veterans

BACKGROUND

Opioids may play a part in the development of atrial fibrillation (AF). Understanding the relationship between opioid exposure and AF can help providers better assess the risk and benefits of prescribing opioids.

OBJECTIVE

To assess the incidence of AF as a function of prescribed opioids and opioid type.

DESIGN

We performed unadjusted and adjusted time-updated Cox regressions to assess the association between opioid exposure and incident AF.

PARTICIPANTS

The national study sample was comprised of Veterans enrolled in the Veterans Health Administration (VHA) who served in support of post-9/11 operations.

MAIN MEASURES

The main predictor of interest was prescription opioid exposure, which was treated as a time-dependent variable. The first was any opioid exposure (yes/no). Secondary was opioid type. The outcome, incident AF, was identified through ICD-9-CM diagnostic codes at any primary care visit after the baseline period.

KEY RESULTS

A total of 609,763 veterans (mean age: 34 years and 13.24% female) were included in our study. Median follow-up time was 4.8 years. Within this cohort, 124,395 veterans (20.40%) were prescribed an opioid. A total of 1,455 Veterans (0.24%) were diagnosed with AF. In adjusted time-updated Cox regressions, the risk of incident AF was higher in the veterans prescribed opioids (hazard ratio [HR]: 1.47; 95% confidence interval [CI]: 1.38-1.57). In adjusted time-updated Cox regressions, both immunomodulating and nonimmunomodulating opioid type was associated with increased risk of incident AF (HR: 1.40; 95% CI: 1.25-1.57 and HR: 1.49; 95% CI: 1.39-1.60), compared to no opioid use, respectively.

CONCLUSIONS

Our findings suggest opioid prescription may be a modifiable risk factor for the development of AF.

Identification and validation of potential biomarkers for atrial fibrillation based on integrated bioinformatics analysis

Background: Globally, the most common form of arrhythmias is atrial fibrillation (AF), which causes severe morbidity, mortality, and socioeconomic burden. The application of machine learning algorithms in combination with weighted gene co-expression network analysis (WGCNA) can be used to screen genes, therefore, we aimed to screen for potential biomarkers associated with AF development using this integrated bioinformatics approach. Methods: On the basis of the AF endocardium gene expression profiles GSE79768 and GSE115574 from the Gene Expression Omnibus database, differentially expressed genes (DEGs) between AF and sinus rhythm samples were identified. DEGs enrichment analysis and transcription factor screening were then performed. Hub genes for AF were screened using WGCNA and machine learning algorithms, and the diagnostic accuracy was assessed by the receiver operating characteristic (ROC) curves. GSE41177 was used as the validation set for verification. Subsequently, we identified the specific signaling pathways in which the key biomarkers were involved, using gene set enrichment analysis and reverse prediction of mRNA-miRNA interaction pairs. Finally, we explored the associations between the hub genes and immune microenvironment and immune regulation. Results: Fifty-seven DEGs were identified, and the two hub genes, hypoxia inducible factor 1 subunit alpha inhibitor (HIF1AN) and mitochondrial inner membrane protein MPV17 (MPV17), were screened using WGCNA combined with machine learning algorithms. The areas under the receiver operating characteristic curves for MPV17 and HIF1AN validated that two genes predicted AF development, and the differential expression of the hub genes was verified in the external validation dataset. Enrichment analysis showed that MPV17 and HIF1AN affect mitochondrial dysfunction, oxidative stress, gap junctions, and other signaling pathway functions. Immune cell infiltration and immunomodulatory correlation analyses showed that MPV17 and HIF1AN are strongly correlated with the content of immune cells and significantly correlated with HLA expression. Conclusion: The identification of hub genes associated with AF using WGCNA combined with machine learning algorithms and their correlation with immune cells and immune gene expression can elucidate the molecular mechanisms underlying AF occurrence. This may further identify more accurate and effective biomarkers and therapeutic targets for the diagnosis and treatment of 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.

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.

The neutrophil-to-lymphocyte ratio is a potential biomarker for the occurrence of atrial fibrillation in patients with obstructive sleep apnea: A BIOMARKER OF AF IN OSA PATIENTS

BACKGROUND

Obstructive sleep apnea (OSA) affects the occurrence of atrial fibrillation (AF) and usually coexists with AF. Chronic inflammation has been identified as an important factor in the development of AF, and the neutrophil-to-lymphocyte ratio (NLR) has been identified as a biomarker that positively correlates with the degree of inflammation. However, little information regarding how NLR correlates with AF in OSA patients.

METHODS

Our study enrolled 368 patients with OSA between September 2018 and April 2023. All data were collected after admission. Independently associated factors were assessed by multivariate logistic regression and then constructed a nomogram to predict AF risk. Nomogram's calculation model was evaluated using receiver operating characteristic (ROC) curves, calibration curves, and decision curve analysis (DCA). The correlation between CHA2DS2-VASc scores and NLR was assessed using Spearman correlation.

RESULTS

Multivariate logistic regression showed that high level log-transformed NLR (OR, 1.664; 95% CI, 1.026-2.699; P = 0.039) was independently associated with the presence of AF in patients with OSA. The concordance index (0.817, 95% CI, 0.770-0.864), ROC curve, calibration curve, and DCA of the nomogram indicated this model had well clinical utility. Also, the nomogram's calculation model could identify patients who are at a higher risk of developing AF, and the CHA2DS2-VASc score was positively correlated with NLR in patients with AF (P < 0.05).

CONCLUSION

The elevated NLR may serve as a promising biomarker for assessing the risk of AF in individuals with OSA. The nomogram's calculation model may be utilized as a tool to estimate the probability of AF occurrence in OSA patients.

Construction and validation of a cuproptosis-related diagnostic gene signature for atrial fibrillation based on ensemble learning

BACKGROUND

Atrial fibrillation (AF) is the most common type of cardiac arrhythmia. Nonetheless, the accurate diagnosis of this condition continues to pose a challenge when relying on conventional diagnostic techniques. Cell death is a key factor in the pathogenesis of AF. Existing investigations suggest that cuproptosis may also contribute to AF. This investigation aimed to identify a novel diagnostic gene signature associated with cuproptosis for AF using ensemble learning methods and discover the connection between AF and cuproptosis.

RESULTS

Two genes connected to cuproptosis, including solute carrier family 31 member 1 (SLC31A1) and lipoic acid synthetase (LIAS), were selected by integration of random forests and eXtreme Gradient Boosting algorithms. Subsequently, a diagnostic model was constructed that includes the two genes for AF using the Light Gradient Boosting Machine (LightGBM) algorithm with good performance (the area under the curve value > 0.75). The microRNA-transcription factor-messenger RNA network revealed that homeobox A9 (HOXA9) and Tet methylcytosine dioxygenase 1 (TET1) could target SLC31A1 and LIAS in AF. Functional enrichment analysis indicated that cuproptosis might be connected to immunocyte activities. Immunocyte infiltration analysis using the CIBERSORT algorithm suggested a greater level of neutrophils in the AF group. According to the outcomes of Spearman's rank correlation analysis, there was a negative relation between SLC31A1 and resting dendritic cells and eosinophils. The study found a positive relationship between LIAS and eosinophils along with resting memory CD4+ T cells. Conversely, a negative correlation was detected between LIAS and CD8+ T cells and regulatory T cells.

CONCLUSIONS

This study successfully constructed a cuproptosis-related diagnostic model for AF based on the LightGBM algorithm and validated its diagnostic efficacy. Cuproptosis may be regulated by HOXA9 and TET1 in AF. Cuproptosis might interact with infiltrating immunocytes in AF.

Advances in basic and translational research in atrial fibrillation

Atrial fibrillation (AF) is a very common cardiac arrhythmia with an estimated prevalence of 33.5 million patients globally. It is associated with an increased risk of death, stroke and peripheral embolism. Although genetic studies have identified a growing number of genes associated with AF, the definitive impact of these genetic findings is yet to be established. Several mechanisms, including electrical, structural and neural remodelling of atrial tissue, have been proposed to contribute to the development of AF. Despite over a century of exploration, the molecular and cellular mechanisms underlying AF have not been fully established. Current antiarrhythmic drugs are associated with a significant rate of adverse events and management of AF using ablation is not optimal, especially in cases of persistent AF. This review discusses recent advances in our understanding and management of AF, including new concepts of epidemiology, genetics and pathophysiological mechanisms. We review the current status of antiarrhythmic drug therapy for AF, new potential agents, as well as mechanism-based AF ablation. This article is part of the theme issue 'The heartbeat: its molecular basis and physiological mechanisms'.

Association between circulating leukocytes and arrhythmias: Mendelian randomization analysis in immuno-cardiac electrophysiology

Background

Cardiac arrhythmia is a common disease associated with high mortality and morbidity. Circulating leukocyte counts, which serve as a biomarker for assessing systemic immune status, have been linked to arrhythmias in observational studies. However, observational studies are plagued by confounding factors and reverse causality, whether alterations in circulating leukocyte components are causally associated with arrhythmias remains uncertain. The present study explored this question based on genetic evidence.

Methods and findings

We performed Mendelian randomization (MR) analysis to evaluate whether alterations in leukocyte counts affect aggregated risk of all types of arrhythmia or risk of five specific types of arrhythmia. Single-nucleotide polymorphisms serving as proxies for leukocyte differential counts were retrieved from the Blood Cell Consortium, and statistical data on arrhythmias were obtained from the UK Biobank), FinnGenand a meta-analysis of genome-wide association studies for atrial fibrillation. We applied inverse variance-weighted method as the primary analysis, complemented by a series of sensitivity analyses. Bidirectional analyses were conducted to assess reverse causality. Finally, multivariable MR was performed to study the joint effects of multiple risk factors. We found that genetically predicted differential leukocyte counts were not significantly associated with aggregated occurrence of all types of arrhythmia. In contrast, each 1-standard deviation increase in lymphocyte count was associated with 46% higher risk of atrioventricular block (OR 1.46, 95% CI 1.11–1.93, p=0.0065). A similar effect size was observed across all MR sensitivity analyses, with no evidence of horizontal pleiotropy. Reverse MR analysis suggested that atrioventricular block was unlikely to cause changes in lymphocyte count. Primary MR analysis based on the inverse-variance weighted method suggested that changes in neutrophil count alter risk of right bundle branch block, and changes in basophil count alter risk of atrial fibrillation. However, these causal relationships were not robust in sensitivity analyses. We found no compelling evidence that neutrophil or lymphocyte counts cause atrial fibrillation.

Conclusion

Our data support higher lymphocyte count as a causal risk factor for atrioventricular block. These results highlight the importance of immune cells in the pathogenesis of specific cardiac conduction disorders.

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.

Characteristics of immune clusters and cell abundance in patients with different subtypes of nonparoxysmal atrial fibrillation

Atrial fibrillation (AF) is the most common sustained arrhythmia in clinical practice. Inflammation plays an important role in the initiation and perpetuation of AF. The present study was conducted to characterize immune clusters in nonparoxysmal AF and to distinguish immune subtypes of nonparoxysmal AF. Immune-related algorithms (CIBERSORT, ESTIMATE, and ssGSEA) were used to evaluate the immune cluster characterization and cell abundance, and multivariable logistics analysis was performed to determine the most relevant immune cells. We identified differentially expressed genes (DEGs) and used consensus clustering analysis to identify nonparoxysmal AF subtypes. Weighted gene coexpression network analysis (WGCNA) was used for finding highly correlated gene sets and attach to external sample traits. And it was conducted twice to identify the immune- and subtype- related modules. Finally, Metascape was used to compare the biological functions of the two nonparoxysmal AF subtypes we obtained. CytoHubba was used to identify the hub genes of these two subtypes. Based on the results of bioinformatics analysis, regulatory T cells, resting NK cells, active mast cells and neutrophils were considered to be closely related to nonparoxysmal AF. The brown module was identified as the most relevant module to the above immune cells by WGCNA. We identified two major nonparoxysmal AF subtypes by consensus clustering analysis and their enriched biological functions by Metascape. The hub genes are TYROBP, PTPRC, ITGB2, SPI1, PLEK, and CSF1R in permanent AF and JAM3, S100P, ARPC5, TRIM34, and GREB1L in persistent AF. This study revealed two major nonparoxysmal AF subtypes and eleven hub genes, which provide potential therapeutic targets for anti-inflammatory treatments of nonparoxysmal AF.

Causal association of peripheral immune cell counts and atrial fibrillation: A Mendelian randomization study

Background

Atrial fibrillation (AF) is the most common and persistent form of arrhythmia. Recently, increasing evidence has shown a link between immune responses and atrial fibrillation. However, whether the immune response is a cause or consequence of AF remains unknown. We aimed to determine whether genetically predicted peripheral immunity might have a causal effect on AF.

Methods

First, we performed Mendelian randomization (MR) analyses using genetic variants strongly associated with neutrophil, eosinophil, basophil, lymphocyte, and monocyte cell counts as instrumental variables (IVs). Lymphocyte counts were then subjected to further subgroup analysis. The effect of immune cell counts on AF risk was measured using summary statistics from genome-wide association studies (GWAS).

Results

Two-sample MR analysis revealed that a higher neutrophil count, basophil count and lymphocyte count had a causal effect on AF [Odds ratio (OR), 1.06, 95% confidence interval (CI), 1.01-1.10, P = 0.0070; OR, 1.10; 95% CI, 1.04-1.17; P = 0.0015; OR, 0.96; 95% CI, 0.93-0.99; P = 0.0359]. In addition, in our further analysis, genetically predicted increases in CD4 + T-cell counts were also associated with an increased risk of AF (OR, 1.04; 95% CI, 1.0-.09; P = 0.0493).

Conclusion

Our MR analysis provided evidence of a genetically predicted causal relationship between higher peripheral immune cell counts and AF. Subgroup analysis revealed the key role of peripheral lymphocytes in AF, especially the causal relationship between CD4 + T cell count and AF. These findings are beneficial for future exploration of the mechanism of AF.

Immune infiltration and immunophenotyping in atrial fibrillation

Atrial fibrillation (AF) is a relatively common arrhythmia in clinical practice. Although significant progress has been achieved in the treatment of AF and its associated complications, research on AF prevention lags behind, mainly due to the lack of a deep understanding of AF pathogenesis. In recent years, as our knowledge has grown, the role of the inflammatory/immune response in the occurrence and progression of AF has gradually gained attention. In this paper, based on existing gene expression data in the Gene Expression Omnibus database, a detailed description of immune infiltration status in AF is presented using a series of analytical methods, including differential analysis, Gene Ontology categorization, Kyoto Encyclopedia of Genes and Genomes enrichment analysis, and weighted gene coexpression network analysis, and analysis tools such as CIBERSORTx and Cytoscape. Several new AF/immune infiltrations-related signature genes were identified, and the AF/immune infiltration pathology was classified based on these immune signature genes, thus providing novel insights into the pathogenesis of AF based on the inflammatory response.

The association between systemic inflammatory response index and new-onset atrial fibrillation in patients with ST-elevated myocardial infarction treated with percutaneous coronary intervention

BACKGROUND

New-onset atrial fibrillation (NOAF) complicating with ST-elevated myocardial infarction (STEMI) patients following percutaneous coronary intervention (PCI) is associated with worse prognosis. The systemic inflammatory response index (SIRI), serves as a novel inflammatory indicator, is found to be predictive of adverse outcomes. The aim of this study is to explore the association between NOAF and SIRI.

METHODS

A retrospective data included 616 STEMI participants treated with PCI in our cardiology department had been analyzed in present investigation, of which being divided into a NOAF or sinus rhythm (SR) group based on the presence or absence of atrial fibrillation. The predictive role of SIRI for in detecting NOAF had been evaluated by the logistic regression analyses and receiver operating characteristic (ROC) curve. Additionally, long-term all-cause mortality between both groups was compared using the Kaplan-Meier test.

RESULTS

NOAF during hospitalization developed in 7.6% of PCI-treated individuals. After multivariate regression analyses, SIRI remains to be an independently predictor of NOAF (odds ratio 1.782, 95% confidence interval 1.675-1.906, P = 0.001). In the ROC curve analysis, SIRI with a cut-off value of 4.86 was calculated to predict NOAF, with 4.86, with a sensitivity of 80.85% and a specificity of 75.57%, respectively (area under the curve (AUC) = 0.826, P < 0.001). Furthermore, pairwise compassion of ROC curves displayed the superiority of SIRI in the prediction of NOAF in comparison with that of neutrophil/lymphocyte or monocyte/lymphocyte (P < 0.05). In addition, the participants in NOAF group had a significantly higher incidence of all-cause death compared to those in SR group after a median of 40-month follow-up (22.0% vs 5.8%, log-rank P < 0.001).

CONCLUSION

SIRI can independently predict NOAF in patients with STEMI after PCI, with being positively correlated to worsened outcomes.

Integrative identification of immune-related key genes in atrial fibrillation using weighted gene coexpression network analysis and machine learning

Background

The immune system significantly participates in the pathologic process of atrial fibrillation (AF). However, the molecular mechanisms underlying this participation are not completely explained. The current research aimed to identify critical genes and immune cells that participate in the pathologic process of AF.

Methods

CIBERSORT was utilized to reveal the immune cell infiltration pattern in AF patients. Meanwhile, weighted gene coexpression network analysis (WGCNA) was utilized to identify meaningful modules that were significantly correlated with AF. The characteristic genes correlated with AF were identified by the least absolute shrinkage and selection operator (LASSO) logistic regression and support vector machine recursive feature elimination (SVM-RFE) algorithm.

Results

In comparison to sinus rhythm (SR) individuals, we observed that fewer activated mast cells and regulatory T cells (Tregs), as well as more gamma delta T cells, resting mast cells, and M2 macrophages, were infiltrated in AF patients. Three significant modules (pink, red, and magenta) were identified to be significantly associated with AF. Gene enrichment analysis showed that all 717 genes were associated with immunity- or inflammation-related pathways and biological processes. Four hub genes (GALNT16, HTR2B, BEX2, and RAB8A) were revealed to be significantly correlated with AF by the SVM-RFE algorithm and LASSO logistic regression. qRT–PCR results suggested that compared to the SR subjects, AF patients exhibited significantly reduced BEX2 and GALNT16 expression, as well as dramatically elevated HTR2B expression. The AUC measurement showed that the diagnostic efficiency of BEX2, HTR2B, and GALNT16 in the training set was 0.836, 0.883, and 0.893, respectively, and 0.858, 0.861, and 0.915, respectively, in the validation set.

Conclusions

Three novel genes, BEX2, HTR2B, and GALNT16, were identified by WGCNA combined with machine learning, which provides potential new therapeutic targets for the early diagnosis and prevention of AF.

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.

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.

Elevated Peripheral T Helper Cells Are Associated With Atrial Fibrillation in Patients With Rheumatoid Arthritis

Patients with rheumatoid arthritis (RA) have a significantly high risk of atrial fibrillation (AF). This study aimed to compare the absolute and relative changes in peripheral T cells in patients with RA who were also affected with and without AF. To help make an early diagnosis and prevent the initiation and progression of AF, the changes in the lymphocyte subsets were assessed in RA patients with and without AF. A propensity score matching (PSM) system (1:3) was used to perform a matched case-control study with 40 RA-AF cases and 120 RA controls. Changes in the erythrocyte sedimentation rate (ESR), C-reactive protein (CRP), anti-citrullinated peptide antibody (ACPA), and rheumatoid factor (RF) were examined. The percentage and absolute number of T, B, natural killer (NK), T helper (Th)1, Th2, Th17, and T-regulatory (Treg) cells in the peripheral blood of patients with and without RA-AF were determined using flow cytometry. Univariate and multivariate analyses were performed to determine the association between peripheral lymphocytes and RA-AF. Demographic data, ESR, CRP, ACPA, and the percentage, as well as the absolute value of B, NK, Th2, and Treg cells, showed no significant differences between the propensity score-matched groups of RA and RA-AF. Meanwhile, the absolute number and percentage of Th1 cells, the absolute number of Th17 cells, the ratio of Th1/Treg, Th17/Treg, and RF were significantly higher in patients with RA-AF than those in the control groups (P < 0.05). Univariate and multivariate logistic regression analyses also revealed that the percentage of Th1 cells, the absolute number of Th17 cells, and the ratio of Th1/Treg were associated with a significantly higher risk of AF. This PSM study demonstrated that the incidence of AF was higher in RA patients with Th cell immunological derangements.

lncRNA KCNQ1OT1 may function as a competitive endogenous RNA in atrial fibrillation by sponging miR‑223‑3p

Atrial fibrillation (AF) is one of the most common forms of cardiac arrhythmia. Novel evidence has indicated that a competing endogenous RNA (ceRNA) mechanism may occur in AF. The present study aimed to identify differentially expressed microRNAs (miRNAs/miRs) in AF and predict their targeting long non-coding RNAs (lncRNAs) to identify a potential ceRNA network involved in AF using bioinformatics analysis. The GSE68475 microarray dataset was downloaded from the Gene Expression Omnibus database and differentially expressed miRNAs in AF were obtained. In addition, right atrial appendage (RAA) tissues from patients with AF were collected to determine the expression levels of the miRNAs identified following bioinformatics analysis using reverse transcription-quantitative PCR (n=8 per group). Subsequently, Gene Ontology (GO) functional term and Kyoto Encyclopedia of Genes and Genomes (KEGG) signaling pathway enrichment analyses of the target genes of differentially expressed miRNAs of interest were performed. The potential upstream lncRNAs targeting the identified miRNAs were predicted using bioinformatics analysis. A dual luciferase reporter assay was used to verify the existence of a targeted relationship between the differentially expressed miRNA and lncRNA of interest. The results identified 43 differentially expressed miRNAs, including 23 upregulated miRNAs. The trends in the expression levels of miR-223-3p were inconsistent between the microarray data and those recorded in the RAA tissues from patients with persistent AF. Therefore, miR-223-3p was selected as the miRNA of interest for further investigations. The target gene of miR-233-3p was found to be enriched in 57 GO terms and 21 KEGG signaling pathways. According to the bioinformatics prediction, 69 lncRNAs targeting miR-223-3p were identified, including the lncRNA growth arrest-specific transcript 5, lncRNA KCNQ1 opposite strand/antisense transcript 1 (KCNQ1OT1) and lncRNA MYC-induced long non-coding RNA. The results from dual luciferase assay confirmed that miR-223-3p was a direct target of KCNQ1OT1. A ceRNA regulatory relationship may exist between KCNQ1OT1 and miR-223-3p in AF, providing therefore a novel potential research target for further studies.

Identification of Hub mRNAs and lncRNAs in Atrial Fibrillation Using Weighted Co-expression Network Analysis With RNA-Seq Data

Atrial fibrillation (AF)/paroxysmal AF (PAF) is the main cause of cardiogenic embolism. In recent years, the progression from paroxysmal AF to persistent AF has attracted more and more attention. However, the molecular mechanism of the progression of AF is unclear. In this study, we performed RNA sequencing for normal samples, paroxysmal AF and persistent AF samples to identify differentially expressed gene (DEG) and explore the roles of these DEGs in AF. Totally, 272 differently expressed mRNAs (DEmRNAs) and 286 differentially expressed lncRNAs (DElncRNAs) were identified in paroxysmal AF compared to normal samples; 324 DEmRNAs and 258 DElncRNAs were found in persistent atrial fibrillation compared with normal samples; and 520 DEmRNAs and 414 DElncRNAs were identified in persistent AF compared to paroxysmal AF samples. Interestingly, among the DEGs, approximately 50% were coding genes and around 50% were non-coding RNAs, suggesting that lncRNAs may also have a crucial role in the progression of AF. Bioinformatics analysis demonstrated that these DEGs were significantly related to regulating multiple AF associated pathways, such as the regulation of vascular endothelial growth factor production and binding to the CXCR chemokine receptor. Furthermore, weighted gene co-expression network analysis (WGCNA) was conducted to identify key modules and hub RNAs and lncRNAs to determine their potential associations with AF. Five hub modules were identified in the progression of AF, including blue, brown, gray, turquoise and yellow modules. Interestingly, blue module and turquoise module were significantly negatively and positively correlated to the progression of AF respectively, indicating that they may have a more important role in the AF. Moreover, the hub protein-protein interaction (PPI) networks and lncRNA-mRNA regulatory network were constructed. Bioinformatics analysis on the hub PPI network in turquoise was involved in regulating immune response related signaling, such as leukocyte chemotaxis, macrophage activation, and positive regulation of α-β T cell activation. Our findings could clarify the underlying molecular changes associated fibrillation, and provide a useful resource for identifying AF marker.

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.

Identifying ceRNA Networks Associated With the Susceptibility and Persistence of Atrial Fibrillation Through Weighted Gene Co-Expression Network Analysis

Background: Atrial fibrillation (AF) is the most common arrhythmia. We aimed to construct competing endogenous RNA (ceRNA) networks associated with the susceptibility and persistence of AF by applying the weighted gene co-expression network analysis (WGCNA) and prioritize key genes using the random walk with restart on multiplex networks (RWR-M) algorithm.

Methods: RNA sequencing results from 235 left atrial appendage samples were downloaded from the GEO database. The top 5,000 lncRNAs/mRNAs with the highest variance were used to construct a gene co-expression network using the WGCNA method. AF susceptibility- or persistence-associated modules were identified by correlating the module eigengene with the atrial rhythm phenotype. Using a module-specific manner, ceRNA pairs of lncRNA–mRNA were predicted. The RWR-M algorithm was applied to calculate the proximity between lncRNAs and known AF protein-coding genes. Random forest classifiers, based on the expression value of key lncRNA-associated ceRNA pairs, were constructed and validated against an independent data set.

Results: From the 21 identified modules, magenta and tan modules were associated with AF susceptibility, whereas turquoise and yellow modules were associated with AF persistence. ceRNA networks in magenta and tan modules were primarily involved in the inflammatory process, whereas ceRNA networks in turquoise and yellow modules were primarily associated with electrical remodeling. A total of 106 previously identified AF-associated protein-coding genes were found in the ceRNA networks, including 16 that were previously implicated in the genome-wide association study. Myocardial infarction–associated transcript (MIAT) and LINC00964 were prioritized as key lncRNAs through RWR-M. The classifiers based on their associated ceRNA pairs were able to distinguish AF from sinus rhythm with respective AUC values of 0.810 and 0.940 in the training set and 0.870 and 0.922 in the independent test set. The AF-related single-nucleotide polymorphism rs35006907 was found in the intronic region of LINC00964 and negatively regulated the LINC00964 expression.

Conclusion: Our study constructed AF susceptibility- and persistence-associated ceRNA networks, linked genetics with epigenetics, identified MIAT and LINC00964 as key lncRNAs, and constructed random forest classifiers based on their associated ceRNA pairs. These results will help us to better understand the mechanisms underlying AF from the ceRNA perspective and provide candidate therapeutic and diagnostic tools.

Systemic immune-inflammation index predicts new-onset atrial fibrillation after ST elevation myocardial infarction

Aim: To investigate the predictive capacity of a systemic immune-inflammation index (SII) in detecting new-onset atrial fibrillation (NOAF) following ST segment elevation myocardial infarction (STEMI). Patients & methods: A total of 402 STEMI patients were enrolled in the study. The patients were divided into two groups according to NOAF development. Results: A cut-off point of 1,228,000 for SII was identified with 60% sensitivity and 78.1% specificity to predict NOAF following STEMI. According to pairwise analysis of receiver operating characteristic curve analysis, the predictive power of SII in detecting NOAF following STEMI was similar to high-sensitive C-reactive protein, and better than neutrophil-to-lymphocyte ratio or platelet-to-lymphocyte ratio. Conclusion: SII can be used as one of the independent predictors of NOAF following STEMI.

Increased Risk of Atrial Fibrillation in Patients with Atopic Triad: A Nationwide Population-Based Study

BACKGROUND

Despite a sharp increase in the global prevalence of allergy over the past decade, the relation between multiple atopic conditions and atrial fibrillation (AF) has not been fully elucidated.

OBJECTIVE

To determine whether there is an association between atopic diseases and AF and to examine the effect of multiple atopic diseases on the incidence of AF.

METHODS

This retrospective population-based study used the database from the 2009 National Health Insurance Services-Health Screening Cohort in Korea. A total of 6,748,564‬ subjects without a previous history of AF were included in the final analysis and observed until 2017. The atopic triad included asthma, allergic rhinitis, and atopic dermatitis. A total of 1,168,196‬ subjects (17.3%) with at least one atopic disease were classified as the atopic group. The primary outcome was new-onset AF.

RESULTS

During a median 7.2 ± 1.0 years of follow-up, 136,253‬ subjects were given the new diagnosis of AF (30,300 in the atopic group and 105,953 in the nonatopic group). The incidence of AF was 3.63/1000 person-years in the atopic group and 2.64/1000 person-years in the nonatopic group. The risk for AF showed a positive correlation with the number of diseases in the atopic triad (adjusted hazard ratio [aHR], 95% confidence interval [CI]: one disease: aHR = 1.15, CI, 1.14-1.17; two diseases: aHR = 1.34, CI, 1.31-1.38; and three diseases: aHR = 1.35, CI, 1.11-1.66; P for trend < .001).

CONCLUSIONS

The atopic triad of asthma, allergic rhinitis, and atopic dermatitis was associated with an increased risk for AF. Moreover, multiple atopic conditions have a higher risk for AF.

Role of Warburg Effect in Cardiovascular Diseases: A Potential Treatment Option

Background:

Under normal conditions, the heart obtains ATP through the oxidation of fatty acids, glucose, and ketones. While fatty acids are the main source of energy in the heart, under certain conditions, the main source of energy shifts to glucose where pyruvate converts into lactate, to meet the energy demand. The Warburg effect is the energy shift from oxidative phosphorylation to glycolysis in the presence of oxygen. This effect is observed in tumors as well as in diseases, including cardiovascular diseases. If glycolysis is more dominant than glucose oxidation, the two pathways uncouple, contributing to the severity of the heart condition. Recently, several studies have documented changes in metabolism in several cardiovascular diseases; however, the specific mechanisms remain unclear.

Methods:

This literature review was conducted by an electronic database of Pub Med, Google Scholar, and Scopus published until 2020. Relevant papers are selected based on inclusion and exclusion criteria.

Results:

A total of 162 potentially relevant articles after the title and abstract screening were screened for full-text. Finally, 135 papers were included for the review article.

Discussion:

This review discusses the effects of alterations in glucose metabolism, particularly the Warburg effect, on cardiovascular diseases, including heart failure, atrial fibrillation, and cardiac hypertrophy.

Conclusion:

Reversing the Warburg effect could become a potential treatment option for cardiovascular diseases.

Potential Target Genes in the Development of Atrial Fibrillation: A Comprehensive Bioinformatics Analysis

Background

Atrial fibrillation (AF) is the most prevalent arrhythmia worldwide. Although it is not life-threatening, the accompanying rapid and irregular ventricular rate can lead to hemodynamic deterioration and obvious symptoms, especially the risk of cerebrovascular embolism. Our study aimed to identify novel and promising genes that could explain the underlying mechanism of AF development.

Material/Methods

Expression profiles GSE41177, GSE79768, and GSE14975 were acquired from the Gene Expression Omnibus Database. R software was used for identifying differentially expressed genes (DEGs), and Gene Ontology and Kyoto Encyclopedia of Genes and Genomes enrichment analyses were subsequently performed. A protein–protein interaction network was constructed in Cytoscape software. Next, a least absolute shrinkage and selection operator (LASSO) model was constructed and receiver-operating characteristic curve analysis was conducted to assess the specificity and sensitivity of the key genes.

Results

We obtained 204 DEGs from the datasets. The DEGs were mostly involved in immune response and cell communication. The primary pathways of the DEGs were related to the course or maintenance of autoimmune and chronic inflammatory diseases. The top 20 hub genes (high scores in cytoHubba) were selected in the PPI network. Finally, we identified 6 key genes (FCGR3B, CLEC10A, FPR2, IGSF6, S100A9, and S100A12) via the LASSO model.

Conclusions

We present 6 target genes that are potentially involved in the molecular mechanisms of AF development. In addition, these genes are likely to serve as potential therapeutic targets.

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

Background

Atrial fibrillation (AF) is the most common persistent arrhythmia. Valvular heart disease (VHD) and AF frequently coexist. In our study, from performing bioinformatics analysis, we sought to identify immune-related genes (IRGs) and explore the role of immune cell infiltration in AF-VHD in depth, aiming at investigating the potential molecular mechanism and developing new therapeutic targets for AF, including AF-VHD.

Methods

The gene expression of the GSE41177 and GSE79768 datasets were downloaded from the Gene Expression Omnibus database. Differentially expressed genes (DEGs) were analyzed via the limma package in Bioconductor with R software. Differentially expressed immune-related genes (DEIRGs) were selected via combination ImmPort database with DEGs, and the enrichment function and pathway analysis were explored. A protein-protein interaction (PPI) network was built with a Search Tool for the Retrieval of Interacting Genes/Proteins plugin in Cytoscape. The CIBERSORT algorithm was used to evaluate immune infiltration in the left atrial (LA) tissues between AF-VHD and sinus rhythm (SR) patients. Finally, a correlation analysis between key DEIRGs and infiltrating immune cells was performed.

Results

A total of 130 DEIRGs were detected. Enrichment function of DEIRGs demonstrated that they are significant in immune and inflammatory responses. The key DEIRGs assessed by the PPI network and involved in both the immune and inflammatory responses were the C-X-C motif chemokine ligand (CXCL) 1, pro-platelet basic protein (PPBP), CXCL12, and C-C motif chemokine ligand 4 (CCL4). The immune infiltration findings indicated that, compared with the LA tissues from SR patients, the tissues from AF-VHD patients contained a higher proportion of gamma delta T cells, but a lower proportion of CD8 and regulatory T cells. The results of correlation analysis demonstrated that CXCL1 was positively correlated with activated mast cells and significantly negatively correlated with resting mast cells. PPBP, CXCL12, and CCL4 were positively correlated with the infiltration of various immune cells, such as neutrophils, plasma cells, and resting dendritic cells.

Conclusions

The key immune-related genes and the differences in immune infiltration in LA tissues play an essential role in the occurrence and progression of AF-VHD.

Does Decreased SNX10 Serve as a Novel Risk Factor in Atrial Fibrillation of the Valvular Heart Disease? - A Case-Control Study

Introduction

Atrial fibrillation (AF) is the most common sustained arrhythmia. Sorting nexin 10 (SNX10) has been reported to be an important regulator in embryonic development and human diseases, however, little is known about its role in cardiac disease. The aim of this study was to investigate the clinical significance of SNX10 expression in AF.

Methods

Nineteen valvular heart disease patients with AF and nine valvular heart disease patients with sinus rhythm (SR) were enrolled. Atrial tissue samples from patients undergoing open heart surgery were examined. Atrial tissues of normal hearts were obtained from two cases’ autopsies. The SNX10 expression and its associations with the degree of fibrosis were analyzed by immunohistochemistry and Masson’s trichrome staining.

Results

SNX10 expression was detected in the cytoplasm of cardiac cells in human myocardial tissue. The SNX10 expression level was higher in the SR group than in the AF group (P=0.023). SNX10 expression was negatively associated with the degree of fibrosis (P=0.017, Spearman rho=-0.447), the New York Heart Association degree (P=0.003, Spearman rho=-0.545), left atrial diameter (P=0.038, Spearman rho=-0.393), right atrial diameter (P=0.043, Spearman rho=-0.386), and the brain natriuretic peptide (BNP) level 24 hours after surgery (P=0.030, Spearman rho=-0.426), but not the BNP level before surgery and 72 hours after surgery. No statistical significance was observed between SNX10 and the level of troponin T and C-reactive protein.

Conclusion

Decreased SNX10 might serve as a potential risk factor in AF of the valvular heart disease.

Integrative transcriptomic, proteomic, and machine learning approach to identifying feature genes of atrial fibrillation using atrial samples from patients with valvular heart disease

Background

Atrial fibrillation (AF) is the most common arrhythmia with poorly understood mechanisms. We aimed to investigate the biological mechanism of AF and to discover feature genes by analyzing multi-omics data and by applying a machine learning approach.

Methods

At the transcriptomic level, four microarray datasets (GSE41177, GSE79768, GSE115574, GSE14975) were downloaded from the Gene Expression Omnibus database, which included 130 available atrial samples from AF and sinus rhythm (SR) patients with valvular heart disease. Microarray meta-analysis was adopted to identified differentially expressed genes (DEGs). At the proteomic level, a qualitative and quantitative analysis of proteomics in the left atrial appendage of 18 patients (9 with AF and 9 with SR) who underwent cardiac valvular surgery was conducted. The machine learning correlation-based feature selection (CFS) method was introduced to selected feature genes of AF using the training set of 130 samples involved in the microarray meta-analysis. The Naive Bayes (NB) based classifier constructed using training set was evaluated on an independent validation test set GSE2240.

Results

863 DEGs with FDR < 0.05 and 482 differentially expressed proteins (DEPs) with FDR < 0.1 and fold change > 1.2 were obtained from the transcriptomic and proteomic study, respectively. The DEGs and DEPs were then analyzed together which identified 30 biomarkers with consistent trends. Further, 10 features, including 8 upregulated genes (CD44, CHGB, FHL2, GGT5, IGFBP2, NRAP, SEPTIN6, YWHAQ) and 2 downregulated genes (TNNI1, TRDN) were selected from the 30 biomarkers through machine learning CFS method using training set. The NB based classifier constructed using the training set accurately and reliably classify AF from SR samples in the validation test set with a precision of 87.5% and AUC of 0.995.

Conclusion

Taken together, our present work might provide novel insights into the molecular mechanism and provide some promising diagnostic and therapeutic targets of AF.

Integrative Identification of Hub Genes Associated With Immune Cells in Atrial Fibrillation Using Weighted Gene Correlation Network Analysis

Background: Atrial fibrillation (AF) is the most common tachyarrhythmia in the clinic, leading to high morbidity and mortality. Although many studies on AF have been conducted, the molecular mechanism of AF has not been fully elucidated. This study was designed to explore the molecular mechanism of AF using integrative bioinformatics analysis and provide new insights into the pathophysiology of AF. Methods: The GSE115574 dataset was downloaded, and Cibersort was applied to estimate the relative expression of 22 kinds of immune cells. Differentially expressed genes (DEGs) were identified through the limma package in R language. Weighted gene correlation network analysis (WGCNA) was performed to cluster DEGs into different modules and explore relationships between modules and immune cell types. Functional enrichment analysis was performed on DEGs in the significant module, and hub genes were identified based on the protein-protein interaction (PPI) network. Hub genes were then verified using quantitative real-time polymerase chain reaction (qRT-PCR). Results: A total of 2,350 DEGs were identified and clustered into eleven modules using WGCNA. The magenta module with 246 genes was identified as the key module associated with M1 macrophages with the highest correlation coefficient. Three hub genes (CTSS, CSF2RB, and NCF2) were identified. The results verified using three other datasets and qRT-PCR demonstrated that the expression levels of these three genes in patients with AF were significantly higher than those in patients with SR, which were consistent with the bioinformatic analysis. Conclusion: Three novel genes identified using comprehensive bioinformatics analysis may play crucial roles in the pathophysiological mechanism in AF, which provide potential therapeutic targets and new insights into the treatment and early detection of AF.

Diabetes Mellitus Promotes Atrial Structural Remodeling and PARP-1/Ikkα/NF-κB Pathway Activation in Mice

BACKGROUND

Diabetes mellitus (DM) has been demonstrated to be linked to atrial fibrillation (AF). However, the underlying mechanisms of the DM-associated increase in AF susceptibility and the potential effects of DM on atrial remodeling remain unclear.

METHODS AND RESULTS

Twenty-five C57BL/6 mice were randomly assigned to the normal/control group (Con, n=10) and model group (n=15). Mice in the model group were administered a high-fat diet combined with multiple injections of low-dose streptozocin (STZ) (35 mg/kg). Eleven mice were ultimately included in DM group. Left atrial tissue structural and inflammatory alterations were assessed. In our study, the atrial weights of DM mice were markedly heavier than those of mice in the Con group. DM mice exhibited significantly increased fasting plasma glucose, fasting insulin, and dyslipidaemia. Furthermore, H&E and Masson's staining revealed broadened interstitial spaces, myocyte disarray and atrial fibrosis in DM mice. The expression levels of the atrial inflammation-associated factor nuclear factor κB (NF-κB) and its pathway were significantly altered in the atria of DM mice.

CONCLUSION

DM could induce atrial structural remodeling and inflammation in mice.

Preceding infection and risk of stroke: An old concept revived by the COVID-19 pandemic

Anecdotal reports and clinical observations have recently emerged suggesting a relationship between COVID-19 disease and stroke, highlighting the possibility that infected individuals may be more susceptible to cerebrovascular events. In this review we draw on emerging studies of the current pandemic and data from earlier, viral epidemics, to describe possible mechanisms by which SARS-CoV-2 may influence the prevalence of stroke, with a focus on the thromboinflammatory pathways, which may be perturbed. Some of these potential mechanisms are not novel but are, in fact, long-standing hypotheses linking stroke with preceding infection that are yet to be confirmed. The current pandemic may present a renewed opportunity to better understand the relationship between infection and stroke and possible underlying mechanisms.

Celiac Disease and Risk of Atrial Fibrillation: A Meta-analysis and Systematic Review

Introduction Several studies have found celiac disease may be associated with a variety of cardiac manifestations. Atrial fibrillation (AF) is one of the most common arrhythmias that can cause significant morbidity. However, the risk of atrial fibrillation in patients with celiac disease according to epidemiological studies remains unclear. The aim of this meta-analysis study is to assess the risk of atrial fibrillation in patients diagnosed with celiac disease compared to controls. Methods A systematic literature review was conducted in MEDLINE, EMBASE, Cochrane databases from inception through December 2017 to identify studies that evaluated the risk of atrial fibrillation in patients with celiac disease. We included randomized controlled trial, cross sectional and cohort studies that reported the odds ratio, relative risk, hazard ratio, and standardized incidence ratio comparing the risk of developing atrial fibrillation among patients with celiac disease, versus patients without celiac disease as control. The Newcastle-Ottawa scale was used to determine the quality of the studies. Effect estimates from individual studies were extracted and combined using random-effect, generic inverse variance method of DerSimonian and Laird. Results Celiac disease is an autoimmune condition. This inflammatory state predisposes patients to develop AF. After a review of the literature, four observational studies with a total of 64,397 participants were enrolled. The association between celiac disease and increased risk of atrial fibrillation was significant, with a pooled OR of 1.38 (95% CI: 1.01-1.88). No publication bias as assessed by the funnel plots and Egger's regression asymmetry test with p = 0.54. However, the heterogeneity of the included studies was high (I2 = 96). Conclusion A significant association between celiac disease and risk of atrial fibrillation was reported in this study. There is a 38% increased risk of atrial fibrillation. Additional studies are needed to clarify the mechanistic link between atrial fibrillation and celiac disease. Some of the limitations of this study are that all were observational studies, some were medical registry-based and there was high heterogeneity between studies.

Metformin improves lipid metabolism and reverses the Warburg effect in a canine model of chronic atrial fibrillation

Background

Previous studies demonstrated impaired lipid metabolism and augmented aerobic glycolysis in AF. The authors aimed to investigate whether the use of metformin, an AMPK activator, could reverse this metabolic remodeling in chronic AF and to explore the underlying mechanisms.

Methods

We conducted chronic AF animal models with 18 beagle dogs and divided them into SR (pacemaker implanted without pacing), AF (pacemaker implanted with sustained pacing at a frequency of 400 beats/min for 6 weeks), and metformin+AF group (daily oral administration of metformin was initiated 1 week before surgery and continued throughout the study period). After electrophysiological measurements, the left atrial appendage tissue samples were taken from the beating heart for further analysis. Protein expression, histological analysis, and biochemical measurements were conducted.

Results

The AF groups showed decreased expression of FAT/CD36, CPT-1, VLCAD, increased concentration of free fatty acid and triglyceride, and increased lipid deposition. The activation of AMPK/PGC-1α/PPARα pathway was decreased. The key factors of the Warburg effect, including HIF-1α, GLUT-1, PDK1, HK, and LDH, increased in AF group compared to SR group. The expression of PDH decreased significantly, accompanied by increased atrial lactate production. The extent of fibrosis increased significantly in the left atrial appendage of AF group. dERP, ∑WOV, and AF inducibility increased while ERP decreased in AF group compared to SR group. The use of metformin attenuated all these changes effectively.

Conclusions

Metformin improves lipid metabolism and reverses the Warburg effect in chronic AF via AMPK activation. It attenuates atrial electrical and structural remodeling.

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.

The Reversal Effect of Sigma-1 Receptor (S1R) Agonist, SA4503, on Atrial Fibrillation After Depression and Its Underlying Mechanism

Aim

Sigma-1 receptors have been investigated and shown to play a protective role in both depression and cardiovascular disease. SA4503, known as a σ1 receptor agonist, regulates cardiac calcium and potassium channels in rat models of depression. However, it remains unknown whether SA4503 can alleviate myocardial inflammation or conduction junctions in the atrium after exposure to chronic mild stress.

Methods and Results

Sprague-Dawley male rats received 28-day treatment with SA4503, simultaneously with chronic mild stress. Behavior measurements were assessed after the daily doses. Additionally, a multielectrode array assessment, electrophysiological study, immunohistochemistry analysis, histological analysis, and Western blot analysis were performed. Depression rats’ hearts showed abnormal electrical activity, including disordered excitation propagation and prolonged total activation time (TAT). In addition, atrial arrhythmias (AAs), induced by burst stimulation, showed higher incidence and longer duration in the depression group compared to the control group. These changes were related to reduced conduction junctions and enhanced spatial heterogeneity. Importantly, depressed rat hearts showed greater expression of inflammatory factors (TGF-α, IL-6, and TGF-β), more collagen distribution in the extracellular matrix, and lower expression of gap junction proteins (CX40 and CX43). Furthermore, SA4503 partially mitigated the above indices in the depression group (P < 0.01 for all groups).

Conclusion

These findings show the effects of the σ1R agonist SA4503; it alleviates atrial myocardial inflammation and conduction junctions after chronic mild stress. SA4503 may be the promising pharmacological agent to treat depression-related AAs by increasing conduction function, improving the expression of connexin 40 and 43, and reducing cardiac myocardial inflammation.

Pinocembrin attenuates autonomic dysfunction and atrial fibrillation susceptibility via inhibition of the NF-κB/TNF-α pathway in a rat model of myocardial infarction

Previous studies indicate that myocardial infarction (MI) may contribute to atrial fibrillation (AF). Emerging evidence has shown that pinocembrin protects myocardial ischemic injury (I/R)-induced cardiac fibrosis and arrhythmias in animals via its anti-inflammatory or antioxidant activities. However, the effects of pinocembrin on MI-induced atrial arrhythmias remain unknown. Thus, this study aimed to investigate the effects of pinocembrin on autonomic dysfunction and AF susceptibility in MI rats and the possible mechanism. In a standard experimental MI model, Sprague-Dawley rats received permanent ligation of the left anterior descending (LAD) coronary artery and were treated with pinocembrin or saline for 6 days. Our results demonstrated that pinocembrin treatment significantly decreased sympathetic activity, augmented parasympathetic activity, improved heart rate variability (HRV), prolonged the atrial effective refractory period (ERP) and action potential duration (APD), shortened activation latency (AL), lowered the indicibility rate of AF, attenuated atrial fibrosis, and decreased concentrations of norepinephrine (NE), tumor necrosis factor-α (TNF-α), interleukin (IL)-1β and IL-6 in the serum and the left atrial (LA). Furthermore, pinocembrin treatment significantly increased the expression levels of Cx43 and Cav1.2 and suppressed the phosphorylation of inhibitor-κBα (IκBα) and the activation of nuclear factor-kappa B (NF-κB)subunit p65. In conclusion, the findings indicate that pinocembrin treatment decreases autonomic remodeling, lowers atrial fibrosis, ameliorates atrial electrical remodeling, and suppresses MI-induced inflammatory responses, which suggests a potential novel strategy for atrial arrhythmias.

The Warburg effect: A new insight into atrial fibrillation

Atrial fibrillation (AF) is the most common sustained arrhythmia. Atrial remodeling, including electrical/structural/autonomic remodeling, plays a vital role in AF pathogenesis. All of these have been shown to contribute continuously to the self-perpetuating nature of AF. The Warburg effect was found to play important roles in tumor and non-tumor disease. Recently, lots of studies documented altered atrial metabolism in AF, but the specific mechanism and the impact of these changes upon AF initiation/progression remain unclear. In this article, we review the metabolic consideration in AF comprehensively and observe the footprints of the Warburg effect. We also summarize the signaling pathway involved in the Warburg effect during AF-HIF-1α and AMPK, and discuss their potential roles in AF maintenance and progression. In conclusion, we give the innovative idea that the Warburg effect exists in AF and promotes the progression of AF. Targeting it may provide new therapies for AF treatment.