CXCR4 and TYROBP mediate the development of atrial fibrillation via inflammation

Identification of common mechanisms and biomarkers of atrial fibrillation and heart failure based on machine learning

AIMS

Atrial fibrillation (AF) is the most common arrhythmia. Heart failure (HF) is a disease caused by heart dysfunction. The prevalence of AF and HF were progressively increasing over time. The co-existence of AF and HF presents a significant therapeutic challenge. In order to provide new ideas for the diagnosis of AF and HF, it is necessary to carry out biomarker related studies.

METHODS AND RESULTS

The training set and validation set data of AF and HF patient samples were downloaded from the GEO database, 'limma' was used to compare the differences in gene expression levels between the disease group and the normal group to screen for differentially expressed genes (DEGs). Weighted correlation network analysis (WGCNA) identified the modules with the highest positive correlation with AF and HF. Functional enrichment and PPI network construction of key genes were carried out. Biomarkers were screened by machine learning. The infiltration of immune cells in AF and HF groups was evaluated by R-packet 'CIBERSORT'. The miRNA network was constructed and potential therapeutic agents for biomarker genes were predicted through the drugbank database. Through WGCNA analysis, it was found that the modules most positively correlated with AF and HF were MEturquoise (r = 0.21, P value = 0.09) and MEbrown (r = 0.62, P value = 8e-12), respectively. We screened 25 genes that were highly correlated with both AF and HF. Lasso regression analysis results showed 7 and 20 core genes in AF and HF groups, respectively. The top 20 important genes in AF and HF groups were obtained as core genes by RF model analysis. Four biomarkers were obtained after the intersection of core genes in four groups, namely, GLUL, NCF2, S100A12, and SRGN. The diagnostic efficacy of four genes in AF validation sets was good (AUC: GLUL 0.76, NCF2 0.64, S100A12 0.68, and SRGN 0.76), as well as in the HF validation set (AUC: GLUL 0.76, NCF2 0.84, S100A12 0.92, and SRGN 0.68). The highest correlation with neutrophils was observed for GLUL, NCF2, and S100A12, while SRGN exhibited the strongest correlation with T cells CD4 memory resting in the AF group. GLUL, NCF2, S100A12, and SRGN were most associated with neutrophils in the HF group. A total of 101 miRNAs were predicted by four genes, and GLUL, NCF2, and S100A12 predicted a total of 10 potential therapeutic agents.

CONCLUSIONS

We identified four biological markers that are highly correlated with AF and HF, namely, GLUL, NCF2, S100A12, and SRGN. Our findings provide theoretical basis for the clinical diagnosis and treatment of AF and HF.

Transcriptomics-based exploration of shared M1-type macrophage-related biomarker in acute kidney injury after kidney transplantation and acute rejection after kidney transplantation

BACKGROUND

Macrophage type 1 (M1) cells are associated with both acute kidney injury (AKI) during kidney transplantation and acute rejection (AR) after kidney transplantation. Our study explored M1-related biomarkers involved in both AKI and AR and their potential biological functions.

METHODS

Based on the Gene Expression Omnibus (GEO) database, the immune cell infiltration levels and differentially expressed genes were examined in AKI and AR in the kidney transplantation; M1-related genes shared in AKI and AR were identified using weighted gene co-expression analysis (WGCNA) system. Subsequently, protein-protein interaction (PPI) networks and machine learning methods to identify Hub genes and construct diagnostic models. Both AKI model and AR rat models were built to validate the expressions of Hub genes and test the injury phenotype, oxidative stress markers, and inflammatory factors. Finally, the transcription factor (TF)-Hub gene and micro-RNA (miRNA)-Hub gene regulatory networks were constructed based on identified Hub genes.

RESULTS

Out of 2167 differential expression genes (DEGs) in AKI and 2100 DEGs in AR, four M1-related Hub genes were obtained by PPI networks and machine learning methods, namely GBP2, TYROBP, CCR5, and TLR8. The calibration curves in the nomogram diagnostic model for these four Hub genes suggested the same predictive probability as an ideal model for AKI and AR after kidney transplantation (AUC values of the area under the ROC curve were all >0.7). The same observations were confirmed in ischemia reperfusion injury (IRI) and AR rat models by identifying common four Hub genes (GBP2, TYROBP, TLR8, and CCR5). Western blots showed that these four Hub genes were significantly different in rat models of IRI and AR (all p<0.05). Compared with the control group, IRI and AR groups showed aggravated histopathological damage and increased secretion of oxidative stress markers and inflammatory factors in rat kidneys (all p<0.05). Finally, TF-Hub and miRNA-Hub gene regulatory networks were constructed to provide a theoretical basis for the regulation of Hub genes.

CONCLUSION

We identified four macrophage M1-related Hub genes shared among AKI and AR after kidney transplantation. These genes may be considered for diagnosis of AKI and AR after kidney transplantation.

Identification of atrial fibrillation-related genes through transcriptome data analysis and Mendelian randomization

Background

Atrial fibrillation (AF) is a common persistent arrhythmia characterized by rapid and chaotic atrial electrical activity, potentially leading to severe complications such as thromboembolism, heart failure, and stroke, significantly affecting patient quality of life and safety. As the global population ages, the prevalence of AF is on the rise, placing considerable strains on individuals and healthcare systems. This study utilizes bioinformatics and Mendelian Randomization (MR) to analyze transcriptome data and genome-wide association study (GWAS) summary statistics, aiming to identify biomarkers causally associated with AF and explore their potential pathogenic pathways.

Methods

We obtained AF microarray datasets GSE41177 and GSE79768 from the Gene Expression Omnibus (GEO) database, merged them, and corrected for batch effects to pinpoint differentially expressed genes (DEGs). We gathered exposure data from expression quantitative trait loci (eQTL) and outcome data from AF GWAS through the IEU Open GWAS database. We employed inverse variance weighting (IVW), MR-Egger, weighted median, and weighted model approaches for MR analysis to assess exposure-outcome causality. IVW was the primary method, supplemented by other techniques. The robustness of our results was evaluated using Cochran's Q test, MR-Egger intercept, MR-PRESSO, and leave-one-out sensitivity analysis. A "Veen" diagram visualized the overlap of DEGs with significant eQTL genes from MR analysis, referred to as common genes (CGs). Additional analyses, including Gene Ontology (GO) enrichment, Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways, and immune cell infiltration studies, were conducted on these intersecting genes to reveal their roles in AF pathogenesis.

Results

The combined dataset revealed 355 differentially expressed genes (DEGs), with 228 showing significant upregulation and 127 downregulated. Mendelian randomization (MR) analysis identified that the autocrine motility factor receptor (AMFR) [IVW: OR = 0.977; 95% CI, 0.956-0.998; P = 0.030], leucine aminopeptidase 3 (LAP3) [IVW: OR = 0.967; 95% CI, 0.934-0.997; P = 0.048], Rab acceptor 1 (RABAC1) [IVW: OR = 0.928; 95% CI, 0.875-0.985; P = 0.015], and tryptase beta 2 (TPSB2) [IVW: OR = 0.971; 95% CI, 0.943-0.999; P = 0.049] are associated with a reduced risk of atrial fibrillation (AF). Conversely, GTPase-activating SH3 domain-binding protein 2 (G3BP2) [IVW: OR = 1.030; 95% CI, 1.004-1.056; P = 0.024], integrin subunit beta 2 (ITGB2) [IVW: OR = 1.050; 95% CI, 1.017-1.084; P = 0.003], glutaminyl-peptide cyclotransferase (QPCT) [IVW: OR = 1.080; 95% CI, 1.010-0.997; P = 1.154], and tripartite motif containing 22 (TRIM22) [IVW: OR = 1.048; 95% CI, 1.003-1.095; P = 0.035] are positively associated with AF risk. Sensitivity analyses indicated a lack of heterogeneity or horizontal pleiotropy (P > 0.05), and leave-one-out analysis did not reveal any single nucleotide polymorphisms (SNPs) impacting the MR results significantly. GO and KEGG analyses showed that CG is involved in processes such as protein polyubiquitination, neutrophil degranulation, specific and tertiary granule formation, protein-macromolecule adaptor activity, molecular adaptor activity, and the SREBP signaling pathway, all significantly enriched. The analysis of immune cell infiltration demonstrated associations of CG with various immune cells, including plasma cells, CD8T cells, resting memory CD4T cells, regulatory T cells (Tregs), gamma delta T cells, activated NK cells, activated mast cells, and neutrophils.

Conclusion

By integrating bioinformatics and MR approaches, genes such as AMFR, G3BP2, ITGB2, LAP3, QPCT, RABAC1, TPSB2, and TRIM22 are identified as causally linked to AF, enhancing our understanding of its molecular foundations. This strategy may facilitate the development of more precise biomarkers and therapeutic targets for AF diagnosis and treatment.

Mining and exploration of appendicitis nursing targets: An observational study

Appendicitis is an inflammation caused by obstruction of the appendiceal lumen or termination of blood supply leading to appendiceal necrosis followed by secondary bacterial infection. The relationship between TYROBP gene and the nursing of appendicitis remains unclear. The appendicitis dataset GSE9579 profile was downloaded from the gene expression omnibus database generated from GPL571. Differentially expressed genes were screened, followed by weighted gene co-expression network analysis, functional enrichment analysis, gene set enrichment analysis, construction and analysis of protein-protein interaction network, Comparative Toxicogenomics Database analysis, and immune infiltration analysis. Heatmaps of gene expression levels were plotted. A total of 1570 differentially expressed genes were identified. According to gene ontology analysis, they were mainly enriched in organic acid metabolic process, condensed chromosome kinetochore, oxidoreductase activity. In Kyoto Encyclopedia of Gene and Genome analysis, they mainly concentrated in metabolic pathways, P53 signaling pathway, PPAR signaling pathway. The soft threshold power in weighted gene co-expression network analysis was set to 12. Through the construction and analysis of protein-protein interaction network, 5 core genes (FCGR2A, IL1B, ITGAM, TLR2, TYROBP) were obtained. Heatmap of core gene expression levels revealed high expression of TYROBP in appendicitis samples. Comparative Toxicogenomics Database analysis found that core genes (FCGR2A, IL1B, ITGAM, TLR2, TYROBP) were closely related to abdominal pain, gastrointestinal dysfunction, fever, and inflammation occurrence. TYROBP gene is highly expressed in appendicitis, and higher expression of TYROBP gene indicates worse prognosis. TYROBP may serve as a molecular target for appendicitis and its nursing.

Low TYROBP expression predicts poor prognosis in multiple myeloma

BACKGROUND

Multiple myeloma (MM) is the second most common refractory hematologic cancer. Searching for new targets and prognostic markers for MM is significant.

METHODS

GSE39754, GSE6477 and GSE24080 were downloaded from the Gene Expression Omnibus (GEO) database. Differentially expressed genes (DEGs) in MM versus healthy people from GSE39754 and GSE6477 were screened using limma package, and MM-related module genes were chosen with the use of Weighted gene co-expression network analysis (WGCNA), and the two were intersected using ggVennDiagram for obtaining MM-related DEGs. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses were carried out. Then, protein-protein interactions (PPI) analysis in String database was used to obtain hub genes, while prognosis was analyzed by survival package in GSE24080. Receiver operating characteristic (ROC) curve was adopted for evaluating diagnostic value of hub genes. Besides, univariable/multivariable Cox regression were employed to screen independent prognostic biomarkers. Gene set enrichment analysis (GSEA) was used to find possible mechanism. Finally, western-blotting and reverse transcription-polymerase chain reaction (RT-PCR) verify TYROBP expression within MM and healthy people. We performed cell adhesion and transwell assays for investigating TYROBP function in MM cell adhesion and migration.

RESULTS

Through differential analyses, 92 MM-related DEGs were obtained. 10 hub genes were identified by PPI and CytoHubba. Their diagnostic and prognostic significance was analyzed. Down-regulation of genes like TYROBP, ELANE, MNDA, and MPO related to dismal MM prognosis. Upon univariable/multivariable Cox regression, TYROBP independently predicted MM prognosis. GSEA pathway was enriched, indicating that TYROBP expression affected MM development via cell adhesion molecular pathway. Upon Western-blotting and RT-PCR assays, TYROBP expression among MM patients decreased relative to healthy donors. Cell adhesion and transwell migration assays revealed increased MM cell adhesion and decreased migration upon TYROBP up-regulation.

CONCLUSION

In summary, TYROBP is a potential prognostic marker for MM.

Construction of LncRNA-mediated CeRNA network for investigating the immune pathogenesis of myocardial infarction

BACKGROUND

Myocardial infarction (MI) is a cardiovascular disease that seriously threatens human health. However, an immune-related competitive endogenous RNA (ceRNA) network has not been reported in MI.

METHODS

The GSE66360, GSE19339, GSE97320, GSE61741, and GSE168281 datasets were acquired from the Gene Expression Omnibus (GEO) database. The differentially expressed genes (DEGs) and differentially expressed miRNAs (DEmiRNAs) from MI patients and healthy controls were screened and an immune-related ceRNA network was constructed. Furthermore, the key long noncoding RNAs(lncRNAs) highly related to the immune mechanism of MI were identified utilizing the random walk with restart algorithm. Finally, the expression of the hub genes was further verified in the GSE66360, GSE19339, and GSE97320 datasets, and quantitativereal-time polymerase chain reaction (qRT-PCR) was performed for the MI patients and healthy controls.

RESULTS

A total of 184 differentially expressed immune-related genes(DE-IRGs) and 432 DE-miRNAs were obtained, and an immune-related ceRNA network comprising 1421 lncRNAs, 61 DE-miRNAs, and 139 DE-IRGs was constructed. According to the order of stress, betweenness, and closeness, NEAT1, KCNQ1OT1, and XIST were identified as key lncRNAs. Moreover, random walk with restart analysis also suggested that NEAT1, KCNQ1OT1, and XIST are key lncRNAs. Subsequently, a ceRNA network of 10 hub genes and 3 lncRNAs was constructed. Finally, we found that the expression of FCER1G and TYROBP significantly differed between MI patients and control individuals in the GSE66360, GSE19339, and GSE97320 datasets. qRT-PCR revealed that the expression of NEAT1, KCNQ1OT1, XIST, FCER1G, and TYROBP was significantly elevated in MI tissue samples compared to healthy control tissue samples.

CONCLUSION

NEAT1, KCNQ1OT1, XIST, FCER1G, and TYROBP are involved in MI and can be used as molecular biomarkers for the screening and diagnosis of MI. Furthermore, the immune system plays an essential role in the onset and progression of MI.

Identification of shared pathogenetic mechanisms between COVID-19 and IC through bioinformatics and system biology

COVID-19 increased global mortality in 2019. Cystitis became a contributing factor in SARS-CoV-2 and COVID-19 complications. The complex molecular links between cystitis and COVID-19 are unclear. This study investigates COVID-19-associated cystitis (CAC) molecular mechanisms and drug candidates using bioinformatics and systems biology. Obtain the gene expression profiles of IC (GSE11783) and COVID-19 (GSE147507) from the Gene Expression Omnibus (GEO) database. Identified the common differentially expressed genes (DEGs) in both IC and COVID-19, and extracted a number of key genes from this group. Subsequently, conduct Gene Ontology (GO) functional enrichment and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis on the DEGs. Additionally, design a protein-protein interaction (PPI) network, a transcription factor gene regulatory network, a TF miRNA regulatory network, and a gene disease association network using the DEGs. Identify and extract hub genes from the PPI network. Then construct Nomogram diagnostic prediction models based on the hub genes. The DSigDB database was used to forecast many potential molecular medicines that are associated with common DEGs. Assess the precision of hub genes and Nomogram models in diagnosing IC and COVID-19 by employing Receiver Operating Characteristic (ROC) curves. The IC dataset (GSE57560) and the COVID-19 dataset (GSE171110) were selected to validate the models' diagnostic accuracy. A grand total of 198 DEGs that overlapped were found and chosen for further research. FCER1G, ITGAM, LCP2, LILRB2, MNDA, SPI1, and TYROBP were screened as the hub genes. The Nomogram model, built using the seven hub genes, demonstrates significant utility as a diagnostic prediction model for both IC and COVID-19. Multiple potential molecular medicines associated with common DEGs have been discovered. These pathways, hub genes, and models may provide new perspectives for future research into mechanisms and guide personalised and effective therapeutics for IC patients infected with COVID-19.

Costunolide ameliorates angiotensin II-induced atrial inflammation and fibrosis by regulating mitochondrial function and oxidative stress in mice: A possible therapeutic approach for atrial fibrillation

Atrial fibrillation (AF) is a cardiac disease characterized by disordered atrial electrical activity. Atrial inflammation and fibrosis are involved in AF progression. Costunolide (COS) is a sesquiterpene lactone containing anti-inflammatory and anti-fibrotic activities. This study aims to explore the underlying mechanisms by which COS protects against AF. Male C57BL/6 mice (8- to 10-week-old) were infused with angiotensin (Ang) II for 3 weeks. Meanwhile, different doses of COS (COS-L: 10 mg/kg, COS-H: 20 mg/kg) were administered to mice by intragastric treatment. The results showed irregular and rapid heart rates in Ang II-treated mice. Moreover, the levels of inflammatory cytokines and fibrotic factors were elevated in mice. COS triggered a reduction of Ang II-induced inflammation and fibrosis, which conferred a protective effect. Mechanistically, mitochondrial dysfunction with mitochondrial respiration inhibition and aberrant ATP levels were observed after Ang II treatment. Moreover, Ang-II-induced excessive reactive oxygen species caused oxidative stress, which was further aggravated by inhibiting Nrf2 nuclear translocation. Importantly, COS diminished these Ang-II-mediated effects in mice. In conclusion, COS attenuated inflammation and fibrosis in Ang-II-treated mice by alleviating mitochondrial dysfunction and oxidative stress. Our findings represent a potential therapeutic option for AF treatment.

Developing Pharmacological Therapies for Atrial Fibrillation Targeting Mitochondrial Dysfunction and Oxidative Stress: A Scoping Review

Atrial fibrillation (AF) is a cardiac arrhythmia caused by electrophysiological anomalies in the atrial tissue, tissue degradation, structural abnormalities, and comorbidities. A direct relationship exists between AF and altered mitochondrial activity resulting from membrane potential loss, contractile dysfunction, or decreased ATP levels. This review aimed to elucidate the role of mitochondrial oxidative mechanisms in AF pathophysiology, the impact of mitochondrial oxidative stress on AF initiation and perpetuation, and current therapies. This review followed the Preferred Reporting Items for Systematic Reviews and the Meta-Analysis Extension for Scoping Reviews. PubMed, Excerpta Medica Database, and Scopus were explored until June 2023 using "MESH terms". Bibliographic references to relevant papers were also included. Oxidative stress is an imbalance that causes cellular damage from excessive oxidation, resulting in conditions such as AF. An imbalance in reactive oxygen species production and elimination can cause mitochondrial damage, cellular apoptosis, and cardiovascular diseases. Oxidative stress and inflammation are intrinsically linked, and inflammatory pathways are highly correlated with the occurrence of AF. AF is an intricate cardiac condition that requires innovative therapeutic approaches. The involvement of mitochondrial oxidative stress in the pathophysiology of AF introduces novel strategies for clinical treatment.

Ablation of CXCR4 expression in cardiomyocytes exacerbates isoproterenol‑induced cell death and heart failure

CXCR4 is a seven‑transmembrane‑spanning Gi‑coupled receptor for the SDF‑1 chemokine and plays a critical role in cardiovascular development and post‑injury repair. However, the specific role of CXCR4 in cardiomyocytes is incompletely understood. It was hypothesized that CXCR4 activation in cardiomyocytes antagonizes β‑adrenoceptor/Gs signaling‑induced cardiac dysfunction. Cardiomyocyte‑specific CXCR4 knockout (CXCR4‑^CMKO) mice were generated by crossing CXCR4^fl/fl and MHC‑Cre^+/‑ mice. Their cardiac structure and function in the basal state are equivalent to that of the control MHC‑Cre^+/‑ littermates until at least 4 months old. However, following continuous subcutaneous administration of isoproterenol (Iso) via an osmotic mini‑pump, the ventricular myocardial contractility, dilation, cardiomyocyte apoptosis, and interstitial fibrosis are worse in CXCR4‑^CMKO mice than in MHC‑Cre^+/‑ littermates. In the cultured H9C2 cardiomyocytes, SDF‑1 treatment markedly attenuated Iso‑induced apoptosis and reduction in phospho‑Akt, and this protective effect was lost by knockdown of CXCR4 or by co‑treatment with Gi inhibitors. In conclusion, CXCR4 promotes cardiomyocyte survival and heart function during β‑adrenergic stress.