Diversity among differentially expressed genes in atrial appendages of atrial fibrillation: The role and mechanism of SPP1 in atrial fibrosis

Multiomics analysis of canine myocardium after circumferential pulmonary vein ablation: Effect of neuropeptide Y on long-term reinduction of atrial fibrillation

Catheter ablation (CA) is an essential method for the interventional treatment of atrial fibrillation (AF), and it is very important to reduce long-term recurrence after CA. The mechanism of recurrence after CA is still unclear. We established a long-term model of beagle canines after circumferential pulmonary vein ablation (CPVA). The transcriptome and proteome were obtained using high-throughput sequencing and TMT-tagged LC-MS/LC analysis, respectively. Differentially expressed genes and proteins were screened and enriched, and the effect of fibrosis was found and verified in tissues. A downregulated protein, neuropeptide Y (NPY), was selected for validation and the results suggest that NPY may play a role in the long-term reinduction of AF after CPVA. Then, the molecular mechanism of NPY was further investigated. The results showed that the atrial effective refractory period (AERP) was shortened and fibrosis was increased after CPVA. Atrial myocyte apoptosis was alleviated by NPY intervention, and Akt activation was inhibited in cardiac fibroblasts. These results suggest that long-term suppression of NPY after CPVA may lead to induction of AF through promoting cardiomyocyte apoptosis and activating the Akt pathway in cardiac fibroblasts, which may make AF more likely to reinduce.

Anoikis and SPP1 in idiopathic pulmonary fibrosis: integrating bioinformatics, cell, and animal studies to explore prognostic biomarkers and PI3K/AKT signaling regulation

OBJECTIVE

This study aims to explore the relevance of anoikis in idiopathic pulmonary fibrosis (IPF) and identify associated biomarkers and signaling pathways.

METHOD

Unsupervised consensus cluster analysis was employed to categorize IPF patients into subtypes. We utilized Weighted Gene Co-Expression Network Analysis (WGCNA) and Protein-Protein Interaction network construction to identify anoikis-related modules and key genes. A prognostic signature was developed using Lasso and multivariate Cox regression analysis. Single-cell sequencing assessed hub gene expression in various cell types, and both cell and animal experiments confirmed IPF-related pathways.

RESULTS

We identified two distinct anoikis-associated subtypes with differing prognoses. WGCNA revealed essential hub genes, with SPP1 being prominent in the anoikis-related signature. The anoikis-related signature is effective in determining the prognosis of patients with IPF. Single-cell sequencing highlighted significant differences in SPP1 expression, notably elevated in fibroblasts derived from IPF patients. In vivo and in vitro experiments demonstrated that SPP1 enhances fibrosis in mouse lung fibroblasts by regulating p27 through the PI3K/Akt pathway.

CONCLUSION

Our research demonstrates a robust prognostic signature associated with anoikis and highlights SPP1 as a pivotal regulator of the PI3K/AKT signaling pathway in pulmonary fibrosis.

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.

TNFSF14/LIGHT promotes cardiac fibrosis and atrial fibrillation vulnerability via PI3Kγ/SGK1 pathway-dependent M2 macrophage polarisation

BACKGROUND

Tumour necrosis factor superfamily protein 14 (TNFSF14), also called LIGHT, is an important regulator of immunological and fibrosis diseases. However, its specific involvement in cardiac fibrosis and atrial fibrillation (AF) has not been fully elucidated. The objective of this study is to examine the influence of LIGHT on the development of myocardial fibrosis and AF.

METHODS

PCR arrays of peripheral blood mononuclear cells (PBMCs) from patients with AF and sinus rhythm was used to identify the dominant differentially expressed genes, followed by ELISA to evaluate its serum protein levels. Morphological, functional, and electrophysiological changes in the heart were detected in vivo after the tail intravenous injection of recombinant LIGHT (rLIGHT) in mice for 4 weeks. rLIGHT was used to stimulate bone marrow-derived macrophages (BMDMs) to prepare a macrophage-conditioned medium (MCM) in vitro. Then, the MCM was used to culture mouse cardiac fibroblasts (CFs). The expression of relevant proteins and genes was determined using qRT-PCR, western blotting, and immunostaining.

RESULTS

The mRNA levels of LIGHT and TNFRSF14 were higher in the PBMCs of patients with AF than in those of the healthy controls. Additionally, the serum protein levels of LIGHT were higher in patients with AF than those in the healthy controls and were correlated with left atrial reverse remodelling. Furthermore, we demonstrated that rLIGHT injection promoted macrophage infiltration and M2 polarisation in the heart, in addition to promoting atrial fibrosis and AF inducibility in vivo, as detected with MASSON staining and atrial burst pacing respectively. RNA sequencing of heart samples revealed that the PI3Kγ/SGK1 pathway may participate in these pathological processes. Therefore, we confirmed the hypothesis that rLIGHT promotes BMDM M2 polarisation and TGB-β1 secretion, and that this process can be inhibited by PI3Kγ and SGK1 inhibitors in vitro. Meanwhile, increased collagen synthesis and myofibroblast transition were observed in LIGHT-stimulated MCM-cultured CFs and were ameliorated in the groups treated with PI3Kγ and SGK1 inhibitors.

CONCLUSION

LIGHT protein levels in peripheral blood can be used as a prognostic marker for AF and to evaluate its severity. LIGHT promotes cardiac fibrosis and AF inducibility by promoting macrophage M2 polarisation, wherein PI3Kγ and SGK1 activation is indispensable.

Identification of key genes and mechanisms of epicardial adipose tissue in patients with diabetes through bioinformatic analysis

Background

In recent years, peri-organ fat has emerged as a diagnostic and therapeutic target in metabolic diseases, including diabetes mellitus. Here, we performed a comprehensive analysis of epicardial adipose tissue (EAT) transcriptome expression differences between diabetic and non-diabetic participants and explored the possible mechanisms using various bioinformatic tools.

Methods

RNA-seq datasets GSE108971 and GSE179455 for EAT between diabetic and non-diabetic patients were obtained from the public functional genomics database Gene Expression Omnibus (GEO). The differentially expressed genes (DEGs) were identified using the R package DESeq2, then Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment were analyzed. Next, a PPI (protein–protein interaction) network was constructed, and hub genes were mined using STRING and Cytoscape. Additionally, CIBERSORT was used to analyze the immune cell infiltration, and key transcription factors were predicted based on ChEA3.

Results

By comparing EAT samples between diabetic and non-diabetic patients, a total of 238 DEGs were identified, including 161 upregulated genes and 77 downregulated genes. A total of 10 genes (IL-1β, CD274, PDCD1, ITGAX, PRDM1, LAG3, TNFRSF18, CCL20, IL1RN, and SPP1) were selected as hub genes. GO and KEGG analysis showed that DEGs were mainly enriched in the inflammatory response and cytokine activity. Immune cell infiltration analysis indicated that macrophage M2 and T cells CD4 memory resting accounted for the largest proportion of these immune cells. CSRNP1, RELB, NFKB2, SNAI1, and FOSB were detected as potential transcription factors.

Conclusion

Comprehensive bioinformatic analysis was used to compare the difference in EAT between diabetic and non-diabetic patients. Several hub genes, transcription factors, and immune cell infiltration were identified. Diabetic EAT is significantly different in the inflammatory response and cytokine activity. These findings may provide new targets for the diagnosis and treatment of diabetes, as well as reduce potential cardiovascular complications in diabetic patients through EAT modification.

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.

Sex-linked genetic mechanisms and atrial fibrillation risk

Atrial fibrillation (AF) is a cardiac condition characterised by an irregular heartbeat, atrial pathology and an elevated downstream risk of thrombosis and heart failure, as well as neurological sequelae including stroke and dementia. The prevalence and presentation of, risk factors for, and therapeutic responses to, AF differ by sex, and this sex bias may be partially explained in terms of genetics. Here, we consider four sex-linked genetic mechanisms that may influence sex-biased phenotypes related to AF and provide examples of each: X-linked gene dosage, X-linked genomic imprinting, sex-biased autosomal gene expression, and male-limited Y-linked gene expression. We highlight novel candidate risk genes and pathways that warrant further investigation in clinical and preclinical studies. Understanding the biological basis of sex differences in AF should allow better prediction of disease risk, identification of novel risk/protective factors, and the development of more effective sex-tailored interventions.