The Molecular Role of Immune Cells in Dilated Cardiomyopathy

IL-17 affects the immune regulation of CD4+ T cells in dilated cardiomyopathy through JAK/STAT pathway

PURPOSE

Studying the effect of interleukin-17 (IL-17) on the mechanism of CD4+ T-cell immune regulation and the Janus kinase/signal transducer and activator of transcription (JAK/STAT) pathway may offer new ideas and methods for the therapy of dilated cardiomyopathy.

METHODS

Naive CD4+ T cells were isolated from mice using a magnetic bead sorting reagent and manipulated by overexpression or knockdown of IL-17. Protein levels of Janus kinase 2 (JAK2), phosphorylated JAK2 (p-JAK2), signal transducer and activator of transcription 3 (STAT3), phosphorylated STAT3 (p-STAT3), matrix metalloproteinase-2 (MMP-2), and matrix metalloproteinase-9 (MMP-9) were determined by Western blotting. Quantitative polymerase chain reaction was used to assess the levels of JAK2, STAT3, MMP-2, and MMP-9. Expression of tumour necrosis factor-alpha (TNF-α), interleukin-1 beta (IL-1β), interleukin-4 (IL-4), and interferon-gamma (IFNγ) was determined by enzyme-linked immunosorbent assay test kits. TNF-α, IL-1β, IL-4, and IFNγ secretion was measured by flow cytometry.

RESULTS

In CD4+ T cells, IL-17 overexpression increased TNF-α, IL-1β, IL-4, IFNγ, p-JAK2, p-STAT3, MMP-2, MMP-9 levels, and apoptosis. Knockdown of IL-17 reduced the levels of TNF-α, IL-1β, IL-4, IFNγ, p-JAK2, p-STAT3, MMP-2, and MMP-9, as well as the level of apoptosis.

CONCLUSION

Through regulation of IL-17 expression in CD4+ T cells, this study reveals its crucial role in regulating the secretion of inflammatory factors, activation of the JAK/STAT signaling pathway, expression of matrix metalloproteinases, and apoptosis of CD4+ T cells.

A brief review on recent advances in diagnostic and therapeutic applications of extracellular vesicles in cardiovascular disease

Extracellular vesicles (EVs) are important mediators of intercellular communication within the cardiovascular system, playing essential roles in physiological homeostasis and contributing to the pathogenesis of various cardiovascular diseases (CVDs). However, their potential as diagnostic biomarkers and therapeutic agents in rare cardiovascular diseases, such as valvular heart disease (VHD) and cardiomyopathies, remains largely unexplored. This review comprehensively emphasizes recent advancements in extracellular vesicle research, explicitly highlighting their growing significance in diagnosing and potentially treating rare cardiovascular diseases, with a particular focus on valvular heart disease and cardiomyopathies. We highlight the potential of extracellular vesicle-based liquid biopsies as non-invasive tools for early disease detection and risk stratification, showcasing specific extracellular vesicle-associated biomarkers (proteins, microRNAs, lipids) with diagnostic and prognostic value. Furthermore, we discussed the therapeutic promise of extracellular vesicles derived from various sources, including stem cells and engineered extracellular vesicles, for cardiac repair and regeneration through their ability to modulate inflammation, promote angiogenesis, and reduce fibrosis. By integrating the findings and addressing critical knowledge gaps, this review aims to stimulate further research and innovation in extracellular vesicle-based diagnostics and therapeutics of cardiovascular disease.

Multi-level transcriptomic analysis of LMNA -related dilated cardiomyopathy identifies disease-driving processes

LMNA- related dilated cardiomyopathy ( LMNA -DCM) is one of the most severe forms of DCM. The incomplete understanding of the molecular disease mechanisms results in lacking treatment options, leading to high mortality amongst patients. Here, using an inducible, cardiomyocyte-specific lamin A/C depletion mouse model, we conducted a comprehensive transcriptomic study, combining both bulk and single nucleus RNA sequencing, and spanning LMNA -DCM disease progression, to identify potential disease drivers. Our refined analysis pipeline identified 496 genes already misregulated early in disease. The expression of these genes was largely driven by disease specific cardiomyocyte sub-populations and involved biological processes mediating cellular response to DNA damage, cytosolic pattern recognition, and innate immunity. Indeed, DNA damage in LMNA -DCM hearts was significantly increased early in disease and correlated with reduced cardiomyocyte lamin A levels. Activation of cytosolic pattern recognition in cardiomyocytes was independent of cGAS, which is rarely expressed in cardiomyocytes, but likely occurred downstream of other pattern recognition sensors such as IFI16. Altered gene expression in cardiac fibroblasts and immune cell infiltration further contributed to tissue-wide changes in gene expression. Our transcriptomic analysis further predicted significant alterations in cell-cell communication between cardiomyocytes, fibroblasts, and immune cells, mediated through early changes in the extracellular matrix (ECM) in the LMNA -DCM hearts. Taken together, our work suggests a model in which nuclear damage in cardiomyocytes leads to activation of DNA damage responses, cytosolic pattern recognition pathway, and other signaling pathways that activate inflammation, immune cell recruitment, and transcriptional changes in cardiac fibroblasts, which collectively drive LMNA -DCM pathogenesis.

Targeting ERBB2 and PIK3R1 as a therapeutic strategy for dilated cardiomyopathy: A single-cell sequencing and mendelian randomization analysis

Background

Dilated cardiomyopathy (DCM) is widely recognized as a significant contributor to heart failure. Nevertheless, the absence of pharmaceutical interventions capable of reversing disease progression and improving prognosis underscores the imperative for additional research in this area.

Methods

First, we identified and evaluated three gene sets, namely "SC-DCM", "EP-DCM" and "Drug", using big data and multiple bioinformatics analysis methods. Accordingly, drug-treatable ("Hub") genes in DCM were identified. Following this, four microarray expression profile datasets were employed to authenticate the expression levels and discriminatory efficacy of "Hub" genes. Additionally, mendelian randomization analysis was conducted to ascertain the causal association between the "Hub genes" and heart failure. Finally, the "DGIdb" was applied to identify "Hub" genes-targeted drugs. The "ssGSEA" algorithm assessed the level of immune cell infiltration in DCM.

Results

Enrichment analysis showed that the "SC-DCM" and "EP-DCM" gene sets were closely associated with DCM. PIK3R1 and ERBB2 were identified as drug-treatable genes in DCM. Additional analysis using MR supported a causal relationship between ERBB2 and heart failure, but not PIK3R1. Moreover, PIK3R1 was positively correlated with immune activation, while ERBB2 was negatively correlated. We found that everolimus was a pharmacological inhibitor for both PIK3R1 and ERBB2. However, no pharmacological agonist was found for ERBB2.

Conclusion

PIK3R1 and ERBB2 are drug-treatable genes in DCM. ERBB2 has a causal effect on heart failure, and its normal expression may play a role in preventing the progression of DCM to heart failure. In addition, there is a cross-expression of PIK3R1 and ERBB2 genes in both DCM and tumors. The adaptive immune system and PIK3R1 may be involved in DCM disease progression, while ERBB2 exerts a protective effect against DCM.

Construction and evaluation of immune-related diagnostic model in patients with heart failure caused by idiopathic dilated cardiomyopathy

OBJECTIVE

The purpose of the study was to construct the potential diagnostic model of immune-related genes during the development of heart failure caused by idiopathic dilated cardiomyopathy.

METHOD

GSE5406 and GSE57338 were downloaded from the GEO website ( https://www.ncbi.nlm.nih.gov/geo/ ). CIBERSORT was used for the evaluation of immune infiltration in idiopathic dilated cardiomyopathy (DCM) of GSE5406. Differently expressed genes were calculated by the limma R package and visualized by the volcano plot. The immune-related genes were downloaded from Immport, TISIDB, and InnateDB. Then the immune-related differential genes (IRDGs) were acquired from the intersection. Protein-protein interaction network (PPI) and Cytoscape were used to visualize the hub genes. Three machine learning methods such as random forest, logical regression, and elastic network regression model were adopted to construct the prediction model. The diagnostic value was also validated in GSE57338.

RESULTS

Our study demonstrated the obvious different ratio of T cell CD4 memory activated, T cell regulatory Tregs, and neutrophils between DCM and control donors. As many as 2139 differential genes and 274 immune-related different genes were identified. These genes were mainly enriched in lipid and atherosclerosis, human cytomegalovirus infection, and cytokine-cytokine receptor interaction. At the same time, as many as fifteen hub genes were identified as the IRDGs (IFITM3, IFITM2, IFITM1, IFIT3, IFIT1, HLA-A, HLA-B, HLA-C, ADAR, STAT1, SAMHD1, RSAD2, MX1, ISG20, IRF2). Moreover, we also discovered that the elastic network and logistic regression models had a higher diagnostic value than that of random forest models based on these hub genes.

CONCLUSION

Our study demonstrated the pivotal role of immune function during the development of heart failure caused by DCM. This study may offer new opportunities for the detection and intervention of immune-related DCM.