Luo Cheng, Tan Baoping, Chu Luoxiang, Chen Liqiang, Zhong Xinglong, Jiang Yangyang, Yan Yuluan, Mo Fanrui, Wang Hong, Yang Fan
Department of Cardiology, Liuzhou Workers' Hospital, The Fourth Affiliated Hospital of Guangxi Medical University, Liuzhou, China.
Medical Science Research Center, The Fourth Affiliated Hospital of Guangxi Medical University, Liuzhou, China.
Front Cardiovasc Med. 2025 Jan 6;11:1460813. doi: 10.3389/fcvm.2024.1460813. eCollection 2024.
Fibroblasts in the fibrotic heart exhibit a heterogeneous biological behavior. The specific subsets of fibroblasts that contribute to progressive cardiac fibrosis remain unrevealed. Our aim is to identify the heart fibroblast (FB) subsets that most significantly promote fibrosis and the related critical genes as biomarkers for ischemic heart disease.
The single nuclei RNA sequencing (snRNA-seq) and bulk RNA sequencing datasets used in this study were obtained from the Gene Expression Omnibus (GEO). The activity of gene sets related to progressive fibrosis was quantified for each FB cluster using the AddmoleculeScore function. Differentially expressed genes (DEGs) for the specific cell cluster with the highest fibrotic transcription dynamics were identified and integrated with bulk RNA sequencing data for analysis. Multiple machine learning models were employed to identify the optimal gene panel for diagnosing ischemic heart disease (IHD) based on the intersected DEGs. The effectiveness and robustness of the gene-derived diagnostic tool were validated using two independent IHD cohorts.Subsequently, we validated the signature genes using a rat post-myocardial infarction heart failure model.
We conducted an analysis on high-quality snRNA-seq data obtained from 3 IHD and 4 cardiac sarcoidosis heart samples, resulting in the identification of 16 FB clusters. Cluster2 exhibited the highest gene activity in terms of fibrosis-related transcriptome dynamics. The characteristic gene expression profile of this FB subset indicated a specific upregulation of COL1A1 and several pro-fibrotic factors, including CCDC102B, GUCY1A3, TEX41, NREP, TCAP, and WISP, while showing a downregulation of NR4A1, an endogenous inhibitor of the TGF- pathway. Consequently, we designated this subgroup as COL1A1NR4A1 FB. Gene set enrichment analysis (GSEA) shows that the gene expression pattern of COL1A1NR4A1 FB was closer to pathways associated with cardiac fibrosis. Through machine learning, ten feature genes from COL1A1NR4A1 FB were selected to construct a diagnostic tool for IHD. The robustness of this new tool was validated using an independent cohort and heart failure rats.
COL1A1NR4A1 FB possess heightened capability in promoting cardiac fibrosis. Additionally, it offers molecular insights into the mechanisms underlying the regulation of the TGF- pathway. Furthermore, the characteristic genes of COL1A1hiNR4A1 FB could serve as valuable tools for diagnosing of IHD.
纤维化心脏中的成纤维细胞表现出异质性生物学行为。导致进行性心脏纤维化的成纤维细胞特定亚群仍未明确。我们的目的是确定最显著促进纤维化的心脏成纤维细胞（FB）亚群以及相关关键基因，作为缺血性心脏病的生物标志物。
本研究中使用的单核RNA测序（snRNA-seq）和批量RNA测序数据集来自基因表达综合数据库（GEO）。使用AddmoleculeScore函数对每个FB簇中与进行性纤维化相关的基因集活性进行量化。鉴定出具有最高纤维化转录动力学的特定细胞簇的差异表达基因（DEG），并与批量RNA测序数据整合进行分析。采用多种机器学习模型，基于交集DEG确定诊断缺血性心脏病（IHD）的最佳基因组合。使用两个独立的IHD队列验证基因衍生诊断工具的有效性和稳健性。随后，我们使用大鼠心肌梗死后心力衰竭模型验证了特征基因。
我们对从3个IHD和4个心脏结节病心脏样本中获得的高质量snRNA-seq数据进行了分析，鉴定出16个FB簇。就纤维化相关转录组动力学而言，Cluster2表现出最高的基因活性。该FB亚群的特征基因表达谱显示COL1A1和几种促纤维化因子（包括CCDC102B、GUCY1A3、TEX41、NREP、TCAP和WISP）特异性上调，而TGF通路的内源性抑制剂NR4A1下调。因此，我们将该亚组命名为COL1A1NR4A1 FB。基因集富集分析（GSEA）表明，COL1A1NR4A1 FB的基因表达模式更接近与心脏纤维化相关的通路。通过机器学习，从COL1A1NR4A1 FB中选择了10个特征基因来构建IHD诊断工具。使用独立队列和心力衰竭大鼠验证了该新工具的稳健性。
COL1A1NR4A1 FB具有增强的促进心脏纤维化的能力。此外，它为TGF通路调控的潜在机制提供了分子见解。此外，COL1A1hiNR4A1 FB的特征基因可作为诊断IHD的有价值工具。
