Jia Fang, Jiang Wei, Zhang Yan, Zhang Lisha, Han Tuo, Liu Danmeng, Xue Jiahong, Deng Fuxue
Department of Endocrinology, The Second Affiliated Hospital of Xi'an Jiaotong University, Xi'an, 710004, Shaanxi, China.
Department of Cardiology, The Second Affiliated Hospital of Xi'an Jiaotong University, Xi'an, 710004, Shaanxi, China.
Cardiovasc Toxicol. 2025 May 9. doi: 10.1007/s12012-025-09999-x.
Although reperfusion therapy can reduce the mortality of myocardial infarction, it results in myocardial ischemia-reperfusion injury (MIRI). The molecular mechanism by which the interferon-γ pathway affects MIRI is unclear, so we addressed this problem by mining transcriptome and single-cell sequencing data. The GSE160516 and GSE83472 datasets, single cell RNA sequencing (scRNA-seq) data of GSE227088 dataset and 182 interferon-γ pathway related genes (IRGs) were retrieved and incorporated into this study. The differentially expressed genes (DEGs) between MIRI and control samples were searched, the candidate genes were obtained by intersecting DEGs with IRGs. The protein-protein interaction (PPI) analysis was utilized for selecting key genes from candidate genes. Moreover, key genes with significant expression and consistent trend in GSE160516 and GSE83472 datasets were selected as biomarkers. The biological functions and regulatory mechanism of biomarkers were investigated by enrichment analysis and predicting the upstream molecules targeting them. Ulteriorly, cell clusters were identified via unsupervised cluster analysis and merged into different cell types by cell annotation. Cell types in which biomarkers observably and differentially expressed were selected as crucial cell types. Finally, cell communication and pseudo-time analysis were implemented based on crucial cell types. Totally 34 candidate genes were searched by overlapping 1,930 DEGs with 182 IRGs. Nine key genes were singled out from candidate genes, of which Myd88 and Trp53 were significantly upregulated in the MIRI samples of GSE160516 and GSE83472 datasets, so they were identified as biomarkers. Besides, they participated in pathways such as ribosome, spliceosome and cell cycle. Myd88 might be simultaneously regulated by mmu-miR-361-3p and mmu-miR-421-3p, and Trp53 could be regulated by Abl1 and Tead2. Totally 25 cell clusters were merged into six cell types, of which three crucial cell types (cardiomyocyte, fibroblast, and macrophage) could interact with each other through receptor-ligand. Pseudo-time analysis revealed states 1, 2, and 5 of macrophages might be associated with MIRI. Two biomarkers (Myd88 and Trp53) related to IRGs in MIRI were mined, providing a reference for elucidating the mechanism of interferon-γ pathway on MIRI.
尽管再灌注治疗可降低心肌梗死的死亡率，但它会导致心肌缺血再灌注损伤（MIRI）。干扰素-γ通路影响MIRI的分子机制尚不清楚，因此我们通过挖掘转录组和单细胞测序数据来解决这个问题。检索了GSE160516和GSE83472数据集、GSE227088数据集的单细胞RNA测序（scRNA-seq）数据以及182个干扰素-γ通路相关基因（IRGs）并纳入本研究。搜索了MIRI与对照样本之间的差异表达基因（DEGs），通过将DEGs与IRGs相交获得候选基因。利用蛋白质-蛋白质相互作用（PPI）分析从候选基因中选择关键基因。此外，选择在GSE160516和GSE83472数据集中具有显著表达且趋势一致的关键基因作为生物标志物。通过富集分析和预测靶向它们的上游分子来研究生物标志物的生物学功能和调控机制。进一步地，通过无监督聚类分析识别细胞簇，并通过细胞注释将其合并为不同的细胞类型。选择生物标志物明显且差异表达的细胞类型作为关键细胞类型。最后，基于关键细胞类型进行细胞通讯和拟时间分析。通过将1930个DEGs与182个IRGs重叠，共搜索到34个候选基因。从候选基因中筛选出9个关键基因，其中Myd88和Trp53在GSE160516和GSE83472数据集的MIRI样本中显著上调，因此它们被确定为生物标志物。此外，它们参与核糖体、剪接体和细胞周期等通路。Myd88可能同时受mmu-miR-361-3p和mmu-miR-421-3p调控，Trp53可能受Abl1和Tead2调控。共将25个细胞簇合并为六种细胞类型，其中三种关键细胞类型（心肌细胞、成纤维细胞和巨噬细胞）可通过受体-配体相互作用。拟时间分析显示巨噬细胞的状态1、2和5可能与MIRI相关。挖掘出两个与MIRI中IRGs相关的生物标志物（Myd88和Trp53），为阐明干扰素-γ通路对MIRI的作用机制提供了参考。
