Septic cardiomyopathy (SCM), a common cardiovascular comorbidity of sepsis, has emerged among the leading causes of death in patients with sepsis. SCM’s pathogenesis is strongly affected by mitochondrial metabolic dysregulation and immune infiltration disorder. However, the specific mechanisms and their intricate interactions in SCM remain unclear. This study employed bioinformatics analysis and drug discovery approaches to identify the regulatory molecules, distinct functions, and underlying interactions of mitochondrial metabolism and immune microenvironment, along with potential interventional strategies in SCM. GSE79962, GSE171546, and GSE167363 datasets were obtained from the Gene Expression Omnibus (GEO) database. Differentially expressed genes (DEGs) and module genes were identified using Limma and Weighted Correlation Network Analysis (WGCNA), followed by functional enrichment analysis. Machine learning algorithms, including support vector machine–recursive feature elimination (SVM–RFE), least absolute shrinkage and selection operator (LASSO) regression, and random forest, were used to screen mitochondria-related hub genes for early diagnosis of SCM. Subsequently, a nomogram was developed based on six hub genes. The immunological landscape was evaluated by single-sample gene set enrichment analysis (ssGSEA). We also explored the expression pattern of hub genes and distribution of mitochondria/inflammation-related pathways in UMAP plots of single-cell dataset. Potential drugs were explored using the Drug Signatures Database (DSigDB). In vivo and in vitro experiments were performed to validate the pathogenetic mechanism of SCM and the therapeutic efficacy of candidate drugs. Six hub mitochondria-related DEGs [MitoDEGs; translocase of inner mitochondrial membrane domain-containing 1 (TIMMDC1), mitochondrial ribosomal protein S31 (MRPS31), F-box only protein 7 (FBXO7), phosphatidylglycerophosphate synthase 1 (PGS1), LYR motif containing 7 (LYRM7), and mitochondrial chaperone BCS1 (BCS1L)] were identified. The diagnostic nomogram model based on the six hub genes demonstrated high reliability and validity in both the training and validation sets. The immunological microenvironment differed between SCM and control groups. The Spearman correlation analysis revealed that hub MitoDEGs were significantly associated with the infiltration of immune cells. Upregulated hub genes showed remarkably high expression in the naive/memory B cell, CD14+ monocyte, and plasma cell subgroup, evidenced by the feature plot. The distribution of mitochondria/inflammation-related pathways varied across subgroups among control and SCM individuals. Metformin was predicted to be the most promising drug with the highest combined score. Its efficacy in restoring mitochondrial function and suppressing inflammatory responses has also been validated. This study presents a comprehensive mitochondrial metabolism and immune infiltration landscape in SCM, providing a potential novel direction for the pathogenesis and medical intervention of SCM.

中文翻译：

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通过综合生物信息学分析和实验验证，对线粒体代谢失调和免疫浸润在脓毒症心肌病中的作用有了新的认识

脓毒症心肌病（SCM）是脓毒症常见的心血管合并症，已成为脓毒症患者死亡的主要原因之一。SCM的发病机制受到线粒体代谢失调和免疫浸润障碍的强烈影响。然而，SCM 中的具体机制及其复杂的相互作用仍不清楚。本研究采用生物信息学分析和药物发现方法来识别调节分子、独特功能以及线粒体代谢和免疫微环境的潜在相互作用，以及 SCM 中潜在的干预策略。GSE79962、GSE171546 和 GSE167363 数据集是从基因表达综合 (GEO) 数据库获得的。使用 Limma 和加权相关网络分析 (WGCNA) 鉴定差异表达基因 (DEG) 和模块基因，然后进行功能富集分析。机器学习算法，包括支持向量机递归特征消除（SVM-RFE）、最小绝对收缩和选择算子（LASSO）回归和随机森林，被用来筛选线粒体相关的中枢基因，以进行 SCM 的早期诊断。随后，基于六个中心基因开发了列线图。通过单样本基因集富集分析（ssGSEA）评估免疫学景观。我们还在单细胞数据集的 UMAP 图中探索了 hub 基因的表达模式以及线粒体/炎症相关通路的分布。使用药物特征数据库（DSigDB）探索潜在药物。通过体内和体外实验来验证SCM的发病机制和候选药物的治疗效果。六个与线粒体相关的中心 DEGs [MitoDEGs; 含有线粒体内膜结构域的转位酶 1 (TIMMDC1)、线粒体核糖体蛋白 S31 (MRPS31)、仅 F-box 蛋白 7 (FBXO7)、磷脂酰甘油磷酸合酶 1 (PGS1)、含有 LYR 基序 7 (LYRM7) 和线粒体伴侣 BCS1 (BCS1L)]被鉴定。基于六个中心基因的诊断列线图模型在训练集和验证集上均表现出较高的可靠性和有效性。SCM 组和对照组之间的免疫微环境不同。Spearman 相关分析显示，中心 MitoDEG 与免疫细胞的浸润显着相关。上调的 hub 基因在幼稚/记忆 B 细胞、CD14+ 单核细胞和浆细胞亚群中表现出非常高的表达，如特征图所示。对照组和 SCM 个体中线粒体/炎症相关通路的分布在不同亚组中有所不同。二甲双胍被预测为综合得分最高的最有前途的药物。其恢复线粒体功能和抑制炎症反应的功效也得到了验证。这项研究展示了 SCM 中全面的线粒体代谢和免疫浸润景观，