Wu Minglong, Zhang Luxin
Department of Obstetrics and Gynecology, The Central Hospital of Wuhan, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, 430014, Hubei, China.
School of Medicine, Jianghan University, Wuhan, China.
Sci Rep. 2025 Sep 1;15(1):32034. doi: 10.1038/s41598-025-17800-w.
Preeclampsia (PE), a devastating pregnancy complication affecting 5% of gravidas worldwide, exhibits poorly characterized connections between mitochondrial dysfunction and immune dysregulation. This study aims to identify integrated mitochondrial-immune biomarkers for preeclampsia by multi-omics analysis of severe PE cohorts, enabling mechanistic insights and diagnostic potential. We developed a novel computational framework integrating multi-omics analysis (GSE10588 transcriptomics), machine learning (LASSO-SVM algorithm), and molecular dynamics simulation. This pipeline systematically identified mitochondrial-immune hubs from 1,589 PE-related DEGs and 188 mitochondrial regulators, followed by in silico validation of therapeutic targets. Fifteen candidate genes were identified by intersecting 1,589 DEGs and 188 MRGs. PPIs revealed associations with the citrate cycle (TCA cycle). Machine learning prioritized ACO1 and OGDH as biomarkers, showing opposite expression trends (ACO1: higher in controls; OGDH: elevated in PE). Both localized to chromosomes 9 and 7, respectively, with nuclear predominance. Enrichment linked them to cytokine-cytokine receptor and neuroactive ligand-receptor pathways. Immune infiltration highlighted correlations with activated NK cells and CD8+ T cells. Regulatory networks implicated lncRNAs (e.g., KCNQ1OT1), miRNAs (e.g., hsa-miR-214-3p), and TFs (e.g., TFAP2A). Drug predictions and docking identified devimistat and acetylcysteine as potential binders. This study establishes ACO1 and OGDH as context-specific mitochondrial-immune coordinators in preeclampsia. We propose a computationally derived dual-target therapeutic strategy, wherein ACO1 agonism aims to restore metabolic homeostasis while OGDH inhibition targets pathological overactivation. These findings, originating from in silico analyses, require preclinical validation through experimental models. Additionally, the mitochondrial-immune scoring system serves as a candidate tool for PE subtyping.
子痫前期（PE）是一种严重的妊娠并发症，影响全球5%的孕妇，其线粒体功能障碍与免疫失调之间的联系尚不明确。本研究旨在通过对重度PE队列进行多组学分析，确定子痫前期的综合线粒体-免疫生物标志物，从而深入了解其机制并挖掘诊断潜力。我们开发了一种新颖的计算框架，整合了多组学分析（GSE10588转录组学）、机器学习（LASSO-SVM算法）和分子动力学模拟。该流程从1589个与PE相关的差异表达基因（DEG）和188个线粒体调节因子中系统地鉴定出线粒体-免疫枢纽，随后对治疗靶点进行了计算机模拟验证。通过交叉分析1589个DEG和188个线粒体调节基因（MRG），确定了15个候选基因。蛋白质-蛋白质相互作用（PPI）揭示了与柠檬酸循环（TCA循环）的关联。机器学习将ACO1和OGDH优先作为生物标志物，显示出相反的表达趋势（ACO1：对照组中较高；OGDH：PE组中升高）。它们分别定位于9号和7号染色体，主要在细胞核中表达。富集分析将它们与细胞因子-细胞因子受体和神经活性配体-受体途径联系起来。免疫浸润突出了与活化的自然杀伤（NK）细胞和CD8+ T细胞的相关性。调控网络涉及长链非编码RNA（lncRNA，如KCNQ1OT1）、微小RNA（miRNA，如hsa-miR-214-3p）和转录因子（TF，如TFAP2A）。药物预测和对接确定了地维司他和乙酰半胱氨酸为潜在结合剂。本研究确定ACO1和OGDH是子痫前期中特定背景下的线粒体-免疫协调因子。我们提出了一种通过计算得出的双靶点治疗策略，其中ACO1激动剂旨在恢复代谢稳态，而OGDH抑制剂则针对病理性过度激活。这些源于计算机模拟分析的发现，需要通过实验模型进行临床前验证。此外，线粒体-免疫评分系统可作为PE亚型分类的候选工具。
