University Creative Research Initiatives Center, Shandong First Medical University, Shandong, 250062, P. R. China.
Key Laboratory of Cancer Proteomics of Chinese Ministry of Health, Xiangya Hospital, Central South University, 87 Xiangya Road, Changsha, Hunan, 410008, P. R. China.
Mass Spectrom Rev. 2020 Sep;39(5-6):471-498. doi: 10.1002/mas.21618. Epub 2020 Feb 4.
The prominent characteristics of mitochondria are highly dynamic and regulatory, which have crucial roles in cell metabolism, biosynthetic, senescence, apoptosis, and signaling pathways. Mitochondrial dysfunction might lead to multiple serious diseases, including cancer. Therefore, identification of mitochondrial proteins in cancer could provide a global view of tumorigenesis and progression. Mass spectrometry-based quantitative mitochondrial proteomics fulfils this task by enabling systems-wide, accurate, and quantitative analysis of mitochondrial protein abundance, and mitochondrial protein posttranslational modifications (PTMs). Multiple quantitative proteomics techniques, including isotope-coded affinity tag, stable isotope labeling with amino acids in cell culture, isobaric tags for relative and absolute quantification, tandem mass tags, and label-free quantification, in combination with different PTM-peptide enrichment methods such as TiO enrichment of tryptic phosphopeptides and antibody enrichment of other PTM-peptides, increase flexibility for researchers to study mitochondrial proteomes. This article reviews isolation and purification of mitochondria, quantitative mitochondrial proteomics, quantitative mitochondrial phosphoproteomics, mitochondrial protein-involved signaling pathway networks, mitochondrial phosphoprotein-involved signaling pathway networks, integration of mitochondrial proteomic and phosphoproteomic data with whole tissue proteomic and transcriptomic data and clinical information in ovarian cancers (OC) to in-depth understand its molecular mechanisms, and discover effective mitochondrial biomarkers and therapeutic targets for predictive, preventive, and personalized treatment of OC. This proof-of-principle model about OC mitochondrial proteomics is easily implementable to other cancer types. © 2020 John Wiley & Sons Ltd. Mass Spec Rev.
线粒体的突出特点是高度动态和可调节，在细胞代谢、生物合成、衰老、凋亡和信号通路中具有关键作用。线粒体功能障碍可能导致多种严重疾病，包括癌症。因此，鉴定癌症中的线粒体蛋白可以提供肿瘤发生和进展的整体视图。基于质谱的定量线粒体蛋白质组学通过实现对线粒体蛋白丰度和线粒体蛋白翻译后修饰（PTM）的系统、准确和定量分析来完成这项任务。多种定量蛋白质组学技术，包括同位素编码亲和标签、稳定同位素标记细胞培养中的氨基酸、相对和绝对定量的同位素标记、串联质量标签和无标记定量，结合不同的 PTM-肽富集方法，如 TiO2 富集的胰蛋白酶磷酸肽和抗体富集的其他 PTM-肽，增加了研究人员研究线粒体蛋白质组的灵活性。本文综述了线粒体的分离和纯化、定量线粒体蛋白质组学、定量线粒体磷酸蛋白质组学、线粒体蛋白参与的信号通路网络、线粒体磷酸蛋白参与的信号通路网络、将线粒体蛋白质组学和磷酸蛋白质组学数据与卵巢癌（OC）的整个组织蛋白质组学和转录组学数据以及临床信息进行整合，以深入了解其分子机制，并发现有效的线粒体生物标志物和治疗靶点，用于预测、预防和个性化治疗 OC。OC 线粒体蛋白质组学的这一原理验证模型易于在其他癌症类型中实施。© 2020 年 John Wiley & Sons Ltd. Mass Spec Rev.
