Vlasblom James, Jin Ke, Kassir Sandy, Babu Mohan
Department of Biochemistry, Research and Innovation Centre, University of Regina, Regina, Saskatchewan S4S 0A2, Canada.
Department of Biochemistry, Research and Innovation Centre, University of Regina, Regina, Saskatchewan S4S 0A2, Canada; Banting and Best Department of Medical Research, University of Toronto, Toronto, Ontario M5S 3E1, Canada; Terrence Donnelly Center for Cellular and Biomolecular Research, University of Toronto, Toronto, Ontario M5S 3E1, Canada.
J Proteomics. 2014 Apr 4;100:8-24. doi: 10.1016/j.jprot.2013.11.008. Epub 2013 Nov 18.
Mitochondria are double membraned, dynamic organelles that are required for a large number of cellular processes, and defects in their function have emerged as causative factors for a growing number of human disorders and are highly associated with cancer, metabolic, and neurodegenerative (ND) diseases. Biochemical and genetic investigations have uncovered small numbers of candidate mitochondrial proteins (MPs) involved in ND disease, but given the diversity of processes affected by MP function and the difficulty of detecting interactions involving these proteins, many more likely remain unknown. However, high-throughput proteomic and genomic approaches developed in genetically tractable model prokaryotes and lower eukaryotes have proven to be effective tools for querying the physical (protein-protein) and functional (gene-gene) relationships between diverse types of proteins, including cytosolic and membrane proteins. In this review, we highlight how experimental and computational approaches developed recently by our group and others can be effectively used towards elucidating the mitochondrial interactome in an unbiased and systematic manner to uncover network-based connections. We discuss how the knowledge from the resulting interaction networks can effectively contribute towards the identification of new mitochondrial disease gene candidates, and thus further clarify the role of mitochondrial biology and the complex etiologies of ND disease.
Biochemical and genetic investigations have uncovered small numbers of candidate mitochondrial proteins (MPs) involved in neurodegenerative (ND) diseases, but given the diversity of processes affected by MP function and the difficulty of detecting interactions involving these proteins, many more likely remain unknown. Large-scale proteomic and genomic approaches developed in model prokaryotes and lower eukaryotes have proven to be effective tools for querying the physical (protein-protein) and functional (gene-gene) relationships between diverse types of proteins. Extension of this new framework to the mitochondrial sub-system in human will likewise provide a universally informative systems-level view of the physical and functional landscape for exploring the evolutionary principles underlying mitochondrial function. In this review, we highlight how experimental and computational approaches developed recently by our group and others can be effectively used towards elucidating the mitochondrial interactome in an unbiased and systematic manner to uncover network-based connections. We anticipate that the knowledge from these resulting interaction networks can effectively contribute towards the identification of new mitochondrial disease gene candidates, and thus foster a deeper molecular understanding of mitochondrial biology as well as the etiology of mitochondrial diseases. This article is part of a Special Issue: Can Proteomics Fill the Gap Between Genomics and Phenotypes?
线粒体是具有双层膜的动态细胞器，参与大量细胞过程，其功能缺陷已成为越来越多人类疾病的致病因素，并且与癌症、代谢和神经退行性（ND）疾病高度相关。生化和遗传学研究已经发现了少量与ND疾病相关的候选线粒体蛋白（MP），但鉴于受MP功能影响的过程具有多样性，以及检测涉及这些蛋白的相互作用存在困难，很可能还有更多未知蛋白。然而，在遗传易处理的模式原核生物和低等真核生物中开发的高通量蛋白质组学和基因组学方法已被证明是查询包括胞质蛋白和膜蛋白在内的不同类型蛋白质之间的物理（蛋白质-蛋白质）和功能（基因-基因）关系的有效工具。在这篇综述中，我们重点介绍了我们小组和其他小组最近开发的实验和计算方法如何能够以无偏见和系统的方式有效地用于阐明线粒体相互作用组，以揭示基于网络的联系。我们讨论了从所得相互作用网络中获得的知识如何能够有效地有助于鉴定新的线粒体疾病基因候选物，从而进一步阐明线粒体生物学的作用以及ND疾病的复杂病因。
生化和遗传学研究已经发现了少量与神经退行性（ND）疾病相关的候选线粒体蛋白（MP），但鉴于受MP功能影响的过程具有多样性，以及检测涉及这些蛋白的相互作用存在困难，很可能还有更多未知蛋白。在模式原核生物和低等真核生物中开发的大规模蛋白质组学和基因组学方法已被证明是查询不同类型蛋白质之间的物理（蛋白质-蛋白质）和功能（基因-基因）关系的有效工具。将这个新框架扩展到人类线粒体亚系统同样将提供一个普遍有用的系统水平视角，以探索线粒体功能背后的进化原理的物理和功能景观。在这篇综述中，我们重点介绍了我们小组和其他小组最近开发的实验和计算方法如何能够以无偏见和系统的方式有效地用于阐明线粒体相互作用组，以揭示基于网络的联系。我们预计，从这些所得相互作用网络中获得的知识能够有效地有助于鉴定新的线粒体疾病基因候选物，从而促进对线粒体生物学以及线粒体疾病病因的更深入分子理解。本文是特刊“蛋白质组学能否填补基因组学与表型之间的空白？”的一部分。
