Wang Jiabin, Yang Jian, Mao Song, Chai Xiaoqiang, Hu Yuling, Hou Xugang, Tang Yiheng, Bi Cheng, Li Xiao
College of Life Sciences, Sichuan University, Ministry of Education Key Laboratory for Bio-resource and Eco-environment, Sichuan Key Laboratory of Molecular Biology and Biotechnology, Chengdu, People's Republic of China.
PLoS One. 2014 Oct 27;9(10):e111187. doi: 10.1371/journal.pone.0111187. eCollection 2014.
Mitochondrion plays a central role in diverse biological processes in most eukaryotes, and its dysfunctions are critically involved in a large number of diseases and the aging process. A systematic identification of mitochondrial proteomes and characterization of functional linkages among mitochondrial proteins are fundamental in understanding the mechanisms underlying biological functions and human diseases associated with mitochondria. Here we present a database MitProNet which provides a comprehensive knowledgebase for mitochondrial proteome, interactome and human diseases. First an inventory of mammalian mitochondrial proteins was compiled by widely collecting proteomic datasets, and the proteins were classified by machine learning to achieve a high-confidence list of mitochondrial proteins. The current version of MitProNet covers 1124 high-confidence proteins, and the remainders were further classified as middle- or low-confidence. An organelle-specific network of functional linkages among mitochondrial proteins was then generated by integrating genomic features encoded by a wide range of datasets including genomic context, gene expression profiles, protein-protein interactions, functional similarity and metabolic pathways. The functional-linkage network should be a valuable resource for the study of biological functions of mitochondrial proteins and human mitochondrial diseases. Furthermore, we utilized the network to predict candidate genes for mitochondrial diseases using prioritization algorithms. All proteins, functional linkages and disease candidate genes in MitProNet were annotated according to the information collected from their original sources including GO, GEO, OMIM, KEGG, MIPS, HPRD and so on. MitProNet features a user-friendly graphic visualization interface to present functional analysis of linkage networks. As an up-to-date database and analysis platform, MitProNet should be particularly helpful in comprehensive studies of complicated biological mechanisms underlying mitochondrial functions and human mitochondrial diseases. MitProNet is freely accessible at http://bio.scu.edu.cn:8085/MitProNet.
线粒体在大多数真核生物的多种生物学过程中起着核心作用,其功能障碍与大量疾病及衰老过程密切相关。系统鉴定线粒体蛋白质组并表征线粒体蛋白质之间的功能联系,对于理解与线粒体相关的生物学功能和人类疾病的潜在机制至关重要。在此,我们展示了一个数据库MitProNet,它为线粒体蛋白质组、相互作用组和人类疾病提供了一个全面的知识库。首先,通过广泛收集蛋白质组数据集编制了哺乳动物线粒体蛋白质清单,并通过机器学习对蛋白质进行分类,以获得一份高可信度的线粒体蛋白质列表。MitProNet的当前版本涵盖1124种高可信度蛋白质,其余的则进一步分类为中可信度或低可信度。然后,通过整合由多种数据集编码的基因组特征,包括基因组背景、基因表达谱、蛋白质-蛋白质相互作用、功能相似性和代谢途径,生成了一个线粒体蛋白质之间的细胞器特异性功能联系网络。该功能联系网络应该是研究线粒体蛋白质生物学功能和人类线粒体疾病的宝贵资源。此外,我们利用该网络使用优先级算法预测线粒体疾病的候选基因。MitProNet中的所有蛋白质、功能联系和疾病候选基因均根据从其原始来源收集的信息进行注释,包括GO、GEO、OMIM、KEGG、MIPS、HPRD等。MitProNet具有用户友好的图形可视化界面,以展示联系网络的功能分析。作为一个最新的数据库和分析平台,MitProNet在全面研究线粒体功能和人类线粒体疾病背后复杂的生物学机制方面应该特别有帮助。可通过http://bio.scu.edu.cn:8085/MitProNet免费访问MitProNet。