Systematic Exploration of the Human Interactome

人类相互作用组的系统探索

• 批准号: 9300934
• 负责人: STEVEN P GYGI
• 金额: $ 66.11万
• 依托单位: HARVARD MEDICAL SCHOOL
• 依托单位国家: 美国
• 财政年份: 2012
• 资助国家: 美国
• 起止时间: 2012-08-21 至 2018-06-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/9300934
• 关键词: Alleles; Architecture; Automobile Driving; Bioinformatics; Biology; Cell Culture Techniques; Cell Line; Cell Lineage; Cells; Collection; Communication; Complex; Data; Data Set; Databases; Defect; Deposition; Detergents; Digitonin; Discipline; Disease; Elements; Follow-Up Studies; Funding; Future; Gene Amplification; Gene Mutation; Genes; Genetic Variation; Genome; Goals; Growth; HCT116 Cells; Health; Hereditary Disease; Human; Human Genetics; Infection; Informatics; Joints; Karyotype; Libraries; Maintenance; Manuscripts; Maps; Measures; Membrane Proteins; Molecular; Molecular Cloning; Molecular Machines; Mutate; Mutation; N-terminal; National Human Genome Research Institute; Online Systems; Open Reading Frames; Organelles; Paper; Pathway Analysis; Pathway interactions; Peptides; Point Mutation; Process; Production; Protein Analysis; Proteins; Proteome; Proteomics; Quality Control; Regulation; Reporting; Research; Research Personnel; Resource Development; Resources; Role; Scientist; Signal Transduction; Site; Specificity; System; Technology; Testing; Translating; Validation; Visit; Work; base; cell type; comparative; experience; gene product; human disease; infrastructure development; insight; mutant; network architecture; next generation; novel; programs; protein complex; protein function; public health relevance; stable cell line

 DESCRIPTION (provided by applicant): The proteome can be viewed as constellations of interacting protein modules, which are organized into organelles, molecular machines, and signal transduction networks. To date, the interaction partners for only a small fraction of expressed proteins are known, yet protein-protein associations are a major driving factor in understanding protein function and activity. During the last 2 years, we have implemented a high-throughput protein interaction pipeline for analysis of protein interactions for ~10,000 human proteins in 293T cells tagged at their C-terminus with an HA tag with joint support by NHGRI and Biogen-Idec. We refer to this as a "first- pass" interactome analysis. This has led so far to the analysis of ~5900 proteins (Sept. 20th, 2014) by AP-MS, 2594 of which have already been deposited in the BioGRID database based on their inclusion in a first paper describing the pipeline. Additional interactions are being released quarterly and all are free to the public. The development of the infrastructure required to perform a project of this scale is unprecedented in the field of interaction proteomics. Indeed, we have developed numerous new informatic and operational elements into a pipeline that allows a small team of scientists to perform an analysis of 500-600 baits by AP-MS per month. Numerous quality control measures have been developed, and comparison of the interaction partners of the first 2594 bait proteins with existing protein interaction databases reveals extensive overlap with the highest quality CORUM database, while also providing experimental evidence for over 20,000 novel interactions that have not been previously reported. The network identifies more than 250 clusters of proteins, and includes both known and novel complexes. In this proposal, we seek to continue the production of this pipeline with 3 major goals, which were chosen as priority objectives based on discussions with NHGRI. In Specific Aim 1, we will complete the analysis of the remaining ORF clones that are available (~4000 clones). We will also re- visit 1,000 ORFs that failed to generate stable cell lines in the first pass analysis, and will examine 1,000 membrane proteins using a different detergent that more readily solubilizes certain classes of membrane proteins than the detergent used in the "first-pass" analysis. In Specific Aim 2, we will test the cell-type specificity of interactions for the first 2600 baits in a second cell line (HCT116), thereby providing validation for a subset of interactions and revealing candidate cell-type-specific interactions. In Specific Aim 3, we will employ a Tandem Mass Tagging approach to compare interactions for ~50 important disease genes and several mutant alleles for each, thereby implementing a highly comparative platform for understanding how disease mutations alter signaling networks, molecular machines, and pathways. All data will be disseminated through public databases and will be subject to extensive bioinformatics and network analysis.

 描述（由申请人提供）：蛋白质组可以被视为相互作用的蛋白质模块的星座，其被组织成细胞器、分子机器和信号转导网络。迄今为止，只有一小部分表达蛋白质的相互作用伴侣是已知的，但蛋白质-蛋白质缔合是理解蛋白质功能和活性的主要驱动因素。在过去的2年中，我们已经实施了一个高通量蛋白质相互作用管道，用于分析293 T细胞中约10，000种人类蛋白质的蛋白质相互作用，这些蛋白质在其C末端标记有HA标签，并得到了NHGRI和Biogen-Idec的联合支持。我们称之为“首过”相互作用组分析。到目前为止，这已经导致了对约5900种蛋白质的分析（9月10日）。2014年20日），其中2594个已经基于其包含在描述管道的第一篇论文中而被保藏在BioGRID数据库中。其他互动每季度发布一次，所有互动都免费向公众开放。执行这种规模的项目所需的基础设施的发展在相互作用蛋白质组学领域是前所未有的。事实上，我们已经开发了许多新的信息和操作元素到一个管道，使一个小团队的科学家进行分析的500-600诱饵AP-MS每月。已经开发了许多质量控制措施，并将前2594种诱饵蛋白与现有的诱饵蛋白的相互作用伴侣进行了比较。 蛋白质相互作用数据库揭示了与最高质量的CORUM数据库的广泛重叠，同时也为以前未报道的20，000多种新的相互作用提供了实验证据。该网络识别了250多个蛋白质簇，包括已知和新的复合物。在本提案中，我们寻求继续生产这一管道，并有3个主要目标，这些目标是根据与NHGRI的讨论选定的优先目标。在特定目标1中，我们将完成对剩余ORF克隆的分析（约4000个克隆）。我们还将重新访问在第一次通过分析中未能产生稳定细胞系的1，000个ORF，并将使用与“第一次通过”分析中使用的去污剂相比更容易溶解某些类别的膜蛋白的不同去污剂来检查1，000个膜蛋白。在具体目标2中，我们将测试细胞类型 在第二细胞系（HCT 116）中的前2600个诱饵的相互作用的特异性，从而为相互作用的子集提供验证并揭示候选细胞类型特异性相互作用。在具体目标3中，我们将采用串联质量标记方法来比较约50个重要疾病基因和每个基因的几个突变等位基因的相互作用，从而实现一个高度比较的平台，以了解疾病突变如何改变信号网络，分子机器和途径。所有数据将通过公共数据库传播，并将进行广泛的生物信息学和网络分析。

项目成果

Architecture of the human interactome defines protein communities and disease networks.

• DOI: 10.1038/nature22366
• 发表时间: 2017-05-25
• 期刊: Nature
• 影响因子: 64.8
• 作者: Huttlin EL;Bruckner RJ;Paulo JA;Cannon JR;Ting L;Baltier K;Colby G;Gebreab F;Gygi MP;Parzen H;Szpyt J;Tam S;Zarraga G;Pontano-Vaites L;Swarup S;White AE;Schweppe DK;Rad R;Erickson BK;Obar RA;Guruharsha KG;Li K;Artavanis-Tsakonas S;Gygi SP;Harper JW
• 通讯作者: Harper JW

Mitochondrial Sirtuin Network Reveals Dynamic SIRT3-Dependent Deacetylation in Response to Membrane Depolarization.

• DOI: 10.1016/j.cell.2016.10.016
• 发表时间: 2016-11-03
• 期刊: CELL
• 影响因子: 64.5
• 作者: Yang, Wen;Nagasawa, Koji;Munch, Christian;Xu, Yingjie;Satterstrom, Kyle;Jeong, Seungmin;Hayes, Sebastian D.;Jedrychowski, Mark P.;Vyas, F. Sejal;Zaganjor, Elma;Guarani, Virginia;Ringel, Alison E.;Gygi, Steven P.;Harper, J. Wade;Haigis, Marcia C.
• 通讯作者: Haigis, Marcia C.

BioPlex Display: An Interactive Suite for Large-Scale AP-MS Protein-Protein Interaction Data.

• DOI: 10.1021/acs.jproteome.7b00572
• 发表时间: 2018-01-05
• 期刊: Journal of proteome research
• 影响因子: 4.4
• 作者: Schweppe DK;Huttlin EL;Harper JW;Gygi SP
• 通讯作者: Gygi SP