Systematic Exploration of the Human Interactome IV

人类相互作用组 IV 的系统探索

• 批准号: 10676848
• 负责人: STEVEN P GYGI
• 金额: $ 76.12万
• 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 2026-05-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10676848
• 关键词: Affinity Chromatography; Architecture; Arizona; Atlases; Benchmarking; Binding; Biological; Biology; Cell Line; Cells; Cellular Structures; Collection; Communities; Coupled; Defect; Detection; Disease; Gene Expression; Genes; HCT116 Cells; Hela Cells; Human; Human Cell Line; Human Cloning; Individual; Knowledge; Libraries; Link; Maps; Mass Spectrum Analysis; Methods; Modeling; Molecular; Open Reading Frames; Organelles; Pathway Analysis; Pattern; Protein Analysis; Protein Isoforms; Proteins; Proteome; Reporting; Resources; Signal Transduction; Specificity; Structure; Universities; Validation; deep learning; design; experimental study; genome resource; genome-wide; human data; human disease; human imaging; human interactome; insight; molecular assembly/self assembly; molecular scale; multi-scale modeling; network architecture; novel; protein complex; protein protein interaction; protein purification; public database; self assembly; synthetic polymer Bioplex; three dimensional structure; web site

SUMMARY The complexities of the proteome are both immense and dynamic, reflecting diverse, context-specific expression of genes in individual cells and distinct isoforms for many proteins. In addition, individual proteins may participate in several distinct protein assemblies during their lifetimes and undergo dynamic signal- dependent re-organization in order to impart unique functions and cellular attributes. Moreover, numerous protein assemblies self-combine and compartmentalize to generate organelles and signaling modules within the cell, which are inherently dynamic. During the past 9 years, we have designed, validated, and applied a platform for the large-scale analysis of protein interaction partners using affinity purification-mass spectrometry (AP-MS) termed BioPlex, which has allowed us to profile interaction partners for over 10,000 nonredundant human bait proteins in two contrasting human cell lines: HEK 293T and HCT116. In addition, we repeated 2000 of these IP’s in three additional cell lines – U2OS, RPE1, and HeLa – to broaden our exploration of cell- specific interactomes. In aggregate, these 25,000 AP-MS experiments in five distinct cell lines have enabled us to define an atlas of nearly 280,000 protein-protein interactions among 15,500 human proteins. The majority have not been reported through independent efforts. The robustness of BioPlex, when benchmarked against other studies and when compared across cell lines, parallels or exceeds available resources, allowing us to broadly define human protein communities, predict functions and localizations of unstudied proteins based on interaction partners, and define a large number of domain-domain enrichments that begin to impart structural architecture upon the network. Finally, by repeating AP-MS experiments for thousands of baits in multiple cell lines, we have created the first and largest proteome-scale, context-specific models of the human interactome, revealing extensive remodeling that reflects the unique biology of each cell line. Yet, as valuable as these BioPlex networks have been for biological discovery, they remain incomplete models of the human interactome. Nearly 1 in 4 proteins do not appear in the BioPlex network at all, and a similar number of proteins appear in these networks as preys only, providing just a partial view of their interactions. Finally, we have only just begun to explore BioPlex in the context of other complementary genomics resources. In this renewal, we seek to greatly enhance and extend our efforts in three major ways: First, we will prosecute an additional 7,500 genes in HEK293T cells. In this way, the BioPlex network will transition from utilizing 50% of all human genes as baits to approximately 90% used as baits, revealing many novel interactions not possible previously. Second, the same 7,500 clones will be examined in HCT116 cells to provide validation and explore cell line specificity. Third, we will integrate BioPlex with complementary genome-scale resources including AlphaFold and the Human Protein Atlas to model protein interactions at scales ranging from molecular interactions to cellular structures.

概括 蛋白质组的复杂性既巨大又动态，反映了潜水员，特定于上下文 许多蛋白质中基因的表达和不同的同工型。另外，单个蛋白质 可以在其一生中参与几个不同的蛋白质组件，并经历动态信号 - 依赖性重组以赋予独特的功能和细胞属性。而且，很多 蛋白质组件自结合并分隔以在内部产生细胞器和信号模块 细胞，本质上是动态的。在过去的9年中，我们设计，验证和应用 使用亲和力纯化质量光谱法对蛋白质相互作用伙伴进行大规模分析的平台 （AP-MS）称为Bioplex，这使我们能够为10,000多个非冗余的互动伙伴介绍互动伙伴 人类诱饵蛋白在两个对比的人类细胞系中：HEK 293T和HCT116。此外，我们重复了 这些IP中的2000个在另外三个细胞系中：U2OS，RPE1和HELA，以扩大我们对细胞的探索 特定的相互作用。总体而言，这25,000个AP-MS实验在五个不同的细胞系中实现了我们 定义15,500种人类蛋白质中近280,000种蛋白质蛋白质相互作用的地图集。多数 尚未通过独立努力进行报告。 Biplex的稳健性，当 其他研究并在跨细胞系进行比较时，相似或超过可用的资源，使我们能够 广泛定义的人类蛋白质群落，预测基于未研究的蛋白质的功能和定位 互动伙伴，并定义大量开始赋予结构性的域域富集 网络上的架构。最后，通过重复多个细胞中数千种诱饵的AP-MS实验 线，我们创建了人类互动组的第一个也是最大的蛋白质组规模，上下文特定的模型， 揭示了大量的重塑，反映了每种细胞系的独特生物学。但是，像这些 Bioplex网络一直用于生物学发现，它们仍然是人类的不完整模型 Interactome。几乎四分之一的蛋白质根本没有出现在Bioplex网络中，并且类似数量的蛋白质 仅作为猎物出现在这些网络中，仅提供部分相互作用的视图。最后，我们只有 刚刚开始在其他完整基因组学资源的背景下探索Bioplex。在这种续约中，我们 寻求通过三种主要方式大大加强和扩大我们的努力：首先，我们将起诉另外 HEK293T细胞中的7,500个基因。这样，Bioplex网络将从使用所有人类的50％过渡 基因作为大约90％用作诱饵的诱饵，揭示了以前无法进行许多新型相互作用。 其次，将在HCT116细胞中检查相同的7,500个克隆以提供验证并探索细胞系 特异性。第三，我们将将Bioplex与完整的基因组规模资源整合到包括Alphafold 和人类蛋白质图集以模拟从分子相互作用到的尺度上的蛋白质相互作用 细胞结构。

项目成果

Proteomic analysis of circulating immune cells identifies cellular phenotypes associated with COVID-19 severity.

• DOI: 10.1016/j.celrep.2023.112613
• 发表时间: 2023-06-27
• 期刊: Cell reports
• 影响因子: 8.8

A Quantitative Tissue-Specific Landscape of Protein Redox Regulation during Aging.

• DOI: 10.1016/j.cell.2020.02.012
• 发表时间: 2020-03-05
• 期刊: Cell
• 影响因子: 64.5
• 作者: Xiao H;Jedrychowski MP;Schweppe DK;Huttlin EL;Yu Q;Heppner DE;Li J;Long J;Mills EL;Szpyt J;He Z;Du G;Garrity R;Reddy A;Vaites LP;Paulo JA;Zhang T;Gray NS;Gygi SP;Chouchani ET
• 通讯作者: Chouchani ET

BioPlexR and BioPlexPy: integrated data products for the analysis of human protein interactions.

• DOI: 10.1093/bioinformatics/btad091
• 发表时间: 2023-03-01
• 期刊: Bioinformatics (Oxford, England)

Dual proteome-scale networks reveal cell-specific remodeling of the human interactome.

• DOI: 10.1016/j.cell.2021.04.011
• 发表时间: 2021-05-27
• 期刊: Cell
• 影响因子: 64.5
• 作者: Huttlin EL;Bruckner RJ;Navarrete-Perea J;Cannon JR;Baltier K;Gebreab F;Gygi MP;Thornock A;Zarraga G;Tam S;Szpyt J;Gassaway BM;Panov A;Parzen H;Fu S;Golbazi A;Maenpaa E;Stricker K;Guha Thakurta S;Zhang T;Rad R;Pan J;Nusinow DP;Paulo JA;Schweppe DK;Vaites LP;Harper JW;Gygi SP
• 通讯作者: Gygi SP