Dynamic protein interaction networks to map molecular origins of envronmental and genomic variations.

动态蛋白质相互作用网络可绘制环境和基因组变异的分子起源。

• 批准号: RGPIN-2021-03216
• 负责人: Michnick, Stephen
• 金额: $ 4.23万
• 依托单位: Université de Montréal
• 依托单位国家: 加拿大
• 项目类别: Discovery Grants Program - Individual
• 财政年份: 2022
• 资助国家: 加拿大
• 起止时间: 2022-01-01 至 2023-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=744381
• 关键词: Dynamic; protein; interaction; networks; map

Problem and Hypotheses: The fate of an organisms is governed by its genome and the environment and the interactions of both with the biochemical networks that make up living cells. A surrogate measurement for the contributions of all biomolecules to cell fate can be found in the spatiotemporal dynamics of protein-protein interaction networks (PINS). We have developed methods to address the dynamics of PINS on a whole proteome scale in the budding yeast Saccharomyces cerevisiae by means of a Protein-fragment Complementation Assay (PCA) using a methotrexate-resistant the enzyme Dihydrofolate Reductase (DHFR) as reporter (Tarassov, et al. Science, 2008; Stynen, Abd Rabbo, et al., Cell, 2018). Surprisingly, we also observed a remarkable overlap between the proteins that respond to drug treatments and human homologues that have been found in genome-wide association studies (GWAS) associated with human disease, suggesting that PCA can link environmental and genomic variation effects to mechanistic consequences. Our central hypothesis is that the dynamics of PINS will accurately capture the biochemical-mechanistic effects of environmental stimuli and genetic variation. At what scale i.e. how many PCA reporters are needed to make accurate predictions of environmental or single nucleotide polymorphisms (SNP) on biochemical mechanisms? To address this question, we will perform two studies: in the short term we will cross PCA containing strains with existing outbred laboratory strains (Sen & Jarosz, Cell, 2018) to establish a benchmark for SNP resolution at which we can predict mechanistic effects from PINs. Second, in the medium term we will implement a new strategy to perform whole protein interactomes in a single test. This massive simplification of DHFR PCA screening will permit us to perform interactomes on large numbers of outbred strains and under a variety of different environmental conditions and to address a variety of fundamental questions in molecular genetics. Objectives: We will ask the following questions: 1 - How is genetic variation reflected in changes in PINs? 2 - How do genetic variation and environmental stress synergize on the structure of the PIN? 3 - At what SNP resolution do PINs resolve genomic effects on biochemical processes? 4-How much are variations in genes or environment reflected in the organization of the PINs, including among orthologs, at hubs and clusters within the network? 5-Can we predict genomic variation associated with human diseases from yeast PIN variation caused by gene and environment effects? These studies will contribute to advancement of knowledge by: 1- Providing a new strategy to directly probe the dynamics of an entire protein interactome. 2 - Revealing principles and mechanisms of dynamic biochemical network organization. 3 - Providing new insight into the principles of evolution of the cellular machinery. 4 - Providing insight into genome-environment interactions with molecular networks.

问题和假设：生物体的命运受其基因组和环境的控制，以及两者与构成活细胞的生化网络的相互作用。可以在蛋白质 - 蛋白质相互作用网络（PIN）的时空动力学中找到对所有生物分子对细胞命运的贡献的替代测量。 We have developed methods to address the dynamics of PINS on a whole proteome scale in the budding yeast Saccharomyces cerevisiae by means of a Protein-fragment Complementation Assay (PCA) using a methotrexate-resistant the enzyme Dihydrofolate Reductase (DHFR) as reporter (Tarassov, et al. Science, 2008; Stynen, Abd Rabbo, et al., Cell, 2018）。出乎意料的是，我们还观察到蛋白质之间对药物治疗的反应和人类同源物的显着重叠，这些蛋白质与人类疾病相关的全基因组关联研究（GWAS）中发现，这表明PCA可以将环境和基因组变异效应与机械后果联系起来。我们的中心假设是，引脚的动力学将准确捕获环境刺激和遗传变异的生化机械效应。在什么规模上，即需要多少个PCA记者来准确预测生化机制上环境或单核苷酸多态性（SNP）？为了解决这个问题，我们将进行两项研究：在短期内，我们将与现有的爆头实验室菌株（Sen＆Jarosz，Cell，2018）跨越PCA，以建立SNP分辨率的基准，以便在该基准中预测销钉中的机械作用。其次，在中期，我们将在单个测试中实施一种新策略来执行全蛋白质相互作用。 DHFR PCA筛选的大规模简化将使我们能够对大量杂种菌株和各种不同的环境条件进行相互作用，并解决分子遗传学中的各种基本问题。目的：我们将提出以下问题：1-针刺变化中的遗传变异如何反映？ 2-遗传变异和环境应力如何协同销结构？ 3-销在什么SNP分辨率下解决了对生化过程的基因组影响？ 4在网络中的集线器和簇中，包括直系同源物在内的引脚组织中反映的基因或环境的变化是多少？ 5-can，我们预测与基因和环境效应引起的酵母销变量相关的基因组变异？这些研究将通过：1-提供一种新策略来直接探测整个蛋白质相互作用组的动态。 2-揭示动态生化网络组织的原理和机制。 3-提供有关蜂窝机械进化原理的新见解。 4-提供与分子网络基因组环境相互作用的见解。