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

问题与假设：生物体的命运取决于其基因组和环境，以及两者与构成活细胞的生化网络的相互作用。蛋白质-蛋白质相互作用网络（PINS）的时空动力学是衡量所有生物分子对细胞命运贡献的一种替代方法。我们已经开发了在芽殖酵母酿酒酵母（Saccharomycescerevisiae）中通过蛋白片段互补测定（PCA）在整个蛋白质组规模上解决PINS动力学的方法，所述蛋白片段互补测定（PCA）使用甲氨蝶呤抗性酶二氢叶酸还原酶（DHFR）作为报告基因（Tarassov等人，Science，2008; Stynen，Abd Rabbo等人，Cell，2018）。令人惊讶的是，我们还观察到对药物治疗有反应的蛋白质与在与人类疾病相关的全基因组关联研究（GWAS）中发现的人类同源物之间存在显著的重叠，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，PINS的动态将准确地捕捉到环境刺激和遗传变异的生化机制效应。在什么样的规模，即需要多少PCA报告，以作出准确的预测环境或单核苷酸多态性（SNP）的生化机制？为了解决这个问题，我们将进行两项研究：在短期内，我们将使含有PCA的菌株与现有的远交实验室菌株杂交（Sen & Jarosz，Cell，2018），以建立SNP分辨率的基准，在该基准下，我们可以预测PIN的机制效应。第二，在中期，我们将实施一项新的策略，在一次测试中进行全蛋白质相互作用组。这种DHFR PCA筛选的大规模简化将使我们能够在各种不同的环境条件下对大量远系繁殖的菌株进行相互作用，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 -遗传变异如何反映在PIN的变化？ 2 -遗传变异和环境压力如何协同作用于PIN的结构？ 3 -在什么样的SNP分辨率下，PIN解决了基因组对生化过程的影响？ 4-基因或环境的变化在多大程度上反映在PIN的组织中，包括直系同源物之间，网络中的枢纽和集群？5-我们能否从基因和环境效应引起的酵母PIN变异预测与人类疾病相关的基因组变异？这些研究将有助于知识的进步：1-提供一种新的策略，直接探测整个蛋白质相互作用组的动力学。 2.揭示动态生化网络组织的原理和机制。 3 -提供了新的见解的原则演变的细胞机制。 4 -提供深入了解基因组与分子网络环境的相互作用。

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.