Computation and functional significance of multi-phenotype genetic interaction ma

描述(由申请人提供)：两个遗传位点之间的上位性表明它们之间存在相互作用，即对表型的综合影响，这超出了基于它们各自影响的预期。计算机模拟和高通量技术的使用使得同时探索几个上位相互作用成为可能，从而产生了上位相互作用网络。这些网络在解释通路功能和进化适应方面发挥着越来越重要的作用，在多性状遗传病的研究和药物联合疗法的开发中也发挥着越来越重要的作用。由于这些原因，越来越多的实验和计算努力集中在收集、模拟和分析上位性相互作用数据。然而，一个经常被忽视的问题是表型选择的重要性，根据表型来定义两个基因之间的相互作用。对单一表型的限制在很大程度上是探索许多可能的遗传变异和表型的组合复杂性的结果。在这里，我们建议利用实验驱动的电子基因组规模的酵母代谢网络模型来生成和研究生物网络中所有可能的表型和扰动的第一个上位性相互作用图。对系统的扰动将是代谢酶基因的缺失，表型将由系统的所有可计算变量组成，即所有细胞内和运输代谢反应速率(通量)。具体地说，我们将计算一组预定义环境条件下所有单扰动和双扰动(基因缺失)的所有通量(表型)，选择适当的上位性度量，然后导出相互作用的三维矩阵(目标1)。所有通量表型的集合将构成一个功能指纹，其中包含代谢基因之间的依赖关系，可用于计划后续实验和生物医学相关应用(如预测疾病和开发疗法)。接下来，我们将通过使用高通量方法来构建适当的菌株和一套稳健的分析来测量大量单酵母和双酵母突变株中选定的通量表型，从而测试这些预测中的相当数量(目标2)。最后，我们将实现一个多表型上位分析的在线平台，用户不仅可以通过该平台下载数据和软件，还可以进行新的计算并生成用户特定的预测和地图(目标3)。我们预计，与单一表型图相比，我们的多表型图将揭示新的相互作用，并将传达更丰富的过程之间关系的观点。我们提出的工作将为分析生物系统中的多表型上位相互作用数据奠定理论、计算和交互可视化的基础。 与公共卫生相关：基因之间相互作用的复杂网络在生物系统中无处不在，这构成了根本障碍，严重限制了我们应对重大生物医学挑战的能力，例如复杂的遗传病以及药物相互作用和副作用。这项提议将通过生成一种新的遗传网络的计算表示来解决这个问题，这将有助于预测、可视化和实验筛选与生物医学相关的相互作用。

DESCRIPTION (provided by applicant): Epistasis between two genetic loci indicates an interaction between them, i.e. a combined effect on phenotype that defies expectations based on their individual effects. The availability of computer simulations and high-throughput technologies makes it possible to explore simultaneously several epistatic interactions, giving rise to epistatic interaction networks. These networks play an increasingly central role in explaining pathway functions and evolutionary adaptation, as well as in the study of multi- trait genetic diseases and in the development of drug combination therapies. For these reasons, a growing number of experimental and computational efforts focus on the collection, simulation and analysis of epistatic interaction data. Yet, an often neglected matter is the importance of the choice of the phenotype relative to which the interaction between two genes is defined. The limitation to a single phenotype is largely a consequence of the combinatorial complexity of exploring many possible genetic variants and phenotypes. Here, we propose to take advantage of experimentally-driven in silico genome- scale models of the metabolic network of the yeast S. cerevisiae to generate and study the first epistatic interaction map for all possible phenotypes and perturbations in a biological network. The perturbations to the system will be the deletions of metabolic enzyme genes, and the phenotypes will consist of all computable variables of the system, i.e. all intracellular and transport metabolic reaction rates (fluxes). Specifically, we will compute all fluxes (phenotypes) for all single and double perturbations (gene deletions) under a set of predefined environmental conditions, choosing an appropriate epistasis metric, and then deriving the three-dimensional matrix of interactions (Aim 1). The set of all flux phenotypes will constitute a functional fingerprint containing dependencies between metabolic genes, which can be used for planning subsequent experiments and for biomedically relevant applications (like predicting disease and developing therapies). Next, we will test a significant number of these predictions by using high throughput methods to construct the appropriate strains and a robust set of assays to measure selected flux phenotypes in a large number of single and double yeast mutants (Aim 2). Finally, we will implement an online platform for multi-phenotype epistasis analyses through which users will be able not only to download data and software, but also to perform novel calculations and generate user-specific predictions and maps (Aim 3). We expect that, compared to single phenotype maps, our multi-phenotype map will reveal novel interactions and will convey a much richer view of the relationships between processes. The work we are proposing will lay the theoretical, computational and interactive visualization foundations for the analysis of multi-phenotype epistatic interaction data in biological systems. PUBLIC HEALTH RELEVANCE: Complex networks of interactions between genes are ubiquitous in biological systems, posing fundamental barriers that severely limit our capacity to address major biomedical challenges, such as complex genetic diseases as well as drug interactions and side- effects. This proposal will address this problem by generating a new computational representation of genetic networks, which will help predict, visualize and experimentally screen biomedically relevant interactions.