Computation and functional significance of multi-phenotype genetic interaction ma

多表型遗传相互作用的计算和功能意义

• 批准号: 7987561
• 负责人: AIMEE M DUDLEY
• 金额: $ 39.62万
• 依托单位: BOSTON UNIVERSITY (CHARLES RIVER CAMPUS)
• 依托单位国家: 美国
• 财政年份: 2010
• 资助国家: 美国
• 起止时间: 2010-09-01 至 2015-08-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/7987561
• 关键词: Address; Adverse effects; Area; Asthma; Behavior; Biochemical Pathway; Biological; Biological Assay; Biology; Biomass; Characteristics; Collaborations; Collection; Combination Drug Therapy; Complex; Computer Simulation; Computer software; DNA Sequence; Data; Data Set; Defect; Dependency; Development; Diabetes Mellitus; Disease; Drug Interactions; Environment; Enzyme Gene; Enzymes; Fingerprint; Foundations; Gene Deletion; Genes; Genetic; Genetic Epistasis; Genome; Genotype; Goals; Growth; Health; Heart Diseases; Hereditary Disease; Human; Imagery; Inborn Errors of Metabolism; Individual; Intracellular Transport; Knowledge; Malignant Neoplasms; Maps; Measurement; Measures; Metabolic; Metabolic Diseases; Metabolism; Methods; Metric; Mining; Modeling; Organism; Outcome; Pathway interactions; Phenotype; Play; Process; Production; Property; Relative (related person); Research; Role; Saccharomyces cerevisiae; System; Testing; Validation; Work; Yeasts; base; biological systems; combinatorial; drug development; expectation; experience; genetic variant; high risk; high throughput technology; human disease; mutant; neglect; novel; pathogenic bacteria; public health relevance; reaction rate; research study; simulation; therapy development; trait; uptake

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.

描述（由申请人提供）：两个遗传基因座之间的上毒表示它们之间的相互作用，即对表型的组合作用，基于其个体效应而违反期望。计算机仿真和高通量技术的可用性使得可以同时探索几次上皮相互作用，从而产生上皮的互动网络。这些网络在解释途径功能和进化适应以及多特质遗传疾病和药物联合疗法的发展中起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工作集中于对上任相互作用数据的集合，模拟和分析。然而，经常被忽略的问题是选择表型相对于两个基因之间相互作用的选择的重要性。单个表型的局限性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，我们建议利用酵母菌的代谢网络的实验驱动的酿酒酵母网络的实验驱动，以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皮相互作用图。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，即所有细胞内和转运代谢反应速率（Fluxes）。具体而言，我们将在一组预定义的环境条件下计算所有单一和双扰动（基因缺失）的所有通量（表型），选择适当的静脉指标，然后得出相互作用的三维矩阵（AIM 1）。所有通量表型的集合将构成一个功能性指纹，其中包含代谢基因之间的依赖性，该基因可用于计划后续实验和生物医学相关的应用（例如预测疾病和发展疗法）。接下来，我们将通过使用高吞吐量方法来构建适当的菌株和一组可靠的测定方法来测量大量的单一和双酵母突变体中所选的通量表型（AIM 2）来测试大量此类预测（AIM 2）。最后，我们将实施一个在线平台，以进行多音型上的上位分析，通过该平台，用户不仅可以下载数据和软件，还可以执行新颖的计算并生成特定用户的预测和地图（AIM 3）。我们希望，与单个表型图相比，我们的多音型图将揭示新的相互作用，并将传达对过程之间关系的更丰富的看法。我们提出的工作将奠定理论，计算和互动可视化基础，以分析生物系统中的多型上皮相互作用数据。 公共卫生相关性：基因之间相互作用的复杂网络在生物系统中无处不在，带来了基本障碍，严重限制了我们应对重大生物医学挑战的能力，例如复杂的遗传疾病以及药物相互作用以及药物相互作用和副作用。该建议将通过生成遗传网络的新计算表示来解决此问题，该计算将有助于预测，可视化和实验筛选与生物医学相关的相互作用。