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: 8136295
• 负责人: AIMEE M DUDLEY
• 金额: $ 44.02万
• 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/8136295
• 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）：两个遗传位点之间的上位表明它们之间存在相互作用，即对表型的综合影响超出了基于其个体影响的预期。计算机模拟和高通量技术的可用性使得同时探索几种上位相互作用成为可能，从而产生上位相互作用网络。这些网络在解释途径功能和进化适应以及多性状遗传疾病的研究和药物组合疗法的开发中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工作集中在上位相互作用数据的收集、模拟和分析上。然而，一个经常被忽视的问题是选择表型的重要性，相对于表型来定义两个基因之间的相互作用。对单一表型的限制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酿酒酵母代谢网络的计算机基因组规模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相互作用图。对系统的扰动将是代谢酶基因的删除，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）。具体来说，我们将在一组预定义的环境条件下计算所有单扰动和双重扰动（基因删除）的所有通量（表型），选择适当的上位度量，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包含代谢基因之间依赖性的功能指纹，可用于规划后续实验和生物医学相关应用（例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构建适当的菌株和一组强大的测定来测试大量的这些预测，以测量大量单酵母和双酵母突变体中选定的通量表型（目标 2）。最后，我们将实现一个用于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，还可以执行新颖的计算并生成用户特定的预测和图谱（目标 3）。我们期望，与单表型图谱相比，我们的多表型图谱将揭示新颖的相互作用，并传达过程之间关系的更丰富的视图。我们提出的工作将为生物系统中多表型上位相互作用数据的分析奠定理论、计算和交互式可视化基础。 公共卫生相关性：基因之间复杂的相互作用网络在生物系统中无处不在，构成了根本性障碍，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和副作用。该提案将通过生成遗传网络的新计算表示来解决这个问题，这将有助于预测、可视化和实验筛选生物医学相关的相互作用。