Methods for large-scale analysis of chemical-genetic interactions

化学-遗传相互作用的大规模分析方法

• 批准号: 8630348
• 负责人: Chad L Myers
• 金额: $ 36.3万
• 依托单位: UNIVERSITY OF MINNESOTA
• 依托单位国家: 美国
• 财政年份: 2010
• 资助国家: 美国
• 起止时间: 2010-08-25 至 2017-04-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/8630348
• 关键词: Address; Animal Model; Bacteria; Big Data; Binding Proteins; Biochemical; Biological Assay; Biological Factors; Biology; Chemicals; Collection; Data; Development; Diagnostic; Disease; Drug Targeting; Escherichia coli; Eukaryotic Cell; FDA approved; Fingerprint; Fission Yeast; Gene Targeting; Genes; Genetic; Genetic Structures; Genome; Genomics; Goals; Health; Human; Human Genome; Lead; Libraries; Life; Maps; Methods; Outcome; Pathway interactions; Pharmaceutical Preparations; Phenotype; Process; Proteins; Research; Saccharomyces cerevisiae; Source; Surveys; Technology; Testing; Therapeutic; Trees; Validation; Work; Yeasts; base; chemical genetics; computer infrastructure; cost; design; drug development; drug discovery; experience; genome sequencing; high throughput screening; improved; infrastructure development; innovation; interest; method development; mutant; next generation sequencing; novel; novel therapeutics; predictive modeling; public health relevance; research and development; screening; small molecule; small molecule libraries; therapeutic development

Project Summary The next-generation sequencing revolution is enabling unprecedented access to causal genes underlying a variety of disease conditions. This information promises to lead to more effective and increasingly personalized therapeutics as new disease mechanisms are characterized and target genes are identified. A critical bottleneck in leveraging this information to the point of defining new treatments, however, is the development of safe and effective therapeutics, which are often small molecules that bind the protein target of interest. Even with a well-defined target, development of small molecule probes is expensive and inefficient, which is why it can take years or even decades of drug development from discovery of the disease mechanism to an FDA approved drug. The proposed research addresses this bottleneck with the long-term goal of rapidly characterizing novel compounds' modes of action to build a comprehensive small molecule library targeting a significant fraction of the human genome. The specific objective of this application is to develop key computational infrastructure for high-throughput chemical genomics approaches, which leverage model organism mutant libraries as a diagnostic for compound target discovery. This objective will be accomplished through three specific aims: (1) the development of an experimental pipeline and computational infrastructure for chemical genetic interaction mapping in S. cerevisiae, S. pombe, and E. coli and application of the approach to large libraries of natural products or synthetic compound libraries, (2) the development of methods for combining chemical-genetic and genetic interactions to predict mode-of-action for large compound libraries, and (3) the development and experimental validation of predictive models for compound synergy. The proposed research is innovative because it closely integrates computational approaches leveraging the structure of genetic interaction networks with optimization of a powerful experimental assay. Furthermore, it challenges the current paradigm of target-centric therapeutic development as well as the notion of an inherent tradeoff in compound screening throughput when chemical genomic approaches are used. The proposed work will demonstrate that chemical genomics can be scaled to accomodate the largest of chemical libraries while providing an unbiased strategy for identifying novel modes of action. Other expected outcomes include (1) the discovery of hundreds of new small molecule probes with precise modes of action, (2) methods for integrating genome-scale data across species to improve the relevance of model organism chemical- genetic data to human health, (3) fundamental characterization of how the diversity of natural products interacts with eukaryotic cells on a global scale, and (4) mechanistic understanding, predictive models, as well as several novel discoveries of compound combinations that act synergistically.

项目摘要 下一代测序革命使人们能够前所未有地获得潜在的致病基因， 各种疾病状况。这些信息有望导致更有效和越来越个性化的 作为新的疾病机制的治疗剂被表征并且靶基因被鉴定。一个关键 然而，利用这些信息来定义新治疗方法的瓶颈是开发 安全有效的治疗药物，通常是结合目标蛋白质的小分子。甚至 由于目标明确，小分子探针的开发既昂贵又低效，这就是为什么它 从疾病机制的发现到FDA的批准， 批准的药物。拟议的研究解决了这一瓶颈，其长期目标是迅速 表征新化合物的作用模式，以构建针对 人类基因组的重要组成部分。该应用程序的具体目标是开发关键 高通量化学基因组学方法的计算基础设施， 生物突变体库作为化合物靶标发现的诊断。这一目标将得以实现 通过三个具体目标：（1）开发实验管道和计算基础设施 化学遗传互作作图。酿酒酵母，S. pombe和E.大肠杆菌的研究及应用 天然产物或合成化合物库的大型库的方法，（2）方法的开发 为了结合化学-遗传和遗传相互作用来预测大型化合物文库的作用模式， （3）化合物协同作用预测模型的建立和实验验证。 拟议的研究是创新的，因为它紧密结合了计算方法 利用遗传相互作用网络的结构，优化强大的实验测定。 此外，它挑战了当前以目标为中心的治疗开发的范式以及 当使用化学基因组方法时，化合物筛选通量的固有权衡。的 拟议的工作将表明，化学基因组学可以扩大规模，以适应最大的化学 库，同时提供用于鉴定新的作用模式的无偏见的策略。其他预期成果 包括（1）发现了数百种具有精确作用模式的新的小分子探针，（2） 用于整合跨物种的基因组规模数据，以提高模式生物化学的相关性， 遗传数据对人类健康的影响，（3）天然产品多样性的基本特征 在全球范围内与真核细胞相互作用，以及（4）机械理解，预测模型，以及 作为协同作用的化合物组合的几个新发现。