Network-based Framework to Decode Novel âÃÂÃÂGain-of-FunctionâÃÂàMutations and their Mechanistic Roles in General Human Diseases

基于网络的框架来解码新的功能获得突变及其在一般人类疾病中的机制作用

• 批准号: 10017306
• 负责人: S. Stephen Yi
• 金额: $ 39.32万
• 依托单位: UNIVERSITY OF TEXAS AT AUSTIN
• 依托单位国家: 美国
• 财政年份: 2019
• 资助国家: 美国
• 起止时间: 2019-09-15 至 2024-08-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10017306
• 关键词: Address; Alleles; Behavior; Binding; Cells; Development; Disease; Engineering; Event; Exhibits; Gene Expression Regulation; Genetic Research; Genomics; Genotype; Glean; Goals; Health; Heart; Heritability; Human Genetics; Investigation; Knowledge; Laboratories; Link; Modeling; Molecular; Mutation; Nature; Network-based; Output; Pathologic; Phenotype; Phosphorylation Site; Proteins; Research; Resolution; Role; Signal Transduction; Specificity; System; Systems Biology; Technology; Therapeutic; Work; base; causal variant; cell type; combinatorial; functional gain; gain of function; gain of function mutation; gene function; genome wide association study; genome-wide; genomic aberrations; human disease; innovation; insight; molecular recognition; novel; protein protein interaction

Traditionally, disease causal mutations were thought to disrupt gene function. However, it becomes more and more clear that many deleterious mutations could exhibit a 'gain-of-function' behavior. Systematic investigation of such mutations has been lacking and largely overlooked. In the last few years it has become more clear that the efficacy and specificity of signal transduction in a cell is, at heart, a problem of molecular recognition and protein interaction. In distinct cell types (with varying genotypes), precise signal transduction controls cell decision, including gene regulation and phenotypic output. When signal transduction goes awry due to gain-of- function mutations, it would give rise to various disease types. Research in my laboratory is focused on developing and utilizing quantitative and molecular technologies to understand protein interaction networks and their perturbations by genomic mutations, bridging genotype and phenotype in health and disease. Our overall goal is to contribute to the understanding of disease mechanisms and of more open ended questions about explanations for 'missing heritability' in genome-wide association studies. We envision that It will be instrumental to push current human genetics research paradigm towards a thorough functional and quantitative modeling of all genomic mutations and their mechanistic molecular interaction events involved in disease development and progression. Therefore, gaining a systems-level understanding of gain-of-function mutations requires to resolve the plastic nature of molecular interactions, and to integrate experimental and computational strategies at the genome scale. Many fundamental questions pertaining to genotype-phenotype relationships remain unresolved. For example, how do interaction networks undergo rewiring upon gain-of- function mutations? Which mutations are key for gene regulation and cellular decisions? Do mutagtions exhit allel-specific behaviors or how do the allelic combinations work to coordinate cellular phenotypes? Is it possible to leverage molecular interaction networks to engineer signal transduction in cells, aiming to cure disease? To begin to address these questions, in this proposal, we will systematically interrogate of gain-of-function disease mutations using a novel network-based systems biology framework. We will then decipher condition-dependent protein-protein interaction perturbations induced by gain-of-function mutations in disorder regions and phosphorylation sites. Finally, we will determine allele-specific and allele-combinatorial effect of gain-of-function mutations on protein interaction network rewiring. Together, this integrative proposal is innovative because it will provide insights in prioritizing driver functional gain-of-function disease mutations, and uncovering individualized molecular mechanisms at a base resolution. Furthermore, it is significant because it will greatly facilitate the functional annotation of a large number of gain-of-function mutations, providing a fundamental link between genotype and phenotype in general human disease.

传统上，疾病因果突变被认为破坏了基因功能。但是，它变成了 越来越清楚的是，许多有害的突变可能表现出“功能奖励”行为。 对这种突变的系统调查一直缺乏，并且在很大程度上被忽略了。在最后几个 几年更清楚的是，单元中信号转导的疗效和特异性 从本质上讲，是分子识别和蛋白质相互作用的问题。在不同的细胞中 类型（具有不同的基因型），精确的信号转导控制细胞决策， 包括基因调节和表型输出。当信号转导因收益而出现问题时 功能突变，会引起各种疾病。我的研究 实验室专注于开发和利用定量和分子技术 了解蛋白质相互作用网络及其通过基因组突变的扰动，桥接 健康与疾病中的基因型和表型。我们的总体目标是为 了解疾病机制和关于解释的更开放的问题 对于全基因组关联研究中的“缺失遗传力”。我们设想 将当前的人类遗传学研究范式推向彻底 所有基因组突变及其机械的功能和定量建模 疾病发展和进展涉及的分子相互作用事件。所以， 获得对功能收益突变的系统级别的理解需要解决 分子相互作用的塑性性质，并整合实验和 基因组量表的计算策略。许多基本问题与 基因型 - 表型关系仍未解决。例如，如何互动 网络会在功能获取突变中重新布线吗？哪些突变是基因的关键 调节和细胞决策？做叛变会敦促特定的Allel特定行为或如何 等位基因组合可以协调细胞表型？是否可以利用分子 旨在治愈疾病的细胞中信号转导的相互作用网络？开始 解决这些问题，在此提案中，我们将系统地询问功能奖励 使用新型基于网络的系统生物学框架进行疾病突变。然后我们将破译 条件依赖性的蛋白质 - 蛋白质相互作用扰动受功能获得的诱导 疾病区域和磷酸化位点的突变。最后，我们将确定 等位基因特异性和等位基因组合效应对蛋白质的功效突变 交互网络重新布线。这项综合提议在一起是创新的，因为 它将提供优先级驾驶员功能功能获得功能的见解 疾病突变，并以基础分辨率揭示个性化的分子机制。 此外，这很重要，因为它将极大地促进功能注释 大量功能收益突变，提供了基本联系 一般人类疾病中的基因型和表型。