Network-based Framework to Decode Novel 'Gain-of-Function' Mutations and their Mechanistic Roles in General Human Disease

项目总结 传统上，疾病原因突变被认为会扰乱基因功能。然而，它变得越来越多 更清楚的是，许多有害的突变可能会表现出一种“功能获得”的行为。系统调查 这种突变的研究一直很少，而且在很大程度上被忽视了。在过去的几年里，情况变得更加明显 从本质上讲，细胞中信号转导的有效性和特异性是一个分子识别和 蛋白质相互作用。在不同的细胞类型(具有不同的基因类型)中，精确的信号转导控制细胞 决策，包括基因调控和表型输出。当信号转导因-增益- 功能突变，就会引发各种疾病类型。我实验室的研究主要集中在 开发和利用定量和分子技术来了解蛋白质相互作用网络和 它们受到基因组突变的干扰，在健康和疾病中架起了基因和表型的桥梁。我们的整体 目标是促进对疾病机制的理解，以及更多关于 全基因组关联研究中“遗漏遗传性”的解释。我们设想，这将是 有助于将当前的人类遗传学研究范式推向彻底的功能和量化 所有基因组突变及其与疾病相关的机械性分子相互作用事件的模拟 发展和进步。因此，获得对功能增益突变的系统级理解 需要解决分子相互作用的可塑性，并将实验和 基因组水平上的计算策略。许多与基因型-表型有关的基本问题 双方的关系仍未得到解决。例如，交互网络如何在获得收益后进行重新布线 功能突变？哪些突变是基因调控和细胞决策的关键？做诱变手术吗？ 等位基因特有的行为或等位基因组合是如何协调细胞表型的？有没有可能？ 利用分子相互作用网络在细胞中设计信号转导，旨在治愈疾病？至 开始解决这些问题，在这个建议中，我们将系统地询问获得性功能疾病 使用一种新的基于网络的系统生物学框架进行突变。然后我们将破译依赖于条件的 无序区和无序区功能增益突变引起的蛋白质相互作用扰动 磷酸化位点。最后，我们将确定等位基因特异性和等位基因组合效应的增益- 蛋白质相互作用网络重新布线的功能突变。总而言之，这一综合建议是创新的 因为它将为确定驾驶员功能获得疾病突变的优先顺序提供洞察力，以及 以碱基分辨率揭示个性化的分子机制。此外，它之所以重要，是因为它 将极大地促进对大量功能增益突变的功能注释，提供了 在一般人类疾病中，基因型和表型之间的基本联系。

PROJECT SUMMARY 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.