Deciphering Functional Consequences of Specific and Combinatorial Mutations in Protein Interaction Networks

破译蛋白质相互作用网络中特定和组合突变的功能后果

• 批准号: 10478000
• 负责人: Nidhi Sahni
• 金额: $ 40.5万
• 依托单位: UNIVERSITY OF TX MD ANDERSON CAN CTR
• 依托单位国家: 美国
• 财政年份: 2020
• 资助国家: 美国
• 起止时间: 2020-09-01 至 2025-08-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10478000
• 关键词: Address; Affect; Alleles; Behavior; Chromosomes; DNA Sequence Alteration; Diagnostic; Diploidy; Disease; Exhibits; Gene Expression; Gene Expression Regulation; Gene Proteins; Genetic Heterogeneity; Genetic Variation; Genome; Genotype; Goals; Human; Knowledge; Lead; Modeling; Mutation; Nature; Patients; Phenotype; Population; Proteins; Stimulus; Structure; System; Technology; Therapeutic; Variant; cellular engineering; combinatorial; driver mutation; exome sequencing; genome sequencing; human disease; improved; infancy; innovation; innovative technologies; knockout gene; patient population; programs; protein folding

PROJECT SUMMARY/ABSTRACT In the recent past, genome and exome sequencing projects have identified millions of genetic mutations across the human populations. Considerable amount of efforts have been made to identify functional or driver mutations by computational predictions or by high-throughput experimental approaches. However, most of these approaches have focused on single mutations and have overlooked the diploid genome structure and the context-specific nature of gene regulation. Studies of disease mutations should take the genotypic composition and inheritance mode into account. In human disease, patients can carry one (monoallelic) or two (biallelic) different mutations on the two alleles, both of which are often expressed. Our recent systematic studies indicate that while a small fraction of disease mutations affect gene expression and protein folding/stability, the majority of these mutations influence protein interaction networks. There is, therefore, a critical need to determine the regulatory mechanisms that underlie biallelic genetic heterogeneity and potentiate functional diversification across patient populations. To address this challenge, we recently developed and pioneered the technology of functional variomics. In characterizing genotype-to-phenotype relationships via interactome networks, a single genotypic variation can lead to either a complete gene knockout-like behavior, or alternatively as interaction- specific changes or “edgetic” perturbations. The mutations on the two alleles of the chromosomes could exhibit allele-specific and allele-combinatorial effect. However, it remains largely unknown how two allelic mutations coordinate together to generate their ultimate functional consequence. In this proposal, we will develop innovative technologies, build a ‘biallelic functionality continuum’ model, and assess the functional effect of monoallelic and biallelic mutations at large scale. We will bridge the current gaps in our knowledge, including: determining the functional impact of large numbers of monoallelic and biallelic mutations of unknown significance, deciphering the extent to which they perturb interactome networks, and interrogating if these perturbations depends on specific contexts. Our long-term goal is to contribute toward a systems-level understanding of the interplay between genetic variations, external stimuli, and functional consequences in cellular networks that can be used for developing improved diagnostic and therapeutic strategies in disease.

项目总结/摘要 在最近的过去，基因组和外显子组测序项目已经确定了数百万个基因突变， 人类人口。已经做出了相当多的努力来鉴定功能或驱动突变 通过计算预测或通过高通量实验方法。然而，其中大多数 这些方法都集中在单一突变上，而忽略了二倍体基因组结构和 基因调控的环境特异性。疾病突变的研究应采取基因型组成 和继承模式。在人类疾病中，患者可以携带一个（单等位基因）或两个（双等位基因） 两个等位基因上的不同突变，这两个等位基因都经常表达。我们最近的系统研究表明 虽然一小部分疾病突变会影响基因表达和蛋白质折叠/稳定性，但大多数疾病突变会影响基因表达和蛋白质折叠/稳定性。 这些突变影响蛋白质相互作用网络。因此，迫切需要确定 双等位基因遗传异质性和增强功能多样化的调节机制 在患者人群中。为了应对这一挑战，我们最近开发并率先推出了 功能变异组学在通过相互作用组网络表征基因型与表型的关系时， 基因型变异可以导致完全基因敲除样行为，或者作为交互作用， 具体的变化或“边缘”扰动。染色体上两个等位基因的突变可以表现出 等位基因特异性和等位基因组合效应。然而，在很大程度上仍然不清楚两个等位基因突变如何 协调在一起以产生它们最终的功能结果。在本提案中，我们将开发 创新技术，建立“双等位基因功能连续体”模型，并评估 单等位基因和双等位基因突变。我们将弥合我们目前的知识差距，包括： 确定大量未知基因的单等位基因和双等位基因突变的功能影响， 重要性，破译他们扰乱相互作用网络的程度，并询问这些 扰动取决于具体的上下文。我们的长期目标是为系统级 了解遗传变异，外部刺激和功能后果之间的相互作用， 细胞网络，可用于开发改进的疾病诊断和治疗策略。