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: 10026526
• 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/10026526
• 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/文摘