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

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: 10582371
• 负责人: S. Stephen Yi
• 金额: $ 20万
• 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/10582371
• 关键词: Address; Affect; Alleles; Alternative Splicing; Behavior; Binding; Bioinformatics; Biological; Cells; Clinical; Complex; Computer Analysis; Computing Methodologies; Development; Disease; Engineering; Event; Exhibits; Gene Expression Regulation; Genes; Genetic Heterogeneity; Genetic Research; Genomics; Genotype; Glean; Goals; Health; Heart; Heritability; Human; Human Genetics; Immune response; Individual; Induced Mutation; Investigation; Knowledge; Laboratories; Lead; Link; Mendelian disorder; Methods; Modeling; Molecular; Mutation; Nature; Network-based; Output; Pain; Pathologic; Patients; Phenotype; Phosphorylation Site; Play; Proteins; Proteome; Proteomics; RNA-Protein Interaction; Research; Resolution; Role; Sensitivity and Specificity; Series; Side; Signal Transduction; Specificity; Stratification; System; Systems Biology; Technology; Therapeutic; Time; Variant; Work; base; causal variant; cell type; combinatorial; disorder subtype; functional gain; functional genomics; gain of function; gain of function mutation; gene function; gene product; genome wide association study; genome-wide; genomic aberrations; human disease; human interactome; individualized medicine; innovation; insight; molecular recognition; novel; precision medicine; protein function; protein misfolding; protein protein interaction

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.

项目总结