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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

基本信息

批准号：
10703220
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
项目类别：
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

项目摘要

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.

项目摘要/摘要在最近的过去，基因组和外显子组测序计划已经确定了数百万的基因突变人类人口。已经做出了大量的努力来确定功能突变或驱动突变通过计算预测或高通量实验方法。然而，这些中的大多数这些方法侧重于单个突变，而忽视了二倍体基因组结构和基因调控的特定于上下文的性质。对疾病突变的研究应以基因成分为基础和继承模式考虑在内。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) 这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，但大多数这些突变中的一部分会影响蛋白质相互作用网络。因此，迫切需要确定构成双等位基因异质性和增强功能多样性的调控机制在所有患者群体中。为了应对这一挑战，我们最近开发并开创了功能变异组学。在通过相互作用组网络表征基因型与表型的关系时，单个基因类型变异可以导致完全的基因敲除样行为，也可以作为交互作用- 具体的变化或“边缘”的扰动。染色体的两个等位基因上的突变可能会表现出等位基因特异性和等位基因组合效应。然而，在很大程度上仍不清楚两个等位基因突变是如何发生的。共同协调，以产生最终的功能后果。在这项提案中，我们将发展创新技术，构建“双等位功能连续体”模型，并评估大规模的单等位基因和双等位基因突变。我们将弥合我们目前在知识方面的差距，包括：确定大量未知单等位基因和双等位基因突变的功能影响意义，破译它们扰乱互动组网络的程度，并询问这些扰动取决于特定的环境。我们的长期目标是为实现系统水平做出贡献了解遗传变异、外部刺激和功能后果之间的相互作用可用于开发改进的疾病诊断和治疗策略的细胞网络。

项目成果

期刊论文数量（6）

专著数量（0）

科研奖励数量（0）

会议论文数量（0）

专利数量（0）

Gain-of-Function Variomics and Multi-omics Network Biology for Precision Medicine.

用于精准医学的功能增益变异组学和多组学网络生物学。

DOI：
10.1007/978-1-0716-3163-8_24
发表时间：
2023
期刊：
Methods in molecular biology (Clifton, N.J.)
影响因子：
0
作者：
Li,MarkM;Awasthi,Sharad;Ghosh,Sumanta;Bisht,Deepa;CobanAkdemir,ZeynepH;Sheynkman,GloriaM;Sahni,Nidhi;Yi,SStephen
通讯作者：
Yi,SStephen

AD-Syn-Net: systematic identification of Alzheimer's disease-associated mutation and co-mutation vulnerabilities via deep learning.

DOI：
10.1093/bib/bbad030
发表时间：
2023-03-19
期刊：
Briefings in bioinformatics
影响因子：
9.5
作者：
通讯作者：

Landscape of MicroRNA Regulatory Network Architecture and Functional Rerouting in Cancer.

DOI：
10.1158/0008-5472.can-20-0371
发表时间：
2023-01-04
期刊：
Cancer research
影响因子：
11.2
作者：
通讯作者：

Pervasive mislocalization of pathogenic coding variants underlying human disorders.

人类疾病背后的致病编码变异普遍存在错误定位。

DOI：
10.1101/2023.09.05.556368
发表时间：
2023
期刊：
bioRxiv : the preprint server for biology
影响因子：
0
作者：
Lacoste,Jessica;Haghighi,Marzieh;Haider,Shahan;Lin,Zhen-Yuan;Segal,Dmitri;Reno,Chloe;Qian,WesleyWei;Xiong,Xueting;Shafqat-Abbasi,Hamdah;Ryder,PearlV;Senft,Rebecca;Cimini,BethA;Roth,FrederickP;Calderwood,Michael;Hill,David;
通讯作者：