Functional Profiling of Human Disease Targets

人类疾病靶标的功能分析

基本信息

批准号：
8625367
负责人：
Michael A Calderwood
金额：
$ 45.27万
依托单位：
DANA-FARBER CANCER INST
依托单位国家：
美国
项目类别：
财政年份：
2014
资助国家：
美国
起止时间：
2014-08-01 至 2018-07-31
项目状态：
已结题

来源：
https://reporter.nih.gov/project-details/8625367
关键词：
Bardet-Biedl Syndrome Biochemical Biological Process Cells Code Complex Congenital Megacolon DNA Sequence Data Diagnostic Disease Etiology Excision Exhibits Gene Mutation Genes Genetic Heterogeneity Genetic Variation Genotype Goals Hereditary Disease Human Human Genetics Human Genome Inborn Genetic Diseases Individual Lead Link Macromolecular Complexes Maps Measures Methods Modeling Molecular Mutation Outcome Patients Phenotype Property Protein-Protein Interaction Map Proteins Proteome Relative (related person)Retinitis Pigmentosa Solutions System Therapeutic Therapeutic Intervention Time Tissues Usher Syndrome Variant Work clinical phenotype disease classification disease-causing mutation disorder subtype genetic variant genome sequencing human disease improved insight knockout gene macromolecule network models next generation sequencing pleiotropism public health relevance trait

项目摘要

PROJECT SUMMARY Our overall goal in this proposal is to functionally analyze mutations in human genes associated with a set of model complex disorders for which a large number of uncharacterized genetic variants have been obtained. With the prospect of knowing the complete genotype of multiple individuals and with increasingly sophisticated ways of measuring phenotypes, biomedicine can now explore genotype-phenotype relationships in mechanistic detail. A fundamental issue to be resolved in the characterization of genotypes is how genetic variation directly relates to phenotype. Our premise is that sequence alone is not sufficient. What is needed is a disruptive shift to better understand the functional and mechanistic molecular consequences of genotypic differences. Our solution to this challenging problem is to investigate the complex macromolecular networks, or "interactomes", formed by large numbers of interacting genes and gene products inside cells and to the perturbations of these networks that occur as a consequence of genetic variation. In characterizing genotype- to-phenotype relationships via an interactome network approach, genotypic variation can lead to either a complete gene knockout, modeled as removal of a node and all of its edges in the network, or alternatively, as interaction-specific perturbation, leading to the removal or strengthening of specific interactions, modeled as edge-specific, or "edgetic" perturbations. We propose that to better understand genotype-phenotype relationships, "edgotypes" should be characterized by systematically establishing the state of node removal versus edgetic perturbations for every biophysical interaction. These strategies will be applied to a small set of complex clinical phenotypes chosen because they exhibit extensive genetic heterogeneity, pleiotropy and phenotypic overlap. These four clinical phenotypes (Usher syndrome; retinitis pigmentosa; Hirschsprung disease; Bardet-Biedl syndrome) have also been studied enough that ample numbers of mutations are known to enable edgotyping profiling at sufficient depth to generate informative disease networks. Study of these four clinical phenotypes should accordingly provide fundamental insights into genotype-phenotype relationships, the impact of DNA sequence variants on specific biological functions, disease modules, and disease classification. Our specific aims are to: i) Generate deep and robust interactome network maps for the selected set of clinical phenotypes, ii) Generate edgotypic maps of perturbed physical and biochemical interactions amongst gene products implicated in the selected set of clinical phenotypes, iii) Exploit edgotyping data computationally to derive mechanistic molecular insights into genotype-phenotype relationships for the selected set of clinical phenotypes.

项目摘要我们在此提案中的总体目标是在人类基因中的功能分析与一组相关的人类基因的突变已经获得了大量未表征的遗传变异的模型复杂疾病。有了了解多个个体的完整基因型，并且越来越复杂测量表型的方法，生物医学现在可以探索基因型 - 表型关系机械细节。在基因型的表征中要解决的基本问题是遗传变异与表型直接相关。我们的前提是仅序列就不够。需要什么破坏性的转变，以更好地了解基因型的功能和机械分子后果差异。我们解决这个具有挑战性问题的解决方案是调查复杂的大分子网络或 “相互作用”，由细胞内部的大量相互作用基因和基因产物形成由于遗传变异而发生的这些网络的扰动。在表征基因型通过Interactome网络方法，基因型变化可以导致一个型 - 型的关系完整的基因敲除，被建模为去除节点及其在网络中的所有边缘，或者以相互作用特异性的扰动，导致去除或加强特定相互作用，被建模为边缘特异性或“ edgetic”扰动。我们提出这一点，以更好地理解基因型 - 表型关系，“ Edgotypes”的特征是系统地建立节点去除状态每种生物物理相互作用的相对于射击扰动。这些策略将应用于选择的一小部分复杂的临床表型，因为它们表现出来广泛的遗传异质性，多效性和表型重叠。这四种临床表型（Usher 综合征；色素性视网膜炎； Hirschsprung疾病； Bardet-Biedl综合征）也已经进行了足够的研究已知大量的突变可以在足够的深度中实现Edgotyping分析以产生信息性疾病网络。对这四种临床表型的研究应相应提供基本对基因型 - 表型关系的见解，DNA序列变体对特定生物学的影响功能，疾病模块和疾病分类。我们的具体目的是： i）为选定的一组临床表型生成深层且坚固的Interactome网络图， ii）生成基因产物中扰动的物理和生化相互作用的EDGOTYPIC图与选定的一组临床表型有关， iii）在计算上利用电子型数据来将机械分子见解推导为基因型 - 表型选择的一组临床表型的关系。