Functional Profiling of Human Disease Targets

人类疾病靶标的功能分析

• 批准号: 9112004
• 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
• 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/9112004
• 关键词: Bardet-Biedl Syndrome; Biochemical; Biological Process; Cells; Code; Complex; Congenital Megacolon; DNA Sequence; DNA Sequence Alteration; Data; Diagnostic; Disease; Etiology; Excision; Exhibits; Genes; Genetic Heterogeneity; Genetic Variation; Genotype; Goals; Health; Hereditary Disease; Human; Human Genetics; Inborn Genetic Diseases; Individual; Lead; Link; Macromolecular Complexes; Maps; Measures; Methods; Modeling; Molecular; Mutation; Outcome; Patients; Phenotype; Property; Protein-Protein Interaction Map; Proteins; Proteome; Retinitis Pigmentosa; System; Therapeutic; Therapeutic Intervention; Time; Tissues; Usher Syndrome; Variant; Work; clinical phenotype; disease classification; disease-causing mutation; disorder subtype; gene product; genetic variant; human disease; human genome sequencing; improved; insight; knockout gene; macromolecule; network models; next generation sequencing; pleiotropism; sequencing platform; trait

DESCRIPTION (provided by applicant): 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.

描述（由申请人提供）：我们在本提案中的总体目标是对与一组模型复杂疾病相关的人类基因突变进行功能分析，其中已经获得了大量未表征的遗传变异。随着了解多个个体完整基因型的前景和越来越复杂的测量表型的方法，生物医学现在可以探索基因型-表型的机制细节关系。在基因型表征中需要解决的一个基本问题是遗传变异如何与表型直接相关。我们的前提是序列本身是不充分的。我们需要的是一种颠覆性的转变，以更好地理解基因型差异的功能和机制分子后果。我们对这个具有挑战性的问题的解决方案是研究复杂的大分子网络，或“相互作用组”，由细胞内大量相互作用的基因和基因产物形成，以及这些网络因遗传变异而发生的扰动。在通过相互作用组网络方法表征基因型-表型关系时，基因型变异可以导致完全的基因敲除，建模为网络中节点及其所有边缘的移除，或者作为相互作用特异性扰动，导致特定相互作用的移除或加强，建模为边缘特异性，或“边缘”扰动。我们建议，为了更好地理解基因型-表型关系，“edgotypes”应该通过系统地建立每个生物物理相互作用的节点移除状态和边缘扰动状态来表征。这些策略将应用于一小部分