Functional Profiling of Human Disease Targets

人类疾病靶标的功能分析

• 批准号: 9320838
• 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 2019-07-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/9320838
• 关键词: Alleles; Bardet-Biedl Syndrome; Biochemical; Biological Process; Biophysics; Cells; Code; Complex; Congenital Megacolon; DNA Sequence; DNA Sequence Alteration; Data; Diagnostic; Disease; Etiology; Excision; Exhibits; 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; 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; improved; insight; knockout gene; macromolecule; network models; next generation sequencing; pleiotropism; public health relevance; sequencing platform; trait; virtual

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)：我们在这项建议中的总体目标是从功能上分析与一组模型复杂疾病相关的人类基因突变，对于这些疾病，已经获得了大量未表征的遗传变异。随着了解多个个体的完整基因型的前景以及越来越复杂的表型测量方法，生物医学现在可以从机械细节上探索基因-表型关系。基因分型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，仅有序列是不够的。我们需要的是一个颠覆性的转变，以更好地理解基因类型差异的功能和机制上的分子后果。我们对这个具有挑战性的问题的解决方案是研究由大量相互作用的基因和基因产物在细胞内形成的复杂的大分子网络，或“相互作用”，以及这些网络由于遗传变异而发生的扰动。在通过相互作用组网络方法描述基因-表型关系时，基因变异可以导致完全的基因敲除，被模拟为移除网络中的一个节点及其所有边缘，或者可替代地，作为交互作用特定的扰动，导致特定的交互作用的移除或加强，被建模为边缘特定的或“边缘”扰动。我们建议，为了更好地理解基因型-表型关系，应该通过系统地建立每个生物物理相互作用的节点移除相对于边缘扰动的状态来表征“edgotype”。这些策略将应用于一小部分 选择复杂的临床表型是因为它们表现出广泛的遗传异质性、多效性和表型重叠。这四种临床表型(Usher综合征；视网膜色素变性；先天性巨结肠症；Bardet-Biedl综合征)也得到了充分的研究，已知有大量的突变能够在足够深的地方进行Edgotype分析，以产生信息丰富的疾病网络。因此，对这四种临床表型的研究应提供对基因-表型关系、DNA序列变异对特定生物功能、疾病模块和疾病分类的影响的基本见解。我们的具体目标是：i)为选定的一组临床表型生成深度和稳健的交互组网络图，ii)生成与选定的临床表型集有关的基因产物之间扰动的物理和生化相互作用的EDG型图，iii)通过计算利用EDGOGING数据来获得对选定的一组临床表型的基因型-表型关系的机械性分子洞察。