Functional Profiling of Human Disease Targets

人类疾病靶标的功能分析

• 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
• 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

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 我们在这项提案中的总体目标是从功能上分析与一组基因相关的人类基因突变。 已经获得了大量未表征的遗传变异的复杂疾病模型。 随着了解多个个体的完整基因型的前景和日益复杂的基因组学的发展， 测量表型的方法，生物医学现在可以探索基因型-表型关系， 机械细节基因型表征中需要解决的一个基本问题是， 变异与表型直接相关。我们的前提是，仅仅序列是不够的。所需要的是 一个破坏性的转变，以更好地了解基因型的功能和机制的分子后果， 差异我们解决这个具有挑战性的问题的方法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， 这些网络的扰动是遗传变异的结果。在描述基因型时- 通过相互作用组网络方法，基因型变异可以导致 完全基因敲除，建模为网络中节点及其所有边的移除，或者替代地，建模为 特定相互作用的扰动，导致特定相互作用的消除或加强，建模为 边缘特异性或"边缘"扰动。我们建议，为了更好地了解基因型-表型 关系，"edgotypes"的特点应该是系统地建立节点删除的状态 与每一个生物物理相互作用的边缘扰动。 这些策略将应用于一小组复杂的临床表型，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）也已得到充分研究 已知足够数量的突变能够在足够的深度进行边缘分型分析， 疾病信息网络。因此，对这四种临床表型的研究应提供基本的 深入了解基因型-表型关系，DNA序列变异对特定生物学特性的影响， 功能、疾病模块和疾病分类。 我们的具体目标是： i）为所选的一组临床表型生成深度和稳健的相互作用组网络图， ii）生成基因产物之间受干扰的物理和生物化学相互作用的边缘型图谱 涉及所选的一组临床表型， iii）通过计算利用边缘分型数据以获得对基因型-表型的机制分子见解 所选临床表型组的关系。