Systematic discovery of nonobvious human disease models by orthologous phenotypes

通过直系同源表型系统地发现非显而易见的人类疾病模型

• 批准号: 7902303
• 负责人: EDWARD M MARCOTTE
• 金额: $ 30.1万
• 依托单位: UNIVERSITY OF TEXAS AT AUSTIN
• 依托单位国家: 美国
• 财政年份: 2009
• 资助国家: 美国
• 起止时间: 2009-08-01 至 2013-07-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/7902303
• 关键词: Affect; Angiogenesis Inhibitors; Animal Model; Autistic Disorder; Biological Models; Breast Cancer Model; Candidate Disease Gene; Cardiovascular Diseases; Communities; Coronary Arteriosclerosis; Defect; Diabetes Mellitus; Diagnostic; Disease; Disease model; Failure; Follow-Up Studies; Genes; Genetic; Genetic Variation; Grant; Hereditary Disease; Human; Lead; Left; Malignant Neoplasms; Methods; Modeling; Nature; Organism; Orthologous Gene; Pathway interactions; Pharmaceutical Preparations; Phenotype; Play; Preclinical Drug Evaluation; Predisposition; Proteins; Research; Role; Screening procedure; Systems Biology; Testing; Work; Wound Healing; Yeast Model System; Yeasts; angiogenesis; antiangiogenesis therapy; antitumor agent; base; cancer therapy; drug discovery; human disease; mouse model; novel strategies; public health relevance; tumor

DESCRIPTION (provided by applicant): Systems biology methods have shown great promise in providing a better understanding of human disease, and in identifying new disease targets. Nonetheless, it remains extraordinarily difficult to identify causal genes in most genetic diseases, in particular highly polygenic disorders, for which current approaches are most limited. These methods also typically leave off once the target is identified, and further research transitions to traditional paradigms for drug discovery. We hypothesize (the 'phenolog' hypothesis) that the identification of equivalent gene networks in humans and model organisms will reveal new candidate disease genes and new model systems for diseases. Moreover, such models can lead to the possibility of pursuing drug discovery based on the networks in the model organisms. We suggest that because pathways can evolve and be repurposed in different organisms that phenologs, similar (or orthologous) gene networks that nonetheless produce different phenotypes, may be present, and that these phenologs provide a basis not just for screening against a single protein, but rather for the identification of and simultaneous drug discovery efforts against multiple different targets in parallel. As an example of the importance of appreciating the evolutionary repurposing of pathways, we identify a yeast model of angiogenesis, and its subsequent application to disease gene and drug discovery. The same theoretical framework suggests a mouse model of autism, a worm model of breast cancer, and more. Our major aim is to test the phenolog hypothesis, primarily using the yeast model to discover new angiogenesis genes & performing yeast-based compound screening to find new classes of anti-angiogenesis inhibitors, suitable as lead compounds for anti-cancer therapies. Phenologs offer the possibility of associating new genes with polygenic diseases, as well as opening up drug screens in model organisms and follow-up studies searching for genetic variation in the candidate disease genes. The theoretical phenolog framework thus has the potential to impact a wide variety of diseases and could potentially affect a large downstream community. PUBLIC HEALTH RELEVANCE: Common diseases, such as coronary artery disease, diabetes, and autism, often arise from effects of many genes, and this polygenic nature complicates traditional methods of discovering causal genes. This grant proposes a novel approach for identifying candidate genes for polygenic diseases, with a specific focus on defects in angiogenesis, failures of which affect wound healing, cardiovascular disease, and tumor malignancy. Anti-angiogenesis drugs play important roles as anti-tumor agents, and the model we propose suggests a path for identifying both angiogenesis candidate genes and new anti-angiogenesis compounds. More generally, this work will increase our understanding of the genetic basis of polygenic diseases and will be a step towards developing genetic diagnostics for susceptibility to these debilitating diseases.

描述（由申请人提供）：系统生物学方法在更好地了解人类疾病和识别新疾病靶标方面显示出巨大的前景。尽管如此，识别大多数遗传疾病的致病基因仍然非常困难，特别是高度多基因疾病，目前的方法最为有限。一旦确定了目标，这些方法通常也会停止，进一步的研究将过渡到传统的药物发现范式。我们假设（“酚类”假设）人类和模型生物中等效基因网络的识别将揭示新的候选疾病基因和新的疾病模型系统。此外，此类模型可以使基于模型生物体网络进行药物发现成为可能。我们认为，由于途径可以在不同的生物体中进化和重新利用，因此酚类化合物、相似的（或直系同源的）基因网络可能会产生不同的表型，并且这些酚类化合物不仅为针对单一蛋白质的筛选提供了基础，而且还为针对多个不同靶点的并行鉴定和同时药物发现工作提供了基础。作为理解途径进化再利用的重要性的一个例子，我们确定了血管生成的酵母模型，及其随后在疾病基因和药物发现中的应用。同样的理论框架提出了自闭症小鼠模型、乳腺癌蠕虫模型等等。我们的主要目标是测试酚类假说，主要使用酵母模型来发现新的血管生成基因，并进行基于酵母的化合物筛选，以找到适合作为抗癌治疗的先导化合物的新型抗血管生成抑制剂。物候学提供了将新基因与多基因疾病关联起来的可能性，以及在模式生物中进行药物筛选以及寻找候选疾病基因中遗传变异的后续研究。因此，理论酚类框架有可能影响多种疾病，并可能影响大型下游群落。公共卫生相关性：冠状动脉疾病、糖尿病和自闭症等常见疾病通常是由许多基因的影响引起的，这种多基因性质使发现致病基因的传统方法变得复杂。该资助提出了一种识别多基因疾病候选基因的新方法，特别关注血管生成缺陷，血管生成失败会影响伤口愈合、心血管疾病和肿瘤恶性肿瘤。抗血管生成药物作为抗肿瘤药物发挥着重要作用，我们提出的模型提出了一条鉴定血管生成候选基因和新的抗血管生成化合物的途径。更一般地说，这项工作将增加我们对多基因疾病遗传基础的理解，并将朝着开发对这些使人衰弱的疾病易感性的遗传诊断迈出一步。