Systematic discovery of nonobvious human disease models by orthologous phenotypes

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确定新的疾病靶点方面显示出巨大的希望。尽管如此，在大多数遗传疾病，特别是高度多基因疾病中确定致病基因仍然非常困难，目前的方法非常有限。一旦确定了靶点，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（“物候”假说），在人类和模式生物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式系统。此外，这样的模型可以导致基于模式生物中的网络进行药物发现的可能性。我们认为，由于途径可以在不同的生物体中进化和被重新利用，因此可能存在物候，即产生不同表型的相似（或同源）基因网络，并且这些物候不仅为筛选单一蛋白质提供了基础，而且还为平行识别和同时发现针对多个不同靶点的药物提供了基础。作为欣赏途径的进化重新定位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。同样的理论框架也适用于自闭症的小鼠模型，乳腺癌的蠕虫模型等等。我们的主要目的是验证物候假说，主要是利用酵母模型发现新的血管生成基因，并进行基于酵母的化合物筛选，以发现新的抗血管生成抑制剂类别，适合作为抗癌治疗的先导化合物。物候学提供了将新基因与多基因疾病联系起来的可能性，也为模式生物的药物筛选和寻找候选疾病基因遗传变异的后续研究开辟了道路。因此，理论物候框架有可能影响各种各样的疾病，并可能影响一个大型下游社区。公共卫生相关性：常见疾病，如冠状动脉疾病、糖尿病和自闭症，往往是由许多基因的作用引起的，这种多基因的性质使发现因果基因的传统方法复杂化。这项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，其失败会影响伤口愈合，心血管疾病和肿瘤恶性。抗血管生成药物作为抗肿瘤药物发挥着重要的作用，我们提出的模型为血管生成候选基因和新的抗血管生成化合物的鉴定提供了一条途径。更一般地说，这项工作将增加我们对多基因疾病的遗传基础的理解，并将朝着开发对这些衰弱性疾病易感性的遗传诊断迈出一步。

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.