Evolving multigenic extensions to lifespan

 大多数生物特征，包括常见疾病，都有很强的遗传基础，但知之甚少。人们对识别这些遗传因素普遍感兴趣，因为它们可以用于识别处于特定疾病风险中的个体，并作为实验手柄来识别新疗法。尽管投入了令人难以置信的资源，但由于大多数疾病的遗传结构的潜在复杂性，大多数致病性遗传变异仍然无法识别。模式生物复杂遗传特征的基础研究应确定可用于识别人类特征中致病性遗传变异的一般原则和方法。在这项研究计划中，我们正在开发一个前所未有的系统在C。elegans通过进化感兴趣的表型的荧光报告基因在模式生物中产生多基因状态。我们将开发一个自动化的微流控，荧光成像和计算机分析系统，以快速测量，分段，并描述在组织特异性方式的转录报告的表达。然后，我们将使用这种成像/分选系统来施加选择压力，以在多代中进化多基因变化以表达。作为原理的证明，我们将把这种方法应用于预测年轻动物寿命的转录报告基因，以进化出寿命更长的动物。然后，可以使用下一代测序快速识别致病突变，并在已知的遗传和细胞网络的背景下仔细研究。这项工作将提高我们对衰老的理解，并在总体上改变我们在模型生物中的方法，以了解复杂遗传疾病中的生物学特征。

 DESCRIPTION: Most biological traits including common diseases have a strong but poorly understood genetic basis. There is general interest in identifying these genetic factors as they can be used to identify individuals that are at risk for a particular disease and as experimental handles to identify novel therapies. Despite an incredible outlay of resources, the majority of causative genetic variants remain unidentified due to the underlying complexity of the genetic architectures of most diseases. Fundamental study of complex genetic traits in model organisms should identify general principles and approaches that can be used to identify causative genetic variants in human traits. In this research proposal, we are developing an unprecedented system in C. elegans to generate multigenic states in model organisms by evolving fluorescent reporters of phenotypes of interest. We will develop an automated microfluidic, fluorescent imaging and computer analysis system to rapidly measure, segment, and describe the expression of a transcriptional reporter in a tissue-specific manner. We will then use this imaging/sorting system to apply selective pressure to evolve multigenic changes to expression over multiple generations. As proof of principal, we will apply this approach to a transcriptional reporter that predicts lifespan in younger animals to evolve longer-lived animals. Causative mutations can then be rapidly identified using next-generation sequencing and carefully studied in the context of known genetic and cellular networks. This work will improve our understanding of aging, and in general transform our approaches in model organisms towards the understanding of biological traits in complex genetic diseases.