The variability of the lifespan phenotype in C.elegans

描述（由申请人提供）：衰老是一个由遗传、环境和机遇共同决定个体死亡时间的过程。生存曲线构成了人口水平上老龄化的人口特征。了解生存曲线对基因干预的反应是指导更结构化的衰老模型发展的关键一步。这些模型可以让我们深入了解与年龄有关的疾病，如阿尔茨海默病和帕金森病以及许多类型的癌症的发生和发展模式的过程。需要大量的人口统计信息来支持这种建模工作，而数据采集在当代研究中仍然是一个有限的步骤。我们最近开发了一种方法，通过在高统计和时间分辨率下自动获取生存曲线，大大加快了秀丽隐杆线虫人口老龄化数据的收集。该方法利用改进的消费电子平板扫描仪对标准条件下培养的蠕虫进行成像。附带的软件会自动将生成的延时视频处理成生存曲线。我们将利用这项技术，以前所未有的规模，将基因和环境的扰动与高精度的人口老龄化特征联系起来。我们将获取并分析大约2000个突变体的高分辨率寿命分布，这些突变体代表了已知影响该生物体寿命的所有经典基因以及各种靶向基因家族。我们将确定生存曲线是否偏离经典老龄化模型的预测，并应用功能数据分析来深入了解人口老龄化特征最易变化的维度的数量和性质。我们将根据基因对生存曲线形状特征的影响将其分类，以便将已知基因功能与人口老龄化特征联系起来，并帮助阐明未知基因功能。这些信息将对未来的机理研究具有重要意义，并将为现有的分子知识提供新的视角。我们将使我们收集的生存曲线广泛可用，从而为秀丽隐杆线虫研究界和人口统计学家提供宝贵的资源。

DESCRIPTION (provided by applicant): Aging is a process shaped by genetics, environment, and chance, all of which conspire to determine an individuals' time of death. A survival curve constitutes the demographic signature of aging at the level of the population. Understanding the survival curve's responsiveness to genetic interventions is a critical step in guiding the development of more structured models of aging. Such models may give insight into the processes that determine the patterns of initiation and progression of age-related diseases, such Alzheimer's and Parkinson's disease and many types of cancer. Large amounts of demographic information are required to support such modeling efforts, and data-acquisition remains a limiting step in contemporary research. We have recently developed a method that greatly accelerates the collection of demographic aging data in C. elegans nematodes, via the automated acquisition of survival curves at high statistical and temporal resolution. This method utilizes modified consumer-electronics flatbed scanners to image worms cultured under standard conditions. Accompanying software automatically processes the resultant time-lapse videos into survival curves. We will use this technology to link genetic and environmental perturbations to a high-precision demographic aging signature at an unprecedented scale. We will acquire and analyze high-resolution lifespan distributions for roughly 2000 mutants representing all classic genes known to affect lifespan in this organism as well as a variety of targeted gene families. We will determine whether survival curves deviate from predictions made by classic aging models and apply functional data analysis to gain insight into the number and nature of dimensions along which the demographic aging signature is most variable. We will group genes into categories in terms of their impact on survival curve shape features, in order to place known gene functions in relation to the demographic aging signature and help illuminate unknown gene functions. This information will be important for future mechanistic studies, and will offer perspective for existing molecular knowledge. We will make our collection of survival curves widely available, thus providing a valuable resource to both the C. elegans research community and demographers. PUBLIC HEALTH RELEVANCE: Animals ranging from worms to humans become frail, disease prone, and more likely to die as they age. We will use an automated method for measuring death times of C. elegans nematodes to probe why some individuals die sooner than others. By observing the effect of 2,000 separate gene mutations on aging worm populations, this project will provide insight about how genes work together to affect the aging process.