The variability of the lifespan phenotype in C.elegans

描述(申请人提供)：衰老是一个由遗传、环境和机会形成的过程，所有这些因素共同决定了一个人的死亡时间。生存曲线在人口层面上构成老龄化的人口特征。了解生存曲线对基因干预的反应是指导发展更具结构性的衰老模型的关键一步。这样的模型可以洞察决定与年龄相关的疾病的发生和发展模式的过程，例如阿尔茨海默氏症和帕金森氏症以及许多类型的癌症。需要大量的人口统计信息来支持这种建模工作，而数据获取在当代研究中仍然是一个限制步骤。我们最近开发了一种方法，通过以高统计和时间分辨率自动获取存活曲线，大大加快了线虫人口老龄化数据的收集。这种方法利用改装的消费电子平板扫描仪对在标准条件下培养的蠕虫进行成像。随附的软件自动将产生的延时视频处理成生存曲线。我们将使用这项技术，以前所未有的规模将遗传和环境扰动与高精度的人口老龄化特征联系起来。我们将获得并分析大约2000个突变体的高分辨率寿命分布，这些突变体代表了这个生物体中所有已知的影响寿命的经典基因以及各种目标基因家族。我们将确定生存曲线是否偏离经典老龄化模型的预测，并应用函数数据分析来洞察人口老龄化特征最可变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对基因进行分类，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。这些信息对未来的机制研究将是重要的，并将为现有的分子知识提供前景。我们将广泛提供我们收集的生存曲线，从而为线虫研究界和人口学家提供宝贵的资源。 与公共卫生相关：从蠕虫到人类的各种动物都变得虚弱、易患疾病，并且更有可能随着年龄的增长而死亡。我们将使用一种自动测量线虫死亡时间的方法来探索为什么一些人比另一些人更早死亡。通过观察2000个单独的基因突变对老化蠕虫种群的影响，该项目将提供关于基因如何共同作用影响衰老过程的洞察力。

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.