MOLECULAR MECHANISMS OF NATURAL LIFESPAN VARIATION

自然寿命变化的分子机制 哪些基因和基因网络负责引起（或延缓）衰老过程？哪些 组件可以被操纵以最大限度地延长寿命？尽管这些问题的基本性质，我们 对控制衰老的细胞机制了解有限。从这个角度来看，我们 总体目标是应用系统生物学方法构建长寿的分子网络， 来自87个天然酵母分离物的多组学数据集。酵母提供了丰富的可用遗传资源， 分子工具以及用于表征衰老机制的已建立的实验范例， 长寿，其相互作用网络已经构建。为了实现这一目标，我们 产生了三组重要的初步数据。首先，我们评估了87个自然人的复制寿命（RLS）。 在三种代谢条件下分离，并揭示了衰老的自然变异的广泛多样性。第二、 我们对这些菌株的基因组进行了测序，以表征它们的遗传多样性。第三，我们发现饮食 线粒体呼吸的限制和激活延长了某些基因型的寿命，而在另一些基因型中， 完全没有反应。基于这些数据，我们将检验遗传变异和 环境因素协调调节转录、翻译和转录的组分， 代谢机制，产生饮食反应和衰老的变化。数据集成的应用 多个组学资源的方法将有助于发现相关的潜在生物过程 与长寿。我们将在以下具体目标中检验这一假设：1）分析转录组， 翻译组和代谢组将这些“内表型”的变异与外部“老化表型”联系起来 在不同的代谢条件下调节寿命。2)整合组学数据的发现， 确定寿命控制的监管网络。这种数据汇总和处理的创新方法， 多维数据分析和基于网络的方法将有助于概念化的分子 老化的自然变化和与老化有关的扰动的机制，在前所未有的规模 和详细程度。最后，这些研究将通过利用我们的 在更复杂的生物体特征的生活史的系统方法方面具有丰富的经验。的 本申请提出了一种利用网络中已建立的指导者和两个共同指导者进行扩展培训的方案， 系统，和计算生物学，以促进PI的发展成为一个独立的研究者， 关于衰老的基础生物学通过研究计划和职业生涯获得的经验，知识和技能 发展活动将使候选人朝着独立调查员的职业发展。

MOLECULAR MECHANISMS OF NATURAL LIFESPAN VARIATION Which genes and gene networks are responsible of causing (or retarding) the aging process? Which of these components can be manipulated to maximize lifespan? Despite the fundamental nature of these questions, we have very limited understanding of the cellular mechanisms governing aging. From this perspective, our overarching goal is to apply systems biology approaches to construct a molecular network of longevity, based on multi-omics datasets from 87 natural yeast isolates. Yeast offers a rich resource of available genetic and molecular tools as well as established experimental paradigms for characterizing mechanisms of aging and longevity, and its interaction networks have been previously constructed. Towards this goal, we have generated three important sets of preliminary data. First, we evaluated replicative lifespan (RLS) of 87 natural isolates under three metabolic conditions and uncovered a wide diversity of natural variation in aging. Second, we sequenced the genomes of these strains to characterize their genetic diversity. Third, we found that dietary restriction and activation of mitochondrial respiration extend lifespan in some genotypes, while in others there is no response at all. Based on this data we will test the hypothesis that genetic variation and environmental factors coordinately regulate components of the transcriptional, translational and metabolic machinery, generating variation in dietary response and aging. Application of data integration methods to multiple omics resources will facilitate the discovery of underlying biological processes associated with longevity. We will test this hypothesis in the following Specific Aims: 1) Analyze transcriptomes, translatomes and metabolomes to link variation in these ‘endo-phenotypes’ to external ‘aging phenotypes’ under different metabolic conditions known to modulate lifespan. 2) Integrate the findings of -omics data and identify regulatory networks of lifespan control. This innovative approach of data aggregation and processing, multidimensional data analysis and network-based methods will aid in conceptualizing the molecular mechanisms that underlie natural variation of aging and aging-related perturbations at an unprecedented scale and level of detail. Finally, these studies will identify general trends in lifespan control by leveraging our extensive experience in systems approaches of life history of traits of more complex organisms. The application proposes a program for expanded training with established mentor and two co-mentors in network, systems, and computational biology to facilitate the PI's development into an independent investigator focusing on basic biology of aging. The experience, knowledge, and skills gained through the research plan and career development activities will carry the candidate forward towards a career as an independent investigator.