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导致(或延缓)衰老过程？这些中的哪一个 可以操纵组件以最大化使用寿命吗？尽管这些问题具有根本性质，但我们 对衰老的细胞机制的了解非常有限。从这个角度来说，我们的 首要目标是应用系统生物学方法构建一个长寿的分子网络，基于 基于87个天然酵母分离物的多重组学数据集。酵母菌提供了丰富的可利用的遗传和 分子工具以及已建立的用于表征衰老机制和 长寿，它的互动网络之前已经构建好了。为了实现这一目标，我们有 产生了三组重要的初步数据。首先，我们评估了87个自然种群的复制寿命 在三种新陈代谢条件下分离的菌株，发现在老化过程中存在广泛的自然变异。第二, 我们对这些菌株的基因组进行了测序，以确定它们的遗传多样性。第三，我们发现饮食 线粒体呼吸的限制和激活在某些基因类型中延长了寿命，而在另一些基因类型中 完全没有反应。基于这些数据，我们将检验这样一种假设，即基因变异和 环境因素协调调节转录、翻译和转录的成分 代谢机制，在饮食反应和衰老方面产生变化。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学过程 长寿的。我们将在以下具体目标中检验这一假设：1)分析转录本， 翻译体和代谢体将这些“内源性表型”的变异与外部“老化表型”联系起来 在不同的新陈代谢条件下调节寿命。2)整合-组学数据和 确定寿命控制的监管网络。这种创新的数据聚合和处理方法， 多维数据分析和基于网络的方法将有助于分子的概念化 衰老和与衰老相关的扰动的自然变化的机制以前所未有的规模 和细节的层次。最后，这些研究将通过利用我们的 对更复杂生物体的生命史的系统研究方法有丰富的经验。这个 申请提出了一项扩展培训计划，包括网络中已建立的导师和两名共同导师， 系统和计算生物学，以促进PI发展成为一个独立的调查者，专注于 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 发展活动将推动候选人迈向独立调查员的职业生涯。

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.