Unbiased identification of interventions that extend lifespan

项目总结/摘要 主要模式生物的寿命可以通过饮食、药物和遗传干预来延长。 这包括小鼠模型，目前有超过15种已知的长寿干预措施。 然而，这些治疗方法是根据具体情况确定的。它们之间的关系 目前尚不清楚，也不知道它们是通过相同还是不同的机制起作用，以及哪些干预措施是 最有效和强大的。因此，需要有系统、无偏见地鉴定寿命 干预哺乳动物。从延长小鼠寿命过渡到延长人类寿命， 既要确定许多延长寿命的干预措施，又要确定寿命控制的一般原则。在这 关于这一点，细胞或生物体寿命缩短或延长的潜力由其代谢状态表示， 这反过来又反映在它的转录组中。以某种方式调整转录组的干预 可以使生物体从短寿命状态转变为长寿命状态。我们定义了基因表达的这种变化 作为长寿标志，并已描述了它们的（i）肝脏，肾脏和大脑在哺乳动物不同30- 倍的寿命;（ii）已知延长小鼠寿命的干预措施;以及（iii）细胞类型不同的人类细胞类型 周转利用这些特征，我们预测并验证了具有潜在寿命的化合物 扩展名.我们建议直接测试这些候选长寿干预措施对小鼠寿命的影响 并将此方法扩展到定义基于高级寿命特征的寿命控制原则， 确定其他长寿干预措施。因此，我们提出了两个广泛的研究方向：（一） 小鼠长寿干预措施的鉴定和验证。我们将首先测试50种化合物对 然后将测试20种性能最好的化合物对寿命的影响。我们将 还分析成功的长寿干预措施的机制和途径，他们的目标， 整合这些信息以定义寿命控制的原则。(ii)无偏识别平台 延长寿命的干预措施。我们将首先开发先进的长寿签名，基于分析 基因表达、代谢产物谱及其在三种寿命延长模型中的整合 （哺乳动物的寿命、细胞类型的寿命和小鼠干预）。然后，签名将用于 预测延长寿命的化合物，这将通过基因表达和基因分析来验证。 生物年龄最后，我们将建立一个筛选化合物和其他干预措施的平台， 广泛的长寿干预措施。在项目完成后，我们将知道哪些长寿 特征是延长寿命干预措施的最佳预测因子，确定介导其效果的途径， 了解寿命是如何调节的，开发一个平台来确定长寿干预措施， 确定了一些减缓小鼠衰老的新药物。有了这些信息，可以为未来设计测试。 在人类中测试长寿干预措施。

PROJECT SUMMARY/ABSTRACT Lifespan of major model organisms can be extended by dietary, pharmacological and genetic interventions. This includes mouse models, for which there are currently more than 15 known longevity interventions. However, these treatments have been identified on a case-by-case approach. The relationship between them is unclear and it is not known if they act through the same or different mechanisms and which interventions are most effective and robust. Accordingly, there is a need for systematic, unbiased identification of longevity interventions in mammals. To transition from extending lifespan in mice to doing this in humans, it is important to both identify many interventions that extend lifespan and define general principles of lifespan control. In this regard, the potential of the cell or organism to live a shorter or longer life is represented by it’s metabolic state, which in turn is reflected in it’s transcriptome. An intervention that adjusts the transcriptome in a certain way may shift an organism from a shorter-lived to a longer-lived state. We define such changes in gene expression as longevity signatures and have described them for (i) liver, kidney and brain across mammals differing 30- fold in lifespan; (ii) interventions known to extend lifespan in mice; and (iii) human cell types differing in cell turnover. Using these signatures, we then predicted and validated compounds with potential for lifespan extension. We propose to directly test these candidate longevity interventions in mice for the effect on lifespan and extend this approach to define principles of lifespan control based on advanced longevity signatures and identification of additional longevity interventions. Accordingly, we propose two broad research directions: (i) Identification and validation of longevity interventions in mice. We will first test 50 compounds for the effect on biological age in mice and then will test the 20 best-performing compounds for the effect on lifespan. We will also analyze successful longevity interventions with regard to mechanisms and pathways they target, and integrate this information to define principles of lifespan control. (ii) Platform for unbiased identification of interventions that extend lifespan. We will first develop advanced longevity signatures, based on the analyses of gene expression, metabolite profiling and their integration across three models of increased lifespan (longevity of mammals, longevity of cell types, and mouse interventions). Then, the signatures will be used to predict compounds that extend lifespan, which will be validated through gene expression and assays of biological age. Finally, we will build a platform for screening of compounds and other interventions and identify a broad range of longevity interventions. At the completion of the project, we will know which longevity signatures are best predictors of lifespan-extending interventions, identify pathways that mediate their effects, understand how lifespan is regulated, develop a platform for the identification of longevity interventions, and identify a number of new agents that slow mouse aging. With this information, tests can be designed for future testing of longevity interventions in humans.