Causes and consequences of lifespan biomarker variation in Caenorhabditis elegans

描述（由申请人提供）：具体目标。在过去的30年里，科学家们从研究其产物导致衰老和寿命差异的基因中学到了很多（JOHNSON 2013； VIJG和SUH 2005）。然而，充分的证据表明，衰老速度也受到非遗传因素的影响（KIRKWOOD和FINCH 2000； MARTIN 2009）。例如，在受控的同质环境中培养的遗传相同的秀丽隐杆线虫最终会达到一个点，其中一些个体仍然能够正常运动，而另一些则不能（HERNDON et al. 2002）。在同一培养皿中培养的基因相同的最短和最长秀丽隐杆线虫的寿命可能相差十倍（数据来自（JOHNSON 1990），由（KIRKWOOD和FINCH 2002）分析）。从50个控制良好的野生型秀丽隐杆线虫实验（同质实验室环境，Kaeberlein实验室）和1000对丹麦同卵双胞胎（异质环境，（HERSKIND et al. 1996））中得出的寿命变异系数（CV；标准差/平均值；比较比例变异的适当统计数据），蠕虫的变异系数为21%，人类的变异系数为23% （Mendenhall et al.未发表）。这些事实表明，非遗传的随机因素（可能包括成人体细胞染色质的表观基因组变化（FRAGA et al. 2005））对衰老速度和寿命的差异有重要影响（见（KIRKWOOD和FINCH 2000））。2005年，汤姆·约翰逊和他的同事发现了寿命异质性的一个预测因子，我在他的实验室里继续这些研究。在同质环境中遗传相同的年轻成年动物中，在小热休克蛋白启动子hsp-16.2的控制下，GFP的表达定义了一个变量，其值预测了随后的寿命（MENDENHALL et al. 2012; REA et al. 2005）。因此，高表达值定义了一种贯穿动物一生的生理状态，这种状态的后果包括延长寿命，增强对随后的热休克的抵抗力，以及降低固定寿命的百分比（CYPSER等人，2013；MENDENHALL等人，2012；REA等人，2005）。重申一下，这种长寿的生理状态在操作上是由一个特定报告基因的高表达来定义的。同样，在酵母中，Brent博士的实验室（coleman - lerner et al. 2005）发现了在同质环境中培养的基因相同的细胞中特定报告基因组合表达差异的原因。这些实验揭示了细胞间在将基因表达为蛋白质的一般能力以及通过特定细胞信号通路的信号传递强度方面的持续差异。因此，他们也在操作上定义了迄今为止尚未确定的生理状态。在前期工作中，我

DESCRIPTION (provided by applicant): Specific Aims. In the past 30 years, scientists have learned a great deal from studying genes whose products contribute to differences in aging and lifespan (JOHNSON 2013; VIJG and SUH 2005). Yet, ample evidence shows that the rate of aging is also affected by non-genetic factors (KIRKWOOD and FINCH 2000; MARTIN 2009). For example, genetically identical C. elegans cultured in controlled homogeneous environments eventually reach a point in which some individuals are still capable of normal movement while others are not (HERNDON et al. 2002). There can be a tenfold difference in lifespan between the shortest and longest lived genetically identical C. elegans cultured on the same Petri dish (data from (JOHNSON 1990), analyzed by (KIRKWOOD and FINCH 2002)). The coefficient of variation for lifespan (CV; standard deviation /mean; an appropriate statistic to compare proportional variation) derived from 50 well controlled wild-type C. elegans experiments (homogeneous laboratory environment, Kaeberlein lab) and 1000 pairs of Danish monozygotic twins (heterogeneous environment, (HERSKIND et al. 1996)), gave values of 21% for worms and 23% for humans (Mendenhall et al. unpublished). These facts suggest that non-genetic, stochastic factors (likely including epigenomic changes to chromatin in the adult soma (FRAGA et al. 2005)) contribute significantly to differences in aging rate and lifespan (reviewed in(KIRKWOOD and FINCH 2000)). In 2005, Tom Johnson and coworkers identified a predictor of heterogeneity in lifespan, and I continued these studies in his lab. In genetically identical young adult animals in homogeneous environments, expression of GFP under control of the small heatshock protein promoter, hsp-16.2, defined a variable, whose value predicted subsequent lifespan (MENDENHALL et al. 2012; REA et al. 2005). High expression values thus defined a physiological state that persisted throughout the life of the animal, a state whose consequences included lengthened lifespan, increased resistance to subsequent heat shock, and a lower percentage of life spent immobilized (CYPSER et al. 2013; MENDENHALL et al. 2012; REA et al. 2005). Restated, this long-lived physiological state was defined, operationally, by high expression of a particular reporter gene. Similarly, in yeast, Dr. Brent's lab (COLMAN-LERNER et al. 2005) identified causes of differences in expression of particular combinations of reporters in genetically identical cells cultured in homogeneous environments. These experiments revealed persistent cell-to-cell differences in general ability to express genes into proteins, and in strength of signal transmission through a particular cell signaling pathway. Thus they also operationally defined hitherto unidentified physiological states. In preliminary work, I have extended my research from Colorado to develop rigorous quantitative methods to quantify, in single cells in tissues of C. elegans, previously identified physiological states, and cell- to-ell and animal-to-animal variation in these states. Over the next five years, I will develop numerous single- copy reporter genes to report on other variables, and make use of additional measurements to cast a wide net for additional physiological states. I will determine which physiological states and cellular processes contribute to differences in long term outcomes including rate of aging and lifespan. The central hypothesis of this proposal is that in biologica systems, variation in processes that are temporally upstream causes variation in downstream system outputs. Thus, these experiments will identify key processes (for examples, differences in the activity of particular signaling pathways during early development, or differences in young adult ability to express genes into proteins) for which variation in these measured processes contributes to distinct long term outcomes, and will shed light on the order in which these occur. They will generate data that will address current theories about aging (including, for example, the free radical theory and the disposable soma theory) and produce and test novel hypotheses about mechanisms that result in cell-to-cell and animal-to-animal differences in the rate of aging and lifespan. Finally, these experiments will identify additional reporter gene biomarkers in C. elegans that can be tested for predictive power in other organisms. During the five years of this project I will: Aim 1 (K99): Continue to develop single-copy reporters (>50) and rigorous reporter quantification methods to allow precise measurement of lifespan reporter biomarkers, in order to cast a wide net to quantify distinct physiological states and cellular processes. Aim 2 (K99): Evaluate preexisting and Aim 1-generated transgenic reporter animals to find which cellular reporter levels, signaling events, cellular processes, and organismic parameters are most variable at different points in the life of the animal, in order to decide on restricted subsets of variables to measure longitudinally in Aim 3. Aim 3 (K99/R00): Quantify the Aim 2 and literature-determined parameters longitudinally, from the E cell of the eight cell embryo all the way to the morbid intestine cells of the elderly hermaphrodite, to generate and test hypotheses on causality of inter-individual and inter-cellular variation in gene expression, lifespan and physiological state, to order reported aging pathologies, to establish which theories of aging are most supported by the new data, and to identify additional biomarkers of aging.