Causes and consequences of lifespan biomarker variation in Caenorhabditis elegans

描述(申请人提供)：具体目标。在过去的30年里，科学家们从研究那些导致衰老和寿命差异的基因中学到了很多东西(Johnson 2013；Vijg and Suh 2005)。然而，大量证据表明，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基因相同的线虫最终达到这样一个点，即一些个体仍然能够正常运动，而另一些个体则不能(Herndon等人。2002年)。在同一培养皿中培养的最短和最长寿命相同的线虫之间的寿命可能相差10倍(数据来自(Johnson 1990)，由(Kirkwood和Finch 2002)分析)。寿命变异系数(CV；标准差/均值；比较比例变异的适当统计数据)来自50个控制良好的野生型线虫实验(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等人)。1996年))，给出了蠕虫21%和人类23%的数值(Mendenall等人)。未出版)。这些事实表明，非遗传、随机因素(可能包括成年体中染色质的表观基因组变化)(Fraga等人。2005年))对老龄化速度和寿命的差异有很大影响(在(Kirkwood和Finch 2000)中进行了回顾)。2005年，汤姆·约翰逊和他的同事发现了寿命异质性的预测因素，我在他的实验室继续进行了这些研究。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控制下的GFP表达定义了一个变量，其值预测了未来的寿命(Mendenall等人。2012年；REA等人。2005)。因此，高表达值定义了一种在动物整个生命过程中持续存在的生理状态，这种状态的后果包括延长寿命，增强对随后热休克的抵抗力，以及较低的固定寿命百分比(CyPSER等人。2013年；门登霍尔等人。2012年；REA等人。2005)。重申，这种长期的生理状态在操作上是由特定报告基因的高表达定义的。同样，在酵母中，Brent博士的实验室(Colman-Lerner等人。2005)确定了在同质环境中培养的遗传相同的细胞中特定组合的报告基因表达差异的原因。这些实验揭示了细胞之间在将基因表达成蛋白质的总体能力以及通过特定细胞信号通路进行信号传输的强度方面的持续差异。因此，它们还在操作上定义了迄今未确定的生理状态。在前期工作中，我 我从科罗拉多州扩展了我的研究范围，以开发严格的定量方法，在线虫组织的单个细胞中量化以前确定的生理状态，以及这些状态下细胞到细胞和动物到动物的变异。在接下来的五年里，我将开发大量的单拷贝报告基因来报告其他变量，并利用额外的测量来为更多的生理状态撒下更大的网。我将确定哪些生理状态和细胞过程会导致长期结果的差异，包括衰老速度和寿命。这一建议的中心假设是，在Biologica系统中，在时间上上游的过程的变化导致下游系统输出的变化。因此，这些实验将确定关键过程(例如，早期发育期间特定信号通路活性的差异，或年轻人将基因表达为蛋白质的能力的差异)，这些测量过程中的变化有助于不同的长期结果，并将阐明这些过程发生的顺序。他们将产生的数据将解决当前关于衰老的理论(例如，包括自由基理论和一次性胞体理论)，并产生和测试关于导致细胞与细胞和动物与动物之间衰老速度和寿命差异的机制的新假说。最后，这些实验将在线虫中识别更多的报告基因生物标记物，这些生物标记物可以在其他生物中测试预测能力。在这个项目的五年中，我将：目标1(K99)：继续开发单拷贝记者(&gt；50)和严格的记者量化方法，以允许精确测量寿命报告生物标记物，以便撒下广泛的网来量化不同的生理状态和细胞过程。目标2(K99)：评估现有的和目标1产生的转基因报告动物，以找出哪些细胞报告水平、信号事件、细胞过程和组织参数在动物生命的不同时间点最可变，以便决定限制的亚群 目标3.目标3(K99/R00)：纵向量化目标2和文献确定的参数，从八细胞胚胎的E细胞一直到老年两性人的病态肠细胞，产生和测试关于基因表达、寿命和生理状态的个体间和细胞间差异的因果关系的假设，对已报道的衰老病理学进行排序，确定哪些衰老理论最受新数据支持，并确定其他衰老生物标记物。

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.