Aging of S. cerevisiae in a Dynamically Changing Environment

说明(申请人提供)：衰老是一个复杂的过程，受遗传和环境因素的影响，“变老”的负面影响可以有多种形式。神经元和干细胞等单个细胞的寿命会影响多细胞生物体衰老的速度和优雅程度。营养应激和遗传不稳定性已被确认为真核生物寿命的关键决定因素。然而，尽管已经确定了许多与衰老有关的重要途径，但限制寿命的基本机制仍不清楚。这项研究的一个障碍是难以跟踪长期行为，不仅在人类和其他长寿哺乳动物中如此，在更简单的模型生物中也是如此。虽然单细胞酿酒酵母是最不复杂的衰老模型，也最容易受到遗传和分子操作的影响，但现有的监测衰老的方法仍然有限，即使在这种快速生长的有机体中也是如此。我们建议使用微流控技术作为研究酿酒酵母老化的实验平台。由于生长环境对真核生物的寿命有很大的影响，我们将开发一种高度并行的微流控设备，能够将不同的细胞群体置于动态环境中。我们将把它与新的图像处理技术相结合，从而能够观察在静态和动态环境中生长的单个细胞的衰老动力学。这一平台的优势是，可以为个体从出生到衰老的过程生成终生统计数据。我们将展示这一平台的潜力，通过关注衰老的关键决定因素-卡路里摄入量-来提供对长期动态的新见解。我们将首先使用微流体梯度平台来表征静态热量限制(CR)对寿命的影响，以使大量细胞处于一定范围的静态葡萄糖浓度下。因为CR可能不需要为了延长寿命而保持恒定，我们接下来将研究动态CR对寿命的影响，以深入了解生物体对低营养水平做出反应的机制。遗传因素对衰老也有很大的影响。基因突变在一生中的积累会导致与衰老相关的疾病的发病，如癌症。酵母是这一现象的一个令人惊讶的有用模型，因为观察到母细胞在达到临界数量的细胞分裂时会切换到基因组不稳定的状态。这种转换导致杂合性缺失(LOH)事件的频繁发生。我们将开发一种使用双色荧光显微镜跟踪LOH事件的方法，并将使用我们的微流控平台来观察LOH频率随CR的变化。最后，酵母中的代谢循环，表现为氧化还原状态的振荡，已被证明受到与寿命延长有关的途径的调节。我们将使用一种感知氧化状态的改良荧光蛋白以及我们的动态微流控平台来确定酵母的寿命与代谢振荡周期之间的关系。

DESCRIPTION (provided by applicant): Aging is a complex process governed by both genetic and environmental factors, and the negative effects of "growing old" can take on many forms. The life spans of individual cells, such as neurons and stem cells, influence the rate and grace with which multi-cellular organisms age. Nutritional stress and genetic instability have been identified as key determinants of life span in eukaryotes. However, while many important pathways involved in aging have been identified, the fundamental mechanisms that limit life span remain undefined. One hindrance to this research is the difficulty in tracking long-term behaviors, not just in humans and other long-lived mammals, but in simpler model organisms as well. While the single-celled S. cerevisiae is the least complicated model for aging and the most amenable to genetic and molecular manipulations, the existing methods for monitoring aging, even in this rapidly growing organism, remain limited. We propose to use microfluidic technology as an experimental platform for the study of aging in S. cerevisiae. As the growth environment has a large impact on the life span of eukaryotes, we will develop a highly parallel microfluidic device with the ability to subject separate populations of cells to a dynamic environment. We will combine this with new image processing techniques, enabling the observation of aging dynamics in single cells growing in both static and dynamic environments. This platform will have the advantage of generating life-long statistics for individual organisms as they progress from birth to old age. We will demonstrate the potential of this platform to provide new insight into long-term dynamics by focusing on a key determinant of aging, caloric intake. We will first characterize the effect of static Calorie Restriction (CR) on life span usinga microfluidic gradient platform to subject large populations of cells to a range of static glucose concentrations. Because CR may not need to be constant in order to extend life span, we will next investigate the effect of dynamic CR on longevity, in order to gain insight into the mechanisms by which an organism responds to low nutrient levels. Genetic factors also have a strong influence on aging. The accumulation of genetic mutations over the course of a lifetime leads to the onset of aging-related diseases, such as cancer. Yeast is a surprisingly useful model for this phenomenon, as mother cells are observed to switch to a state of genomic instability when they reach a critical number of cell divisions. This switch leads to the frequent occurrence of loss-of-heterozygosity (LOH) events. We will develop a method for employing two-color fluorescence microscopy to track LOH events, and we will use our microfluidic platform to observe changes in LOH frequency in response to CR. Finally, a metabolic cycle in yeast, manifested by oscillations in redox state, has been shown to be regulated by pathways involved in life span extension. We will use a modified fluorescent protein that senses oxidative state along with our dynamic microfluidic platform to determine how life span is related to the period of metabolic oscillations in yeast.