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.

Public Health Relevance

The aging process is governed by both the environment and genetics. Environments that provide sub-optimal levels of nutrients have been shown to greatly increase life span in numerous model organisms, from yeasts to mammals. We will develop a highly parallel microfluidic platform to observe and characterize the interplay between environmental and genetic factors that affect important aspects of eukaryotic aging.

Agency
National Institute of Health (NIH)
Institute
National Institute of General Medical Sciences (NIGMS)
Type
Research Project (R01)
Project #
1R01GM095964-01A1
Application #
8297341
Study Section
Cellular Mechanisms in Aging and Development Study Section (CMAD)
Program Officer
Maas, Stefan
Project Start
2012-04-01
Project End
2016-03-31
Budget Start
2012-04-01
Budget End
2013-03-31
Support Year
1
Fiscal Year
2012
Total Cost
$258,609
Indirect Cost
$68,609
Name
University of California San Diego
Department
Biostatistics & Other Math Sci
Type
Schools of Arts and Sciences
DUNS #
804355790
City
La Jolla
State
CA
Country
United States
Zip Code
92093