This project will develop tools and data necessary to accurately predict lifespan from the activity of genes that respond to environmental conditions such as food intake or stress exposure. It is not fully understood how genes and the environment determine an individual's specific longevity outcome. For example, it is known that calorie reduction or reduction in oxidative stress can lead to increased longevity, but precisely predicting longevity from this information alone is not possible. Given the difficulties of studying aging in humans, the roundworm, Caenorhabditis elegans, will be used as a model to link the activity of specific genes known to affect aging. A mathematical model of the relationship between environment, genes, and longevity will be generated to reveal which genes and environments are most important in determining aging and longevity and to enable the prediction of lifespan. This information will be used in future work that focuses on whether these predictions can be extended to other organisms, and ultimately to humans. Through this work, two graduate students and one postdoctoral researcher will receive interdisciplinary training. In addition, this project will support a Latinx-directed STEM initiative for high-school students.
The project's main goal is to develop an experimental and analytical pipeline that will enable the establishment of rules of lifespan determination in the model organism Caenorhabditis elegans. Mathematical and statistical models that can predict lifespan of an individual based on lifelong activity of key aging pathways will be developed. The research tests whether variability in activity of key genes (due to biological noise or environmental or genetic perturbations) is propagated to variability in lifespan. To address this question, a microfluidic platform that enables monitoring of multiple individuals throughout their lifespan, while quantifying the activity of key aging pathways through endogenous fluorescent reporters, will be developed. The proposed platform will allow data pairing, where gene activity at the single individual level can be paired with its lifespan. Experimental tools that enable simultaneous quantitative analysis of in vivo gene activity and combinatorial environmental perturbations will be developed. These tools hinge on microfluidics, computer vision, automation, and new reporter strains engineered by CRISPR to track gene activity under endogenous regulatory control. The acquired data sets will be used to derive data-driven mathematical models that describe the stochastic dynamics of gene activity and its downstream phenotypic outcome: lifespan. Novel statistical techniques will be combined with mathematical models to infer phenotypic heterogeneity and robustness within populations. Interdisciplinary training will be provided to two graduate students and one postdoctoral researcher, and the award will support an educational program directed at broaden participation of Latinx high-school students in science.
This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.
