A platform for in vivo lifelong tracking of aging genes and lifespan prediction under precise environments in C. elegans.

PROJECT SUMMARY Aging is an integrative phenotype subject to a complex interplay of genetic, environmental, and life history factors, and a key risk factor for a multitude of human diseases. Research in model organisms has enabled the identification of key evolutionary conserved genetic pathways that play a role in aging. In particular, the model organism Caenorhabditis elegans has been crucial in our current understanding of the genetic and environmental regulation of lifespan. Although a multitude of pathways are known to affect longevity, how these pathways jointly respond to upstream stimuli, and how they integrate this information to drive lifespan is far from understood. A major limitation to answer this question is the technical difficulty associated with studying the spatiotemporal activity of multiple pathways throughout lifespan, and under precise stimuli. The overarching goal of this proposal is to quantitatively determine how the spatiotemporal activity patterns of key aging regulators change in response to environmental inputs, and how these lifelong spatiotemporal gene activity patterns drive longevity in C. elegans. To answer this question, we will develop an experimental and analysis pipeline that addresses the major limitations to acquiring lifelong information of gene activity in live animals, under precise stimuli conditions. We will develop a platform to allow high-throughput combinatorial stimuli delivery and live imaging of C. elegans. The multidimensional data sets acquired will contain information about longevity, spatiotemporal activity of key aging transcription factors, and environmental exposures. These data will be used to derive models that reveal how these multiple pathways jointly drive a lifespan outcome. This pipeline will enable quantitative longitudinal, and non-destructive acquisition of lifespan and live monitoring of protein abundance of key nodes in the aging network. Moreover, we will use these tools to identify the optimal environmental conditions that can maximize lifespan. Furthermore, the developed experimental platform will have applicability not only in aging studies, but also in stress response, metabolism, toxicological studies, etc. The findings from this work will be the grounding work to study genome-wide transcriptomic changes, and correlate molecular spatiotemporal dynamics of large- scale aging-associated networks with lifespan, thus opening new avenues in aging research.