Lifelong Analysis of Key Aging Genes as Determinants of Lifespan in C. elegans

Abstract

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, research on 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 a variety of environmental conditions. In this work, we present a system that enables in vivo tracking the endogenous spatiotemporal activity of key aging genes throughout C. elegans lifespan. This system hinges on an integrative experimental platform based on microfluidics, computer vision, and tagging of endogenous genes via CRISPR/Cas9 genetic engineering approaches. In contrast to traditional transgene expression, CRISPR/Cas9 enables insertion of a tag at precise genomic locations. This results in fluorescent protein levels representative of the endogenously expressed genes, and where all isoforms can be analyzed. Studying endogenous protein levels, however, poses a significant challenge, as these reporters are extremely dim in comparison to traditional multi-copy insertion transgenes. To address this limitation, we have developed computer vision approaches to quantitatively determine the spatial location and levels of said proteins, which can be used as a metric for gene activity. Furthermore, the use of microfluidic devices enables culture, stimulation, and longitudinal high-resolution imaging of animal populations under precise environmental conditions. Taking advantage of our computer vision algorithms, we can quantify protein levels, cellular compartmentalization, and tissue localization. Using this approach, we have studied the key transcription factor, DAF-16/FOXO, the main regulator of Insulin/Insulin-like Signaling in C. elegans. Under a variety of exposures to dietary restriction, a well-known regulator of lifespan that acts through DAF-16, we have observed patterns of activity that have not been identified with traditional transgenes. Integrating lifespan measurements under varied environmental conditions with quantitative analysis from DAF-16 lifelong spatiotemporal activity, we are exploring the predictive power of this key transcription factor at the tissue-level, using statistical and mathematical models. We are working on expanding our analysis to additional lifespan regulators to better understand how these interact in driving lifespan.