Systems variation underlying the genetics of aging

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PROJECT SUMMARY Aging is currently the most important correlate of chronic illness in the United States. A fundamental question is whether aging is itself causal of disease or if aging is the result of generalized accumulation of failures among the many complex systems that underlie normal function, with the diseases associated with old age simply being the most extreme form of this failure. From a systems biology perspective, this question can be phrased as whether the degradation in complex functional regulatory networks associated with aging is caused by a limited set of central components/nodes or whether aging-associated decline is generated by heterogeneous failure across the entire network which then leads to an inevitable crossing of a critical frailty threshold. We aim to test these hypotheses using a comprehensive network analysis of age-specific changes in gene expression and protein abundance using the nematode Caenorhabditis elegans as a model system. Specifically, we aim to (1) determine age-specific changes in the gene regulatory network at a cellular resolution, defining subcomponents that are specifically correlated with lifespan and central healthspan measures, (2) use natural genetic variation to systematically perturb the age-specific regulatory network in order to determine the regulatory structure and causal connections within the network, and (3) test functional hypotheses about the emergent structure of the age-specific regulatory network and relate network properties to individual variation in longevity, using knockouts and over- expression constructs. Our approach has three unique elements. First, we use microfluidic techniques to image gene expression reporters at a cellular and sub-cellular level of resolution, allowing our network approaches to be tissue specific. Because this approach is high-throughput and nondestructive, these imaging experiments will also inform the temporal dynamics of the networks. Second, we use natural genetic variation coupled with whole genome sequencing to first perturb network structure and then map genetic causation, thereby allowing directionality across the network to be established. Third, we achieve this high level of mapping precision by conducting bulk segregant analysis (extreme QTL) on samples that have been sorted for differential gene expression, longevity and healthspan biomarkers using custom-designed microfluidic devices. These approaches will allow us to reconstruct the tissue-specific age-associated regulatory network, to examine and functionally validate emergent properties of changes in network structure and function during aging, and to couple these changes to individual variation in longevity.