Aging involves the progressive deterioration of multiple organismal functions; chief among them is the ability to maintain cellular and tissue homeostasis. Because observable aging-related phenotypes are caused by changes in hundreds, if not thousands of molecular components and interactions among them, a network-based systems analysis is needed to describe and study how dynamic properties of genetic networks change across lifespan. To address this challenge, we will combine modeling and experimentation to capture the complexity of the deterioration of cellular processes as an organism ages, which promises to reveal features of aging biology that are unlikely to be captured with single-gene approaches. We focus on the proteostasis network (PN), a large set of molecular processes that determine the integrity of the proteome, because deterioration of the proteome reverberates through all cellular processes and is a central player in aging and aging-related diseases. We will analyze the network dynamics and emergent properties of the complex proteostasis system using modeling, organismal biology and quantitative genomes in the nematode Caenorhabditis elegans in the following three aims: (1) To identify the mechanisms of developmental acceleration. We will use whole-animal transcriptomics to establish the dynamics of response to proteotoxic stress in young animals and determine the features that deteriorate during aging. Then, using experimental data, we will develop a quantitative model that will relate changes in dynamics of gene expression to cellular manifestation of loss of proteostasis. This framework will allow us to investigate the origins of variability among individuals in age-related decline. (2) To define the neuroendocrine signals that control the germline. We will develop a quantitative understanding of the effects of insults to specific tissues on aging at the organismal level, by tissue specific damage or enhancement of the PN, to establish whether insults work synergistically, independently or antagonistically. (3) Validation of the systems properties of the proteostasis network to predict organismal decline and gene editing of network components to ameliorate aging. Because predicting the course of aging remains a challenge, we will leverage the power of our unique panel of hundreds of natural strains to test whether the diagnostic tools we devised can accurately predict PN genotypes that are particularly prone to premature decline in proteostasis. After determining the components of the PN that are altered across natural strains, we will correct the natural genetic variants that cause PN malfunction with the goal of improving physiological performance in genetic backgrounds that would otherwise experience early demise. These experiments will serve as a test case for developing individualized interventions for healthy aging.
|Effective start/end date||7/1/18 → 6/30/22|
- National Institute of General Medical Sciences (3R01GM126125-01A1S1)