Aging is a process shaped by genetics, environment, and chance, all of which conspire to determine an individuals'time of death. A survival curve constitutes the demographic signature of aging at the level of the population. Understanding the survival curve's responsiveness to genetic interventions is a critical step in guiding the development of more structured models of aging. Such models may give insight into the processes that determine the patterns of initiation and progression of age-related diseases, such Alzheimer's and Parkinson's disease and many types of cancer. Large amounts of demographic information are required to support such modeling efforts, and data-acquisition remains a limiting step in contemporary research. We have recently developed a method that greatly accelerates the collection of demographic aging data in C. elegans nematodes, via the automated acquisition of survival curves at high statistical and temporal resolution. This method utilizes modified consumer-electronics flatbed scanners to image worms cultured under standard conditions. Accompanying software automatically processes the resultant time-lapse videos into survival curves. We will use this technology to link genetic and environmental perturbations to a high-precision demographic aging signature at an unprecedented scale. We will acquire and analyze high-resolution lifespan distributions for roughly 2000 mutants representing all classic genes known to affect lifespan in this organism as well as a variety of targeted gene families. We will determine whether survival curves deviate from predictions made by classic aging models and apply functional data analysis to gain insight into the number and nature of dimensions along which the demographic aging signature is most variable. We will group genes into categories in terms of their impact on survival curve shape features, in order to place known gene functions in relation to the demographic aging signature and help illuminate unknown gene functions. This information will be important for future mechanistic studies, and will offer perspective for existing molecular knowledge. We will make our collection of survival curves widely available, thus providing a valuable resource to both the C. elegans research community and demographers.
Animals ranging from worms to humans become frail, disease prone, and more likely to die as they age. We will use an automated method for measuring death times of C. elegans nematodes to probe why some individuals die sooner than others. By observing the effect of 2,000 separate gene mutations on aging worm populations, this project will provide insight about how genes work together to affect the aging process.
| Apfeld, Javier; Fontana, Walter (2017) Age-Dependence and Aging-Dependence: Neuronal Loss and Lifespan in a C. elegans Model of Parkinson's Disease. Biology (Basel) 7: |
| Stroustrup, Nicholas; Anthony, Winston E; Nash, Zachary M et al. (2016) The temporal scaling of Caenorhabditis elegans ageing. Nature 530:103-7 |
| Romero-Aristizabal, Catalina; Marks, Debora S; Fontana, Walter et al. (2014) Regulated spatial organization and sensitivity of cytosolic protein oxidation in Caenorhabditis elegans. Nat Commun 5:5020 |
| Fernandes de Abreu, Diana Andrea; Caballero, Antonio; Fardel, Pascal et al. (2014) An insulin-to-insulin regulatory network orchestrates phenotypic specificity in development and physiology. PLoS Genet 10:e1004225 |
| Stroustrup, Nicholas; Ulmschneider, Bryne E; Nash, Zachary M et al. (2013) The Caenorhabditis elegans Lifespan Machine. Nat Methods 10:665-70 |
| Hulme, S Elizabeth; Shevkoplyas, Sergey S; McGuigan, Alison P et al. (2010) Lifespan-on-a-chip: microfluidic chambers for performing lifelong observation of C. elegans. Lab Chip 10:589-97 |
| Kolokotrones, Tom; Van Savage; Deeds, Eric J et al. (2010) Curvature in metabolic scaling. Nature 464:753-6 |
| Feret, Jerome; Danos, Vincent; Krivine, Jean et al. (2009) Internal coarse-graining of molecular systems. Proc Natl Acad Sci U S A 106:6453-8 |
