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A hierarchical process model links behavioral aging and lifespan in C. elegans.

Natasha OswalOlivier M F MartinSofia StroustrupMonika Anna Matusiak BrucknerNicholas Stroustrup
Published in: PLoS computational biology (2022)
Aging involves a transition from youthful vigor to geriatric infirmity and death. Individuals who remain vigorous longer tend to live longer, and within isogenic populations of C. elegans the timing of age-associated vigorous movement cessation (VMC) is highly correlated with lifespan. Yet, many mutations and interventions in aging alter the proportion of lifespan spent moving vigorously, appearing to "uncouple" youthful vigor from lifespan. To clarify the relationship between vigorous movement cessation, death, and the physical declines that determine their timing, we developed a new version of the imaging platform called "The Lifespan Machine". This technology allows us to compare behavioral aging and lifespan at an unprecedented scale. We find that behavioral aging involves a time-dependent increase in the risk of VMC, reminiscent of the risk of death. Furthermore, we find that VMC times are inversely correlated with remaining lifespan across a wide range of genotypes and environmental conditions. Measuring and modelling a variety of lifespan-altering interventions including a new RNA-polymerase II auxin-inducible degron system, we find that vigorous movement and lifespan are best described as emerging from the interplay between at least two distinct physical declines whose rates co-vary between individuals. In this way, we highlight a crucial limitation of predictors of lifespan like VMC-in organisms experiencing multiple, distinct, age-associated physical declines, correlations between mid-life biomarkers and late-life outcomes can arise from the contextual influence of confounding factors rather than a reporting by the biomarker of a robustly predictive biological age.
Keyphrases
  • physical activity
  • mental health
  • metabolic syndrome
  • deep learning
  • high resolution
  • machine learning
  • high throughput
  • insulin resistance
  • photodynamic therapy
  • climate change
  • fluorescence imaging
  • life cycle