Lifespan distributions
Aging research has made considerable progress, particularly in the nematode C.elegans, since the discovery in the mid 1980s of the first single-gene mutation that extends the worm’s lifespan. Since then, more than 50 genes (and many conditions) have been reported to significantly extend lifespan. This has led to the identification of broad classes of physiological processes – metabolic rate and respiration, reproduction, sensory perception, stress responses and associated developmental states (e.g. dauer) – that guide efforts at elucidating causes and molecular mechanisms of aging.
At the level of an individual, aging describes how a system approaches failure. Aging is a process shaped by multiple stochastic, genetic, and environmental factors, all of which conspire to set a range of possible death times. At the level of a population, it is the endpoint of the aging process—death—that comes into focus. A large number of individuals sample a probability distribution of death times, yielding a survival curve that constitutes the demographic signature of aging. The survival curve is a central piece of statistical data referred to or produced by researchers regardless of which aspects of aging they study. The negative derivative of the logarithm of the survival curve is the mortality or hazard rate. It estimates the probability that some member of a population fails at a particular time. At the population-level, the phenomenon of aging is defined as the time-dependency of this mortality rate. The impact of mutations or environmental conditions on this phenotype provides a starting point for hypothesizing about the molecular mechanisms of aging. Moreover, the shape of the hazard rate and its responsiveness to genetic interventions are critical in guiding the development of more structured computational models of aging.
One objective of our lab is to establish an inexpensive and robust technology for the
automated measurement of high-quality survival curves of C.elegans populations. One major advantage of
such a method is to substantially increase the phenotypic scope of screens for aging research.
Our goal is to provide enabling technology to perform a detailed survey of
high-resolution survival curves for each mutant or knock-down, and subsequently screen for specific survival
curve features. Such an approach enables not only a more sensitive systematic screening for genes that
affect maximum lifespan, but also the identification of genes that strongly affect, for example, the
variance – and perhaps only the variance – of the lifespan distribution. Such a system would have many uses. High-
resolution survival curves would be an ideal way to classify genes in terms of subtle impacts on the aging
phenotype, such as onset of aging, fastest aging rate (a second derivative), onset of plateauing, and
asymptotic hazard rate.
Molecular physiology
In "Lifespan distribution" (above), we study aging at the level of a population, where the endpoint of the aging process—death— comes into focus. In contrast, at the level of an individual, aging describes how a system approaches failure. The next generation of investigations must begin to measure the aging process itself – in vivo, in single organisms, and at different levels of organization. Once we can monitor observables that report physiological state in specific tissues of individual organisms over their entire lifespan, we can start quantifying correlations between age-related events at the cellular level and age-related phenomena at the whole-organism level.
Our mid-term objective in this project consists in establishing an experimental protocol to track
physiological states in specific cells throughout the life of the animal. Such physiological states
include mitochondrial activity (e.g. pH gradient across the inner mitochondrial membrane), redox state,
and dynamics of protein expression. In vivo data of this kind is then analyzed to detect events,
or event patterns, that are indicative of biological age in the sense that information about
their nature and timing permits a more reliable prediction of remaining lifespan than information about
time since adulthood alone. In a subsequent step we probe the dependencies between cellular and
organismic aging by detecting relative shifts in the timing of age
indicators in response to lifespan-altering genetic and environmental interventions of increasing spatial and
temporal detail. This will clarify whether and how aging processes are coupled across organizational levels
and guide us towards the molecular processes that underlie these dependencies.
Microfluidics for C.elegans aging research
In collaboration with the Whitesides Lab at Harvard's Department of Chemistry we develop microfluidics technology to facilitate quantitative longitudinal measurements in single organisms.
