The C. elegans Lifespan Machine and its application to the temperature scaling of lifespan

Lifespan results from the complex interaction between genetic, environmental and stochastic factors, and therefore varies widely even among isogenic individuals. In C. elegans , the action of molecular mechanisms on aging can be inferred from their statistical effects on the distribution of lifespans within populations. However, such investigations are hindered by limitations in the methods available for collecting lifespan data. To enable the rapid collection of survival curves at any desired statistical resolution, we developed an automated platform for determining the lifespans of large populations of nematodes

The C. elegans Lifespan Machine

The measurement of lifespan pervades aging research. Because lifespan results from complex interactions between genetic, environmental and stochastic factors, it varies widely even among isogenic individuals. The action of molecular mechanisms on lifespan is therefore visible only through their statistical effects on populations. Survival assays in C. elegans provided critical insights into evolutionarily conserved determinants of aging. To enable the rapid acquisition of survival curves at arbitrary statistical resolution, we developed a scalable imaging and analysis platform to observe nematodes over multiple weeks across square meters of agar surface at 8 μm resolution. The method generates a permanent visual record of individual deaths from which survival curves are constructed and validated, producing data consistent with the manual method for several mutants in both standard and stressful environments. Our approach allows rapid, detailed reverse-genetic and chemical screens for effects on survival and enables quantitative investigations into the statistical structure of aging

The temporal scaling of Caenorhabditis elegans ageing

The process of ageing makes death increasingly likely, but involves a random aspect that produces a wide distribution of lifespan even in homogeneous populations1,2. The study of this stochastic behaviour may link molecular mechanisms to the ageing process that determines lifespan. Here, by collecting high-precision mortality statistics from large populations, we observe that interventions as diverse as changes in diet, temperature, exposure to oxidative stress, and disruption of genes including the heat shock factor hsf-1, the hypoxia-inducible factor hif-1, and the insulin/IGF-1 pathway components daf-2, age-1, and daf-16 all alter lifespan distributions by an apparent stretching or shrinking of time. To produce such temporal scaling, each intervention must alter to the same extent throughout adult life all physiological determinants of the risk of death. Organismic ageing in Caenorhabditis elegans therefore appears to involve aspects of physiology that respond in concert to a diverse set of interventions. In this way, temporal scaling identifies a novel state variable, r(t), that governs the risk of death and whose average decay dynamics involves a single effective rate constant of ageing, kr. Interventions that produce temporal scaling influence lifespan exclusively by altering kr. Such interventions, when applied transiently even in early adulthood, temporarily alter kr with an attendant transient increase or decrease in the rate of change in r and a permanent effect on remaining lifespan. The existence of an organismal ageing dynamics that is invariant across genetic and environmental contexts provides the basis for a new, quantitative framework for evaluating how and how much specific molecular processes contribute to the aspect of ageing that determines lifespan

An Insulin-to-Insulin Regulatory Network Orchestrates Phenotypic Specificity in Development and Physiology

Insulin-like peptides (ILPs) play highly conserved roles in development and physiology. Most animal genomes encode multiple ILPs. Here we identify mechanisms for how the forty Caenorhabditis elegans ILPs coordinate diverse processes, including development, reproduction, longevity and several specific stress responses. Our systematic studies identify an ILP-based combinatorial code for these phenotypes characterized by substantial functional specificity and diversity rather than global redundancy. Notably, we show that ILPs regulate each other transcriptionally, uncovering an ILP-to-ILP regulatory network that underlies the combinatorial phenotypic coding by the ILP family. Extensive analyses of genetic interactions among ILPs reveal how their signals are integrated. A combined analysis of these functional and regulatory ILP interactions identifies local genetic circuits that act in parallel and interact by crosstalk, feedback and compensation. This organization provides emergent mechanisms for phenotypic specificity and graded regulation for the combinatorial phenotypic coding we observe. Our findings also provide insights into how large hormonal networks regulate diverse traits

Measuring and modeling interventions in aging

Many dietary, pharmaceutical, and genetic interventions have been found to increase the lifespan of laboratory animals. Several are now being explored for clinical application. To understand the physiologic action and therapeutic potential of interventions in aging, researchers must build quantitative models. Do interventions delay the onset of aging? Slow it down? Merely ameliorate some of its symptoms? If interventions slow some aging mechanisms but accelerate others, can we detect or predict the systemic consequences? Statistical and analytic models provide a crucial framework in which to answer these questions and clarify the systems-level effect of molecular interventions in aging. This review provides a brief survey of approaches to modeling lifespan data and places them in the context of recent experimental work.This work was supported by the MEIC Excelencia award BFU2017-88615-P, and by an award from the Glenn Foundation for Medical Research

Measuring and modeling interventions in aging

Many dietary, pharmaceutical, and genetic interventions have been found to increase the lifespan of laboratory animals. Several are now being explored for clinical application. To understand the physiologic action and therapeutic potential of interventions in aging, researchers must build quantitative models. Do interventions delay the onset of aging? Slow it down? Merely ameliorate some of its symptoms? If interventions slow some aging mechanisms but accelerate others, can we detect or predict the systemic consequences? Statistical and analytic models provide a crucial framework in which to answer these questions and clarify the systems-level effect of molecular interventions in aging. This review provides a brief survey of approaches to modeling lifespan data and places them in the context of recent experimental work.This work was supported by the MEIC Excelencia award BFU2017-88615-P, and by an award from the Glenn Foundation for Medical Research

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

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.This project was funded by the European Research Council (ERC) under the European Union’s Horizon 2020 research and innovation programme (Grant agreement No. 852201), the Spanish Ministry of Economy, Industry and Competitiveness (MEIC) to the EMBL partnership, the Centro de Excelencia Severo Ochoa (CEX2020-001049-S, MCIN/AEI /10.13039/501100011033), the CERCA Programme/Generalitat de Catalunya, the MEIC Excelencia award BFU2017-88615-P, and an award from the Glenn Foundation for Medical Research to NS. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript

The effect of interventions and mutations on the slope of the linear model relating VMC and death times.

Parameter estimates of βv and the p-value testing the hypothesis (1-βv)! = 0. Confidence intervals and p-values were obtained via bootstrapping.</p

Lifespan Machine Technology Update.

A description of the new approaches to image analysis, including detection of death-associated contraction and expansion and partitioning of lifespan into distinct behavioral and morphological stages. (PDF)</p