While many factors are known to modulate the mean lifespan of a population, less is known about how these factors alter the aging process on an individual level. Zhang et al. recently showed that within a wild-type population, long-lived and short-lived animals differed in two ways (Zhang et al., 2016). First, the rate of physiological decline was slower in long-lived individuals, as might be expected. The second, however, was counter-intuitive: the additional lifespan of longer-lived individuals was primarily due to differences toward the end of the lifespan. That is, long-lived animals exhibited longer periods of low physiological function, or ‘extended twilight’ (Zhang et al., 2016).

A different picture was suggested by a study using automated assays of lifespan in the ‘Lifespan machine’ (Stroustrup et al., 2016). In this study it was reported that various genetic and environmental perturbations do not fundamentally change the shape of the survival curve, but rather only compress or dilate it in time. This result was interpreted as suggesting that the aging process in C. elegans is, at least at some point in its pathway, controlled by a single process describable by a single variable corresponding to the rate of aging (Stroustrup et al., 2016).

We sought to determine to what extent ‘extended twilight’ and/or scaling effects apply at the behavioral level in mutants with altered aging. The concept of a universal scaling parameter in aging would suggest that the short and long-lived individuals within any strain (whether with normal, short, or long mean lifespan) would resemble their short and long-lived counterparts in the reference strain, but with a temporal scaling (Figure 4a). If the variations in aging rate among individuals in any isogenic strain are governed by similar factors, we would expect that short and long-lived individuals would display similar late-life characteristics as their wild type counterparts. If, on the other hand, short-lived strains as a whole physiologically more closely resemble short-lived individuals of a wild type population, we might expect them to display late-life characteristics similar to these short-lived individuals (Figure 4b). Similarly, long-lived strains might display a range of late-life decays or alternatively collectively resemble long-lived worms in the reference strain.

Figure 4 Download asset Open asset Potential aging models. (a) Model 1: Temporal scaling results in identical patterns of behavioral decline when data are normalized by lifespan. Idealized decline curves for wild type (black dot-dashed), short-lived (red solid), and long-lived (green dashed) strains. Decline curves are shown as a function of chronological time (left) and fraction of life (right). (b) Model 2: Long-lived and short-lived strains resemble long-lived and short-lived wild type worms with respect to behavioral decline. Idealized decline curves for a short-lived (red solid) and a long-lived (green dashed) strain. Decline curves are shown as a function of chronological time (left) and fraction of life (right). https://doi.org/10.7554/eLife.26652.017

Wild-type strain N2 worms exhibited an initial decline followed by a ‘plateau’ period of nearly constant spontaneous and stimulated activity and response duration and latency (Figure 3g–j). When we compared the behavior of the shortest-lived and longest-lived quartile of N2 worms, we found that their behavioral declines were qualitatively different. The longest-lived animals exhibited a ‘decline and plateau’ phenotype, in which an initial rapid decline in behavioral capacity is later replaced by a very gradual decline for the remainder of life (Figure 5a,h). By contrast, the shortest-lived animals showed only the rapid decline in behavior before dying (Figure 5a,g). The result that long-lived animals experience a long period of low behavior are consistent with the ‘extended twilight’ reported by Zhang et al. (Zhang et al., 2016).

Figure 5 with 1 supplement with 1 supplement see all Download asset Open asset Mutants with short and long lifespan display patterns of late-life behavioral decline that resemble short and long-lived worms from a wild type population. (a) N2 behavior over time for the lowest quartile (solid curve) and highest quartile (dashed curve) of survivors. (b) Data from panel (a) plotted as a fraction of each individual’s life. (c) daf-16 behavior over time. (d) daf-16 behavior over fraction of life. (e) daf-2 behavior over time. (f) daf-2 behavior over fraction of life. (g) Comparison of N2 lowest survivor quartile and daf-16. (h) Comparison of N2 highest survivor quartile with daf-2. https://doi.org/10.7554/eLife.26652.018

Short-lived daf-16 mutants declined at a similar rate to N2, but did not exhibit any plateau phase; instead, daf-16 worms die after their initial behavioral decline (Figure 3g–j, Figure 5c,d). A similar effect was seen in daf-16 response duration and response latency, which do not level off but decrease or increase, respectively, at a similar rate until the time of death. Comparing the activity history of the shortest-lived N2 worms to that of daf-16 as a whole, we found a striking correspondence between the behavioral decline of the two groups (Figure 5g). These results show that the behavioral decline of daf-16 animals is not a scaled version of the wild type distribution of decline, but instead resembles the short-lived individuals in a wild-type population.

Long-lived daf-2(e1370) mutants, in which behavioral quiescence has been previously reported (Gems et al., 1998; Gaglia and Kenyon, 2009), exhibited a decline in stimulated activity akin to that observed in N2 and daf-16 followed by a nearly constant low level of stimulated activity and response behaviors for the remainder of life (Figure 3h). Spontaneous activity in daf-2, on the other hand, declined to near zero within 10 days of adulthood, where it remained until death. Even at very young chronological age (before day 5), daf-2 mutants perform less well than N2 for each behavior metric scored (Figure 3g–j).

The ‘decline and plateau’ phenotype of the longest-lived N2 animals was also evident in both short-lived and long-lived daf-2 animals (Figure 5e,f). Long-lived strains age-1, tax-4, and unc-31 also exhibited the ‘decline and plateau’ phenotype (Figure 3—figure supplement 1, Figure 5—figure supplement 1). These results show that aging behavior of daf-2 and other long-lived animals, like that of daf-16 animals, does not resemble a scaled version of wild type. Instead, they resemble the longest-lived individuals in a wild-type population, in that they exhibit a long plateau period of low locomotory function during late life.

In order to further characterize inter-individual differences in aging, we next sought to quantify the shape of behavioral decline. We analyzed individual behavioral decline as a fraction of life and calculated early and late decline rates (Figure 5b,d,f, Figure 5—figure supplement 1, Figure 6a–c) (see Materials and methods). We then calculated the difference in decline rates to quantify the overall shape of behavioral decline. We found that the change in decline rate negatively correlated with lifespan in all strains tested (N2: R = −0.32, p=8.0×10−12; daf-16: R = −0.18, p=0.0054; daf-2: R = −0.25, p=0.016; age-1: R = −0.43, p=8.0×10−7; tax-4: R = −0.41, p=4.2×10−4; unc-31: R = −0.49, p=1.6×10−8; lite-1: R = −0.54, p=3.9×10−8, aak-2: R = −0.23, p=0.012) (Figure 6a–e), indicating that the shape of behavioral decline differed signficantly for individuals with differing lifespans. We observed a smooth transition between the shape of aging behavior between short-lived and long-lived individuals within each strain (Figure 6a–c, Figure 6—figure supplement 1). Furthermore, our results suggest that there exists a relationship between change in decline rate and lifespan that lies along a continuum across strains in addition to between individuals of the same strain (Figure 6e,f). Therefore, our results suggest that while behavioral decline does not temporally scale with lifespan, the stochastic sources of variability between isogenic individuals modulate the shape of aging along the same axis of variability as between short and long lived strains. For example, variability in the rate of aging may reflect a variability in the nuclear localization of DAF-16 and the activation of its targets.

Figure 6 with 1 supplement with 1 supplement see all Download asset Open asset Shape of behavioral decline changes continuously with lifespan across individuals and strains. (a) Change in decline rate (pixels/life fraction) versus lifespan for individual N2 animals. (b) Same data as in panel (a) presented for daf-16 mutants. (c) Same data as in panel (a) presented for daf-2 mutants. (d) Change in decline rate (pixels/life fraction) in lowest (white) to highest (dark gray) survivor quartiles. *, p<0.05; **, p<0.01; ***, p<0.001. (e) Change in decline rate (pixels/life fraction) versus lifespan for multiple strains. (f) Mean decline rate change (pixels/life fraction) plotted against mean lifespan for each strain tested. Correlation coefficient r = −0.94, p=0.0006. (g) Standard deviation of decline rate change (pixels/life fraction) plotted against mean lifespan for each strain tested. Correlation coefficient r = −0.70, p=0.055. https://doi.org/10.7554/eLife.26652.020

Finally, we investigated the level of inter-individual variability in the rate of aging. We found that the standard deviation of decline rate change generally decreased with average lifespan (Figure 6g) (R = −.70, p=0.055). That is, longer-lived strains exhibited less individual variability than shorter-lived strains. Under a temporal scaling model, both the mean decline rate change and standard deviation of decline rate change would be equal across strains with different lifespans. Therefore, our data argue against a temporal scaling model of aging.