Aging trajectories

Alexander Markov, "Elements" 

The dynamics of aging is traditionally described with the help of "survival curves", showing what proportion of individuals survive to a certain age. The shape of the survival curves (and hence the nature of the dependence of mortality on age) varies greatly among different organisms. With the help of an automated system for monitoring the life and death of populations of the nematode Caenorhabditis elegans, it was possible to show that various factors that prolong or shorten life do not change the shape of the survival curve, but only stretch or compress it over time. Apparently, this means that aging is a programmed process, the course of which is difficult to fundamentally change, although it can be slowed down or accelerated.

"Aging", depending on the context and the research task, may mean different aspects of age-related changes, but most often this term refers to the growth with age of only one indicator, namely mortality. It is customary to depict the dynamics of aging in the form of "survival curves", where age is postponed along the abscissa axis, and the proportion of individuals living to such an age is postponed along the ordinate axis. If the scale along the ordinate axis is logarithmic, then the slope of the survival curve at each point reflects the mortality characteristic of a given age.

The shape of the survival curve varies greatly among different species of living beings (Fig. 1). In species with pronounced aging, the curve is convex upwards (if the earliest stages of development are not taken into account, that is, embryonic and infant mortality are discarded): young individuals rarely die, and with age, mortality is steadily increasing. This is what the survival curves look like in humans and many other animals, including such classic model objects as the fruit fly and the roundworm Caenorhabditis elegans.

mortality does not change with age, that is, there is virtually no aging. In this case, the survival curve on a logarithmic scale has the form of an inclined straight line. This is how the survival curves of any ageless objects look like – for example, atoms of a radioactive isotope. They have a constant half-life, and the probability of "death" of each individual atom at each moment of time is the same and does not depend at all on the time of formation (age) of this atom. This means that the object does not age. It is important not to confuse the absence of aging with immortality: the first means no increase in mortality with age, the second means zero mortality; the first occurs in wildlife, the second does not.

Finally, there are species in which mortality not only does not increase with age, but even decreases. These species with "negative aging" have concave survival curves. Some algae, turtles and trees have such a happy property (Fig. 1, bottom row).

As animals from the first group, that is, those with pronounced aging, of course, we are most interested in convex survival curves. We like their almost horizontal beginning, but we don't like the increasing steepness of the slope at the end at all. Gerontologists are trying hard to find out the causes of the increase in mortality with age, and considerable success has been achieved along this path (see the links at the end of the news). But there are still many unresolved issues, including quite fundamental ones.

For example, everyone knows that you can die from a huge variety of different reasons. Apparently, this is why mortality is always stochastic: even if you take a sample of genetically identical organisms of the same age and put them in exactly the same conditions, their life expectancy will not be the same. Someone will die earlier, someone later. We will never get a survival curve for a cohort of identical organisms in the form of two perpendicular segments: zero mortality until some age, and then the simultaneous death of all individuals. On the contrary, we will get a smoothed curve that differs little from the typical one for this type.

At the same time, it is not clear whether the causes that determine the dynamics of aging (the increase in mortality with age) are as numerous and diverse as the causes of death. At first glance, it seems that this should be the case. For example, some factors may have a stronger impact on the likelihood of death at a young age, others on mortality in old age. In this case, by arbitrarily changing these factors, we can arbitrarily change the shape of the survival curve.

On the other hand, it may also turn out that the dynamics of age-related changes in mortality is in some sense "programmed". Maybe the impact of all the numerous potential causes of death is mediated by certain systemic properties of the body, which naturally change over time, following their internal rhythm. Then the shape of the survival curve characteristic of this species will be resistant to disturbances.

To understand this, it is necessary to statistically analyze large amounts of data on the lifespan of organisms in different conditions.

One of the most convenient model objects for such studies is the nematode C.elegans. It has a short life cycle, rapid reproduction, it is easy to breed in the laboratory, and most importantly, many genetic and environmental factors have already been found that affect its life expectancy. It's a small matter: to put a sufficient number of tests and measure the life expectancy in a variety of experimental populations contained in different conditions in order to collect the necessary statistics.

But manually counting dead worms in hundreds of Petri dishes several times a day is a laborious task. I say this with knowledge, because that's what Elena Naimark and I are doing right now, only not on worms, but on fruit flies. A single survival curve based on a cohort of 100-200 individuals is worth a lot of effort.

Biologists from Harvard Medical School in Boston (USA) solved this problem radically by developing a device for simultaneous observation of multiple populations of C. elegans and automatic construction of survival curves. This remarkable unit, called the "Lifespan Machine", is a large scanner on which many Petri dishes with a nutrient medium and populations of C.elegans are installed. Worms in such cups can be seen in the light. The scanner is connected to a computer with software that continuously analyzes images coming from the scanner, identifies worms, monitors their movements and "guesses" about the death of a particular worm when it finally stops moving. At the exit, the researcher receives ready-made survival curves. The authors described their invention in a separate article published in 2013 in the journal Nature Methods (N. Stroustrup et al., 2013. The Caenorhabditis elegans Lifespan Machine).

In a new article published in Nature, the authors present results based on data on the life expectancy of more than 100,000 individuals.

To begin with, the researchers obtained survival curves for genetically homogeneous populations grown at different temperatures. It is known that the lifespan of C. elegans (like many other cold-blooded animals) depends very much on temperature: at +20°They live 40 times longer than at +33°C.

Comparing the obtained graphs, the authors noticed that the temperature does not affect the shape of the survival curve at all, but only affects its scale along the horizontal (time) axis. In other words, an increase in temperature uniformly compresses all stages of worm aging over time, but does not change their ratio. Therefore, any of the obtained curves can be obtained from any other by simply changing the scale along the horizontal axis (Fig. 2). Thus, the only change in the age dynamics of mortality that occurs with temperature changes is "temporal scaling". A large amount of data allowed us to show the high statistical reliability of this conclusion.

The authors began to check these factors one by one and in most cases received a positive response.

So, for many animals, the link between aging and oxidative stress is shown. By adding various amounts of an oxidant (tert-butylhydroperoxide) to the nutrient medium, the authors found that this acts in the same way as an increase in temperature: the survival curves shrink over time, exactly maintaining their shape. The same effect is caused by various mutations of genes that affect the work of the signaling cascade involving insulin / insulin-like growth factor 1. It is known about this cascade that its activity affects life expectancy in many animals, including mice, fruit flies and C.elegans. The shape of the survival curve at the same time, as it turned out, also does not change – only the scale along the time axis. The "temporary scaling" of aging also occurs if worms are fed with dead (UV-killed) E. coli bacteria, and not live ones. Worms on such a diet live longer, but the shape of the survival curve remains unchanged.

However, there were two exceptions to the rule. The mutation of the eat-2 gene, which disrupts the eating behavior of worms, and the mutation of the nuo-6 gene, which disrupts the mitochondria and makes the worm generally inhibited (such worms consume less oxygen, move slower, but live longer), as it turned out, still change the shape of the survival curve, making it flatter (in other words, the spread in life expectancy increases). However, even in these mutants, other influences affecting life expectancy, including temperature changes, lead only to "time scaling".

From this, the authors conclude that "time scaling" is a typical reaction of mortality dynamics to environmental and genetic influences. It turns out that the influence of almost any factor on the dynamics of mortality can be described by a single number reflecting the magnitude of scaling. One of the possible interpretations of the discovered regularity is that there is a certain systemic property of the organism, which can be conditionally called "resistance" or "resilience" (resilience, r), on which the probability of death from any of the many possible causes depends. This "state variable" r declines along a certain trajectory: 

dr/dt = −k•F(r), 

where F(r) is some unknown function describing the basic trajectory of aging, that is, a decrease in r. At the same time, most of the genetic and environmental influences that change life expectancy affect only k, which leads to time scaling.

The authors also checked what would happen if the factor that changes the rate of aging does not act during the entire life of the worm, but only at some stage of it. To do this, they placed the worms first in some temperature conditions, and then in others. The results obtained are consistent with the assumption that temperature directly affects k, and this effect is reversible and has no inertia. In other words, while the worm lives at a temperature of 30 ° C, it ages along a "fast" trajectory characteristic of this temperature, but it is worth lowering the temperature to 20 ° C, as the aging trajectory immediately changes to "slow", and the rest of the worm's life will age at a speed characteristic of 20 °C. It should be clarified that only the effect of temperature on k is reversible, but not on the final lifespan. Days spent at high temperatures shorten the life of the worm irreversibly. During these days, he manages to age a lot, and there is no way to return it. You can slow down the rate of decrease in r by reducing the temperature, but no rejuvenation will occur, of course. Aging will continue from the point that was reached by the end of the high temperature period.

It remains unclear to what extent all this applies to other animals, including humans. After all, C.elegans is a rather specific creature. Its development is very rigidly determined, the fate of each embryonic cell is precisely predetermined (see: The development of worms begins with the tail, "Elements", 11/23/2006). It may be related to this and the predestination of the aging trajectory in C. elegans. If you look at Fig. 1, you can see that in three different samples of Homo sapiens (modern Japanese women, Swedes born in 1881, hunter-gatherers ache), the shape of the survival lines differs markedly: in Japanese women it is the most convex, in hunter-gatherers it is the most flat. These differences, which cannot be reduced to a time scale, are obviously explained by the level of development of medicine and other benefits of civilization, which reduce mortality at all ages, except the latest.

Source: Stroustrup et al., The temporal scaling of Caenorhabditis elegans aging // Nature. Published online 27 January 2016.

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03.02.2015