Source of information on the evolution of aging/senescence

Source of information on the evolution of aging/senescence

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Do you know a good review (published peer-reviewed or an online course or whatever) that offers a good overview of all hypothesis explaining the various patterns linked with aging?

I'd like this review to make the following things clear; antagonist pleiotropy, Gompertz and Weibull models and what theoretical explanations are underlying these models, mutation accumulation, reliability theory, eventually group selection, age-specific efficiency of natural selection, telomere length, the definition of senescence, and all not-cited hypothesis to explain the patterns of aging/senescence.

I don't know about just one source that fits everything you've asked, so here's a custom bibliography to get you started instead.

General Sources

I first learned about biogerontology from the excellent series of introductory essays at João Pedro de Magalhães' website,; I definitely recommend starting here.

A short book written by two experts, for Scientific American Publishers, is Finch and Ricklefs' Aging: A Natural History. "The" book on the Evolutionary Theory of Aging is Michael Rose's Evolutionary Biology of Aging. "The" book on comparative biogerontology is Finch's Longevity, Senescence, and the Genome. A "pop" account of recent research (esp. to do with caloric restriction and long-lived mutants, which have people excited right now, and the search for longevity drugs) is David Stipp's The Youth Pill. A constantly-updated-with-new-editions-reference is The Handbook of the Biology of Aging. In 2008 (just missing the 2009 rapamycin excitement), Cold Spring Harbor Press published a collection on Molecular Biology of Aging. The Gerontological Society of America has an e-textbook out, that I think is going to be updated on the regular, called Molecular and Cellular Biology of Aging.

The Definition and Measurement of Aging

Usually, people define senescence as an increase in "mortality" with age. Mortality roughly means vulnerability; the older you are, the easier it is to die. Mortality is measured/estimated demographically, in terms of (hypothetical or actual) cohort survivorship. There are two quantities usually used: one for discrete-time, and one for continuous-time. Where $N_x$ the number of survivors still alive in a cohort at age $x$,

$q_x = frac{{N_x}-{N_{x+1}}}{N_x}$ discrete-time; The Age-Specific Probability of Death.

$mu_x = -frac{N^{'}_{x}}{N_x}$ instantaneous; The Force of Mortality.

The first one is the fraction, of those alive at age $x$, who will die before age $x+1$. This estimates the probability that an individual aged $x$ will die before age $x+1$. This is intuitive: to age means that an 80-year-old is less likely to make it to 81, than a 20-year-old is to make it to 21. Notice that $q_x$ is bounded between 0 and 1.

The second one is the instantaneous rate of death, normalized against cohort size at that instant (whereas the first was the number of deaths in an interval, normalized against cohort size at the beginning of the interval). It is the instantaneous, age-specific, per-capita death rate. It can be visualized as the slope of the survivorship curve, divided by its height, times -1 to make it positive. It is not a probability. It has a dimension of rate ("per-second", "per-hour", etc) and it has no upper bound.

This way of defining senescence is described in Nature Scitable's Aging and its Demographic Measurement (although the authors give an erroneous definition of the force of mortality). It is discussed and justified intuitively, and at length, in Peter Medawar's The Definition and Measurement of Senescence.

Sometimes, you might want to count a decline in fecundity as aging, too, even if mortality wasn't changing. Or, you might want to define senescence in terms of various kinds of physiological performance. This is really a matter of taste, I suppose, or of what kinds of question you're trying to answer. Ageing or Senescence is just the part of getting older which, on top of having more birthdays, is somehow or other "bad" for you.

But the most common criterion is that $q_x$ or $mu_x$ grows with age.

Mainstream Evolutionary Theory of Aging:

The mainstream idea is that aging evolves because of the declining force of natural selection with age. Natural selection "cares less" about things that happen late in life, than about things that happen early in life.

  • Mutation Accumulation (MA): the theory proposed in Peter Medawar's An Unsolved Problem of Biology. (Theory that aging is due to genes that are silent or neutral in early life, and deleterious in late life.)
  • Antagonistic Pleiotropy (AP): the theory proposed in George C. Williams' Pleiotropy, Natural Selection, and the Evolution of Senescence. (Theory that aging is due to genes that are beneficial in early life, and deleterious late in life.)
  • Disposable Soma: Proposed by Thomas Kirkwood and Robin Holliday; sometimes considered a version of AP. (Theory that aging is due to a tradeoff, in turn possibly due to allocation of limited resources, between reproduction and maintenance; selection favours less than perfect maintenance. Can be considered a version of AP where the "early beneficial effect" is increased maintenance, and the "late deleterious effect" is decreased survival.)

Summarized in Nature Scitable's The Evolution of Aging. Note that both MA and AP could be partly true; both kinds of genes could exist.

Both MA and AP are commonly cited as predicting that higher extrinsic mortality should lead to the evolution of earlier / faster senescence; but things may be more complicated than this.

The Declining Force of Natural Selection: Hamilton's Formalization

Medawar's and Williams' insights were verbal. William Hamilton did the math, and showed that certain formal indicators of the strength of selection on a genetic effect, necessarily declined with the age at which that effect occurs. These indicators were I think: 1) the partial derivative of $r$ (the intrinsic rate of increase / malthusian parameter, which he took to be fitness) with respect to the force of mortality at age $x$; and 2) the partial derivative of $r$ with respect to fecundity at age $x$. (The first starts declining at the age of first reproduction; the second starts declining immediately.)

In passing, this also demonstrated that a remark R. A. Fisher had previously made, and which Medawar had accepted, was fallacious. Fisher and Medawar had assumed that the strength of natural selection was proportional to age-specific reproductive value; but since reproductive value can increase with age, and Hamilton's indicators can't, reproductive value can't be measuring the sensitivity of $r$.

  • Hamilton's paper: The Moulding of Senescence by Natural Selection.
  • Charlesworth summarizes Hamilton's results and puts them in context in Fisher, Medawar, Hamilton and the Evolution of Aging.
  • Charlesworth's book Evolution in Age-Structured Populations is supposed to be quite important and to have furthered and refined stuff in this area.
  • Apparently things might be a bit more complicated than this? Baudisch has claimed that although Hamilton's indicators must decrease with age, other indicators, which aren't obviously unreasonable, can increase.

Gompertz, Weibull, and Gompertz-Makeham

The Gompertz equation is intended to describe how the Force of Mortality $mu_x$ grows with age. The Gompertz equation was come up with empirically, not for any theoretical reason; it just seemed to Benjamin Gompertz to fit the data quite well, for a good stretch of adult lifespan. The same goes for the Weibull equation, as far as I know, and for the Gompertz-Makeham equation.

  • Gompertz is an exponential growth equation: $mu_x = mu_0 cdot e^{Gx}$ (where $G$ is a parameter). It looks like a straight line when graphed on a semilog plot.
  • Weibull is a power growth equation: $mu_x = A cdot x^B$ (where $A$ and $B$ are parameters). It looks like a straight line when graphed on a log-log plot.
  • Gompertz-Makeham adds a constant to the Gompertz equation: $mu_x = mu_0 cdot e^{Gx}+C$. This ruins the elegance of the semilog-graphing, alas; it doesn't look like a straight line.

Apparently some people have tried to show how eg. Gompertz can evolve in MA models, but the generality of this is questionable (see Box 2 in this Trends in Ecology and Evolution paper). Gavrilov and Gavrilova (proponents of the "reliability theory of aging", have said something about Gompertz and Weibull being special: ("Both the Gompertz and the Weibull failure laws have their fundamental explanation rooted in reliability theory… and are the only two theoretically possible limiting extreme value distributions for systems whose life spans are determined by the first failed component… In other words, as the system becomes more and more complex (contains more vital components, each being critical for survival), its life span distribution may asymptotically approach one of the only two theoretically possible limiting distributions-either Gompertz or Weibull (depending on the early kinetics of failure of system components).") I don't know what that means.

Gompertz is the most commonly cited / used equation, I think. It's supposed to be more accurate than Weibull. Gompertz-Makeham I guess is more accurate, but of course it would be - it has an extra adjustible parameter, which, if it isn't helping, you can just set to zero!

Sometimes these equations are erroneously presented in terms of $q_x$ instead of $mu_x$. This is erroneous historically, but also because $q_x$ has an upper bound of 1 and couldn't possibly obey any of the equations, at least not indefinitely.

Sometimes people find it more intuitive to describe Gompertz' exponential growth in terms of how long it takes $mu_x$ to double; in humans, this "Mortality Rate Doubling Time" is about eight years.

Note that the equation does not accurately describe early life; mortality often starts high in infants, before falling to its minimum, and then starting to grow again. Thus $mu_0$ isn't the actual mortality rate at age 0, but the backwards-extrapolated mortality rate you would have had at age 0, if you were perfectly Gompertzian (or whatever).

It also may not describe very late life. A hot topic right now is "late-life mortality deceleration", where the exponential growth seems to slow down such that Gompertz overpredicts mortality in the very old; whether this is so, and why, is debated.

Reliability Theory and the Reliability Theory of Aging

"Reliability Theory" itself is just a mathematical framework for describing survival / failure rates and stuff. (Engineers use it to quantify stuff like, the rate at which parts have their first breakage, or whatever.) Some of the quantities it describes are basically the same as some analogous quantities in demography / life histories, if "first failure" is taken to mean death. For example, the "Survival Function" $S(t)$ is the probability, at time $0$, that failure will not yet have occurred by time $t$; this is obviously analogous to the probability $l_x$ that a newborn will still be alive at age $x$.

Systems can be made of parts; if you know the survival/reliability patterns of the parts, and how they go together, you can tell something about the survival/reliability of the whole system. To illustrate, compare two kinds of reliability structure:

  • A "series reliability structure" is one where, if any part fails, the system fails. An example would be a block held up by a chain; if any link breaks the block falls.
  • A "parallel reliability structure" is one where, if all parts fail, the system fails. An example would be a block held up by a number of individual rings, attached separately; only when the last ring breaks, does the block fall.

"The Reliability Theory of Aging and Longevity" is a specific theory proposed by Leonid Gavrilov and Natalia Gavrilova, intended to explain patterns of organismal failure ("death") in terms of component failure, and how the components are put together. If I recall, they propose that aging organisms are made up of redundant blocks of non-aging components; supposedly, this can explain near-Gompertzian aging over most of adult life, as well as apparent late-life mortality deceleration.

Group Selection

Heresy! Most evolutionary biologists / gerontologists, I assume, are happy with the mainstream theory and don't take the group selection theory seriously. Iunno, try Mitteldorf? Here's a paper and he also wrote a pop book with Dorion Sagan. On group selection in general as an explanation for altruism, I think David Sloan Wilson's the guy trying the hardest to defend it (or "a new version of it").

Molecular Mechanisms

Iunno, try The Hallmarks of Aging? The trouble is we really don't know what mechanisms cause aging. There's some good ideas, but finding evidence is haaaaard. One problem is that, if you want to know why some species live longer than others, and you're looking for physiological correlations, body size is a huge confound (elephants and whales live longer than mice and dogs), and so might be phylogeny.

Integrative Systems Biology, Comparative Genomics, and Ageing

We developed the Human Ageing Genomic Resources (HAGR) to help understand how the genome regulates human ageing. Because ageing is a complex process involving the interplay of multiple genes and proteins with each other and with the environment, we believe that studying its multiple components as a whole is imperative to fully comprehend ageing and more accurately pinpoint how to intervene in it. HAGR was developed to provide the most accurate and complete information possible to permit such integrative analyses. While we cannot offer a full description of ageing, systems biology, or comparative genomics, we provide herein a brief description of our scientific strategy in order for users to better use the resources available in HAGR. For more information about ageing, we refer to our parent website.

Arguably there are two key questions concerning ageing (de Magalhaes & Toussaint 2004): 1) What are the genetic determinants of ageing, both in terms of longevity differences between individuals and species differences in ageing? 2) Which changes occur across the lifetime to increase vulnerability, for example in a person from age 30 to age 70 to increase the chance of dying by roughly 30-fold (Figure 1)? HAGR was developed to facilitate studies that help answer both these questions. This is particularly timely because a variety of high-throughput technologies, including next-generation sequencing platforms (de Magalhaes et al. 2010), are now available that generate large amounts of data, which means there is a need to collect and systematically organize what we know about the genetics and genomics of ageing.

Figure 1: On one hand, comparative genomics can be used to study species differences in ageing. In parallel, we may study the changes people, or animals, endure while they age. These complementary approaches can help decipher the human ageing process and ultimately lead to interventions that extend life and health by manipulating ageing. Notice how the area of the circles decreases as we study species progressively more distant to humans, since it is expected that species evolutionary more distant from humans are less likely to share mechanisms of ageing that are relevant in humans.

One basic principle behind our approach is that the genome regulates rate of ageing in mammals, including humans, to a large extent (Miller 1999 de Magalhaes 2003). Therefore, in theory, it is possible to study how the human genome regulates ageing and age-related deterioration through computational approaches. The human genome is indecipherable by itself, however. To harness its information one powerful approach is comparative genomics (Ureta-Vidal et al. 2003). That is, we must compare the human genome to that of other organisms to understand which regions of the genome do what. As such, having fully sequenced genomes allows researchers to study the evolution of ageing with unprecedented detail. Even though the nature of the human ageing process remains unclear (de Magalhaes 2005), it is undeniable that certain genes make humans age slower than other primates and about 30 times slower than mice and rats. Finding those genes has tremendous biomedical applications and is one of the reasons why we created a database of ageing in animals to study the evolution of ageing. Following the same rationale, studying long-lived animals may allow us to identify adaptations that contribute to longevity and disease resistance (de Magalhaes 2006), and we are involved in genome sequencing and analysis projects.

Differences in longevity between individuals of the same species, including humans, are also important determinants of longevity and for this we developed the LongevityMap featuring genetic variants associated with human longevity. Moreover, we created GenAge, a database of genes related to ageing. Most researchers agree that ageing is a complex, multigenic process. GenAge allows us to focus on the genes and pathways more likely to be involved in ageing, in humans and in model organisms, and it allows us to study the interactions between the genes and how they together modulate longevity. Network analyses, in fact, are now an emerging paradigm to study how genes interact with each and with the environment to determine a whole phenotype.

In addition to understanding the genetic basis for phenotypic variation in ageing and longevity, it is also crucial to elucidate the changes that contribute to age-related degeneration. We have derived a common molecular signatures of ageing from gene expression data and made this available as part of GenAge. Moreover, our gene-centric databases, GenAge, GenDR and the LongevityMap, help interpret results from large-scale approaches, including gene expression profiling, to gain insights on the molecular drivers of the process of ageing.

In HAGR, we try interpret what we know about ageing in model organisms in light of human biology. Using a system-level approach that incorporates data from multiple sources, we attempt to build a more coherent model of the genetic and molecular mechanisms of human ageing (de Magalhaes & Toussaint 2004). Defining a gene as related to human ageing is subjective. We used different criteria to define different pathways and advise researchers to look at our gene database, as described elsewhere. By building and constantly upgrading GenAge we aim to provide resources and directions for research in biogerontology.


Aging, which we broadly define as the time-dependent functional decline that affects most living organisms, has attracted curiosity and excited imagination throughout the history of humankind. However, it is only 30 years since a new era in aging research was inaugurated after the isolation of the first long-lived strains in Caenorhabditis elegans (Klass, 1983). Nowadays, aging is subjected to scientific scrutiny based on the ever-expanding knowledge of the molecular and cellular bases of life and disease. The current situation of aging research exhibits many parallels with that of cancer research in previous decades. The cancer field gained major momentum in 2000 with the publication of a landmark paper that enumerated six hallmarks of cancer (Hanahan and Weinberg, 2000), and that has been recently expanded to ten hallmarks (Hanahan and Weinberg, 2011). This categorization has helped to conceptualize the essence of cancer and its underlying mechanisms.

At first sight, cancer and aging may seem opposite processes: cancer is the consequence of an aberrant gain of cellular fitness, while aging is characterized by a loss of fitness. At a deeper level, however, cancer and aging may share common origins. The time-dependent accumulation of cellular damage is widely considered the general cause of aging (Gems and Partridge, 2013 Kirkwood, 2005 Vijg and Campisi, 2008). Concomitantly, cellular damage may occasionally provide aberrant advantages to certain cells, which can eventually produce cancer. Therefore, cancer and aging can be regarded as two different manifestations of the same underlying process, namely, the accumulation of cellular damage. In addition, several of the pathologies associated with aging, such as atherosclerosis and inflammation, involve uncontrolled cellular overgrowth or hyperactivity (Blagosklonny, 2008). Based on this conceptual framework, a series of critical questions have arisen in the field of aging regarding the physiological sources of aging-causing damage, the compensatory responses that try to re-establish homeostasis, the interconnection between the different types of damage and compensatory responses, and the possibilities to intervene exogenously to delay aging.

Here, we have attempted to identify and categorize the cellular and molecular hallmarks of aging. We propose nine candidate hallmarks that are generally considered to contribute to the aging process and together determine the aging phenotype ( Figure 1 ). Given the complexity of the issue, we have emphasized current understanding of mammalian aging, while recognizing pioneer insights from simpler model organisms (Gems and Partridge, 2013 Kenyon, 2010). Each ‘hallmark’ should ideally fulfil the following criteria: (i) it should manifest during normal aging (ii) its experimental aggravation should accelerate aging and (iii) its experimental amelioration should retard the normal aging process and, hence, increase healthy lifespan. This set of ideal requisites is met to varying degrees by the proposed hallmarks, an aspect that will be discussed in detail for each of them. The last criterion is the most difficult to achieve, even if restricted to just one aspect of aging. For this reason, not all the hallmarks are fully supported yet by interventions that succeed in ameliorating aging. This caveat is tempered by the extensive interconnectedness between the aging hallmarks, implying that experimental amelioration of one particular hallmark may impinge on others.

The scheme enumerates the nine hallmarks described in this review: genomic instability, telomere attrition, epigenetic alterations, loss of proteostasis, deregulated nutrient-sensing, mitochondrial dysfunction, cellular senescence, stem cell exhaustion, and altered intercellular communication.

Evolution of ageing as a tangle of trade-offs: energy versus function

Despite tremendous progress in recent years, our understanding of the evolution of ageing is still incomplete. A dominant paradigm maintains that ageing evolves due to the competing energy demands of reproduction and somatic maintenance leading to slow accumulation of unrepaired cellular damage with age. However, the centrality of energy trade-offs in ageing has been increasingly challenged as studies in different organisms have uncoupled the trade-off between reproduction and longevity. An emerging theory is that ageing instead is caused by biological processes that are optimized for early-life function but become harmful when they continue to run-on unabated in late life. This idea builds on the realization that early-life regulation of gene expression can break down in late life because natural selection is too weak to optimize it. Empirical evidence increasingly supports the hypothesis that suboptimal gene expression in adulthood can result in physiological malfunction leading to organismal senescence. We argue that the current state of the art in the study of ageing contradicts the widely held view that energy trade-offs between growth, reproduction, and longevity are the universal underpinning of senescence. Future research should focus on understanding the relative contribution of energy and function trade-offs to the evolution and expression of ageing.

1. Introduction

It is indeed remarkable that after a seemingly miraculous feat of morphogenesis a complex metazoan should be unable to perform the much simpler task of merely maintaining what is already formed.

Ageing, or senescence, is a physiological deterioration of an organism with advancing age, which reduces reproductive performance and increases the probability of death [1,2]. Despite the fact that ageing reduces Darwinian fitness, it is ubiquitous and represents an integral part of the life course of most species on Earth [2,3]. It was originally believed that ageing is restricted only to humans, captive animals, and livestock because animals in nature die from predation, competition, and parasites before they senesce. Therefore, ageing was predicted to lie largely outside the realm of natural selection. However, this view has been overturned in recent years by a string of outstanding studies in natural populations that have definitively demonstrated that ageing also occurs in the wild and is very common (reviewed in [3]). Nevertheless, there is a remarkable diversity in the patterns of ageing across the tree of life, with some species showing negligible rates of senescence in either age-specific reproduction, mortality, or both [4]. To explain this variation, evolutionary biologists and biogerontologists have sought to understand why ageing evolves, what determines variation in lifespan and rates of ageing, what are the proximal causes of ageing, and are they evolutionarily conserved? Such an understanding requires an integrated approach in which evolutionary concepts are used to guide the research into the mechanisms of ageing, while knowledge of the mechanisms is then used to support or reject different evolutionary theories.

2. Why do organisms age?

This key and enduring question requires qualification: are we asking the precise reasons why you or I might be ageing, or alternatively why ageing evolved in the first place? The failure to clearly distinguish between these proximate and ultimate questions, and why it even matters has, arguably, resulted in a fair amount of confusion within the broader study of ageing. The aim of this review is to marry these approaches and to facilitate a more complete understanding of the biology of ageing by integrating the latest mechanistic advances with the evolutionary theory. Through this, we will promote the idea that these approaches are complementary, synergistic, and can help in the development of age-specific life-history theory.

Evolutionary theories of ageing have been extensively reviewed [1,2,5,6] and are only briefly summarized here. These theories rely on the axiom that selection maximizes fitness, not necessarily lifespan. Therefore, ageing is associated with selective processes to build vehicles for successful reproduction [7,8]. The key idea underpinning the evolutionary theory of ageing is that the strength of natural selection on a trait declines after sexual maturation and with advancing age [9–13] resulting in Haldane's [14] famous ‘selection shadow’ (figure 1). This is because non-ageing-related extrinsic mortality reduces the probability of late-life reproduction and old individuals in the population have already produced a large part of their lifetime reproduction and passed on their genes resulting in a decline in selection gradients on mortality and fertility [11–13,17]. This single, fundamentally important insight led to the formulation of the major theories of ageing.

Figure 1. The strength of age-specific selection is maximized during pre-reproductive development but declines after sexual maturation with advancing adult age and reaches zero at the age of last reproduction [11–13,15]. The colours along the selection gradient line represent the effect of an antagonistically pleiotropic (AP) allele on fitness across the life course, from positive green early in life to strongly negative red in late life. The shading of the background represents the effect of an AP allele on lifespan across the life course, from neutral white to strongly negative black. The classic AP allele, as envisioned by Williams [10], will have a positive effect on fitness during development but a negative effect on fitness in late life. However, the effects of such an AP allele on lifespan will vary across the life course depending on whether the trade-off between lifespan and other fitness-related traits is based on energy or function. The negative effect on lifespan can result from competitive energy allocation between development, growth, and reproduction on the one hand, and somatic maintenance on the other hand, resulting in energy trade-offs as suggested by the ‘disposable soma’ theory [16]. Under energy trade-offs, damage accumulation due to insufficient repair starts early in life and accumulates through the ages until the demise of an organism and lifespan extension is always costly. However, functional trade-offs result from suboptimal regulation of gene expression in late life resulting in suboptimal physiological function. Under functional trade-offs, optimizing gene expression in adulthood improves both fitness and lifespan, without developmental costs. (Online version in colour.)

Mutation accumulation: in this, mutations with late-life effects can accumulate and be transmitted through the germ line [9]. Ageing here occurs due to the summation of randomly acquired deleterious effects that are manifested only late in life [18]. Following from the premise of the ‘selection shadow’, ageing at late ages has relatively little impact upon an organism's overall fitness. Early formulations of MA assumed narrow ‘windows’ for mutational effects during the life course of the organism, and models based on such assumptions predicted a rapid increase in mortality after the end of reproduction, or ‘walls of death’ that are rarely seen in nature. Subsequent models considered the possibility for positive mutational effects across adjacent age-classes and explored the extent to which MA could occur even if genes ultimately responsible for ageing had mildly deleterious effects in early life [6,19,20]. These models allowed for post-reproductive lifespan, a more gradual increase in mortality rate with age and a decline in mortality rates at a very late age. MA theory makes no specific assumptions about which types of pathways should underpin ageing, as the accumulation of mutational effects could in theory occur across random loci. MA has some support (reviewed in [21]), though recent discussions have highlighted that it may not be consistent with the discovery of the molecular signalling pathways that potentially underpin ageing across many animal groups and appear to be evolutionarily conserved [21–23].

Antagonistic pleiotropy: is the evolutionary theory of ageing which recognizes that genes often have multiple or pleiotropic effects and that a beneficial effect of a gene early in an organism's life can be strongly selected even if that gene causes a negative effect later in life [10]. Because selection gradients on survival and fertility decline with age, early-life beneficial effects are likely to be strongly positively selected, and deleterious late-life effects can persist because selection is weak and cannot eliminate them. Antagonistic pleiotropy (AP) emphasizes the importance and inevitability of trade-offs between different life-history traits across early and late life. To ascertain whether MA or AP was the dominant paradigm, many studies have examined whether enhanced success in reproduction early in life is inevitably associated with decreased lifespan or increased ageing. Laboratory evolution experiments have successfully selected for increased late-life fitness and observed decreased early-life fitness as a correlated response [24,25] in line with AP theory. Others selected directly for increased survival and observed decreased reproductive output [26]. The identification of individual alleles with AP effects has also strengthened support [27–29]. As noted above, Williams originally suggested the types of loci that could show antagonistic effects and, while at first an abstract concept, the finding of genes with the appropriate profile of antagonistic effects provides intriguing support for AP. One example is found in the sword tail fish (Xiphophorus cortezi), in which individuals that carry the dominant Xmrk oncogene simultaneously have increased the risk of melanoma and a selective size advantage [30].

3. Energy trade-offs between growth, reproduction, and longevity

While the logic of the AP theory of ageing [10] is straightforward, supported by mathematical modelling [11] and quantitative and molecular genetics [2,27], it does not explain which physiological processes actually result in organismal senescence. Connecting evolutionary and mechanistic explanations for ageing is important because (i) this knowledge builds a general understanding of the ageing process, (ii) knowing which physiological processes contribute to organismal senescence could provide powerful tests of ultimate ageing theory. Perhaps the most accomplished physiological/mechanistic account of AP to date is the ‘disposable soma’ theory of ageing (DST) [7,16,31]. While this model was developed as an independent evolutionary theory of ageing, and is sometimes presented as such in the literature alongside MA and AP, we agree with many researchers in the fields of evolutionary biology, ecology, and biogerontology that DST represents a physiological explanation of AP.

The premise of DST is that most organisms develop in environments in which resources are limited at least during some part of their lives. Because growth, reproduction, and somatic maintenance require energy, it is reasonable to expect that limited resources will be allocated between these different traits to maximize fitness. These are the energy trade-offs that underlie the DST and more broadly life-history theory itself [32]. Cellular damage occurs constantly and can result from direct damage to the genome and from accumulation of insoluble protein compounds that interfere with cell function. While organisms possess many maintenance and repair mechanisms that can be deployed for genome repair as well as to re-fold or clear away misfolded proteins, it may ultimately be beneficial to invest in such maintenance and repair only to maximize the organismal function during the expected period of life, which will be determined by environmental mortality risk [31]. There is no benefit of investing in high fidelity and long-term maintenance and repair to produce an organism that shows negligible senescence, but which is highly likely to be quickly predated or killed by pathogens.

(a) Increased reproduction accelerates ageing and vice versa

There is a wealth of evidence to support the existence of genetic trade-offs between early- and late-life fitness. Classic experimental evolution studies in Drosophila revealed negative early- versus late-life genetic correlations for fitness by selecting flies for early or late age at reproduction [15,33,34]. Follow-up studies in Drosophila and other invertebrates [25,35,36] using selection regimes that controlled for potentially confounding factors such as larval density also confirmed that enhanced late-life reproduction and survival was negatively correlated with early-life reproduction. Such studies are often cited in direct support of the DST [37]. However, while they provide evidence for a genetic correlation that is consistent with AP, they do not identify whether the underlying mechanisms are as predicted by the DST.

More direct support for the DST comes from the growing number of reports of trade-offs between investment in early-life performance and late-life performance in natural populations [38–41]. These ‘ageing in the wild’ studies have contributed three major advances: first, to help dispel the myth that ageing in nature is rare second, to provide evidence that early–late life trade-offs shape individual life histories in natural populations and third, to show that ageing in nature is plastic and depends strongly on the early-life environment.

Further evidence for DST comes from the experimental studies of natural populations. Field experiments with Collared Flycatchers (Ficedula albicollis) on the Swedish island of Gotland have shown that birds that reared an experimentally enlarged brood of nestlings laid smaller clutches later in life than did control birds [42,43]. Subsequent studies in other birds have shown that artificially increased brood size can also negatively affect survival [44–46]. Similarly, artificially increased litter sizes are associated with reduced survival in bank voles (Clethrionomys glareolus) [47]. Interestingly, in several mammalian studies, experimental increases to litter sizes did not result in reduced survival or reproduction of the parents, but instead reduced offspring size or survival [48–50]. These results suggest that there are significant costs of reproduction, and that animals can differentially allocate their investment between parental survival, offspring number, and offspring quality. These experimental studies come closest to linking increased reproduction with accelerated ageing, but do not yet identify the underlying mechanisms involved.

Hence, while numerous studies provide evidence for potential energy trade-offs in natural settings, the important additional steps are: (i) to demonstrate energy reallocation (e.g. [51]), (ii) to identify mechanisms that contribute to these trade-offs in order to evaluate the relative importance of the DST in the evolution and expression of ageing.

4. Trade-offs between reproduction and survival can be uncoupled

Despite cross-taxonomic support for the idea that competitive energy allocation between growth, survival, and reproduction can contribute to senescence, the last two decades have seen an increase in a number of studies that challenge the centrality of energy trade-offs in ageing (reviewed in [15,52–56]). For example, reproduction increases metabolism, which leads to increased generation of reactive oxygen species (ROS) that can contribute to cellular damage and senescence. However, studies in fruit flies suggest that direct experimental reduction in ROS production via mitochondrial uncoupling proteins (UCPs) extends lifespan without a concomitant decrease in fecundity or physical activity [57]. Similarly, experimental downregulation of the nutrient-sensing target-of-rapamycin (TOR) signalling pathway in Drosophila melanogaster extends lifespan both in sterile flies (via rapamycin, [58]) and in fertile flies without negative effects on reproduction (via torin, [59]).

Some of the strongest empirical evidence against the energy allocation trade-offs being the universal cause of ageing comes from experimental studies that have directly uncoupled increased longevity from reduced fecundity. For example, downregulation of the evolutionarily conserved insulin/IGF-1 signalling (IIS) pathway that shapes development, growth, reproduction, and longevity increases lifespan but can also lead to arrested development and/or reduced early-life reproduction. However, a classic study by Dillin et al. [60] showed that the negative effects of reduced IIS on reproduction and positive effects on longevity can be uncoupled depending upon when during the life course of the organism the changes to IIS occur. Early-life downregulation of IIS signalling by RNA interference knockdown of daf-2 gene expression in Caenorhabditis elegans starting at the egg stage or in early larval development extended lifespan, but resulted in reduced early-life fecundity. However, allowing the nematodes to develop normally and reach sexual maturity prior to IIS downregulation completely eliminated the negative effects of this manipulation on development and reproduction but prolonged lifespan to the same extent. This study definitively showed that while daf-2 expression underpins negative genetic correlation between reproduction and survival, this correlation can be uncoupled by precise age-specific optimization of gene function. Essentially, wild-type levels of daf-2 expression contribute to senescence and shorten the life of the worm not because of accumulation of unrepaired molecular damage starting from early life onwards, but because of the damage directly created during adulthood. Hence, optimizing gene expression in adulthood reduced damage and increased lifespan, without negative consequences for other life-history traits.

These findings prompt the question of what kind of damage might be created by suboptimal gene expression leading to suboptimal IIS function in adulthood. There is good evidence that misfolded protein aggregates that accumulate in cells with age contribute to cellular senescence, and several studies have linked reduced protein synthesis with increased longevity [61–65]. IIS signalling promotes protein synthesis [66,67] and, consequently, exceptionally long-lived daf-2 C. elegans mutants whose IIS signalling is reduced exhibit a marked reduction in translation [68]. This suggests that superfluous protein synthesis in adulthood contributes to cellular senescence and organismal death. Nevertheless, protein synthesis is not the only anabolic process controlled by IIS signalling, and recent work in C. elegans has also linked superfluous yolk production in late life to senescence [69].

There are two objections to the idea that tinkering with age-specific gene expression can postpone ageing and increase lifespan without apparent fitness costs. First, it is possible that worms with reduced IIS signalling in adulthood are underperforming across different environments. Arguing against this, IIS mutants are known to be resistant to a wide range environmental stressors and exhibit increased tolerance to heat, cold, certain pathogens, to oxidative stress caused by visible light and radiation. These studies suggest that downregulation of IIS signalling in adulthood could improve the fitness of C. elegans nematodes across a range of ecologically relevant environments, although more research is necessary to fully test this assertion. The second objection is the potential for deleterious inter-generational effects of experimentally adjusted physiology. Most studies focus on the trade-off between longevity and offspring number, but increased investment into the parental soma could come at the cost of offspring quality. There are at least two different routes through which the putative trade-off between parental longevity and offspring quality can be realized [55]. First, increased reproduction can result in reduced parental investment in terms of quantity and/or quality of resources provided by the parents to the offspring. Second, increased investment into parental soma can be traded-off with investment into germline maintenance and repair resulting in increased number of de novo germline mutations in offspring. However, recent work has showed that the offspring of parents treated with daf-2 RNAi during adulthood had higher reproduction, similar lifespan, and higher Darwinian fitness than their control counterparts [70]. Taken together, these studies suggest that wild-type IIS signalling in nematodes optimizes development at the cost of reduced survival in adulthood and reduced offspring fitness, making the wild-type daf-2 an AP allele whose late-life cost does not result directly from energy trade-offs.

5. Function trade-offs: from Williams to the developmental theory of ageing

(a) Origins of the theory

While damage accumulation resulting from energy trade-offs between reproduction and maintenance is generally viewed as the leading physiological/mechanistic explanation for the evolution and expression of ageing via AP, it is interesting that Williams himself used a very different example to illustrate the action of a putative AP allele [10]. He envisioned an allele with a beneficial effect on bone calcification in the developing organism, but that causes calcification of arteries in adulthood. Such an allele could have an overall positive effect on fitness and become established in the population. This is an example of a functional trade-off, where the same physiological process is beneficial for fitness in early-life (e.g. during development), but detrimental for fitness in late life (e.g. post-sexual maturation). Williams suggested that selection could lead to the evolution of a modifier gene to suppress excessive calcification of arteries with age, but noted that such suppression is unlikely ever to be fully effective given weak late-life selection [10].

(b) The developmental theory of ageing

Williams' ideas have been developed further in recent decades following the above logic, by explicitly linking the development of an organism to its senescence with advancing age [5,8,71–74]. In its broadest sense, the developmental theory of ageing (DTA) argues that ageing and longevity are shaped by the physiological processes that are optimized for early-life development, growth, and reproduction and are not sufficiently optimized for late-life function [8,71]. Importantly, there is a clear distinction between the classical damage accumulation paradigm as envisioned by the DST and the DTA. Damage accumulation hypotheses, including the DST, predict that increased investment in somatic maintenance will reduce cellular damage and increase longevity at the cost of reduced growth and reproduction. Because the organisms are predicted to optimize energy allocation between life-history traits to maximize fitness, experimental reallocation of energy should result in fitness costs. Contrary to this, the DTA predicts that it is possible to optimize age-specific gene expression to increase longevity without incurring costs to growth and reproduction, because longevity is curtailed by suboptimal physiology in adulthood rather than by the lack of resources for somatic maintenance. Hence, the DTA offers an explanation for the results of daf-2 studies in C. elegans, which exemplify how modification of gene expression can have negative fitness consequences when applied during development but positive when applied during adulthood [60,70]. Consistent with this, an RNAi screen of 2700 genes involved in C. elegans development identified 64 different genes that are detrimental when deactivated during development, but which extend longevity when deactivated in adulthood [75,76]. It will be instrumental for our understanding of ageing to study the fitness consequences of age-specific optimization of gene expression across a broad array of physiological processes. The decline in selection gradients with age makes it logical that ageing evolves as a combined effect of many alleles with beneficial early-life and detrimental late-life effects, precisely because weak selection struggles to fully optimize gene expression in late life.

(c) Excessive biosynthesis as a proximate cause of ageing

Recently, the concept that ageing results as a consequence of suboptimal gene function in later life has been mechanistically linked with the idea that superfluous nutrient-sensing signalling during adulthood can lead to excessive biosynthesis resulting in cellular hypertrophy, cellular senescence, and organismal senescence. The ‘hyperfunction’ idea provides a direct mechanistic mode-of-action hypothesis to explain how the continuation of a developmental programme can cause damage in late life and contribute to ageing [5,72,74,77]. In doing so, this hypothesis has moved the field forward by stimulating new studies aimed specifically at identifying the negative consequences of superfluous biosynthesis with advancing age. It has support from in vitro studies of cell cultures which suggest that, when cell proliferation is arrested, high levels of growth signalling can trigger the cells to transition from a quiescent to senescent state, while reduced growth signalling reduces cellular hypertrophy and senescence [77,78]. Cellular hyperfunction is predicted to result in organismal senescence and death, and demonstrating this link is vital for future tests of the ‘hyperfunction’ hypothesis. Most recently, Ezcurra et al. [69] showed that IIS signalling in nematodes promotes the conversion of gut biomass to yolk. While this process contributes to reproduction and is beneficial early in life, it is detrimental to the survival of ageing non-reproducing nematodes. Thus, one important factor that causes death in old worms is not an accumulation of unrepaired damage but the opposite: active yet costly conversion of gut biomass to unused yolk in the cells, resulting in intestinal atrophy and senescent obesity in old worms.

(d) Age-specific trade-offs in optimal function

The ‘hyperfunction’ hypothesis is firmly rooted in the logic of age-specific trade-offs in gene function championed by Williams [10] and further developed by Hamilton [11]. This hypothesis provides a clear and detailed physiological explanation for how excessive biosynthesis in late life can contribute to cellular and organismal senescence by linking the quantitative genetic AP theory with the proximate DTA theory via nutrient-sensing molecular signalling within cells. Nevertheless, we believe that this concept can and should be broadened further, to encompass all possible ways in which suboptimal gene expression leading to suboptimal physiology in late life can contribute to the evolution and expression of ageing. For example, excessive biosynthesis may prove to be an important physiological mechanism of senescence, but there is no reason to expect it to operate across all taxa. Just as hyperfunction of nutrient-sensing signalling seems to play an important role in the age-related demise of nematodes, other physiological processes, some running ‘too high’ and some running ‘too low’, can contribute to ageing [8,71]. Moreover, in some cases, the developmental programme can actively downregulate certain physiological processes that would be beneficial in late life. For example, heat-shock resistance is actively downregulated in C. elegans nematodes upon sexual maturation, resulting in accumulation of insoluble protein compounds in the cells leading to disrupted proteostasis and, ultimately, senescence [79]. This ‘hypofunction’ of molecular chaperones has prompted researchers to suggest that sexual maturation marks the onset of ageing in C. elegans, much in line with Hamilton's [11] predictions. Understanding which physiological processes that shape senescence are more prevalent in different taxonomic groups, and why, should be the focus of research on the biology of ageing.

6. The hidden costs of longevity

The study of the evolutionary ecology of ageing has been driven by the search for energy trade-offs between life-history traits. Above, we have emphasized the role of non-energy-based trade-offs in the evolution of ageing (see also figure 2). However, if energy-based trade-offs are not detectable in standard life-history assays, it may be fruitful to ask where the classic energy-based costs of longevity might sometimes be hidden and perhaps overlooked. First, perhaps the most obvious reason for the lack of fitness costs of lifespan extension is that fitness may often be assessed in one or two structurally simple environments. Laboratory studies using small, rapidly reproducing organisms provide a fertile ground for evaluation of the fitness consequences of genetic, environmental, or pharmacological lifespan extensions across a wide range of complex, ecologically relevant environments. There is untapped power in these systems to run controlled experiments in which animals encounter natural fluctuations of light, temperature, humidity, food and mate availability, presence of pathogens and predators across their life course, as well as across generations. While such experiments may be challenging and expensive, they are certainly feasible. Recent studies in related fields suggested that adding complexity to laboratory evolution can provide important novel insights into the evolutionary processes [80–82]. Second, fitness effects of lifespan extension are often assessed within one, and rarely two, generations. Measures of the quality of offspring, or perhaps grand-offspring, may represent an important and missing fitness component. The offspring of parents whose somatic performance has been artificially improved could experience a reduced quantity and/or quality of developmental resources, reduced parental care, or increased germline mutation rate. Such inter-generational trade-offs between parental longevity and offspring fitness have been demonstrated recently, but much more work is needed to establish whether such effects are common.

Figure 2. AP is a population genetic theory of ageing postulating that ageing evolves via alleles that have positive fitness effects early in life and negative fitness effects late in life [10]. Because the strength of age-specific selection is maximized early in life [11–13], such alleles can be beneficial and will be selected for. There are two proximate physiological mechanisms that account for AP: energy and function trade-offs between development, growth, reproduction, and survival. The DST [7,16,31] focuses on the energy trade-offs between growth, reproduction, and survival, while the DTA [8,71] focuses on gene expression optimized for development and early-life function. The relative importance of these two processes in the evolution of ageing is unknown, as most studies testing for age-specific fitness do not identify the physiological mechanisms. (Online version in colour.)

7. Integrated view of ageing: ultimate and proximate reasons

An integrated view, drawing together the proximate and ultimate concepts discussed above and building from the rapidly accumulating new knowledge is likely to revolutionize the study of ageing over the next few decades. We advocate this approach to fully evaluate the relative importance of different ageing pathways, and to use these data to distinguish between different evolutionary hypotheses (figure 2). Both energy and function trade-offs are likely to contribute to the evolution and expression of ageing across organisms. Nevertheless, it is possible and necessary to establish their relative importance in the evolution of ageing across taxa and in shaping individual differences in age-related physiological decline. Understanding and quantifying the contribution of different types of trade-offs will not only help answer why do most organisms senesce as they grow old, but also will guide the efforts in the field of applied biogerontology.

Studies of ageing in laboratory model organisms, such as yeast, nematodes, fruit flies, and mice, have provided a wealth of mechanistic detail underpinning ageing-related traits such as lifespan, fecundity, and locomotion. However, they have much less often estimated fitness consequences of experimental manipulations. More studies explicitly linking gene function with age-specific fitness are required to understand whether old animals generally die from unrepaired damage that they slowly accumulated over their lives or from newly acquired damage resulting from a rogue developmental programme.

Studies of ageing in wild populations have often focused on correlations between measures of reproductive effort and survival, without exploring the underlying physiology. For example, high early-life reproductive performance is often associated with accelerated senescence in the wild, but no study as yet has causatively linked increased reproduction to damage accumulation to senescence. However, there is scope for experimental work with vertebrate species in natural and semi-natural environments. For example, dietary restriction-mimicking compounds that downregulate nutrient-sensing signalling and prolong lifespan in laboratory studies, such as rapamycin, can be administered at different ages via food and the effects on Darwinian fitness evaluated accordingly.

8. Conclusion

Trade-offs underlie the evolution of ageing. The two proximate theories focus on either imperfect repair due to competitive energy allocation (the DST) or imperfect function due to suboptimal gene expression after sexual maturation (the DTA). While both rely on the principle of AP, they make distinct predictions with respect to the relationship between growth, reproduction, and survival. We need to understand how trade-offs work in order to distinguish whether they are primarily energy-based or function-based. Distinguishing between these mechanisms may have profound practical consequences. For example, should DTA represent the dominant paradigm, it could significantly boost our chances for increasing healthy lifespan via optimization of late-life physiology.

While many studies claim to uncouple reduced reproduction from increased survival, we need inter-generational studies assessing Darwinian fitness in realistically complex and ecologically relevant environments because selection can favour different traits in different contexts. It is unlikely that ageing in any one taxon will be entirely driven by either energy or function trade-offs, not the least because some trade-offs may involve both. However, their relative importance in the evolution of ageing across different taxa could certainly differ. For example, ageing in C. elegans might be driven primarily by suboptimal gene expression in adulthood, while ageing in mice by competitive energy allocation. At this point, we do not have the final answer even for these two extensively studied model organisms, let alone other animals. Until we know the answer across the broad range of taxonomic groups, ageing will remain an unsolved problem in biology.


The post-reproductive segment of the human lifespan is often considered to be an adaptive feature of aging, since post-reproductive women can make significant contributions to the fitness of their children and grandchildren [1,2]. Although there is published evidence for the adaptive significance of the post-reproductive life stage in humans [3], few formal empirical analyses have been conducted to conclusively demonstrate that this segment of the life history has been shaped by natural selection. Evolutionary theory predicts that an extended post-reproductive lifespan should evolve only when post-reproductive females can contribute significantly to the fitness of their offspring or relatives. Such fitness contributions can occur only when post-reproductive maternal care is required for the last young that mothers produce [4], or when animals have extended social networks allowing post-reproductive females to contribute significantly to the fitness of close relatives, as in humans [5,6]. Where such kin networks exist, any advantage of post-reproductive lifespan must also be sufficient to offset the waning force of natural selection associated with the low probability of females surviving to advanced ages [5,6]. If no such post-reproductive contribution to fitness exists, then any post-reproductive lifespan that we see should represent a variable and random segment of the life history incidental to differences in aging rates of reproductive and somatic tissues.

The phenomenon of post-reproductive lifespan is by no means limited to human females, or even to female primates. Midlife cessation of ovulation, followed by a post-reproductive lifespan of up to a third of the reported species maximum lifespan, has been reliably documented in a wide range of female vertebrates in captivity or under other favorable conditions. These species include guppies (Poecilia reticulata) [7], platyfish [8], Japanese quail [9,10,11], budgerigar “parakeets” [12], laboratory rats and mice [13,14,15], opossums[16], and primates. [17,18,19]. Most of these animals lack well-developed kin networks and engage in very limited, if any, maternal care. In some (e.g., platyfish and Japanese quail), males also exhibit midlife loss of reproductive capacity [8,20,11]. These observations suggest that, rather than being a result of kin selection, midlife fertility loss and post-reproductive lifespan may be correlates of extended lifespan under particularly favorable conditions characterized by good nutrition and unusually low rates of mortality from parasites, predators, disease, and accident.

The strongest evidence for the adaptive significance of post-reproductive lifespan in humans comes from historical, multigenerational demographic records for 18th and 19th century populations in Canada and Finland [3]. In analyses of data of this type, survival of grandmothers has been associated with improved reproductive success in their offspring. These analyses also suggest that this “grandmother effect” requires active behavioral interactions between older females and kin. The effect was detected only when grandmothers lived in the same town as kin receiving the benefit and accrued only by grandchildren that survived past weaning. The positive effect of grandmothering was present even after controlling statistically for potentially confounding factors, such as socioeconomic status, temporal trends in survival, or geography.

Comparable analyses have been performed for other species. Packer et al. [4] report on baboons and lions, both of which live in extended family groups in which grandmothers have extended interactions with kin. In both species, however, females generally live only long enough to care for their last-born offspring, and no positive effect of grandmothers' post-reproductive survival was detected. Female salmon, like lions and baboons, live long enough after spawning to enhance the fitness of their offspring. They prevent the nest site from being reused by late-arriving spawners such reuse causes a substantial reduction in the viability of the eggs [21].

We have evaluated the pre-reproductive, reproductive, and post-reproductive lifespan in guppies in relation to selective regimes shaping the evolution of their early life histories and patterns of senescence. We compared life-history traits in guppies from high- versus low-predation environments in Trinidad. Guppies from high-predation environments co-occur with predators that frequently prey on guppies, and particularly on large, adult-size classes. Guppies from low-predation environments co-occur with just a single species of fish, Rivulus hartii, that feeds on guppies only occasionally, and tends to feed on small, immature-size classes. These two types of localities are often found in the same drainage in close proximity of one another, separated by barrier waterfalls that exclude larger predators but not guppies or Rivulus.

In nature, guppies from high-predation localities sustain higher mortality rates than their counterparts from low-predation localities [22]. Furthermore, guppies from high-predation localities attain maturity at an earlier age, produce more offspring per litter, and reproduce more often than guppies from low predation localities [23,24], which is the predicted evolutionary response to higher adult mortality rates [6]. In addition, we have manipulated the mortality rates that guppies experience in nature with guppy or predator introduction experiments and have shown that these life history patterns can evolve in relatively brief intervals of time [25,26,27]. We have also documented senescence in natural populations in the form of an acceleration of mortality rate with age [28].

We compared the patterns of aging and senescence in the second laboratory-born generation of guppies from high- versus low-predation environments. We evaluated guppies from the Yarra and Oropuche drainages and included a high- and low-predation environment from each drainage, for a total of four populations. Since genetic data show that guppies adapted to these regimes independently in each locality [29,30], this approach provides duplicate studies of adaptation of guppy populations to the presence or absence of predators. Classical evolutionary senescence theory predicts that animals from high-predation environments will experience earlier or more rapid senescence than those from low-predation environments, either as a byproduct of intense selection for increased investment in reproduction early in life or because of the accumulation of deleterious mutations affecting older individuals [31,32]. We reported previously that the early life histories of high-predation guppies in this experiment were different from those from low-predation localities in the same drainage in a fashion that is consistent with our earlier comparisons [33]: They matured earlier, produced more young per litter, and reproduced more frequently. Contrary to expectation, however, we found that when guppies were reared in the lab free from predation, those from high-predation environments have lower initial mortality rates, lower mortality rates throughout their lives, and longer median lifespans [34]. Furthermore, guppies from high-predation localities have higher fecundity throughout their lives.

Here we consider in more detail why guppies from high-predation environments have longer median lifespans and, specifically, whether the length of post-reproductive lifespan varies in guppies that have evolved under different mortality regimes. Life-history theory predicts that selection will shape only segments of the life history that contribute directly to fitness or are correlated with other life-history variables. Specifically, selection should favor the evolution of an extended post-reproductive lifespan only if post-reproductive individuals can contribute to their inclusive fitness in some way, either by caring for their own young, other kin, or grand-offspring. Since guppies are livebearers and provide no parental care after birth, we predicted that there should be no direct selection for an extended post-reproductive lifespan in this species. Any post-reproductive lifespan we observe, therefore, should represent a by-product of different aging rates of different parts of the soma or, alternatively, a correlate of selection for traits—including reproductive characteristics—that are adaptive earlier in the life history.

Unlike birds and mammals, which produce the vast majority of oocytes before birth, fish are generally believed to produce new oocytes throughout their lives. For this reason, fish have been suggested to show little or no reproductive senescence [35]. The empirical evidence for such unlimited reproductive capacity, however, is based on isolated observations of very old, fertile individuals and a few small-scale laboratory studies of senescence in fish few comparative studies have directly addressed reproductive aging in fish. Here we formally evaluate, for the first time, whether or not there is reproductive senescence with an extended post-reproductive lifespan in a species of fish.


A Model for Change

In the model presented here, individuals will compete in a landscape representing the world where they live. The environment will be represented by a square two-dimensional grid with sites, so that each individual will live and compete for the resources in one of the sites. Periodic boundary conditions will be assumed, so that no boundary effects are observed. Each site will have a carrying capacity , that is, it can only sustain one individual at a time. Whenever more than individuals share the same site, they will compete for the local resources and only one will survive. Time will be measured in discrete steps, each time step corresponding to one generation, that is, the time the organisms need to produce new viable offspring. New offspring does not represent all the children of one individual, since only individuals who are at reproductive age are modeled. As such, if one species has many children and most of them die before reaching maturity, only the surviving child is described in the model and all others are assumed to have died between time steps.

While most traditional evolutionary models work with a stationary environment as basis, which can be a very good first approximation, real world conditions are not unchanging. Climatic cycles happen, predators and prey evolve together in constant evolution, new diseases appear and replace old ones. Trying to model all those aspects and how they change with time would be a daunting task, with too many yet unsolved questions, and that would also unnecessarily complicate the model. Instead of doing that, an approximation will be adopted here. This will be implemented by proposing a type of fitness function, that captures the influence of the environment and the changing conditions. Unlike fitness functions of Evolutionary Game Theory [39]–[41], the one we will use here is not exactly the final payoff of a game the individuals play. But it plays a similar role, as it is related to the likelihood an individual will survive sharing resources with a competitor.

Let be a fitness function, such that whenever there is competition between two (or more) agents in one site, the probability that agent will prevail is proportional to . Given agent and agent competing for the resources in a site at time , agent will survive with probability . The larger is, the more likely is to survive, but there will always be a chance that less fit individuals would survive (except, of course, when ).

At each time step, surviving individuals produce offspring. Each offspring is born at a distance from its parent, where is measured in units of the grid size and it inherits the fitness of its parents, except for small deviations, due to mutation. For simplicity, if organism is the parent of organism , the fitness of will be , where , with equal probabilities for the three possible results. This represents small changes. Cases where rare, large and usually detrimental mutations happen will not be included in the model here, as strong detrimental mutations would almost certainly not survive until adulthood. The mutation here only represents the fact that surviving offspring can be a little different from their parents . Also, in order to represent the fact that conditions change, is diminished by a constant value every time step, so that , where for every individual .

Some comments about the meaning of are necessary here. As described above, might represent a rate of change in the environment. However, it also plays the mathematical role of keeping the fitness from exploding due to mutation. We will see that and are related in a way that makes it very hard to define as a simple measurement of environment change. Environment change can have an impact on but an exact interpretation of its values can be misleading due to its connection with the mutation.

Two types of animals are introduced, those who die of senescence and those who will only die due to other competition. At first, both types will always start with the same values for their parameters. In the model, all organisms who suffer the effects of senescence and will die at the same programmed age, . It should be noted that, for very small values of , aging is clearly a very important disadvantage as many individuals will die too fast. On the other hand, very large values of have a very small effect, because almost no individuals would survive long enough to reach a very large . Tests for as large as show basically equal chances for both species, since so few individuals survive that much, and any aging effects become almost negligible. Therefore, we will limit the simulations to values of between and in the following Sections. In order to introduce the possibility of random deaths, disassociated from the competition, a chance that each organism will die at each time step could also be easily introduced. Preliminary tests with different values of showed little difference in the final chances of extinction for each species. Therefore, this will not be further explored in this paper.


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Each animal in the Darwinian theater is exposed to a number of abiotic and biotic risk factors causing mortality. Several of these risk factors are intimately associated with the act of energy acquisition as such and with the amount of reserve the organism has available from this acquisition for overcoming temporary distress. Because a considerable fraction of an individual’s lifetime energy acquisition is spent on somatic maintenance, there is a close link between energy expenditure on somatic maintenance and mortality risk. Here, we show, by simple life-history theory reasoning backed up by empirical cohort survivorship data, how reduction of mortality risk might be achieved by restraining allocation to somatic maintenance, which enhances lifetime fitness but results in aging. Our results predict the ubiquitous presence of senescent individuals in a highly diverse group of natural animal populations, which may display constant, increasing, or decreasing mortality with age. This suggests that allocation to somatic maintenance is primarily tuned to expected life span by stabilizing selection and is not necessarily traded against reproductive effort or other traits. Due to this ubiquitous strategy of modulating the somatic maintenance budget so as to increase fitness under natural conditions, it follows that individuals kept in protected environments with very low environmental mortality risk will have their expected life span primarily defined by somatic damage accumulation mechanisms laid down by natural selection in the wild.

A secreted signature of aging cells

Senescent cells undergo an irreversible and permanent arrest of cell division and are hallmarks of both the aging process and multiple chronic diseases. Senescent cells -- and more importantly the factors they secrete, known collectively as the senescence-associated secretory phenotype (SASP) -- are widely accepted as drivers of aging and multiple age-related diseases.

A new study publishing on January 16 in the open-access journal PLOS Biology from Drs. Nathan Basisty, Judith Campisi, Birgit Schilling (Buck Institute for Research on Aging) and colleagues extensively profiles the SASP in human cells. They show that a core secreted protein "signature" of senescent cells is enriched with aging biomarkers found in human plasma.

The study utilizes a comprehensive and unbiased technique called mass spectrometry combined with bioinformatics to develop secreted protein signatures of senescent cells. The researchers' results show that the SASP is about ten-fold more complex than is currently appreciated, allowing them to propose new signatures of senescent cells -- both 'core' signatures shared across all senescent cells and signatures that identify specific subsets of senescent cells.

Mouse studies have demonstrated that the targeted removal of senescent cells has beneficial effects on cardiac, vascular, metabolic, neurological, renal, pulmonary and musculoskeletal functions. Therefore, the selective elimination of senescent cells or inhibition of the SASP that they secrete are promising therapeutic approaches to treat age-related diseases in humans. Development of drugs that eliminate senescent cells, known as senolytics, or drugs that inhibit the SASP, known as senomorphics, requires molecular markers to assess the abundance of senescent cells. However, there are currently no simple reliable secreted biomarkers to measure the senescent cell burden in humans.

"We hope that these biomarker signatures will help us measure the burden of senescent cells in human biofluids, such as plasma, to aid the translation of senescence-targeted therapies into the clinic," says Dr Basisty. "We believe that the proteins secreted by senescent cells will also be important biomarkers for aging, neurodegenerative diseases, and other diseases marked by the presence of senescent cells."

Along with this study, the researchers launched the SASP Atlas, a curated database of proteins secreted by senescent cells. This resource can be used by others in the research community to identify proteins originating from senescent cells in their own research.