Experimental Evolution of Life- History

Digital Comprehensive Summaries of Uppsala Dissertations from the Faculty of Science and Technology 1178

Experimental Evolution of Life- history

Testing the Evolutionary Theories of Ageing

HWEI-YEN CHEN

ACTA UNIVERSITATIS UPSALIENSIS ISSN 1651-6214 ISBN 978-91-554-9034-8 UPPSALA urn:nbn:se:uu:diva-231948 2014 Dissertation presented at Uppsala University to be publicly examined in Friessalen, EBC, Norbyvägen 14, Uppsala, Tuesday, 28 October 2014 at 14:00 for the degree of Doctor of Philosophy. The examination will be conducted in English. Faculty examiner: Professor Tracey Chapman (University of East Anglia).

Abstract Chen, H.-y. 2014. Experimental Evolution of Life-history. Testing the Evolutionary Theories of Ageing. Digital Comprehensive Summaries of Uppsala Dissertations from the Faculty of Science and Technology 1178. 43 pp. Uppsala: Acta Universitatis Upsaliensis. ISBN 978-91-554-9034-8.

Ageing reduces fitness, but how ageing evolves is still unclear. Evolutionary theory of ageing hinges on the fundamental principal that the force of natural selection declines with age. This principle has yielded two important predictions: 1) the evolution of faster ageing in populations under high rate of extrinsic mortality; and 2) the evolution of faster ageing in a sex that experiences higher rates of extrinsic mortality. However, an emerging new theory argues that when the extrinsic mortality is not random but instead selects on traits showing positive genetic correlation with lifespan, increased mortality should lead to the evolution of increased lifespan. Such condition-dependent mortality is also expected to increase the robustness in the population, resulting in increased deceleration of mortality in late-life. Similarly, high sex-specific mortality can result in increased sex-specific selection on traits that have positive pleiotropic effects on lifespan in the affected sex. This thesis is based on two experimental evolution studies in Caenorhabditis remanei. The first experiment was designed to disentangle the effects of the rate (high or low) and the source (random or condition-dependent) of mortality on the evolution of lifespan and ageing. Reduced lifespan evolved under high rate of random mortality, whereas high condition-dependent mortality, imposed by heat-shock, led to the evolution of increased lifespan (Paper I). However, while female reproduction increased under condition- dependent mortality, male reproduction suffered, suggesting a role for sexual antagonism in maintaining genetic variation for fitness (Paper II). Besides, long lifespan and high fecundity evolved at a cost of slow juvenile growth rate in females (Paper III). Moreover, high condition- dependent mortality led to the evolution of lower rate of intrinsic mortality in late-life (Paper IV). The second experiment showed that evolution of sexual dimorphism in lifespan is driven by the factors that cause sex-specific mortality and cannot be predicted from differences in mortality rate alone. Specifically, high condition-dependent mortality renders males less prone to ageing than females despite higher rates of male mortality (Paper V). The strength of this thesis is the reconfirmation of the earlier findings combined with support for the new theory. Rather than further complicating the matter, the inclusion of the new ideas should help explain some empirical results that are inconsistent with the classic theory, as well as provide a more comprehensive picture of ageing evolution.

Keywords: senescence, ageing, longevity, mortality, experimental evolution, Caenorhabditis remanei

Hwei-yen Chen, Department of Ecology and Genetics, Animal ecology, Norbyvägen 18 D, Uppsala University, SE-752 36 Uppsala, Sweden.

ISSN 1651-6214 ISBN 978-91-554-9034-8 urn:nbn:se:uu:diva-231948 (http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-231948) List of Papers

This thesis is based on the following papers, which are referred to in the text by their Roman numerals.

I Chen, H-y. and Maklakov, A. A. (2012) Longer life span evolves under high rates of condition-dependent mortality. Cur- rent Biology, 22(22): 2140–2143 II Chen, H-y.*, Spagopoulou, F.*, and Maklakov, A. A. Evolu- tion of male reproductive ageing under differential risks and causes of death. Submitted manuscript. III Lind, M. I., Chen, H-y., Guevara Gil, A. C., Zwoinska M. K. and Maklakov, A. A. Slow development as an evolutionary cost of long life. Submitted manuscript. IV Chen, H-y., Zajitschek, F., and Maklakov, A. A. (2013) Why ageing stops: heterogeneity explains late-life mortality deceleration in nematodes. Biology Letters, 9(5): 20130217 V Chen, H-y. and Maklakov, A. A. (2014) Condition dependence of male mortality drives the evolution of sex differences in longevity. Current Biology, in press.

* Shared first authorship

Reprints were made with permission from the respective publishers.

The following papers were written during the course of my doctoral studies but are not part of the present dissertation.

Chen, H-y. and Maklakov, A. A. 2013. The worm that lived: Evolution of rapid aging under high extrinsic mortality revisited. Worm 2(3): e23704.

Rogell, B., Chen, H-y., Uebbing, S. and Maklakov, A. A. Bridging the gap between evolutionary and molecular causes of ageing in a nematode. In preparation.

Contents

Introduction ...... 7 Why do organisms age? ...... 7 Why do organisms stop ageing? ...... 12 Why do sexes differ in ageing? ...... 13 Materials and Methods ...... 15 Caenorhabditis remanei ...... 15 Experimental evolution ...... 15 Lifespan assay, fitness assay, and growth rate assay ...... 20 Statistical analysis ...... 21 Results and discussion ...... 23 Evolution of ageing ...... 23 Cessation of ageing ...... 27 Sex differences in ageing ...... 28 Conclusions and future perspectives ...... 29 Svensk Sammanfattning ...... 31 Acknowledgement (and fun) ...... 35 References ...... 39

Introduction

Why do organisms age? Ageing, a physiological deterioration that decreases fertility and increases the probability of death with advancing age, reduces Darwinian fitness but afflicts nearly all living beings (Finch 1990; Hughes and Reynolds 2005). Given that natural selection has shaped such enormous complexity in meta- zoans, as well as the large number of repair mechanisms, the failure of simply maintaining what is already formed is indeed remarkable (Williams 1957). Evolutionary theories of ageing strive to explain this paradox, as well as the extensive variation in lifespan among species (Rose 1991; Hughes and Reynolds 2005). The fundamental idea that lies behind is that the strength of natural selection declines with age – organisms are inevitably exposed to various extrinsic hazards, either biotic or abiotic, so that few individuals can survive to old age and contribute to future generations (Medawar 1952; Wil- liams 1957; Hamilton 1966). This decline has been proposed to result in two genetic processes that contribute to ageing: mutation accumulation, suggest- ed by Peter Medawar in 1952 (Medawar 1952), and antagonistic pleiotropy theory, put forward by George Williams in 1957 (Williams 1957). In mutation accumulation scenario, ageing arises because selection cannot effectively weed out deleterious mutations whose effects are confined to old ages (Medawar 1952). On the other hand, antagonistic pleiotropy theory involves trade-off between early- versus late-life fitness – genes that increase fitness in early-life will be favoured by selection even if they have a pleiotropic cost in late-life (Williams 1957). The central prediction used to test Medawar-Williams theories is that increased extrinsic mortality should lead to the evolution of more rapid ageing and shorter intrinsic lifespan, because high rate of extrinsic mortality should leave a wider selection “shadow” in late-life (Williams et al. 2006). This prediction has been employed in comparative, field, and laboratory studies. In comparative studies, for example, the longer lifespan observed in birds, bats, and arboreal mammals has been attributed to their ability to fly or climb trees, which protects them from predators and unfavourable environ- ments (Calder 1983; Holmes and Austad 1995; Shattuck and Williams 2010). Similarly, field studies suggest that the variation in mortality risk among habitats can contribute to variation in ageing and lifespan (Dudycha 2001).

7 Two testable corollaries were further derived by Edney and Gill (1968) – because ageing evolution depends on the strength of age-specific selection, it should be possible to postpone ageing by increasing selection at later ages, and to accelerate ageing by shorten effective lifespan. Both corollaries have been tested and generally supported by laboratory selection studies. Briefly, three independent sets of selection lines in three different Drosophila species showed that ageing can be postponed when selection strength was made to be greater at later ages by delaying the age of reproduction (Luckinbill et al. 1984; Rose 1984; Wattiaux 1968a,b). On the other hand, evolution of accelerated ageing was tested in Tribolium beetles (Sokal 1970; Mertz 1975) as well as in Drosophila flies (Mueller 1987) by “truncating” effective lifespan and increasing selection on early reproduction. Although the results were not as clear as postponing ageing, perhaps because of potential false positives created by inbreeding depression, early reproduction could indeed result in the evolution of accelerated ageing. The strongest and most recent support comes from an experimental evolution study done by Stearns et al. in 2000. The experimental design is mostly closely related to the theory – replicated populations of Drosophila melanogaster were kept under either high or low adult mortality (HAM or LAM, respectively). After many generations, HAM lines indeed evolved increased intrinsic mortality, shorter lifespan, and a shift in peak fecundity to an earlier age, compared to LAM lines.

Condition-dependent mortality and the evolution of ageing However, despite its dominant role and overwhelming acceptance, Meda- war-Williams prediction has been recently challenged on theoretical grounds, and a closer examination of the evidence opens room for alternative interpretation (Abrams 1993; Williams and Day 2003; Abrams 2004; Wil- liams et al. 2006). For example, in Stearns et al. experiments, although the rate of extrinsic mortality was strictly controlled, the mortality was applied haphazardly (Stearns et al. 2000); therefore the surviving cohort was more or less a random sample of the previous generation. This is arguably rather uncommon in nature, where the main source of mortality is likely to involve selection – either by biotic agents (e.g. predation or infectious disease) or by environmental stressors (e.g. increased temperature or humidity) (Williams et al. 2006). Selection is likely to be non-random and result in changes in genetic composition; for example, heat-shock could select against susceptibility to increased temperature. Importantly, when the trait under selection is positively linked with organism’s general condition, non-random mortality can lead to the evolution of physiological robustness (Abrams 2004). More broadly, it can be argued that individuals in more robust condition are more likely to survive most types of extrinsic mortality hazards (Williams and Day 2003; Abrams 2004; Williams et al. 2006), and strong non-random, condition-dependent mortality can thus alter the evolution of ageing through the

8 genetic correlation between the focal trait and lifespan. For example, increased lifespan in birds and bats can be alternatively interpreted as an evolutionary by-product of selection on aerobic efficiency (Lane 2011) – high aerobic demand requires highly integrated mitochondrial and nuclear ge- nomes with low free-radical leak, which is one of the potential proximate mechanisms of ageing (Hekimi et al. 2011). The theoretical model of the effects of condition-dependent mortality on the evolution of ageing predicts a variety of outcomes, including a scenario where increased mortality selects for decreased physiological deterioration and slower intrinsic ageing (Williams and Day 2003). Nevertheless, direct experimental test of the role of condition-dependent mortality in the evolution of ageing was lacking until Paper I. Prior to this work, the best support came from a study in guppies (Poecilia reticulata), which showed that high predation risk was associated with longer lifespan in the natural populations (Reznick et al. 2004). However, without a strict control of population size and density, explanations alternative to condition-dependence could not be excluded (Reznick et al. 2004). In Paper I, I directly tested the role of condition-dependent mortality by creating four experimental evolution regimes. Replicated populations of the dioecious nematode Caenorhabditis remanei were subjected to either high or low rate of extrinsic mortality, within which half of the populations experienced condition-dependent mortality imposed by heat-shock, while the other half experienced random extrinsic mortality. This experimental scheme simultaneously tested the Medawar-Williams prediction (i.e. evolution of faster ageing under high extrinsic mortality rate) and the role of condition- dependence (i.e. evolution of slower ageing under high condition-dependent extrinsic mortality). After 12 generations of experimental evolution, two distinct trajectories of lifespan evolution were observed. First, in line with the Medawar- Williams prediction, when the extrinsic mortality was applied as a random hazard, worms evolved shorter lifespan under high rate of mortality. By contrast, when the same rates of mortality were applied using heat-shock, increased mortality resulted in the evolution of prolonged lifespan. The general pattern observed in random mortality regime agreed with the classic life-history trade-off that reduced lifespan evolves as a cost of increased fecundity under high extrinsic mortality. Surprisingly, evolution of increased lifespan under high condition-dependent mortality was achieved without a trade-off with reproduction, because high condition-dependent mortality lines enjoyed the same level of female lifetime reproduction as high random mortality lines. In fact, high condition-dependent mortality lines had higher fecundity that control lines.

9 The worm who lived: in search of trade-offs under condition- environment interactions Williams and Day’s models predict that condition-environment interactions can lead to slower ageing in some cases; however, the reproductive performance and overall fitness of the population is not expected to increase under increased mortality. The absence of the survival cost of reproduction observed under condition-dependent mortality implies that reduced lifespan is not an inescapable result of increased investment in reproduction. Neverthe- less, selection for increased physiological robustness may cause trade-offs with other aspects of fitness, for example, with juvenile survival or growth rate. Besides, since sexes often have different optimal phenotypic values for fitness but sex-specific optimization is constrained by the shared genome, trade-offs may be manifested in a sex-specific way, such that increased adaptation in one trait is positively linked with fitness in one sex but negatively in the opposite sex. Since my original study focused only on female reproduction, it was possible that males are paying the cost of increased lifespan. Paper II and Paper III aimed to explore potential evolutionary costs of increased lifespan, stress resistance and female fecundity in C. remanei populations evolving under high condition-dependent mortality. Specifically, Paper II focused on sex-specificity of trade-offs, and Paper III looked at the trade-offs with pre-adult life-history traits.

Condition-dependent mortality and sex-specific pleiotropy Classic theories of ageing evolution focused on trade-offs between early- versus late-life fitness (Williams 1957). Theories predict that late survival should be sacrificed under high rate of extrinsic mortality. However, the life- history pattern observed in condition-dependent mortality regime in Experi- mental Evolution I contradicted this prediction – increased mortality under heat-shock resulted in the evolution of prolonged lifespan with no corresponding reduction in female fecundity. Besides, selection under heat stress also led to rapid evolution in resistance to increased ambient temperature – post heat-shock survival increased significantly in five generations, particularly in high mortality treatment regime (Chen and Maklakov 2013). Simultaneous increase in resistance, longevity and reproduction was un- anticipated and was not necessarily predicted by Williams and Day’s original model. Moreover, the results raised a question of the maintenance of genetic variation for fitness in the original population, which the selection lines were derived from. Several explanations were discussed in Paper I. In Paper II, I examined the role of sexually antagonistic selection resulting in intra-locus sexual conflict (IaSC). IaSC arises because sexes often have different optima in life-history traits due to anisogamy and sexual selection (Parker et al. 1972; Trivers 1972; Parker 1979; Rice 1984; Rice and Chip- pindale 2001; Arnqvist et al. 2003). Generally, males benefit from increasing

10 investment in early reproduction, whereas females maximize fitness by pre- serving energy for future reproduction (Bonduriansky et al. 2008; Adler and Bonduriansky 2014). Nevertheless, sex-specific optimization can be constrained by intersexual genetic correlation due to the shared genome (Lande 1980; Bonduriansky and Chenoweth 2009). Shared genes are likely to produce sexually antagonistic fitness effects, since they are expected to produce similar phenotypic values in the sexes even when the phenotype is positively associated with fitness in one sex and negatively in the opposite sex. Genetic variation for sexually antagonistic genes is thus maintained at a polymorphic compromise by their opposing fitness effects between sexes. Paper II focused on the evolution of male reproductive ageing using experimental lines derived from Experimental Evolution I. These experimental lines differ in their evolutionary history (high or low rate of extrinsic mortality imposed by stress (heat-shock) or as a random hazard) and, as a result, in a number of traits such as lifespan, stress resistance, and female fecundity, thus providing a great opportunity to study the evolution of sex-specific trade-off under condition-environment interactions. Paper II aimed to an- swer the following questions: 1) Do males evolve increased early-life reproduction under increased rate of extrinsic mortality? 2) Does condition- dependence of extrinsic mortality affect the evolution of reproduction in males? 3) Is there a trade-off between resistance to heat shock and fertility in males? 4) Can sexually antagonistic selection explain high genetic variation for female fitness found in these populations?

Slow development as an evolutionary cost of long lifespan Life-history theory deals with the question of how natural selection shapes major life events of an organism so as to maximize its genetic representation in future generations. Major life-history traits are often negatively correlated with each other, such that increased fitness in one trait evolves at the cost of decreased fitness in another trait (Stearns 1992). Several lines of mutually compatible and possibly inter-connected theories have pointed to a trade-off between growth and lifespan, suggesting that long lifespan may evolve at a cost of slow growth rate (and vice versa). Be- cause growth consumes a substantial portion of available resources in a finite resource pool (Wieser 1994), increased growth will allocate away energy that could have been otherwise invested in other physiological functions, such as reproduction or immunity (Yearsley et al. 2004; Mangel and Munch 2005). Besides, increased growth rate is associated with higher level of oxi- dative stress, reduced maintenance, and more rapid telomere shortening. These findings imply that fast-growing individuals may sacrifice physiological integrity for growth rate, such that they grow more rapidly but the increased growth rate leads to a ‘jerry-built’ body that is more susceptible to metabolic damages (Metcalfe and Monaghan 2003; Dmitriew 2011). Be- sides, an emerging theory suggests that ageing evolves because it shares the

11 same genetic program with developmental growth; and as this program con- tinues to run at full capacity after sexual maturation, it becomes detrimental in later ages (Blagosklonny 2012; Gems and Partridge 2013). A key prediction here is that the evolution of increased lifespan will be linked with slow juvenile growth rate. Indeed, negative relationship between growth rate and lifespan has been found in artificial selection studies in fruit flies (Lints and Soliman 1977) and mice (Eklund and Bradford 1977), as well as in experimental studies in lizards (Olsson and Shine 2002) and sticklebacks (Lee et al. 2013). The higher rate of ageing in large dog breeds might also be attributed to artificial selection on increased growth rate (Kraus et al. 2013). Most importantly, selection for early reproduction often results in decreased lifespan and increased growth rate (Gasser et al. 2000); therefore, it is possible that lifespan evolution observed in some classic studies was caused by accelerated growth (Gems and Partridge 2013). Paper I showed that condition-dependence of mortality could lead to increased lifespan under high rate of extrinsic mortality without associated cost of reproduction; however, correlated evolution in other life-history traits are yet to be ex- plored. If increased lifespan evolves at a cost of slower growth, then the trade-off between lifespan and growth rate should result in prolonged development and/or reduced body size at sexual maturity in long-lived selection lines. Paper III examined these pre-adult life-history traits and the role of growth in the evolution of ageing and lifespan.

Why do organisms stop ageing? The process of ageing is often described as an exponential increase in age- specific mortality rate with age (Charlesworth and Partridge 1997). Howev- er, in many taxa, ranging from flies to humans, the increase in mortality in early- to mid-life decelerates or even ceases in very late ages, forming a distinct mortality “plateau” (Carey et al. 1992; Curtsinger et al. 1992; Charles- worth and Partridge 1997; Vaupel et al. 1998). Currently, there are two major sets of theories explaining the evolution of late-life mortality deceleration: heterogeneity theory suggests that the deceleration arises from variation in robustness among individuals in a population (Vaupel et al. 1979), while so-called Hamiltonian theory proposes that mortality deceleration is a conse- quence of the diminishing strength of natural selection with age (Mueller et al. 2011). Individuals in a population differ in many ways, some of which are readi- ly quantifiable, such as body size; others are less easily understood, such as fitness or “robustness”. Robustness is a lifelong intrinsic property that de- termines an individual’s likelihood of survival – less robust individuals are more likely to die (Vaupel et al. 1979). Mortality deceleration can arise because selection removes less-robust individuals when they are young, leav-

12 ing more robust individuals who survive selection to define the mortality rate of the population in late ages (Carey et al. 1992; Curtsinger et al. 1992; Brooks et al. 1994; Vaupel 2010). Although robustness involves both genetic and environmental components, at least some versions of heterogeneity theory predict that selection for physiological robustness should alter the rate and/or the timing of onset of mortality deceleration (Steinsaltz 2005). Hamilton’s formal analysis of senescence suggests that the strength of natural selection continuously declines from the age of first reproduction (Hamilton 1966). When organisms stop reproducing, selection strength eventually declines to zero and thus can no longer distinguish differences in fitness among post-reproductive age classes. Age-specific mortality therefore decelerates in parallel with selection strength (Rauser et al. 2009). Therefore, mortality deceleration should evolve in accordance with the rate of extrinsic mortality, such that increased extrinsic mortality should lead to earlier deceleration of mortality (Mueller et al. 2011). The effect of different sources of mortality (e.g. random or condition-dependent) is beyond the scope of this scenario – populations should evolve similar mortality deceleration when the rate of extrinsic mortality, and hence the strength of age- specific selection, is held constant (Rauser et al. 2009). Paper IV aimed to solve this puzzle. In the first experimental evolution study, independent manipulation of the source and the rate of extrinsic mortality enabled direct testing of these two contemporary theories of late-life mortality deceleration. Heterogeneity theory predicts that extrinsic mortality source should affect the evolution of mortality deceleration, as condition- dependent mortality should lead to increased robustness. On the other hand, Hamiltonian theory predicts that mortality deceleration should be solely determined by the rate of extrinsic mortality, such that increased extrinsic mortality should result in earlier onset of deceleration.

Why do sexes differ in ageing? Males and females age at different rates and have different longevities across the animal kingdom; for example, in reindeer, female life expectancy can be more than twice as long as male life expectancy (Clutton-Brock and Isvaran 2007). However, what causes the sex-difference in lifespan remains one of the unsolved problems in evolutionary biology. Males, in most species, are more exposed to extrinsic mortality factors than females (Comfort 1979; Finch 1990; Promislow and Harvey 1990; Promislow 1992; Vieira et al. 2000). Sexual selection often drives the evolution of costly male traits, for example, exaggerated sexual advertisement and increased same-sex fighting (Promislow 1992; Promislow et al. 1992; Promislow et al. 1994; Liker and Szekely 2005; Kruger and Nesse 2006; Clutton-Brock and Isvaran 2007; Nussey et al. 2009). Such male-biased ex-

13 trinsic mortality should result in the evolution of more rapid ageing in males in comparison to females (Williams 1957; Trivers 1985; Promislow 1992; Carranza et al. 2004; Clutton-Brock and Isvaran 2007). However, despite being widely accepted, a direct experimental test of this prediction – that increased sex-specific mortality leads to the evolution of shorter intrinsic lifespan in the focal sex – is still lacking. Moreover, as condition-dependent mortality can select for more robust individuals and therefore alter the trajectory of the evolution of ageing (Paper I), sexual selection acting on male condition and whole-organism performance (Lailvaux and Irschick 2006) may favour alleles with positive pleiotropic effects on lifespan and somatic maintenance (Bonduriansky et al. 2008; Maklakov and Lummaa 2013). Paper V provides the first experimental examination that simultaneously tests two distinct mechanisms that can contribute to the evolution of sexually dimorphic life-histories: 1) increased sex-biased random mortality resulting in the evolution of reduced lifespan in the affected sex; and 2) increased sex- biased condition-dependent mortality leading to the evolution of prolonged lifespan in the affected sex. In Paper V, replicated populations of an obli- gately sexually reproducing nematode C. remanei were subjected to male- biased extrinsic mortality rates, with male-specific mortality induced either as a random hazard (i.e. testing mechanism 1) or in a condition-dependent manner (i.e. testing mechanism 2). Paper V demonstrates that sex-difference in lifespan evolves depending on the factors that cause sex-specific mortality (i.e. whether mortality is random or condition-dependent) and cannot be predicted from the mortality rate alone, reconciling the finding in Paper I and further emphasizing the key role of condition-dependent mortality in the evolution of ageing.

14 Materials and Methods

Caenorhabditis remanei C. remanei is small, free-living nematode worm commonly found in decom- posing fruits, woods or other plant material, where it feeds on microbes, such as bacteria. Life cycle of C. remanei features four juvenile stages (L1 ~ L4) with one alternative dauer stage, followed by an adult stage. Occasionally, C. remanei is found as dauers associated with small terrestrial invertebrates, such as isopods, beetles or snails (Baird 1999; Kiontke 2006; Félix and Braendle 2010). Caenorhabditis nematodes exhibit two major sex systems, dioecious (having males and females) and hermaphroditic (having males and hermaph- rodites, which can reproduce by self-fertilizing). Dioecious species experi- ence stronger sexual selection than hermaphroditic species (LaMunyon and Wanless 1999; Artieri et al. 2008). Besides, dioecious species are more robust in terms of resistance to environmental stresses and immunity (Amrit et al. 2010). Mating system also correlates with sexual dimorphism in lifespan – sex-difference in lifespan is more pronounced in dioecious species than in hermaphroditic species (McCulloch and Gems 2003). In most Caenorhabdi- tis species, males are the longer-lived sex (McCulloch and Gems 2003). The small size (~ 1mm) and short generation time (3~4 days) make C. remanei an excellent system for experimental evolution. Like its famous cousin C. elegans, C. remanei can be easily maintained in laboratories on agar plates seeded with a lawn of Escherichia coli (Stiernagle 2006). Be- sides, C. remanei is a dioecious species and harbors high level of genetic diversity (Cutter et al. 2006b), which further facilitates experimental evolution studies. All experiments in this thesis used the wild-type strain SP8 of C. remanei, provided by N. Timmermeyer from the Animal Evolutionary Ecology group in Tuebingen University in Germany. SP8 is a genetically heterogeneous strain created by crossing three C. remanei strains that were isolated from different locations: SB146 isolated from Freiburg in Germany, MY31 isolated from Tuebingen in Germany, and PB206 isolated from Ohio in the United States.

Experimental evolution Experimental evolution is concerned with observing evolutionary processes experimentally in real time: a series of replicated experimental populations,

15 typically derived from one base population (‘ancestral’), is subjected to a novel condition planned by the experimenter, and the independent evolutionary changes occurring in these populations after multiple generations of exposure are measured (Futuyma and Bennett 2011). Experimental evolution is sometimes described as ‘laboratory natural selection’, but experimental evolution study can also be conducted in the field (Kawecki et al. 2012), although such study should involve extensive a priori knowledge of the environment of a study site. Notably, experimental evolution is most suitably used as a method to test evolutionary hypothesis (what can happen), and should not be used to prove past evolutionary history or predict future evolutionary direction (what did happen or what will happen) (Futuyma and Bennett 2011). However, with appropriate design, experimental evolution can be informative to infer the evolution in the wild (what might have happened or what is likely to happen). The power of experimental evolution lies in the replication of evolutionary responses and the control over the environment where evolution occurs. By replicating populations exposed to the predefined environment, the experimenter can thus quantify the consistency of evolutionary outcome associated with the novel condition (Futuyma and Bennett 2011). Experimental evolution studies the evolutionary outcomes associated with the predefined environmental change. Therefore, the results are best evaluat- ed if the ancestral condition is known. In systems where populations can be preserved alive but prevented from evolving (e.g. seeds in dormant stage; nematodes cryo-preserved in glycerol), ancestral population and the respective evolved populations can be assayed simultaneously, thus the observed evolutionary changes can be directly attributed to the treatment (Kawecki et al. 2012). However, when preservation is not possible, a control treatment regime should be included. Intuitively, control treatment can be an ‘unse- lected’ regime, in which no selection is applied. Alternatively, control treatment can involve selection, with the degree of environmental change being strictly defined and controlled (Kawecki et al. 2012).

Experimental Evolution I Experiment Evolution I aimed to disentangle the effect of extrinsic mortality source on the evolution of ageing from the effect of extrinsic mortality rate. In Experimental Evolution I, total 16 experimental populations were subjected to either high or low rate of extrinsic mortality (High or Low, H or L), within which half of the populations experienced condition-dependent mortality (Condition-dependent, C-d), whereas the other half experienced random mortality hazards (Random, R), for total 12 generations (Figure 1). This 2 × 2 design resulted in four life-history regimes: 1) High Random mortality (HR); 2) Low Random mortality (LR); 3) High Condition-dependent (HC-d) mortality imposed exposure to increased temperature and 4) Low Condition- dependent (LC-d) mortality imposed by exposure to increased temperature

16 (four populations per regime). The first and second regimes (HR and LR) enabled to test the classic Medawar-Williams’ prediction for accelerated ageing under increased mortality. At the same time, the third and fourth regimes (HC-d and LC-d) directly tested the effect of increased extrinsic mortality imposed by an environmental hazard in a condition-dependent manner on the evolution of intrinsic lifespan and actuarial ageing. The strength of the experimental design is that, by maintaining the same mortality rates across Random and Condition-dependent mortality regimes, the four regimes served as control for each other, thus eliminates the need of an ancestral ‘control’ regime. For example, HR and HC-d populations differed only in the source of mortality, but their mortality rate matched with each other. Heat-shock was used to impose condition-dependent mortality. As soil- dwellers, Caenorhabditis nematodes naturally encounter variable tempera- tures and have evolved both adaptation to local temperature and physiological plasticity to changes in ambient temperature (Cutter et al. 2006a). Caeno- rhabditis nematodes are known to be thermotolerant (Kiontke 2006), and resistance to increased temperature is associated with longer lifespan (Yang and Wilson 2000; Gainutdinov et al. 2007), better survival and immunity (Amrit et al. 2010). Indeed, exposure to heat shock increases the proportion of “robust” individuals in the population (Yashin et al. 2002). Moreover, the so-called heat shock proteins (HSPs) that are responsible for thermotolerance are in fact generally involved in physiological processes mediating defense to a wide range of stresses (Sørensen et al. 2003).

total 12 transfers

heat−shock C−d 90% death per transfer

random R 90% death per transfer

heat−shock C−d 10% death per transfer

random R 10% death per transfer

Figure 1 Experimental design of Experimental Evolution I

17 Experimental Evolution II Experimental Evolution II aimed to test two mechanisms that can contribute to the evolution of sexual dimorphism in lifespan: 1) increased sex-specific mortality rate that is predicted to result in the evolution of shorter lifespan in the affected sex; and 2) increased sex-specific condition-dependent mortality leading to selection on whole-organism performance and the evolution of prolonged lifespan in the affected sex. See Figure 2 for experimental design. To simplify the issue, males were selected as the focal sex; however in principal either (or both) of the sexes can be studied. In total, eight experimental populations were subjected to male-biased extrinsic mortality rate (80% death in males v.s. 50% death in females per generation, for total 20 generations). Then, a crucial distinction was made with regard to male- specific mortality: in half of the populations, males experienced condition- dependent mortality (Condition-dependent, C-d); in the other half of the populations, males experienced random mortality (Random, R). Female- specific mortality was applied as random hazard in both regimes. Experimental Evolution II employs a “time machine” approach to study the evolution of sex-difference in lifespan (Stiernagle 2006). By taking ad- vantage of Caenorhabditis nematodes’ cryo-preservability, evolved populations after 20 bouts of experimental evolution (Evolved, E) were compared to their respective ancestral populations that were cryo-preserved at generation one prior to the experimental evolution (Ancestral, A). This approach allows contemporaneous lifespan assays in the evolved as well as ancestral populations but prevents the ancestral populations from evolving, thus ena- bling direct quantification of the independent evolutionary responses in each of the replicated populations (Kawecki et al. 2012). For example, the degree of sexual dimorphism in lifespan in AR and in ER populations tests the classic prediction that faster sex-specific ageing should evolve under sex-biased mortality. Condition-dependent mortality was imposed on male chemotaxis during mate searching, which incorporates locomotor activity and chemosensory ability. Experimental males and female pheromone source were placed at the opposing long ends of a rectangular slide of agar, and mortality was induced by selecting the first 20% of males that arrived at the pheromone spot. Ran- dom mortality was induced haphazardly.

18 generation 1 generation 20

selection selection 20% death 20% death

C−d

ancestral evolved (cryo−preserved) (cryo−preserved)

random random 20% death 20% death

ancestral evolved (cryo−preserved) (cryo−preserved)

Figure 2 Experimental design of Experimental Evolution II

19 Lifespan assay, fitness assay, and growth rate assay All assays were conducted after the experimental evolution followed by 2-3 generations without selection to reduce potential environmental effects.

Lifespan assay Replicates of same-sex age-synchronized worms derived from experimental populations were maintained as normally reproducing cohorts with corresponding number of background ‘tester’ worms of the opposite sex derived from the base SP8 population. Lifespan of experimental individuals was recorded every three or every four days. Worms that failed to respond to gentle prodding by a soft worm picker were considered as dead. Alive worms, together with corresponding number of background worms, were transferred to fresh medium after scoring to avoid mixing with progeny. The measurement continued until the last individual died. Lifespan was measured in both sexes in Experimental I and II.

Female fitness assay Focal females derived from the experimental populations were kept with corresponding number of sexually matured background ‘tester’ males from the base SP8 population, of which the number was adjusted throughout the assay to maintain 1:1 sex ratio. Every other day following establishment, females were transferred to fresh medium with food, and were left on the medium to lay eggs for three hours. The assay continued until the last female died, and these three-hour snapshots were added up as an index of female lifetime fecundity. Female fitness was assayed in Experimental Evolution I.

Male fitness assay This assay was conducted on an individual basis for each male throughout the male’s lifespan, from day one of adulthood until death. Every three days, one focal male was paired with five sexually matured virgin ‘tester’ females derived from SP8 population for three hours, followed by three hours of egg- laying after the focal male was removed. The number of eggs produced by the five females within this three-hour egg-laying period was used as an estimation of the focal male’s age-specific fertility. On the days when the assay was not performed, each of the males was paired with one mature SP8 ‘holding’ female to reduce males’ escaping tendency and to standardize their mating status. λind, an individual-based, rate-sensitive estimation of absolute fitness which combines lifetime fertility and reproductive schedule (McGraw and Caswell 1996; Brommer et al. 2002; Metcalf and Pavard 2007), was calcu- lated for each of the males. Essentially, λind is an analogous measurement of intrinsic population growth rate derived from Euler-Lotka equation using age-structured projection matrices: for individual i, fitness λind is the largest

20 -1 root of Σ(fx)(lx)(λind) = 1, where fx is fertility at age x and lx is survival at age x (either 1 or 0). Male fitness was assayed in Experimental Evolution I.

Growth rate assay Growth rate assay focused on the development time to and the body size at sexual maturity of females. Sexual maturity of females can be easily determined by the presence of a mating plug, secreted by its mating partners, on the side of the body. For each of the replicated populations, age- synchronized eggs were derived by collecting eggs laid by 10 mated females within 1 hr. The eggs hatched and developed to L4 larval stage after 54 hrs. Male L4 larvae were then removed and replaced by the same number of sexually matured males from SP8 populations. These plates were monitored every hour from 59 hours after egg laying. For every female, the development time from egg to sexual maturity and the size at sexual maturity was measured.

Statistical analysis In general, lifespan, fitness data and growth rate were analyzed using general linear mixed-effect model (GLMM) with restricted maximum likelihood approach (REML) with relevant factors and interactions (e.g. mortality source, mortality rate, sex, experimental history, age and age at last reproduction) included as fixed effects, and individual and/or population (when appropriate) nested within treatment regimes included as random effects. GLMM analysis was conducted using the R programming language. Mortality data were fitted to the Gompertz family of nested models (Gompertz, Gompertz-Makeham, Logistic and Logistic-Makehem) using maximum-likelihood approach implemented in WinModest software bx (Pletcher 1998). The Gompertz model, µx = ae , describes the exponential increasing in mortality with age, where µx is the age-specific mortality at age x, a is the baseline mortality rate and b is the exponential increase in mortali- bx bx ty with age (rate-of-senescence). Logistic models, µx = ae /[1 + (as/b)(e - 1)], account for the deceleration of the rate of mortality in late-life (s). Lo- gistic models are reduced to Gompertz model when such deceleration equals to zero. Finally, Makeham models include an additional constant defining age-independent mortality; for example, parameter c in Gompertz-Makeham bx model, µx = c + ae . Comparisons of parameters was done by fitting general linear model to individual parameters derived from each population, or by comparing the log likelihoods (LLs) of constrained models (null hypothesis; where the parameter estimates were constrained to be the same), with uncon- strained models (alternative hypothesis; where all parameter values were allowed to vary). The significance was estimated by using the value of -

21 2 2(LLnull hypothesis – LLalternative hypothesis) with X distribution and degrees of free- dom equal to the number of constraint parameters (here, 1). To estimate the inflection points of late-life mortality deceleration (i.e. when the exponential increase in age-specific mortality stopped), 5000 replicates of mortality parameter estimates were resampled by randomly sam- pling 95% of the original mortality data without replacement. These estimates were used to calculate the inflection points (as the mean of the 5000 inflection points of curves fitted to the re-sampled data) and their 95% con- fidence intervals, which were then fitted by a general linear model.

22 Results and discussion

Evolution of ageing

Condition-dependent mortality and the evolution of ageing Paper I corroborates the hypothesis that condition-dependence alters the evolution of life-history traits in response to extrinsic mortality. Lifespan evolution was significantly modified by mortality source under different mortality rates, resulting in source by rate interaction (Figure 3). The lifespan pattern observed under random mortality regimes agrees with the classic prediction that high mortality leads to shorter lifespan – populations exposed to high rate of mortality evolved reduced lifespan compared to populations exposed to low rate of mortality (HR v.s. LR; Figure 3). Conversely, condition-dependent selection reversed the trajectory of lifespan evolution, resulting in longer lifespan under increased extrinsic mortality (HC-d v.s. LC-d; Figure 3). This contrasting pattern emphasizes the significance of condition-dependence on lifespan evolution; for example, HC-d populations outlived HR populations by ~20%, despite experiencing the same rate of extrinsic mortality. 15 Condition−dependent Random 14 13 12 Lifespan (days) Lifespan 11 10 Low High Mortality Figure 3 Evolution of lifespan under differential life-histories

Mean lifespan (± SE) of worms derived from high or low mortality rate regimes when the mortality source was either condition-dependent (black) or random (grey).

23 The extension in lifespan was not a result of trade-off with reproduction, as females from long-lived line showed high level of fecundity (Figure 4) – there was a significant effect of mortality rate with high mortality populations being more fecund, but no effect of mortality source. 350 Condition−dependent Random 300 250 Female Fecundity Female 200 150 Low High Mortality Figure 4 Evolution of female fecundity under differential life-histories

Mean lifetime fecundity index (± SE) of females derived from high or low mortality rate regimes when the mortality source was either condition-dependent (black) or random (grey).

24 Condition-dependent mortality and sex-specific pleiotropy Paper II showed that male reproductive ageing trades-off with resistance to heat-shock but not with lifespan per se; for example, while LR and LC-d populations had similar lifespan (Figure 3), LR males showed greater early fertility than stress-resistant LC-d males (Figure 5). Thus, evolution under heat-shock had a strong impact on the evolution of male reproduction, espe- cially reproduction in early ages (Figure 5). It is important to note that there was no difference in male fertility between LC-d and HC-d lines suggesting that even relatively small increase in heat-shock resistance results in a tradeoff with male fertility. In summary, selection imposed by heat-shock resulted in better thermoresistance in both sexes (Chen and Maklakov 2013) and an increase in female fecundity (Figure 4), but also in a reduction in male early fertility, suggesting an intra-locus sexual conflict over the optimal resolution of a trade-off between stress resistance and reproduction. 100 80 60 specific fertility − 40 Age

Condition−dependent Random 20 1 4 7 10 13 Age (Days)

Figure 5 Male reproductive ageing

Mean (± SE) age-specific fertility of males evolved under condition-dependent mortality (black) or random (grey) mortality.

25 Slow development as an evolutionary cost of long life Paper III showed that condition-dependent mortality by heat-shock led to the evolution of longer development time compared to random mortality (Figure 6). There was no interaction between mortality source and mortality rate, although HC-d populations had the highest absolute values for development time. HC-d populations also had the smallest absolute body size at sexual maturity, however no significant difference was found between selection regimes. 0.042 ) 2 m 0.040 m 0.038 0.036 Female size ( size Female

0.034 HC−d LC−d HR LR 0.032 2.80 2.85 2.90 2.95 Female development time (days)

Figure 6 Development time under differential risks and causes of mortality.

Development time and size at sexual maturity of females from random (grey) or condition dependent mortality regime (black). High mortality rate is designated by closed circles and low rate by crossed circles.

26 Cessation of ageing The role of mortality source and mortality rate in shaping the late-life mortality deceleration can be tested by pair wise comparisons of the deceleration in mortality (s) between experimental regimes with (i) the same rate but different sources (heterogeneity theory), or (ii) the same source but different rates of extrinsic mortality (Hamiltonian theory). In the first set of comparisons, the deceleration was more rapid in C-d regimes than in R regimes in both sexes when the mortality rate was high, and in males when the rate was low (LLR test; P < 0.05). Contrarily, in the second set of comparisons, all pairs except for one (HC-d v.s. LC-d females) showed the same deceleration in mortality (LLR test; P > 0.05). The results were the same when using GLMM on parameters derived from population-based analyses (Figure 7). Condition-dependent mortality also resulted in earlier onset of mortality deceleration in both sexes (P = 0.013), while there was no effect of extrinsic mortality rate on inflection points. These results, summarized in Paper IV, support the theory that late-life mortality deceleration arises from compositional heterogeneity of robustness among individuals in a populations. Populations selected for increased robustness under heat-shock showed an earlier and more rapid deceleration of mortality in late ages than populations exposed to random mortality, despite experiencing the same rate of extrinsic mortality.

3.0 Condition−dependent Random 2.5 2.0 1.5 1.0 life deceleration (s) deceleration life − 0.5 Late 0.0 Low High Mortality rate

Figure 7 Evolution of late-life mortality deceleration (s)

The effect of extrinsic mortality rate and extrinsic mortality source (condition- dependent: black; random: grey) on intrinsic mortality deceleration (mean ± SE).

27 Sex differences in ageing Experimental Evolution II created replicate populations that evolved under identical rate of male-specific mortality but differed in male-specific selection (random (R) v.s. condition-dependent (C-d)). A “time machine” approach was used to compare sex-specific longevities in the evolved populations to their own ancestral populations cryo-preserved at generation one. While female lifespan did not change in both regimes, male lifespan evolved in the opposing directions in R and C-d populations (Figure 8). Un- der random extrinsic mortality, male-biased mortality resulted in the evolution of reduced lifespan in males (Figure. 8). Conversely, when mortality rate was increased in a condition-dependent fashion, males evolved prolonged intrinsic lifespan (Figure. 8) thereby widening the lifespan gap between the sexes. Paper V clearly illustrates how variation in sex-limited mortality affects the evolution of lifespan and shapes sexual dimorphism in this trait. The classic prediction that sex-biased extrinsic mortality leads to shorter lifespan in the affected sex is supported by the lifespan pattern observed in random mortality regimes – the evolution of monomorphism in lifespan as a result of increased mortality in the longer-lived sex. By contrast, males experiencing to condition-dependent mortality evolved longer lifespan despite higher rate of mortality. In the absence of the correlated response in females, increase in male lifespan led to increased sexual dimorphism in this trait.

19 Condition−dependent:Male Condition−dependent:Female Random:Male Random:Female 18 17 16 Longevity (days) Longevity 15 14 Ancestral Evolved History

Figure 8 Evolution of sex-difference in lifespan

Mean lifespan (± SE) of males (circles) and females (triangles) evolving under condition-dependent (black) or random (grey) extrinsic mortality measured contempo- raneously in ancestral (generation 1) and evolved (generation 20) populations.

28 Conclusions and future perspectives

Senescence evolves because the strength of selection declines with age. Be- cause all organisms eventually succumb to extrinsic hazards (e.g. starvation, predation or parasitism), old age classes are left under “selection shadow”: mutations that reduce fitness in late ages are not seen by selection and can accumulate, limiting the intrinsic lifespan of the organisms. Nevertheless, mortality in nature is often non-random; therefore, the sur- vivors can represent a non-random sample of the initial cohort. Medawar- Williams theories describe how ageing evolves under increased or decreased extrinsic mortality, but these theories do not take into account how the extrinsic mortality is imposed. Nature is a highly competitive place, where survival typically depends on resistance to the agents of mortality (e.g. predators, parasites, or adverse abiotic conditions). Increased environmental hazard can favour increased performance and reduced senescence in physiological traits that affect the susceptibility to this hazard. When such physiological traits are positively linked with general condition (defined as a total pool of resources that can be allocated to reproduction and somatic maintenance), such condition-dependent mortality can lead to the evolution of more phys- iologically robust organisms and increased survival. This thesis demonstrates that evolution of ageing is tightly associated with the source of mortality and that condition-dependent selection can re- verse the classic scenario, resulting in the evolution of more robust individuals with prolonged lifespan under increased mortality rates (Figure 3). More- over, condition-dependence of mortality also has profound influence on the deceleration of late-life mortality rates (“mortality plateaus”) (Figure 7), as well as the evolution of sexual dimorphism in ageing (Figure 8). The evolution of ageing in response to trait-specific mortality depends on the covariation between the trait in question and lifespan (Williams and Day 2003). More broadly speaking, selection on one trait can affect the evolution of other trait(s) through the underlying genetic correlation. When the correlation is positive, increase in trait-specific mortality should promote the evolution of robustness in the correlated trait(s). On the other hand, when the correlation is negative, improved performance in one trait will evolve at a cost of performance in the other. This implies that the overall fitness does not necessarily increase under condition-dependent selection, because decreased fitness should be observed in traits that are negatively correlated with the

29 trait under selection or with lifespan. For example, the evolution of long lifespan can be associated with slow development at juvenile stage. Besides, males and females are often constrained in achieving their sex- specific life-history optimization because they share most of their genome – what is best for females may be suboptimal for males. An increase in the phenotypic value of a trait may benefit one sex but at the same time decrease fitness of the opposite sex. Therefore, the improved physiological robustness and/or lifespan may result in different fitness consequences in the two sexes. For example, an increase in heat resistance may be associated with increased fitness in females but be detrimental for males. Nevertheless, while some traits are characterized by high intersexual genetic correlation, other traits exhibit substantial amount of sex-specific genetic variation, which facilitates the evolution of sexual dimorphism in response to sex-specific selection. Experimental Evolution I showed that lifespan evolved in response to selection on heat-shock resistance in both sexes (Figure 3); meanwhile, increased female fecundity and decreased male fertility were associated with strong thermoresistance (Figure 5). In Experimental Evolution II, male lifespan and mating success increased under male-specific selection on whole-organism performance – locomotion during mate search; however, lifespan of females did not respond via intersexual genetic correlation, possibly because heritability of lifespan is largely sex-limited (Lehtovaara et al. 2013) (Figure 8). If we want to predict the evolutionary response in lifespan following an increase in extrinsic mortality rate in a species, or in a particular sex of the species, we have to consider the source of mortality – Does mortality impose selection on a particular trait? Is expression of this trait condition- dependent? Is this trait genetically correlated with lifespan? What is the ex- tent of intersexual genetic correlation for this trait? The strength of this thesis is the reconfirmation of the earlier findings combined with support for the new theory. Although the inclusion of the new ideas may appear to make things more complex, it should provide a more comprehensive picture of ageing evolution. Moreover, the new theory may in fact help explaining some of the empirical results that are inconsistent with the classic theory. Future studies should focus on testing the effect of different ecologically relevant mortality sources, either biotic or abiotic, on the evolution of age-specific life-histories in a variety of model and non-model organisms.

30 Svensk Sammanfattning

Ingen av oss blir yngre – vi får alla rynkor och grått hår, blir känsligare för sjukdomar och får dessutom högre risk att få funktionshinder ju äldre vi blir. Ur ett biologiskt perspektiv definieras åldrande som den fysiologiska för- sämringen som leder till lägre fertilitet och högre risk att avlida vid ökad ålder. Givetvis är åldrande inte fördelaktigt. Givet hur enormt komplicerade våra kroppar är, och den stora mängd repareringsmekanismer som kroppen har utvecklat, kan det tyckas anmärkningsvärt att evolutionen helt enkelt misslyckas med att bibehålla något som redan har formats. Varför åldras vi? Evolutionsbiologer har länge sökt den underliggande förklaringen. Flera orsaker finns, och den första av dem är mutationer. Mu- tationer är en naturlig process som förändrar DNA-koden i vår arvsmassa. Mutationer sker i en konstant takt, men de flesta är skadliga för bäraren. Mutationernas effekt kan dock vara åldersbunden: medan vissa mutationer enbart visar sig hos unga individer kan andra mutationer ha en effekt som uppträder sent i livet. Den andra faktorn är det naturliga urvalet (selektionen). Man kan beskriva det naturliga urvalet som en ”mutations-sil”, som silar bort skadliga mutationer och låter gynnsamma mutationer öka i frekvens. Det naturliga urvalet är dock inte konstant genom hela livet, utan dess styrka minskar med ökad ålder. Eftersom organismer ofrånkomligen exponeras för ett antal yttre mortalitetsfaktorer såsom sjukdomar, predation och parasitism så är det få individer som överlever till hög ålder och kan bidra till framtida generationer. Ju större den yttre mortalitetsrisken är, desto färre individer överlever och desto snabbare minskar styrkan hos det naturliga urvalet. Kon- sekvensen är att de äldre åldersklasserna är evolutionärt sett mindre viktiga än de yngre i sitt genetiska bidrag till framtida generationer. Sammantaget så är mutationer med negativa effekter som enbart uttrycks sent i livet dolda för det naturliga urvalet, och kan öka i populationen. Med ett stort antal sådana mutationer ackumulerade, kommer populationen att åldras pga. mutationernas effekt. Detta brukar kallas inneboende åldrande, dvs. att populationen försämras av sig själv utan påverkan av externa mortalitetsfaktorer. Hastig- heten hos det inneboende åldrandet är därför beroende på det naturliga urvalets styrka – det åldersspecifika inneboende åldrandet ökar exponentiellt med selektionens minskande styrka med ökad ålder. Vid riktigt hög ålder har dock selektionen helt försvunnit, och då förväntas ökningen i inneboende åldrande plana ut.

31 Det ovan beskrivna scenariot kallas mutationsackumulerings-scenariot för evolutionen av åldrande, det föreslogs 1952 av Peter Medawar som en av de viktiga genetiska processerna som ligger bakom åldrande. Ett annat scenario kallas antagonistisk pleiotropi, och föreslogs 1957 av George Williams. En- ligt det här scenariot beskrivs åldrandet som ett resultat av en kompromiss mellan fitness tidigt och sent i livet. Eftersom de äldre årsklasserna har en mindre effekt på fitness kommer mutationer som ökar fitness tidigt i livet öka i frekvens, även om de har negativa effekter sent i livet. Flera viktiga förutsägelser kan härledas ur de här scenarierna: 1) Ökad mortalitet leder till en snabbare minskning av det naturliga urvalets styrka, ett större åldersspann kommer då att vara dolt för selektionen och därför kommer ett snabbare åldrande att evolvera. 2) Eftersom populationens inneboende åldersspecifika mortalitet kommer att öka och minska parallellt med styrkan hos det naturliga urvalet, kommer en tidigare minskning av den inneboende dödligheten evolvera under en ökad mortalitetsrisk. 3) Om risken att dö av yttre faktorer skiljer sig åt för de två könen, kommer det kön som har störst mortalitetsrisk att evolvera ett snabbare åldrande. Nya teoretiska modeller har dock föreslagit att om den yttre mortaliteten inte drabbar individer slumpmässigt utan istället selekterar på egenskaper som är positivt korrelerade med livslängd, så kommer ökad yttre mortalitet istället leda till evolution av en längre livslängd. Exempelvis kan den yttre mortaliteten orsakas av predation, som selekterar på bytesdjurets snabbhet, och om snabbhet också är korrelerat med lång livslängd så kommer ett ökat predationstryck att leda till att förmågan att springa snabbt evolverar i bytes- populationen, men därmed också en längre livslängd. Generellt kan man säga att en sådan konditionsberoende (tillståndsberoende) mortalitet kommer att gynna en ökad fysik robusthet, och mer robusta individer har större san- nolikhet att klara av de flesta sorters yttre mortalitetsfaktorer. Den här teorin ger ett antal tydliga förutsägelser, till exempel att ökad konditionsberoende mortalitet kommer att leda till längre livslängd, och att hög könsspecifik mortalitet kan leda till könsspecifik selektion på hela organismens kondition som korrelerar positivt med livslängd i det påverkade könet. Dessutom bör proportionen robusta individer öka i en population som utsätts för tillstånds- beroende mortalitet. Eftersom robusthet är direkt kopplat till en individs chans att överleva, kan ökad robusthet i en population påverka dess ålders- specifika mortalitetsmönster, som till exempel mortaliteten sent i livet. Den här avhandlingen baseras på två selektionsexperiment i Caenor- habditis remanei, en art av nematoder (rundmaskar) med både hanar och honor. Det första experimentet var ämnat att separera effekten av mortalitets- intensitet (hög eller låg) och mortalitetskälla (slumpmässig eller konditionsberoende) på evolutionen av livslängd och åldrande. Efter experimentell evolution i 12 generationer utvecklades en kortare livslängd i behandlingen med hög intensitet av slumpmässig mortalitet. Om mortaliteten istället var konditionsberoende, orsakad av värmechock, så evolverade istället en längre

32 livslängd (Kapitel 1). De här resultaten följde de klassiska förutsägelserna, och bekräftade samtidigt att konditionsberoende mortalitet förändrar hur yttre mortalitet påverkar evolutionen av åldrande. Dessutom påverkade kon- ditionsberoendet även sänkningen av den inneboende mortaliteten: populat- ioner som evolverat med intensiv konditionsberoende mortalitet uppvisade en tidigare påbörjan av den inneboende mortalitetsminskningen sent i livet, vilket tyder på att konditionsberoende mortalitet resulterade i en ökning av den övergripande robustheten (Kapitel V). Vid evolution av ökad livslängd förväntar man sig en minskad reproduktion, men överraskande nog ökade istället reproduktionen i de långlivade populationerna som var utsatta för hög konditionsberoende mortalitet så att den var lika hög som i de kortlivade populationerna som evolverat under hög slumpmässig mortalitet. Det tyder på att den förlängda livslängden under konditionsberoende selektion inte var ett resultat av en avvägning mellan livslängd och reproduktion, utan att kostnaden med att evolvera ett långt liv låg nog annanstans. I två uppföljnings- experiment undersöktes den potentiella kostnaden med långt liv, Kapitel II handlar om könsspecifika avvägningar, medan Kapitel III fokuserar på en egenskap under uppväxten. I Kapitel II fann vi att även om honorna hade en ökad reproduktion, så betalade hanarna priset genom en lägre fertilitet tidigt i livet, vilket resulterade i en lägre reproduktion sett över hela livet. Det be- tyder att det finns en avvägning mellan livslängd och reproduktion, men att den avvägningen kan vara könsspecifik. I Kapitel III undersökte vi juvenil tillväxt och storlek vid könsmognad, och vi fann att konditionsberoende mortalitet som drabbade vuxna individer resulterade i en längre utvecklings- tid till könsmognad, och individerna som evolverat under intensiv konditionsberoende mortalitet hade också en mindre storlek vid könsmognad. Därmed kunde vi visa att en långsammare juvenil tillväxt var det evolution- ära priset som betalades för ett långt liv. Vi fortsatte sedan ned till den mole- kylära nivån. Vi undersökte genuttrycket hos de selekterade linjerna, och fann att genuttrycket var associerat med viktiga livshistorie-egenskaper och var välbalanserat mellan gener involverade i reproduktion, livslängd och stress-resistans (Kapitel IV). Det andra selektionsexperimentet fokuserade på evolutionen av könsskillnader i livslängd under slumpmässig eller konditionsberoende könsspecifik mortalitet. Vi ökade mortaliteten hos hanarna genom att selektera på förflyttningsförmåga och kemosensorisk förmåga (”doftsinne”) på antingen ett konditionsberoende eller slumpmässigt sätt. Resultatet visade att könsskillnaden i livslängd (hanar lever normalt längst) beror på orsaken till mortalitet och inte om det är hög eller låg dödlighet: hög konditionsberoende mortalitet resulterade i evolution av hanar som åld- rades mindre än honorna, trots den högre intensiteten av mortalitet hos hanarna (Paper VI).

Acknowledgement (and fun)

This is probably the first part (and possibly the only part) of my entire thesis that you read, but it was the last thing I wrote, simply because there are too many people I would like to thank! Of course, first I would like to express my never-ending gratitude to my supervisor Alexei Maklakov. Alex has been such a great support during my years in Uppsala. None of the work in this thesis could be done without him. Alex has taught me so much about science and about being a scientist – from designing an experiment to analysis, and from writing up the results to sub- mission. Alex is the best illustration of an enthusiastic scientist who loves science and enjoys it. To me, he is not only the greatest supervisor, but also a special and important mentor and friend. Thank you for your encourage- ment, your support, and your understanding along the way. My deepest thank to my nematode pals in the lab – Martyna, my first lab fellow, thank you for all the discussions and inspirations! You have no idea how happy I was when you joined the lab. Thank you for keeping me ac- companied during the weekends and the late evenings in the lab or in the office (and sometimes in the gym too)! To Foteini, I still remember the nights we spent together in the lab. Now I think about it, it was fun, but I’m sure this wasn’t exactly the way we felt when doing the work. Thank you for staying with me, you’re the one who made all the difficult work possible! Martin, the toughest guy in the lab. You have stimulated me to become less afraid and more interested in equations – which opens a new path for me. I’ve enjoyed the discussions with you very much, and thank you for helping out with the abstract! Anna and Mara “Wonderful”, thank you for keeping the lab energetic! I felt very lost after you both left Uppsala, but I’m sure we will see each other at some point. I would like to thank Elisabeth for being the greatest office mate, who of- fers sophisticated scientific discussion along with delicious chocolate! Noth- ing can be better than that! Thank you for all your help, big and small, and for listening to my complaints and for all the little afternoon chatting. I also want to thank Felix for helping me with statistical analysis in Paper IV. Thank you for your warmest concerning for me, I feel very supported and touched when seeing your email. To Elena and her fluffy cat Cooper, I miss you so much! Elena, you had always been bringing positive atmosphere to the group. And Cooper – me,

35 Ding and our little cat Luna all miss you very, very much! You’re such a fluffy firend. We all love you. Thank Bjorn for taking care of the difficult part of the gene expression paper. I feel guilty when I see you looking lost and tired, and I hope this will be worked out soon! There are so many people who have worked in the lab that I would like to thank for their support and help – Margo, Katja, Maria, David, and many more! Karoline, practically my third supervisor – thank you for teaching me so much about nematodes, and for cheering me up when I was down. If it wasn’t for you I could have easily spent two years fighting with agar plates and ended up with nothing. It was you who made this thesis become true! Cosima and Frederik, the loveliest couple I’ve ever met. Thank you both for being my greatest music partners, and for your delicious Austrian and German cuisines! Special thank to Cosima – I had so much fun hanging out with you! We had fika and went for berry picking (and were cooking jam until two in the morning!). Now with your baby boy Jacob we might have to slightly change our plan, but certainly we will keep in touch! Zooekologi people – Goran, Karl, Julieta, Leanne, Isobel, Johanna, Muri- elle, Masahito, Mats, Eryn, Mirjam, Josefin, Biao, Reija, Gunilla, Anders, just to name a few, and Urban, Jochen and Ted – thank you all for standing with me alone the time during my PhD studies! Throughout my PhD I have came across quite a few people at various occasions, and now is my opporturnity to thank them! To Jennifer and Rose from Eugene, it was great to have met you at the conferences! Thank you for sharing your nematode “technologies” with me. And to Nadine, thank you for sending me SP8! These wonderful worms have literally built up our research work. To Andrea from BMC, thank you for letting me visit your lab and for the E. coli strains you shared. I would also like to thank Russell for your inspiring discussion on Paper II, your comments and suggestions are very helpful! Special thank to Professor Koos Boomsma in Copenhagen, Denmark, who offered me an opportunity to work on those amazing leaf-cutting ants, and the Happy Digging Team in Panama! It was my first fieldwork in my life (hopefully not the last), and it was great! I honestly have never been like that dirty ever, but I had so much fun. Thank Guo-jie, Sarah, Ryan, Rachelle, Joanito, and all the hardcore ant people there! I would also like to thank SystBio people for letting me parasitize your fika room and events. I’ve met many great friends there! Yoshiaki, the Guitar Hero, the Superstar! Thank you for playing for us on various occasions, particularly on the wedding party! I hope we will see you and Leanne in Japan one day! To my Taiwanese friends – thank you all for supporting me during my Uppsala days! Lillian, Michael, and my friend-beyond-age-difference Tere-

36 sa, you guys are the most loving family, and it feels like you are already my family! Thank Lillian for feeding me with your tasty Taiwanese food to re- lieve my homesickness; thank Michael for your insightful discussion and your Tai-chi sessions! Thank you Teresa – we communicate without words, and I know you love me too! Thank Leo, Alicia, Frank, Nancy, Christ, Kai-chun, Ying-hsiu, Guei-bao, Hsin-ho, Yu-ting, Reverb, Ching-en, Leanne, Rita, Kevin and Yifan – you have made my Uppsala days so much more fun! I will always remember those boats we built together for Valborg, the BBQs we had on those chilly days, the crazy shopping trips we went together, and all the gaming evenings! Last but certainly not the least – Ding and Luna, my beloved family. You have been my source of strength and happiness. I couldn’t imagine how my life would have been without you! Ding always knows when I need a cup of coffee or a bit of chocolate after a full day of lab work, and no matter what happens he supports me with no judgment. Thank you for staying with me for the past years! Luna, you fuzzy little cutie! Thank you for keeping my knees warm in the evenings. But I have to say – it would be great if you can stop attacking my toes.

Now, the fun part…

A few metaphors were implemented in the cover picture, but none of the (few) people I asked actually grasped the ideas. So here is your try: 1. Find the keynote pattern that themed this thesis. 2. Interpret the meaning of the pattern. 3. Apparently there are many worms, larvae and eggs in the cover picture. So, what is your impression of nematode work?

Answers are on the next page!

38 References

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43 Acta Universitatis Upsaliensis Digital Comprehensive Summaries of Uppsala Dissertations from the Faculty of Science and Technology 1178 Editor: The Dean of the Faculty of Science and Technology

A doctoral dissertation from the Faculty of Science and Technology, Uppsala University, is usually a summary of a number of papers. A few copies of the complete dissertation are kept at major Swedish research libraries, while the summary alone is distributed internationally through the series Digital Comprehensive Summaries of Uppsala Dissertations from the Faculty of Science and Technology. (Prior to January, 2005, the series was published under the title “Comprehensive Summaries of Uppsala Dissertations from the Faculty of Science and Technology”.)

ACTA UNIVERSITATIS UPSALIENSIS Distribution: publications.uu.se UPPSALA urn:nbn:se:uu:diva-231948 2014