Molecules, divergence times and the evolution of life-histories

Transcription

1 Molecules, divergence times and the evolution of life-histories Nicolas Lartillot, Raphael Poujol, Frederic Delsuc September 2010 Nicolas Lartillot (Universite de Montréal) Life history evolution September / 23

2 The molecular clock ρ = µ f 0 substitution rate = mutation rate fraction of neutral mutations Kimura 1982, after Pauling and Zuckerkandl 1962

3 Variations of the substitution rate AARDVARK GOLDENMOLE TENRECID ELEPHANTULUS MACROSCELIDES ELEPHANT HYRAX SIRENIAN ANTEATER SLOTH ARMADILLO CAT DOG PANGOLIN FLYINGFOX PHYLLOSTOMID HORSE RHINO TAPIR COW DELPHINOID WHALE HIPPO PIG LLAMA HEDGEHOG SHREW MOLE CAVIOMORPH MOUSE RAT SCIURID PIKA RABBIT FLYINGLEMUR HUMAN LEMUR TREESHREW 0.1 subs per site concatenation of 13 nuclear genes, 38 placentals

4 Variations of the substitution rate Generation-time effect time = AARDVARK GOLDENMOLE TENRECID ELEPHANTULUS MACROSCELIDES ELEPHANT HYRAX SIRENIAN ANTEATER SLOTH ARMADILLO CAT DOG PANGOLIN FLYINGFOX PHYLLOSTOMID HORSE RHINO TAPIR COW DELPHINOID WHALE HIPPO PIG LLAMA HEDGEHOG SHREW MOLE CAVIOMORPH MOUSE RAT SCIURID PIKA RABBIT FLYINGLEMUR HUMAN LEMUR TREESHREW 0.1 subs per site concatenation of 13 nuclear genes, 38 placentals

5 Salmon 5.2 Introduction Percent sequence divergence is estimated from restriction fragment length polymorphism (RFLP) analysis of mtdna. Mya, million years ago; Myr, million years. *C. S. Baker and S.R.P., unpublished data. The metabolic rate hypothesis available. Regression analyses show that both are SUPPLEMENT highly for metabolic rate versus P = for generation correlated with the silent rate. However, in a multiple regression inofvalidation the silent rate studies versusof generation aroundtime 3% and [seemeta- variables are highly correlated, the analysis suggests that the time). Although multiple regression can be a weak tool when respirometry bolic rate, only the latter had a significant 13 coefficient (P = n Speakman (17)]. For individuals, it provides a value apparent relationship of silent rate to generation time may be an artifact. tility because the precision of the estimate is lower, 101 For the cytochrome b data in Fig. 1B, neither generation h because this is the only reliable method for estimaty energy1-. demands, such individual estimates are gen- silent rate by either parametric or nonparametric tests. How- time nor metabolic rate shows a significant correlation with referableato having nothing at all. ever, generation time and metabolic rate together yield a key question in the context of the current discussion significant multiple regression analysis (R2 = 0.673, F = 0 nks between energetics and aging is whether resting 6.187, P = 0.035), indicating that both factors may be playing 100 important roles in a synergistic fashion. ism provides 8 a reasonable proxy for daily rates of In other cases, inspections of molecular rate data do not expenditure. This would be the case, for example, if follow the predictions of the generation time hypothesis. For ntributed cn a substantial amount to the total expendiudies that have documented both resting metabolism relative to primates despite their shorter generation time (10). example, whales have a slow rate of nuclear DNA evolution ly energy expenditure are available for 73 species of Rates of single-copy DNA evolution are slower in marsupials 10, than in placental mammals independant of generation time ammal (weighing kg), allowing 0 evaluation of this 4 (ref. 8; but see ref. 43). Substitution rates in nuclear ribosomal genes are 8-fold slower in salamanders than in mam on [reviewed in Speakman (19)]. 10These data10indicate average in small mammals RMRMass contributes (k) 35% of mals (44), despite their shorter generation times. l DEE as For mtdna rates, Hasegawa and Kishino (12) failed to fmd FIG. estimated 2. Relationship by DLW between [n 73 rate of(19)]. mtdnathis is a sequence diver- of the (% total, change Martin per butmillion and it is not years) Palumbia and majority. body size 1993 Hence, (in kg) forin various tution rate. Furthermore, the silent rate in shark mtdnas is FIGURE an association 2 Relationship between generation between time mass and specific mtdna daily substi-energy expenditure measured by portiongence vertebrates. Data are from Table 2. 1, Mice; 2, dogs; 3, humanchimpanzee; 4, horses; 5, bears; 6, geese; 7, Speakman the doubly labeled 1995 water method and body evaluating the utility of RMR as a proxy for DEE this 5-7 times whales; slower than 8, in newts; 9, mass [data derived from original primatesdata or ungulates reviewed despite in Speakman broadly (19)]. ion clearly frogs; indicates 10, tortoise; that 11, it salmon; is deficient. 12, sea turtles; However, 13, sharks. thisboxes similar ranges of generation times for the three groups (14). t be therepresent only the range of rates and body sizes for a given taxon. Solid Similarly, mtdna divergence rates of newts and frogs, whose smaller criterion for body, evaluating higher the usefulness mass-specific of lines are a measure of drawn totaltoenergy pass through metabolism. the boxes. Dashed For line example, represents the generation metabolic times are on the order of 3-5 years (44), are slower hypothesis of rate constancy. than those of primates (34). Rates of mtdna evolution are ight also be useful, despite having a low absolute between DEE and body mass. As an overall trend, therefore, mutagenic effect of metabolism: tion to DEE, if there was a fixed ratio between the describing higher the nature substitution of energetics scaling rate at the interspecific R might contribute only 35% of the total, but if it was level RMR does capture the essence of the relationship between total in daily mitochondrial energy demands (by DNA DLW) and body mass. It 5%, then effect a relationship should betweenbe RMR more and life pronounced span alidly reflect a relationship between DEE and life span. is important to remember, however, that at the level of individual species RMR does not function quite so advantageously. ct, ratios between RMR and DEE vary from 1.4 to 8.0 maximum frequency at 2.6 RMR (Fig. 1). RMR Because the relationship between DEE and body mass has Nicolas Lartillot (Universite de Montréal) Life history theevolution obverse exponent of the relationship September between 2010 longevity 4 / 23

6 Introduction The longevity G Model (and mass) hypothesis MAD S. Someya, T.A. Prolla / Mechanisms of Ageing and Development xxx (2010) xxx xxx 5 Someya and Prolla 2010 Fig. 2. Potential role of mitochondrial apoptosis in aging of long-lived cells. During aging, mitochondrial ROS production steadily increases, leading to DNA damage and the activation of a p53-mediated transcriptional response. p53 transcriptional targets include pro-apoptotic genes such as Bak and Bax. p53 also directly triggers mitochondrial apoptosis by binding to and promoting the oligomerization of pro-apoptotic Bak protein in the outer mitochondrial membrane. Chronic activation of this pathway is likely to negatively impact tissues dependent on non-regenerating long-lived cells, such as the cochlea, brain, and heart. 330 An important conclusion derived from our studies of the role of Cheng, A.G., Cunningham, L.L., Rubel, E.W., Mechanisms of hair cell death and 362 protection. Curr. Opin. Otolaryngol. Head Neck Surg. 13, ROS and mitochondrial apoptosis in AHL is that cells may not need Chinnery, P.F., Elliott, C., Green, G.R., Rees, A., Coulthard, A., Turnbull, D.M., Griffiths, to be irreversibly damaged by ROS in order to enter the T.D., The spectrum of hearing loss due to mitochondrial DNA defects mitochondrial apoptotic program. This key conclusion is supported Brain 123 (Pt 1), by the observation that Bak / Culmsee, C., Mattson, M.P., p53 in neuronal apoptosis. Biochem. Biophys. Res mice do not display cochlear cell Commun. 331, loss and display normal hearing at middle age, despite the fact that Darrat, I., Ahmad, N., Seidman, K., Seidman, M.D., Auditory research involving larger body, 336 theselonger animals have no evidence life: of reduced higher ROS (Someya et al., risks antioxidants. of Curr. Opin. somatic Otolaryngol. Head Neck Surg. mutations , Derin, A., Agirdir, B., Derin, N., Dinc, O., Guney, K., Ozcaglar, H., Kilincarslan, S., ). Presumably, the level of ROS that is produced in cochlear The effects of L-carnitine on presbyacusis in the rat model. Clin. Otolaryngol cells during aging is sufficient to trigger the Bak-mediated Allied Sci. 29, apoptotic program, but not sufficient to impair cellular function. Deschauer, M., Muller, T., Wieser, T., Schulte-Mattler, W., Kornhuber, M., Zierz, S., Hearing impairment is common in various phenotypes of the mitochon- DNA 340 Thus, the cell loss associated with AHL is an active process that can 375 selection for lower mutation rate indrial larger A3243G mutation. Arch. animals Neurol. 58, be blocked by Bak inhibition, in the absence of deleterious effects Dirks, A.J., Hofer, T., Marzetti, E., Pahor, M., Leeuwenburgh, C., Mitochondrial to the target tissue. If this paradigm is applicable to other tissues DNA mutations, energy metabolism and apoptosis in aging muscle. Ageing Res impacted by cell loss during aging, a significant component of the Rev. 5, Dirks, A.J., Leeuwenburgh, C., Aging and lifelong calorie restriction result in effect should 344 aging process bemaymore pharmacologically pronounced blocked by improving the adaptations inof skeletal mitochondrial muscle apoptosis repressor, apoptosis-inducing factor, DNA mitochondrial antioxidant defense system and by blocking X-linked inhibitor of apoptosis, caspase-3, and caspase-12. Free Radic. Biol mitochondrial apoptosis. Med. 36, Edwards, M.G., Anderson, R.M., Yuan, M., Kendziorski, C.M., Weindruch, R., Prolla, 384 T.A., Gene expression profiling of aging reveals activation of a p References mediated transcriptional program. BMC Genomics 8, Erster, S., Mihara, M., Kim, R.H., Petrenko, O.,Moll, U.M., In vivo mitochondrial 387 Nicolas Lartillot (Universite 348 de Administration Montréal) Aging, A.o., Administration on Aging, Life Aging history Statistics Web evolution p53 translocation triggers a rapid first wave of cell death in response to DNA September / 23 NCORRECTED PROOF

7 Introduction The nearly-neutral model (Ohta, 1972, Kimura, 1979) fit unfit 1/N 2 1/N 1 γ s1=5/n1 N1 > N2 coding sequence s2=5/n2 0e+00 2e 04 4e 04 6e 04 8e 04 1e 03 substitution rate as a function of population size s dn ds = ω 1 N α Nicolas Lartillot (Universite de Montréal) Life history evolution September / 23

8 The causes of rate variations the generation-time hypothesis shorter generations, higher rate per Myr the metabolic rate hypothesis smaller body, higher mass-specific metabolic rate mutagenic effect of metabolism: higher substitution rate The longevity (and mass) hypothesis larger body, longer life: selection for lower mutation rate The effect of population size (Ohta 1973, Kimura 1979) ω = dn/ds: fraction of effectively neutral non-syn. mutations ω decreases when population size increases testing correlations between rates and life history traits

9 Estimating divergence times: the relaxed clock model r j up sequence alignment l = r "t &'&#'&'#('&#')'#***# r j &'(#'('#('&#')'#***# &'(#'&'#()&#')'#***# &'(#'&'#()&#')'#***# %# $# "# "t = t 2 # t 1 Brownian process x t N(x 0, νt) x t = ln r t (Thorne et al 1998, Lepage et al 2007, Rannala and Yang 2007)

10 Estimating divergence times: the relaxed clock model r j up sequence alignment l = r "t &'&#'&'#('&#')'#***# r j &'(#'('#('&#')'#***# &'(#'&'#()&#')'#***# &'(#'&'#()&#')'#***# %# $# "# "t = t 2 # t 1 Sampling posterior density by MCMC parameter vector: θ = (ν, r, t, Q) p(θ D) = p(d θ)p(θ) p(d) (Thorne et al 1998, Lepage et al 2007, Rannala and Yang 2007)

11 Divergence times and substitution rates PLATYPUS MONODELPHI DIDELPHIS ARMADILLO SLOTH ANTEATER SIRENIAN HYRAX ELEPHANT AARDVARK SHEARELESH LOEARELESH TENRECID GOLDENMOLE TREESHREW STREPSIRRH HUMAN FLYINGLEMU RABBIT PIKA SCIURID RAT MOUSE CAVIOMORPH MOLE SHREW HEDGEHOG LLAMA PIG HIPPO WHALE DELPHINOID COW TAPIR RHINO HORSE PHYLLOSTOM FLYINGFOX PANGOLIN DOG CAT Myrs KT carnivores chiropteres perissodactyls cetartiodactyls eulipotyphlans rodents lagomorphs primates afrotherians xenarthrans marsupials monotremes

12 Introduction Linear regression on leaf values log substitution rate log generation "#$%&''$ time Methodological weaknesses points are not independent (phylogenetically related) sequential method: error propagation no feedback of rate variations on life-history evolution Nicolas Lartillot (Universite de Montréal) Life history evolution September / 23

13 Coupling life-history and substitution rate variations &'#"()# *+,-#./00# 01*02#3/4# r 3 r 2 r 1 sequence alignment $ " = 2 #1 ' & ) %#1 1 ( covariance matrix l 2 = r 2 "t "()# )2(5# 5)# 789#898#987#8:8#222# 787#878#987#8:8#222# 789#878#9:7#8:8#222# (6))# 789#878#9:7#8:8#222# %# $# "# kg Joint estimation (Bayesian MCMC) divergence times, covariances, rates, and life-history evolution

14 Introduction Generalization substitution parameters rate of synonymous substitution non-synonymous / synonymous ratio equilibrium GC (3 positions) codon model (Goldman Yang, Muse Gaut 1994) life-history traits sexual maturity mass maximum lifespan metabolic rate Priors Inverse-Wishart prior on the covariance matrix uniform on divergence times + fossil calibrations (Springer et al, 2003) Data nuclear concatenation: 13 genes in 41 mammals mitochondrial gene: cytochrome b in 410 mammals Nicolas Lartillot (Universite de Montréal) Life history evolution September / 23

15 Results Nuclear data: covariance matrix ds ds dn/ds mat. long. mass met. dn/ds maturity longevity mass red: blue: light shade: positive negative not significant metabolic rate strong correlations between life-history traits ds correlates negatively with body mass, gen. time and longevity R 2 : life-history variations explain 40% of synonymous rate. weak but significant effect on dn/ds (effective population size?) Nicolas Lartillot (Universite de Montréal) Life history evolution September / 23

16 Results Nuclear data: covariance matrix ds ds dn/ds mat. long. mass met. dn/ds maturity longevity mass red: blue: light shade: positive negative not significant metabolic rate Multiple regressions (ds versus life-history traits) unconclusive concatenation: mass and generation time both contribute gene-dependent (e.g. BRCA, 140 taxa: only generation-time) Nicolas Lartillot (Universite de Montréal) Life history evolution September / 23

17 Results Mitochondrial data (cytochrome b) ds ds dn/ds gc1 gc2 gc3 mat. mass long. dn/ds gc1 gc2 gc3 maturity red: blue: light shade: positive negative not significant mass longevity no apparent generation-time effect on ds mass (or metabolism) sufficient predictors of ds positive correlation between dn/ds and mass / longevity equilibrium gc negatively correlates with ds Nicolas Lartillot (Universite de Montréal) Life history evolution September / 23

18 Inferring divergence times and body size evolution 1 kg 10 kg 100 kg 1000 kg CAT DOG PANGOLIN FLYINGFOX PHYLLOSTOMID HORSE RHINO TAPIR COW DELPHINOID WHALE HIPPO PIG LLAMA HEDGEHOG SHREW MOLE CAVIOMORPH MOUSE RAT SCIURID PIKA RABBIT FLYINGLEMUR HUMAN LEMUR TREESHREW GOLDENMOLE TENRECID ELEPHANTULUS MACROSCELIDES AARDVARK ELEPHANT HYRAX SIRENIAN ANTEATER SLOTH ARMADILLO DIDELPHIS MONODELPHIS PLATYPUS carnivores chiropteres perissodactyls cetartiodactyls eulipotyphlans rodents lagomorphs primates afrotherians xenarthrans marsupials monotremes 100 KT 0 Myrs

19 The evolution of body size 1 kg CAT DOG PANGOLIN Cow Whale ancestor 10 kg 100 kg 1000 kg FLYINGFOX PHYLLOSTOMID HORSE RHINO TAPIR COW DELPHINOID WHALE HIPPO PIG LLAMA HEDGEHOG SHREW MOLE post. density coupled uncoupled CAVIOMORPH MOUSE RAT log 10 Mass (g) SCIURID PIKA RABBIT Hippo Whale ancestor FLYINGLEMUR HUMAN LEMUR TREESHREW GOLDENMOLE TENRECID ELEPHANTULUS MACROSCELIDES AARDVARK ELEPHANT HYRAX SIRENIAN ANTEATER SLOTH ARMADILLO DIDELPHIS post. density coupled < KT uncoupled MONODELPHIS PLATYPUS log 10 Mass (g) 100 KT 0 Myrs

20 The evolution of body size CAT 1 kg 10 kg DOG PANGOLIN FLYINGFOX PHYLLOSTOMID HORSE Pakicetids 100 kg RHINO TAPIR 1000 kg COW DELPHINOID WHALE HIPPO PIG LLAMA HEDGEHOG SHREW MOLE (Thewissen et al, 2001) CAVIOMORPH MOUSE RAT SCIURID PIKA RABBIT Hippo Whale ancestor FLYINGLEMUR HUMAN LEMUR TREESHREW GOLDENMOLE TENRECID ELEPHANTULUS MACROSCELIDES AARDVARK ELEPHANT HYRAX SIRENIAN ANTEATER SLOTH ARMADILLO DIDELPHIS post. density coupled < KT uncoupled MONODELPHIS PLATYPUS log 10 Mass (g) 100 KT 0 Myrs

21 The evolution of body size Cow Whale ancestor CAT 1 kg 10 kg 100 kg 1000 kg DOG PANGOLIN FLYINGFOX PHYLLOSTOMID HORSE RHINO TAPIR COW DELPHINOID WHALE HIPPO PIG LLAMA HEDGEHOG SHREW post. density coupled > KT coupled < KT uncoupled MOLE CAVIOMORPH MOUSE log 10 Mass (g) RAT SCIURID PIKA Hippo Whale ancestor RABBIT FLYINGLEMUR HUMAN LEMUR TREESHREW GOLDENMOLE TENRECID ELEPHANTULUS MACROSCELIDES AARDVARK ELEPHANT HYRAX SIRENIAN ANTEATER SLOTH ARMADILLO post. density coupled > KT coupled < KT uncoupled DIDELPHIS MONODELPHIS PLATYPUS log 10 Mass (g) 100 KT 0 Myrs

22 Placentals and the KT boundary post. density unconstrained Age of placentals 10 grams, 10 days Age (Myrs) constrained (10 grams 10 days) KT CAT DOG PANGOLIN FLYINGFOX PHYLLOSTOM HORSE RHINO TAPIR COW DELPHINOID WHALE HIPPO PIG LLAMA HEDGEHOG SHREW MOLE CAVIOMORPH MOUSE RAT SCIURID PIKA RABBIT FLYINGLEMU HUMAN STREPSIRRH TREESHREW GOLDENMOLE TENRECID LOEARELESH SHEARELESH AARDVARK ELEPHANT HYRAX SIRENIAN ANTEATER SLOTH ARMADILLO DIDELPHIS MONODELPHI PLATYPUS Laurasiatheria Euarchontoglires Afrotheria Xenarthra 100 KT 0 Myrs

23 Results Conclusions integrated approach for correlating rates and phenotypes gives mechanistic insights about causes of rate variations helps reconstructing evolution of life-history potential impact on divergence times estimation Perspectives including data about body size of fossil taxa investigating burst models (punctuated equilibria) reconstructing variations of population size (using dn/ds) working at a larger phylogenetic scale (bilaterians) covariant models for cis-regulatory sequences Software availability (coevol) Nicolas Lartillot (Universite de Montréal) Life history evolution September / 23

24 aa composition and temperature in euryarcheota Pyrococcus Pyrococcus Pyrococcus Thermococcus Palaeococcus Thermococcus Thermococcus Thermococcus Aciduliprofundum Thermoplasma Picrophilus Methanopyrus Methanothermobacter Methanobrevibacter Methanosphaera Methanocaldococcus Methanococcus Methanococcus Methanococcus Methanococcus Archaeoglobus Methanocella Methanosaeta Methanococcoides Methanosarcina Methanosarcina Methanosarcina Methanocorpusculum Methanospirillum Methanoculleus Methanoregula Methanosphaerula Halobacterium Natrialba Natronomonas Halorhabdus Haloarcula Halomicrobium Halorubrum Halogeometricum Haloquadratum 7750 amino acids LG matrix + non homog. a.a. freq + GC + Temperature A C D E F G H I K L M covariance matrix N P Q R S T V W Y gc t A C D E F G H I K L M N P Q R S T V W Y gc t

25 aa composition and temperature in euryarcheota Pyrococcus Pyrococcus Pyrococcus Thermococcus Palaeococcus Thermococcus Thermococcus Thermococcus Aciduliprofundum Thermoplasma Picrophilus Methanopyrus Methanothermobacter Methanobrevibacter Methanosphaera Methanocaldococcus Methanococcus Methanococcus Methanococcus Methanococcus Archaeoglobus Methanocella Methanosaeta Methanococcoides Methanosarcina Methanosarcina Methanosarcina Methanocorpusculum Methanospirillum Methanoculleus Methanoregula Methanosphaerula Halobacterium Natrialba Natronomonas Halorhabdus Haloarcula Halomicrobium Halorubrum Halogeometricum Haloquadratum temperature A C D E F G H I K L M N P Q R S T V Y GC A C D E F G H I K L M N P Q R S T V Y

26 Acknowledgments Montreal Raphael Poujol Jean-Christophe Grenier Hervé Philippe Ottawa Nicolas Rodrigue Montpellier Frédéric Delsuc Nicolas Galtier Sylain Glémin Benoit Nabholz