molecules, divergence times and the evolution of life

Molecules, divergence times andthe evolution of life-histories

Nicolas Lartillot, Raphael Poujol, Frederic Delsuc

September 2010

Nicolas Lartillot (Universite de Montréal) Life history evolution September 2010 1 / 23

The molecular clock

ρ = µ× f0substitution rate = mutation rate× fraction of neutral mutations

Kimura 1982, after Pauling and Zuckerkandl 1962

Variations of the substitution rate

Generation-time effect

time

=⇒

TREESHREWLEMUR

HUMANFLYINGLEMUR

RABBITPIKA

SCIURIDRAT

MOUSECAVIOMORPH

MOLESHREWHEDGEHOG

LLAMAPIG

HIPPOWHALE

DELPHINOIDCOW

TAPIRRHINO

HORSEPHYLLOSTOMID

FLYINGFOXPANGOLIN

DOGCAT

ARMADILLOSLOTH

ANTEATERSIRENIAN

HYRAXELEPHANT

MACROSCELIDESELEPHANTULUS

TENRECIDGOLDENMOLE

AARDVARK

0.1 subs per site

Nicolas Lartillot (Universite de Montréal) BIN6009 10/05/2009 1 / 1concatenation of 13 nuclear genes, 38 placentals

Introduction

The metabolic rate hypothesis

Proc. Natl. Acad. Sci. USA 90 (1993) 4089

Table 2. Estimates of percent sequence divergence, time of divergence from the fossil record, divergence rate, and average adult bodysize for a variety of vertebrates

% sequence Divergence Divergence Body size, GenerationTaxon difference time, Mya rate, %/Myr kg time, yr Ref(s).

HomeothermRodentsMus musculus-M. spretus 11.3 1-3 3.8-11.3 0.020 0.2-0.3 25

DogsCanis latrans-C. lupus 2.7-4.2 1-2 1.8-3.5 10-55 2-3 26

Horses 7.8 3-5 1.5-2.6 100-400 3-4 27Bears

Ursus arctos-U. maritimus 0.8-1.2 0.5 1.6-2.4 150-300 4-5 28U. arctos-U. americanus 6.9-7.1 3.5 1.9-2.0 150-300 4-5 28U. maritimus-U. americanus 7.6-7.7 4.4 1.7-1.8 150-300 4-5 28

PrimatesHomo sapiens-Pan troglodytes 10.5 5 2.1 33-55 8-15 29

WhalesMegaptera novanglieae-Baleanoptera physalus 6.0 6-15 1.0-0.4 40,000 5-10 *

GeeseBranta-Anser 9.0 4-5 1.8-2.3 1-2 2-3 30

PoikilothermTortoise

East vs. west Gopherus 5.3 5.5 0.95 0.5-3 17-20 31Gopherus-Xerobates 11.2 15-23 0.48-0.75 0.5-3 17-20 31

Sea turtlesGeminate Chelonia 0.6 1.5-3.0 0.2-0.40 50-70 >20 13, 32Kemp and Ridley's 1.2 3-4 0.30-0.40 30-50 >20 13, 33

AmphibiansNewts 8.0 8-12 0.67-1.0 0.020 2-3 34Frogs 8.3 11-12 0.69-0.75 0.050 2-3 34

SharksSphyrna-Galeocerdo 17 55 0.31 60-100 10-15 14, 21

Teleost fishSalmon 5.2 6-10 0.9-0.5 1-4 1-4 21

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 Mya, million years ago;Myr, million years.*C. S. Baker and S.R.P., unpublished data.

available. Regression analyses show that both are highlycorrelated with the silent rate. However, in a multiple re-gression of the silent rate versus generation time and meta-bolic rate, only the latter had a significant 13 coefficient (P =

1-.

a

0

8

cn

101

100

10,-2 -.1 0 1 2 3 4

10 10 10 10 10 10 10

Mass (k)

FIG. 2. Relationship between rate of mtDNA sequence diver-gence (% change per million years) and body size (in kg) for variousvertebrates. Data are from Table 2. 1, Mice; 2, dogs; 3, human-chimpanzee; 4, horses; 5, bears; 6, geese; 7, whales; 8, newts; 9,frogs; 10, tortoise; 11, salmon; 12, sea turtles; 13, sharks. Boxesrepresent the range of rates and body sizes for a given taxon. Solidlines are drawn to pass through the boxes. Dashed line represents thehypothesis of rate constancy.

0.009 for metabolic rate versus P = 0.349 for generationtime). Although multiple regression can be a weak tool whenvariables are highly correlated, the analysis suggests that theapparent relationship of silent rate to generation time may bean artifact.For the cytochrome b data in Fig. 1B, neither generation

time nor metabolic rate shows a significant correlation withsilent rate by either parametric or nonparametric tests. How-ever, generation time and metabolic rate together yield asignificant multiple regression analysis (R2 = 0.673, F =6.187, P = 0.035), indicating that both factors may be playingimportant roles in a synergistic fashion.

In other cases, inspections of molecular rate data do notfollow the predictions of the generation time hypothesis. Forexample, whales have a slow rate of nuclear DNA evolutionrelative to primates despite their shorter generation time (10).Rates of single-copy DNA evolution are slower in marsupialsthan in placental mammals independant of generation time(ref. 8; but see ref. 43). Substitution rates in nuclear ribo-somal genes are 8-fold slower in salamanders than in mam-mals (44), despite their shorter generation times.For mtDNA rates, Hasegawa and Kishino (12) failed to fmd

an association between generation time and mtDNA substi-tution rate. Furthermore, the silent rate in shark mtDNAs is5-7 times slower than in primates or ungulates despite broadlysimilar ranges of generation times for the three groups (14).Similarly, mtDNA divergence rates ofnewts and frogs, whosegeneration times are on the order of 3-5 years (44), are slowerthan those of primates (34). Rates of mtDNA evolution are

Evolution: Martin and Palumbi

Martin and Palumbi 1993

taneous respirometry in validation studies of around 3% [seereview in Speakman (17)]. For individuals, it provides a valueof less utility because the precision of the estimate is lower,although because this is the only reliable method for estimat-ing daily energy demands, such individual estimates are gen-erally preferable to having nothing at all.

The key question in the context of the current discussionabout links between energetics and aging is whether restingmetabolism provides a reasonable proxy for daily rates ofenergy expenditure. This would be the case, for example, ifRMR contributed a substantial amount to the total expendi-ture. Studies that have documented both resting metabolismand daily energy expenditure are available for 73 species ofsmall mammal (weighing !4 kg), allowing evaluation of thissuggestion [reviewed in Speakman (19)]. These data indicatethat on average in small mammals RMR contributes 35% ofthe total DEE as estimated by DLW [n " 73 (19)]. This is alarge proportion of the total, but it is not a majority. Hence, interms of evaluating the utility of RMR as a proxy for DEE thiscalculation clearly indicates that it is deficient. However, thisneed not be the only criterion for evaluating the usefulness ofRMR as a measure of total energy metabolism. For example,RMR might also be useful, despite having a low absolutecontribution to DEE, if there was a fixed ratio between thetwo. RMR might contribute only 35% of the total, but if it wasalways 35%, then a relationship between RMR and life spanmight validly reflect a relationship between DEE and life span.

In fact, ratios between RMR and DEE vary from 1.4 to 8.0with a maximum frequency at 2.6# RMR (Fig. 1). RMRclearly does not have a fixed ratio to DEE, making its use as aproxy for DEE tenuous. An example may clarify this point.The Kangaroo rat (Dipodomys merriami) weighing 28.7 g hasan RMR of 15.8 kJ d$1. This is almost identical to the RMRof the small marsupial mouse (Antechinus) of almost equalbody mass (25.7 g), with an RMR of 15.6 kJ d$1, yet thedirectly measured DEE of the Kangaroo rat at 34.3 kJ d$1 (24)in winter is only half the DEE of the marsupial mouse at 72.0kJ d$1 (25). In spite of this wide range of ratios between RMRand DEE, across a wide range of species there was still a strongnegative relationship between DEE (per gram body mass) andbody mass (Fig. 2), which has approximately the same scalingexponent as that found between RMR (per gram body mass)and body mass. This relationship occurs because the range ofDEE covers a 50-fold range of variation, but the variability intranslating RMR to DEE only differs in the range two- toeightfold. As the ratio of RMR to DEE does not differ system-atically with body mass (19), the overall nature of the relationbetween RMR and body mass is mirrored in the relation

between DEE and body mass. As an overall trend, therefore,describing the nature of energetics scaling at the interspecificlevel RMR does capture the essence of the relationship be-tween total daily energy demands (by DLW) and body mass. Itis important to remember, however, that at the level of indi-vidual species RMR does not function quite so advantageously.

Because the relationship between DEE and body mass hasthe obverse exponent of the relationship between longevityand body mass, it remains true that despite the inadequacies ofusing RMR at the individual species level, the product of thetwo relationships (total energy expenditure per gram per lifespan) is mass invariant. On average a gram of tissue in a smallanimal expends the same amount of energy over the course ofan entire life as does a gram of tissue from a large animal.

The empirical data are compromised by the confoundingeffect of body size

Using the scaling exponents in relation to body mass todeduce similarities in the scaling effects of different traitscannot be used to infer causality between those traits. This isbecause there is a confounding effect of body mass itself, withalmost all physiological traits varying in relation to body mass.Hence, given that longevity also varies in relation to bodymass, by comparing the respective scaling exponents it ispossible to draw inferences that almost any trait that varieswith an exponent around $0.25 to $0.3 is an important “lifehistory invariant” controlling longevity [e.g., food-intakescales with an exponent around $0.29 (26)]. Hence, lifetimefood intake per kg scales as 10.7 M$0.29 # 237 M0.29 " 2537M0.00, which is a “mass-invariant” trait. Using the scalingargument to infer causality, one might reason that life span iscontrolled by the amount of food (energy) that is eaten. Inother words, animals have a fixed amount of food to eat intheir lives, which is actually equal to 2537 MJ per kg of bodymass, and once they have eaten that amount, they die. [Usingan average energy content for food of about 4 MJ/kg (wetweight) and an average body mass of an adult human of 80 kg,this calculation leads to the prediction that the amount of foodthat determines our life span is 50,740 kg or about 50 metrictons.] The idea that a fixed amount of total food intake“determines” our life span by direct causality is extremelyunlikely. Yet, exactly the same arguments are brought into

FIGURE 1 Histogram showing the distribution of ratios of restingmetabolic rate to daily energy expenditure (DEE/RMR) measuredacross 73 species of small mammal [reviewed in Speakman (19)].

FIGURE 2 Relationship between mass specific daily energy ex-penditure measured by the doubly labeled water method and bodymass [data derived from original data reviewed in Speakman (19)].

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Introduction

The longevity (and mass) hypothesis

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An important conclusion derived from our studies of the role ofROS and mitochondrial apoptosis in AHL is that cells may not needto be irreversibly damaged by ROS in order to enter themitochondrial apoptotic program. This key conclusion is supportedby the observation that Bak!/! mice do not display cochlear cellloss and display normal hearing at middle age, despite the fact thatthese animals have no evidence of reduced ROS (Someya et al.,2009). Presumably, the level of ROS that is produced in cochlearcells during aging is sufficient to trigger the Bak-mediatedapoptotic program, but not sufficient to impair cellular function.Thus, the cell loss associated with AHL is an active process that canbe blocked by Bak inhibition, in the absence of deleterious effectsto the target tissue. If this paradigm is applicable to other tissuesimpacted by cell loss during aging, a significant component of theaging process may be pharmacologically blocked by improving themitochondrial antioxidant defense system and by blockingmitochondrial apoptosis.

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Culmsee, C., Mattson, M.P., 2005. p53 in neuronal apoptosis. Biochem. Biophys. Res.Commun. 331, 761–777.

Darrat, I., Ahmad, N., Seidman, K., Seidman, M.D., 2007. Auditory research involvingantioxidants. Curr. Opin. Otolaryngol. Head Neck Surg. 15, 358–363.

Derin, A., Agirdir, B., Derin, N., Dinc, O., Guney, K., Ozcaglar, H., Kilincarslan, S., 2004.The effects of L-carnitine on presbyacusis in the rat model. Clin. Otolaryngol.Allied Sci. 29, 238–241.

Deschauer, M., Muller, T., Wieser, T., Schulte-Mattler, W., Kornhuber, M., Zierz, S.,2001. Hearing impairment is common in various phenotypes of the mitochon-drial DNA A3243G mutation. Arch. Neurol. 58, 1885–1888.

Dirks, A.J., Hofer, T., Marzetti, E., Pahor, M., Leeuwenburgh, C., 2006. MitochondrialDNA mutations, energy metabolism and apoptosis in aging muscle. Ageing Res.Rev. 5, 179–195.

Dirks, A.J., Leeuwenburgh, C., 2004. Aging and lifelong calorie restriction result inadaptations of skeletal muscle apoptosis repressor, apoptosis-inducing factor,X-linked inhibitor of apoptosis, caspase-3, and caspase-12. Free Radic. Biol.Med. 36, 27–39.

Edwards, M.G., Anderson, R.M., Yuan, M., Kendziorski, C.M., Weindruch, R., Prolla,T.A., 2007. Gene expression profiling of aging reveals activation of a p53-mediated transcriptional program. BMC Genomics 8, 80.

Erster, S., Mihara, M., Kim, R.H., Petrenko, O., Moll, U.M., 2004. In vivomitochondrialp53 translocation triggers a rapid first wave of cell death in response to DNAdamage that can precede p53 target gene activation. Mol. Cell. Biol. 24, 6728–6741.

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Fischel-Ghodsian, N., 2003. Mitochondrial deafness. Ear Hear. 24, 303–313.Fortunato, G., Marciano, E., Zarrilli, F., Mazzaccara, C., Intrieri, M., Calcagno, G.,

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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 theactivation of a p53-mediated transcriptional response. p53 transcriptional targets include pro-apoptotic genes such as Bak and Bax. p53 also directly triggers mitochondrialapoptosis by binding to and promoting the oligomerization of pro-apoptotic Bak protein in the outer mitochondrial membrane. Chronic activation of this pathway is likely tonegatively impact tissues dependent on non-regenerating long-lived cells, such as the cochlea, brain, and heart.

S. Someya, T.A. Prolla /Mechanisms of Ageing and Development xxx (2010) xxx–xxx 5

G Model

MAD 10424 1–6

Please cite this article in press as: Someya, S., Prolla, T.A., Mitochondrial oxidative damage and apoptosis in age-related hearing loss.Mech. Ageing Dev. (2010), doi:10.1016/j.mad.2010.04.006

Someya and Prolla 2010

larger body, longer life: higher risks of somatic mutationsselection for lower mutation rate in larger animalseffect should be more pronounced in mitochondrial DNA


Introduction

The nearly-neutral model(Ohta, 1972, Kimura, 1979)

unfit

fit

coding sequence

1/N2

1/N1

●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●

0e+00 2e−04 4e−04 6e−04 8e−04 1e−03

5010

015

0

s

γγ

s1=5/N1

s2=5/N2

N1 > N2

substitution rate as a function of population size

dNdS

= ω ∼ 1Nα


The causes of rate variationsthe generation-time hypothesis

shorter generations, higher rate per Myr

the metabolic rate hypothesissmaller body, higher mass-specific metabolic ratemutagenic effect of metabolism: higher substitution rate

The longevity (and mass) hypothesislarger 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. mutationsω decreases when population size increases

⇒ testing correlations between rates and life history traits

Estimating divergence times: the relaxed clock model

!"#!$#!%#

!

l = r "t

!

"t = t2# t

1

!

rj up

!

rj &'(#'('#('&#')'#***#

&'&#'&'#('&#')'#***#

&'(#'&'#()&#')'#***#

&'(#'&'#()&#')'#***#

sequence alignment

Brownian process

xt ∼ N(x0, νt)xt = ln rt

(Thorne et al 1998, Lepage et al 2007, Rannala and Yang 2007)

Estimating divergence times: the relaxed clock model

!"#!$#!%#

!

l = r "t

!

"t = t2# t

1

!

rj up

!

rj &'(#'('#('&#')'#***#

&'&#'&'#('&#')'#***#

&'(#'&'#()&#')'#***#

&'(#'&'#()&#')'#***#

sequence alignment

Sampling posterior density by MCMCparameter 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)

Divergence times and substitution rates

PLATYPUSMONODELPHIDIDELPHISARMADILLOSLOTHANTEATERSIRENIANHYRAXELEPHANTAARDVARKSHEARELESHLOEARELESHTENRECIDGOLDENMOLETREESHREWSTREPSIRRHHUMANFLYINGLEMURABBITPIKASCIURIDRATMOUSECAVIOMORPHMOLESHREWHEDGEHOGLLAMAPIGHIPPOWHALEDELPHINOIDCOWTAPIRRHINOHORSEPHYLLOSTOMFLYINGFOXPANGOLINDOGCAT

0100 MyrsKT

carnivoreschiropteresperissodactyls

cetartiodactyls

eulipotyphlans

rodentslagomorphs

primates

afrotherians

xenarthransmarsupialsmonotremes

Introduction

Linear regression on leaf values! !

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!

!

!

!

!

!

!!

!

!

!!

!

!

!

!

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!

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!

!

!

!

!

!

!

!

!

!

!

!

!

!

2.0 2.5 3.0

!0

.6!

0.4

!0

.20

.0

log generation time

log s

ubstitu

tion r

ate

!"#$%&''$

Methodological weaknessespoints are not independent (phylogenetically related)sequential method: error propagationno feedback of rate variations on life-history evolution


Coupling life-history and substitution rate variations

!"#!$#!%#

&'#"()#

!

" =2 #1

#1 1

$

% &

'

( )

*+,-#./00#

01*0!2#3/!4#

"()#

)2(5#

5)#

(6))#

covariance

matrix

kg

!

r 1

!

r 3

!

r 2

!

l2

= r 2"t

789#898#987#8:8#222#

787#878#987#8:8#222#

789#878#9:7#8:8#222#

789#878#9:7#8:8#222#

sequence alignment

Joint estimation (Bayesian MCMC)divergence times, covariances, rates, and life-history evolution

Introduction

Generalization

substitution parametersrate of synonymous substitutionnon-synonymous / synonymous ratioequilibrium GC (3 positions)

codon model (Goldman Yang, Muse Gaut 1994)

life-history traitssexual maturitymassmaximum lifespanmetabolic rate

PriorsInverse-Wishart prior on the covariance matrixuniform on divergence times + fossil calibrations (Springer et al, 2003)

Datanuclear concatenation: 13 genes in 41 mammalsmitochondrial gene: cytochrome b in 410 mammals


Results

Nuclear data: covariance matrix

Nicolas Lartillot (Universite de Montréal) BIN6009 10/05/2009 1 / 1

dS

dN/dS

maturity

longevity

mass

metabolic rate

dS dN/dS mat. long. mass met.

red: positive

blue: negative

light shade: not significant

strong correlations between life-history traitsdS correlates negatively with body mass, gen. time and longevityR2: life-history variations explain ∼ 40% of synonymous rate.weak but significant effect on dN/dS (effective population size?)


Results

Nuclear data: covariance matrix


dS

dN/dS

maturity

longevity

mass

metabolic rate

dS dN/dS mat. long. mass met.

red: positive

blue: negative


Multiple regressions (dS versus life-history traits)unconclusiveconcatenation: mass and generation time both contributegene-dependent (e.g. BRCA, 140 taxa: only generation-time)


Results

Mitochondrial data (cytochrome b)


dS

dN/dS

maturity

longevity

mass

dS dN/dS mat. long. mass gc1 gc2 gc3

gc1

gc2

gc3

red: positive

blue: negative


no apparent generation-time effect on dSmass (or metabolism) sufficient predictors of dSpositive correlation between dN/dS and mass / longevityequilibrium gc negatively correlates with dS


Inferring divergence times and body size evolution

PLATYPUSMONODELPHISDIDELPHISARMADILLOSLOTHANTEATERSIRENIANHYRAXELEPHANTAARDVARKMACROSCELIDESELEPHANTULUSTENRECIDGOLDENMOLETREESHREWLEMURHUMANFLYINGLEMURRABBITPIKASCIURIDRATMOUSECAVIOMORPHMOLESHREWHEDGEHOGLLAMAPIGHIPPOWHALEDELPHINOIDCOWTAPIRRHINOHORSEPHYLLOSTOMIDFLYINGFOXPANGOLINDOGCAT

0100 MyrsKT

1 kg

10 kg

100 kg

1000 kg

carnivoreschiropteresperissodactyls

cetartiodactyls

eulipotyphlans

rodentslagomorphs

primates

afrotherians

xenarthransmarsupialsmonotremes

The evolution of body size


0100 MyrsKT

1 kg

10 kg

100 kg

1000 kg


1 2 3 4 5 6 7 8

0.0

0.2

0.4

0.6

0.8

Hippo Whale ancestor

log10 Mass (g)p

ost.

de

nsity

coupled < KT uncoupled

1 2 3 4 5 6 7 8

0.0

0.2

0.4

0.6

0.8

Cow Whale ancestor

log10 Mass (g)

po

st.

de

nsity

coupled uncoupled



0100 MyrsKT

1 kg

10 kg

100 kg

1000 kg


Pakicetids

(Thewissen et al, 2001)

1 2 3 4 5 6 7 8

0.0

0.2

0.4

0.6

0.8


log10 Mass (g)p

ost.

de

nsity

coupled < KT uncoupled



0100 MyrsKT

1 kg

10 kg

100 kg

1000 kg


1 2 3 4 5 6 7 8

0.0

0.2

0.4

0.6

0.8


log10 Mass (g)p

ost.

de

nsity

coupled < KT

coupled > KT

uncoupled

1 2 3 4 5 6 7 8

0.0

0.2

0.4

0.6

0.8

Cow Whale ancestor

log10 Mass (g)

po

st.

de

nsity

coupled < KT

coupled > KT

uncoupled

Placentals and the KT boundary

PLATYPUSMONODELPHIDIDELPHISARMADILLOSLOTHANTEATERSIRENIANHYRAXELEPHANTAARDVARKSHEARELESHLOEARELESHTENRECIDGOLDENMOLETREESHREWSTREPSIRRHHUMANFLYINGLEMURABBITPIKASCIURIDRATMOUSECAVIOMORPHMOLESHREWHEDGEHOGLLAMAPIGHIPPOWHALEDELPHINOIDCOWTAPIRRHINOHORSEPHYLLOSTOMFLYINGFOXPANGOLINDOGCAT

0100 MyrsKT


Laurasiatheria

Euarchontoglires

Afrotheria

Xenarthra

130 120 110 100 90 80 70 60

0.0

00

.02

0.0

40

.06

0.0

80

.10

Age of placentals

Age (Myrs)

po

st.

de

nsity

10 grams, 10 daysunconstrained

KT

constrained

(!10 grams !10 days)

Results

Conclusionsintegrated approach for correlating rates and phenotypesgives mechanistic insights about causes of rate variationshelps reconstructing evolution of life-historypotential impact on divergence times estimation

Perspectivesincluding data about body size of fossil taxainvestigating 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)www.phylobayes.org


aa composition and temperature in euryarcheota

HaloquadratumHalogeometricum

HalorubrumHalomicrobiumHaloarcula

HalorhabdusNatronomonas

NatrialbaHalobacterium

MethanosphaerulaMethanoregula

MethanoculleusMethanospirillum

MethanocorpusculumMethanosarcina

MethanosarcinaMethanosarcinaMethanococcoides

MethanosaetaMethanocella

ArchaeoglobusMethanococcusMethanococcus

MethanococcusMethanococcus

MethanocaldococcusMethanosphaera

MethanobrevibacterMethanothermobacter

MethanopyrusPicrophilus

ThermoplasmaAciduliprofundum

ThermococcusThermococcus

ThermococcusPalaeococcus

ThermococcusPyrococcusPyrococcus

Pyrococcus

7750 amino acidsLG matrix +

non homog. a.a. freq +GC + Temperature

A

A

C

C

D

D

E

E

F

F

G

G

H

H

I

I

K

K

L

L

M

M

N

N

P

P

Q

Q

R

R

S

S

T

T

V

V

W

W

Y

Y

gc

gc

t

t

Nicolas Lartillot (Universite de Montréal) Mutation Selection models 10/12/2009 1 / 1covariance matrix

aa composition and temperature in euryarcheota

HaloquadratumHalogeometricum

HalorubrumHalomicrobiumHaloarcula

HalorhabdusNatronomonas

NatrialbaHalobacterium

MethanosphaerulaMethanoregula

MethanoculleusMethanospirillum

MethanocorpusculumMethanosarcina

MethanosarcinaMethanosarcinaMethanococcoides

MethanosaetaMethanocella

ArchaeoglobusMethanococcusMethanococcus

MethanococcusMethanococcus

MethanocaldococcusMethanosphaera

MethanobrevibacterMethanothermobacter

MethanopyrusPicrophilus

ThermoplasmaAciduliprofundum

ThermococcusThermococcus

ThermococcusPalaeococcus

ThermococcusPyrococcusPyrococcus

Pyrococcus

temperature

A C D E F G H I K L M N P Q R S T V Y

−0.

050.

000.

05

GC

A C D E F G H I K L M N P Q R S T V Y

−0.

6−

0.4

−0.

20.

00.

20.

40.

6

Acknowledgments

MontrealRaphael PoujolJean-Christophe GrenierHervé Philippe

OttawaNicolas Rodrigue

MontpellierFrédéric DelsucNicolas GaltierSylain GléminBenoit Nabholz

molecules, divergence times and the evolution of life

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