thomas bataillon - présentation mee 2013
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Fitness Landscapes & Dynamics of Adapta4on & … what can we infer from pa9erns of phenotypic and molecular evolu4on? Thomas Bataillon Bioinforma4cs Research Center (BiRC), Aarhus University, Denmark. ISEM, Universite de Montpellier ( Un4l July 2013)
Muta4on & Fitness landscapes & Evolu4on Why do I care ?
Open ended vs. Close evolu6on Evolu6onary Poten6al Proper6es of beneficial muta6ons? • BIG or small effects ? à Distribu6on of fitness effects (DFE) • DOMINANT or recessive, etc? • Ecologically specialized or broadly beneficial ?
Which model can account for the proper4es of muta4ons that ma9er for adapta4on? à What data do we have to challenge models?
Mutation fitness effect, s
Predic4ng DFE
Heuristics “In a well adapted population, virtually (almost) all mutations with a measurable fitness effect will suck”
à Extreme Value Theory • Gillespie’s seminal work (1984, …) • Orr (2002,…)
Explicit fitness landscape models “Current level of adaptation matters as well as the genetic architecture underlying fitness”
Fisher’s “geometric” landscape
Other landscapes e.g. stick breaking
Distribu4ons of fitness effects and extreme value theory: “look on the right …”
A. H. Orr, The Distribu,on of Fitness Effects Among Beneficial Muta,on, Gene6cs 2003.
J. H. Gillespie, Molecular evolu,on over the muta,onal landscape, Evolu6on,1984
Extreme value theory
Fitness
EVT limi4ng distribu4on: Generalized Pareto distribu4on
κ< -‐1
κ=-‐1
Beisel et al Gene4cs 2007…
Explicit fitness landscape
Genotypes à Fitness Genotypesà Phenotype(s)àFitness DFE Expected dynamics of fitness over 4me Expected level of molecular //ism etc.
Martin & Lenormand Evolution 2006 Chevin et al Evolution 2010 Lourenço et al Evolution 2011
DFEs & Experimental Evolu4on data
• What kind of data? – Fitness of strains differing by single step from an ancestral strain – Fitness trajectory over 4me – Snapshot of genomic diversity over 4me
Assaying collec4on of genotypes “one step” away from a wild type
Selective
Count
0
20
40
60
80
100
120
140
Permissive
Absolute fitness0.3 0.4 0.5 0.6 0.7 0.8 0.9 1.0 1.1 1.2
0
20
40
60
80
100
120
140
Kassen & Bataillon Nat Gen 2006, Bataillon et al Gene4cs 2011
EVT limi4ng distribu4on: Generalized Pareto distribu4on
κ< -‐1
κ=-‐1
Beisel et al Gene4cs 2007…
Inferring the distribu4on of beneficial muta4ons fixed / on their way to fixa4on
Schoustra et al PLOS Biol 2009
Inferring parameters of Fisher’s phenotypic landscape with Exp Evolu4on data
with L. Perfeito A. Sousa & I Gordo (IGC, Portugal)
• DATA (E. coli) • Patterns of fitness decline in a mutation accumulation experiment • 50 lines ca 230 gens
• Patterns of fitness recovery over 240 generations
• MODEL: Fisher’s geometric fitness landscape • PARAMETERS
– Genome wide mutation rate U – Number of indep traits underlying fitness n – Mean effect of a mutation – Distance to opimum (here ZERO)
Temporal dynamics of fitness b
d f
Trindade et al Phil Trans Roy Soc 2010
ABC in a nutshell
• Principle :approximate the likelihood or posterior distribu4on of the parameters • Replace the whole data D by a joint set of summary sta4s4cs • Simulate data under your (pet) model
• Prac4ce • Choose summary sta4s4cs that have worked in known contexts • Use (crude) rejec4on sampling to approximate P(S) • Validate with simulated data
Proper4es of Fisher’s fitness landscape as inferred by experimental data
Fitness decline depends on star4ng fitness Δ fitness o
ver 1
0 bo
9len
ecks
Fitness recovery also depends on ini4al fitness
DFEs predicted by the data under Fisher’s model
Sta4s4cal performance of the approx. Bayesian framework
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5e−04 2e−03 5e−03 2e−02 5e−02 2e−01
5e−0
42e−0
35e−0
32e−0
25e−0
22e−0
1
Estimating U
True U
ABC
est
imat
e of
Uunbiasedactual bias (lowess)
Es4mated U
True genome-‐wide muta4on rate (U)
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5 10 15 20
510
15
Estimating Number of dimension (ndim)
True ndim
ABC
est
imat
e of
ndi
m
unbiasedactual bias (lowess)
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0.0 0.1 0.2 0.3 0.4 0.5 0.6
0.0
0.1
0.2
0.3
0.4
0.5
Estimating Rmax
True Rmax
ABC
est
imat
e of
Rm
ax
unbiasedactual bias (lowess)
Inferring DFE from polymorphism and divergence (1)
Assump4ons: 1. Synonymous muta4ons are
to first approxima4on neutral
2. Non-‐synonymous muta4ons might be neutral deleterious or beneficialà DFE
Bad muta4ons are ojen pre9y rare à examine site frequency spectrum (SFS)
Hvilsom et al. PNAS 2012
Inferring DFE from polymorphism and divergence (2)
• SFS based methods assume: – Stable popula4on or some
explicit model – Neutral synonymous – Non-‐Synonymous
polymorphism is always deleterious
– a frac4on α of non-‐syn divergence are beneficial.
(but see RSS models) Keightley & Eyre-‐Walker MBE 2009
|Ns|<1 1<|Ns|<10 10<|Ns|<100
|Ns|>100
Hvilsom et al. PNAS 2012
Puong it all together… can we steer way from dangerous assump4ons?
−15 −10 −5 0
020
4060
8010
012
0
S=4Nes
EPn
or E
Dn
φ(S) rescaledEPnEDn
−15 −10 −5 0 5
020
4060
8010
0
S=4Nes
EPn(
S) a
nd E
Dn(
S)
EPsEPnESnEDn
Single stat summaries of polymorphism & divergence alone are not good enough
à SFS or MK counts
Expecta6on for the direc6on Of Selec6on
−100 −80 −60 −40 −20 0
−0.4
−0.3
−0.2
−0.1
0.0
0.1
S
Dire
ctio
n O
f Sel
ectio
n (d
os)
dos ≡Dn
Dn +Ds−
PnPn +Ps
Mean DOS
var(DOS)
Expecta6on for MK counts
−15 −10 −5 0 5
020
4060
8010
0
S=4Nes
EPn(
S) a
nd E
Dn(
S)
EPsEPnESnEDn
Structure of an extended MK model
Data
Pol Syn
Freq
uenc
y
0 5 10 15 20 25
02
46
810
Singleton Syn
Freq
uenc
y
2 4 6 8 10 12
05
1015
Diverg. Syn
Freq
uenc
y
0 5 10 15 20 25 30
02
46
8
Pol Non Syn
dataS$PNobs
Freq
uenc
y
0 5 10 15 20 25
02
46
810
12
Singleton Non Syn
dataS$SNobs
Freq
uenc
y
0 5 10 15
05
1015
Diverg. Non Syn
dataS$DNobs
Freq
uenc
y
0 5 10 20 30
02
46
8
Hierarchical model for counts of polymorphism & divergence
ξ η Δ are indepPoisson with means functions of Ne, r, Φµ Φs
Φs�smax,smean,Β�
s
ΦΜ�Α,Β�
Μ
Θ
Ne
Η1, Η2,..., Ηn�1
Sr
Ξ1, Ξ2,..., Ξn�1 �syn �ns
“Chimps in a nutshell” Gonder M K et al. PNAS 2011;108:4766-4771
12 wild-‐born unrelated chimpanzees (CENTRAL) Aboume Amelie Ayrton Bakoumba Benefice Chiquita Cindy Lalala Makokou Masuku Noemie Susi
Pääbo, Nature 421, 409-‐412(23 January 2003) doi:10.1038/nature01400
6 Western
11 Eastern
(Chimp)Polymorphism & (Chimp) divergence
Chimpanzee Human (hg 19)
Human – Chimp ancestor Divergence
Orangutan
Numbers of coding SNPs and fixed differences with humans
Autosome X chromosome Number of synonymous sites called 3287414 172476 Number of non-synonymous sites called 11380785 600624
Number of synonymous SNPs 32942 808 Number of non-synonymous SNPs 26462 617
Synonymous divergence with humans 32548 1223 Non-synonymous divergence with humans 20632 1054
DFEs inferred from exome polymorphism & divergence
−60 −40 −20 0
0.00
0.02
0.04
0.06
0.08
DFE inferred from exome Pol & divergence
S= 4Nes
φ(S)
Deleterious Beneficial
chromosome Xchromosome 4, 7, 9, 10
• n=87-‐90 windows comprising 10kb of called exon material
• Varia4on in muta4on rate • Poor fit with 7 parameters
rela4ve to a saturated model
fitness effects of deleterious muta4ons on autosomes Vs. X chromosome
Purifying selec4on at least as efficient on the X chromosome
!"
!#$"
!#%"
!#&"
!#'"
!#("
!#)"
!#*"
+,-./"01,23-" 4/5657"6151213/8,."
9151213/8,." :137"6151213/8,."
;<3/=-" >,38?1"Distribu4on of fitness effects in human popula4ons show weaker selec4on (values from Eyre-‐Walker and Keightley 2009)
|Ns|<1 1<|Ns|<10 10<|Ns|<100
|Ns|>100
0.005 0.010 0.015 0.020
050
100
150
!
Den
sity
priorMC approx Posterior
−1 0 1 2 3 4 5 6
0.00
0.05
0.10
0.15
0.20
0.25
density.default(x = margPost, bw = 0.35)
Smax
Density
Many thanks to • Experimental Evolu4on
– Rees Kassen, Sijmen Schoustra & Gordo group – Guillaume Mar4n, Thomas Lenormand and Paul Joyce for numerous discussions
• Pa9erns of molecular polymorphism & divergence – Mikkel Schierup, Thomas Mailund, C. Hvilsom, Yu Qian (chimp exomes)
– Nicolas Gal4er & Sylvain Glemin (MK-‐DFE). • Money UM2, FNU, French Embassy in O9awa, ERC.
Cleaning of the exome data • Minimize rela4onship between
coverage and human-‐chimpanzee divergence
• Restrict analyses to exons with > 20X and < 100X coverage in all 12 individuals
• Exclude exons in duplicated regions
è 48% of all exons included. For these, genotypes of SNPs could be called in all individuals (12 Central Chimpanzees)
log10(length in bp)4.5 5.0 5.5 6.0 6.5 7.0 7.5
050
100
150
200
250
300
350
A non sophis4cated survey for Sweeps
• Bin exome data in con4guous windows comprising 10kb of exon (n=2069)
• Use polymorphism and divergence to compute – Synonymous divergence (Ds) to
control for varia4on in muta4on rate
– Standardized measure of polymorphism per window
– Index for direc4on of selec4on DoS = Dn/(Dn+Ds) -‐ Pn/(Pn+Ps)
!!!
MODEL
Φs�smax,smean,Β�
s
ΦΜ�Α,Β�
Μ
Θ
Ne
Η1, Η2,..., Ηn�1
Sr
Ξ1, Ξ2,..., Ξn�1 �syn �ns
DATA
40/37
Assaying 18 mutants in 96 new environments
How specialized are beneficial mutations?
41/37
Assaying Top 18 mutants in 96 new environments How specialized are mutants?
→ They are NOT
A non sophis4cated survey for Sweeps reveals a major Sweep on Chr 3
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−0.4
0.0
ch3$start
DO
S
−2.0
−0.5
1.0
ch3$start
Std
. Pol
C
−2.0
−0.5
ch3$start
Std
. Pol
E
0 20 40 60 80 100 120
−3.0
−1.0
coordinate CHR3
Std
. Pol
W
THE DATA : Experimental set up (P. fluorescens)
• Use a single strain (SBW25) • Use an4bio4c resistance to ‘trap’
new single step resistance muta4ons – 2016 popula4ons assayed – n = 673 mutants collected
• Replicated assays to characterize pleiotropic fitness effects of muta4ons
• Compare with the “wild” type …
LB
500 cells
~ 108 cells
Agar + nalidixic acid
What does natural selec4on ‘see’?
Selective
Count
0
20
40
60
80
100
120
140
Permissive
Absolute fitness0.3 0.4 0.5 0.6 0.7 0.8 0.9 1.0 1.1 1.2
0
20
40
60
80
100
120
140
Wild type
Wild type has zero fitness
LB + an6bio6c
LB
• 673 nalR mutants isolated (from 2016 screened)
• 28 mutants fi9er than wild type
Kassen & Bataillon Nat Gen 2006
45/37
BIOLOG Setting
Assaying 18 mutants in 96 new environments How specialized are beneficial mutations?
Are GxE important relative to a random set of mutation ?
Random 63 Top 18
46/37
Assaying Top 18 mutants in 96 new environments → A variety of shapes for fitness effect
Assaying Top 18 mutants in 96 new environments H0: muta4on effects are exponen4al
Ha: muta4on effects are GPD
pvalues
Freq
uency
0.0 0.2 0.4 0.6 0.8 1.0
0 5
10
15
Combined p-‐values (Fisher’s procedure) p=0.0002
95 Different carbon source +Water