Post on 13-Jan-2016
transcript
Distribution of Mutation Effects and Adaptation in an RNA Virus
Christina Burch
UNC Chapel Hill
We know a lot about selection
J. W. Dudley, R. J. Lambert, Plant Breed. Rev. 24 (part 1), 79 (2004).
Ronald Fisher
R = h2S
We know less about the resulting adaptations.
J. W. Dudley, R. J. Lambert, Plant Breed. Rev. 24 (part 1), 79 (2004).
Original population range
Ronald Fisher
The Goal:
Measure the distribution of spontaneous mutation effects.
-0.4 -0.3 -0.2 -0.1 0 0.1
mutation effect (s)
Pro
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The Data
We conduct laboratory evolution experiments using microbes so that we can monitor evolution in
real time.
bacteriophage+
bacteria
Growing bacteriophage in the lab
Assaying fitness of phage genotypes
Small population
Large population
Small population
Small population
Small population
Small population
Small population
Small population
Small population
-1.5
-1.25
-1
-0.75
-0.5
-0.25
0
0.25
0 10 20 30 40 50
Generation
Log(
fitne
ss)
Fitness Loss
-1.5
-1.25
-1
-0.75
-0.5
-0.25
0
0.25
0 10 20 30 40 50
Generation
Log(
fitne
ss)
Fitness Loss
Genome sequencing reveals that one mutation was acquired right here
-1.5
-1.25
-1
-0.75
-0.5
-0.25
0
0.25
0 10 20 30 40 50
Generation
Log(
fitne
ss)
Fitness Loss
Statistics can give the same answer, and statistics are much cheaper!
Large population
Large population
Adaptation
Generation
Log(
fitne
ss)
-1.5
-1.25
-1
-0.75
-0.5
-0.25
0
0.25
0 25 50 75 100
Adaptation
Generation
Log(
fitne
ss)
-1.5
-1.25
-1
-0.75
-0.5
-0.25
0
0.25
0 25 50 75 100
Genome sequencing of the endpoint reveals TWO new mutations.
Adaptation
Generation
Log(
fitne
ss)
-1.5
-1.25
-1
-0.75
-0.5
-0.25
0
0.25
0 25 50 75 100
Again, statistics can give the same answer.
The Goal:
Measure the distribution of spontaneous mutation effects.
-0.4 -0.3 -0.2 -0.1 0 0.1
mutation effect (s)
Pro
ba
bili
ty d
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sity
A slightly simpler goal:
Measure the distribution of spontaneous mutation effects in a well adapted genome.
-0.4 -0.3 -0.2 -0.1 0 0.1
mutation effect (s)
Pro
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bili
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sity
The Goal: Measure the distribution of spontaneous mutation effects in a well adapted genome.
Burch, C. L. et al. (2007) Genetics 176:467-476.
…40 days…
…40 days…
…40 days…
.
.
.10 lineages
Genome sequence at the start and end of the experiment tells us how many mutations accumulated.
Accumulated Mutations.
LineageSegment /nt mutationa,b
Gene orRegion Functional consequence
A S/a1378gS/c2164tS/a2453gM/a804gL/c489t
P9P53’ UTR1st IGRP7
K13RA182V
S11L
B L/a270g P14 M1V; start codon lost
C S/t1867cS/g2141aS/c2627tM/a491gM/t760cM/a3660gL/a5166gL/g5774a
P5P53’UTRP101st IGRP13P1P1
V83ASilent
K42R
E51GN406DSilent
We also measure fitness every day.p
laq
ue a
rea
transfer
Fitness measures, alone, allow identification of many mutations.
Effects of observed mutations
0
5
10
Num
ber
of m
uta
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s
0 0.1 0.2 0.3 0.4 0.5
mutation effect (s)
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0 0.1 0.2 0.3 0.4 0.5
Nu
mb
er
of
mu
tatio
ns Observed Sample
0 0.1 0.2 0.3 0.4 0.5
mutation effect (s)
Pro
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sity Unknown Population of
Spontaneous Mutations
Estimating distribution shapes by Maximum Likelihood
Excellent correspondence between the likelihood analysis and the molecular data
Genome sequencing:
56 total mutations.
32 non-synonymous mutations.
Maximum Likelihood Estimates
# deleterious mutations = 34
Average effect (s) = 0.142
Burch, C. L. et al. (2007) Genetics 176:467-476.
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Acknowledgements
• Phyllis Driscoll UNC Biology• Sebastien Guyader
• Mihee Lee UNC Statistics• Dan Samarov • Haipeng Shen
National Institutes of Health