distribution of mutation effects and adaptation in an rna virus christina burch unc chapel hill
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.
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mutation effect (s)
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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
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Generation
Log(
fitne
ss)
Fitness Loss
-1.5
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0 10 20 30 40 50
Generation
Log(
fitne
ss)
Fitness Loss
Genome sequencing reveals that one mutation was acquired right here
-1.5
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Generation
Log(
fitne
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Fitness Loss
Statistics can give the same answer, and statistics are much cheaper!
Large population
Large population
Adaptation
Generation
Log(
fitne
ss)
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Adaptation
Generation
Log(
fitne
ss)
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Genome sequencing of the endpoint reveals TWO new mutations.
Adaptation
Generation
Log(
fitne
ss)
-1.5
-1.25
-1
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-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.
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mutation effect (s)
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A slightly simpler goal:
Measure the distribution of spontaneous mutation effects in a well adapted genome.
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mutation effect (s)
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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
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Num
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s
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mutation effect (s)
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Nu
mb
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mutation effect (s)
Pro
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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