1. Distribution of Mutation Effects and Adaptation in an RNA Virus Christina Burch UNC Chapel Hill

3. We know a lot about selection J. W. Dudley, R. J. Lambert, Plant Breed. Rev. 24 (part 1), 79 (2004). Ronald Fisher R = h 2 S

4. 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

5. The Goal: Measure the distribution of spontaneous mutation effects. -0.4-0.3-0.2-0.100.1 mutation effect (s) Probability density

6. The Data We conduct laboratory evolution experiments using microbes so that we can monitor evolution in real time.

7. bacteriophage + bacteria Growing bacteriophage in the lab

8. Assaying fitness of phage genotypes

9. Small population Large population

10. Small population

17. -1.5 -1.25 -0.75 -0.5 -0.25 0 0.25 01020304050 Generation Log(fitness) Fitness Loss

18. -1.5 -1.25 -0.75 -0.5 -0.25 0 0.25 01020304050 Generation Log(fitness) Fitness Loss Genome sequencing reveals that one mutation was acquired right here

19. -1.5 -1.25 -0.75 -0.5 -0.25 0 0.25 01020304050 Generation Log(fitness) Fitness Loss Statistics can give the same answer, and statistics are much cheaper!

20. Large population

22. Adaptation Generation Log(fitness) -1.5 -1.25 -0.75 -0.5 -0.25 0 0.25 0255075100

23. Adaptation Generation Log(fitness) -1.5 -1.25 -0.75 -0.5 -0.25 0 0.25 0255075100 Genome sequencing of the endpoint reveals TWO new mutations.

24. Adaptation Generation Log(fitness) Again, statistics can give the same answer.

25. The Goal: Measure the distribution of spontaneous mutation effects. -0.4-0.3-0.2-0.100.1 mutation effect (s) Probability density

26. A slightly simpler goal: Measure the distribution of spontaneous mutation effects in a well adapted genome. -0.4-0.3-0.2-0.100.1 mutation effect (s) Probability density

27. 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…. 10 lineages

28. Genome sequence at the start and end of the experiment tells us how many mutations accumulated. Accumulated Mutations. Lineage Segment / nt mutation a,b Gene or RegionFunctional consequence AS/a1378g S/c2164t S/a2453g M/a804g L/c489t P9 P5 3’ UTR 1 st IGR P7 K13R A182V S11L BL/a270gP14M1V; start codon lost CS/t1867c S/g2141a S/c2627t M/a491g M/t760c M/a3660g L/a5166g L/g5774a P5 3’UTR P10 1 st IGR P13 P1 V83A Silent K42R E51G N406D Silent

29. We also measure fitness every day. plaque area transfer

30. Fitness measures, alone, allow identification of many mutations.

31. Effects of observed mutations 0 5 10 Number of mutations 00.10.20.30.40.5 mutation effect (s)

32. 0 5 10 00.10.20.30.4 0.5 Number of mutations Observed Sample 00.10.20.30.40.5 mutation effect (s) Probability density Unknown Population of Spontaneous Mutations Estimating distribution shapes by Maximum Likelihood

33. 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. 0 5 10 00.10.20.30.40.5 s probability density

34. Acknowledgements Phyllis DriscollUNC Biology Sebastien Guyader Mihee Lee UNC Statistics Dan Samarov Haipeng Shen National Institutes of Health