Sex, viruses, and the statistical physics of evolution

Transcription

1 Sex, viruses, and the statistical physics of evolution

2 Cartoon of Evolution A TC G Selection Genotypes Mutation & Recombination Mutation...ATACG... Sex & Recombination Selection...ATGCG... Richard Neher 2

3 Tree of Life - billions of years source: tree of life, tolweb.org 4 billion years Richard Neher 3

4 Phylogenetic tree of Fruitflies - millions of years ~40 Millions of years image from: insects.eugenes.org/species/ Richard Neher 4

5 Evolution of Influenza A - few years PB2 Influenza A global epidemic aa # aa # aa # A/Weiss/43 A/AA/Huston/1945 A/AA/Marton/1943 A/Iowa/1943 A 3 aa 98 # aa #5 A/Bel/42 A/Alaska/1935 A/PR/8/34 A/Henry/1936 A/Phila/1935 A/Melbourne/35 A/WSN/1933 A/Brevig Mission/1/ A/Chile/1/83 A/Memphis/39/1983 A/Christs Hospital/157/1982 A/Memphis/1/1984 A/Tonga/14/1984 A/New Zealand/7/1983 A/India/6263/1980 A/Memphis/7/1980 A/Baylor/4052/1981 A/Brazil/11/1978 A/Hong Kong/117/77 A/Lackland/3/1978 A/Memphis/10/1978 A/USSR/90/1977 A/Arizona/14/1978 A/Tientsin/78/1977 A/Roma/1949 A/Albany/4835/1948 A/Albany/4836/1950 A/Albany/12/1951 A/Denver/57 A/Fort Worth/50 A/Malaysia/54 A/FORT MONMOUTH/1/47 A/Cam/46 A/Hickox/1940 B I C aa #7 100 D A/Texas/36/91 F A/Memphis/12/1986 A/Memphis/3/1987 A/New York/2924-1/1986 A/Taiwan/01/1986 A/Texas/2922-3/1986 A/Singapore/6/1986 A/Baylor/11515/82 V E II 1 aa # III IV 100 A/Canterbury/01/2001 A/Canterbury/106/2004 A/Memphis/5/2003 A/New York/8/ A/Otago/5/2005 J A/New York/223/2003 A/New York/291/2002 A/Wellington/1/2001 A/Memphis/1/2001 A/New York/239/2001 A/New Caledonia/20/1999 A/New South Wales/24/1999 A/TW/4845/99 A/New York/233/2000 A/Hong Kong/470/97 A/TW/3355/97 A/Nanchang/13/1996 A/Charlottesville/31/95 A/Memphis/6/1996 A/New York/653/1996 A/TW/130/96 A/New York/694/1995 G A/Hong Kong/427/98 A/South A/New Australia/44/2000 York/146/ H VI I IX VIII Evolution as cause of epidemics Nelson et al., 2008 Richard Neher 5

6 Human immunodeficiency virus (HIV) 100nm HIV budding from an immune cell Rapid evolution is a hallmark of HIV infections Richard Neher 6

7 HIV Richard Neher 7

8 HIV Virus escapes the immune system by continuous evolution Richard Neher 7

9 Evolution in a single patient (blood samples every 6 month) ~ 8% divergence in 10 years == many millions of years in Drosophila Time (years since seroconversion) Lemey et al., 2006, Shankarappa et al Richard Neher 8

10 Evolution of HIV The virus has to change: Escape the immune system and drug resistance Resistance to drugs (protease inhibitors): Drug sensitive: PQITLWQRPLVTIKIGGQLKEALLDTGADDTVLEEMNLPGRWKPKMIGGIGGFIKVRQYDQILIEICGHKAIGTVLVGPTPVNIIGRNLLTQIGCTLNF Drug resistant: PQITLWQRPLVTIKVGGQLTEALLDTGADDTILEDMTLPGRWKPKIVGGIGGFIKVRQYDQVPIEICGHKVISTVLIGPTPCNIIGRNLMTQIGLTLNF Richard Neher 9

11 Evolution of HIV The virus has to change: Escape the immune system and drug resistance Resistance to drugs (protease inhibitors): Drug sensitive: PQITLWQRPLVTIKIGGQLKEALLDTGADDTVLEEMNLPGRWKPKMIGGIGGFIKVRQYDQILIEICGHKAIGTVLVGPTPVNIIGRNLLTQIGCTLNF Drug resistant: PQITLWQRPLVTIKVGGQLTEALLDTGADDTILEDMTLPGRWKPKIVGGIGGFIKVRQYDQVPIEICGHKVISTVLIGPTPCNIIGRNLMTQIGLTLNF High mutation rate: μ=3x10-5 /generation and site Richard Neher 9

12 Evolution of HIV The virus has to change: Escape the immune system and drug resistance Resistance to drugs (protease inhibitors): Drug sensitive: PQITLWQRPLVTIKIGGQLKEALLDTGADDTVLEEMNLPGRWKPKMIGGIGGFIKVRQYDQILIEICGHKAIGTVLVGPTPVNIIGRNLLTQIGCTLNF Drug resistant: PQITLWQRPLVTIKVGGQLTEALLDTGADDTILEDMTLPGRWKPKIVGGIGGFIKVRQYDQVPIEICGHKVISTVLIGPTPCNIIGRNLMTQIGLTLNF High mutation rate: μ=3x10-5 /generation and site Large population: N=10 10 viruses Richard Neher 9

13 Evolution of HIV The virus has to change: Escape the immune system and drug resistance Resistance to drugs (protease inhibitors): Drug sensitive: PQITLWQRPLVTIKIGGQLKEALLDTGADDTVLEEMNLPGRWKPKMIGGIGGFIKVRQYDQILIEICGHKAIGTVLVGPTPVNIIGRNLLTQIGCTLNF Drug resistant: PQITLWQRPLVTIKVGGQLTEALLDTGADDTILEDMTLPGRWKPKIVGGIGGFIKVRQYDQVPIEICGHKVISTVLIGPTPCNIIGRNLMTQIGLTLNF High mutation rate: μ=3x10-5 /generation and site human immune cell Large population: N=10 10 viruses Viral sex Richard Neher 9

14 Recombination in viruses - Influenza 100nm 11 genes on 8 segments 16 H (hemagglutinin) subtypes 9 N (neuraminidase) subtypes H1N1, H2N2, H3N2, H5N1 are common Richard Neher 10

15 Recombination in viruses - Influenza 100nm 11 genes on 8 segments 16 H (hemagglutinin) subtypes 9 N (neuraminidase) subtypes H1N1, H2N2, H3N2, H5N1 are common Pandemics often follow reassortments, e.g. pandemics 1957 (H2N2) and 1968 (H3N2) Reassortment is frequent in waterfowl and swine, where many subtypes circulate. Richard Neher 10

16 Population Spreading of beneficial mutations Mutant individuals reproduce faster (Drug resistant strain) Time Fixation probability: ~s Sweep time: ~ ln(ns) / s Richard Neher 11

17 Evolution in asexual and sexual organisms Small Population Time Sequential innovations: rate ~ N Richard Neher 12

18 Evolution in asexual and sexual organisms Small Population Large Population Time Sequential innovations: rate ~ N Many concurrent mutations: rate ~ log N Most good mutations are wasted! Time Richard Neher 12

19 Evolution in asexual and sexual organisms Small Population E V O L U T I O N IN S E X U A L A N D A S E X U A L POPUL.AT1ONS Time POPULATION E V O L U T I O N IN S E X U A L A N D A S E X U A L POPUL.AT1ONS SMALL Large Population Sequential innovations: rate ~ N Many concurrent mutations: a n d rate ~ log N FIGURE 1. Evolution i n s e x u a l and a s e x u a l populations. The hatched shaded a r e a s show the i n c r e a s e d number of mutant individuals following t h e occurrence of a favorable mutation. T h e a b s c i s s a I s time. Modified from Muller (1932). Most good mutations are wasted! s i s small, and therefore that p changes very slowly s o t h a t i t i s appropriate to replace addition by integration. T h i s l e a d s to LARGE POPULATION LARGE POPULATION Time In the a b s e n c e of recombination, p follows the logistic curve Conventional wisdom: rate ~ N RA Fisher (1930), H Muller (1932), M Kimura and J Crow (1965) Time Richard Neher 12

20 Quantifying the benefits of recombination [for the virus] Example: drug resistance of HIV Drug sensitive: PQITLWQRPLVTIKIGGQLKEALLDTGADDTVLEEMNLPGRWKPKMIGGIGGFIKVRQYDQILIEICGHKAIGTVLVGPTPVNIIGRNLLTQIGCTLNF Drug resistant: PQITLWQRPLVTIKVGGQLTEALLDTGADDTILEDMTLPGRWKPKIVGGIGGFIKVRQYDQVPIEICGHKVISTVLIGPTPCNIIGRNLMTQIGLTLNF Hypothetical distribution of drug resistance mutations Resistance mutations Richard Neher 13

21 Quantifying the benefits of recombination [for the virus] Example: drug resistance of HIV Drug sensitive: PQITLWQRPLVTIKIGGQLKEALLDTGADDTVLEEMNLPGRWKPKMIGGIGGFIKVRQYDQILIEICGHKAIGTVLVGPTPVNIIGRNLLTQIGCTLNF Drug resistant: PQITLWQRPLVTIKVGGQLTEALLDTGADDTILEDMTLPGRWKPKIVGGIGGFIKVRQYDQVPIEICGHKVISTVLIGPTPCNIIGRNLMTQIGLTLNF Hypothetical distribution of drug resistance mutations Drug resistance increases due to selection on existing mutations Resistance mutations Richard Neher 13

22 Quantifying the benefits of recombination [for the virus] Example: drug resistance of HIV Drug sensitive: PQITLWQRPLVTIKIGGQLKEALLDTGADDTVLEEMNLPGRWKPKMIGGIGGFIKVRQYDQILIEICGHKAIGTVLVGPTPVNIIGRNLLTQIGCTLNF Drug resistant: PQITLWQRPLVTIKVGGQLTEALLDTGADDTILEDMTLPGRWKPKIVGGIGGFIKVRQYDQVPIEICGHKVISTVLIGPTPCNIIGRNLMTQIGLTLNF Hypothetical distribution of drug resistance mutations novel mutation Drug resistance increases due to selection on existing mutations Novel mutations keep the wave going Resistance mutations Richard Neher 13

23 Quantifying the benefits of recombination [for the virus] Example: drug resistance of HIV Drug sensitive: PQITLWQRPLVTIKIGGQLKEALLDTGADDTVLEEMNLPGRWKPKMIGGIGGFIKVRQYDQILIEICGHKAIGTVLVGPTPVNIIGRNLLTQIGCTLNF Drug resistant: PQITLWQRPLVTIKVGGQLTEALLDTGADDTILEDMTLPGRWKPKIVGGIGGFIKVRQYDQVPIEICGHKVISTVLIGPTPCNIIGRNLMTQIGLTLNF Hypothetical distribution of drug resistance mutations novel mutation recombination Drug resistance increases due to selection on existing mutations Novel mutations keep the wave going Resistance mutations Via recombination mutations can keep up with the wave and make it Richard Neher 13

24 Surfing of beneficial mutations ! Richard Neher 14

25 Surfing of beneficial mutations ! Richard Neher 14

26 Evolutionary benefits of recombination Conventional wisdom: rate of evolution ~ N holds only for very frequent recombination Realistic recombination: rate of evolution ~ r 2 log N To slow down evolution, one has to target recombination rather than the population size!! Richard Neher 15

27 The more recombination, the better? Is there a cost to recombination? Richard Neher 16

28 Genetic interactions So far: Fitness = # beneficial mutations BUT: An organism is more than the sum of its parts Richard Neher 17

29 Genetic interactions So far: Fitness = # beneficial mutations BUT: An organism is more than the sum of its parts Richard Neher 17

30 Genetic interactions So far: Fitness = # beneficial mutations BUT: An organism is more than the sum of its parts Richard Neher 17

31 The cost of recombination A recombined soccer team is worse than the parents. Richard Neher 18

32 The cost of recombination A recombined soccer team is worse than the parents. Relay racing teams don t have that problem! Richard Neher 18

33 Genetic interaction and recombination Different reassortments of Influenza A: Recombination explores -- selection amplifies the best Richard Neher 19

34 Genetic interaction and recombination Different reassortments of Influenza A: Recombination explores -- selection amplifies the best no recombination moderate recombination frequent recombination Richard Neher 19

35 Allele vs genotype selection Strength of Interaction Rel. epistasis Genotype selection Clonal Competition (CC) Quasi Linkage Equilibrium (QLE) Allele selection Recombination rate/selection Mating freq./selection Richard Neher 20

36 The success of selection 0.4 Selection on the properties of the parts. All Stars team F final 0.3 Selection on the combinations the best clubs recombination Richard Neher 21

37 Connecting theory and observations Large data bases of HIV sequences and drug resistance allow us to study: The recombination rate in HIV Selection strength of HIV evolution without drugs Drug resistance mutations: team players or independent? Does recombination vary from patient to patient? Does it vary at different stages of the disease? With modern sequencing technology we can soon monitor viral populations at unprecedented resolution. Richard Neher 22

38 Connecting theory and observations Large data bases of HIV sequences and drug resistance allow us to study: The recombination rate in HIV Selection strength of HIV evolution without drugs Drug resistance mutations: team players or independent? Does recombination vary from patient to patient? Does it vary at different stages of the disease? With modern sequencing technology we can soon monitor viral populations at unprecedented resolution. Do such observations, together with theoretical insight, explain the differences between drugs and patients? Richard Neher 22

39 Connecting theory and observations Large data bases of HIV sequences and drug resistance allow us to study: The recombination rate in HIV Selection strength of HIV evolution without drugs Drug resistance mutations: team players or independent? Does recombination vary from patient to patient? Does it vary at different stages of the disease? With modern sequencing technology we can soon monitor viral populations at unprecedented resolution. Do such observations, together with theoretical insight, explain the differences between drugs and patients? What can we learn about the evolutionary process in general? Richard Neher 22

40 Sequencing Costs per Base Cost per base [$] 10!1 10!2 10!3 10!4 Data Exponential decay:! = 2.5 years 10! year Richard Neher 23

41 Experiment & Theory Darwin s Theory Observations: Paleontology Diversity of species Richard Neher 24

42 Experiment & Theory Darwin s Theory Quantitative Theory Dynamics Experiments in the Lab Bacteria or viruses Sequencing and Phenotyping Observations: Paleontology Diversity of species Richard Neher 24

43 Collaborators Boris Shraiman, KITP Daniel Fisher, Stanford Thomas Leitner, LANL Richard Neher 25