Loss and Recovery of Genetic Diversity in Adapting Populations of HIV

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The evolution of drug resistance in HIV occurs by the fixation of specific, well-known, drug-resistance mutations, but the underlying population genetic processes are not well understood. By analyzing within-patient longitudinal sequence data, we make four observations that shed a light on the underlying processes and allow us to infer the short-term effective population size of the viral population in a patient. Our first observation is that the evolution of drug resistance usually occurs by the fixation of one drug-resistance mutation at a time, as opposed to several changes simultaneously. Second, we find that these fixation events are accompanied by a reduction in genetic diversity in the region surrounding the fixed drug-resistance mutation, due to the hitchhiking effect. Third, we observe that the fixation of drug-resistance mutations involves both hard and soft selective sweeps. In a hard sweep, a resistance mutation arises in a single viral particle and drives all linked mutations with it when it spreads in the viral population, which dramatically reduces genetic diversity. On the other hand, in a soft sweep, a resistance mutation occurs multiple times on different genetic backgrounds, and the reduction of diversity is weak. Using the frequency of occurrence of hard and soft sweeps we estimate the effective population size of HIV to be ( confidence interval ). This number is much lower than the actual number of infected cells, but much larger than previous population size estimates based on synonymous diversity. We propose several explanations for the observed discrepancies. Finally, our fourth observation is that genetic diversity at non-synonymous sites recovers to its pre-fixation value within 18 months, whereas diversity at synonymous sites remains depressed after this time period. These results improve our understanding of HIV evolution and have potential implications for treatment strategies.

Vyšlo v časopise: Loss and Recovery of Genetic Diversity in Adapting Populations of HIV. PLoS Genet 10(1): e32767. doi:10.1371/journal.pgen.1004000
Kategorie: Research Article
prolekare.web.journal.doi_sk: https://doi.org/10.1371/journal.pgen.1004000

Souhrn

Zdroje

1. HaydenEJ, FerradaE, WagnerA (2011) Cryptic genetic variation promotes rapid evolutionary adaptation in an RNA enzyme. Nature 474: 92–95.

2. LauringAS, FrydmanJ, AndinoR (2013) The role of mutational robustness in RNA virus evolution. Nat Rev Microbiol 11: 327–336.

3. Maynard SmithJ, HaighJ (1974) The hitchhiking effect of a favorable gene. Genet Res 23: 23–35.

4. PenningsP, HermissonJ (2006) Soft sweeps II: Molecular population genetics of adaptation from recurrent mutation or migration. Molecular Biology and Evolution 23: 1076–1084.

5. KouyosRD, AlthausCL, BonhoefferS (2006) Stochastic or deterministic: what is the effective population size of HIV-1? Trends Microbiol 14: 507–511.

6. Leigh-BrownAJ (1997) Analysis of hiv-1 env gene sequences reveals evidence for a low effective number in the viral population. Proceedings of the National Academy of Sciences of the United States of America 94: 1862–1865.

7. HaaseAT (1994) The role of active and covert infections in lentivirus pathogenesis. Annals of the New York Academy of Sciences 724: 75–86.

8. CoffinJM (1995) HIV population dynamics in vivo - implications for genetic variation, pathogenesis, and therapy. Science 267: 483–489.

9. FrostSDW, NijhuisM, SchuurmanR, BoucherCAB, BrownAJL (2000) Evolution of lamivudine resistance in human immunodeficiency virus type 1-infected individuals: the relative roles of drift and selection. Journal of Virology 74: 6262–6268.

10. RouzineIM, CoffinJM (1999) Linkage disequilibrium test implies a large effective population number for HIV in vivo. Proceedings of the National Academy of Sciences 96: 10758–10763.

11. Maldarelli (2013) HIV populations are large and accumulate high genetic diversity in nonlinear fashion. J Virol 87: 10313–23 doi: 10.1128/JVI.01225-12

12. BachelerLT, AntonED, KudishP, BakerD, BunvilleJ, et al. (2000) Human immunodeficiency virus type 1 mutations selected in patients failing efavirenz combination therapy. Antimicrobial Agents and Chemotherapy 44: 2475–2484.

13. KarasovT, MesserP, PetrovD (2010) Evidence that adaptation in drosophila is not limited by mutation at single sites. PLoS Genetics 6: e1000924.

14. NairS, NashD, SudimackD, JaideeA, BarendsM, et al. (2007) Recurrent gene amplification and soft selective sweeps during evolution of multidrug resistance in malaria parasites. Molecular Biology and Evolution 24: 562–573.

15. BoltzVF, AmbroseZ, KearneyMF, ShaoW, KewalramaniVN, et al. (2012) Ultrasensitive allele-specific PCR reveals rare preexisting drugresistant variants and a large replicating virus population in macaques infected with a simian immunodeficiency virus containing human immunodeficiency virus reverse transcriptase. Journal of Virology 86: 12525–30.

16. KaplanNL, HudsonRR, LangleyCH (1989) The “hitchhiking effect” revisited. Genetics 123: 887–899.

17. NijhuisM, BoucherCa, SchipperP, LeitnerT, SchuurmanR, et al. (1998) Stochastic processes strongly inuence HIV-1 evolution during suboptimal protease-inhibitor therapy. Proceedings of the National Academy of Sciences of the United States of America 95: 14441–6.

18. PenningsP, HermissonJ (2006) Soft sweeps III: The signature of positive selection from recurrent mutation. PLoS Genetics 2: e186.

19. AbramME, FerrisAL, ShaoW, AlvordWG, HughesSH (2010) Nature, position, and frequency of mutations made in a single cycle of HIV-1 replication. Journal of Virology 84: 9864–78.

20. ZhangY, ImamichiH, ImamichiT, LaneC, JF, et al. (1997) Drug resistance during indinavir therapy is caused by mutations in the protease gene and in its gag substrate cleavage sites. Journal of Virology 71: 6662–6670.

21. PenningsPS (2012) Standing genetic variation and the evolution of drug resistance in HIV. PLoS Comput Biol 8: e1002527.

22. SanjuánR, BorderíaAV (2011) Interplay between RNA structure and protein evolution in HIV-1. Molecular Biology and Evolution 28: 1333–8.

23. SongYS, SteinrückenM (2012) A simple method for finding explicit analytic transition densities of diffusion processes with general diploid selection. Genetics 190: 1117–1129.

24. Alon U (2006) An Introduction to Systems Biology: Design Principles of Biological Circuits. Chapman and Hall/CRC, 1 edition.

25. Malecot G (1969). The Mathematics of Heredity. Revised, edited and translated by D.M. Yermanos.

26. NeiM, MaruyamaT, ChakrabortyR (1975) The bottleneck effect and genetic variability in populations. Evolution 29: 1–10.

27. GordoI, DionisioF (2005) Nonequilibrium model for estimating parameters of deleterious mutations. Physical Review E 71: 031907.

28. LohmuellerKE, IndapAR, SchmidtS, BoykoAR, HernandezRD, et al. (2008) Proportionally more deleterious genetic variation in European than in African populations. Nature 451: 994–U5.

29. KiezunA, PulitSL, FrancioliLC, van DijkF, SwertzM, et al. (2013) Deleterious alleles in the human genome are on average younger than neutral alleles of the same frequency. PLoS Genetics 9: e1003301.

30. DesaiMM, FisherDS (2007) Beneficial mutation-selection balance and the effect of linkage on positive selection. Genetics 176: 1759–1798.

31. SchiffelsS, SzöllősiGJ, MustonenV, LässigM (2011) Emergent neutrality in adaptive asexual evolution. Genetics 189: 1361–1375.

32. NeherRA, ShraimanBI (2011) Genetic draft and quasi-neutrality in large facultatively sexual populations. Genetics 188: 975–96.

33. WalczakAM, NicolaisenLE, PlotkinJB, DesaiMM (2012) The structure of genealogies in the presence of purifying selection: A fitness-class coalescent. Genetics 190: 753–779.

34. DesaiMM, WalczakAM, FisherDS (2012) Genetic diversity and the structure of genealogies in rapidly adapting populations. Genetics 193: 565–585.

35. RouzineIM, CoffinJM (2010) Multi-site adaptation in the presence of infrequent recombination. Theoretical Population Biology 77: 189–204.

36. RouzineIM, WakeleyJ, CoffinJM (2003) The solitary wave of asexual evolution. Proceedings of the National Academy of Sciences of the United States of America 100: 587–592.

37. RouzineIM, BrunetE, WilkeCO (2008) The traveling-wave approach to asexual evolution: Muller's ratchet and speed of adaptation. Theoretical Population Biology 73: 24–46.

38. NeherRA, ShraimanBI, FisherDS (2010) Rate of adaptation in large sexual populations. Genetics 184: 467–81.

39. GoodBH, RouzineIM, BalickDJ, HallatschekO, DesaiMM (2012) Distribution of fixed beneficial mutations and the rate of adaptation in asexual populations. Proceedings of the National Academy of Sciences of the United States of America 109: 4950–5.

40. GoyalS, BalickDJ, JerisonER, NeherRA, ShraimanBI, et al. (2012) Dynamic mutation-selection balance as an evolutionary attractor. Genetics 191: 1309–1319.

41. NeherRA, LeitnerT (2010) Recombination rate and selection strength in HIV intra-patient evolution. PLoS Computational Biology 6: e1000660.

42. BatorskyR, KearneyMF, PalmerSE, MaldarelliF, RouzineIM, et al. (2011) Estimate of effective recombination rate and average selection coef- ficient for HIV in chronic infection. Proceedings of the National Academy of Sciences of the United States of America 108: 5661–5666.

43. MullinsJI, HeathL, HughesJP, KichaJ, StyrchakS, et al. (2011) Mutation of HIV-1 genomes in a clinical population treated with the mutagenic nucleoside KP1461. PLoS One 6: e15135.

44. WeissmanDB, BartonNH (2012) Limits to the rate of adaptive substitution in sexual populations. PLoS Genetics 8: e1002740.

45. JohnsonV, Brun-VézinetF, ClotetB, GünthardH, KuritzkesD, et al. (2010) Update of the drug resistance mutations in HIV-1: December 2010. Top HIV Med 18: 156–163.

46. ManskyLM, TeminHM (1995) Lower in vivo mutation rate of human immunodeficiency virus type 1 than that predicted from the fidelity of purified reverse transcriptase. Journal of Virology 69: 5087–94.