リケラボ論文検索は、全国の大学リポジトリにある学位論文・教授論文を一括検索できる論文検索サービスです。

リケラボ 全国の大学リポジトリにある学位論文・教授論文を一括検索するならリケラボ論文検索大学・研究所にある論文を検索できる

リケラボ 全国の大学リポジトリにある学位論文・教授論文を一括検索するならリケラボ論文検索大学・研究所にある論文を検索できる

大学・研究所にある論文を検索できる 「Identifying Potentially Beneficial Genetic Mutations Associated with Monophyletic Selective Sweep and a Proof-of-Concept Study with Viral Genetic Data」の論文概要。リケラボ論文検索は、全国の大学リポジトリにある学位論文・教授論文を一括検索できる論文検索サービスです。
リケラボ

コピーが完了しました

URLをコピーしました

論文の公開元へ論文の公開元へ
書き出し

Identifying Potentially Beneficial Genetic Mutations Associated with Monophyletic Selective Sweep and a Proof-of-Concept Study with Viral Genetic Data

Furuse, Yuki 京都大学 DOI:10.1128/mSystems.01151-20

2021.02

概要

Genetic mutations play a central role in evolution. For a significantly beneficial mutation, a one-time mutation event suffices for the species to prosper and predominate through the process called "monophyletic selective sweep." However, existing methods that rely on counting the number of mutation events to detect selection are unable to find such a mutation in selective sweep. We here introduce a method to detect mutations at the single amino acid/nucleotide level that could be responsible for monophyletic selective sweep evolution. The method identifies a genetic signature associated with selective sweep using the population genetic test statistic Tajima's D We applied the algorithm to ebolavirus, influenza A virus, and severe acute respiratory syndrome coronavirus 2 to identify known biologically significant mutations and unrecognized mutations associated with potential selective sweep. The method can detect beneficial mutations, possibly leading to discovery of previously unknown biological functions and mechanisms related to those mutations.IMPORTANCE In biology, research on evolution is important to understand the significance of genetic mutation. When there is a significantly beneficial mutation, a population of species with the mutation prospers and predominates, in a process called "selective sweep." However, there are few methods that can find such a mutation causing selective sweep from genetic data. We here introduce a novel method to detect such mutations. Applying the method to the genomes of ebolavirus, influenza viruses, and the novel coronavirus, we detected known biologically significant mutations and identified mutations the importance of which is previously unrecognized. The method can deepen our understanding of molecular and evolutionary biology.

論文の公開元へ

この論文で使われている画像

関連論文

関連論文をもっと見る

参考文献

1. Loewe L, Hill WG. 2010. The population genetics of mutations: good, bad and indifferent. Philos Trans R Soc Lond B Biol Sci 365:1153–1167. https:// doi.org/10.1098/rstb.2009.0317.

2. Frost SDW, Magalis BR, Kosakovsky Pond SL. 2018. Neutral theory and rap- idly evolving viral pathogens. Mol Biol Evol 35:1348–1354. https://doi.org/10.1093/molbev/msy088.

3. Taubenberger JK, Kash JC. 2010. Influenza virus evolution, host adapta- tion, and pandemic formation. Cell Host Microbe 7:440–451. https://doi.org/10.1016/j.chom.2010.05.009.

4. Li W, Zhang C, Sui J, Kuhn JH, Moore MJ, Luo S, Wong SK, Huang IC, Xu K, Vasilieva N, Murakami A, He Y, Marasco WA, Guan Y, Choe H, Farzan M. 2005. Receptor and viral determinants of SARS-coronavirus adaptation to human ACE2. EMBO J 24:1634–1643. https://doi.org/10.1038/sj.emboj.7600640.

5. Letko M, Seifert SN, Olival KJ, Plowright RK, Munster VJ. 2020. Bat-borne virus diversity, spillover and emergence. Nat Rev Microbiol 18:461–471. https://doi.org/10.1038/s41579-020-0394-z.

6. Sawyer SL, Emerman M, Malik HS. 2004. Ancient adaptive evolution of the primate antiviral DNA-editing enzyme APOBEC3G. PLoS Biol 2:e275. https://doi.org/10.1371/journal.pbio.0020275.

7. Sauter D, Kirchhoff F. 2019. Key viral adaptations preceding the AIDS pan- demic. Cell Host Microbe 25:27–38. https://doi.org/10.1016/j.chom.2018.12.002.

8. Suzuki Y. 2006. Natural selection on the influenza virus genome. Mol Biol Evol 23:1902–1911. https://doi.org/10.1093/molbev/msl050.

9. Smith DJ, Lapedes AS, de Jong JC, Bestebroer TM, Rimmelzwaan GF, Osterhaus ADME, Fouchier RAM. 2004. Mapping the antigenic and genetic evolution of influenza virus. Science 305:371–376. https://doi.org/10.1126/ science.1097211.

10. Neher RA, Bedford T, Daniels RS, Russell CA, Shraiman BI. 2016. Prediction, dynamics, and visualization of antigenic phenotypes of seasonal influ- enza viruses. Proc Natl Acad Sci U S A 113:E1701–E1709. https://doi.org/ 10.1073/pnas.1525578113.

11. Monto AS, McKimm-Breschkin JL, Macken C, Hampson AW, Hay A, Klimov A, Tashiro M, Webster RG, Aymard M, Hayden FG, Zambon M. 2006. Detection of influenza viruses resistant to neuraminidase inhibitors in global surveillance during the first 3 years of their use. Antimicrob Agents Chemother 50:2395–2402. https://doi.org/10.1128/AAC.01339-05.

12. Kiso M, Mitamura K, Sakai-Tagawa Y, Shiraishi K, Kawakami C, Kimura K, Hayden FG, Sugaya N, Kawaoka Y. 2004. Resistant influenza A viruses in children treated with oseltamivir: descriptive study. Lancet 364:759–765. https://doi.org/10.1016/S0140-6736(04)16934-1.

13. Wang B, Dwyer DE, Blyth CC, Soedjono M, Shi H, Kesson A, Ratnamohan M, McPhie K, Cunningham AL, Saksena NK. 2010. Detection of the rapid emergence of the H275Y mutation associated with oseltamivir resistance in severe pandemic influenza virus A/H1N1 09 infections. Antiviral Res 87:16–21. https://doi.org/10.1016/j.antiviral.2010.04.002.

14. Steinbrück L, McHardy AC. 2011. Allele dynamics plots for the study of ev- olutionary dynamics in viral populations. Nucleic Acids Res 39:e4. https:// doi.org/10.1093/nar/gkq909.

15. Orr HA. 1998. The population genetics of adaptation: the distribution of factors fixed during adaptive evolution. Evolution 52:935–949. https://doi.org/10.2307/2411226.

16. Suzuki Y, Gojobori T. 1999. A method for detecting positive selection at single amino acid sites. Mol Biol Evol 16:1315–1328. https://doi.org/10.1093/oxfordjournals.molbev.a026042.

17. Yang Z, Nielsen R. 2000. Estimating synonymous and nonsynonymous sub- stitution rates under realistic evolutionary models. Mol Biol Evol 17:32–43. https://doi.org/10.1093/oxfordjournals.molbev.a026236.

18. Kosakovsky Pond SL, Poon AFY, Leigh Brown AJ, Frost SDW. 2008. A maximum likelihood method for detecting directional evolution in protein sequences and its application to influenza A virus. Mol Biol Evol 25:1809–1824. https://doi.org/10.1093/molbev/msn123.

19. Zhang J, Kumar S. 1997. Detection of convergent and parallel evolution at the amino acid sequence level. Mol Biol Evol 14:527–536. https://doi.org/10.1093/oxfordjournals.molbev.a025789.

20. Charlesworth B. 2009. Fundamental concepts in genetics: effective popu- lation size and patterns of molecular evolution and variation. Nat Rev Genet 10:195–205. https://doi.org/10.1038/nrg2526.

21. Pavlidis P, Alachiotis N. 2017. A survey of methods and tools to detect recent and strong positive selection. J Biol Res (Thessalon) 24:7. https:// doi.org/10.1186/s40709-017-0064-0.

22. Klingen TR, Reimering S, Loers J, Mooren K, Klawonn F, Krey T, Gabriel G, McHardy AC. 2018. Sweep Dynamics (SD) plots: computational identifica- tion of selective sweeps to monitor the adaptation of influenza A viruses. Sci Rep 8:373. https://doi.org/10.1038/s41598017-18791-z.

23. Rasmussen DA, Stadler T. 2019. Coupling adaptive molecular evolution to phylodynamics using fitness-dependent birth-death models. Elife 8: e45562. https://doi.org/10.7554/eLife.45562.

24. Alachiotis N, Stamatakis A, Pavlidis P. 2012. OmegaPlus: a scalable tool for rapid detection of selective sweeps in whole-genome datasets. Bioinfor- matics 28:2274–2275. https://doi.org/10.1093/bioinformatics/bts419.

25. Pavlidis P, Živkovi´c D, Stamatakis A, Alachiotis N. 2013. SweeD: likelihoodbased detection of selective sweeps in thousands of genomes. Mol Biol Evol 30:2224–2234. https://doi.org/10.1093/molbev/mst112.

26. Tajima F. 1989. Statistical method for testing the neutral mutation hy- pothesis by DNA polymorphism. Genetics 123:585–595. https://doi.org/ 10.1093/genetics/123.3.585.

27. Kosakovsky Pond SL, Frost SDW. 2005. Not so different after all: a compar- ison of methods for detecting amino acid sites under selection. Mol Biol Evol 22:1208–1222. https://doi.org/10.1093/molbev/msi105.

28. Kosakovsky Pond SL, Frost SDW, Muse SV. 2005. HyPhy: hypothesis test- ing using phylogenies. Bioinformatics 21:676–679. https://doi.org/10.1093/bioinformatics/bti079.

29. Malvy D, McElroy AK, de Clerck H, Günther S, van Griensven J. 2019. Ebola virus disease. Lancet 393:936–948. https://doi.org/10.1016/S0140-6736 (18)33132-5.

30. WHO Ebola Response Team. 2014. Ebola virus disease in West Africa—the first 9 months of the epidemic and forward projections. N Engl J Med 371:1481–1495. https://doi.org/10.1056/NEJMoa1411100.

31. Ueda MT, Kurosaki Y, Izumi T, Nakano Y, Oloniniyi OK, Yasuda J, Koyanagi Y, Sato K, Nakagawa S. 2017. Functional mutations in spike glycoprotein of Zaire ebolavirus associated with an increase in infection efficiency. Genes Cells 22:148–159. https://doi.org/10.1111/gtc.12463.

32. Urbanowicz RA, McClure CP, Sakuntabhai A, Sall AA, Kobinger G, Müller MA, Holmes EC, Rey FA, Simon-Loriere E, Ball JK. 2016. Human adaptation of Ebola virus during the West African outbreak. Cell 167:1079–1087.e5. https://doi.org/10.1016/j.cell.2016.10.013.

33. Marzi A, Chadinah S, Haddock E, Feldmann F, Arndt N, Martellaro C, Scott DP, Hanley PW, Nyenswah TG, Sow S, Massaquoi M, Feldmann H. 2018. Recently identified mutations in the Ebola virus-Makona genome do not alter pathogenicity in animal models. Cell Rep 23:1806–1816. https://doi.org/10.1016/j.celrep.2018.04.027.

34. Wong G, He S, Leung A, Cao W, Bi Y, Zhang Z, Zhu W, Wang L, Zhao Y, Cheng K, Liu D, Liu W, Kobasa D, Gao GF, Qiu X. 2018. Naturally occurring single mutations in Ebola virus observably impact infectivity. J Virol 93: e01098-18. https://doi.org/10.1128/JVI.01098-18.

35. Furuse Y, Suzuki A, Kishi M, Nukiwa N, Shimizu M, Sawayama R, Fuji N, Oshitani H. 2010. Occurrence of mixed populations of influenza A viruses that can be maintained through transmission in a single host and poten- tial for reassortment. J Clin Microbiol 48:369–374. https://doi.org/10.1128/JCM.01795-09.

36. Moscona A. 2009. Global transmission of oseltamivir-resistant influenza. N Engl J Med 360:953–956. https://doi.org/10.1056/NEJMp0900648.

37. Furuse Y, Suzuki A, Oshitani H. 2009. Large-scale sequence analysis of M gene of influenza A viruses from different species: mechanisms for emer- gence and spread of amantadine resistance. Antimicrob Agents Chemo- ther 53:4457–4463. https://doi.org/10.1128/AAC.00650-09.

38. Centers for Disease Control and Prevention. 2009. Oseltamivir-Resistant novel influenza A (H1N1) virus infection in two immunosuppressed patients—Seattle, Washington, 2009. Morb Mortal Wkly Rep 58:893–896.

39. Correia V, Abecasis AB, Rebelo-de-Andrade H. 2018. Molecular footprints of selective pressure in the neuraminidase gene of currently circulating human influenza subtypes and lineages. Virology 522:122–130. https:// doi.org/10.1016/j.virol.2018.07.002.

40. Meijer A, Lackenby A, Hungnes O, Lina B, Van Der Werf S, Schweiger B, Opp M, Paget J, Van De Kassteele J, Hay A, Zambon M, European Influenza Surveillance Scheme. 2009. Oseltamivir-resistant influenza virus A (H1N1), Europe, 2007–08 season. Emerg Infect Dis 15:552–560. https://doi.org/10.3201/eid1504.181280.

41. Rambaut A, Pybus OG, Nelson MI, Viboud C, Taubenberger JK, Holmes EC. 2008. The genomic and epidemiological dynamics of human influenza A virus. Nature 453:615–619. https://doi.org/10.1038/nature06945.

42. Rogers MB, Song T, Sebra R, Greenbaum BD, Hamelin M-E, Fitch A, Twaddle A, Cui L, Holmes EC, Boivin G, Ghedin E. 2015. Intrahost dynam- ics of antiviral resistance in influenza A virus reflect complex patterns of segment linkage, reassortment, and natural selection. mBio 6:e02464-14. https://doi.org/10.1128/mBio.02464-14.

43. Yen HL, Herlocher LM, Hoffmann E, Matrosovich MN, Monto AS, Webster RG, Govorkova EA. 2005. Neuraminidase inhibitor-resistant influenza viruses may differ substantially in fitness and transmissibility. Antimi- crob Agents Chemother 49:4075–4084. https://doi.org/10.1128/AAC.49.10.4075-4084.2005.

44. Wiersinga WJ, Rhodes A, Cheng AC, Peacock SJ, Prescott HC. 2020. Patho- physiology, transmission, diagnosis, and treatment of coronavirus disease 2019 (COVID-19): a review. JAMA 324:782–793. https://doi.org/10.1001/ jama.2020.12839.

45. Korber B, Fischer WM, Gnanakaran S, Yoon H, Theiler J, Abfalterer W, Hengartner N, Giorgi EE, Bhattacharya T, Foley B, Hastie KM, Parker MD, Partridge DG, Evans CM, Freeman TM, de Silva TI, McDanal C, Perez LG, Tang H, Moon-Walker A, Whelan SP, LaBranche CC, Saphire EO, Montefiori DC, Angyal A, Brown RL, Carrilero L, Green LR, Groves DC, Johnson KJ, Keeley AJ, Lindsey BB, Parsons PJ, Raza M, Rowland-Jones S, Smith N, Tucker RM, Wang D, Wyles MD. 2020. Tracking changes in SARS-CoV-2 Spike: evidence that D614G increases infectivity of the COVID-19 virus. Cell 182:812–827.e19. https://doi.org/10.1016/j.cell.2020.06.043.

46. Hou YJ, Chiba S, Halfmann P, Ehre C, Kuroda M, Dinnon KH, Leist SR, Schäfer A, Nakajima N, Takahashi K, Lee RE, Mascenik TM, Graham R, Edwards CE, Tse LV, Okuda K, Markmann AJ, Bartelt L, de Silva A, Margolis DM, Boucher RC, Randell SH, Suzuki T, Gralinski LE, Kawaoka Y, Baric RS. 2020. SARS-CoV-2 D614G variant exhibits efficient replication ex vivo and transmission in vivo. Science 370:1464–1468. https://doi.org/10.1126/ science.abe8499.

47. Grubaugh ND, Hanage WP, Rasmussen AL. 2020. Making sense of muta- tion: what D614G means for the COVID-19 pandemic remains unclear. Cell 182:794–795. https://doi.org/10.1016/j.cell.2020.06.040.

48. Volz E, Hill V, McCrone JT, Price A, Jorgensen D, O’Toole Á, Southgate J, Johnson R, Jackson B, Nascimento FF, Rey SM, Nicholls SM, Colquhoun RM, da Silva Filipe A, Shepherd J, Pascall DJ, Shah R, Jesudason N, Li K, Jarrett R, Pacchiarini N, Bull M, Geidelberg L, Siveroni I, Goodfellow I, Loman NJ, Pybus OG, Robertson DL, Thomson EC, Rambaut A, Connor TR, Koshy C, Wise E, Cortes N, Lynch J, Kidd S, Mori M, Fairley DJ, Curran T, McKenna JP, Adams H, Fraser C, Golubchik T, Bonsall D, Moore C, Caddy SL, Khokhar FA, Wantoch M, Reynolds N, Warne B, Maksimovic J, Spellman K, McCluggage K, John M, Beer R, COG-UK Consortium, et al. 2021. Evaluating the effects of SARS-CoV-2 spike mutation D614G on transmissibility and pathogenicity. Cell 184:64–75.e11. https://doi.org/10.1016/j.cell.2020.11.020.

49. van Dorp L, Richard D, Tan CCS, Shaw LP, Acman M, Balloux F. 2020. No evi- dence for increased transmissibility from recurrent mutations in SARS-CoV-2. Nat Commun 11:5986. https://doi.org/10.1038/s41467-020-19818-2.

50. Chavali PL, Stojic L, Meredith LW, Joseph N, Nahorski MS, Sanford TJ, Sweeney TR, Krishna BA, Hosmillo M, Firth AE, Bayliss R, Marcelis CL, Lindsay S, Goodfellow I, Woods CG, Gergely F. 2017. Neurodevelop- mental protein Musashi-1 interacts with the Zika genome and pro- motes viral replication. Science 357:83–88. https://doi.org/10.1126/ science.aam9243.

51. Jopling CL, Yi MK, Lancaster AM, Lemon SM, Sarnow P. 2005. Molecular biology: modulation of hepatitis C virus RNA abundance by a liver-spe- cific microRNA. Science 309:1577–1581. https://doi.org/10.1126/science.1113329.

52. Lim CS, Brown CM. 2018. Know your enemy: successful bioinformatic approaches to predict functional RNA structures in viral RNAs. Front Microbiol 8:2582. https://doi.org/10.3389/fmicb.2017.02582.

53. Hutchinson EC, von Kirchbach JC, Gog JR, Digard P. 2010. Genome pack- aging in influenza A virus. J Gen Virol 91:313–328. https://doi.org/10.1099/vir.0.017608-0.

54. Fay JC, Wu CI. 2000. Hitchhiking under positive Darwinian selection. Genetics 155:1405–1413.

55. Messer PW, Neher RA. 2012. Estimating the strength of selective sweeps from deep population diversity data. Genetics 191:593–605. https://doi.org/10.1534/genetics.112.138461.

56. Geoghegan JL, Holmes EC. 2018. Evolutionary virology at 40. Genetics 210:1151–1162. https://doi.org/10.1534/genetics.118.301556.

57. Cantor RM, Lange K, Sinsheimer JS. 2010. Prioritizing GWAS results: a review of statistical methods and recommendations for their application. Am J Hum Genet 86:6–22. https://doi.org/10.1016/j.ajhg.2009.11.017.

58. Luksza M, Lässig M. 2014. A predictive fitness model for influenza. Nature 507:57–61. https://doi.org/10.1038/nature13087.

59. Villa M, Lässig M. 2017. Fitness cost of reassortment in human influenza. PLoS Pathog 13:e1006685. https://doi.org/10.1371/journal.ppat.1006685.

60. Benjamini Y, Hochberg Y. 1995. Controlling the false discovery rate: a practical and powerful approach to multiple testing. J R Stat Soc Ser B 57:289–300. https://doi.org/10.1111/j.2517-6161.1995.tb02031.x.

61. Kumar S, Stecher G, Tamura K. 2016. MEGA7: Molecular Evolutionary Genet- ics Analysis version 7.0 for bigger datasets. Mol Biol Evol 33:1870–1874. https://doi.org/10.1093/molbev/msw054.

62. Murrell B, Moola S, Mabona A, Weighill T, Sheward D, Kosakovsky Pond SL, Scheffler K. 2013. FUBAR: a fast, unconstrained Bayesian approxima- tion for inferring selection. Mol Biol Evol 30:1196–1205. https://doi.org/10.1093/molbev/mst030.

63. Murrell B, Wertheim JO, Moola S, Weighill T, Scheffler K, Kosakovsky Pond SL. 2012. Detecting individual sites subject to episodic diversify- ing selection. PLoS Genet 8:e1002764. https://doi.org/10.1371/journal.pgen.1002764.

64. Stamatakis A. 2014. RAxML version 8: a tool for phylogenetic analysis and post-analysis of large phylogenies. Bioinformatics 30:1312–1313. https:// doi.org/10.1093/bioinformatics/btu033.

参考文献をもっと見る