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Codon usage of HIV regulatory genes is not determined by nucleotide composition

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Abstract

Codon usage bias can be a result of either mutational bias or selection for translational efficiency and/or accuracy. Previous data has suggested that nucleotide composition constraint was the main determinant of HIV codon usage, and that nucleotide composition and codon usage were different between the regulatory genes, tat and rev, and other viral genes. It is not clear whether translational selection contributed to the codon usage difference and how nucleotide composition and translational selection interact to determine HIV codon usage. In this study, a model of codon bias due to GC composition with modification for the A-rich third codon position was used to calculate predicted HIV codon frequencies based on its nucleotide composition. The predicted codon usage of each gene was compared with the actual codon frequency. The predicted codon usage based on GC composition matched well with the actual codon frequencies for the structural genes (gag, pol and env). However, the codon usage of the regulatory genes (tat and rev) could not be predicted. Codon usage of the regulatory genes was also relatively unbiased showing the highest effective number of codons (ENC). Moreover, the codon adaptation index (CAI) of the regulatory genes showed better adaptation to human codons when compared to other HIV genes. Therefore, the early expressed genes responsible for regulation of the replication cycle, tat and rev, were more similar to humans in terms of codon usage and GC content than other HIV genes. This may help these genes to be expressed efficiently during the early stages of infection.

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Abbreviations

RSCU:

Relative synonymous codon usage

ENC:

Effective number of codon

CAI:

Codon adaptation index

References

  1. Gouy M, Gautier C (1982) Codon usage in bacteria: correlation with gene expressivity. Nucleic Acids Res 10:7055–7074

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  2. Gustafsson C, Govindarajan S, Minshull J (2004) Codon bias and heterologous protein expression. Trends Biotechnol 22:346–353

    Article  CAS  PubMed  Google Scholar 

  3. Hu X, Shi Q, Yang T, Jackowski G (1996) Specific replacement of consecutive AGG codons results in high-level expression of human cardiac troponin T in Escherichia coli. Protein Expr Purif 7:289–293

    Article  CAS  PubMed  Google Scholar 

  4. Deng T (1997) Bacterial expression and purification of biologically active mouse c-Fos proteins by selective codon optimization. FEBS Lett 409:269–272

    Article  CAS  PubMed  Google Scholar 

  5. Kotula L, Curtis PJ (1991) Evaluation of foreign gene codon optimization in yeast: expression of a mouse IG kappa chain. Biotechnology (NY) 9:1386–1389

    Article  CAS  Google Scholar 

  6. Burgess-Brown NA, Sharma S, Sobott F, Loenarz C, Oppermann U, Gileadi O (2008) Codon optimization can improve expression of human genes in Escherichia coli: A multi-gene study. Protein Expr Purif 59:94–102

    Article  CAS  PubMed  Google Scholar 

  7. Wong EH, Smith DK, Rabadan R, Peiris M, Poon LL (2010) Codon usage bias and the evolution of influenza A viruses. Codon usage biases of influenza virus. BMC Evol Biol 10:253

    Article  PubMed  PubMed Central  Google Scholar 

  8. Belalov IS, Lukashev AN (2013) Causes and implications of codon usage bias in RNA viruses. PLoS One 8:e56642. doi:10.1371/journal.pone.0056642

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  9. Bulmer M (1987) Coevolution of codon usage and transfer RNA abundance. Nature 325:728–730

    Article  CAS  PubMed  Google Scholar 

  10. Ermolaeva MD (2001) Synonymous codon usage in bacteria. Curr Issues Mol Biol 3:91–97

    CAS  PubMed  Google Scholar 

  11. Haas J, Park EC, Seed B (1996) Codon usage limitation in the expression of HIV-1 envelope glycoprotein. Curr Biol 6:315–324

    Article  CAS  PubMed  Google Scholar 

  12. Chen SL, Lee W, Hottes AK, Shapiro L, McAdams HH (2004) Codon usage between genomes is constrained by genome-wide mutational processes. Proc Natl Acad Sci USA 101:3480–3485

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  13. Sharp PM, Stenico M, Peden JF, Lloyd AT (1993) Codon usage: mutational bias, translational selection, or both? BiochemSoc Trans 21:835–841

    Article  CAS  Google Scholar 

  14. Knoepfel SA, Di Giallonardo F, Däumer M, Thielen A, Metzner KJ (2011) In-depth analysis of G-to-A hypermutation rate in HIV-1 env DNA induced by endogenous APOBEC3 proteins using massively parallel sequencing. J Virol Methods 171:329–338

    Article  CAS  PubMed  Google Scholar 

  15. Imahashi M, Nakashima M, Iwatani Y (2012) Antiviral mechanism and biochemical basis of the human APOBEC3 family. Front Microbiol 3:250

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  16. Romani B, Engelbrecht S, Glashoff RH (2009) Antiviral roles of APOBEC proteins against HIV-1 and suppression by Vif. Arch Virol 154:1579–1588

    Article  CAS  PubMed  Google Scholar 

  17. Pandit A, Sinha S (2011) Differential trends in the codon usage patterns in HIV-1 genes. PLoS One 6:e28889. doi:10.1371/journal.pone.0028889

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  18. van der Kuyl AC, Berkhout B (2012) The biased nucleotide composition of the HIV genome: a constant factor in a highly variable virus. Retrovirology 9:92. doi:10.1186/1742-4690-9-92

    Article  PubMed  PubMed Central  Google Scholar 

  19. Palidwor GA, Perkins TJ, Xia X (2010) A general model of codon bias due to GC mutational bias. PLoS One 5:e13431. doi:10.1371/journal.pone.0013431

    Article  PubMed  PubMed Central  Google Scholar 

  20. van Hemert FJ, Berkhout B, Lukashov VV (2007) Host-related nucleotide composition and codon usage as driving forces in the recent evolution of the Astroviridae. Virology 361:447–454

    Article  PubMed  Google Scholar 

  21. Butt AM, Nasrullah I, Qamar R, Tong Y (2016) Evolution of codon usage in Zika virus genomes is host and vector specific. Emerg Microbes Infect 5:e107. doi:10.1038/emi.2016.106

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  22. Gilchrist MA, Coombs D (2006) Evolution of virulence: interdependence, constraints, and selection using nested models. Theor Popul Biol 69:145–153

    Article  PubMed  Google Scholar 

  23. Kypr J, Mrazek J (1987) Unusual codon usage of HIV. Nature 327:20

    Article  CAS  PubMed  Google Scholar 

  24. Stothard P (2000) The sequence manipulation suite: JavaScript programs for analyzing and formatting protein and DNA sequences. Biotechniques 28:1102–1104

    CAS  PubMed  Google Scholar 

  25. Puigbò P, Bravo IG, Garcia-Vallve S (2008) CAIcal: a combined set of tools to assess codon usage adaptation. Biol Direct 3:38. doi:10.1186/1745-6150-3-38

    Article  PubMed  PubMed Central  Google Scholar 

  26. Nakamura Y, Gojobori T, Ikemura T (2000) Codon usage tabulated from international DNA sequence databases. Nucleic Acids Res 28:292

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  27. Wright F (1990) The ‘effective number of codons’ used in a gene. Gene 87:23–29

    Article  CAS  PubMed  Google Scholar 

  28. Fuglsang A (2006) Estimating the “effective number of codons”: the Wright way of determining codon homozygosity leads to superior estimates. Genetics 172:1301–1307

    Article  PubMed  PubMed Central  Google Scholar 

  29. Sharp PM, Li WH (1987) The codon Adaptation Index—a measure of directional synonymous codon usage bias, and its potential applications. Nucleic Acids Res 15:1281–1295

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  30. Turner BG, Summers MF (1999) Structural biology of HIV. J Mol Biol 285:1–32

    Article  CAS  PubMed  Google Scholar 

  31. Bulmer M (1991) The selection-mutation-drift theory of synonymous codon usage. Genetics 129:897–907

    CAS  PubMed  PubMed Central  Google Scholar 

  32. Cattaneo R, Schmid A, Eschle D, Baczko K, ter Meulen V, Billeter MA (1988) Biased hypermutation and other genetic changes in defective measles viruses in human brain infections. Cell 55:255–265

    Article  CAS  PubMed  Google Scholar 

  33. Samuel CE (2012) ADARs: viruses and innate immunity. Curr Top Microbiol Immunol 353:163–195. doi:10.1007/82_2011_148

    CAS  PubMed  Google Scholar 

  34. Jayan GC, Casey JL (2002) Increased RNA editing and inhibition of hepatitis delta virus replication by high-level expression of ADAR1 and ADAR2. J Virol 76:3819–3827

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  35. Doria M, Neri F, Gallo A, Farace MG, Michienzi A (2009) Editing of HIV-1 RNA by the double-stranded RNA deaminase ADAR1 stimulates viral infection. Nucleic Acids Res 37:5848–5858

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  36. Sapiro AL, Deng P, Zhang R, Li JB (2015) Cis regulatory effects on A-to-I RNA editing in related Drosophila species. Cell Rep 11:697–703

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  37. Keating CP, Hill MK, Hawkes DJ, Smyth RP, Isel C, Le SY et al (2009) The A-rich RNA sequences of HIV-1 pol are important for the synthesis of viral cDNA. Nucleic Acids Res 37:945–956

    Article  CAS  PubMed  Google Scholar 

  38. Meintjes PL, Rodrigo AG (2005) Evolution of relative synonymous codon usage in Human Immunodeficiency Virus type-1. J Bioinform Comput Biol 3:157–168

    Article  CAS  PubMed  Google Scholar 

  39. Vabret N, Bailly-Bechet M, Najburg V, Müller-Trutwin M, Verrier B, Tangy F (2012) The biased nucleotide composition of HIV-1 triggers type I interferon response and correlates with subtype D increased pathogenicity. PLoS One 7:e33502. doi:10.1371/journal.pone.0033502

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  40. Ray SK, Baruah VJ, Satapathy SS, Banerjee R (2014) Cotranslational protein folding reveals the selective use of synonymous codons along the coding sequence of a low expression gene. J Genet 93:613–617

    Article  PubMed  Google Scholar 

  41. Yu CH, Dang Y, Zhou Z, Wu C, Zhao F, Sachs MS et al (2015) Codon usage influences the local rate of translation elongation to regulate co-translational protein folding. Mol Cell 59:744–754

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  42. van Weringh A, Ragonnet-Cronin M, Pranckeviciene E, Pavon-Eternod M, Kleiman L, Xia X (2011) HIV-1 modulates the tRNA pool to improve translation efficiency. Mol Biol Evol 28:1827–1834

    Article  PubMed  PubMed Central  Google Scholar 

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Acknowledgements

This work was financial supported by the Thailand Research Fund through the Royal Golden Jubilee Ph.D. Program (Grant No. PHD/0030/2556) and a research grant (Grant No. IRN60W0002) from Thailand Research Fund.

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Correspondence to Prasert Auewarakul.

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Handling editor: Li Wu.

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Phakaratsakul, S., Sirihongthong, T., Boonarkart, C. et al. Codon usage of HIV regulatory genes is not determined by nucleotide composition. Arch Virol 163, 337–348 (2018). https://doi.org/10.1007/s00705-017-3597-5

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  • DOI: https://doi.org/10.1007/s00705-017-3597-5

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