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Evidence of Increased Adaptation of Omicron SARS-CoV-2 Codons to Humans

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Comparative Genomics (RECOMB-CG 2024)

Part of the book series: Lecture Notes in Computer Science ((LNBI,volume 14616))

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Abstract

Viruses are highly dependent on their hosts to carry out cellular mechanisms and cause productive infection. Thus, they undergo extensive adaptations to the host intracellular machinery, which occur over the evolution of the virus, and during the emergence of new viral strains with different properties. One aspect of viral adaptation is related to the efficiency of recruiting the host’s gene expression machinery and specifically the translation machinery. This process can be partially detected using measures of codon usage bias (CUB).

While previous studies in the field suggested that there is an adaptation of codons in the viral genome to the host, none of them studied these adaptations among the different strains of the same virus over time. Thus, in this study, we focused on the SARS-CoV-2 and demonstrated for the first time that the omicron strain has an increased codon usage adaptation to humans in the early gene ORF1ab compared to previous strains. In addition, our findings indicate that the observed differences in CUB scores were primarily attributed to non-synonymous mutations. This conclusion holds for additional human-infecting viruses.

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References

  1. Whitaker-Dowling, P., Youngner, J.S.: “VIRUS-HOST CELL INTERACTIONS,” in Encyclopedia of Virology, pp. 1957–1961. Elsevier (1999)

    Google Scholar 

  2. Lucas, M., Karrer, U., Lucas, A., Klenerman, P.: Viral escape mechanisms - escapology taught by viruses. Int. J. Exp. Pathol. 82(5), 269–286 (2008)

    Google Scholar 

  3. Goz, E., Zafrir, Z., Tuller, T.: Universal evolutionary selection for high dimensional silent patterns of information hidden in the redundancy of viral genetic code. Bioinformatics 34(19), 3241–3248 (2018)

    Google Scholar 

  4. Bahir, I., Fromer, M., Prat, Y., Linial, M.: Viral adaptation to host: a proteome-based analysis of codon usage and amino acid preferences. Mol. Syst. Biol. 5(1), 311 (2009)

    Google Scholar 

  5. Crill, W.D., Wichman, H.A., Bull, J.J.: Evolutionary reversals during viral adaptation to alternating hosts. Genetics 154(1), 27–37 (2000)

    Google Scholar 

  6. Sanjuán, R., Domingo-Calap, P.: Mechanisms of viral mutation. Cell. Mol. Life Sci. 73(23), 4433–4448 (2016)

    Google Scholar 

  7. Greenbaum, B.D., Levine, A.J., Bhanot, G., Rabadan, R.: Patterns of evolution and host gene mimicry in influenza and other RNA viruses. PLoS Pathog. 4(6), e1000079 (2008)

    Google Scholar 

  8. Elena, S.F., Sanjuán, R.: Adaptive value of high mutation rates of RNA viruses: separating causes from consequences. J. Virol. 79(18), 11555–11558 (2005)

    Google Scholar 

  9. Stern, A., Andino, R.: Viral Evolution. Viral Pathog. 233–240 (2016)

    Google Scholar 

  10. Holmes, E.C., Drummond, A.J.: The evolutionary genetics of viral emergence, pp. 51–66 (2007)

    Google Scholar 

  11. Duffy, S., Shackelton, L.A., Holmes, E.C.: Rates of evolutionary change in viruses: patterns and determinants. Nat. Rev. Genet. 9(4), 267–276 (2008)

    Google Scholar 

  12. Wong, E.H., Smith, D.K., Rabadan, R., Peiris, M., Poon, L.L.: Codon usage bias and the evolution of influenza A viruses. Codon usage biases of influenza virus. BMC Evol. Biol. 10, 253 (2010)

    Google Scholar 

  13. Khandia, R., et al.: Analysis of nipah virus codon usage and adaptation to hosts. Front. Microbiol. 10, 439603 (2019)

    Google Scholar 

  14. Cristina, J., Moreno, P., Moratorio, G., Musto, H.: Genome-wide analysis of codon usage bias in ebolavirus. Virus Res. 196, 87–93 (2015)

    Google Scholar 

  15. Biswas, K., et al.: Codon usage bias analysis of citrus tristeza virus: higher codon adaptation to citrus reticulata host. Viruses 11(4), 331 (2019)

    Google Scholar 

  16. Li, G., et al.: Evolutionary and genetic analysis of the VP2 gene of canine parvovirus. BMC Genomics 18(1), 534 (2017)

    Google Scholar 

  17. Cristina, J., Fajardo, A., Soñora, M., Moratorio, G., Musto, H.: A detailed comparative analysis of codon usage bias in Zika virus. Virus Res. 223, 147–152 (2016)

    Google Scholar 

  18. Moratorio, G., Iriarte, A., Moreno, P., Musto, H., Cristina, J.: A detailed comparative analysis on the overall codon usage patterns in West Nile virus. Infect. Genet. Evol. 14, 396–400 (2013)

    Google Scholar 

  19. Jenkins, G.M., Holmes, E.C.: The extent of codon usage bias in human RNA viruses and its evolutionary origin. Virus Res. 92(1), 1–7 (2003)

    Google Scholar 

  20. Belalov, I.S., Lukashev, A.N.: Causes and implications of codon usage bias in RNA viruses. PLoS ONE 8(2), e56642 (2013)

    Google Scholar 

  21. Parvez, M.K., Parveen, S.: Evolution and emergence of pathogenic viruses: past, present, and future. Intervirology 60(1–2), 1–7 (2017)

    Google Scholar 

  22. Pybus, O.G., Tatem, A.J., Lemey, P.: Virus evolution and transmission in an ever more connected world. Proc. R. Soc. B Biol. Sci. 282(1821), 20142878 (2015)

    Google Scholar 

  23. LaTourrette, K., Garcia-Ruiz, H.: Determinants of virus variation, evolution, and host adaptation. Pathogens 11(9), 1039 (2022)

    Google Scholar 

  24. Ojosnegros, S., Beerenwinkel, N.: Models of RNA virus evolution and their roles in vaccine design. Immunome Res. 6(Suppl 2), S5 (2010)

    Google Scholar 

  25. Hie, B., Zhong, E.D., Berger, B., Bryson, B.: Learning the language of viral evolution and escape. Science 371(6526), 284–288 (2021)

    MathSciNet  Google Scholar 

  26. Marz, M., et al.: Challenges in RNA virus bioinformatics. Bioinformatics 30(13), 1793–1799 (2014)

    Google Scholar 

  27. Elena, S.F.: “Restrictions to RNA virus adaptation: an experimental approach”, Antonie van Leeuwenhoek. Int. J. Gen. Mol. Microbiol. 81(1–4), 135–142 (2002)

    Google Scholar 

  28. Hanna, R., Dalvi, S., Sălăgean, T., Pop, I.D., Bordea, I.R., Benedicenti, S.: Understanding COVID-19 pandemic: molecular mechanisms and potential therapeutic strategies. An evidence-based review. J. Inflamm. Res. 14, 13–56 (2021)

    Google Scholar 

  29. De Maio, N., Walker, C.R., Turakhia, Y., Lanfear, R., Corbett-Detig, R., Goldman, N.: Mutation rates and selection on synonymous mutations in SARS-CoV-2. Genome Biol. Evol. 13(5), evab087 (2021)

    Google Scholar 

  30. Magazine, N., Zhang, T., Wu, Y., McGee, M.C., Veggiani, G., Huang, W.: Mutations and evolution of the SARS-CoV-2 spike protein. Viruses 14(3), 640 (2022)

    Google Scholar 

  31. Nambou, K., Anakpa, M.: Deciphering the co-adaptation of codon usage between respiratory coronaviruses and their human host uncovers candidate therapeutics for COVID-19. Infect. Genet. Evol. 85, 104471 (2020)

    Google Scholar 

  32. Tao, K., et al.: The biological and clinical significance of emerging SARS-CoV-2 variants. Nat. Rev. Genet. 22(12), 757–773 (2021)

    Google Scholar 

  33. Planas, D., et al.: Reduced sensitivity of SARS-CoV-2 variant delta to antibody neutralization. Nature 596(7871), 276–280 (2021)

    Google Scholar 

  34. Hu, J., et al.: Increased immune escape of the new SARS-CoV-2 variant of concern omicron. Cell. Mol. Immunol. 19(2), 293–295 (2022)

    Google Scholar 

  35. Chavda, V., Bezbaruah, R., Deka, K., Nongrang, L., Kalita, T.: The delta and omicron variants of SARS-CoV-2: what we know so far. Vaccines 10(11), 1926 (2022)

    Google Scholar 

  36. Kumar, S., Thambiraja, T.S., Karuppanan, K., Subramaniam, G.: Omicron and delta variant of SARS-CoV-2: a comparative computational study of spike protein. J. Med. Virol. 94(4), 1641–1649 (2022)

    Google Scholar 

  37. Davidson, A.M., Wysocki, J., Batlle, D.: Interaction of SARS-CoV-2 and other coronavirus with ACE (Angiotensin-Converting Enzyme)-2 as their main receptor. Hypertension 76(5), 1339–1349 (2020)

    Google Scholar 

  38. Hamming, I., Timens, W., Bulthuis, M., Lely, A., Navis, G., van Goor, H.: Tissue distribution of ACE2 protein, the functional receptor for SARS coronavirus. A first step in understanding SARS pathogenesis. J. Pathol. 203(2), 631–637 (2004)

    Google Scholar 

  39. Zhang, H., Penninger, J.M., Li, Y., Zhong, N., Slutsky, A.S.: Angiotensin-converting enzyme 2 (ACE2) as a SARS-CoV-2 receptor: molecular mechanisms and potential therapeutic target. Intensive Care Med. 46(4), 586–590 (2020)

    Google Scholar 

  40. Lan, J., et al.: Structure of the SARS-CoV-2 spike receptor-binding domain bound to the ACE2 receptor. Nature 581(7807), 215–220 (2020)

    Google Scholar 

  41. Peronace, C., et al.: The first identification in Italy of SARS-CoV-2 omicron BA. 4 harboring KSF141_del: a genomic comparison with omicron sub-variants. Biomedicines 10(8), 1839 (2022)

    Google Scholar 

  42. Chakraborty, C., Bhattacharya, M., Sharma, A.R., Dhama, K., Agoramoorthy, G.: A comprehensive analysis of the mutational landscape of the newly emerging omicron (B.1.1.529) variant and comparison of mutations with VOCs and VOIs. GeroScience 44(5), 2393–2425 (2022)

    Google Scholar 

  43. Plotkin, J.B., Dushoff, J.: Codon bias and frequency-dependent selection on the hemagglutinin epitopes of influenza A virus. Proc. Natl. Acad. Sci. 100(12), 7152–7157 (2003)

    Google Scholar 

  44. Koyama, T., Platt, D., Parida, L.: Variant analysis of SARS-CoV-2 genomes. Bull. World Health Organ. 98(7), 495–504 (2020)

    Google Scholar 

  45. Chatterjee, S., Bhattacharya, M., Nag, S., Dhama, K., Chakraborty, C.: A detailed overview of SARS-CoV-2 omicron: its sub-variants, mutations and pathophysiology, clinical characteristics, immunological landscape, immune escape, and therapies. Viruses 15(1), 167 (2023)

    Google Scholar 

  46. Hatcher, E.L., et al.: Virus variation resource – improved response to emergent viral outbreaks. Nucleic Acids Res. 45(D1), D482–D490 (2017)

    Google Scholar 

  47. Hodcroft, E.B.: CoVariants: SARS-CoV-2 Mutations and Variants of Interest (2021)

    Google Scholar 

  48. O’Leary, N.A., et al.: Reference sequence (RefSeq) database at NCBI: current status, taxonomic expansion, and functional annotation. Nucleic Acids Res. 44(D1), D733–D745 (2016). https://doi.org/10.1007/978-3-319-21602-7_8

    Article  Google Scholar 

  49. Alexaki, A., et al.: Codon and codon-pair usage tables (CoCoPUTs): facilitating genetic variation analyses and recombinant gene design. J. Mol. Biol. 431(13), 2434–2441 (2019)

    Google Scholar 

  50. Chan, P.P., Lowe, T.M.: GtRNAdb: a database of transfer RNA genes detected in genomic sequence. Nucleic Acids Res. 37, D93–D97 (2009)

    Google Scholar 

  51. Chan, P.P., Lowe, T.M.: GtRNAdb 2.0: an expanded database of transfer RNA genes identified in complete and draft genomes. Nucleic Acids Res. 44(D1), D184–D189 (2016)

    Google Scholar 

  52. Sabi, R., Tuller, T.: Modelling the efficiency of codon–tRNA interactions based on codon usage bias. DNA Res. 21(5), 511–526 (2014)

    Google Scholar 

  53. Sharp, P.M., Li, W.-H.: The codon adaptation index-a measure of directional synonymous codon usage bias, and its potential applications. Nucleic Acids Res. 15(3), 1281–1295 (1987)

    Google Scholar 

  54. Reis, M.D.: Solving the riddle of codon usage preferences: a test for translational selection. Nucleic Acids Res. 32(17), 5036–5044 (2004)

    Google Scholar 

  55. Hernandez-Alias, X., Benisty, H., Schaefer, M.H., Serrano, L.: Translational efficiency across healthy and tumor tissues is proliferation-related. Mol. Syst. Biol. 16(3), e9275 (2020)

    Google Scholar 

  56. Pechmann, S., Frydman, J.: Evolutionary conservation of codon optimality reveals hidden signatures of cotranslational folding. Nat. Struct. Mol. Biol. 20(2), 237–243 (2013)

    Google Scholar 

  57. Needleman, S.B., Wunsch, C.D.: A general method applicable to the search for similarities in the amino acid sequence of two proteins. J. Mol. Biol. 48(3), 443–453 (1970)

    Google Scholar 

  58. Sievers, F., et al.: Fast, scalable generation of high-quality protein multiple sequence alignments using Clustal Omega. Mol. Syst. Biol. 7(1), 539 (2011)

    Google Scholar 

  59. Davison, A.C., Hinkley, D.V.: Bootstrap Methods and their Application. Cambridge University Press, Cambridge (1997)

    Google Scholar 

  60. Bergman, S., Tuller, T.: Widespread non-modular overlapping codes in the coding regions*. Phys. Biol. 17(3), 031002 (2020)

    Google Scholar 

  61. Lauring, A.S., Frydman, J., Andino, R.: The role of mutational robustness in RNA virus evolution. Nat. Rev. Microbiol. 11(5), 327–336 (2013)

    Google Scholar 

  62. Wang, R., Chen, J., Wei, G.-W.: Mechanisms of SARS-CoV-2 evolution revealing vaccine-resistant mutations in Europe and America. J. Phys. Chem. Lett. 12(49), 11850–11857 (2021)

    Google Scholar 

  63. Emam, M., Oweda, M., Antunes, A., El-Hadidi, M.: Positive selection as a key player for SARS-CoV-2 pathogenicity: insights into ORF1ab, S and E genes. Virus Res. 302, 198472 (2021)

    Google Scholar 

  64. V’kovski, P., Kratzel, A., Steiner, S., Stalder, H., Thiel, V.: Coronavirus biology and replication: implications for SARS-CoV-2. Nat. Rev. Microbiol. 19(3), 155–170 (2021)

    Google Scholar 

  65. Mioduser, O., Goz, E., Tuller, T.: Significant differences in terms of codon usage bias between bacteriophage early and late genes: a comparative genomics analysis. BMC Genomics 18(1), 866 (2017)

    Google Scholar 

  66. Manrubia, S., Lazaro, E.: Viral evolution. Phys. Life Rev. 3(2), 65–92 (2006)

    Google Scholar 

  67. Domingo, E., Holland, J.J.: RNA virus mutations and fitness for survival. Annu. Rev. Microbiol. 51(1), 151–178 (1997)

    Google Scholar 

  68. Bull, R.A., et al.: Sequential bottlenecks drive viral evolution in early acute hepatitis C virus infection. PLoS Pathog. 7(9), e1002243 (2011)

    Google Scholar 

  69. Domingo-Calap, P.: Viral evolution and Immune responses. J. Clin. Microbiol. Biochem. Technol. 5(2), 013–018 (2019)

    Google Scholar 

  70. Mordstein, C., et al.: Transcription, mRNA export, and immune evasion shape the codon usage of viruses. Genome Biol. Evol. 13(9), 1–14 (2021)

    Google Scholar 

  71. Nijhuis, M., Deeks, S., Boucher, C.: Implications of antiretroviral resistance on viral fitness. Curr. Opin. Infect. Dis. 14(1), 23–28 (2001)

    Google Scholar 

  72. Domingo, E., Menéndez-Arias, L., Holland, J.J.: RNA virus fitness. Rev. Med. Virol. 7(2), 87–96 (1997)

    Google Scholar 

  73. Gao, Y., et al.: Structure of the RNA-dependent RNA polymerase from COVID-19 virus. Science 368(6492), 779–782 (2020)

    Google Scholar 

  74. Daczkowski, C.M., Dzimianski, J.V., Clasman, J.R., Goodwin, O., Mesecar, A.D., Pegan, S.D.: Structural insights into the interaction of coronavirus papain-like proteases and interferon-stimulated gene product 15 from different species. J. Mol. Biol. 429(11), 1661–1683 (2017)

    Google Scholar 

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This research was funded by the DFG.

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Davidson, A. et al. (2024). Evidence of Increased Adaptation of Omicron SARS-CoV-2 Codons to Humans. In: Scornavacca, C., Hernández-Rosales, M. (eds) Comparative Genomics. RECOMB-CG 2024. Lecture Notes in Computer Science(), vol 14616. Springer, Cham. https://doi.org/10.1007/978-3-031-58072-7_13

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