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Dissimilation of synonymous codon usage bias in virus–host coevolution due to translational selection

Nature Ecology & Evolution volume 4, pages 589–600 (2020)Cite this article

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Abstract

Eighteen of the 20 amino acids are each encoded by more than one synonymous codon. Due to differential transfer RNA supply within the cell, synonymous codons are not used with equal frequency, a phenomenon termed codon usage bias (CUB). Previous studies have demonstrated that CUB of endogenous genes trans-regulates the translational efficiency of other genes. We hypothesized similar effects for CUB of exogenous genes on host translation, and tested it in the case of viral infection, a common form of naturally occurring exogenous gene translation. We analysed public Ribo-Seq datasets from virus-infected yeast and human cells and showed that virus CUB trans-regulated tRNA availability, and therefore the relative decoding time of codons. Manipulative experiments in yeast using 37 synonymous fluorescent proteins confirmed that an exogenous gene with CUB more similar to that of the host would apply decreased translational load on the host per unit of expression, whereas expression of the exogenous gene was elevated. The combination of these two effects was that exogenous genes with CUB overly similar to that of the host severely impeded host translation. Finally, using a manually curated list of viruses and natural and symptomatic hosts, we found that virus CUB tended to be more similar to that of symptomatic hosts than that of natural hosts, supporting a general deleterious effect of excessive CUB similarity between virus and host. Our work revealed repulsion between virus and host CUBs when they are overly similar, a previously unrecognized complexity in the coevolution of virus and host.

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Fig. 1: Codon usage of viral genes with excessive translation impacted the decoding time of codons in yeast.
Fig. 2: Impact of IAV codon usage on decoding time in infected human cells.
Fig. 3: Manipulative experiments in yeast elucidate the regulatory effects of CUB on host translation.
Fig. 4: CUB similarity between virus, natural host and symptomatic host.
Fig. 5: Schematic diagram of the regulatory role of virus CUB and its evolutionary implication.

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Data availability

For the yeast Ribo-Seq data underpinning Fig. 1, all accession numbers for publicly available datasets are listed in Supplementary Table 1. For the human Ribo-Seq data underpinning Fig. 2, the original dataset is available from NCBI SRA under accession nos. SRR3623932 and SRR3623937. The raw data underlying Fig. 3 are shown in Supplementary Fig. 6 and Supplementary Table 3. Species identified as virus or its natural/symptomatic hosts are listed in Supplementary Table 5, with their genomic sequences obtained from NCBI GenBank.

Code availability

Custom R codes were used in data analysis and are available at Github (https://github.com/chenfengokha/CUB).

References

  1. Muto, A. & Osawa, S. The guanine and cytosine content of genomic DNA and bacterial evolution. Proc. Natl Acad. Sci. USA 84, 166–169 (1987).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  2. Xia, X. Maximizing transcription efficiency causes codon usage bias. Genetics 144, 1309–1320 (1996).

    CAS  PubMed  PubMed Central  Google Scholar 

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

    CAS  PubMed  PubMed Central  Google Scholar 

  4. Hershberg, R. & Petrov, D. A. Selection on codon bias. Annu. Rev. Genet. 42, 287–299 (2008).

    Article  CAS  PubMed  Google Scholar 

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

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  6. Xia, X. How optimized is the translational machinery in Escherichia coli, Salmonella typhimurium and Saccharomyces cerevisiae? Genetics 149, 37–44 (1998).

    CAS  PubMed  PubMed Central  Google Scholar 

  7. Tuller, T., Waldman, Y. Y., Kupiec, M. & Ruppin, E. Translation efficiency is determined by both codon bias and folding energy. Proc. Natl Acad. Sci. USA 107, 3645–3650 (2010).

    Article  PubMed  PubMed Central  Google Scholar 

  8. Robinson, M. et al. Codon usage can affect efficiency of translation of genes in Escherichia coli. Nucleic Acids Res. 12, 6663–6671 (1984).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  9. Sorensen, M. A., Kurland, C. G. & Pedersen, S. Codon usage determines translation rate in Escherichia coli. J. Mol. Biol. 207, 365–377 (1989).

    Article  CAS  PubMed  Google Scholar 

  10. Xia, X. A major controversy in codon–anticodon adaptation resolved by a new codon usage index. Genetics 199, 573–579 (2015).

    Article  CAS  PubMed  Google Scholar 

  11. Akashi, H. Synonymous codon usage in Drosophila melanogaster: natural selection and translational accuracy. Genetics 136, 927–935 (1994).

    CAS  PubMed  PubMed Central  Google Scholar 

  12. Stoletzki, N. & Eyre-Walker, A. Synonymous codon usage in Escherichia coli: selection for translational accuracy. Mol. Biol. Evol. 24, 374–381 (2006).

    Article  CAS  PubMed  Google Scholar 

  13. Johnston, T. C., Borgia, P. T. & Parker, J. Codon specificity of starvation induced misreading. Mol. Gen. Genet. 195, 459–465 (1984).

    Article  CAS  PubMed  Google Scholar 

  14. Johnston, T. C. & Parker, J. Streptomycin-induced, third-position misreading of the genetic code. J. Mol. Biol. 181, 313–315 (1985).

    Article  CAS  PubMed  Google Scholar 

  15. Plotkin, J. B. & Kudla, G. Synonymous but not the same: the causes and consequences of codon bias. Nat. Rev. Genet. 12, 32–42 (2010).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  16. Ikemura, T. Correlation between the abundance of Escherichia coli transfer RNAs and the occurrence of the respective codons in its protein genes. J. Mol. Biol. 146, 1–21 (1981).

    Article  CAS  PubMed  Google Scholar 

  17. Qian, W., Yang, J. R., Pearson, N. M., Maclean, C. & Zhang, J. Balanced codon usage optimizes eukaryotic translational efficiency. PLoS Genet. 8, e1002603 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  18. Akashi, H. & Eyre-Walker, A. Translational selection and molecular evolution. Curr. Opin. Genet. Dev. 8, 688–693 (1998).

    Article  CAS  PubMed  Google Scholar 

  19. Kudla, G., Murray, A. W., Tollervey, D. & Plotkin, J. B. Coding-sequence determinants of gene expression in Escherichia coli. Science 324, 255–258 (2009).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  20. Shah, P., Ding, Y., Niemczyk, M., Kudla, G. & Plotkin, J. B. Rate-limiting steps in yeast protein translation. Cell 153, 1589–1601 (2013).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  21. Frumkin, I. et al. Codon usage of highly expressed genes affects proteome-wide translation efficiency. Proc. Natl Acad. Sci. USA 115, E4940–E4949 (2018).

    Article  PubMed  PubMed Central  Google Scholar 

  22. Tian, L., Shen, X., Murphy, R. W. & Shen, Y. The adaptation of codon usage of +ssRNA viruses to their hosts. Infect. Genet. Evol. 63, 175–179 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  23. Albers, S. & Czech, A. Exploiting tRNAs to boost virulence. Life (Basel) 6, 4 (2016).

    Google Scholar 

  24. 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, 311 (2009).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  25. Lucks, J. B., Nelson, D. R., Kudla, G. R. & Plotkin, J. B. Genome landscapes and bacteriophage codon usage. PLoS Comput. Biol. 4, e1000001 (2008).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  26. Gardin, J. et al. Measurement of average decoding rates of the 61 sense codons in vivo. eLife 3, e03735 (2014).

    Article  PubMed Central  Google Scholar 

  27. Camacho, C. et al. BLAST+: architecture and applications. BMC Bioinformatics 10, 421 (2009).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  28. Schmitt, M. J. & Breinig, F. The viral killer system in yeast: from molecular biology to application. FEMS Microbiol. Rev. 26, 257–276 (2002).

    Article  CAS  PubMed  Google Scholar 

  29. Ribas, J. C. & Wickner, R. B. Saccharomyces cerevisiae L-BC double-stranded RNA virus replicase recognizes the L-A positive-strand RNA 3′ end. J. Virol. 70, 292–297 (1996).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  30. Ingolia, N. T., Ghaemmaghami, S., Newman, J. R. S. & Weissman, J. S. Genome-wide analysis in vivo of translation with nucleotide resolution using ribosome profiling. Science 324, 218–223 (2009).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  31. Zinshteyn, B., Gilbert, W. V. & Copenhaver, G. P. Loss of a conserved tRNA anticodon modification perturbs cellular signaling. PLoS Genet. 9, e1003675 (2013).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  32. Pop, C. et al. Causal signals between codon bias, structure, and the efficiency of translation and elongation. Mol. Syst. Biol. 10, 770 (2014).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  33. Artieri, C. G. & Fraser, H. B. Accounting for biases in riboprofiling data indicates a major role for proline in stalling translation. Genome Res. 24, 2011–2021 (2014).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  34. McManus, C. J., May, G. E., Spealman, P. & Shteyman, A. Ribosome profiling reveals post-transcriptional buffering of divergent gene expression in yeast. Genome Res. 24, 422–430 (2014).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  35. Dana, A. & Tuller, T. The effect of tRNA levels on decoding times of mRNA codons. Nucleic Acids Res. 42, 9171–9181 (2014).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  36. Bercovich-Kinori, A. et al. A systematic view on influenza induced host shutoff. eLife 5, e18311 (2016).

    Article  PubMed  PubMed Central  Google Scholar 

  37. Chen, S. et al. Codon-resolution analysis reveals a direct and context-dependent impact of individual synonymous mutations on mRNA level. Mol. Biol. Evol. 34, 2944–2958 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  38. Presnyak, V. et al. Codon optimality is a major determinant of mRNA stability. Cell 160, 1111–1124 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  39. Mignon, C. et al. Codon harmonization—going beyond the speed limit for protein expression. FEBS Lett. 592, 1554–1564 (2018).

    Article  CAS  PubMed  Google Scholar 

  40. Angov, E., Legler, P. M. & Mease, R. M. in Heterologous Gene Expression in E. coli. Methods in Molecular Biology (Methods and Protocols) vol. 705 (eds Evans Jr, T. & Xu, M. Q.) 1–13 (Humana Press, 2011).

  41. Yang, J. R., Chen, X. & Zhang, J. Codon-by-codon modulation of translational speed and accuracy via mRNA folding. PLoS Biol. 12, e1001910 (2014).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

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

    Article  CAS  PubMed  Google Scholar 

  43. Cherry, J. M. et al. Saccharomyces genome database: the genomics resource of budding yeast. Nucleic Acids Res. 40, D700–D705 (2012).

    Article  CAS  PubMed  Google Scholar 

  44. Icho, T. & Wickner, R. B. The double-stranded RNA genome of yeast virus L-A encodes its own putative RNA polymerase by fusing two open reading frames. J. Biol. Chem. 264, 6716–6723 (1989).

    CAS  PubMed  Google Scholar 

  45. Bruenn, J. A. A closely related group of RNA-dependent RNA polymerases from double-stranded RNA viruses. Nucleic Acids Res. 21, 5667–5669 (1993).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  46. Zerbino, D. R. et al. Ensembl 2018. Nucleic Acids Res. 46, D754–D761 (2018).

    Article  CAS  PubMed  Google Scholar 

  47. Nagalakshmi, U. et al. The transcriptional landscape of the yeast genome defined by RNA sequencing. Science 320, 1344–1349 (2008).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  48. Cai, Y. & Futcher, B. Effects of the yeast RNA-binding protein Whi3 on the half-life and abundance of CLN3 mRNA and Other targets. PLoS ONE 8, e84630 (2014).

    Article  CAS  Google Scholar 

  49. Langmead, B. & Salzberg, S. L. Fast gapped-read alignment with Bowtie 2. Nat. Methods 9, 357 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  50. Tarailo-Graovac, M. & Chen, N. Using RepeatMasker to identify repetitive elements in genomic sequences. Curr. Protoc. Bioinformatics 25, 4.10.1–4.10.14 (2009).

    Article  Google Scholar 

  51. Tuller, T. et al. An evolutionarily conserved mechanism for controlling the efficiency of protein translation. Cell 141, 344–354 (2010).

    Article  CAS  PubMed  Google Scholar 

  52. Crick, F. H. C. Codon–anticodon pairing: the wobble hypothesis. J. Mol. Biol. 19, 548–555 (1966).

    Article  CAS  PubMed  Google Scholar 

  53. Chan, P. P. & Lowe, T. M. GtRNAdb: a database of transfer RNA genes detected in genomic sequence. Nucleic Acids Res. 37, D93–D97 (2008).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  54. Holstege, F. C. et al. Dissecting the regulatory circuitry of a eukaryotic genome. Cell 95, 717–728 (1998).

    Article  CAS  PubMed  Google Scholar 

  55. 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, 1281–1295 (1987).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  56. Drummond, D. A. & Wilke, C. O. Mistranslation-induced protein misfolding as a dominant constraint on coding-sequence evolution. Cell 134, 341–352 (2008).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  57. You, E. et al. Codon usage bias analysis for the spermidine synthase gene from Camellia sinensis (L.) O. Kuntze. Genet. Mol. Res. 14, 7368–7376 (2015).

    Article  CAS  PubMed  Google Scholar 

  58. Liu, Q., Hu, H. & Wang, H. Mutational bias is the driving force for shaping the synonymous codon usage pattern of alternatively spliced genes in rice (Oryza sativa L.). Mol. Genet. Genomics 290, 649–660 (2015).

    Article  CAS  PubMed  Google Scholar 

  59. Jia, X. et al. Non-uniqueness of factors constraint on the codon usage in Bombyx mori. BMC Genomics 16, 356 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  60. Shen, X., Wang, H., Wang, M. & Liu, B. The complete mitochondrial genome sequence of Euphausia pacifica (Malacostraca: Euphausiacea) reveals a novel gene order and unusual tandem repeats. Genome 54, 911–922 (2011).

    Article  CAS  PubMed  Google Scholar 

  61. Gu, W., Zhou, T. & Wilke, C. O. A universal trend of reduced mRNA stability near the translation-initiation site in prokaryotes and eukaryotes. PLoS Comput. Biol. 6, e1000664 (2010).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  62. Gietz, R. D. in Yeast Genetics: Methods and Protocols (eds Smith, J. S. & Burke, D. J.) 1–12 (Springer, 2014).

  63. Dobin, A. & Gingeras, T. R. Mapping RNA-seq reads with STAR. Curr. Protoc. Bioinformatics 51, 11.14.11–11.14.19 (2015).

    Article  Google Scholar 

  64. Kramer, E. B., Vallabhaneni, H., Mayer, L. M. & Farabaugh, P. J. A comprehensive analysis of translational missense errors in the yeast Saccharomyces cerevisiae. RNA 16, 1797–1808 (2010).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  65. Hulo, C. et al. ViralZone: a knowledge resource to understand virus diversity. Nucleic Acids Res. 39, D576–D582 (2011).

    Article  CAS  PubMed  Google Scholar 

  66. Prabhakaran, R., Chithambaram, S. & Xia, X. Escherichia coli and Staphylococcus phages: effect of translation initiation efficiency on differential codon adaptation mediated by virulent and temperate lifestyles. J. Gen. Virol. 96, 1169–1179 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  67. Bailly-Bechet, M., Vergassola, M. & Rocha, E. Causes for the intriguing presence of tRNAs in phages. Genome Res. 17, 1486–1495 (2007).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  68. Chithambaram, S., Prabhakaran, R. & Xia, X. Differential codon adaptation between dsDNA and ssDNA phages in Escherichia coli. Mol. Biol. Evol. 31, 1606–1617 (2014).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  69. Chithambaram, S., Prabhakaran, R. & Xia, X. The effect of mutation and selection on codon adaptation in Escherichia coli bacteriophage. Genetics 197, 301–315 (2014).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

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Acknowledgements

We thank X. He, W. Qian, Z. Zhou, Z. Wu, W. Shi and Z. Li for their comments on the manuscript. This work was supported by the National Special Research Program of China for Important Infectious Diseases (grant number 2018ZX10302103 to X.C.), the National Key R&D Program of China (grant nos. 2017YFA0103504 to X.C. and 2018ZX10301402 to J.-R.Y. and Z.H.), the National Natural Science Foundation of China (grant nos. 31671320, 31871320 and 81830103 to J.-R.Y. and 31771406 to X.C.), the start-up grant from ‘100 Top Talents Program’ of Sun Yat-sen University (grant nos. 50000-18821112 to X.C. and 50000-18821117 to J.-R.Y.), and the US National Institutes of Health grant R01GM103232 to J.Z.

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Authors and Affiliations

  1. Program in Cancer Research, Zhongshan School of Medicine, The Fifth Affiliated Hospital, Sun Yat-sen University, Guangzhou, China

    Feng Chen & Jian-Rong Yang

  2. Department of Biomedical Informatics, Zhongshan School of Medicine, Sun Yat-sen University, Guangzhou, China

    Feng Chen, Peng Wu, Yutong Hou & Jian-Rong Yang

  3. Key Laboratory of Tropical Disease Control, Ministry of Education, Sun Yat-sen University, Guangzhou, China

    Feng Chen & Jian-Rong Yang

  4. Department of Medical Genetics, Zhongshan School of Medicine, Sun Yat-sen University, Guangzhou, China

    Shuyun Deng & Xiaoshu Chen

  5. Zhongshan School of Medicine, Sun Yat-sen University, Guangzhou, China

    Heng Zhang

  6. Department of Obstetrics and Gynecology, Precision Medicine Institute, The First Affiliated Hospital, Sun Yat-sen University, Guangzhou, China

    Zheng Hu

  7. Department of Ecology and Evolutionary Biology, University of Michigan, Ann Arbor, MI, USA

    Jianzhi Zhang, Xiaoshu Chen & Jian-Rong Yang

  8. RNA Biomedical Institute, Sun Yat-sen Memorial Hospital, Sun Yat-sen University, Guangzhou, China

    Jian-Rong Yang

  9. Guangdong Provincial Key Laboratory of Biomedical Imaging, The Fifth Affiliated Hospital, Sun Yat-sen University, Zhuhai, Guangdong Province, China

    Jian-Rong Yang

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Contributions

J.-R.Y., X.C. and J.Z. conceived the idea, designed and supervised the study. F.C., P.W., S.D., H.Z. and Y.H. conducted experiments and acquired data, S.D., Z.H., X.C. and J.-R.Y. contributed new reagents/analytic tools. F.C., P.W., S.D., H.Z. and J.-R.Y. analysed data. F.C., J.Z., X.C. and J.-R.Y. wrote the paper.

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Correspondence to Jianzhi Zhang, Xiaoshu Chen or Jian-Rong Yang.

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Supplementary Information

Supplementary Figs. 1–9, Tables 1, 2 and 5–8 and text.

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Supplementary Table

Supplementary Table 3. Expression levels of mCherry and YFP in yeast strains using flow cytometry and RT–qPCR. Supplementary Table 4. Raw data of translation error rates in yeast strains.

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Chen, F., Wu, P., Deng, S. et al. Dissimilation of synonymous codon usage bias in virus–host coevolution due to translational selection. Nat Ecol Evol 4, 589–600 (2020). https://doi.org/10.1038/s41559-020-1124-7

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