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Mutation Patterns of Human SARS-CoV-2 and Bat RaTG13 Coronavirus Genomes Are Strongly Biased Towards C>U Transitions, Indicating Rapid Evolution in Their Hosts

Matyášek, Roman, Kovařík, Aleš·2020·Genes
DOI 10.3390/genes11070761PMC PMC7397057

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Abstract

The pandemic caused by the spread of SARS-CoV-2 has led to considerable interest in its evolutionary origin and genome structure. Here, we analyzed mutation patterns in 34 human SARS-CoV-2 isolates and a closely related RaTG13 isolated from Rhinolophus affinis (a horseshoe bat). We also evaluated the CpG dinucleotide contents in SARS-CoV-2 and other human and animal coronavirus genomes. Out of 1136 single nucleotide variations (~4% divergence) between human SARS-CoV-2 and bat RaTG13, 682 (60%) can be attributed to C>U and U>C substitutions, far exceeding other types of substitutions. An accumulation of C>U mutations was also observed in SARS-CoV2 variants that arose within the human population. Globally, the C>U substitutions increased the frequency of codons for hydrophobic amino acids in SARS-CoV-2 peptides, while U>C substitutions decreased it. In contrast to most other coronaviruses, both SARS-CoV-2 and RaTG13 exhibited CpG depletion in their genomes. The data suggest that C-to-U conversion mediated by C deamination played a significant role in the evolution of the SARS-CoV-2 coronavirus. We hypothesize that the high frequency C>U transitions reflect virus adaptation processes in their hosts, and that SARS-CoV-2 could have been evolving for a relatively long period in humans following the transfer from animals before spreading worldwide.

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Figure 1 Nucleotide substitution spectra in coronavirus genomes. (a) Cumulative frequency of nucleotides in human SARS-CoV-2 genomes. Positive and negative values indicate enrichment and depletion of bases in the SARS-CoV-2 (MN908947) relative to the RaTG13 (MN996532) reference, respectively. (b) Nucleotide substitution rates for each of the four nucleotides between RaTG13 and SARS-CoV-2 genomes expressed as observed/expected ratios. The direction of changes is from RaTG13 to SARS-CoV-2. Calculations are according to Equations (1)–(3) in the Methods section. (c) SNV characteristics in the human SARS-CoV-2 isolates. The data were pooled from 85 variants identified in 33 genomes (each genome was compared to the reference (MN908947) (Table S4). Enrichments of transition/transversion (d) and (C>U + U>C)/(G>A + A>G) ratios (e) between bat RaTG13 and SARS-CoV-2 (MN908947) and among the SARS-CoV-2 isolates relative to corresponding expected values.
Figure 1 Nucleotide substitution spectra in coronavirus genomes. (a) Cumulative frequency of nucleotides in human SARS-CoV-2 genomes. Positive and negative values indicate enrichment and depletion of bases in the SARS-CoV-2 (MN908947) relative to the RaTG13 (MN996532) reference, respectively. (b) Nucleotide substitution rates for each of the four nucleotides between RaTG13 and SARS-CoV-2 genomes expressed as observed/expected ratios. The direction of changes is from RaTG13 to SARS-CoV-2. Calculations are according to Equations (1)–(3) in the Methods section. (c) SNV characteristics in the human SARS-CoV-2 isolates. The data were pooled from 85 variants identified in 33 genomes (each genome was compared to the reference (MN908947) (Table S4). Enrichments of transition/transversion (d) and (C>U + U>C)/(G>A + A>G) ratios (e) between bat RaTG13 and SARS-CoV-2 (MN908947) and among the SARS-CoV-2 isolates relative to corresponding expected values.CC BY · PubMed Central
Figure 2 Phylogenetic relationships between human SARS-CoV-2 viruses. The maximum-likelihood tree was constructed from the alignment of 34 whole genome sequences (Table S1). Statistical support calculated as the approximate likelihood-ratio test [29] was 98% for three branches indicated in colors. The reference Wu-Hu-1 (M908947) sequence fell within the “L” group. N—number of sequences in each branch followed by the frequency of substitution type.
Figure 2 Phylogenetic relationships between human SARS-CoV-2 viruses. The maximum-likelihood tree was constructed from the alignment of 34 whole genome sequences (Table S1). Statistical support calculated as the approximate likelihood-ratio test [29] was 98% for three branches indicated in colors. The reference Wu-Hu-1 (M908947) sequence fell within the “L” group. N—number of sequences in each branch followed by the frequency of substitution type.CC BY · PubMed Central
Figure 3 Changes in the hydrophobic properties of amino acids in nonsynonymous substitutions. The Y axis represents shifts in hydrophobicity values between replaced amino acids. The data were obtained from single-nucleotide variations (SNVs) in the SARS-CoV-2 sequences (Table S5).
Figure 3 Changes in the hydrophobic properties of amino acids in nonsynonymous substitutions. The Y axis represents shifts in hydrophobicity values between replaced amino acids. The data were obtained from single-nucleotide variations (SNVs) in the SARS-CoV-2 sequences (Table S5).CC BY · PubMed Central
Figure 4 Sequence variation between the bat RaTG13 and Sars-CoV-2 (MN908947) genomes. (a) A graphical representation of SNVs. The height of the columns reflects the number of SNVs in a 100-nt sliding window. (b) Alignment of the ACE2 receptor-binding domains (RBD). Coordinates are according to a spike protein reading frame in the SARS-CoV-2 genome. The direction of mutations is from the bat RaTG13 to human SARS-CoV-2 sequence. The differences between SARS-CoV-2 and bat RaTG13 sequences are highlighted. Amino acids in a contact zone are boxed. The site at position 22984 (asterisk) was also polymorphic among human SARS-CoV-2 isolates (Table S4 sheet 3). The occurrences of distinct substitutions in synonymous and nonsynonymous sites in (c) the whole ACE2 receptor-binding domain (RBD) and (d) a contact zone sub-domain.
Figure 4 Sequence variation between the bat RaTG13 and Sars-CoV-2 (MN908947) genomes. (a) A graphical representation of SNVs. The height of the columns reflects the number of SNVs in a 100-nt sliding window. (b) Alignment of the ACE2 receptor-binding domains (RBD). Coordinates are according to a spike protein reading frame in the SARS-CoV-2 genome. The direction of mutations is from the bat RaTG13 to human SARS-CoV-2 sequence. The differences between SARS-CoV-2 and bat RaTG13 sequences are highlighted. Amino acids in a contact zone are boxed. The site at position 22984 (asterisk) was also polymorphic among human SARS-CoV-2 isolates (Table S4 sheet 3). The occurrences of distinct substitutions in synonymous and nonsynonymous sites in (c) the whole ACE2 receptor-binding domain (RBD) and (d) a contact zone sub-domain.CC BY · PubMed Central
Figure 5 CpG depletion analysis in coronavirus genomes. (a) Bar charts showing CpGOBS/EXP in individual genomes. The calculations are according to Equation (4) in the Methods section. (b) A statistical representation of CpG depletions in individual groups. The Wuhan-Hu-1 accession (MN908947) was included in the “Human” group as a representative of SARS-CoV-2. Note that, except for RaTG13 (MN996532), bat coronaviruses show no or little CpG depletion.
Figure 5 CpG depletion analysis in coronavirus genomes. (a) Bar charts showing CpGOBS/EXP in individual genomes. The calculations are according to Equation (4) in the Methods section. (b) A statistical representation of CpG depletions in individual groups. The Wuhan-Hu-1 accession (MN908947) was included in the “Human” group as a representative of SARS-CoV-2. Note that, except for RaTG13 (MN996532), bat coronaviruses show no or little CpG depletion.CC BY · PubMed Central

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Citations

Cites

Research works printed but not linked: 36

  1. Zhou, 2020, A pneumonia outbreak associated with a new coronavirus of probable bat origin, Nature, 579, 270, 10.1038/s41586-020-2012-7 · DOI · Paper page
  2. Kandeel, M., Ibrahim, A., Fayez, M., and Al-Nazawi, M. (2020). From SARS and MERS CoVs to SARS-CoV-2: Moving toward more biased codon usage in viral structural and nonstructural genes. J. Med. Virol. · DOI
  3. Wrapp, 2020, Cryo-EM structure of the 2019-nCoV spike in the prefusion conformation, Science, 367, 1260, 10.1126/science.abb2507 · DOI · Paper page
  4. Zhang, T., Wu, Q., and Zhang, Z. (2020). Probable pangolin origin of SARS-CoV-2 associated with the COVID-19 outbreak. Curr. Biol. · DOI
  5. Andersen, 2020, The proximal origin of SARS-CoV-2, Nat. Med., 26, 450, 10.1038/s41591-020-0820-9 · DOI · Paper page
  6. Frederico, 1993, Cytosine deamination in mismatched base pairs, Biochemistry, 32, 6523, 10.1021/bi00077a005 · DOI
  7. Poole, 2001, Confounded cytosine! Tinkering and the evolution of DNA, Nat. Rev. Mol. Cell Biol., 2, 147, 10.1038/35052091 · DOI
  8. Jabbari, 2004, Cytosine methylation and CpG, TpG (CpA) and TpA frequencies, Gene, 333, 143, 10.1016/j.gene.2004.02.043 · DOI
  9. Stevens, 2013, Estimating absolute methylation levels at single-CpG resolution from methylation enrichment and restriction enzyme sequencing methods, Genome Res., 23, 1541, 10.1101/gr.152231.112 · DOI
  10. Bird, 2002, DNA methylation patterns and epigenetic memory, Genes Dev., 16, 6, 10.1101/gad.947102 · DOI · Paper page
  11. Kypr, 1989, Nucleotide composition bias and CpG dinucleotide content in the genomes of HIV and HTLV 1/2, Biochim. Biophys. Acta, 1009, 280, 10.1016/0167-4781(89)90114-0 · DOI
  12. Cheng, X., Virk, N., Chen, W., Ji, S., Sun, Y., and Wu, X. (2013). CpG usage in RNA viruses: Data and hypotheses. PLoS ONE, 8. · DOI
  13. Upadhyay, 2013, CpG dinucleotide frequencies reveal the role of host methylation capabilities in parvovirus evolution, J. Virol., 87, 13816, 10.1128/JVI.02515-13 · DOI
  14. Anwar, 2016, Source of CpG depletion in the HIV-1 genome, Mol. Biol. Evol., 33, 3205, 10.1093/molbev/msw205 · DOI
  15. Gojobori, 1982, Patterns of nucleotide substitution in pseudogenes and functional genes, J. Mol. Evol., 18, 360, 10.1007/BF01733904 · DOI
  16. Ochman, 2003, Neutral mutations and neutral substitutions in bacterial genomes, Mol. Biol. Evol., 20, 2091, 10.1093/molbev/msg229 · DOI
  17. Wang, 2016, Astonishing 35S rDNA diversity in the gymnosperm species Cycas revoluta Thunb, Chromosoma, 125, 683, 10.1007/s00412-015-0556-3 · DOI
  18. Bulmer, 1986, Neighboring base effects on substitution rates in pseudogenes, Mol. Biol. Evol., 3, 322
  19. Keller, I., Bensasson, D., and Nichols, R.A. (2007). Transition-transversion bias is not universal: A counter example from grasshopper pseudogenes. PLoS Genet., 3. · DOI
  20. Duchene, S., Ho, S.Y., and Holmes, E.C. (2015). Declining transition/transversion ratios through time reveal limitations to the accuracy of nucleotide substitution models. BMC Evol. Biol., 15. · DOI
  21. Rozas, 1997, DnaSP version 2.0: A novel software package for extensive molecular population genetics analysis, Comput. Appl. Biosci., 13, 307
  22. Pettersen, 2004, UCSF Chimera–A visualization system for exploratory research and analysis, J. Comput. Chem., 25, 1605, 10.1002/jcc.20084 · DOI
  23. Kyte, 1982, A simple method for displaying the hydropathic character of a protein, J. Mol. Biol., 157, 105, 10.1016/0022-2836(82)90515-0 · DOI
  24. Gouy, 2010, SeaView version 4: A multiplatform graphical user interface for sequence alignment and phylogenetic tree building, Mol. Biol. Evol., 27, 221, 10.1093/molbev/msp259 · DOI
  25. Tang, 2020, On the origin and continuing evolution of SARS-CoV-2, Natl. Sci. Rev., 7, 1012, 10.1093/nsr/nwaa036 · DOI
  26. Anisimova, 2006, Approximate likelihood-ratio test for branches: A fast, accurate, and powerful alternative, Syst. Biol., 55, 539, 10.1080/10635150600755453 · DOI · Paper page
  27. Nei, 1986, Simple methods for estimating the numbers of synonymous and nonsynonymous nucleotide substitutions, Mol. Biol. Evol., 3, 418
  28. Duffy, S. (2018). Why are RNA virus mutation rates so damn high?. PLoS Biol., 16. · DOI
  29. Pachetti, 2020, Emerging SARS-CoV-2 mutation hot spots include a novel RNA-dependent-RNA polymerase variant, J. Transl. Med., 18, 179, 10.1186/s12967-020-02344-6 · DOI
  30. Minskaia, 2006, Discovery of an RNA virus 3′->5′ exoribonuclease that is critically involved in coronavirus RNA synthesis, Proc. Natl. Acad. Sci. USA, 103, 5108, 10.1073/pnas.0508200103 · DOI
  31. Sethna, 1991, Minus-strand copies of replicating coronavirus mRNAs contain antileaders, J. Virol., 65, 320, 10.1128/jvi.65.1.320-325.1991 · DOI
  32. Roberts, 2013, An APOBEC cytidine deaminase mutagenesis pattern is widespread in human cancers, Nat. Genet., 45, 970, 10.1038/ng.2702 · DOI
  33. Opi, 2006, Monomeric APOBEC3G is catalytically active and has antiviral activity, J. Virol., 80, 4673, 10.1128/JVI.80.10.4673-4682.2006 · DOI
  34. Upadhyay, M., and Vivekanandan, P. (2015). Depletion of CpG Dinucleotides in papillomaviruses and polyomaviruses: A role for divergent evolutionary pressures. PLoS ONE, 10. · DOI
  35. Klinman, 1997, Contribution of CpG motifs to the immunogenicity of DNA vaccines, J. Immunol., 158, 3635, 10.4049/jimmunol.158.8.3635 · DOI · Paper page
  36. Zhang, 2000, Rates of conservative and radical nonsynonymous nucleotide substitutions in mammalian nuclear genes, J. Mol. Evol., 50, 56, 10.1007/s002399910007 · DOI · Paper page

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Metadata

Authors
Matyášek, R., & Kovařík, A.
Affiliations
Laboratory of Molecular Epigenetics, Institute of Biophysics, Academy of Sciences of the Czech Republic, Královopolská 135, 61265 Brno, Czech Republic
Venue
Genes
Publisher
MDPI AG
Published
2020-07-07
Volume
11
Issue
7
Pages
761
ISSN
2073-4425
DOI
10.3390/genes11070761
PMC
PMC7397057
Cited by
78
References
3
Language
en
Licence
creativecommons.org/licenses/by/4.0/ (vor)
Funders
  • Grantová Agentura České Republiky (20-14133J, 20-28029S)
  • Akademie Věd České Republiky (68081707)

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