Mutations in the SARS-CoV-2 genome could confer resistance to pre-existing antibodies and/or increased transmissibility. The recently emerged Omicron subvariants exhibit a strong tendency for immune evasion, suggesting adaptive evolution. However, because previous studies have been limited to specific lineages or subsets of mutations, the overall evolutionary trajectory of SARS-CoV-2 and the underlying driving forces are still not fully understood. Here we analysed all open-access SARS-CoV-2 genomes (up to November 2022) and correlated the mutation incidence and fitness changes with the impacts of mutations on immune evasion and ACE2 binding affinity. Our results show that the Omicron lineage had an accelerated mutation rate in the RBD region, while the mutation incidence in other genomic regions did not change dramatically over time. Mutations in the RBD region exhibited a lineage-specific pattern and tended to become more aggregated over time, and the mutation incidence was positively correlated with the strength of antibody pressure. Additionally, mutation incidence was positively correlated with changes in ACE2 binding affinity, but with a lower correlation coefficient than with immune evasion. In contrast, the effect of mutations on fitness was more closely correlated with changes in ACE2 binding affinity than with immune evasion. Our findings suggest that immune evasion and ACE2 binding affinity play significant and diverse roles in the evolution of SARS-CoV-2.
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Ozono, S. et al. SARS-CoV-2 D614G spike mutation increases entry efficiency with enhanced ACE2-binding affinity. Nat. Commun. 12, 848 (2021).
Liu, L. et al. Striking antibody evasion manifested by the Omicron variant of SARS-CoV-2. Nature 602, 676–681 (2022).
Telenti, A., Hodcroft, E. B. & Robertson, D. L. The evolution and biology of SARS-CoV-2 variants. Cold Spring Harb. Perspect. Med. https://doi.org/10.1101/cshperspect.a041390 (2022).
Markov, P. V., Katzourakis, A. & Stilianakis, N. I. Antigenic evolution will lead to new SARS-CoV-2 variants with unpredictable severity. Nat. Rev. Microbiol. 20, 251–252 (2022).
Plante, J. A. et al. Spike mutation D614G alters SARS-CoV-2 fitness. Nature 592, 116–121 (2021).
Liu, Y. et al. The N501Y spike substitution enhances SARS-CoV-2 infection and transmission. Nature 602, 294–299 (2022).
Wang, P. et al. Antibody resistance of SARS-CoV-2 variants B.1.351 and B.1.1.7. Nature 593, 130–135 (2021).
Baum, A. et al. Antibody cocktail to SARS-CoV-2 spike protein prevents rapid mutational escape seen with individual antibodies. Science 369, 1014–1018 (2020).
Greaney, A. J. et al. Comprehensive mapping of mutations in the SARS-CoV-2 receptor-binding domain that affect recognition by polyclonal human plasma antibodies. Cell Host Microbe 29, 463–476.e466 (2021).
Greaney, A. J. et al. Complete mapping of mutations to the SARS-CoV-2 spike receptor-binding domain that escape antibody recognition. Cell Host Microbe 29, 44–57.e49 (2021).
Cao, Y. et al. Omicron escapes the majority of existing SARS-CoV-2 neutralizing antibodies. Nature 602, 657–663 (2022).
Starr, T. N. et al. Shifting mutational constraints in the SARS-CoV-2 receptor-binding domain during viral evolution. Science 377, 420–424 (2022).
Cao, Y. et al. BA.2.12.1, BA.4 and BA.5 escape antibodies elicited by Omicron infection. Nature 608, 593–602 (2022).
Cao, Y. et al. Imprinted SARS-CoV-2 humoral immunity induces convergent Omicron RBD evolution. Nature 614, 521–529 (2022).
Focosi, D., Quiroga, R., McConnell, S., Johnson, M. C. & Casadevall, A. Convergent evolution in SARS-CoV-2 spike creates a variant soup from which new COVID-19 waves emerge. Int. J. Mol. Sci. 24, 2264 (2023).
Ito, J. et al. Convergent evolution of SARS-CoV-2 Omicron subvariants leading to the emergence of BQ.1.1 variant. Nat. Commun. 14, 2671 (2023).
Tuekprakhon, A. et al. Antibody escape of SARS-CoV-2 Omicron BA.4 and BA.5 from vaccine and BA.1 serum. Cell 185, 2422–2433 e2413 (2022).
Spratt, A. N. et al. Continued complexity of mutations in Omicron sublineages. Biomedicines 10, 2593 (2022).
Turakhia, Y. et al. Ultrafast Sample placement on Existing tRees (UShER) enables real-time phylogenetics for the SARS-CoV-2 pandemic. Nat. Genet. 53, 809–816 (2021).
Dhar, M. S. et al. Genomic characterization and epidemiology of an emerging SARS-CoV-2 variant in Delhi, India. Science 374, 995–999 (2021).
van Dorp, L. et al. No evidence for increased transmissibility from recurrent mutations in SARS-CoV-2. Nat. Commun. 11, 5986 (2020).
Richard, M. et al. Factors determining human-to-human transmissibility of zoonotic pathogens via contact. Curr. Opin. Virol. 22, 7–12 (2017).
Hadfield, J. et al. Nextstrain: real-time tracking of pathogen evolution. Bioinformatics 34, 4121–4123 (2018).
Tay, J. H., Porter, A. F., Wirth, W. & Duchene, S. The emergence of SARS-CoV-2 variants of concern is driven by acceleration of the substitution rate. Mol. Biol. Evol. 39, msac013 (2022).
Hill, V. et al. The origins and molecular evolution of SARS-CoV-2 lineage B.1.1.7 in the UK. Virus Evol. 8, veac080 (2022).
Mallapaty, S. Where did Omicron come from? Three key theories. Nature 602, 26–28 (2022).
Robbiani, D. F. et al. Convergent antibody responses to SARS-CoV-2 in convalescent individuals. Nature 584, 437–442 (2020).
McCallum, M. et al. N-terminal domain antigenic mapping reveals a site of vulnerability for SARS-CoV-2. Cell 184, 2332–2347 e2316 (2021).
Tonkin-Hill, G. et al. Patterns of within-host genetic diversity in SARS-CoV-2. eLife https://doi.org/10.7554/eLife.66857 (2021).
Deinhardt-Emmer, S. et al. Early postmortem mapping of SARS-CoV-2 RNA in patients with COVID-19 and the correlation with tissue damage. eLife https://doi.org/10.7554/eLife.60361 (2021).
Lauring, A. S. & Andino, R. Quasispecies theory and the behavior of RNA viruses. PLoS Pathog. 6, e1001005 (2010).
Shen, Z. et al. Genomic diversity of severe acute respiratory syndrome-coronavirus 2 in patients with coronavirus disease 2019. Clin. Infect. Dis. 71, 713–720 (2020).
Park, Y. J. et al. Imprinted antibody responses against SARS-CoV-2 Omicron sublineages. Science 378, 619–627 (2022).
Martin, D. P. et al. The emergence and ongoing convergent evolution of the SARS-CoV-2 N501Y lineages. Cell 184, 5189–5200 e5187 (2021).
Yue, C. et al. ACE2 binding and antibody evasion in enhanced transmissibility of XBB.1.5. Lancet Infect. Dis. 23, 278–280 (2023).
Ou, J. et al. V367F mutation in SARS-CoV-2 spike RBD emerging during the early transmission phase enhances viral infectivity through increased human ACE2 receptor binding affinity. J. Virol. 95, e0061721 (2021).
Moulana, A. et al. Compensatory epistasis maintains ACE2 affinity in SARS-CoV-2 Omicron BA.1. Nat. Commun. 13, 7011 (2022).
Yang, L., Liu, S., Tsoka, S. & Papageorgiou, L. G. Mathematical programming for piecewise linear regression analysis. Expert Syst. Appl. 44, 156–167 (2016).
Gkioulekas, I. & Papageorgiou, L. G. Piecewise regression analysis through information criteria using mathematical programming. Expert Syst. Appl. 121, 362–372 (2019).
Starr, T. N. et al. Deep mutational scanning of SARS-CoV-2 receptor binding domain reveals constraints on folding and ACE2 binding. Cell 182, 1295–1310 e1220 (2020).
Otwinowski, J., McCandlish, D. M. & Plotkin, J. B. Inferring the shape of global epistasis. Proc. Natl Acad. Sci. USA 115, E7550–E7558 (2018).
Subramanian, A. et al. Gene set enrichment analysis: a knowledge-based approach for interpreting genome-wide expression profiles. Proc. Natl Acad. Sci. USA 102, 15545–15550 (2005).
Starr, T. N. et al. Deep mutational scans for ACE2 binding, RBD expression, and antibody escape in the SARS-CoV-2 Omicron BA.1 and BA.2 receptor-binding domains. PLoS Pathog. 18, e1010951 (2022).
Ma, W., Fu, H., Jian, F., Cao, Y. & Li, M. Immune evasion and ACE2 binding affinity contribute to SARS-CoV-2 evolution data and code. Zenodo https://zenodo.org/record/7954439 (2023).
We thank all the scientists around the globe for performing SARS-CoV-2 sequencing and surveillance analysis. This study was funded by the National Natural Science Foundation of China (grant no. 82161148009 to M.L.), the Strategic Priority Research Program of the Chinese Academy of Sciences (grant no. XDB38030400 to M.L.) and the Key Collaborative Research Program of the Alliance of International Science Organizations (grant no. ANSO-CR-KP-2022-09 to M.L.).
The authors declare no competing interests.
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a) The correlation between nonsynonymous mutation count and collection date. Two hundred sequences were randomly selected from each month based on the collection date. The segmented regression line was fitted using automatic piecewise linear regression, and the mutation rate was estimated as the slope of the regression line. We conducted 100 samplings and added the resulting median and standard deviation of the estimated mutation rate on top of the regression line. T1 represents Dec 24, 2019, which is the collection date of the first open-access SARS-CoV-2 sequence. b) The correlation between nonsynonymous mutation count and collection date for major lineages. c) The correlation between synonymous mutation count and collection date. D) The correlation between synonymous mutation count and collection date for major lineages.
The color denoted the ratio of the mutation’s frequency to the frequency of all mutations in the particular lineage. The top five mutations occurring most frequently in each lineage are shown, while the sites with just one high-frequency mutation were excluded. The mutations that had been fixed in the lineage were labeled in grey. The antibody epitope groups that were evaded by the mutation in the right panel are labeled in the left panel.
The ‘Top 5’ denotes the top five most frequent mutations observed in at least one lineage while ‘Other’ denotes the rest mutations. The unadjusted p-values were obtained from the two-sided Fisher’s exact test.
Extended Data Fig. 4 The correlation between the mutation incidence and immune escape score and ACE2 binding score at the individual mutation level in the SARS-CoV-2 RBD region.
Each dot in the plot represents an individual mutation. The Pearson correlation coefficient and two-sided unadjusted p-value are shown in each plot. The shading represents the 99% confidence interval.
Extended Data Fig. 5 The comparison of early-stage mutations and later-stage mutations in the SARS-CoV-2 RBD region in B and BA.2 lineages.
The early-stage mutations (Early, n=66 for B and n=34 for BA.2) were those identified in sequences collected at the first quartile of the time distribution of all mutations (the threshold for B macro-lineages: Oct 31, 2020; BA.2 lineage: May 12, 2022) and other mutations were defined as later-stage mutations (Later, n=66 for B and n=37 for BA.2). B lineage includes all its sub-lineages except the Omicron lineage. The upper part figures show the ACE2 binding score and immune escape score for different mutation types, while the bottom part figures show the proportion of ACE2 binding-enhancing and immune escape mutations for different mutation types. Random shows the background distribution of the metrics for different mutation types. The centre line denotes the median value, the black cross represents the mean value, the box represents the interquartile range (IQR), the whiskers extend to the furthest data point in each wing that is within 1.5 times the IQR value, and points represent outliers. The unadjusted p-value was obtained from the two-sided Wilcoxon test.
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Ma, W., Fu, H., Jian, F. et al. Immune evasion and ACE2 binding affinity contribute to SARS-CoV-2 evolution. Nat Ecol Evol 7, 1457–1466 (2023). https://doi.org/10.1038/s41559-023-02123-8
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