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Immune evasion and ACE2 binding affinity contribute to SARS-CoV-2 evolution

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Abstract

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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Fig. 1: The mutations in the SARS-CoV-2 genome.
Fig. 2: Convergent evolution of the RBD sequences in the SARS-CoV-2 genome.
Fig. 3: The correlation between the incidence of RBD mutations and humoral immune pressure.
Fig. 4: Immune evasion and ACE2 binding affinity drove the evolution of SARS-CoV-2.

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Data availability

All data files generated in this study were uploaded to the GitHub website (https://github.com/ipplol/SARS2EVO)44.

Code availability

All custom scripts used in this study were uploaded to the GitHub website (https://github.com/ipplol/SARS2EVO)44.

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Acknowledgements

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.).

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Authors and Affiliations

  1. Beijing Institute of Genomics, Chinese Academy of Sciences, and China National Center for Bioinformation, Beijing, China

    Wentai Ma, Haoyi Fu & Mingkun Li

  2. University of Chinese Academy of Sciences, Beijing, China

    Wentai Ma, Haoyi Fu & Mingkun Li

  3. Biomedical Pioneering Innovation Center (BIOPIC), Peking University, Beijing, China

    Fanchong Jian & Yunlong Cao

  4. Changping Laboratory, Beijing, China

    Yunlong Cao

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Contributions

M.L. designed the study. W.M. and M.L. wrote the manuscript with input from all authors. W.M., H.F. and F.J. performed the bioinformatics analyses. Y.C. and F.J. generated and supervised the analysis of the DMS and neutralization data.

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Correspondence to Yunlong Cao or Mingkun Li.

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Nature Ecology & Evolution thanks Oscar MacLean, Aiping Wu and the other, anonymous, reviewer(s) for their contribution to the peer review of this work. Peer reviewer reports are available.

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Extended data

Extended Data Fig. 1 The correlation between mutation count and collection date.

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.

Extended Data Fig. 2 The convergent evolution of the RBD region in the SARS-CoV-2 genome.

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.

Extended Data Fig. 3 The spectrum of non-synonymous mutations in the RBD region.

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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