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. 2024 Sep 2;10(1):veae071.
doi: 10.1093/ve/veae071. eCollection 2024.

SARS-CoV-2 variant replacement constrains vaccine-specific viral diversification

Bethany L Dearlove  1   2 , Anthony C Fries  3 , Nusrat J Epsi  2   4 , Stephanie A Richard  2   4 , Anuradha Ganesan  2   4   5 , Nikhil Huprikar  5 , David A Lindholm  6   7 , Katrin Mende  2   4   7 , Rhonda E Colombo  2   4   6   8 , Christopher Colombo  8 , Hongjun Bai  1   2 , Derek T Larson  9 , Evan C Ewers  9 , Tahaniyat Lalani  2   4   10 , Alfred G Smith  10 , Catherine M Berjohn  4   6   11 , Ryan C Maves  12 , Milissa U Jones  13 , David Saunders  6 , Carlos J Maldonado  14 , Rupal M Mody  15 , Samantha E Bazan  16 , David R Tribble  4 , Timothy Burgess  4 , Mark P Simons  4 , Brian K Agan  2   4 , Simon D Pollett  2   4 , Morgane Rolland  1   2
Affiliations

SARS-CoV-2 variant replacement constrains vaccine-specific viral diversification

Bethany L Dearlove et al. Virus Evol. .

Abstract

Coronavirus disease 2019 (COVID-19) vaccine breakthrough infections have been important for all circulating severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) variant periods, but the contribution of vaccine-specific SARS-CoV-2 viral diversification to vaccine failure remains unclear. This study analyzed 595 SARS-CoV-2 sequences collected from the Military Health System beneficiaries between December 2020 and April 2022 to investigate the impact of vaccination on viral diversity. By comparing sequences based on the vaccination status of the participant, we found limited evidence indicating that vaccination was associated with increased viral diversity in the SARS-CoV-2 spike, and we show little to no evidence of a substantial sieve effect within major variants; rather, we show that rapid variant replacement constrained intragenotype COVID-19 vaccine strain immune escape. These data suggest that, during past and perhaps future periods of rapid SARS-CoV-2 variant replacement, vaccine-mediated effects were subsumed with other drivers of viral diversity due to the massive scale of infections and vaccinations that occurred in a short time frame. However, our results also highlight some limitations of using sieve analysis methods outside of placebo-controlled clinical trials.

Keywords: SARS-CoV-2; sieve analysis; vaccine breakthrough; variants.

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Conflict of interest statement

The views expressed are those of the authors and do not reflect the official policy of the Uniformed Services University of the Health Sciences (USUHS), Department of the Army, Department of the Navy, the Department of the Air Force, the Department of Defense or the US Government, and the Henry M. Jackson Foundation for the Advancement of Military Medicine, Inc. (HJF). The investigators have adhered to the policies for protection of human subjects as prescribed in 45 CFR 46. Drs Berjohn, Fries, Smith, Mody, Huprikar, Lindholm, Jones, Larson, Ewers; Ms Bazan; and Drs Saunders, Maldonado, Simons, Tribble, and Burgess are service members or employees of the US Government. This work was prepared as part of their official duties. Title 17 USC §105 provides that “Copyright protection under this title is not available for any work of the United States Government.” Title 17 USC §101 defines a US Government work as a work prepared by a military service member or employee of the US Government as part of that person’s official duties.

Figures

Figure 1.
Figure 1.
Circulation of variants in the South and West census bureau regions of the USA during the EPICC study. (a) The proportion of sequences broken down by variant from the EPICC study. WHO-named VOIs are included in a single category, with nonvariant spike D614G-containing lineages also separated out. (b) The proportion of sequences by variant in GISAID for the same period as the EPICC study, downloaded on 28 April 2022. (c) Variant prevalence in a 60-day sliding window for unvaccinated (solid line) and vaccine breakthrough (dashed line) groups for VOCs with at least five sequences per group. Gray lines denote the rolling averages from GISAID over the same period.
Figure 2.
Figure 2.
Sequences from unvaccinated and vaccine breakthrough infections are intermingled in the phylogenetic tree. Maximum likelihood tree reconstructed from spike nucleotide sequences with IQ-TREE and rooted by Wuhan-Hu-1; tip points are slightly jittered for readability. Inset: comparison of pairwise distances between tips of the same lineage for the unvaccinated and vaccine breakthrough cases. Asterisks give significance levels for P values: **** P < .0001; *** P < .001; ** P < .1, * P < .05; ns, not significant.
Figure 3.
Figure 3.
Spike divergence for sequences sampled in the South census region, calculated using the Hamming distance from Wuhan-Hu-1. (a) Overall distribution of Hamming distance split by vaccination group, broken down by lineage. (b) Comparison of Hamming distance distributions over time. Differences between vaccination groups and between time points compared with the Wilcoxon rank sum test and Bonferroni adjusted for multiple testing. (c) Linear regression of the Hamming distance and sample collection date. (d) As in (c), but for Delta sequences only. Asterisks give significance levels for P values: **** P < .0001; *** P < .001; ** P < .01; * P < .05; ns, not significant.
Figure 4.
Figure 4.
Local sieve analysis of site diversity for (a) overall in the South census region and (b) broken down by year quarter. A proportion >0 shows higher diversity from Wuhan-Hu-1 in the vaccine breakthrough group, whereas proportion <0 shows higher diversity in the unvaccinated group. Significant sites from the permutation test after adjustment for multiple testing are labeled by their Wuhan-Hu-1 index (214ins = insertion after site 214). Only quarters with more than five sequences available in both groups were analyzed.
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