No abstract

Bivalent mRNA vaccine boosters expressing Omicron BA.5 spike and ancestral D614G spike were introduced to attempt to boost waning antibody titers and broaden coverage against emerging SARS-CoV-2 lineages. Previous reports showed that peak serum neutralizing antibody (NAb) titers against SARS-CoV-2 variants following bivalent booster were similar to peak titers following monovalent booster. It remains unknown whether these antibody responses would diverge over time. We assessed serum virus-neutralizing titers in 41 participants who received three monovalent mRNA vaccine doses followed by bivalent booster, monovalent booster, or BA.5 breakthrough infection at one month and three months after the last vaccine dose or breakthrough infection using pseudovirus neutralization assays against D614G and Omicron subvariants (BA.2, BA.5, BQ.1.1, and XBB.1.5). There was no significant difference at one month and three months post-booster for the two booster cohorts. BA.5 breakthrough patients exhibited significantly higher NAb titers at three months against all Omicron subvariants tested compared against monovalent and bivalent booster cohorts. There was a 2-fold drop in mean NAb titers in the booster cohorts between one and three month time points, but no discernible waning of titers in the BA.5 breakthrough cohort over the same period. Our results suggest that NAb titers after boosting with one dose of bivalent mRNA vaccine are not higher than boosting with monovalent vaccine. Perhaps inclusion of D614G spike in the bivalent booster exacerbates the challenge posed by immunological imprinting. Hope remains that a second bivalent booster could induce superior NAb responses against emerging variants.

SARS-CoV-2 Omicron BA.2.75 has diversified into multiple subvariants with additional spike mutations, and several are expanding in prevalence, particularly CH.1.1 and BN.1. Here, we investigated the viral receptor affinities and neutralization evasion properties of major BA.2.75 subvariants actively circulating in different regions worldwide. We found two distinct evolutionary pathways and three newly identified mutations that shaped the virological features of these subvariants. One phenotypic group exhibited a discernible decrease in viral receptor affinities, but a noteworthy increase in resistance to antibody neutralization, as exemplified by CH.1.1, which is apparently as resistant as XBB.1.5. In contrast, a second group demonstrated a substantial increase in viral receptor affinity but only a moderate increase in antibody evasion, as exemplified by BN.1. We also observed that all prevalent SARS-CoV-2 variants in the circulation presently, except for BN.1, exhibit profound levels of antibody evasion, suggesting this is the dominant determinant of virus transmissibility today.

Cells and tissues have a remarkable ability to adapt to genetic perturbations via a variety of molecular mechanisms. Nonsense-induced transcriptional compensation, a form of transcriptional adaptation, has recently emerged as one such mechanism, in which nonsense mutations in a gene can trigger upregulation of related genes, possibly conferring robustness at cellular and organismal levels. However, beyond a handful of developmental contexts and curated sets of genes, to date, no comprehensive genome-wide investigation of this behavior has been undertaken for mammalian cell types and contexts. Moreover, how the regulatory-level effects of inherently stochastic compensatory gene networks contribute to phenotypic penetrance in single cells remains unclear. Here we combine computational analysis of existing datasets with stochastic mathematical modeling and machine learning to uncover the widespread prevalence of transcriptional adaptation in mammalian systems and the diverse single-cell manifestations of minimal compensatory gene networks. Regulon gene expression analysis of a pooled single-cell genetic perturbation dataset recapitulates our model predictions. Our integrative approach uncovers several putative hits-genes demonstrating possible transcriptional adaptation-to follow up on experimentally, and provides a formal quantitative framework to test and refine models of transcriptional adaptation.