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Genetic and Structural Data on the SARS-CoV-2 Omicron BQ.1 Variant Reveal Its Low Potential for Epidemiological Expansion.

Fabio ScarpaDaria SannaDomenico BenvenutoAlessandra BorsettiIlenia AzzenaMarco CasuPier Luigi FioriMarta GiovanettiAntonello MarouttiGiancarlo CeccarelliArnaldo CarusoFrancesca CaccuriRoberto CaudaAntonio CassoneStefano PascarellaMassimo Ciccozzi
Published in: International journal of molecular sciences (2022)
The BQ.1 SARS-CoV-2 variant, also known as Cerberus, is one of the most recent Omicron descendant lineages. Compared to its direct progenitor BA.5, BQ.1 has some additional spike mutations in some key antigenic sites, which confer further immune escape ability over other circulating lineages. In such a context, here, we perform a genome-based survey aimed at obtaining a complete-as-possible nuance of this rapidly evolving Omicron subvariant. Genetic data suggest that BQ.1 represents an evolutionary blind background, lacking the rapid diversification that is typical of a dangerous lineage. Indeed, the evolutionary rate of BQ.1 is very similar to that of BA.5 (7.6 × 10 -4 and 7 × 10 -4 subs/site/year, respectively), which has been circulating for several months. The Bayesian Skyline Plot reconstruction indicates a low level of genetic variability, suggesting that the peak was reached around 3 September 2022. Concerning the affinity for ACE2, structure analyses (also performed by comparing the properties of BQ.1 and BA.5 RBD) indicate that the impact of the BQ.1 mutations may be modest. Likewise, immunoinformatic analyses showed moderate differences between the BQ.1 and BA5 potential B-cell epitopes. In conclusion, genetic and structural analyses on SARS-CoV-2 BQ.1 suggest no evidence of a particularly dangerous or high expansion capability. Genome-based monitoring must continue uninterrupted for a better understanding of its descendants and all other lineages.
Keyphrases
  • sars cov
  • genome wide
  • dna methylation
  • respiratory syndrome coronavirus
  • copy number
  • big data
  • machine learning
  • climate change
  • human health
  • coronavirus disease
  • high intensity
  • data analysis