Background SARS-CoV-2 recombinants involving the divergent Delta and Omicron lineages have been described, and one of them, “Kraken” (XBB.1.5), has recently been a matter of concern. Recombination requires the coexistence of two SARS-CoV-2 strains in the same individual. Only a limited number of studies have focused on the identification of co-infections and are restricted to co-infections involving the Delta/Omicron lineages. Methods We performed a systematic identification of SARS-CoV-2 co-infections throughout the pandemic (7609 different patients sequenced), not biassed towards the involvement of highly divergent lineages. Through a comprehensive set of validations based on the distribution of allelic frequencies, phylogenetic consistency, re-sequencing, host genetic analysis and contextual epidemiological analysis, these co-infections were robustly assigned. Results Fourteen (0.18%) co-infections with ≥ 8 heterozygous calls (8–85 HZs) were identified. Co-infections were identified throughout the pandemic and involved an equal proportion of strains from different lineages/sublineages (including pre-Alpha variants, Delta and Omicron) or strains from the same lineage. Co-infected cases were mainly unvaccinated, with mild or asymptomatic clinical presentation, and most were at risk of overexposure associated with the healthcare environment. Strain segregation enabled integration of sequences to clarify nosocomial outbreaks where analysis had been impaired due to co-infection. Conclusions Co-infection cases were identified throughout the pandemic, not just in the time periods when highly divergent lineages were co-circulating. Co-infections involving different lineages or strains from the same lineage were occurring in the same proportion. Most cases were mild, did not require medical assistance and were not vaccinated, and a large proportion were associated with the hospital environment.

Computational de novo protein design tailors proteins for target structures and oligomerisation states with high stability, which allows overcoming many limitations of natural proteins when redesigned for new functions. Despite significant advances in the field over the past decade, it remains challenging to predict sequences that will fold as stable monomers in solution or binders to a particular protein target; thereby requiring substantial experimental resources to identify proteins with the desired properties. To overcome this, here we leveraged the large amount of design data accumulated in the last decade, and the breakthrough in protein structure prediction from last year to investigate on improved ways of selecting promising designs before experimental testing. We collected de novo proteins from previous studies, 518 designed as monomers of different folds and 2112 as binders against the Botulinum neurotoxin, and analysed their structures with AlphaFold2, RoseTTAFold and fragment quality descriptors in combination with other properties related to surface interactions. These features showed high complementarity in rationalizing the experimental results, which allowed us to generate quite accurate machine learning models for predicting well-folded monomers and binders with a small set of descriptors. Cross-validating designs with varied orthogonal computational techniques should guide us for identifying design imperfections, rescuing designs and making more robust design selections before experimental testing.

The emergence of the Omicron variant of SARS-CoV-2 represented a challenge to the treatment of COVID-19 using monoclonal antibodies. Only Sotrovimab maintained partial activity, allowing it to be used in high-risk patients infected with the Omicron variant.

Centers for Disease Control and Prevention guidelines consider SARS-CoV-2 reinfection when sequential COVID-19 episodes occur > 90 days apart. However, genomic diversity acquired over recent COVID-19 waves could mean previous infection provides insufficient cross-protection. We used genomic analysis to assess the percentage of early reinfections in a sample of 26 patients with 2 COVID-19 episodes separated by 20–45 days. Among sampled patients, 11 (42%) had reinfections involving different SARS-CoV-2 variants or subvariants. Another 4 cases were probable reinfections; 3 involved different strains from the same lineage or sublineage. Host genomic analysis confirmed the 2 sequential specimens belonged to the same patient. Among all reinfections, 36.4% involved non-Omicron, then Omicron lineages. Early reinfections showed no specific clinical patterns; 45% were among unvaccinated or incompletely vaccinated persons, 27% were among persons <18 years of age, and 64% of patients had no risk factors. Time between sequential positive SARS-CoV-2 PCRs to consider reinfection should be re-evaluated.