High number of SARS-CoV-2 persistent infections uncovered through genetic analysis of samples from a large community-based surveillance study (preprint)

Persistent severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infections may act as viral reservoirs that could seed future outbreaks 1-5, give rise to highly divergent lineages 6-8, and contribute to cases with post-acute Coronavirus disease 2019 (COVID-19) sequelae (Long Covid) 9,10. However, the population prevalence of persistent infections, their viral load kinetics, and evolutionary dynamics over the course of infections remain largely unknown. We identified 381 infections lasting at least 30 days, of which 54 lasted at least 60 days. These persistently infected individuals had more than 50% higher odds of self-reporting Long Covid compared to the infected controls, and we estimate that 0.09-0.5% of SARS-CoV-2 infections can become persistent and last for at least 60 days. In nearly 70% of the persistent infections we identified, there were long periods during which there were no consensus changes in virus sequences, consistent with prolonged presence of non-replicating virus. Our findings also suggest reinfections with the same major lineage are rare and that many persistent infections are characterised by relapsing viral load dynamics. Furthermore, we found a strong signal for positive selection during persistent infections, with multiple amino acid substitutions in the Spike and ORF1ab genes emerging independently in different individuals, including mutations that are lineage-defining for SARS-CoV-2 variants, at target sites for several monoclonal antibodies, and commonly found in immunocompromised patients 11-14. This work has significant implications for understanding and characterising SARS-CoV-2 infection, epidemiology, and evolution.

Viral burdens are associated with age and viral variant in a population-representative study of SARS-CoV-2 that accounts for time-since-infection related sampling bias. (preprint)

In this study, we evaluated the impact of viral variant, in addition to other variables, on within-host viral burdens, by analysing cycle threshold (Ct) values derived from nose and throat swabs, collected as part of the UK COVID-19 Infection Survey. Because viral burden distributions determined from community survey data can be biased due to the impact of variant epidemiology on the time-since-infection of samples, we developed a method to explicitly adjust observed Ct value distributions to account for the expected bias. Analysing the adjusted Ct values using partial least squares regression, we found that among unvaccinated individuals with no known prior infection, the average Ct value was 0.94 lower among Alpha variant infections, compared those with the predecessor strain, B.1.177. However, among vaccinated individuals, it was 0.34 lower among Delta variant infections, compared to those with the Alpha variant. In addition, the average Ct value decreased by 0.20 for every 10 year age increment of the infected individual. In summary, within-host viral burdens are associated with age, in addition to the interplay of vaccination status and viral variant.

Lineage replacement and evolution captured by the United Kingdom Covid Infection Survey (preprint)

The Office for National Statistics COVID-19 Infection Survey is a large household-based surveillance study based in the United Kingdom. Here, we report on the epidemiological and evolutionary dynamics of SARS-CoV-2 determined by analysing sequenced samples collected up until 13th November 2021. We observed four distinct sweeps or partial-sweeps, by lineages B.1.177, B.1.1.7/Alpha, B.1.617.2/Delta, and finally AY.4.2, a sublineage of B.1.617.2, with each sweeping lineage having a distinct growth advantage compared to their predecessors. Evolution was characterised by steady rates of evolution and increasing diversity within lineages, but with step increases in divergence associated with each sweeping major lineage, leading to a faster overall rate of evolution and fluctuating levels of diversity. These observations highlight the value of viral sequencing integrated into community surveillance studies to monitor the viral epidemiology and evolution of SARS-CoV-2, and potentially other pathogens, particularly as routine PCR testing is phased out or in settings where large-scale sequencing is not feasible.

Computational analysis of the effect of SARS-CoV-2 variant Omicron Spike protein mutations on dynamics, ACE2 binding and propensity for immune escape (preprint)

The recently reported Omicron (B.1.1.529) SARS-CoV-2 variant has a large number of mutations in the Spike (S) protein compared to previous variants. Here we evaluate the potential effect of Omicron S mutations on S protein dynamics and ACE2 binding as contributing factors to infectivity as well as propensity for immune escape. We define a consensus set of mutations from 77 sequences assigned as Omicron in GISAID as of November 25. We create structural models of the Omicron S protein in the open and closed states, as part of a complex with ACE2 and for each of 77 complexes of S bound to different antibodies with known structures. We have previously utilized Dynamical Signatures (DS) and the Vibrational Entropy Score (VDS) to evaluate the propensity of S variants to favour the open state. Here, we introduce the Binding Influence Score (BIS) to evaluate the influence of mutations on binding affinity based on the net gain or loss of interactions within the protein-protein interface. BIS shows excellent correlation with experimental data (Pearson correlation coefficient of 0.87) on individual mutations in the ACE2 interface for the Alpha, Beta, Gamma, Delta and Omicron variants combined. On the one hand, the DS of Omicron highly favours a more rigid open state and a more flexible closed state with the largest VDS of all variants to date, suggesting a large increase in the chances to interact with ACE2. On the other hand, the BIS shows that apart from N501Y, all other mutations in the interface reduce ACE2 binding affinity. VDS and BIS show opposing effects on the overall effectiveness of Omicron mutations to promote binding to ACE2 and therefore initiate infection. To evaluate the propensity for immune escape we calculated the net change of favourable and unfavourable interactions within each S-antibody interface. The net change of interactions shows a positive score (a net increase of favourable interactions and decrease of unfavourable ones) for 41 out of 77 antibodies, a nil score for 15 and a negative score for 21 antibodies. Therefore, in only 28% of S-antibody complexes (21/77) we predict some level of immune escape due to a weakening of the interactions with Omicron S. Considering that most antibody epitopes and the mutations are within the S-ACE2 interface our results suggest that mutations within the RBD of Omicron may give rise to only partial immune escape, which comes at the expense of reduced ACE2 binding affinity. However, this reduced ACE2 affinity appears to have been offset by increasing the occupancy of the open state of the Spike protein.