Explore the vast range of titles available.

Coronaviruses express their structural and accessory genes via a set of subgenomic RNAs, whose synthesis is directed by transcription regulatory sequences (TRSs) in the 5′ genomic leader and upstream of each body open reading frame. In SARS-CoV-2, the TRS has the consensus AAACGAAC; upon searching for emergence of this motif in the global SARS-CoV-2 sequences, we find that it evolves frequently, especially in the 3′ end of the genome. We show well-supported examples upstream of the Spike gene—within the nsp16 coding region of ORF1b—which is expressed during human infection, and upstream of the canonical Envelope gene TRS, both of which have evolved convergently in multiple lineages. The most frequent neo-TRS is within the coding region of the Nucleocapsid gene, and is present in virtually all viruses from the B.1.1 lineage, including the variants of concern Alpha, Gamma, Omicron and descendants thereof. Here, we demonstrate that this TRS leads to the expression of a novel subgenomic mRNA encoding a truncated C-terminal portion of Nucleocapsid, which is an antagonist of type I interferon production and contributes to viral fitness during infection. We observe distinct phenotypes when the Nucleocapsid coding sequence is mutated compared to when the TRS alone is ablated. Our findings demonstrate that SARS-CoV-2 is undergoing evolutionary changes at the functional RNA level in addition to the amino acid level.

The emergence of successive Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) variants of concern (VOCs) during 2020 to 2022, each exhibiting increased epidemic growth relative to earlier circulating variants, has created a need to understand the drivers of such growth. However, both pathogen biology and changing host characteristics—such as varying levels of immunity—can combine to influence replication and transmission of SARS-CoV-2 within and between hosts. Disentangling the role of variant and host in individual-level viral shedding of VOCs is essential to inform Coronavirus Disease 2019 (COVID-19) planning and response and interpret past epidemic trends. Using data from a prospective observational cohort study of healthy adult volunteers undergoing weekly occupational health PCR screening, we developed a Bayesian hierarchical model to reconstruct individual-level viral kinetics and estimate how different factors shaped viral dynamics, measured by PCR cycle threshold (Ct) values over time. Jointly accounting for both interindividual variation in Ct values and complex host characteristics—such as vaccination status, exposure history, and age—we found that age and number of prior exposures had a strong influence on peak viral replication. Older individuals and those who had at least 5 prior antigen exposures to vaccination and/or infection typically had much lower levels of shedding. Moreover, we found evidence of a correlation between the speed of early shedding and duration of incubation period when comparing different VOCs and age groups. Our findings illustrate the value of linking information on participant characteristics, symptom profile and infecting variant with prospective PCR sampling, and the importance of accounting for increasingly complex population exposure landscapes when analysing the viral kinetics of VOCs. Trial Registration: The Legacy study is a prospective observational cohort study of healthy adult volunteers undergoing weekly occupational health PCR screening for SARS-CoV-2 at University College London Hospitals or at the Francis Crick Institute ( NCT04750356 ) (22,23). The Legacy study was approved by London Camden and Kings Cross Health Research Authority Research and Ethics committee (IRAS number 286469). The Legacy study was approved by London Camden and Kings Cross Health Research Authority Research and Ethics committee (IRAS number 286469) and is sponsored by University College London Hospitals. Written consent was given by all participants.

The emergence of SARS-CoV-2 variants and COVID-19 vaccination have resulted in complex exposure histories. Rapid assessment of the effects of these exposures on neutralising antibodies against SARS-CoV-2 infection is crucial for informing vaccine strategy and epidemic management. We aimed to investigate heterogeneity in individual-level and population-level antibody kinetics to emerging variants by previous SARS-CoV-2 exposure history, to examine implications for real-time estimation, and to examine the effects of vaccine-campaign timing. Our Bayesian hierarchical model of antibody kinetics estimated neutralising-antibody trajectories against a panel of SARS-CoV-2 variants quantified with a live virus microneutralisation assay and informed by individual-level COVID-19 vaccination and SARS-CoV-2 infection histories. Antibody titre trajectories were modelled with a piecewise linear function that depended on the key biological quantities of an initial titre value, time the peak titre is reached, set-point time, and corresponding rates of increase and decrease for gradients between two timing parameters. All process parameters were estimated at both the individual level and the population level. We analysed data from participants in the University College London Hospitals–Francis Crick Institute Legacy study cohort (NCT04750356) who underwent surveillance for SARS-CoV-2 either through asymptomatic mandatory occupational health screening once per week between April 1, 2020, and May 31, 2022, or symptom-based testing between April 1, 2020, and Feb 1, 2023. People included in the Legacy study were either Crick employees or health-care workers at three London hospitals, older than 18 years, and gave written informed consent. Legacy excluded people who were unable or unwilling to give informed consent and those not employed by a qualifying institution. We segmented data to include vaccination events occurring up to 150 days before the emergence of three variants of concern: delta, BA.2, and XBB 1.5. We split the data for each wave into two categories: real-time and retrospective. The real-time dataset contained neutralising-antibody titres collected up to the date of emergence in each wave; the retrospective dataset contained all samples until the next SARS-CoV-2 exposure of each individual, whether vaccination or infection. We included data from 335 participants in the delta wave analysis, 223 (67%) of whom were female and 112 (33%) of whom were male (median age 40 years, IQR 22–58); data from 385 participants in the BA.2 wave analysis, 271 (70%) of whom were female and 114 (30%) of whom were male (41 years, 22–60); and data from 248 participants in the XBB 1.5 wave analysis, 191 (77%) of whom were female, 56 (23%) of whom were male, and one (<1%) of whom preferred not to say (40 years, 21–59). Overall, we included 968 exposures (vaccinations) across 1895 serum samples in the model. For the delta wave, we estimated peak titre values as 490·0 IC50 (95% credible interval 224·3–1515·9) for people with no previous infection and as 702·4 IC50 (300·8–2322·7) for people with a previous infection before omicron; the delta wave did not include people with a previous omicron infection. For the BA.2 wave, we estimated peak titre values as 858·1 IC50 (689·8–1363·2) for people with no previous infection, 1020·7 IC50 (725·9–1722·6) for people with a previous infection before omicron, and 1422·0 IC50 (679·2–3027·3) for people with a previous omicron infection. For the XBB 1.5 wave, we estimated peak titre values as 703·2 IC50 (415·0–3197·8) for people with no previous infection, 1215·9 IC50 (511·6–7338·7) for people with a previous infection before omicron, and 1556·3 IC50 (757·2–7907·9) for people with a previous omicron infection. Our study shows the feasibility of real-time estimation of antibody kinetics before SARS-CoV-2 variant emergence. This estimation is valuable for understanding how specific combinations of SARS-CoV-2 exposures influence antibody kinetics and for examining how COVID-19 vaccination-campaign timing could affect population-level immunity to emerging variants. Wellcome Trust, National Institute for Health Research University College London Hospitals Biomedical Research Centre, UK Research and Innovation, UK Medical Research Council, Francis Crick Institute, and Genotype-to-Phenotype National Virology Consortium.

SARS-CoV-2 variant Omicron rapidly evolved over 2022, causing three waves of infection due to sub-variants BA.1, BA.2 and BA.4/5. We sought to characterise symptoms and viral loads over the course of COVID-19 infection with these sub-variants in otherwise-healthy, vaccinated, non-hospitalised adults, and compared data to infections with the preceding Delta variant of concern (VOC). In a prospective, observational cohort study, healthy vaccinated UK adults who reported a positive polymerase chain reaction (PCR) or lateral flow test, self-swabbed on alternate weekdays until day 10. We compared participant-reported symptoms and viral load trajectories between infections caused by VOCs Delta and Omicron (sub-variants BA.1, BA.2 or BA.4/5), and tested for relationships between vaccine dose, symptoms and PCR cycle threshold (Ct) as a proxy for viral load using Chi-squared (X2) and Wilcoxon tests. 563 infection episodes were reported among 491 participants. Across infection episodes, there was little variation in symptom burden (4 [IQR 3-5] symptoms) and duration (8 [IQR 6-11] days). Whilst symptom profiles differed among infections caused by Delta compared to Omicron sub-variants, symptom profiles were similar between Omicron sub-variants. Anosmia was reported more frequently in Delta infections after 2 doses compared with Omicron sub-variant infections after 3 doses, for example: 42% (25/60) of participants with Delta infection compared to 9% (6/67) with Omicron BA.4/5 (X.sup.2 P < 0.001; OR 7.3 [95% CI 2.7-19.4]). Fever was less common with Delta (20/60 participants; 33%) than Omicron BA.4/5 (39/67; 58%; X.sup.2 P = 0.008; OR 0.4 [CI 0.2-0.7]). Amongst infections with an Omicron sub-variants, symptoms of coryza, fatigue, cough and myalgia predominated. Viral load trajectories and peaks did not differ between Delta, and Omicron, irrespective of symptom severity (including asymptomatic participants), VOC or vaccination status. PCR Ct values were negatively associated with time since vaccination in participants infected with BA.1 ([beta] = -0.05 (CI -0.10-0.01); P = 0.031); however, this trend was not observed in BA.2 or BA.4/5 infections. Our study emphasises both the changing symptom profile of COVID-19 infections in the Omicron era, and ongoing transmission risk of Omicron sub-variants in vaccinated adults.

Background Little data exist on the COVID-19 vaccine response in African countries who despite having high disease burden, have low COVID-19 mortality rates. We investigated the longitudinal immune response in a West-African urban population upon COVID-19 vaccination, two years after the start of the pandemic. Methods The HERITAGE study is a prospective cohort study of 301 residents of Accra, Ghana. Participants received two doses of a COVID-19 vaccine (AZD1222 or BNT162b2) from December 2021 and were followed-up for 12 months. COVID-19 status was determined by RT-PCR at seven time points. Serological responses, including anti-Nucleocapsid IgG, anti-Spike IgG and live-virus neutralisation were determined at four time points during the 12 months follow-up. Results COVID-19 positivity was 19.3% at baseline and reduced rapidly upon vaccination. Serological analyses indicated previous exposure to SARS-CoV-2 in 80.5% of the HERITAGE participants. After vaccination, neutralising antibody titres (NAbTs) against six different SARS-CoV-2 variants significantly ( p  < 0.001) increased, with fold changes (FC) ranging from 1.87 to 4.59. Highest NAbTs were recorded in the previously exposed group. Participants without prior exposure showed a continues increase in NAbTs between months 3 and 12 for circulating variants (Omicron B.A2 (FC 2.44, p  < 0.001) and XBB.1.5 (FC 1.91, p  = 0.05)). By comparison a matched cohort from the UK-based LEGACY study showed generally lower NAbTs at baseline (HERITAGE vs LEGACY for Wild-type: 250.3 vs 141.3, p  < 0.0001, for A.27 84.6 vs 43.2, p  = 0.0129, for Eta 159.7 vs 118.1, p  = 0.3428, for Delta 158.6 vs 10.0, p  < 0.0001, for Omicron B.A2 153.7 vs 10.0, p  < 0.0001) and after receiving the vaccine (HERITAGE vs LEGACY for Wild-type: 882.6 vs 337.7, p  < 0.0001, for A.27 552.0 vs 227.7, p  = 0.0001, for Eta 682.2 vs 295.3, p  < 0.0001, for Delta 557.6 vs 165.1, p  < 0.0001, for Omicron B.A2 283.3 vs 124.2, p  < 0.0001). NAbTs kinetics between the two cohorts were more similar when analysis was restricted to previously unexposed participants when adjusted for circulating variants during the sampling period. Conclusions Two doses of AZD1222 or BNT162b2 significantly increased existing NAbTs against SARS-CoV-2 in a highly exposed population, showing durable boosting of pre-existing infection-induced immunity. This indicates the importance of considering local population exposure in vaccination design and deployment.

IntroductionPeople with multiple sclerosis (pwMS) receiving B-cell depleting disease-modifying therapy (BCD-DMT) are vulnerable to severe COVID-19. Data on vaccine immunogenicity in this patient group are incomplete. In the context of the rapid evolution of SARS-CoV-2 2020–22, we compared vaccine responses in pwMS and healthy vaccinated adults (HVA) after three doses of messenger RNA vaccine encoding Ancestral SARS-CoV-2 Spike.MethodsIn this prospective observational cohort study, we collected serum from 226 pwMS prevaccine and postvaccine and quantified neutralising antibody titres (nAbT) in a high-throughput live virus assay against SARS-CoV-2 Ancestral, Alpha, Delta, Omicron BA.1, BA.2 and BA.5. We compared nAbT in pwMS and HVA, matched by age, sex, vaccine type, number of doses and time since exposure, using Wilcoxon signed-rank and χ2 tests. We further investigated nAbT vaccine response in pwMS on BCD-DMTs or non-depleting DMTs.ResultsPrior to third vaccination, nAbTs against nearly all variants tested were significantly lower (p<0.05) in pwMS taking BCD therapy than those in HVA or B-cell replete pwMS, and were not significantly boosted following vaccination. In contrast, B-cell replete pwMS versus HVAs exhibited equivalent prevaccination nAbTs against all variants, which were comparably boosted against most variants following vaccination. Consequently, differences in nAbTs against all variants tested were further magnified between B-cell replete and B-cell depleted pwMS post-third vaccination. Across the entire cohort, there were no COVID-19 hospitalisations or deaths. Notably, sera collected prior to the pandemic from pwMS demonstrated pre-existing, pan-coronavirus neutralising activity against seasonal HCoV-OC43 and SARS-CoV-2 variants.ConclusionsPwMS taking BCD therapy have limited antibody boosting following repeated COVID-19 vaccination. However, the absence of severe outcomes in pwMS, despite reduced immunogenicity, suggests a lower threshold for effective protection than previously reported. These findings support more nuanced risk stratification in clinical policy.