ABSTRACT
SARS-CoV-2 omicron subvariants BA.1 and BA.2 became dominant in many countries in early 2022. These subvariants are now being displaced by BA.4 and BA.5. While natural infection with BA.1/BA.2 provides some protection against BA.4/BA.5 infection, the duration of this protection remains unknown. We used the national Portuguese COVID-19 registry to investigate the waning of protective immunity conferred by prior BA.1/BA.2 infection towards BA.5. We divided the individuals infected during the period of BA.1/BA.2 dominance (>90% of sample isolates) in successive 15-day intervals and determined the risk of subsequent infection with BA.5 over a fixed period. Compared with uninfected people, one previous infection conferred substantial protection against BA.5 re-infection at 3 months (RR=0.12; 95% CI: 0.11-0.12). However, although still significant, the protection was reduced by two-fold at 5 months post-infection (RR=0.24; 0.23-0.24). These results should be interpreted in the context of vaccine breakthrough infections, as the vaccination coverage in the individuals included in the analyses is >98% since the end of 2021. This waning of protection following BA.1/BA.2 infection highlights the need to assess the stability and durability of immune protection induced with the adapted vaccines (based on BA.1) over time.
ABSTRACT
The SARS-CoV-2 omicron BA.5 subvariant is progressively displacing earlier subvariants, BA.1 and BA.2, in many countries. One possible explanation is the ability of BA.5 to evade immune responses elicited by prior BA.1 and BA.2 infections. The impact of BA.1 infection on the risk of reinfection with BA.5 is a critical issue because adapted vaccines under current clinical development are based on BA.1. We used the national Portuguese COVID-19 registry to analyze the risk of BA.5 infection in individuals without a documented infection or previously infected during periods of distinct variants predominance (Wuhan-Hu-1, alpha, delta, BA.1/BA.2). National predominance periods were established according to the national SARS-CoV-2 genetic surveillance data (when one variant represented >90% of the sample isolates). We found that prior SARS-CoV-2 infection reduced the risk for BA.5 infection. The protection effectiveness, related to the uninfected group, for a first infection with Wuhan-Hu-1 was 52.9% (95% CI, 51.9 - 53.9%), for Alpha 54.9% (51.2 - 58.3%), for Delta 62.3% (61.4 - 63.3%), and for BA.1/BA.2 80.0% (79.7 - 80.2%). The results ought to be interpreted in the context of breakthrough infections within a population with a very high vaccine coverage (>98% of the study population completed the primary vaccination series). In conclusion, infection with BA.1/BA.2 reduces the risk for breakthrough infections with BA.5 in a highly vaccinated population. This finding is critical to appraise the current epidemiological situation and the development of adapted vaccines.
ABSTRACT
Considerable effort was made to better understand why some people suffer from severe COVID-19 while others remain asymptomatic. This has led to important clinical findings; people with severe COVID-19 generally experience persistently high levels of inflammation, slower viral load decay, display a dysregulated type-I interferon response, have less active natural killer cells and increased levels of neutrophil extracellular traps. How these findings are connected to the pathogenesis of COVID-19 remains unclear. We propose a mathematical model that sheds light on this issue. The model focuses on cells that trigger inflammation through molecular patterns: infected cells carrying pathogen-associated molecular patterns (PAMPs) and damaged cells producing damage-associated molecular patterns (DAMPs). The former signals the presence of pathogens while the latter signals danger such as hypoxia or the lack of nutrients. Analyses show that SARS-CoV-2 infections can lead to a self-perpetuating feedback loop between DAMP expressing cells and inflammation. It identifies the inability to quickly clear PAMPs and DAMPs as the main contributor to hyperinflammation. The model explains clinical findings and the conditional impact of treatments on disease severity. The simplicity of the model and its high level of consistency with clinical findings motivate its use for the formulation of new treatment strategies.
ABSTRACT
Resistance mutations to monoclonal antibody (mAb) therapy has been reported, but in the non-immunosuppressed population, it is unclear if in vivo emergence of SARS-CoV-2 resistance mutations alters either viral replication dynamics or therapeutic efficacy. In ACTIV-2/A5401, non-hospitalized participants with symptomatic SARS-CoV-2 infection were randomized to bamlanivimab (700mg or 7000mg) or placebo. Treatment-emergent resistance mutations were significantly more likely detected after bamlanivimab 700mg treatment than placebo (7% of 111 vs 0% of 112 participants, P=0.003). There were no treatment-emergent resistance mutations among the 48 participants who received bamlanivimab 7000mg. Participants with emerging mAb resistant virus had significantly higher pre-treatment nasopharyngeal and anterior nasal viral load. Intensive respiratory tract viral sampling revealed the dynamic nature of SARS-CoV-2 evolution, with evidence of rapid and sustained viral rebound after emergence of resistance mutations, and worsened symptom severity. Participants with emerging bamlanivimab resistance often accumulated additional polymorphisms found in current variants of concern/interest and associated with immune escape. These results highlight the potential for rapid emergence of resistance during mAb monotherapy treatment, resulting in prolonged high level respiratory tract viral loads and clinical worsening. Careful virologic assessment should be prioritized during the development and clinical implementation of antiviral treatments for COVID-19.
ABSTRACT
SARS-CoV-2 is a human pathogen that causes infection in both the upper respiratory tract (URT) and the lower respiratory tract (LRT). The viral kinetics of SARS-CoV-2 infection and how they relate to infectiousness and disease progression are not well understood. Here, we develop data-driven viral dynamic models of SARS-CoV-2 infection in both the URT and LRT. We fit the models to viral load data from patients with likely infection dates known, we estimated that infected individuals with a longer incubation period had lower rates of viral growth, took longer to reach peak viremia in the URT, and had higher chances of presymptomatic transmission. We then developed a model linking viral load to infectiousness. We found that to explain the substantial fraction of transmissions occurring presymptomatically, the infectiousness of a person should depend on a saturating function of the viral load, making the logarithm of the URT viral load a better surrogate of infectiousness than the viral load itself. Comparing the roles of target-cell limitation, the innate immune response, proliferation of target cells and spatial infection in the LRT, we found that spatial dissemination in the lungs is likely to be an important process in sustaining the prolonged high viral loads. Overall, our models provide a quantitative framework for predicting how SARS-CoV-2 within-host dynamics determine infectiousness and represent a step towards quantifying how viral load dynamics and the immune responses determine disease severity.
