Biology subjects

Meijers, M.

Publications and source records attributed to Meijers, M..

4 recordsLinked to original sources

bioRxiv2025

Detection of low pre-existing humoral immunity against influenza virus H5N1 clade 2.3.4.4b in unexposed individuals

The spill-over of Influenza A virus H5N1 clade 2.3.4.4b from cattle to humans highlights the risk of a human H5N1 pandemic. Given the impact of pre-existing immunity on the course and severity of viral infections, we comprehensively assessed the humoral immunity against the H5N1 A/Texas/37/2024 isolate in H5N1-naive individuals. To this end, we performed complementary binding and neutralization assays on 66 subjects and ranked activities among a panel of 76 influenza A virus isolates. We detected low but distinct cross-neutralizing titers against A/Texas/37/2024 with a 3.9 to 15.6-fold reduction compared to selected H1N1 or H3N2 strains. By cloning and evaluating 136 monoclonal antibodies from memory B cells, we identified potent A/Texas/37/2024-neutralizing monoclonal antibodies in five out of six investigated individuals. These antibodies cross-neutralize H1, compete with antibodies targeting the HA stem, and protect mice from lethal H5N1 challenge. Our findings demonstrate partial pre-existing humoral immunity to A/Texas/37/2024 in H5N1-naive individuals.

immunology↗

bioRxiv2024

Concepts and methods for predicting viral evolution

The seasonal human influenza virus undergoes rapid evolution, leading to significant changes in circulating viral strains from year to year. These changes are typically driven by adaptive mutations, particularly in the antigenic epitopes, the regions of the viral surface protein haemagglutinin targeted by human antibodies. Here we describe a consistent set of methods for data-driven predictive analysis of viral evolution. Our pipeline integrates four types of data: (1) sequence data of viral isolates collected on a worldwide scale, (2) epidemiological data on incidences, (3) antigenic characterization of circulating viruses, and (4) intrinsic viral phenotypes. From the combined analysis of these data, we obtain estimates of relative fitness for circulating strains and predictions of clade frequencies for periods of up to one year. Furthermore, we obtain comparative estimates of protection against future viral populations for candidate vaccine strains, providing a basis for pre-emptive vaccine strain selection. Continuously updated predictions obtained from the prediction pipeline for influenza and SARS-CoV-2 are available on the website previr.app.

biophysics↗

bioRxiv2022

Vaccination shapes evolutionary trajectories of SARS-CoV-2

The large-scale evolution of the SARS-CoV-2 virus has been marked by rapid turnover of genetic clades. New variants show intrinsic changes, notably increased transmissibility, as well as anti-genic changes that reduce the cross-immunity induced by previous infections or vaccinations1-4. How this functional variation shapes the global evolutionary dynamics has remained unclear. Here we show that selection induced by vaccination impacts on the recent antigenic evolution of SARS-CoV-2; other relevant forces include intrinsic selection and antigenic selection induced by previous infections. We obtain these results from a fitness model with intrinsic and antigenic fitness components. To infer model parameters, we combine time-resolved sequence data5, epidemiological records6,7, and cross-neutralisation assays8-10. This model accurately captures the large-scale evolutionary dynamics of SARS-CoV-2 in multiple geographical regions. In particular, it quantifies how recent vaccinations and infections affect the speed of frequency shifts between viral variants. Our results show that timely neutralisation data can be harvested to identify hotspots of antigenic selection and to predict the impact of vaccination on viral evolution.

biophysics↗

bioRxiv2020

Predicting in vivo escape dynamics of HIV-1 from a broadly neutralizing antibody

Broadly neutralizing antibodies are promising candidates for treatment and prevention of HIV-1 infections. Such antibodies can temporarily suppress viral load in infected individuals; however, the virus often rebounds by escape mutants that have evolved resistance. In this paper, we map an in vivo fitness landscape of HIV-1 interacting with broadly neutralizing antibodies, using data from a recent clinical trial. We identify two fitness factors, antibody dosage and viral load, that determine viral reproduction rates reproducibly across different hosts. The model successfully predicts the escape dynamics of HIV-1 in the course of an antibody treatment, including a characteristic frequency turnover between sensitive and resistant strains. This turnover is governed by a dosage-dependent fitness ranking, resulting from an evolutionary tradeoff between antibody resistance and its collateral cost in drug-free growth. Our analysis suggests resistance-cost tradeoff curves as a measure of antibody performance in the presence of resistance evolution.

biophysics↗