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Netzl, A.

Publications and source records attributed to Netzl, A..

5 recordsLinked to original sources

NGS-RFA/FRA: A High Throughput Experimental and Computational Pipeline for Selective Mutational Scanning in Parallel

Influenza viruses evade vaccine and infection mediated immunity by accumulating mutations in their hemagglutinin (HA) protein. Predicting this evolution might be possible via selective mutational scanning (SMS) - the generation of many specific mutants of interest from currently circulating viruses and characterizing their escape potential and fitness with high accuracy (Mogling 2016). However, this task is challenging, even when focusing on a reduced set of key HA positions (Koel et al. 2013). Here we describe a high-throughput SMS method to address this challenge. Our approach consists of a three-stage pipeline: (1) a parallel optimized virus rescue process that generates balanced target mutant virus libraries (2) an assay to assess replicative fitness and neutralisation of these variants as a mixture, and (3) a bespoke statistical model to quantify statistically significant differences between these observables. We tested the pipeline on libraries of up to 134 variants finding excellent correlation to classical hemagglutination inhibition (HI) and plaque growth assays used to assess antigenic phenotype and replicative fitness respectively, as well as remarkable repeatability overall. Notably, the method reduces the timeline required to carry out such assessments with classical methods from about a year to several weeks. By enabling rapid and efficient characterization of influenza virus variants, this approach has the potential to greatly enhance surveillance efforts, transforming reactive monitoring into proactive forecasting.

evolutionary biology↗

Direct comparison of SARS-CoV-2 variant specific neutralizing antibodies in human and hamster sera

Antigenic characterization of newly emerging SARS-CoV-2 variants is important to assess their immune escape and judge the need for future vaccine updates. As exposure histories for human sera become more and more complex, animal sera may provide an alternative for antigenic characterization of new variants. To bridge data obtained from animal sera with human sera, we here analyzed neutralizing antibody titers in human and hamster first infection sera in a highly controlled setting using the same live-virus neutralization assay performed in one laboratory. Using a Bayesian framework, we found that titer fold changes in hamster sera corresponded well to human sera and that hamster sera generally exhibited higher reactivity. Our results indicate that sera from infected hamsters are a good surrogate for the antigenic characterization of new variants.

immunology↗

Comparative Analysis of SARS-CoV-2 Antigenicity across Assays and in Human and Animal Model Sera

The antigenic evolution of SARS-CoV-2 requires ongoing monitoring to judge the immune escape of newly arising variants. A surveillance system necessitates an understanding of differences in neutralization titers measured in different assays and using human and animal sera. We compared 18 datasets generated using human, hamster, and mouse sera, and six different neutralization assays. Titer magnitude was lowest in human, intermediate in hamster, and highest in mouse sera. Fold change, immunodominance patterns and antigenic maps were similar among sera. Most assays yielded similar results, except for differences in fold change in cytopathic effect assays. Not enough data was available for conclusively judging mouse sera, but hamster sera were a consistent surrogate for human first-infection sera.

bioinformatics↗

Mapping SARS-CoV-2 antigenic relationships and serological responses

During the SARS-CoV-2 pandemic, multiple variants escaping pre-existing immunity emerged, causing concerns about continued protection. Here, we use antigenic cartography to analyze patterns of cross-reactivity among a panel of 21 variants and 15 groups of human sera obtained following primary infection with 10 different variants or after mRNA-1273 or mRNA-1273.351 vaccination. We find antigenic differences among pre-Omicron variants caused by substitutions at spike protein positions 417, 452, 484, and 501. Quantifying changes in response breadth over time and with additional vaccine doses, our results show the largest increase between 4 weeks and >3 months post-2nd dose. We find changes in immunodominance of different spike regions depending on the variant an individual was first exposed to, with implications for variant risk assessment and vaccine strain selection. One sentence summaryAntigenic Cartography of SARS-CoV-2 variants reveals amino acid substitutions governing immune escape and immunodominance patterns.

immunology↗

Analysis of SARS-CoV-2 Omicron Neutralization Data up to 2021-12-22

The rapid spread of the Omicron BA.1 (B.1.1.529.1) SARS-CoV-2 (Severe Acute Respiratory Syndrome Coronavirus 2) variant in 2021 resulted in international efforts to quickly assess its escape from immunity generated by vaccines and previous infections. Numerous laboratories published BA.1 neutralization data as preprints and reports. We collated this data in real time and regularly presented updates of the aggregated results in US, European and WHO research and advisory settings. Here, we retrospectively analyzed the accuracy of these aggregations from 85 different sources published during a time period from 2021/12/08 up to 2022/08/14. We found that the mean titer fold change from wild type-like variants to BA.1, a standard measure of a variants immune escape, remained stable after the first 15 days of data reporting in people who were twice vaccinated, and incoming data increased the confidence in this quantity. Further, it is possible to build reliable, stable antigenic maps from this collated data already after one month of incoming data. We here demonstrate that combining early reports from variable, independent sources can rapidly indicate a new virus variants immune escape and can therefore be of immense benefit for public health.

immunology↗