NGS-RFA/FRA: A High Throughput Experimental and Computational Pipeline for Selective Mutational Scanning in Parallel
TL;DR
The method reduces the timeline required to carry out such assessments with classical methods from about a year to several weeks, and has the potential to greatly enhance surveillance efforts, transforming reactive monitoring into proactive forecasting.
Abstract
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 (Mögling 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.