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

Influenza viruses can avoid immunity through accumulated mutations in their hemagglutinin (HA) protein, making it difficult to predict their evolution. Selective mutational scanning (SMS) - creating specific mutants of interest from circulating viruses and evaluating their escape potential and fitness with precision - is a promising solution (Mogling 2016).

However, applying SMS to a small number of key HA positions remains challenging (Koel et al. 2013). The authors present a high-throughput SMS method to overcome this challenge. The method comprises three stages: (1) a parallel virus rescue process that generates evenly distributed target mutant virus libraries, (2) an assay to evaluate replicative fitness and neutralization of these variants simultaneously, and (3) a custom statistical model to determine statistically significant differences between these observations.

The researchers validated the pipeline using libraries of up to 134 variants, finding strong correlation with the classical hemagglutination inhibition (HI) and plaque growth assays used for antigenic phenotype and replicative fitness assessment, respectively. The method also demonstrated exceptional repeatability across the board.

Importantly, the approach significantly shortens the time required for such evaluations, from around a year using classical techniques to just a few weeks. By facilitating rapid and efficient characterization of influenza virus variants, this technique has the potential to greatly enhance surveillance efforts, shifting reactive monitoring into proactive forecasting.