High-Throughput Identification of Loss-of-Function Mutations for Anti-Interferon Activity in the Influenza A Virus NS Segment

Abstract

Viral proteins often display several functions which require multiple assays to dissect their genetic basis. Here, we describe a systematic approach to screen for loss-of-function mutations that confer a fitness disadvantage under a specified growth condition. Our methodology was achieved by genetically monitoring a mutant library under two growth conditions, with and without interferon, by deep sequencing. We employed a molecular tagging technique to distinguish true mutations from sequencing error. This approach enabled us to identify mutations that were negatively selected against, in addition to those that were positively selected for. Using this technique, we identified loss-of-function mutations in the influenza A virus NS segment that were sensitive to type I interferon in a high-throughput fashion. Mechanistic characterization further showed that a single substitution, D92Y, resulted in the inability of NS to inhibit RIG-I ubiquitination. The approach described in this study can be applied under any specified condition for any virus that can be genetically manipulated. IMPORTANCETraditional genetics focuses on a single genotype-phenotype relationship, whereas high-throughput genetics permits phenotypic characterization of numerous mutants in parallel. High-throughput genetics often involves monitoring of a mutant library with deep sequencing. However, deep sequencing suffers from a high error rate (ϳ0.1 to 1%), which is usually higher than the occurrence frequency for individual point mutations within a mutant library. Therefore, only mutations that confer a fitness advantage can be identified with confidence due to an enrichment in the occurrence frequency. In contrast, it is impossible to identify deleterious mutations using most next-generation sequencing techniques. In this study, we have applied a molecular tagging technique to distinguish true mutations from sequencing errors. It enabled us to identify mutations that underwent negative selection, in addition to mutations that experienced positive selection. This study provides a proof of concept by screening for loss-of-function mutations on the influenza A virus NS segment that are involved in its anti-interferon activity.

Annotating Protein Functional Residues by Coupling High-Throughput Fitness Profile and Homologous-Structure Analysis

“…High-throughput genetics have been applied to a number of viral, bacterial, and cellular proteins (16, 33–38, 111, 112). Here, point mutations were randomly introduced into segment 2 of influenza A/WSN/33 virus through error-prone PCR.…”

“…Large amounts of information generated with current technologies demand more effective approaches to determine structure-function relationships. Coupling mutagenesis with high-throughput sequencing, high-throughput fitness profiling provides a sensitive and unbiased way to identify the essential residues of targeted proteins (16, 33–37, 104–107). The same principle applies to other proteins/organisms, as long as the proper functional measurement can be made (37).…”

Comprehensive mapping of avian influenza polymerase adaptation to the human host

“…Here, we map all single amino-acid mutations to an avian influenza PB2 protein that enhance growth in human cells versus avian cells. We do so by leveraging deep mutational scanning (Boucher et al, 2014;Fowler and Fields, 2014), which previously has only been used to measure the functional effects of mutations to several influenza proteins in mammalian cells (Ashenberg et al, 2017;Bloom, 2014;Doud and Bloom, 2016;Du et al, 2018;Jiang et al, 2016;Lee et al, 2018;Wu et al, 2014Wu et al, , 2015. We show that comparative deep mutational scanning in human versus avian cells identifies numerous human-adaptive mutations that have never before been described.…”

Deep Mutational Scanning Identifies Sites in Influenza Nucleoprotein That Affect Viral Inhibition by MxA

“…This massively parallel experimental technique involves generating a library of mutants, imposing a functional selection, and using deep sequencing to determine the frequency of each mutation before and after selection. Deep mutational scanning has already been used to examine the functional effects of most mutations to several influenza proteins [28][29][30][31][32][33][34][35].…”