OP7, a novel influenza A virus defective interfering particle: production, purification, and animal experiments demonstrating antiviral potential

The novel influenza A virus (IAV) defective interfering particle “OP7” inhibits IAV replication in a co-infection and was previously suggested as a promising antiviral agent. Here, we report a batch-mode cell culture-based production process for OP7. In the present study, a seed virus containing standard virus (STV) and OP7 was used. The yield of OP7 strongly depended on the production multiplicity of infection. To inactivate infectious STV in the OP7 material, which may cause harm in a potential application, UV irradiation was used. The efficacy of OP7 in this material was preserved, as shown by an in vitro interference assay. Next, steric exclusion chromatography was used to purify and to concentrate (~ 13-fold) the UV-treated material. Finally, administration of produced OP7 material in mice did not show any toxic effects. Furthermore, all mice infected with a lethal dose of IAV survived the infection upon OP7 co-treatment. Thus, the feasibility of a production workflow for OP7 and its potential for antiviral treatment was demonstrated. Key points • OP7 efficacy strongly depended on the multiplicity of infection used for production • Purification by steric exclusion chromatography increased OP7 efficacy • OP7-treated mice were protected against a lethal infection with IAV

Sequencing Framework for the Sensitive Detection and Precise Mapping of Defective Interfering Particle-Associated Deletions across Influenza A and B Viruses

ABSTRACT The mechanisms and consequences of defective interfering particle (DIP) formation during influenza virus infection remain poorly understood. The development of next-generation sequencing (NGS) technologies has made it possible to identify large numbers of DIP-associated sequences, providing a powerful tool to better understand their biological relevance. However, NGS approaches pose numerous technical challenges, including the precise identification and mapping of deletion junctions in the presence of frequent mutation and base-calling errors, and the potential for numerous experimental and computational artifacts. Here, we detail an Illumina-based sequencing framework and bioinformatics pipeline capable of generating highly accurate and reproducible profiles of DIP-associated junction sequences. We use a combination of simulated and experimental control data sets to optimize pipeline performance and demonstrate the absence of significant artifacts. Finally, we use this optimized pipeline to reveal how the patterns of DIP-associated junction formation differ between different strains and subtypes of influenza A and B viruses and to demonstrate how these data can provide insight into mechanisms of DIP formation. Overall, this work provides a detailed roadmap for high-resolution profiling and analysis of DIP-associated sequences within influenza virus populations. IMPORTANCE Influenza virus defective interfering particles (DIPs) that harbor internal deletions within their genomes occur naturally during infection in humans and during cell culture. They have been hypothesized to influence the pathogenicity of the virus; however, their specific function remains elusive. The accurate detection of DIP-associated deletion junctions is crucial for understanding DIP biology but is complicated by an array of technical issues that can bias or confound results. Here, we demonstrate a combined experimental and computational framework for detecting DIP-associated deletion junctions using next-generation sequencing (NGS). We detail how to validate pipeline performance and provide the bioinformatics pipeline for groups interested in using it. Using this optimized pipeline, we detect hundreds of distinct deletion junctions generated during infection with a diverse panel of influenza viruses and use these data to test a long-standing hypothesis concerning the molecular details of DIP formation.

Illumina-based sequencing framework for accurate detection and mapping of influenza virus defective interfering particle-associated RNAs

The mechanisms and consequences of defective interfering particle (DIP) formation during influenza virus infection remain poorly understood. The development of next generation sequencing (NGS) technologies has made it possible to identify large numbers of DIP-associated sequences, providing a powerful tool to better understand their biological relevance. However, NGS approaches pose numerous technical challenges including the precise identification and mapping of deletion junctions in the presence of frequent mutation and base-calling errors, and the potential for numerous experimental and computational artifacts. Here we detail an Illumina-based sequencing framework and bioinformatics pipeline capable of generating highly accurate and reproducible profiles of DIP-associated junction sequences. We use a combination of simulated and experimental control datasets to optimize pipeline performance and demonstrate the absence of significant artifacts. Finally, we use this optimized pipeline to generate a high-resolution profile of DIP-associated junctions produced during influenza virus infection and demonstrate how this data can provide insight into mechanisms of DIP formation. This work highlights the specific challenges associated with NGS-based detection of DIP-associated sequences, and details the computational and experimental controls required for such studies.