Production of Defective Interfering Particles of Influenza A Virus in Parallel Continuous Cultures at Two Residence Times—Insights From qPCR Measurements and Viral Dynamics Modeling

Abstract

Defective interfering particles (DIPs) are a natural byproduct of influenza A virus (IAV) replication. DIPs interfere with the propagation and spread of infectious standard virus (STV), reduce virus yields by competing for viral and cellular resources, and induce antiviral responses. These properties open exciting possibilities for the development of DIP-based antivirals. Exploring options for cell culture-based DIP production, we have established a fully continuous cultivation process, where one bioreactor is used to grow cells that are fed to two bioreactors operated in parallel for virus production. This system allows head-to-head comparisons of STV and DIP replication dynamics over extended time periods. Cultivations were performed at two residence times (RT, 22 and 36 h) using MDCK suspension cells grown in a fully defined medium. For infection, we used a virus seed generated by reverse genetics containing STVs and a known DIP carrying a deletion in segment 1 (delS1(1)). Four days post infection, DIPs achieved maximum concentrations of 7.0·109 virions/mL and 8.4·109 virions/mL for RTs of 22 and 36 h, respectively. Furthermore, oscillations in virus titers with two to three maxima were found for DIP accumulation at 36 and 22 h RT, respectively. To complement the study, a basic mathematical model using simple kinetics and a reasonable number of parameters to describe DIP-propagation in continuous cultures was established. Upon fitting the model individually to each of the two data sets, oscillations in the viral dynamics and the cell population dynamics were described well. Modeling suggests that both STV inactivation and virus degradation have to be taken into account to achieve good agreement of simulations and experimental data for longer RTs. Together, the high DIP titers obtained, and the successful simulation of the experimental data showed that the combination of continuous bioreactors and mathematical models can enable studies regarding DIP dynamics over extended time periods and allow large scale manufacturing of DIP-based antivirals.

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

“…The interference assay was performed in independent experiments (n = 3), each using one preparation. Error bars indicate standard deviation Previously, a process for continuous production of DIPs was reported (Frensing et al 2013;Tapia et al 2019). However, the here proposed batch process may hold several advantages.…”

“…To ensure that observed interfering effects were not caused by conventional DIPs, a segment-specific RT-PCR was performed. The method was previously used to detect deleted (and short) DI vRNAs of IAV of segment 1-8 (Seg1-8) in cell culture (Frensing et al 2013;Tapia et al 2019). Figure 2c shows the results for Seg1, Seg2, and Seg3, which are most prone for the formation of conventional DI vRNAs.…”

“…Furthermore, it allows for better defined process conditions and the possibility to monitor the production. Therefore, the production process can be characterized in depth, and reproducible product quality can be ensured (Frensing et al 2014;Swick et al 2014;Tapia et al 2019;Wasik et al 2018). Interfering efficacy of processed OP7 material.…”

“…Therefore, DIPs are only capable of replication upon co-infection with an infectious standard virus (STV), compensating for the missing or truncated protein. Yet, in such a co-infection scenario, STV replication is inhibited and suppressed, and mainly non-infectious DIPs are released (Frensing et al 2013;Frensing et al 2014;Tapia et al 2019;Von Magnus 1951). One explanation for this interference is the shorter DI vRNA, which may amplify faster compared with the full-length vRNA.…”

Computational Strategies to Combat COVID-19: Useful Tools to Accelerate SARS-CoV-2 and Coronavirus Research

“…COPASI has been used to model various aspects of virology, including mechanisms of action [76][77][78][79], pharmaceutical interventions [80], virus life-cycle [81], vaccine design [82] and dynamics of epidemics [83][84][85].…”