Model-based analysis of influenza A virus replication in genetically engineered cell lines elucidates the impact of host cell factors on key kinetic parameters of virus growth

Abstract

The best measure to limit spread of contagious diseases caused by influenza A viruses (IAVs) is annual vaccination. The growing global demand for low-cost vaccines requires the establishment of high-yield production processes. One possible option to address this challenge is the engineering of novel vaccine producer cell lines by manipulating gene expression of host cell factors relevant for virus replication. To support detailed characterization of engineered cell lines, we fitted an ordinary differential equation (ODE)-based model of intracellular IAV replication previously established by our group to experimental data obtained from infection studies in human A549 cells. Model predictions indicate that steps of viral RNA synthesis, their regulation and particle assembly and virus budding are promising targets for cell line engineering. The importance of these steps was confirmed in four of five single gene overexpression cell lines (SGOs) that showed small, but reproducible changes in early dynamics of RNA synthesis and virus release. Model-based analysis suggests, however, that overexpression of the selected host cell factors negatively influences specific RNA synthesis rates. Still, virus yield was rescued by an increase in the virus release rate. Based on parameter estimations obtained for SGOs, we predicted that there is a potential benefit associated with overexpressing multiple host cell genes in one cell line, which was validated experimentally. Overall, this model-based study on IAV replication in engineered cell lines provides a step forward in the dynamic and quantitative characterization of IAV-host cell interactions. Furthermore, it suggests targets for gene editing and indicates that overexpression of multiple host cell factors may be beneficial for the design of novel producer cell lines.

Model-based approach for predicting the impact of genetic modifications on product yield in biopharmaceutical manufacturing—Application to influenza vaccine production

“…In a first step, experimental data from a previous study on optimization of influenza vaccine production [6] were used to perform a bootstrap parameter estimation with an intracellular deterministic model [16] and an adjusted viral fusion rate (see Materials and methods). The full set of intracellular data [6] comprises flow cytometric measurements of nuclear vRNP import dynamics and the intracellular concentration of viral RNA (mRNA, cRNA, vRNA) of the viral genome segment 5. Furthermore, cell-specific virus yields were measured in the early phase of virus release upon infection of A549 cells at MOI of 1.…”

“…So far, in the literature a series of experimental studies can be found, which focus on HCFs affecting the viral replication cycle in a positive or negative way resulting in an increased or decreased virus yield [5][6][7][8][9]. Based on this knowledge, engineering of novel producer cell lines by overexpressing the genes of selected HCFs is a promising option to improve the production process.…”

“…A direct consideration of the gene variability is a challenging problem and requires a mathematical description of cellular transcription and translation for the corresponding HCFs. Furthermore, interactions of HCFs with the viral life cycle, which itself is still subject of current research [5][6][7][8][9], would have to be taken into account for such detailed description. Alternatively, variability introduced by the gene editing method can be captured by the variance of kinetic parameters.…”

“…Bootstrap parameter estimates were determined on the basis of experimental data sets for SGOs and used to simulate the population balance model of the viral replication process. In a former study, the virus yields of MGOs were analyzed using a random combination of median SGO parameter values in a single cell model [6]. In our contribution, we present a more sophisticated approach, where the random selection of median values is no longer required.…”

“…In our contribution, we present a more sophisticated approach, where the random selection of median values is no longer required. To this end, we evaluated four different strategies to generate distributed parameter sets for MGOs based on parameter sets which were estimated from experimental data of their corresponding infected SGOs [6,17]. In the following, the most suitable strategy was used to generate new parameter distributions for potential MGOs to predict the virus yield in model simulations.…”