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

A large group of biopharmaceuticals is produced in cell lines. The yield of such products can be increased by genetic engineering of the corresponding cell lines. The prediction of promising genetic modifications by mathematical modeling is a valuable tool to facilitate experimental screening. Besides information on the intracellular kinetics and genetic modifications the mathematical model has to account for ubiquitous cell-to-cell variability. In this contribution, we establish a novel model-based methodology for influenza vaccine production in cell lines with overexpressed genes. The manipulation of the expression level of genes coding for host cell factors relevant for virus replication is achieved by lentiviral transduction. Since lentiviral transduction causes increased cell-to-cell variability due to different copy numbers and integration sites of the gene constructs we use a population balance modeling approach to account for this heterogeneity in terms of intracellular viral components and distributed kinetic parameters. The latter are estimated from experimental data of intracellular viral RNA levels and virus titers of infection experiments using cells overexpressing a single host cell gene. For experiments with cells overexpressing multiple host cell genes, only final virus titers were measured and thus, no direct estimation of the parameter distributions was possible. Instead, we evaluate four different computational strategies to infer these from single gene parameter sets. Finally, the best computational strategy is used to predict the most promising candidates for future modifications that show the highest potential for an increased virus yield in a combinatorial study. As expected, there is a trend to higher yields the more modifications are included.

Multiscale modeling of influenza A virus replication in cell cultures predicts infection dynamics for highly different infection conditions.

Influenza A viruses (IAV) are commonly used to infect animal cell cultures for research purposes and vaccine production. Their replication is influenced strongly by the multiplicity of infection (MOI), which ranges over several orders of magnitude depending on the respective application. So far, mathematical models of IAV replication have paid little attention to the impact of the MOI on infection dynamics and virus yields. To address this issue, we extended an existing model of IAV replication in adherent MDCK cells with kinetics that explicitly consider the time point of cell infection. This modification does not only enable the fitting of high MOI measurements, but also the successful prediction of viral release dynamics of low MOI experiments using the same set of parameters. Furthermore, this model allows the investigation of defective interfering particle (DIP) propagation in different MOI regimes. The key difference between high and low MOI conditions is the percentage of infectious virions among the total virus particle release. Simulation studies show that DIP interference at a high MOI is determined exclusively by the DIP content of the seed virus while, in low MOI conditions, it is predominantly controlled by the de novo generation of DIPs. Overall, the extended model provides an ideal framework for the prediction and optimization of cell culture-derived IAV manufacturing and the production of DIPs for therapeutic use.

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.

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.

Cell Bank Origin of MDCK Parental Cells Shapes Adaptation to Serum-Free Suspension Culture and Canine Adenoviral Vector Production.

Phenotypic variation in cultured mammalian cell lines is known to be induced by passaging and culture conditions. Yet, the effect these variations have on the production of viral vectors has been overlooked. In this work we evaluated the impact of using Madin-Darby canine kidney (MDCK) parental cells from American Type Culture Collection (ATCC) or European Collection of Authenticated Cell Cultures (ECACC) cell bank repositories in both adherent and suspension cultures for the production of canine adenoviral vectors type 2 (CAV-2). To further explore the differences between cells, we conducted whole-genome transcriptome analysis. ECACC's MDCK showed to be a less heterogeneous population, more difficult to adapt to suspension and serum-free culture conditions, but more permissive to CAV-2 replication progression, enabling higher yields. Transcriptome data indicated that this increased permissiveness is due to a general down-regulation of biological networks of innate immunity in ECACC cells, including apoptosis and death receptor signaling, Janus kinase/signal transducers and activators of transcription (JAK/STAT) signaling, toll-like receptors signaling and the canonical pathway of nuclear factor kappa-light-chain-enhancer of activated B cells (NF-κB) signaling. These results show the impact of MDCK source on the outcome of viral-based production processes further elucidating transcriptome signatures underlying enhanced adenoviral replication. Following functional validation, the genes and networks identified herein can be targeted in future engineering approaches aiming at improving the production of CAV-2 gene therapy vectors.

Mathematical model calibrated to in vitro data predicts mechanisms of antiviral action of the influenza defective interfering particle "OP7".

Defective interfering particles (DIPs) are regarded as potent broad-spectrum antivirals. We developed a mathematical model that describes intracellular co-infection dynamics of influenza standard virus (STV) and "OP7", a new type of influenza DIP discovered recently. Based on experimental data from in vitro studies to calibrate the model and confirm its predictions, we deduce OP7's mechanisms of interference, which were yet unknown. Simulations suggest that the "superpromoter" on OP7 genomic viral RNA enhances its replication and results in a depletion of viral proteins. This reduces STV genomic RNA replication, which appears to constitute an antiviral effect. Further, a defective viral protein (M1-OP7) likely causes the deficiency of OP7's replication. It appears unable to bind to genomic viral RNAs to facilitate their nuclear export, a critical step in the viral life cycle. An improved understanding of OP7's antiviral mechanism is crucial toward application in humans as a prospective antiviral treatment strategy.

De-novo reconstruction and identification of transcriptional gene regulatory network modules differentiating single-cell clusters.

Single-cell RNA sequencing (scRNA-seq) technology provides an unprecedented opportunity to understand gene functions and interactions at single-cell resolution. While computational tools for scRNA-seq data analysis to decipher differential gene expression profiles and differential pathway expression exist, we still lack methods to learn differential regulatory disease mechanisms directly from the single-cell data. Here, we provide a new methodology, named DiNiro, to unravel such mechanisms de novo and report them as small, easily interpretable transcriptional regulatory network modules. We demonstrate that DiNiro is able to uncover novel, relevant, and deep mechanistic models that not just predict but explain differential cellular gene expression programs. DiNiro is available at https://exbio.wzw.tum.de/diniro/.

Proteomic meta-study harmonization, mechanotyping and drug repurposing candidate prediction with ProHarMeD.

Proteomics technologies, which include a diverse range of approaches such as mass spectrometry-based, array-based, and others, are key technologies for the identification of biomarkers and disease mechanisms, referred to as mechanotyping. Despite over 15,000 published studies in 2022 alone, leveraging publicly available proteomics data for biomarker identification, mechanotyping and drug target identification is not readily possible. Proteomic data addressing similar biological/biomedical questions are made available by multiple research groups in different locations using different model organisms. Furthermore, not only various organisms are employed but different assay systems, such as in vitro and in vivo systems, are used. Finally, even though proteomics data are deposited in public databases, such as ProteomeXchange, they are provided at different levels of detail. Thus, data integration is hampered by non-harmonized usage of identifiers when reviewing the literature or performing meta-analyses to consolidate existing publications into a joint picture. To address this problem, we present ProHarMeD, a tool for harmonizing and comparing proteomics data gathered in multiple studies and for the extraction of disease mechanisms and putative drug repurposing candidates. It is available as a website, Python library and R package. ProHarMeD facilitates ID and name conversions between protein and gene levels, or organisms via ortholog mapping, and provides detailed logs on the loss and gain of IDs after each step. The web tool further determines IDs shared by different studies, proposes potential disease mechanisms as well as drug repurposing candidates automatically, and visualizes these results interactively. We apply ProHarMeD to a set of four studies on bone regeneration. First, we demonstrate the benefit of ID harmonization which increases the number of shared genes between studies by 50%. Second, we identify a potential disease mechanism, with five corresponding drug targets, and the top 20 putative drug repurposing candidates, of which Fondaparinux, the candidate with the highest score, and multiple others are known to have an impact on bone regeneration. Hence, ProHarMeD allows users to harmonize multi-centric proteomics research data in meta-analyses, evaluates the success of the ID conversions and remappings, and finally, it closes the gaps between proteomics, disease mechanism mining and drug repurposing. It is publicly available at https://apps.cosy.bio/proharmed/ .

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.

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.

Modeling the intracellular replication of influenza A virus in the presence of defective interfering RNAs.

Like many other viral pathogens, influenza A viruses can form defective interfering particles (DIPs). These particles carry a large internal deletion in at least one of their genome segments. Thus, their replication depends on the co-infection of cells by standard viruses (STVs), which supply the viral protein(s) encoded by the defective segment. However, DIPs also interfere with STV replication at the molecular level and, despite considerable research efforts, the mechanism of this interference remains largely elusive. Here, we present a mechanistic mathematical model for the intracellular replication of DIPs. In this model, we account for the common hypothesis that defective interfering RNAs (DI RNAs) possess a replication advantage over full-length (FL) RNAs due to their reduced length. By this means, the model captures experimental data from yield reduction assays and from studies testing different co-infection timings. In addition, our model predicts that one important aspect of interference is the competition for viral proteins, namely the heterotrimeric viral RNA-dependent RNA polymerase (RdRp) and the viral nucleoprotein (NP), which are needed for encapsidation of naked viral RNA. Moreover, we find that there may be an optimum for both the DI RNA synthesis rate and the time point of successive co-infection of a cell by DIPs and STVs. Comparing simulations for the growth of DIPs with a deletion in different genome segments suggests that DI RNAs derived from segments which encode for the polymerase subunits are more competitive than others. Overall, our model, thus, helps to elucidate the interference mechanism of DI RNAs and provides a novel hypothesis why DI RNAs derived from the polymerase-encoding segments are more abundant in DIP preparations.

Reprint of "Modeling the intracellular replication of influenza A virus in the presence of defective interfering RNAs.

Like many other viral pathogens, influenza A viruses can form defective interfering particles (DIPs). These particles carry a large internal deletion in at least one of their genome segments. Thus, their replication depends on the co-infection of cells by standard viruses (STVs), which supply the viral protein(s) encoded by the defective segment. However, DIPs also interfere with STV replication at the molecular level and, despite considerable research efforts, the mechanism of this interference remains largely elusive. Here, we present a mechanistic mathematical model for the intracellular replication of DIPs. In this model, we account for the common hypothesis that defective interfering RNAs (DI RNAs) possess a replication advantage over full-length (FL) RNAs due to their reduced length. By this means, the model captures experimental data from yield reduction assays and from studies testing different co-infection timings. In addition, our model predicts that one important aspect of interference is the competition for viral proteins, namely the heterotrimeric viral RNA-dependent RNA polymerase (RdRp) and the viral nucleoprotein (NP), which are needed for encapsidation of naked viral RNA. Moreover, we find that there may be an optimum for both the DI RNA synthesis rate and the time point of successive co-infection of a cell by DIPs and STVs. Comparing simulations for the growth of DIPs with a deletion in different genome segments suggests that DI RNAs derived from segments which encode for the polymerase subunits are more competitive than others. Overall, our model, thus, helps to elucidate the interference mechanism of DI RNAs and provides a novel hypothesis why DI RNAs derived from the polymerase-encoding segments are more abundant in DIP preparations.