Highlights d Endothelial cells communicate mechanically in a basement membrane material d A defined stiffness range, plasticity, and the presence of laminin are mandatory d Cells remodel the matrix forming stiff bridges, which precede cell protrusions d These processes are an organizing principle for vascular network formation

Angiogenesis is of high clinical relevance as it plays a crucial role in physiological (e.g., tissue regeneration) and pathological processes (e.g., tumor growth). Besides chemical signals, such as VEGF, the relationship between cells and the extracellular matrix (ECM) can influence endothelial cell behavior during angiogenesis. Previously, in terms of the connection between angiogenesis and mechanical factors, researchers have focused on shear forces due to blood flow. However, it is becoming increasingly important to include the direct influence of the ECM on biological processes, such as angiogenesis. In this context, we focus on the stiffness of the surrounding ECM and the adhesion of cells to the ECM. Furthermore, we highlight the mechanical cues during the main stages of angiogenesis: cell migration, tip and stalk cells, and vessel stabilization. It becomes clear that the different stages of angiogenesis require various chemical and mechanical cues to be modulated by/modulate the stiffness of the ECM. Thus, changes of the ECM during tumor growth represent additional potential dysregulations of angiogenesis in addition to erroneous biochemical signals. This awareness could be the basis of therapeutic approaches to counteract specific processes in tumor angiogenesis.

Cancer cell migration is influenced by cellular phenotype and behavior as well as by the mechanical and chemical properties of the environment. Furthermore, many cancer cells show plasticity of their phenotype and adapt it to the properties of the environment. Here, we study the influence of fiber stiffness, confinement, and adhesion properties on cancer cell migration in porous collagen gels. Collagen gels with soft fibers abrogate migration and promote a round, non-invasive phenotype. Stiffer collagen fibers are inherently more adhesive and lead to the existence of an adhesive phenotype and in general confined migration due to adhesion. Addition of TGF-β lowers adhesion, eliminates the adhesive phenotype and increases the amount of highly motile amoeboid phenotypes. Highest migration speeds and longest displacements are achieved in stiff collagen fibers in pores of about cell size by amoeboid phenotypes. This elucidates the influence of the mechanical properties of collagen gels on phenotype and subsequently migration and shows that stiff fibers, cell sized pores, and low adhesion, are optimal conditions for an amoeboid phenotype and efficient migration.

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.

Cell culture-derived defective interfering particles (DIPs) are considered for antiviral therapy due to their ability to inhibit influenza A virus (IAV) production. DIPs contain a large internal deletion in one of their eight viral RNAs (vRNAs) rendering them replication-incompetent. However, they can propagate alongside their homologous standard virus (STV) during infection in a competition for cellular and viral resources. So far, experimental and modeling studies for IAV have focused on either the intracellular or the cell population level when investigating the interaction of STVs and DIPs. To examine these levels simultaneously, we conducted a series of experiments using highly different multiplicities of infections for STVs and DIPs to characterize virus replication in Madin-Darby Canine Kidney suspension cells. At several time points post infection, we quantified virus titers, viable cell concentration, virus-induced apoptosis using imaging flow cytometry, and intracellular levels of vRNA and viral mRNA using real-time reverse transcription qPCR. Based on the obtained data, we developed a mathematical multiscale model of STV and DIP co-infection that describes dynamics closely for all scenarios with a single set of parameters. We show that applying high DIP concentrations can shut down STV propagation completely and prevent virus-induced apoptosis. Interestingly, the three observed viral mRNAs (full-length segment 1 and 5, defective interfering segment 1) accumulated to vastly different levels suggesting the interplay between an internal regulation mechanism and a growth advantage for shorter viral RNAs. Furthermore, model simulations predict that the concentration of DIPs should be at least 10000 times higher than that of STVs to prevent the spread of IAV. Ultimately, the model presented here supports a comprehensive understanding of the interactions between STVs and DIPs during co-infection providing an ideal platform for the prediction and optimization of vaccine manufacturing as well as DIP production for therapeutic use.