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Quantifying the Impact of Cell-to-Cell Transmission on Viral Spread
Over time, viruses have evolved several strategies to infect host cells, which can be roughly categorized into two distinct modes of viral transmission, namely cell-free and cell-to-cell. Various studies have opted for the latter to play the dominant role in infections with human immunodeficiency virus (HIV) and hepatitis C virus (HCV). How to quantify the exact contribution of each transmission mode to the infection dynamics, without biasing results due to the experimental protocol, remains to be answered to allow for more efficient drug and vaccine development. Previously, mathematical models based on mass-action kinetics were fitted to bulk measurements obtained from in vitro cell cultures cultivated in aqueous media. This combination of experiment and modeling is very specific and might not be appropriate to describe other situations such as in vivo studies. To allow generalization, the effect of modeling strategy and experimental protocol on the predicted contributions of cell-to-cell spread to the infection dynamics should be studied in more detail. In this thesis, I therefore analyzed possible consequences of varying extracellular microenvironments and types of target cells on the different transmission modes during an infection and how certain modeling strategies affect predictions. In a first step, I studied the impact of varying extracellular microenvironments on in vitro spread of HIV. To this end, I developed a detailed mass-action kinetics model combining experiments and mathematical modeling to describe HIV infection dynamics in aqueous media and two types of collagen. My results show that for HIV infection of motile target cells the contribution of transmission modes varies dependent on the environment. Next, I moved from motile to stationary cells analyzing cell-to-cell transmission of HCV among immobile hepatocytes. To account for the local nature of cell-to-cell spread in stationary target cells, I developed an agent-based model, which combines deterministic intracellular viral processes with stochastic intercellular infection events. This model was applied to spatially-resolved data, describing HCV infection dynamics obtained from an in vitro experiment conducted on hepatocytes in a monolayer. I found that the contribution of cell-to-cell transmission to the infection dynamics is dominant in the early stages of the experiment. However, depending on the composition of the used serum, the contribution can shift towards a stronger role of cell-free spread during the experiment. Moreover, I investigated how to reconcile bulk measurement data with mass-action kinetics models in the context of viral cell-to-cell transmission among stationary target cells. Fitting complex models to describe various aspects of viral infections such as cell-to-cell transmission usually requires sufficiently resolved data. Nonetheless, experimental protocols often only allow bulk measurements, which are generally combined with mass-action kinetics models that assume a well-mixed system. In the context of locally confined movement of target cells infected by viruses, which are capable of infecting via cell-to-cell transmission, this assumption is likely violated. Therefore, I developed an extension of a previously published model, incorporating the decreasing proportion of cells contributing to cell-to-cell transmission with progression of infection. The extended model then allowed correct quantification of the infection dynamics. In summary, I developed different modeling approaches to analyze the contribution of cell-to-cell transmission to the infection dynamics of HIV and HCV under varying conditions. Furthermore, I provide an extension to a mass-action kinetics model to allow correct description of cell-to-cell transmission in the context of stationary cells while still keeping a simple structure, which can be fitted to population-level data. All approaches can be adapted to other viruses to allow quantification of transmission modes, which will help to guide drug and vaccine development more efficiently.
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500 Naturwissenschaften und Mathematik
500 Natural sciences and mathematics
570 Biowissenschaften, Biologie
570 Life sciences