Epithelial stratification shapes infection dynamics

Abstract

Infections of stratified epithelia collectively represent a large burden on global health. Experimental models provide a means to understand how the cell dynamics themselves influence the outcomes of these infections. Mathematical approaches are needed to improve quantification and theoretical advancement of these complex systems. Here, we develop a general ecology-inspired model for stratified epithelial dynamics, which allows us to simulate infections and to estimate parameters that are difficult to measure with organotypic cell cultures. To explore how epithelial cell dynamics affect infection dynamics, we focus on two contrasting pathogens of the cervicovaginal epithelium: Chlamydia trachomatis and Human papillomaviruses. We find that key infection symptoms stem from differential interactions with the layers, while clearance and pathogen burden are bottom-up processes. Cell protective responses to infections (e.g. increased cell proliferation) generally lowered pathogen load but there were specific effects based on infection strategies. These generic responses by the epithelium, then, will have varying results depending on the pathogen's infection strategy. Our modeling approach opens new perspectives for 3D tissue culture experimental systems of infections and, more generally, for developing and testing hypotheses related to infections of stratified epithelia.

Modelling the evolution of viral oncogenesis

“…Finally, another aspect we simplified has to do with the structure of the host tissue. Indeed, for viruses infecting tissues with 3D structures, such as epithelia, this structure could directly impact infection duration [27]. However, this would require virus-specific models since oncoviruses infect different tissues.…”

“…All the parameter estimates and initial conditions that are similar across all three viral groups were kept constant to facilitate the comparisons between life cycles. These are shown in Supplementary Information and chosen to be biologically realistic (see [27] for more details). Figure 3 illustrates typical time series for each of the three infection models.…”

Validated models of immune response to virus infection

“…The most used model is the standard viral dynamics model (Figure 1), which was introduced over 20 years ago (reviewed in [14,15]). The model has since been successfully applied to study a variety of virus infections, including HIV [16], HCV[17], IAV [9], West Nile virus (WNV) [18], Dengue virus (DENV) [19], Adenovirus (ADV) [20], RSV [21], yellow fever virus (YFV) [22], ZV [23], BKV [24,25], and HPV [26,27], among others. These viruses range from acute to chronic and have varied sites of infection (e.g., lung versus liver) and pathologies (e.g., pneumonia versus cirrhosis).…”

“…However, the generation and efficacy of various cellular (e.g., macrophages, T cells, or B cells) and soluble (e.g., type I interferons (IFNs) or antibodies (Abs)) host immune factors responsible for controlling viral spread have also been assessed mathematically (Figure 1). In addition, some models have examined how spatial heterogeneity and cell-to-cell viral spread influences viral kinetics [26,28–32]. The use of different functional forms, such as saturating or time-dependent functions [20,33–35], can achieve more complex dynamics than those in Figure 1 while simultaneously retaining model simplicity.…”

“…Kinetic models yield substantial insight about the rates of virus production and clearance, the complex host-pathogen interactions that govern the viral infection, and the formation of protective immunity for a variety of viruses (reviewed in [9,54–59], among others, and herein). In addition, an understanding of how direct cell-to-cell viral spread and spatial heterogeneity influences the disease course is beginning to be understood for viruses like HSV, HIV, HCV, HBV, and HPV [26,28–32]. How population kinetics correlate to tissue-level measurements is also increasing for IAV [9].…”