0 avis
Epithelial stratification shapes infection dynamics
Archive ouverte
International audience. Infections of stratified epithelia contribute to a large group of common diseases, such as dermatological conditions and sexually transmitted diseases. To investigate how epithelial structure affects infection dynamics, we develop a general ecology-inspired model for stratified epithelia. Our model allows us to simulate infections, explore new hypotheses and estimate parameters that are difficult to measure with tissue cell cultures. We focus on two contrasting pathogens: Chlamydia trachomatis and Human papillomaviruses (HPV). Using cervicovaginal parameter estimates, we find that key infection symptoms can be explained by differential interactions with the layers, while clearance and pathogen burden appear to be bottom-up processes. Cell protective responses to infections (e.g. mucus trapping) generally lowered pathogen load but there were specific effects based on infection strategies. 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. Author summary Many epithelia are stratified in layers of cells and their infection can result in many pathologies, from rashes to cancer. It is important to understand to what extent the epi-thelial structure determines infection dynamics and outcomes. To aid experimental and clinical studies, we develop a mathematical model that recreates epithelial and infection dynamics. By applying it to a virus, human papillomavirus (HPV), and a bacteria, chla-mydia, we show that considering stratification improves our general understanding of disease patterns. For instance, the duration of infection can be driven by the rate at which the stem cells of the epithelium divide. Having a general model also allows us to investigate and compare hypotheses. This ecological framework can be modified to study specific pathogens or to estimate parameters from data generated in 3D skin cell culture experiments. PLOS Computational Biology | https://doi.
Consulter en ligne
Suggestions
Du même auteur
