In silico modelling of toll-like receptor signalling pathways in human epidermal keratinocytes allows for prediction of immune responses to encountered antigens.

Abstract

The cutaneous environment plays a pivotal role in the regulation of immune responses. A key aspect of this immune mediation are the keratinocytes which make up a large proportion of the epidermal layers. Their position in the epidermis means that they are often the first cell type to detect the invasion of pathogenic antigens, and are therefore responsible for initiating signalling pathways which dictate the severity of an immune response by releasing proinflammatory cytokines such as IL-8. In particular, TLR2 is known to detect the microbe S. aureus, which is commonly found in the cutaneous microbiome, particularly of those with atopic dermatitis and is known as a common cause for cutaneous infection and inflammation. Although experimental work has been carried out regarding the effects of S. aureus in relation to skin inflammatory responses, it is often difficult to investigate these responses in detail at the molecular level, particularly when a number of different experimental conditions are needed. Taking a systems biology approach can bridge the gap between global immune responses and in-depth molecular observations by using mathematics to describe the TLR2 signalling pathways in response to microbial ligands and utilising the model to predict how immune regulation will change under different conditions. A systematic review of TLR signalling pathways in human epidermal keratinocytes allowed for a global view of the interactions mediating immune responses. From this, an ordinary differential equations model was constructed to allow for quantitative modelling and predictions of keratinocyte immune responses following exposure to S. aureus. Model parameterisation was conducted using a genetic algorithm with rank selection which had been thoroughly tested on multiple systems biology models. After parameterising the TLR2 model with detailed time course data, it was possible to predict changes in IL-8 immune response following a change to ligand dose. Similarly, the TLR2 model provided accurate predictions of keratinocyte IL-8 secretion following exposure to lysed S. aureus. By modifying the TLR2 model to include a feedback loop, it was also possible to replicate the oscillatory dynamics observed in data from keratinocytes in a Th2 cytokine environment, suggesting that in an atopic dermatitis-like environment, molecular feedback pathways may be sensitised to show an amplification in oscillatory dynamics