(190f) Towards a Dynamic Understanding of Pathogen Detection and Response

Towards a Dynamic Understanding of Pathogen Detection and Response

            Pathogen detection and the ensuing immune response play a direct role in determining the severity of an infection. Recently, our group has identified key signaling mechanisms that determine when and to what degree the innate immune response is activated during infection with type A influenza viruses and showed that the degree of the immune response was associated with the tissue pathology. This allowed us to create the first mathematical model that can predict the timing and magnitude of the innate immune response to influenza infection which we validated using de novo infection data.  We now report on our efforts to extend this model to be able to predict the host response to additional types of virus. We used specially constructed mutant influenza viruses whose ability to antagonize the immune response (by limiting interferon production) has been reduced. These viruses result in an early immune response relative to their respective wild-type viruses and tend to cause a less severe disease. From mice infected with the mutant viruses, we collected gene expression and protein data and determined the quantity of virus. We have found that only a slight change of a single parameter in the original model was necessary to predict the dynamics of the host response in the new infections. The change can be linked to the different binding affinities between the viruses and a key host protein – thus the model can be re-parameterized with a minimal number of experiments. The ability to develop an accurate model of the host response is the first step in developing advanced therapies which seek to manipulate the immune response – as opposed to directly targeting the virus – to manage disease severity and we believe such therapies will be more robust to virus mutations and applicable to a wide range of pathogens.