Systems Analysis of the Impact of Interferon-Lambda Signaling on Vaccine Response

Influenza viruses cause annual epidemics resulting in 3 to 5 million cases of severe illness, and 250,000 to 500,000 deaths. Vaccination against influenza significantly reduces the burden of infection, however, vaccination success is limited in certain risk groups such as immunocompromised patients. Various factors significantly impact the vaccination success, such as age, immunosuppressive drugs and chronic disease. In particular, single nucleotide polymorphisms (SNPs) in signaling pathways of the immune system have been identified as an additional factor for effective vaccination.

Several independent groups identified SNPs in the IFN-lambda signaling pathway that significantly influence the antiviral response and probably the immune response to vaccines as well. Several other in vitro and in vivo studies indicate the importance of IFN-lambda. However, the role of IFN-lambda in vaccine response is not yet understood and the biological mechanism of its activity remains largely unknown.

We are using a combined computational and experimental approach to study the
IFN-lambda signaling pathway in immune cells. In particular, we are investigating the impact of genetic variants on the immune response to influenza vaccination in healthy individuals and immunocompromised patients. The genetic variant effects are mapped into a computational model of the IFN-lambda signaling pathway, which enables the manipulation of relevant variables and the determination of downstream effects. The developed model will be integrated in a multi-scale computational model of the vaccine response to explore vaccine outcomes based on genetic and clinical parameters. A better understanding of how genetic variants influence the vaccine response may help to develop novel effective vaccine therapies for immunocompromised patients.

Keywords: Signaling models, mathematical modeling in immunology, quantitative flow cytometry, interferons, immune response, mixed-effect models



 Janina Linnik

Janina Linnik
ETH Zurich
Computational Systems Biology Group
Mattenstrasse 26
CH - 4058 Basel

Phone: +41 61 387 31 97