Journal
BIOINFORMATICS
Volume 37, Issue 2, Pages 229-235Publisher
OXFORD UNIV PRESS
DOI: 10.1093/bioinformatics/btaa691
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Funding
- Alfons und Gertrud Kassel-Stiftung
- Deutsche Forschungsgemeinschaft [HE7707/5-1]
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The research team developed a stochastic model that successfully represents antibody cross-reactive data and validated it with cross-reaction data of different influenza strains. They found that changes in time of infection and the B-cells population are important for successful antibody cross-reaction, while the affinity threshold of B-cells between consecutive infections is a necessary condition.
Motivation: Influenza viruses are a cause of large outbreaks and pandemics with high death tolls. A key obstacle is that flu vaccines have inconsistent performance, in the best cases up to 60% effectiveness, but it can be as low as 10%. Uncovering the hidden pathways of how antibodies (Abs) induced by one influenza strain are effective against another, cross-reaction, is a central vexation for the design of universal flu vaccines. Results: We conceive a stochastic model that successfully represents the antibody cross-reactive data from mice infected with H3N2 influenza strains and further validation with cross-reaction data of H1N1 strains. Using a High-Performance Computing cluster, several aspects and parameters in the model were tested. Computational simulations highlight that changes in time of infection and the B-cells population are relevant, however, the affinity threshold of B-cells between consecutive infections is a necessary condition for the successful Abs cross-reaction. Our results suggest a 3-D reformulation of the current influenza antibody landscape for the representation and modeling of cross-reactive data.
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