Computer Model Predicts Vaccine Immune Efficiency

Researchers have developed a multidisciplinary bioinformatics method that may enable them to predict the immune efficiency of a vaccine without having to expose individuals to infection.

Investigators at Emory University (Atlanta, GA, USA) worked with the classical YF-17D vaccine against yellow fever. This vaccine has been in use for more than 70 years and vaccinated more than 500 million people so far.

To determine how the vaccine interacts with the human immune system at the genomic level the investigators injected it into 15 healthy individuals and evaluated the T cell and antibody responses in their blood.

They reported in the November 23, 2008, online edition of the journal Nature Immunology that vaccination induced the activity of a group of specific genes. These genes regulated virus innate sensing and type I interferon production. Computational analyses identified a gene signature, including complement protein C1qB and eukaryotic translation initiation factor 2 alpha kinase 4 – a modulator of the integrated stress response – that correlated with and predicted YF-17D CD8+ T cell responses with up to 90% accuracy. A distinct signature, including B cell growth factor TNFRS17, predicted the neutralizing antibody response with up to 100% accuracy.

"Using a bioinformatics approach, we were able to identify distinct gene signatures that correlated with the T cell response and the antibody response induced by the vaccine,” explained senior author Dr. Bali Pulendran, professor of pathology and laboratory medicine at Emory University. "To determine whether these gene signatures could predict immune response, we vaccinated a second group of individuals and were able to predict with up to 90% accuracy which of the vaccinated individuals would develop a strong T or B cell immunity to yellow fever.”

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