Predicting Vaccine Immunity
Emory scientists have found a way to predict vaccine immunity without exposing people to infection.
Using immunology, genomics, and bioinformatics, they confronted a long-standing challenge in vaccine development: determining immunity or effectiveness long after vaccination and, often, only after exposure to infection.
Researchers with the Yerkes National Primate Research Center and the Emory Vaccine Center used the yellow fever vaccine (YF-17D) as a model. The vaccine, given to nearly half a billion people over the past 70 years, is one of the most successful ever developed. Yet little is known about the immunological mechanisms that make it so. A team led by Yerkes researcher Bali Pulendran worked with the Emory Vaccine Center, the Georgia Institute of Technology, and the Institute for Systems Biology in Seattle to determine why YF-17D is effective.
The researchers vaccinated 15 healthy people with YF-17D and studied the T cell and antibody responses in their blood. Analysis of gene expression patterns in white blood cells showed that in most of the people the vaccine induced a network of genes involved in the early innate immune response against viruses. Through bioinformatics, the researchers identified distinct gene signatures that correlated with T cell response and the antibody response induced by the vaccine.
“To determine whether these gene signatures could predict immune response, we vaccinated a second group of people and were able to predict with up to 90% accuracy which of those vaccinated would develop a strong T or B cell immunity to yellow fever,” says Pulendran.
Now his team is working to determine whether this approach can be used to predict the effectiveness of flu and other vaccines. If successful, the process could speed up evaluation of new and emerging vaccines and identification of people likely to resist immunity.
