In the first study of its kind, researchers at the Yerkes National Primate Research Center and Emory Vaccine Center, Emory University, have developed a multidisciplinary approach involving immunology, genomics and bioinformatics to predict the immunity of a vaccine without exposing individuals to infection. This approach addresses a long-standing challenge in the development of vaccines--that of only being able to determine immunity or effectiveness long after vaccination and, often, only after being exposed to infection.

The study, which used the yellow fever vaccine (YF-17D) as a model, is available in the online edition of Nature Immunology and represents a long awaited step forward in vaccine immunology and predictive health.

YF-17D is one of the most successful vaccines ever developed and has been administered to nearly half a billion people over the last 70 years.

"A single shot of the vaccine induces immunity in many people for nearly 30 years," says Bali Pulendran, PhD, lead Yerkes researcher of the study and professor in the Department of Pathology and Laboratory Medicine at Emory University School of Medicine. "Despite the great success of the yellow fever vaccine, little has been known about the immunological mechanisms that make it effective," he continues.

Pulendran's team, including graduate student Troy Querec, PhD, in collaboration with Rafi Ahmed, PhD, director of the Emory Vaccine Center, Eva Lee, PhD, director of the Center for Operations Research in Medicine and Healthcare at Georgia Institute of Technology and Alan Aderem, PhD, Institute for Systems Biology in Seattle, sought to determine what makes such a vaccine effective so researchers can design new vaccines against global pandemics and emerging infections that repeat the success of this model vaccine.

The researchers used YF-17D to predict the body's ability shortly after immunization to stimulate a strong and enduring immunity. Researchers vaccinated 15 healthy individuals with YF-17D and studied the T cell and antibody responses in their blood. There was a striking variation in these responses between individuals. Analysis of gene expression patterns in white blood cells revealed in the majority of the individuals the vaccine induced a network of genes involved in the early innate immune response against viruses.

A major challenge in the study involved the identification of discriminatory gene signatures -- among over 50,000 signatures per individual -- that can predict the responses of T cells and antibodies. Lee has developed powerful modeling, computational feature selection and predictive tools that overcome shortcomings of existing techniques, which often have limited ability to handle data sets involving heterogeneous, large-scale, ill-separated and mixed biological and medical data. Her approach offers a very robust classification framework that effectively handles such data sets and derives a classifier that often provides higher prediction accuracy and lower misclassification errors than classifiers derived from other methods.

"Using such 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," says Pulendran. "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 percent accuracy which of the vaccinated individuals would develop a strong T or B cell immunity to yellow fever," continues Pulendran.

Pulendran and his colleagues are now working to determine whether this approach can be used to predict the effectiveness of other vaccines, including flu vaccines. The ability to successfully predict the immunity and effectiveness of vaccines would facilitate the rapid evaluation of new and emerging vaccines, and the identification of individuals who are unlikely to be protected by a vaccine.

"This type of research is essential to answer fundamental questions that can lead to better vaccinations and prevention of disease. Yerkes, as one of only eight National Institutes of Health-designated national primate research centers, is uniquely positioned to carry out such diverse research," says Stuart Zola, PhD, director, Yerkes Research Center.

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Funding for this study was provided in part by the National Institute of Allergy and Infectious Diseases (NIAID) at the National Institutes of Health (NIH), as part of the U19 Cooperative Centers for Translational Research on Human Immunology and Biodefense. Dr. Lee's research is supported partially by the National Science Foundation, and the National Center for Research Resources at the National Institutes of Health, as part of the U54 Clinical and Translational Science Awards.

Reference: Systems biology approach predicts immunogenicity of the yellow fever vaccine in humans. Nature Immunology, early online publication. Troy D. Querec, Rama S. Akondy, Eva K. Lee, Weiping Cao, Helder I. Nakaya, Dirk Teuwen, Ali Pirani, Kim Gernert, Jiusheng Deng, Bruz Marzolf, Kathleen Kennedy, Haiyan Wu, Soumaya Bennouna, Herold Oluoch, Joseph Miller, Ricardo Z. Vencio, Mark Mulligan, Alan Aderem, Rafi Ahmed and Bali Pulendran.

The Center for Operations Research in Medicine and HealthCare, founded in 1999 with partial support from the National Science Foundation and the Whitaker Foundation, is a collaborative education and research center established between the School of Industrial and Systems Engineering at Georgia Institute of Technology and medical and healthcare researchers in different disciplines. The Center's mission is to foster interdisciplinary education and research efforts involving the development and application of sophisticated techniques from the field of operations research to problems in medicine and healthcare.
Focusing on biomedicine and health systems, researchers in the center perform systems modeling, design and develop algorithms and software, and utilize decision theory analysis to advance various domains within medicine. Specific research areas include computational genomics, health risk prediction, disease diagnosis and early detection, optimal treatment strategies and drug delivery, healthcare outcome analysis and treatment prediction, public health and medical preparedness, large-scale medical decision analysis, quality improvement and clinical operations management.