4.5 Review

Identifying gnostic predictors of the vaccine response

Journal

CURRENT OPINION IN IMMUNOLOGY
Volume 24, Issue 3, Pages 332-336

Publisher

CURRENT BIOLOGY LTD
DOI: 10.1016/j.coi.2012.03.010

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Funding

  1. NIAID NIH HHS [U19 AI082630, N01 AI050025, U19AI057266, R01AI091493, U19AI082630, R37 AI048638-09A1, U19AI090023, U54AI057157, R38 AI140299, R37 AI048638, U19 AI090023-03, U19 AI090023, R01 AI091493-03, R01 AI091493, R37AI48638, U19 AI057266, HHSN266200700006C, U54 AI057157] Funding Source: Medline
  2. NIDDK NIH HHS [R01DK057665, R01 DK057665, R37 DK057665] Funding Source: Medline

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Molecular predictors of the response to vaccination could transform vaccine development. They would allow larger numbers of vaccine candidates to be rapidly screened, shortening the development time for new vaccines. Gene-expression based predictors of vaccine response have shown early promise. However, a limitation of gene-expression based predictors is that they often fail to reveal the mechanistic basis of their ability to classify response. Linking predictive signatures to the function of their component genes would advance basic understanding of vaccine immunity and also improve the robustness of vaccine prediction. New analytic tools now allow more biological meaning to be extracted from predictive signatures. Functional genomic approaches to perturb gene expression in mammalian cells permit the function of predictive genes to be surveyed in highly parallel experiments. The challenge for vaccinologists is therefore to use these tools to embed mechanistic insights into predictors of vaccine response.

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