Utilizing population variation, vaccination, and systems biology to study human immunology

•Genetic diversity and varied environmental exposures and life histories give rise to diverse immune states.•Genetic and phenotypic variations constitute natural ‘perturbations’ in humans.•These variations are essential to robustly establish correlations among parameters.•Population-based, systems-l...

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Bibliographic Details
Published inTrends in immunology Vol. 36; no. 8; pp. 479 - 493
Main Author Tsang, John S.
Format Journal Article
LanguageEnglish
Published England Elsevier Ltd 01.08.2015
Elsevier Limited
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Online AccessGet full text
ISSN1471-4906
1471-4981
1471-4981
DOI10.1016/j.it.2015.06.005

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Summary:•Genetic diversity and varied environmental exposures and life histories give rise to diverse immune states.•Genetic and phenotypic variations constitute natural ‘perturbations’ in humans.•These variations are essential to robustly establish correlations among parameters.•Population-based, systems-level studies of vaccination provide predictors of immune-response quality. The move toward precision medicine has highlighted the importance of understanding biological variability within and across individuals in the human population. In particular, given the prevalent involvement of the immune system in diverse pathologies, an important question is how much and what information about the state of the immune system is required to enable accurate prediction of future health and response to medical interventions. Towards addressing this question, recent studies using vaccination as a model perturbation and systems-biology approaches are beginning to provide a glimpse of how natural population variation together with multiplexed, high-throughput measurement and computational analysis can be used to uncover predictors of immune response quality in humans. Here I discuss recent developments in this emerging field, with emphasis on baseline correlates of vaccination responses, sources of immune-state variability, as well as relevant features of study design, data generation, and computational analysis.
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ISSN:1471-4906
1471-4981
1471-4981
DOI:10.1016/j.it.2015.06.005