Cookies on this website

We use cookies to ensure that we give you the best experience on our website. If you click 'Accept all cookies' we'll assume that you are happy to receive all cookies and you won't see this message again. If you click 'Reject all non-essential cookies' only necessary cookies providing core functionality such as security, network management, and accessibility will be enabled. Click 'Find out more' for information on how to change your cookie settings.

UNLABELLED: Despite increases in vaccination coverage, reductions in influenza-related mortality have not been observed. Better vaccines are therefore required and influenza challenge studies can be used to test the efficacy of new vaccines. However, this requires the accurate post-challenge classification of subjects by outcome, which is limited in current methods that use artificial thresholds to assign 'symptomatic' and 'asymptomatic' phenotypes. We present data from an influenza challenge study in which 22 healthy adults (11 vaccinated) were inoculated with H3N2 influenza (A/Wisconsin/67/2005). We generated genome-wide gene expression data from peripheral blood taken immediately before the challenge and at 12, 24 and 48 h post-challenge. Variation in symptomatic scoring was found amongst those with laboratory confirmed influenza. By combining the dynamic transcriptomic data with the clinical parameters this variability can be reduced. We identified four subjects with severe laboratory confirmed influenza that show differential gene expression in 1103 probes 48 h post-challenge compared to the remaining subjects. We have further reduced this profile to six genes (CCL2, SEPT4, LAMP3, RTP4, MT1G and OAS3) that can be used to define these subjects. We have used this gene set to predict symptomatic infection from an independent study. This analysis gives further insight into host-pathogen interactions during influenza infection. However, the major potential value is in the clinical trial setting by providing a more quantitative method to better classify symptomatic individuals post influenza challenge. KEY MESSAGE: Differential gene expression signatures are seen following influenza challenge. Expression of six predictive genes can classify response to influenza challenge. The genomic influenza response classification replicates in an independent dataset.

Original publication

DOI

10.1007/s00109-014-1212-8

Type

Journal article

Journal

J Mol Med (Berl)

Publication Date

01/2015

Volume

93

Pages

105 - 114

Keywords

Adolescent, Adult, Female, Gene Expression Profiling, Humans, Influenza A Virus, H3N2 Subtype, Influenza Vaccines, Influenza, Human, Male, Middle Aged, Young Adult