Temporal dynamics of host molecular responses differentiate symptomatic and asymptomatic influenza a infection.

Abstract

Exposure to influenza viruses is necessary, but not sufficient, for healthy human hosts to develop symptomatic illness. The host response is an important determinant of disease progression. In order to delineate host molecular responses that differentiate symptomatic and asymptomatic Influenza A infection, we inoculated 17 healthy adults with live influenza (H3N2/Wisconsin) and examined changes in host peripheral blood gene expression at 16 timepoints over 132 hours. Here we present distinct transcriptional dynamics of host responses unique to asymptomatic and symptomatic infections. We show that symptomatic hosts invoke, simultaneously, multiple pattern recognition receptors-mediated antiviral and inflammatory responses that may relate to virus-induced oxidative stress. In contrast, asymptomatic subjects tightly regulate these responses and exhibit elevated expression of genes that function in antioxidant responses and cell-mediated responses. We reveal an ab initio molecular signature that strongly correlates to symptomatic clinical disease and biomarkers whose expression patterns best discriminate early from late phases of infection. Our results establish a temporal pattern of host molecular responses that differentiates symptomatic from asymptomatic infections and reveals an asymptomatic host-unique non-passive response signature, suggesting novel putative molecular targets for both prognostic assessment and ameliorative therapeutic intervention in seasonal and pandemic influenza.

Department

Description

Provenance

Citation

Published Version (Please cite this version)

10.1371/journal.pgen.1002234

Publication Info

Huang, Yongsheng, Aimee K Zaas, Arvind Rao, Nicolas Dobigeon, Peter J Woolf, Timothy Veldman, N Christine Øien, Micah T McClain, et al. (2011). Temporal dynamics of host molecular responses differentiate symptomatic and asymptomatic influenza a infection. PLoS Genet, 7(8). p. e1002234. 10.1371/journal.pgen.1002234 Retrieved from https://hdl.handle.net/10161/8945.

This is constructed from limited available data and may be imprecise. To cite this article, please review & use the official citation provided by the journal.

Scholars@Duke

Zaas

Aimee Kirsch Zaas

Professor of Medicine

Medical education
Genomic applications for diagnosis of infectious diseases
Genomic applications for prediction of infectious diseases

McClain

Micah Thomas McClain

Associate Professor of Medicine

Unless otherwise indicated, scholarly articles published by Duke faculty members are made available here with a CC-BY-NC (Creative Commons Attribution Non-Commercial) license, as enabled by the Duke Open Access Policy. If you wish to use the materials in ways not already permitted under CC-BY-NC, please consult the copyright owner. Other materials are made available here through the author’s grant of a non-exclusive license to make their work openly accessible.