A host transcriptional signature for presymptomatic detection of infection in humans exposed to influenza H1N1 or H3N2.


There is great potential for host-based gene expression analysis to impact the early diagnosis of infectious diseases. In particular, the influenza pandemic of 2009 highlighted the challenges and limitations of traditional pathogen-based testing for suspected upper respiratory viral infection. We inoculated human volunteers with either influenza A (A/Brisbane/59/2007 (H1N1) or A/Wisconsin/67/2005 (H3N2)), and assayed the peripheral blood transcriptome every 8 hours for 7 days. Of 41 inoculated volunteers, 18 (44%) developed symptomatic infection. Using unbiased sparse latent factor regression analysis, we generated a gene signature (or factor) for symptomatic influenza capable of detecting 94% of infected cases. This gene signature is detectable as early as 29 hours post-exposure and achieves maximal accuracy on average 43 hours (p = 0.003, H1N1) and 38 hours (p-value = 0.005, H3N2) before peak clinical symptoms. In order to test the relevance of these findings in naturally acquired disease, a composite influenza A signature built from these challenge studies was applied to Emergency Department patients where it discriminates between swine-origin influenza A/H1N1 (2009) infected and non-infected individuals with 92% accuracy. The host genomic response to Influenza infection is robust and may provide the means for detection before typical clinical symptoms are apparent.





Published Version (Please cite this version)


Publication Info

Woods, Christopher W, Micah T McClain, Minhua Chen, Aimee K Zaas, Bradly P Nicholson, Jay Varkey, Timothy Veldman, Stephen F Kingsmore, et al. (2013). A host transcriptional signature for presymptomatic detection of infection in humans exposed to influenza H1N1 or H3N2. PLoS One, 8(1). p. e52198. 10.1371/journal.pone.0052198 Retrieved from https://hdl.handle.net/10161/8944.

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Christopher Wildrick Woods

Wolfgang Joklik Distinguished Professor of Global Health

1. Emerging Infections
2. Global Health
3. Epidemiology of infectious diseases
4. Clinical microbiology and diagnostics
5. Bioterrorism Preparedness
6. Surveillance for communicable diseases
7. Antimicrobial resistance


Micah Thomas McClain

Associate Professor of Medicine

Aimee Kirsch Zaas

Professor of Medicine

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

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