A Host Transcriptional Signature for Presymptomatic Detection of Infection in Humans Exposed to Influenza H1N1 or H3N2

Christopher W Woods,Alfred O Hero,Aimee K Zaas,Stephen F Kingsmore,Geoffrey S Ginsburg,Bradly P Nicholson,Seth Glickman,Micah T Mcclain,Robert Lambkin-Williams,Anthony G Gilbert,Minhua Chen,Lawrence Carin,Timothy Veldman,Joseph E Lucas,Elizabeth Ramsburg,Yongsheng Huang,Jay Varkey

doi:10.1371/journal.pone.0052198

Abstract

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.

Highlights

Infectious disease diagnostics traditionally rely heavily on pathogen detection [1,2,3]
The results provide clear evidence that a biologically relevant peripheral blood gene expression signature can distinguish influenza infection with a remarkable degree of accuracy across the two strains
We have defined the performance of the blood gene expression signature over time throughout the complete course of human influenza infection

Summary

Introduction

Infectious disease diagnostics traditionally rely heavily on pathogen detection [1,2,3]. Previous studies with dengue, melioidosis, tuberculosis, candidiasis, and sepsis have focused on diagnosis in patients as they present with active disease [7,8,9,10,11,12], we utilized human influenza challenge cohorts with a defined inoculation event coupled with dense serial sampling to explore the ability of modern genomic and statistical techniques to accurately classify individuals with multiple subtypes of influenza infection as early as possible following viral exposure Through this method, we have demonstrated the potential for a robust host gene response signature in pre-symptomatic human infection and suggest the utility of this approach for detecting pandemic H1N1 infection in an acute care setting

Methods

Results

Conclusion