Title |
A Host Transcriptional Signature for Presymptomatic Detection of Infection in Humans Exposed to Influenza H1N1 or H3N2
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Published in |
PLOS ONE, January 2013
|
DOI | 10.1371/journal.pone.0052198 |
Pubmed ID | |
Authors |
Christopher W. Woods, Micah T. McClain, Minhua Chen, Aimee K. Zaas, Bradly P. Nicholson, Jay Varkey, Timothy Veldman, Stephen F. Kingsmore, Yongsheng Huang, Robert Lambkin-Williams, Anthony G. Gilbert, Alfred O. Hero, Elizabeth Ramsburg, Seth Glickman, Joseph E. Lucas, Lawrence Carin, Geoffrey S. Ginsburg |
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. |
X Demographics
Geographical breakdown
Country | Count | As % |
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United States | 2 | 67% |
Australia | 1 | 33% |
Demographic breakdown
Type | Count | As % |
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Scientists | 3 | 100% |
Mendeley readers
Geographical breakdown
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United Kingdom | 2 | 1% |
Netherlands | 1 | <1% |
Austria | 1 | <1% |
Brazil | 1 | <1% |
Taiwan | 1 | <1% |
Denmark | 1 | <1% |
United States | 1 | <1% |
Unknown | 131 | 94% |
Demographic breakdown
Readers by professional status | Count | As % |
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Researcher | 37 | 27% |
Student > Ph. D. Student | 31 | 22% |
Student > Bachelor | 14 | 10% |
Student > Master | 12 | 9% |
Student > Doctoral Student | 7 | 5% |
Other | 21 | 15% |
Unknown | 17 | 12% |
Readers by discipline | Count | As % |
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Agricultural and Biological Sciences | 37 | 27% |
Medicine and Dentistry | 20 | 14% |
Immunology and Microbiology | 19 | 14% |
Biochemistry, Genetics and Molecular Biology | 15 | 11% |
Computer Science | 13 | 9% |
Other | 12 | 9% |
Unknown | 23 | 17% |