Autor: |
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 |
Jazyk: |
angličtina |
Rok vydání: |
2013 |
Předmět: |
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Zdroj: |
PLoS ONE, Vol 8, Iss 1, p e52198 (2013) |
Druh dokumentu: |
article |
ISSN: |
1932-6203 |
DOI: |
10.1371/journal.pone.0052198 |
Popis: |
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. |
Databáze: |
Directory of Open Access Journals |
Externí odkaz: |
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