A host transcriptional signature for presymptomatic detection of infection in humans exposed to influenza H1N1 or H3N2.
Date
2013
Editors
Journal Title
Journal ISSN
Volume Title
Repository Usage Stats
views
downloads
Citation Stats
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.
Type
Department
Description
Provenance
Subjects
Citation
Permalink
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.
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.
Collections
Scholars@Duke
Christopher Wildrick Woods
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
Aimee Kirsch Zaas
Medical education
Genomic applications for diagnosis of infectious diseases
Genomic applications for prediction of infectious diseases
Lawrence Carin
Lawrence Carin earned the BS, MS, and PhD degrees in electrical engineering at the University of Maryland, College Park, in 1985, 1986, and 1989, respectively. In 1989 he joined the Electrical Engineering Department at Brooklyn Polytechnic Institute (now part of NYU) as an Assistant Professor, and became an Associate Professor there in 1994. In September 1995 he joined the Electrical and Computer Engineering (ECE) Department at Duke University, where he is now a Professor. He was ECE Department Chair from 2011-2014, and Vice Provost and Vice President for Research from 2014-2020. He was the Provost at King Abdullah University of Science & Technology (KAUST) from 2020-2023, returning to Duke in 2023. From 2003-2014 he held the William H. Younger Distinguished Professorship, and since 2018 he has held the James L. Meriam Distinguished Professorship. Dr. Carin's research focuses on machine learning (ML) and artificial intelligence (AI). He publishes widely in the main ML/AI forums, and has addressed many applications of AI, including in medicine and security. He was co-founder of the small business Signal Innovations Group, which was acquired by BAE Systems in 2014, and in 2017 he co-founded the company Infinia ML, which was acquired by Aspirion in 2023. He is an IEEE Fellow.
Geoffrey Steven Ginsburg
Dr. Geoffrey S. Ginsburg's research interests are in the development of novel paradigms for developing and translating genomic information into medical practice and the integration of personalized medicine into health care.
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.