Gene expression-based classifiers identify Staphylococcus aureus infection in mice and humans.
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Staphylococcus aureus causes a spectrum of human infection. Diagnostic delays and uncertainty lead to treatment delays and inappropriate antibiotic use. A growing literature suggests the host's inflammatory response to the pathogen represents a potential tool to improve upon current diagnostics. The hypothesis of this study is that the host responds differently to S. aureus than to E. coli infection in a quantifiable way, providing a new diagnostic avenue. This study uses Bayesian sparse factor modeling and penalized binary regression to define peripheral blood gene-expression classifiers of murine and human S. aureus infection. The murine-derived classifier distinguished S. aureus infection from healthy controls and Escherichia coli-infected mice across a range of conditions (mouse and bacterial strain, time post infection) and was validated in outbred mice (AUC>0.97). A S. aureus classifier derived from a cohort of 94 human subjects distinguished S. aureus blood stream infection (BSI) from healthy subjects (AUC 0.99) and E. coli BSI (AUC 0.84). Murine and human responses to S. aureus infection share common biological pathways, allowing the murine model to classify S. aureus BSI in humans (AUC 0.84). Both murine and human S. aureus classifiers were validated in an independent human cohort (AUC 0.95 and 0.92, respectively). The approach described here lends insight into the conserved and disparate pathways utilized by mice and humans in response to these infections. Furthermore, this study advances our understanding of S. aureus infection; the host response to it; and identifies new diagnostic and therapeutic avenues.
Aged, 80 and over
Gene Expression Profiling
Mice, 129 Strain
Mice, Inbred BALB C
Mice, Inbred C3H
Mice, Inbred C57BL
Mice, Inbred NOD
Mice, Inbred Strains
Oligonucleotide Array Sequence Analysis
Reproducibility of Results
Sensitivity and Specificity
Published Version (Please cite this version)10.1371/journal.pone.0048979
Publication InfoAhn, Sun Hee; Tsalik, Ephraim L; Cyr, Derek D; Zhang, Yurong; van Velkinburgh, Jennifer C; Langley, Raymond J; ... Fowler, Vance G (2013). Gene expression-based classifiers identify Staphylococcus aureus infection in mice and humans. PLoS One, 8(1). pp. e48979. 10.1371/journal.pone.0048979. Retrieved from https://hdl.handle.net/10161/13321.
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Florence McAlister Distinguished Professor of Medicine
Determinants of Outcome in Patients with Staphylococcus aureus Bacteremia Antibacterial ResistancePathogenesis of Bacterial Infections Tropical medicine/International Health
Professor of Medicine
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.
Associate Research Professor in the Social Science Research Institute
This author no longer has a Scholars@Duke profile, so the information shown here reflects their Duke status at the time this item was deposited.
Associate Professor of Medicine
My research is focused on understanding the dynamic between host and pathogen so as to discover and develop host-response markers that can diagnose and predict health and disease. This new and evolving approach to diagnosing illness has the potential to significantly impact individual as well as public health considering the rise of antibiotic resistance. With any potential infectious disease diagnosis, it is difficult, if not impossible, to determine at the time of presentation
Professor of Medicine
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
Professor of Medicine
Medical education Genomic applications for diagnosis of infectious diseases Genomic applications for prediction of infectious diseases
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