Systematic comparison of published host gene expression signatures for bacterial/viral discrimination.
Abstract
<h4>Background</h4>Measuring host gene expression is a promising diagnostic strategy
to discriminate bacterial and viral infections. Multiple signatures of varying size,
complexity, and target populations have been described. However, there is little information
to indicate how the performance of various published signatures compare to one another.<h4>Methods</h4>This
systematic comparison of host gene expression signatures evaluated the performance
of 28 signatures, validating them in 4589 subjects from 51 publicly available datasets.
Thirteen COVID-specific datasets with 1416 subjects were included in a separate analysis.
Individual signature performance was evaluated using the area under the receiving
operating characteristic curve (AUC) value. Overall signature performance was evaluated
using median AUCs and accuracies.<h4>Results</h4>Signature performance varied widely,
with median AUCs ranging from 0.55 to 0.96 for bacterial classification and 0.69-0.97
for viral classification. Signature size varied (1-398 genes), with smaller signatures
generally performing more poorly (P < 0.04). Viral infection was easier to diagnose
than bacterial infection (84% vs. 79% overall accuracy, respectively; P < .001). Host
gene expression classifiers performed more poorly in some pediatric populations (3
months-1 year and 2-11 years) compared to the adult population for both bacterial
infection (73% and 70% vs. 82%, respectively; P < .001) and viral infection (80% and
79% vs. 88%, respectively; P < .001). We did not observe classification differences
based on illness severity as defined by ICU admission for bacterial or viral infections.
The median AUC across all signatures for COVID-19 classification was 0.80 compared
to 0.83 for viral classification in the same datasets.<h4>Conclusions</h4>In this
systematic comparison of 28 host gene expression signatures, we observed differences
based on a signature's size and characteristics of the validation population, including
age and infection type. However, populations used for signature discovery did not
impact performance, underscoring the redundancy among many of these signatures. Furthermore,
differential performance in specific populations may only be observable through this
type of large-scale validation.
Type
Journal articleSubject
HumansBacterial Infections
Virus Diseases
Diagnosis, Differential
Cohort Studies
Gene Expression Profiling
Publications
Adult
Child
Host-Pathogen Interactions
Validation Studies as Topic
Genetic Association Studies
Transcriptome
Datasets as Topic
Biomarkers
COVID-19
SARS-CoV-2
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https://hdl.handle.net/10161/24746Published Version (Please cite this version)
10.1186/s13073-022-01025-xPublication Info
Bodkin, Nicholas; Ross, Melissa; McClain, Micah T; Ko, Emily R; Woods, Christopher
W; Ginsburg, Geoffrey S; ... Tsalik, Ephraim L (2022). Systematic comparison of published host gene expression signatures for bacterial/viral
discrimination. Genome medicine, 14(1). pp. 18. 10.1186/s13073-022-01025-x. Retrieved from https://hdl.handle.net/10161/24746.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.
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Show full item recordScholars@Duke
Micah Thomas McClain
Associate Professor of Medicine
Ephraim Tsalik
Adjunct Associate Professor in the Department of Medicine
My research at Duke has 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 pre
Christopher Wildrick Woods
Wolfgang Joklik Distinguished Professor of Global Health
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
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