A miRNA Host Response Signature Accurately Discriminates Acute Respiratory Infection Etiologies.
Abstract
Background: Acute respiratory infections (ARIs) are the leading indication for antibacterial
prescriptions despite a viral etiology in the majority of cases. The lack of available
diagnostics to discriminate viral and bacterial etiologies contributes to this discordance.
Recent efforts have focused on the host response as a source for novel diagnostic
targets although none have explored the ability of host-derived microRNAs (miRNA)
to discriminate between these etiologies. Methods: In this study, we compared host-derived
miRNAs and mRNAs from human H3N2 influenza challenge subjects to those from patients
with Streptococcus pneumoniae pneumonia. Sparse logistic regression models were used
to generate miRNA signatures diagnostic of ARI etiologies. Generalized linear modeling
of mRNAs to identify differentially expressed (DE) genes allowed analysis of potential
miRNA:mRNA relationships. High likelihood miRNA:mRNA interactions were examined using
binding target prediction and negative correlation to further explore potential changes
in pathway regulation in response to infection. Results: The resultant miRNA signatures
were highly accurate in discriminating ARI etiologies. Mean accuracy was 100% [88.8-100;
95% Confidence Interval (CI)] in discriminating the healthy state from S. pneumoniae
pneumonia and 91.3% (72.0-98.9; 95% CI) in discriminating S. pneumoniae pneumonia
from influenza infection. Subsequent differential mRNA gene expression analysis revealed
alterations in regulatory networks consistent with known biology including immune
cell activation and host response to viral infection. Negative correlation network
analysis of miRNA:mRNA interactions revealed connections to pathways with known immunobiology
such as interferon regulation and MAP kinase signaling. Conclusion: We have developed
novel human host-response miRNA signatures for bacterial and viral ARI etiologies.
miRNA host response signatures reveal accurate discrimination between S. pneumoniae
pneumonia and influenza etiologies for ARI and integrated analyses of the host-pathogen
interface are consistent with expected biology. These results highlight the differential
miRNA host response to bacterial and viral etiologies of ARI, offering new opportunities
to distinguish these entities.
Type
Journal articleSubject
bacterial infectionshost-pathogen interaction
micro RNA
molecular diagnostics
personalized medicine
respiratory tract infections
transcriptome
viral infections
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https://hdl.handle.net/10161/21657Published Version (Please cite this version)
10.3389/fmicb.2018.02957Publication Info
Poore, Gregory D; Ko, Emily R; Valente, Ashlee; Henao, Ricardo; Sumner, Kelsey; Hong,
Christopher; ... Tsalik, Ephraim L (2018). A miRNA Host Response Signature Accurately Discriminates Acute Respiratory Infection
Etiologies. Frontiers in microbiology, 9(DEC). pp. 2957. 10.3389/fmicb.2018.02957. Retrieved from https://hdl.handle.net/10161/21657.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
Thomas Burke
Manager, Systems Project
Geoffrey Steven Ginsburg
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.
Ricardo Henao
Assistant Professor in Biostatistics and Bioinformatics
Erich Senin Huang
Assistant Professor in Biostatistics and Bioinformatics
Chief Data Officer for Quality, Duke HealthDirector of Duke ForgeDirector of Duke
CrucibleAssistant Dean for Biomedical InformaticsDr. Huang’s research interests span
applied machine learning, research provenance and data infrastructure. Projects include
building data provenance tools funded by the NIH’s Big Data to Knowledge program,
regulatory science funded by the Burroughs Wellcom
Emily Ray Ko
Assistant Professor of Medicine
Micah Thomas McClain
Associate Professor of Medicine
Ephraim Tsalik
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
Christopher Wildrick Woods
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
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