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A miRNA Host Response Signature Accurately Discriminates Acute Respiratory Infection Etiologies.

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Date
2018-01
Authors
Poore, Gregory D
Ko, Emily R
Valente, Ashlee
Henao, Ricardo
Sumner, Kelsey
Hong, Christopher
Burke, Thomas W
Nichols, Marshall
McClain, Micah T
Huang, Erich S
Ginsburg, Geoffrey S
Woods, Christopher W
Tsalik, Ephraim L
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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 article
Subject
bacterial infections
host-pathogen interaction
micro RNA
molecular diagnostics
personalized medicine
respiratory tract infections
transcriptome
viral infections
Permalink
https://hdl.handle.net/10161/21657
Published Version (Please cite this version)
10.3389/fmicb.2018.02957
Publication 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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Scholars@Duke

Burke

Thomas Burke

Manager, Systems Project
Ginsburg

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.
Henao

Ricardo Henao

Assistant Professor in Biostatistics and Bioinformatics
Huang

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
Ko

Emily Ray Ko

Assistant Professor of Medicine
McClain

Micah Thomas McClain

Associate Professor of Medicine
Tsalik

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
Woods

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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