Nasopharyngeal Protein Biomarkers of Acute Respiratory Virus Infection.

Abstract

Infection of respiratory mucosa with viral pathogens triggers complex immunologic events in the affected host. We sought to characterize this response through proteomic analysis of nasopharyngeal lavage in human subjects experimentally challenged with influenza A/H3N2 or human rhinovirus, and to develop targeted assays measuring peptides involved in this host response allowing classification of acute respiratory virus infection. Unbiased proteomic discovery analysis identified 3285 peptides corresponding to 438 unique proteins, and revealed that infection with H3N2 induces significant alterations in protein expression. These include proteins involved in acute inflammatory response, innate immune response, and the complement cascade. These data provide insights into the nature of the biological response to viral infection of the upper respiratory tract, and the proteins that are dysregulated by viral infection form the basis of signature that accurately classifies the infected state. Verification of this signature using targeted mass spectrometry in independent cohorts of subjects challenged with influenza or rhinovirus demonstrates that it performs with high accuracy (0.8623 AUROC, 75% TPR, 97.46% TNR). With further development as a clinical diagnostic, this signature may have utility in rapid screening for emerging infections, avoidance of inappropriate antibacterial therapy, and more rapid implementation of appropriate therapeutic and public health strategies.

Department

Description

Provenance

Citation

Published Version (Please cite this version)

10.1016/j.ebiom.2017.02.015

Publication Info

Burke, Thomas W, Ricardo Henao, Erik Soderblom, Ephraim L Tsalik, J Will Thompson, Micah T McClain, Marshall Nichols, Bradly P Nicholson, et al. (2017). Nasopharyngeal Protein Biomarkers of Acute Respiratory Virus Infection. EBioMedicine, 17. pp. 172–181. 10.1016/j.ebiom.2017.02.015 Retrieved from https://hdl.handle.net/10161/23218.

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.

Scholars@Duke

Burke

Thomas Burke

Manager, Systems Project
Henao

Ricardo Henao

Associate Professor of Biostatistics & Bioinformatics
Soderblom

Erik James Soderblom

Associate Research Professor of Cell Biology

Director, Proteomics and Metabolomics Core Facility

Tsalik

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 presentation what the underlying cause of illness is.  For example, acute respiratory illness is among the most frequent reasons for patients to seek care. These symptoms, such as cough, sore throat, and fever may be due to a bacterial infection, viral infection, both, or a non-infectious condition such as asthma or allergies.  Given the difficulties in making the diagnosis, most patients are inappropriately given antibacterials.  However, each of these etiologies (bacteria, virus, or something else entirely) leaves a fingerprint embedded in the host’s response. We are very interested in finding those fingerprints and exploiting them to generate new approaches to understand, diagnose, and manage disease.

These principles also apply to sepsis, defined as life-threatening organ dysfunction caused by a dysregulated host response to infection. Just as with acute respiratory illness, it is often difficult to identify whether infection is responsible for a patient’s critical illness.  We have embarked on a number of research programs that aim to better identify sepsis; define sepsis subtypes that can be used to guide future clinical research; and to better predict sepsis outcomes.  These efforts have focused on many systems biology modalities including transcriptomics, miRNA, metabolomics, and proteomics.  Consequently, our Data Science team has utilized these highly complex data to develop new statistical methods, furthering both the clinical and statistical research communities.

These examples are just a small sampling of the breadth of research Dr. Tsalik and his colleagues have conducted.  

In April 2022, Dr. Tsalik has joined Danaher Diagnostics as the VP and Chief Scientific Officer for Infectious Disease, where he is applying this experience in biomarkers and diagnostics to shape the future of diagnostics in ID. 

Thompson

J. Will Thompson

Adjunct Assistant Professor in the Department of Pharmacology & Cancer Biology

Dr. Thompson's research focuses on the development and deployment of proteomics and metabolomics mass spectrometry techniques for the analysis of biological systems. He served as the Assistant Director of the Proteomics and Metabolomics Shared Resource in the Duke School of Medicine from 2007-2021. He currently maintains collaborations in metabolomics and proteomics research at Duke, and develops new tools for chemical analysis as a Principal Scientist at 908 Devices in Carrboro, NC.

McClain

Micah Thomas McClain

Associate Professor of Medicine
Moseley

Martin Arthur Moseley

Adjunct Professor in the Department of Cell Biology
Woods

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

Ginsburg

Geoffrey Steven Ginsburg

Adjunct Professor in the Department 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.


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.