Rapid, Sample-to-Answer Host Gene Expression Test to Diagnose Viral Infection


<jats:title>Abstract</jats:title> <jats:sec> <jats:title>Background</jats:title> <jats:p>Distinguishing bacterial, viral, or other etiologies of acute illness is diagnostically challenging with significant implications for appropriate antimicrobial use. Host gene-expression offers a promising approach although no clinically useful tests have yet been developed to accomplish this. Here, Qvella’s FAST™ HR process was developed to quantify previously identified host gene-expression signatures in whole blood in <45 minutes.</jats:p> </jats:sec> <jats:sec> <jats:title>Methods</jats:title> <jats:p>Whole blood was collected from 128 human subjects (mean age 47, range 18-88) with clinically adjudicated, microbiologically confirmed viral infection, bacterial infection, non-infectious illness, or healthy controls. Stabilized mRNA was released from cleaned and stabilized RNA-surfactant complexes using e-lysisTM, an electrical process providing a qRT-PCR-ready sample. Threshold cycle values (CT) for 10 host response targets were normalized to HPRT1 expression, a control mRNA. The transcripts in the signature were specifically chosen to discriminate viral from non-viral infection (bacterial, non-infectious illness, or healthy). Classification accuracy was determined using cross-validated sparse logistic regression.</jats:p> </jats:sec> <jats:sec> <jats:title>Results</jats:title> <jats:p>Reproducibility of mRNA quantification was within 1 cycle as compared to the difference seen between subjects with viral vs. non-viral infection (up to 5.0 normalized CT difference). Classification of 128 subjects into viral or non-viral etiologies demonstrated 90.6% overall accuracy compared to 82.0% for procalcitonin (p=0.06). FASTTM HR achieved rapid and accurate measurement of the host response to viral infection in less than 45 minutes.</jats:p> </jats:sec> <jats:sec> <jats:title>Conclusions</jats:title> <jats:p>These results demonstrate the ability to translate host gene expression signatures to clinical platforms for use in patients with suspected infection.</jats:p> </jats:sec>





Published Version (Please cite this version)


Publication Info

Tsalik, Ephraim L, Ayeaye Khine, Abdossamad Talebpour, Alaleh Samiei, Vilcy Parmar, Thomas W Burke, Micah T Mcclain, Geoffrey S Ginsburg, et al. (2019). Rapid, Sample-to-Answer Host Gene Expression Test to Diagnose Viral Infection. Open Forum Infectious Diseases, 6(11). 10.1093/ofid/ofz466 Retrieved from https://hdl.handle.net/10161/21654.

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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. 


Thomas Burke

Manager, Systems Project

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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