A blood-based host gene expression assay for early detection of respiratory viral infection: an index-cluster prospective cohort study.


BACKGROUND:Early and accurate identification of individuals with viral infections is crucial for clinical management and public health interventions. We aimed to assess the ability of transcriptomic biomarkers to identify naturally acquired respiratory viral infection before typical symptoms are present. METHODS:In this index-cluster study, we prospectively recruited a cohort of undergraduate students (aged 18-25 years) at Duke University (Durham, NC, USA) over a period of 5 academic years. To identify index cases, we monitored students for the entire academic year, for the presence and severity of eight symptoms of respiratory tract infection using a daily web-based survey, with symptoms rated on a scale of 0-4. Index cases were defined as individuals who reported a 6-point increase in cumulative daily symptom score. Suspected index cases were visited by study staff to confirm the presence of reported symptoms of illness and to collect biospecimen samples. We then identified clusters of close contacts of index cases (ie, individuals who lived in close proximity to index cases, close friends, and partners) who were presumed to be at increased risk of developing symptomatic respiratory tract infection while under observation. We monitored each close contact for 5 days for symptoms and viral shedding and measured transcriptomic responses at each timepoint each day using a blood-based 36-gene RT-PCR assay. FINDINGS:Between Sept 1, 2009, and April 10, 2015, we enrolled 1465 participants. Of 264 index cases with respiratory tract infection symptoms, 150 (57%) had a viral cause confirmed by RT-PCR. Of their 555 close contacts, 106 (19%) developed symptomatic respiratory tract infection with a proven viral cause during the observation window, of whom 60 (57%) had the same virus as their associated index case. Nine viruses were detected in total. The transcriptomic assay accurately predicted viral infection at the time of maximum symptom severity (mean area under the receiver operating characteristic curve [AUROC] 0·94 [95% CI 0·92-0·96]), as well as at 1 day (0·87 [95% CI 0·84-0·90]), 2 days (0·85 [0·82-0·88]), and 3 days (0·74 [0·71-0·77]) before peak illness, when symptoms were minimal or absent and 22 (62%) of 35 individuals, 25 (69%) of 36 individuals, and 24 (82%) of 29 individuals, respectively, had no detectable viral shedding. INTERPRETATION:Transcriptional biomarkers accurately predict and diagnose infection across diverse viral causes and stages of disease and thus might prove useful for guiding the administration of early effective therapy, quarantine decisions, and other clinical and public health interventions in the setting of endemic and pandemic infectious diseases. FUNDING:US Defense Advanced Research Projects Agency.






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Publication Info

McClain, Micah T, Florica J Constantine, Bradly P Nicholson, Marshall Nichols, Thomas W Burke, Ricardo Henao, Daphne C Jones, Lori L Hudson, et al. (2020). A blood-based host gene expression assay for early detection of respiratory viral infection: an index-cluster prospective cohort study. The Lancet. Infectious diseases. 10.1016/s1473-3099(20)30486-2 Retrieved from https://hdl.handle.net/10161/21651.

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Micah Thomas McClain

Associate Professor of Medicine

Thomas Burke

Manager, Systems Project

Ricardo Henao

Associate Professor in Biostatistics & Bioinformatics

Daphne Jones

Medical Instructor in the Department 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 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. 


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.


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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