COVID-19 Diagnostics: New Blood-Based Host Gene Expression Assay Test Accurately Identifies Viral Infection Before Symptoms Even Develop!

COVID-19 Diagnostics: Researchers from Duke University Medical Center-North Carolina have identified biomarkers that accurately identify numerous viral infections across the clinical stages of disease, advancing a potential new way to guide treatment, quarantine decisions, and other clinical and public health interventions in the setting of endemic and pandemic infectious diseases.


 
The unique blood-based test uses a gene expression assay to correctly predict nine different respiratory viral infections including influenza, enterovirus, adenovirus and coronaviruses known to cause common colds. It shows the body's genes responding to a pathogen before symptoms are present. The test is also now being assessed to see possible usage in the SARS-CoV-2 context which the study team says will most probably work just as  well.
 
The initial study results showing the effectiveness of the new test on the 9 different respiratory viral infections are published in the journal: The Lancet Infectious Diseases https://www.thelancet.com/journals/laninf/article/PIIS1473-3099(20)30486-2/fulltext#%20
 
Dr Micah McClain, M.D., Ph.D., Associate Professor in the Department of Medicine at Duke and lead author the  study told Thailand Medical News,  "While our research was conducted prior to the COVID-19 pandemic, our data show that these biomarkers of viral infection are present and detectable before clinical disease develops and thus could form the basis of novel approaches to early identification and management of emerging viral outbreaks and pandemics.”
 
Dr McClain said additional studies are ongoing to determine the genomic markers' effectiveness in detecting SARS-CoV-2, the strain of coronavirus that causes COVID-19. Preliminary findings from those studies have been posted on a public website and the data are currently under peer review. https://www.medrxiv.org/content/10.1101/2020.07.20.20155507v1
 
The study team at the Duke Center for Applied Genomics and Precision Medicine have been working for years to develop and fine-tune tests that quickly distinguish bacterial infections from viral infections, a public health need to assure that antibiotics are properly prescribed.
 
Antibiotics are ineffective against viruses, and their improper use has fueled the rise of treatment-resistant bacteria.
 
The present research advances that work and provides a clinically applicable approach to not only identify a viral infection, but to do so before symptoms develop and often before standard viral PCR tests become positive.
 
The study team enrolled 1,465 college students at Duke between 2009 and 2015 and monitored them for the entire academic year for the presence and severity of eight symptoms of respiratory tract infections. Participants filled out a daily web-based survey, rating symptoms on a scale of 0-4.
Index cases were defined as study participants who reported a 6-point increase in a cumulative daily symptom score.
 
Biospecimens were collected from 264 index cases with clinical illness, of whom 150 had a respiratory viral cause confirmed by traditional PCR testing of nasopharyngeal samples.
 
All close contacts of the sick study participants ie roommates, close friends and partners considered to be at increased risk of developing symptoms were monitored for five days for symptoms and viral shedding. The researchers also measured gene expression responses using the blood-based 36-gene RT-PCR assay.
 
It was found that of the 555 close contacts enrolled and sampled, 162 developed symptoms of respiratory tract infection during observation, of whom 106 had confirmed illness based on traditional viral PCR testing.
 
Significantly, for most of the study participants, the gene expression test accurately predicted viral infection up to three days before maximum symptoms, often prior to any symptom onset or detectable viral shedding.
 
Importantly for influenza, the assay was 99% accurate in predicting illness, 95% accurate for adenovirus and 93% accurate for the cold-causing coronavirus strain.
 
The study team said that the new diagnostic platform  has permitted real-world validation of a transcriptomic signature in peripheral blood that is capable of accurately identifying exposed but apparently healthy individuals who will go on to develop symptomatic viral infection. In the majority of participants (62%), the gene signature was present even before viral shedding was detected. The promise of this approach is highlighted by the discriminative ability of the genomic signature 2–3 days before maximum illness, when most individuals had minimal or no symptoms (90% and 78% of participants were asymptomatic or had subclinical illness at the T–3 and T–2 timepoints, respectively). Furthermore, mild symptoms at early timepoints were clinically vague and typical of seasonal allergies, mild chronic obstructive pulmonary disorder flares, or even symptoms due to sequelae of chronic smoking.
 
The team said, ‘Genomic analysis to classify viral infection among asymptomatic individuals or those with common, non-specific upper respiratory symptoms would be valuable. The accuracy of the transcriptomic signature across nine different respiratory viruses, each with specific incubation times and variable clinical progression and duration, also highlights the potential of this approach. Additionally, the gene signature used represents conserved antiviral response signaling pathways that are active, and thus discriminatory, across the spectrum of illness, not only at early timepoints. This broad applicability is vital because in a real-world setting, the location of an individual along the continuum of infection at the moment of testing would be unknown.”
 
Although the transcriptomic signature accurately classified a wide spectrum of viral respiratory tract infections, it had the highest accuracy in a subset of viruses, including influenza and coronavirus. Influenza is one of the most important viruses to identify early because of its contribution to pandemics, in which existing diagnostics might perform poorly (especially for emerging strains as observed in 2009), questions of triage and quarantine are paramount, and effective therapeutic options are readily available and most effective when provided early in disease.
 
Similarly, The global outbreak of a novel, highly virulent coronavirus: SARS-CoV-2 is ongoing, and it is evident that tools that can aid in assessment of individuals with potential exposure, especially tools that can diagnose infection before detectable viral shedding, would be a valuable to existing epidemiological and molecular approaches in outbreak settings. The experience suggests that the ability to accurately detect exposure to these types of emerging infections will only become increasingly important over time.
 
Dr McClain concluded, "Our research demonstrates the potential of this gene expression-based testing approach. We can use the body's natural immune response signals to detect a viral infection with a high degree of accuracy even at a time when individuals have been exposed to the pathogen but don't yet feel sick."
 
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