A change of thinking on how to diagnose respiratory infections could help clinicians determine whether a patient has a viral, bacterial, or fungal infection and prevent unnecessary use of antibiotics, an important topic recently covered by BioNews Texas Contributing Science Editor Charles W. Moore. The new test is based on a blood-based gene expression signature and was developed at Duke University Medical Center by Aimee Zaas and her colleagues. The researchers have shown in a proof-of-concept study that the test can distinguish whether a patient has a respiratory viral infection or non-viral infection and this can be accomplished in an emergency department venue. The article is available in Science Translational Medicine (Sept. 18, 2013).
Generally, for infectious disease, diagnosis orients itself around trying to identify traces of a pathogen which requires prior experience of the pathogen. Instead of the more conventional approach, the new test evaluates how strongly particular host genes are expressed in the presence of the pathogen.
According to James Mahony from McMaster University of Canada (not involved in the study), “It’s an interesting paper. It takes a different approach from what we traditionally do in a microbiology lab, which is look for pathogens. Looking for host transcriptional patterns indicative of a viral infection versus a non-viral infection . . . is an interesting approach, and totally different from what I do as a clinical virologist”.
Mike Loeffelholz, a pathologist and clinical microbiologist at the University of Texas Medical Branch at Galveston (UTMB), also not involved in the study adds, “Viruses do not have one single conserved genetic sequence that you can target and detect all viruses, unlike bacteria, where we can detect ribosomal RNA sequences. In order to broadly detect viruses, you have to have these massively multiplexed pathogen-specific assays. So if you can detect—instead of the actual viral pathogens—a host response signature . . . it gives you a single test for many different kinds of viral infections.”
For the current study, Zaas and colleagues looked at two cohorts of individuals that were experimentally infected with influenza A H3N2 or H1N1 and evaluated the performance of their RT-PCR (reverse transcriptase polymerase chain reaction)-based assay. Additionally, the test was performed on 102 patients from the emergency department presenting with fever along with 41 healthy volunteers. The individuals who where experimentally infected with H3N2 and H1N1 were identified with 100 percent and 87 percent accuracy respectively. Patients taken from the emergency department resulted in 89 percent viral infections with a false-negative rate of 6 percent. The test not only was able to identify influenza infections but also found rhinovirus infections.
Coauthors Geoffrey Ginsburg and Christoper Woods remark that, the current study sets a basis for future evaluations with a host-based diagnostic approach. According to Christopher Woods, an associate professor of medicine and global health at Duke and chief of infectious diseases at the Durham VA Medical Center in North Carolina, “When developing diagnostics for pathogens using an RT-PCR platform . . . there are issues still with sample errors due to the frequency of the pathogen’s genomic DNA in any particular sample. Those samples that have lower numbers of pathogen copies to be detect become very well-suited for a host response-based diagnostic. One can envision, ultimately, using a combined approach that is complementary going after both the pathogens and the host response concurrently”.
This test has the ability to detect a wide range of viral infections, which makes it interesting in that if an outbreak occurred at some point, it might be possible to test some part of the population and know who had contact with the pathogen or were infected without even knowing what virus it is. The only drawback at the moment is that it takes 12 hours to get results from the test and in an outbreak situation time is critical. So, it has been suggested that the test is better for the doctor’s office at the moment. Ideally, it would be nice if the test could be used in all clinical situations.
Photo from http://www.gene-quantification.de.