This web page was produced as an assignment for an undergraduate course at Davidson College.
Data obtained from nose swabs and blood samples can be used to compare host responses among patients to a variety of acute respiratory illnesses (ARIs), including severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), and ultimately to generate a method to accurately discriminate between SARS-CoV-2 infection and other ARIs from NP swabs.
Since the end of 2019, Coronavirus Disease-19 (COVID-19), caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), has become a worldwide pandemic accounting for over three million deaths worldwide and over half a million in the United States alone (“COVID”). The number of COVID-19 tests in the United States alone has totaled over 400 million since they became available to the public in the beginning of 2019 (“Total”). At the moment, COVID-19 testing relies on a process known as reverse transcription quantitative polymerase chain reaction (RT-PCR) which can generate false-negative results due to the fact that viral loads, a measurement of the relative amount of a virus present in an organism, can be very low in some patients and fluctuate significantly over the course of an illness. In one study conducted at Tongji Hospital in Wuhan, China demonstrated that 8.9% (48/540) of individuals had their results change from negative to positive on consecutive tests (Li et al. 3). Due to the drawbacks of current testing methods, researchers set out to develop a separate testing technique, based on the host response instead of viral load, which they believed would prove to be useful as a complementary tool for differential diagnosis of SARS-CoV-2, as well as other respiratory infections caused by viruses or bacteria (Ng et al. 1).
Experimenters obtained host response data from individuals with a variety of acute respiratory illnesses, including COVID-19, using both nasopharyngeal (NP) swab samples, more commonly referred to as “nose swabs”, and whole-blood (WB) samples. The experimenters also recorded whether each individual with COVID-19 had a mild and severe cases of the disease to help determine the magnitude to which this affected the results and ultimately the accuracy of the diagnosis. The NP and WB samples were used to analyze the viral transcriptome from RNA sequencing (RNA-seq) to better understand the host response to better understand the host response to infection. RNA-sequencing uses RNA transcripts to quantify how an organism’s DNA is being transcribed. The term transcriptome refers to the total of all the messenger RNA (mRNA) molecules expressed by an organism. With the transcriptome generated via RNA-seq, as well as details, such as age, weight, and disease severity, from each of the individuals from whom the samples were obtain, the experimenters were able to analyze a large variety of factors to determine if any correlations could be found. Some of the factors analyzed including how cell types and proportions compared between SARS-CoV-2 and other infections or how host response differed between patients hospitalized for COVID-19 and those who did not require hospitalization. With a better understanding of how COVID-19 affected gene expression, thanks to their thorough analysis, the research hypothesized that they would be able to generate a classifier that could better differentiate between SARS-CoV-2 infections and other ARIs. The researchers were eventually able to produce a classifier with greater than 85% overall accuracy, using NP swab samples, which they believe would be especially useful as a complementary diagnostic tool for SARS-CoV-2 infection. Their classifier consisted of two layers and was able to distinguish between infections using information from only 19 total genes, 8 genes in layer one and 11 genes in layer two. The first layer distinguishes between SARS-CoV-2 and non-viral ARIs, while the second layer distinguishes between SARS-CoV-2 and other viral ARIs. Along with this classifier, the experimenters were also able to identify a set of differentially expressed genes (DEGs) that were associated with COVID-19 disease severity. This means that these genes tend to be either over or under expressed in patients with more severe cases of the disease. In the case of the DEGs identified for this paper, all of them demonstrated increased expression in hospitalized patients compared to those who did not require hospitalization.
The findings of this paper are extremely relevant in the context of the current COVID-19 pandemic worldwide. More accurate testing is extremely important in our response to the pandemic, as it allows us to better understate the current state of the pandemic. Specifically, the minimization of false negative test results, a major focus of this study, is extremely important. People who receive false negative test results believe that they do not have the virus when they actually do. When this happens, it leads to a greater possibility of these people spreading without knowing they are doing so. Another promising aspect of this study is the fact that the researchers were able to obtain their results without having to obtain additional samples. People are less likely to get tested if they know that they have to give multiple samples, simply due to the fact that providing additional samples is not only time consuming but also often uncomfortable, in the case of IVs for WB samples for example. There are two important questions that this study addresses, but ultimately leaves unanswered, thus providing areas for potential future studies. First, how useful is this classifier in the diagnosis of SARS-CoV-2 infection in either asymptomatic or presymtomatic patients? Second, would long term studies validate the accuracy of the DEGs in predicting disease severity in COVID-19 patients? If future studies can prove that these methods are accurate in their predictions and diagnoses, then they would be extremely helpful in addressing the COVID-19 pandemic.
Callen Davidson is currently enrolled at Davidson College. Contact him at cadavidson@davidson.edu.
References:
COVID Live Update: 150,188,881 Cases and 3,162,699 Deaths from the Coronavirus – Worldometer [WWW Document], n.d. URL https://www.worldometers.info/coronavirus/ (accessed 4.28.21).
Li, Y., Yao, L., Li, J., Chen, L., Song, Y., Cai, Z., Yang, C., 2020. Stability issues of RT-PCR testing of SARS-CoV-2 for hospitalized patients clinically diagnosed with COVID-19. Journal of Medical Virology 92, 903–908. https://doi.org/10.1002/jmv.25786
Ng, D.L., Granados, A.C., Santos, Y.A., Servellita, V., Goldgof, G.M., Meydan, C., Sotomayor-Gonzalez, A., Levine, A.G., Balcerek, J., Han, L.M., Akagi, N., Truong, K., Neumann, N.M., Nguyen, D.N., Bapat, S.P., Cheng, J., Martin, C.S.-S., Federman, S., Foox, J., Gopez, A., Li, T., Chan, R., Chu, C.S., Wabl, C.A., Gliwa, A.S., Reyes, K., Pan, C.-Y., Guevara, H., Wadford, D., Miller, S., Mason, C.E., Chiu, C.Y., 2021. A diagnostic host response biosignature for COVID-19 from RNA profiling of nasal swabs and blood. Science Advances 7, eabe5984. https://doi.org/10.1126/sciadv.abe5984
Total COVID-19 tests [WWW Document], n.d. . Our World in Data. URL https://ourworldindata.org/grapher/full-list-total-tests-for-covid-19 (accessed 4.28.21)
© Copyright 2020 Department of Biology, Davidson College, Davidson, NC 28036
