While the majority of those infected with SARS-CoV-2 experience only mild symptoms, a significant minority progress to severe or critical symptoms with high rates of mortality. Many risk factors associated with more serious symptoms such as respiratory failure and pneumonia were identified early in the pandemic, namely age and comorbidities such as obesity and diabetes.
However, a worrying number of younger individuals otherwise considered healthy have been seen to transition from the early viral stage, with typical mild symptoms, to life-threatening extreme inflammation, usually 7-12 days following symptom onset.
In a paper recently uploaded to the bioRxiv* preprint server, the genetic link between COVID-19 severity and gene expression is investigated, hoping to allow early identification of otherwise healthy individuals that will likely require hospitalization if infected.
How was the study performed?
The group assembled a group of 53 adults confirmed to have COVID-19 by PCR and categorized them based on symptom severity by a number of variables, with those with severe symptoms defined as requiring respiratory support. Blood samples were collected from each participant and underwent transcriptomic profiling using RNA sequencing, wherein gene expression and post-translational modifications can be identified by amplification and characterization of RNA populations (mRNA, miRNA, sRNA, etc.).
Of the 53 participants, 20 were considered to have severe symptoms, 19 moderate, and 14 mild. Besides the fact that the vast majority of mild cases concerned white individuals while representing only half of the moderate or severe cases, other demographic characteristics between groups were comparable, with a median age of 62 and half of participants being male. There were, on average, 4, 9, or 6.5 days from symptom onset to sample collection in mild, moderate, or severe individuals, respectively, and all severe cases were eventually hospitalized, with one participant dying.
Differential gene expression in severe COVID-19
In comparing differences in gene expression between those with or without severe COVID-19, the group notes that around half of genes are expressed differentially between groups, around 7,500 individual genes (±~1,000, depending on the adjustment of variables such as race, sex, and BMI). Of the genes differentially expressed in those with severe COVID-19, the group identified 74 pathways significantly upregulated, including those associated with tumor necrosis factor α (TNFα) signaling and platelet activation and aggregation, while 25 pathways were found to be significantly downregulated, mainly associated with host RNA metabolism and T-cell regulation.
Upregulated nuclear factor kappa B, TNFα, and platelet activation and aggregation pathways have been widely observed in those infected with SARS-CoV-2, associated with severe illness and a high incidence of arterial clotting. The group notes that these pathways are typically activated during bacterial or parasitic challenges. This is possibly induced by the activation of macrophages that bear great biological similarity with osteoclasts, which are activated during parasitic infections such as malaria. Other reports have also observed downregulated T-cell and Th17 activation amongst those with severe COVID-19, and several other novel dysregulated pathways were also identified in this work. However, deeper analysis was outside the scope of this study.
Gene expression as a COVID-19 prognosis indicator
Owing to the unique gene expression profile generated in those with severe COVID-19, the group suggests that the method may be used to predict the future course of SARS-CoV-2 infection. Thus they set out gene-specific expression thresholds for 18 genes of interest. Upon feeding back the data gathered from the participants in the study, the method was shown to have a sensitivity of 100%, specificity of 85%, and an error rate of 9%, given that 5 participants were misclassified with severe instead of moderate illness.
An additional 100 individuals were gathered, half of which had been admitted to the hospital, and their weighted gene expression risk score was assessed in the same way. In this case, a sensitivity and specificity of 84% and 74%, respectively, were achieved.
While measurements of inflammatory markers and viral characteristics can be useful in predicting the outcome of COVID-19, they have often proven unreliable, particularly in atypical severe cases amongst the otherwise healthy. This study has demonstrated that genome-wide expression profiling may make a suitable alternative, providing a non-biased identification of biomarkers that may be useful in predicting the course of the disease by characterizing how the infection has influenced gene expression.
Besides use as a prognostic tool, deep characterization of how gene expression changes over the course of COVID-19 may allow identification of novel targets for future therapeutic intervention or otherwise provide clues as to how severe SARS-CoV-2 infection can be mitigated or avoided.
bioRxiv publishes preliminary scientific reports that are not peer-reviewed and, therefore, should not be regarded as conclusive, guide clinical practice/health-related behavior, or treated as established information.