New research has shed light on the reasons why some people develop severe COVID-19 symptoms while others experience no symptoms at all. From quite early in the pandemic, risk factors were identified that were linked to severe COVID-19 infections including pre-existing illnesses such as diabetes, a high body mass index, and age, but those risk factors alone did not account for many severe cases and deaths, such as individuals with no known health conditions who were young and not overweight.
An international team of researchers from the University of Sheffield in the UK and Stanford University in the United States conducted a multiomic analysis that revealed cell-type-specific molecular determinants of COVID-19 severity and identified more than 1,000 genes that were linked to the development of severe COVID-19.
Identifying patterns in vast amounts of data is a considerable challenge, so the researchers developed a machine learning tool to identify the genetic basis for diseases such as COVID-19 that are poorly understood. If the genetic factors that lead to the development of severe disease are not known, it limits the opportunity for early intervention and the provision of treatments that could reduce disease severity and prevent death.
The researchers used multiple large data sets for their study, including genetic information from healthy human lung tissue which allowed them to identify gene expression for 19 different lung cell types. Data was also used from the COVID-19 Host Genetics Initiative, which was one of the largest genetic studies of critically ill coronavirus patients. That dataset was used to identify genetic clues as to why some individuals were at a higher risk of developing severe disease than others. Mutations that were present or absent in individuals with severe COVID-19 suggested the mutations may be involved in the severity of the disease. The researchers also used data that described the different regions of the genome that were important for different cell types within lung tissue and overlapped the mutations onto the cell-specific genomes, which allowed them to identify which genes were dysfunctional within each cell type.
The researchers’ machine learning tool – RefMap – identified more than 1,000 risk genes across 19 cell types that accounted for 77% of the SNP-based heritability for severe disease. While a broad range of genes increased risk, the greatest number were related to the immune system. “Genetic risk is particularly focused within natural killer (NK) cells and T cells, placing the dysfunction of these cells upstream of severe disease, “explained the researchers. “Mendelian randomization and single-cell profiling of human NK cells support the role of NK cells and further localize genetic risk to CD56bright NK cells, which are key cytokine producers during the innate immune response. Rare variant analysis confirms the enrichment of severe-disease-associated genetic variation within NK-cell risk genes.”
The study has significantly improved understanding of why some people have more serious COVID-19 symptoms than others and has identified potential therapeutic targets. “Our findings lay the foundation for a genetic test that can predict who is born with an increased risk for severe COVID-19,” said study lead, Michael P. Snyder, Ph.D., of the Department of Genetics at Stanford University. “Imagine there are 1,000 changes in DNA linked to severe COVID-19. If you have 585 of these changes, that might make you pretty susceptible, and you’d want to take all the necessary precautions.”
You can read more about the study in the paper – Multiomic analysis reveals cell-type-specific molecular determinants of COVID-19 severity – which was recently published in Cell Systems. DOI: 10.1016/j.cels.2022.05.007