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New analysis uncovers specific genes associated with severe COVID-19

 25 October 2021   Research News

Researchers at the MRC London Institute of Medical Sciences (LMS) have used 3D genomic analysis to identify which genes increase the likelihood of severe COVID-19 infection.

Large-scale collaborative research has scanned the genomes of many individuals throughout the COVID-19 pandemic, looking for clues as to why the virus effects some people worse than others. These efforts have identified a multitude of DNA mutations preferentially found in people who suffer severe COVID-19 infection. However, converting data collected through these genome-wide association studies (GWAS) into effective COVID-19 treatments has proven difficult.

Many of the severe COVID-associated mutations do not reside in the genes themselves, but instead are found in enhancers, stretches of DNA that control in what cells and under what conditions genes are switched on or off. Many enhancers are not located beside the genes they control, instead they reside large genomic distances away. This has made it difficult for researchers to unpick how COVID-associated mutations are changing gene expression.

However, while enhancers may be far from their regulated genes along linear DNA, they typically localise in close proximity within the 3D space of the cell’s nucleus. Therefore, assessing which genes the COVID-associated enhancer mutations interact with in a nuclear 3D space could provide answers.

To test this, the LMS Functional Gene Control group, led by Dr Mikhail Spivakov combined genetic data from COVID-19 association studies with their high-resolution catalogue of 3D DNA contacts, compiled via a technique known as Capture Hi-C. The catalogue contains a 3D map of enhancer-gene interactions, thereby letting the researchers know which gene expression alterations are likely driving, or at least contributing towards an increased sensitivity to COVID-19 infection.

This integrated data analysis identified 251 genes implicated in severe COVID-19 infection. Many of the genes appear to be involved in immune functions, such as interferon response, others are linked to lung biology and interestingly, many play roles in the nervous system, including genes acting in the synapses: connections between nerve cells.

Notably, a number of genes linked with severe COVID-19 form part of the so-called NOD pathway that triggers a response to pathogens that have entered the cell. Drugs targeting this pathway already exist meaning there could be opportunities for repurposing them as COVID-19 treatment.

Dr Helen Ray-Jones, joint senior author and postdoc in the LMS Functional Gene Control Group, commented on the findings: “Our analysis has shone a light on the genes that may underpin genetic associations with severe COVID-19 infection.

“Many of the genes we have identified were not uncovered using other strategies. I hope that this insight will strengthen our understanding of COVID-19 pathology and better equip us in identifying novel treatments against this virus.”

This work is based on collaborative studies between the LMS and start-up company Enhanc3D Genomics, with the lead authorship shared between the company’s Dr Michiel Thiecke and Ms Emma Yang, a former Imperial College Masters student in the LMS  Functional Gene Control group who has now started a PhD at Edinburgh.

Commenting on the importance of collaboration, Group Leader and senior author Dr Mikhail Spivakov said: “The COVID-19 pandemic has further highlighted the importance of collaboration in scientific research. COVID-19 diagnostic tools, vaccines and other treatments that we have today are the result of international collaboration.

“By working alongside the COVID-19 Host Genetics Initiative we have been able to deepen the understanding of COVID-19 pathology, paving the way for novel treatments. Further studies are now required to carefully validate individual gene candidates.”

Prioritisation of candidate genes underpinning COVID-19 host genetic traits based on high-resolution 3D chromosomal topology’ was published in Frontiers in Genetics on 25 October 2021.