New COVID-19 study says specific protein on cell surface can predict disease severity

A recent study by researchers from the University of Copenhagen revealed that examination of a specific protein on the cell surface can identify who is at risk of a major illness from the coronavirus. The research, published in the journal, EMBO Molecular Medicine, promises to provide a much-needed boost to fight against COVID-19.  

News agency ANI quoted assistant professor Rajan Gogna, the main author of the study, as saying, "Cells have a so-called fitness status, and by analysing it we could predict hospitalisation or death in COVID-19 patients, potentially making such a biomarker an earlier prediction tool, especially because it can be detected from the common nasal swap COVID-19 tests." 

Gogna went on to explain that whenever a cell's fitness status is low, it means the cell is not developing properly, either because it is old, unreliable, have a malfunctioning metabolism, or is disease-prone. The study team had observed earlier in 2021 that the fitness status of the cell is represented in proteins known as flower proteins. These flower proteins are found on the cell surface and are produced in two forms.    

Researchers explain importance of 'flower proteins' in fight against COVID-19

While talking about the two forms of the protein, Gogna said that one signals the neighbouring cells, indicating that this cell is performing well in one way or another. while the other signals the surrounding tissue that this specific cell is not functioning properly and has a low fitness rating. If a cell's fitness isn't high enough, it will be phased out and destroyed by the surrounding cells. 

The flower protein expression may reliably predict hospitalisation or death risks of COVID patients, as well as understand whether a patient will have a less severe infection or not. The researchers believe that this could be especially useful in the early stages of the COVID-19 disease. 

With an efficiency of 78.7%, this technique could predict who required hospitalisation. The forecast was 93.9% correct with COVID-19 patients who would not have a major illness, says associate professor and group leader Kyoung Jae Won, who used machine learning to examine the data. 

Further, the researchers used a post-mortem study of diseased lung tissue from deceased COVID-19 patients to identify the flower proteins' biological function in acute lung damage, which is the most common cause of death from the disease. They also conducted observational research utilising nasal swab specimens to see if protein expression could correctly predict hospitalisation or mortality. 

Rajan Gogna further revealed that the flower protein's expression of cell fitness might potentially explain why some individuals do not respond well to COVID-19 and give away to identify high-risk persons ahead of time.  This finding has the ability to save their lives by warning them to take extra precautions to protect themselves. 

Gogna further added that cell fitness is influenced by a variety of factors in human bodies and does not always change with age. Although age has an influence, the researchers have witnessed many examples in their database where patients as old as 80 have a very high lung fitness profile, which is the key location wherein cell fitness is assessed to predict COVID-19 infection results. Due to the sheer persistence of COVID-19 and growing incidence and mortality in countries around the world despite vaccinations, the researchers believe their finding is relevant and will help battle the disease in the future. 

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