Study indicates underlying molecular sources in COVID-19 variability

Humans have different susceptibilities to SARS-CoV-2, the virus responsible for the ongoing COVID-19 pandemic, and eventually develop varying degrees of fatigue, fever, and breathing problems—which are common symptoms of the disease. Scientists are still unaware of factors that may describe this variation.

Study indicates underlying molecular sources in COVID-19 variability
Sika Zheng is an associate professor of biomedical sciences at UC Riverside. Image Credit: University of California, Riverside/Zheng laboratory.

Now, researchers from the University of California, Riverside, and the University of Southern California may have an answer to this puzzle.

For the first time, the researchers demonstrated that there might be underlying molecular sources in the observed COVID-19 variability. The study, published in the Informatics in Medicine Unlocked, could lead to the development of effective therapeutic and prophylactic strategies against the COVID-19 disease.

Based on biomarkers and molecular profiles of individuals, one would hope to develop better medical tests to accommodate these variations in monitoring virus transmission and disease pathology, which helps guide mitigation and treatment options.”

Sika Zheng, Study Lead, School of Medicine, University of California, Riverside

Zheng is also an associate professor of biomedical sciences at UC Riverside School of Medicine,

The human host molecules are hijacked by the SARS-CoV-2 virus for fusion and replication. This allows the virus to attack the human cellular functions. The term “SARS-CoV-2 host genes” is used to collectively refer to these human host molecules.

Using large-scale genomics, proteomics, and transcriptomics, the team systematically examined the expression, variations, and sex- and age-dependency of the SARS-CoV-2 host genes in the human population.

Initially, the researchers found that the similarity of host gene expression is usually associated with tissue vulnerability to the infection caused by SARS-CoV-2. Among the six most inconsistently expressed genes in the population, the team identified ACE2, CLEC4M, and CLEC4G, which are known to communicate with the spike protein of the SARS-CoV-2 virus.

When these genes are increasingly expressed, they may increase the chance of being infected and of developing serious symptoms. PKP2 and SLC27A2 are other variable genes and both are known to block the replication of viruses; PTGS2 is yet another gene that mediates the response to fever. In addition, the authors detected genetic variants related to the variable expression of these genes.

Zheng believes that the expression profiles of these marker genes may allow scientists to better categorize the risk groups.

More comprehensive risk assessment can better guide the early stage of vaccine distribution. Tests can also be developed to include these molecular markers to better monitor disease progression. They can also be used to stratify patients to assess and ultimately enhance treatment effectiveness.”

Sika Zheng, Study Lead, School of Medicine, University of California, Riverside

Apart from detecting the most variable SARS-CoV-2 host genes, the study results indicate that genetic and multiple biological factors are implicated in the population variation in SARS-CoV-2 infection and the severity of symptoms.

Of course, these will need confirmation with more data. But the results indicate a potential value of a large scale eQTL project to profile genotypes and transcriptome of COVID-19 patients.”

Sika Zheng, Study Lead, School of Medicine, University of California, Riverside

The team has planned to further examine large-scale genotypes as well as transcriptome data of COVID-19 patients when made available and to improve the outcomes for greater association and precision.

Source:
Journal reference:

Chena, L & Zheng, S (2020) Understand variability of COVID-19 through population and tissue variations in expression of SARS-CoV-2 host genes. Informatics in Medicine Unlocked. doi.org/10.1016/j.imu.2020.100443.

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