Medicines

Study investigates the links between COVID-19 severity, gene expression in immune cells

New research shows the genetic differences that are linked to COVID-19 cases that are severe can affect our immune cells.

The study, conducted by scientists at La Jolla Institute for Immunology (LJI) is the first to provide a comprehensive examination of the links between COVID-19 severity and gene expression in a variety of types of immune cells. This research could help guide the development of new COVID-19 therapies to improve the function of immune cells.

The researchers found that a gene found in non-classical monoocytes, which is a cell type that is part of the body’s “first responder” group of innate immune cells, could be a possible target for COVID-19 treatments.

“This study highlights the potential of human genetics to reveal novel pathways linked to disease,” says LJI Professor Pandurangan Vijayanand, M.D., Ph.D., senior author of the Nature Communications study.

Many genetic variations, known as polymorphisms, have been identified by the science community. They are called “severe COVID-19 risk variants”. These genetic variants can be related to gene expression and may influence the severity of the case. Researchers haven’t been able to determine which immune cells were most affected by these risk factors.

In the study, Vijayanand and his colleagues combined the genetic information of patients from the COVID-19 Host Genetic Initiative and LJI’s open-access Database of Immune Cell Epigenomes (DICE) to define the genes and the cell types that are affected by these risk variants. The team examined 13 subtypes of the body’s key immune and anti-virus cells: T cells B cells monocytes, NK cells and.

There are a variety of immune cell types, and all contribute small functions to the global picture. We need to examine each immune cell type individually to understand how the immune system works to react to COVID.”

Benjamin Schmiedel, Ph.D. is the primary author of the study as well as an instructor at LJI.

The researchers found several significant connections between genetic variants and genes. One of them was a risk variant that affected 12 of the 13 types of cells studied. This severe COVID-19-risk variant in chromosome 21 was associated with the absence of the receptor on cells known as IFNAR2. This receptor is part a signaling pathway that alerts your immune system to infection. This new association could explain why some people are unable to create a strong immune response to SARS-2.

A risk variant on chromosome 12 showed the most powerful impact in monocytes not classified as classical, a type of immune cell that is innate and patrols the body and emits signaling molecules to alert other immune cells to threats. The risk variant led monocytes that are not classified as classical to decrease the expression of a gene known as OAS1. The absence of OAS1 expression could hobble the body’s defenses by reducing the expression of a family of proteins that typically degrade viral RNA and activates the immune system’s antiviral responses.

Schmiedel says that non-classical monocytes “are extremely rare and understudied type of cells.” They comprise only 2percent of the immune cells.

Schmiedel is hoping to conduct more pre-clinical assessments to determine the role of these genes in COVID-19 pathogenesis. “That we can pinpoint these genetic mechanisms is a big step towards the future,” he says. “We can combine the information we have with our research on immune cell function to discover potential therapeutic targets.

Journal reference:

Schmiedel, B.J., et al. (2021) COVID-19 genetic risk variants are linked to expression of multiple genes in various immune cell types. Nature Communications. doi.org/10.1038/s41467-021-26888-3.

Content Source: https://www.news-medical.net/news/20211121/Study-explores-connections-between-COVID-19-severity-and-gene-expression-in-immune-cells.aspx

Gemma Wilson

Gemma is a journalism graduate with keen interest in covering business news – specifically startups. She has as a keen eye for technologies and has predicted quite a few successful startups over the last couple of years.

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