Scientists Map Networks of Disease-Associated Immune Genes

The New Immune Networks Have Implications for Developing Immunotherapies and Understanding Autoimmune Diseases

By Sarah C.P. Williams | | July 11, 2022

Alex Marson, MD, PhD

Photo of Alex Marson, MD, PhD, by Steve Babuljak

Using new technologies to study thousands of genes simultaneously within immune cells, researchers at Gladstone Institutes, UC San Francisco (UCSF), and Stanford School of Medicine have created the most detailed map yet of how complex networks of genes function together. The new insights into how these genes relate to each other shed light on both the basic drivers of immune cell function and on immune diseases.

“These results help us flesh out a systematic network map that can serve as an instruction manual for how human immune cells function and how we can engineer them for our benefit,” says Alex Marson, MD, PhD, director of the Gladstone-UCSF Institute of Genomic Immunology and co-senior author of the new study, published in Nature Genetics.

The study, conducted in collaboration with Jonathan Pritchard, PhD, professor of genetics and of biology at Stanford School of Medicine, is also critical to better understand how variations in a person’s genes are connected to their risk of autoimmune disease.

Immune Insights from CRISPR

Researchers know that when the immune system’s T cells—white blood cells that can fight infections and cancer—become activated, levels of thousands of proteins within the cells change. They also know that many of the proteins are interconnected such that changes in the level of one protein can cause changes in the level of another.

Scientists represent these connections among proteins and genes as networks that look somewhat like a subway map. Mapping these networks is important because they can help explain why mutations in two different immune genes might lead to the same disease, or how a drug might have an impact on many immune proteins at once.

In the past, scientists have mapped out a part of these networks by removing the gene for each protein, one at a time, and studying the impact on other genes and proteins, as well as on immune cells’ overall function. But this kind of “downstream” approach only reveals half the picture.