Multidisciplinary Team from Gladstone Institutes and UC San Francisco SAN FRANCISCO—May 24, 2024— In a scientific feat that broadens our knowledge of genetic changes that shape brain development or lead to psychiatric disorders, a team of researchers combined high-throughput experiments and machine learning to analyze more than 100,000 sequences in human brain cells—and identify over 150 variants that likely cause disease.
The study, from scientists at Gladstone Institutes and University of California, San Francisco (UCSF), establishes a comprehensive catalog of genetic sequences involved in brain development and opens the door to new diagnostics or treatments for neurological conditions such as schizophrenia and autism spectrum disorder. Findings appear in the journal Science.
“We collected a massive amount of data from sequences in noncoding regions of DNA that were already suspected to play a big role in brain development or disease,” says Senior Investigator Katie Pollard, PhD, who also serves as director of the Gladstone Institute for Data Science and Biotechnology. “We were able to functionally test more than 100,000 of them to find out whether they affect gene activity, and then pinpoint sequence changes that could alter their activity in disease.”
Pollard co-led the sweeping study with Nadav Ahituv, PhD, professor in the Department of Bioengineering and Therapeutic Sciences at UCSF and director of the UCSF Institute for Human Genetics. Much of the experimental work on brain tissue was led by Tomasz Nowakowski, PhD, associate professor of neurological surgery in the UCSF Department of Medicine.
In all, the team found 164 variants associated with psychiatric disorders and 46,802 sequences with enhancer activity in developing neurons, meaning they control the function of a given gene.
These “enhancers” could be leveraged to treat psychiatric diseases in which one copy of a gene is not fully functional, Ahituv says: “Hundreds of diseases result from one gene not working properly, and it may be possible to take advantage of these enhancers to make them do more.”
Organoids and Machine Learning Take the Spotlight
Beyond identifying enhancers and disease-linked sequences, the study holds significance in two other key areas.
First, the scientists repeated parts of their experiment using a […]
Scientists leverage machine learning to decode gene regulation in the developing human brain
