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Transcriptomic Data May Provide New Way to Classify, Compare Brain Disorders

NEW YORK — Researchers have examined the transcriptomic relationships between genes implicated in various brain disorders, which could provide a new approach to classify and compare brain diseases based on their gene expression signatures.

"This new way of classifying diseases based on gene expression data is sometimes different from the way brain diseases are classified based on their symptoms," co-corresponding author Michael Hawrylycz, an investigator at the Allen Institute for Brain Science, said.

"Based on these new transcriptomic associations of diseases, maybe we can look at a new way of developing treatments for brain diseases," he added.

For this study, published in PLOS Biology on Thursday, Hawrylycz and Yashar Zeighami from McGill University, along with their colleagues, examined gene expression data from the Allen Human Brain Atlas that was derived from six neurotypical brain samples across 104 brain structures. They in particular examined the expression of genes linked to 40 common human brain diseases, including neurodegenerative, psychiatric, and substance abuse disorders.

For each disease, the researchers used data from the DisGeNET database to identify associated genes. They then examined the expression of those genes within the brain samples from six neurotypical individuals.

Based on their shared gene expression across different brain regions, the researchers found that many diseases with varied symptoms in fact clustered together.

For example, language development disorders, obsessive-compulsive disorder, and temporal lobe epilepsy — which are phenotypically diverse conditions — fell in the same gene expression-based group. "Gene expression is pooling them in a way which is different from conventional phenotypic manifestation," Hawrylycz said.

The researchers further focused on 24 diseases that affect the cerebral cortex. By studying single-nucleus gene expression data from different cell types such as inhibitory, excitatory and non-neuronal types in the middle temporal gyrus, they homed in on the cell types implicated in the various diseases. Genes from psychiatric, movement, and substance abuse disorders showed the highest cell type specificity compared to tumors, developmental disorders, and neurodegenerative diseases. 

A striking finding was the increased variability of excitatory cell types in psychiatric diseases.

"While there have been several lines of evidence that inhibitory cell types are impaired in the psychiatric disorders depression, bipolar disorder, and schizophrenia, results here indicate that excitatory pathways may be equally important," the authors wrote.

Finally, the researchers compared the results from human samples with single-cell mouse gene expression data to tease out human-specific cell type differences. While the same cell types were implicated in various diseases for both humans and rats, the genetic signatures differed, they found.

"Through mapping of homologous cell types between mouse and human, most disease risk genes are found to act in common cell types, while having species-specific expression in those types and preserving similar phenotypic classification within species," the authors wrote.

As a next step, the researchers would like to study the effect of each gene implicated in a particular disease. "Some genes may have a dramatic effect, some may have a lesser effect, but we don't have the perfect information on that right now," Hawrylycz said, adding that his group is already working on profiling single-cell data in Alzheimer's disease, in addition to single genes.