Public Release: 

Columbia Team develops treatments for depression

Understanding the molecular mechanisms of resistance to antidepressants

Data Science Institute at Columbia

Major depressive disorder is a debilitating illness that affects more than 350 million people around the world. The most common treatments for depression are Selective Serotonin Reuptake Inhibitors (SSRIs), drugs such as Prozac that increase serotonin levels in some regions of the brain. About half of the patients who take the pills, however, do not respond to treatment.

This team is thus trying to understand the molecular mechanisms of such treatment resistance. Ultimately, they would like to be able to predict which people will respond to antidepressant drugs before they begin treatment, and to develop new treatments that can circumvent antidepressant resistance in the millions of people who do not respond now to antidepressants.

This is a research collaboration between the Neuroscience group at Columbia University Medical Center, where Rene Hen is an expert in basic and translational research and neuropsychiatric disorders, and the Functional Genomics group at Columbia Engineering, where Sergey Kalachikov is a genomicist with expertise in molecular biology, data analysis and statistics. Columbia's Data Science Institute supports the research with a $100,000 Seed Fund Grant.

They and other researchers have shown that an area of the brain called the hippocampal dentate gyrus plays a critical role in a person's response to antidepressants. Dentate gyrus is part of the brain that is mainly responsible for learning and new memories and one of the few areas of the brain where new neurons are born during adulthood. Recently, while studying gene activity in neurons in the dentate gyrus, the team identified specific regulatory pathways and genes associated with the lack of response to antidepressant treatment.

In particular, they found a strong association between treatment resistance and regulation of dendritic spines, small protrusions on the surface of neuronal cells in the brain that are responsible for connections between neurons. Moreover, ten of the candidate genes found by Hen and Kalachikov are among the 13 genes associated with depression identified by a genetics consortium working in collaboration with 23andMe. That correlation of genes in both studies supports the team's preliminary results.

The team is now using a combination of data science and experimental approaches to pinpoint the mechanisms underlying resistance to antidepressants. Applying computational genomics, they will integrate several types of their own data with publicly available data on antidepressant resistance, including information on gene expression, behavior, and neuronal cell morphology. Then, using mice as animal models of depression, they will validate their predictions experimentally by monitoring the effect of antidepressants on the dendritic spines in the brains of the mice. The study will reveal targets for genetic manipulations for a future research project that will include single-cell analysis to find particular neuronal types in the brain that are involved in treatment resistance.

"We feel very privileged to be able to contribute to solving this problem," says Kalachikov. "A plethora of regulatory pathways are involved, and there are difficulties in carrying out this kind of analysis at the level required for precision medicine. I hope that in a year or two we will have a good picture of what's going on in critical areas of the brain, in the dentate gyrus in particular, that prevent antidepressants from working in half the people who try them, and that we will be able to predict genetic mechanisms in the body that can be targeted by antidepressants. If we succeed, these new targets and treatments could allow millions of people to lead healthier and happier lives."

###

Disclaimer: AAAS and EurekAlert! are not responsible for the accuracy of news releases posted to EurekAlert! by contributing institutions or for the use of any information through the EurekAlert system.