Skip to main content

Transforming the understanding
and treatment of mental illnesses.

Celebrating 75 Years! Learn More >>

 Archived Content

The National Institute of Mental Health archives materials that are over 4 years old and no longer being updated. The content on this page is provided for historical reference purposes only and may not reflect current knowledge or information.

PsychENCODE - the molecular architecture of the brain


[intro music]

Mental disorders have a genetic underpinning. That means that there are some genetic factors, risk factor -- you know, variation of genes -- that actually increase the risk. Most of them don't make you ill, but they increase the risk that you may develop a mental illness. What we didn't know is what happens in the brain. We knew that the brain is much more complex than we anticipated. There are many more cell types. There are different areas of the brain that likely have different functions. And some areas are more likely to be involved in the expression of a mental disorder than others. So some years ago, we decided to start this program that we all PsychENCODE. Let's look at the brain specifically. Let's see if we can link genetic signals, genetic regulation to molecules in particular regions of the brain and particular cell types and let's see if we can associate that with disease. And guess what? The PsychENCODE project came through. And indeed, we are now seeing the first signs of molecular perturbations. We're at the beginning....I cannot overstate how early we are. But I can confidently say for the first time that we have a beginning of an understanding of the biology, the molecular pathophysiology of mental disorders -- schizophrenia, bipolar and autism spectrum disorder.  What we also learned from this first pass of this initiative is how development – how important developmental timelines are for brain development and risk for mental disorders. We actually see perturbations in gene expression networks that can be linked to specific disease, for example, autism. So with the PsychENCODE project, we're actually able to identify several hundred new risk genes for mental disorders. Schizophrenia in particular. And they were actually able to see, to identify human specific genes that are expressed across development that are risk genes for schizophrenia. There are specific genes that are unique to humans. And that these unique genes are involved in the pathogenesis of this mental disorder.

This needed concerted effort. many investigators had to come together and had to do this collectively. And that's where they could get the number of brains and samples to make scientific inferences. And that's why we founded this collaborative consortium for them to share in real time the data that they generate.  They have provided some biological insight into what could be the critical time windows during development of the brain where these genes could influence the disease process. There's no single gene that contributes. And its polygenic nature is also seen at the transcriptome level. And the regulation of this transcriptome is globally impaired. So the RNA, or the gene expression, changes on a global scale. They developed these computational models to predict risk much better than the previous ones. So by integrating data across different levels – transcriptome, epigenome, proteome, and regulome – they are able to build much better predictive models of the disease. When you combine all this information in a computational method that learns from these different data sets, this learning model tells us the likelihood of that disease occurring when you have this particular variation at each level, in each data set. By comparing readouts – gene expression data, which is transcriptome between schizophrenia, autism, bipolar – from cross disorder analysis, they could see convergent molecular pathways. So these are distinct diseases, but there are some aspects of this where the biology is similar. there are network modules. The genes interact with each other in a way to influence the disease process. if we can find clues early on, biological clues, we can intervene early on. While we are building and generating more data, analyzing this data to find basic mechanisms, there’s an opportunity also for drug discovery.