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Genomic analysis of the gene regulatory landscape of the developing neocortex

Nerve Growth, Stanford Neurosciences Institute

The most recently evolved part of the human brain is called the neocortex. Responsible for higher order thinking, feeling and sensing, the neocortex consists of many different types of neurons, each with a specialized function. Remarkably, this vastly complex structure arises from a uniform mass of proto-neurons. Proper brain development depends on these proto-neurons differentiating themselves from one another and connecting with each other in a predictable and appropriate manner. When this process goes wrong, it can result in connectivity disorders, such as autism, intellectual disability and obsessive-compulsive disorder. Autism alone affects 1 in 110 U.S. children, but the genetic basis of autism and other connectivity disorders is still unknown. I seek to understand how our genes encode the instructions for neurons in the neocortex to properly arise during normal brain development. This knowledge will then allow us to understand how genetic mutations perturb development leading to human disease.

Key to proper neuronal development is a class of master regulatory proteins collectively called transcription factors or TFs. Some neurons manufacture more of a particular TF than others. Variable TF levels therefore help distinguish different types of neurons. TFs work by reading and interpreting the instructions for making each neuronal subtype. These instructions are literally written in DNA as “the genetic code” and tell the cell which proteins to make, when to make them, and how much to make of each. The catch is that there are all sorts of TFs, but each TF subtype can only read certain sequences of DNA. TFs thereby help generate only the specific proteins needed for making each type of neuron according to the genetic instructions being read.

But what if there’s a typo, or a mutation, in the genetic code? Often, mutations have no functional consequences, and the neocortex develops just fine. But sometimes the consequences are dire. A current challenge is figuring out which mutations are benign and which are malignant, because most mutations fall in regions of DNA that do not code for protein, making them difficult to classify. I can use this to my advantage, however, given that TFs “read” these non-protein-coding instructive regions of DNA. I aim to identify the main TFs important for making each type of neuron, where they “read” DNA, and the proteins produced downstream. I will use this information to construct the genetic networks important for building a whole neocortex and identify regions of DNA that potentially harbor malignant mutations.

To do this, I have optimized a system for taking a snapshot of TF activity in subtypes of neurons at various time points during mouse brain development, generating a “Twitter feed” of neuronal development over time. Each snapshot looks like this: when a TF “reads” a bit of the genetic code, it leaves a footprint where the DNA, once tightly coiled around itself, is now exposed. These exposed bits are different for different cell types and change over time. I’ll use a technique called Next Generation Sequencing to identify all the exposed bits of DNA in each cell type as well as the unique sets of TFs and other proteins made in each cell. I’ll then use computational tools to put it all together. I’ll ask what’s different and common between each cell type as well as which bits of DNA are absolutely crucial for making various cell types. These crucial pieces of DNA will then point where to look for malignant mutations as well as where to direct novel therapeutics for treating human connectivity disorders.


Lead Researcher(s): 

Sponsors: Susan McConnell (Biology), and Gill Bejerano (Computer Science and Developmental Biology)

Funding Type: 
Postdoctoral Fellowship
Award Year: