Dr. Tim Reddy, Assistant Professor of Biostatistics & Bioinformatics, Duke University

What do you do if you’re being chased by a bear? Run! How do you do it? With a little help from cortisol – a hormone that regulates part of the body’s response to stress. Cortisol reduces inflammation in your joints and mobilizes glucose into your body to give your muscles energy to run.

While cortisol occurs naturally in the body, it is also the most commonly prescribed anti-inflammatory drug in the world because it reduces inflammation so effectively. However, people with systemic auto-immune diseases, like those with rheumatoid arthritis, cannot take cortisol as a treatment because of the increased risk of contracting diseases like diabetes, even though it would probably help with pain management.

As part of the Genomics of Gene Regulation Project, Tim Reddy’s lab has been collaborating with Greg Crawford’s lab to understand how the body responds to cortisol. “If we can understand how cortisol regulates inflammation versus metabolism, we can start to separate the two,” Reddy said. “And that’s a big step towards coming up with the types of drugs that can treat people with auto-immune diseases.”

There are more than 12,000 locations in the human genome that might be important for the cortisol response. The Crawford Lab completed open chromatin mapping to figure out all of the interesting regions of the genomes and how those regions change with hormones. The Reddy Lab developed the Enhancer Cluster Model to test all of those locations at once, which has never been done before. These findings are published in the Aug. 25 issue of Cell.

They discovered that some of the locations respond directly to the hormone (direct-response sites) while others (indirect-response sites) depend on having the direct-response sites nearby. Those indirect-response sites are more like to vary between different cells, which helps to explain why some cells respond to cortisol one way and other cells respond in a different way.

Think of the body like a house. The human genome is like the electrical system in the house. Several power outlets – direct-response sites – are hooked into that electrical system; appliances –indirect-response sites – are plugged into the power outlets. Household appliances only work when they are plugged in, so, as a result, the appliances in the house end up clustered around the outlets because their cords are only so long. The same happens in the genome. Indirect-response sites end up clustered around the direct-response sites.

“What we are beginning to unravel here,” Reddy said, “is that in different types of cells, the core component we have identified stays the same, but the core component interacts with other regions of the genome, and it’s those interactions that modify the body’s response and determine the cortisol response.”

In other words, if you plug a toaster into the power outlet in your home, it will do something very different than a hair dryer plugged into that same outlet, even though the wiring in the outlet is the same.

“Now that we are beginning to understand how these clusters arise and work together to regulate genes, we can start to think about coming up with ways to make the stress response more specific” says Chris Vockley, lead author on the study and a graduate student Duke’s Cell Biology department.

One direction the team will focus on is seeing if they can use this new knowledge to rationally redesign the cortisol response. With the help of the Gersbach lab, they are using genome editing strategies to modify different regions of the genome. They will use the Enhancer Cluster Model to find the regions they want to modify if they want to direct an immune response, as well as the regions they want to modify if they want a metabolic response.

“Up until now, if we didn’t have this model, we would be guessing,” Reddy said. “This model is giving us some guideposts on how we can start to modify that response and actually come up with these more specific therapies.”

Story originally published on the Duke Center for Genomic and Computational Biology website on August 25, 2016. View original

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