Research News

Schizophrenia genetics are notoriously complicated, especially when it comes to asking how DNA mutations actually cause disease. Cleveland Clinic scientists are developing computational analysis, artificial intelligence and imaging techniques to make their research less of a picture and more of a movie – with free, behind-the-scenes commentary. The technology lets them watch how schizophrenia-associated mutations affect DNA in living neurons, in real time. 

The project, led by Ming Hu, PhD, and Liangqi "Frank" Xie, PhD, has received nearly $4 million in funding from the National Institute of Mental Health.

Is schizophrenia genetic? 

Schizophrenia is genetic; it runs in families. Scientists have found hundreds of DNA mutations, or variants, linked to schizophrenia risk. However, progress in researching these variants, causal genes and the underlying mechanisms has been slow. 

Most schizophrenia risk variants aren't actually found in genes themselves; they're in regulatory regions. Regulatory regions in our DNA turn other genes on or off and adjust the intensity of the genes' expression. 

"For most of the schizophrenia risk-associated variants we know about, we haven't determined how those mutations disrupt the regulation of their target genes," Dr. Hu says.  

Reading the roadmap of genetic architecture  

To learn what genes a regulatory region regulates, scientists isolate and sequence DNA strands that physically touch each other. This approach has three main limits: 

1. Scientists must fix cells and freeze the DNA in place, so they can only analyze one snapshot in time. But DNA in the nucleus constantly reshapes itself into new loops to regulate different genes over time. 
2. Our DNA is over six feet long – it needs to coil and fold in on itself to fit in our cells’ nuclei, which are 10,000x smaller. There are thousands of potential shapes our genomes can fold into, and billions of potential ways genes can touch each other. 

Dr. Hu is an expert in making better technologies to predict 3D DNA architecture. His team pioneered a software called SnapHiC, a computer-based method that both predicts DNA touchpoints in single cells and highlights the most promising sets of genes and regulatory regions. 

Dr. Hu’s lab is building on this technology to develop new statistical and AI methods that identify complex DNA interactions with even higher speed and accuracy.

Watching gene regulation in live cells in real time 

Dr. Hu approached Dr. Xie to help confirm what his computational models had predicted. The Xie Lab specializes in making live-cell imaging technologies that capture DNA interactions in live cells in real time.  

“Seeing is believing,” Dr. Hu says. “If we can keep cells alive in their native state, we can better measure the molecular dynamics that are relevant to disease.” 

Here's how Dr. Xie's technique works: 

• The lab adds a protein called a “fluorescent marker,” which lights up under a microscope, to the regulatory region they want to study. 

• Different fluorescent markers are added to areas that might touch the regulatory region.

• Each combination of fluorescent markers generates a different-colored light when they touch.

• The newly generated color is evidence that a specific gene is touching, and being controlled by, the regulatory region. 

Every time the lights change, researchers can infer how the DNA changed shape and what new genes the regulatory region controls. 

"Our technology is like upgrading from reading the alt-text of a screenshot to reading the full script of a movie," Dr Hu says. "Frank's technology lets us watch the most important scenes in IMAX." 

Drs. Hu and Xie have combined their techniques to see how different schizophrenia-associated mutations regulate neuronal gene expression in live neurons in real time. Dr. Hu predicts which genes are important with his computational algorithms, and Dr. Xie watches those genes under the microscope. 

"Schizophrenia is only one application of our technology, but we believe this approach can be applied to other complex human diseases," Dr. Hu says. "If we understand the mechanisms behind these types of genetic variants, future technologies may be able to restore those pathways and provide entirely new treatments. " 

About this study 

In addition to Drs. Hu (contact PI) and Xie (MPI), this research team includes MPI Dr. Yun Li, PhD, University of North Carolina at Chapel Hill (UNC); and co-investigators Yin Shen, PhD, University of California, San Francisco; Bing Ren, PhD, New York Genome Center, Columbia University; and Huaxiu Yao, PhD, UNC.