Zhengqing Ouyang Awarded $2 Million NIH Grant to Create 3D Depiction of RNA Structures

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Zhengqing Ouyang, associate professor of biostatistics, has received a five-year, $2 million grant from the National Institutes of Health (NIH) to develop 3D models of all types of RNA using sophisticated large-data technologies, with the aim of understanding the relationship between the structure of RNA molecules and human health and disease.

The ultimate goal is developing statistical and computational methodologies to reconstruct RNA 3D structures in living cells to identify potential targets for diagnosing and treating diseases that result from RNA misfolding.

DNA, the human body’s genetic fingerprint, is transcribed into RNA, which in turn provides proteins with the information that different types of cells need to function. The dynamic structure of RNA determines its function, but little is known about the folding of RNA in the 3D space.

“I have been intrigued by the macromolecule RNA as a long-term interest of my research,” says Ouyang. “RNA does not merely transfer genetic information from DNA to protein. It’s important in various biological processes and ultimately affects human health.”

But uncovering the complex structure of RNA is very challenging. “There have been many approaches, and they are very tedious, traditionally working on one RNA at a time,” Ouyang says.

In the Ouyang Lab, researchers will study the structures of thousands of different RNA molecules simultaneously. “We want to generate 3D models for all kinds of RNAs,” Ouyang says.

He likens a human cell to a factory filled with machinery. “Each part of the machinery needs to be carefully designed with a certain structure. These different structures work together to make the whole machinery operate properly.”

Similarly, he says, “The structure of RNA is crucial in making the cell function properly. There are numerous molecular interactions that are mediated by RNA structure. This changes dynamically from cell state to cell state and cell type to cell type. And the misfolding of RNA may result in dysregulation of the cell, and that ultimately leads to disease.”

His team will use their newly developed statistical and computational methods to analyze high-throughput sequencing data from living cells that can provide information on the spatial interaction of RNA. 

“This will give us a hint about how the RNAs are interacting with each other, even though their DNA counterparts are far away in the genome. So, we can study them all at once—their shapes and interactions in various categories, whether it’s a coding or non-coding RNA,” Ouyang says.

These sophisticated computational machine-learning techniques will enable the researchers to create models of all RNA 3D structures in living cells—the entire “3D structurome.” 

“Once we have the 3D models of all kinds of RNAs and in all different cell types, then we can study how the RNA shapes change dynamically across cell types,” Ouyang says. “This global RNA picture will provide insights between RNA structure and function across cell types, and then ultimately point to the mechanism of human diseases.”