MOBE: A Base Editor That Multitasks without Mix-ups

A single mutation in a person’s genome can be the difference between health and disease, and with base editing, researchers can recreate these mutations in a laboratory to study how genetic changes make cells go haywire. But genetic underpinnings of complex diseases involve multiple mutations, and current base editing strategies fall short.

“We think it's really important to functionally characterize specific combinations of mutations,” said Alexis Komor, a chemist at the University of California, San Diego. “We need the tools to be able to multiplex.”

Unfortunately, multiplexing isn’t as easy as throwing together multiple sets of base editors, especially if researchers want to make different types of edits at the same time: say, turning a cytosine to a thymine at one position, and an adenine to a guanine at another position.

The machinery is complex and includes both an enzyme to make the desired modification to the base and a guide RNA to point the complex to the correct site in the genome. But, when multiple base editors are in play at the same time, the guide RNA intended for one base editor can interact with the components of the other base editor, leading the wrong enzyme complex to its target site and creating the wrong type of edit at that site.

To fix this problem, Komor’s team developed four new multiplexed orthogonal base editor (MOBE) systems that allow researchers to make multiple types of edits at the same time without fear of crosstalk between the machinery for each modification.¹ The systems, published in Nature Biotechnology, open the door to building models to study complex genetic diseases driven by multiple mutations.

The researchers leveraged complementary pairs of RNA and protein molecules, called aptamers and coat proteins, that selectively bind to each other. By incorporating the RNA aptamer into the guide RNA and the coat protein into the corresponding base editor protein complex, they forced the correct pairs of guide RNA and base editor to work together and minimize incorrect pairings.

“When we thought it up, it was a very kind of simple idea, in theory,” Komor said. “Getting it to actually work and doing all the protein engineering was a Herculean effort.”

Quinn Cowan, a biochemist in Komor’s lab who led the work, tested hundreds of different configurations of the RNA and protein components until he found the optimal approach to maximize the specificity of the edits and minimize crosstalk, where one base editor makes its edit at the target position of the other editor. Typically, when combining base editors that target adenines and cytosines, the crosstalk rate is 30 percent. But with MOBE, crosstalk dropped to five percent. As many as one quarter of the cells ended up with the correct pair of edits.

“I see a lot of utility here with this platform,” said Krishanu Saha, a biomedical engineer at the University of Wisconsin-Madison, who was not involved in this study. “What's exciting about the strategy that this team developed is how modular it is.” The system can be used for many different types of base editors, and even fine-tuned further by changing the way different components are linked together. Saha also notes that this modularity could make it easier to deliver to cells than other bulkier multiplexed editors.

While some members of Komor’s team improve the performance of the base editing enzymes, others use the system to create genetic models of complex diseases by editing multiple underlying mutations into cell lines. For example, in the study, the researchers used MOBE to edit the pairs of mutations that cause Kallmann syndrome, a hormonal disorder, and anencephaly, a neural disorder, into cell lines. They could then study how these mutations affected features of the cells, such as their transcriptional profiles or morphology.

Saha is optimistic about potential applications of MOBE in his own work creating cell therapies. Certain mutations can make T cells more potent for immunotherapy, for example, and with MOBE, researchers can do large screens of many combinations of mutations to see which yields the most therapeutically promising T cells.

“Expanding the scope of disease modeling and cell engineering, to me, is super exciting,” Saha said. “It would push the application of these genome-writing tools to another level.”