A small genome editing nuclease has a big impact

WWhen a chef develops a new recipe, he methodically adds and removes individual ingredients to see how each changes the final dish. When scientists try to understand the role of genes in the body, they use a similar tactic using genome editing. Currently the most popular tool in their toolbox CRISPRwith applications ranging from cancer therapies to treatments for genetic diseases such as sickle cell anemia and β-thalassemia.¹ However, this genome editing product still has its limitations.

“It is very difficult to put the genes that code for these proteins into the viruses that are used for their delivery into the cells,” says Tautvydas Karvelisa genome biologist at Vilnius University. Even when CRISPR nucleases are delivered directly into cells, their large protein sizes pose limitations. For example, the commonly used Cas9 is about 1,400 amino acid residues long.

From a recent study published in Nature methods, Gerald Schwanka genome biologist at the University of Zurich, and his team described a small but efficient nuclease that works as well as some of the current Cas proteins, but is less than half the size.²

“It’s like a new class of tools that can be used for genome editing, not just as a principle,” says Karvelis, who was not involved in the study.

In 2021, Karvelis discovered a compact RNA-guided protein that can cut DNA: TnpB.³ Compared to other CRISPR nucleases, TnpB is much smaller, with approximately 400 amino acid residues. However, TnpB has lower editing efficiency and limited target range.

That is why Schwank wanted to improve TnpB’s performance. He and his team optimized TnpB for gene editing in mammals and engineered variants of the proteins to improve targeting. They also built a machine learning model to predict how well a guide RNA will perform for a set of target sequences, saving future users the headache of multiple trials.

When unmodified, TnpB’s editing efficiency is between zero and 20 percent, which is lower than that of the smallest CRISPR-Cas9 ortholog, CjCas9. “We asked if we could really make TnpB efficient enough to use it,” Schwank said. So the team did two things. They optimized the codon sequence of TnpB for mammalian cells and attached a small tag to the protein that guided it to the nucleus. The team observed a 4.4-fold increase in the editing efficiency of this modified nuclease, surpassing most commonly used RNA-guided endonucleases, including CjCas9. They called this variant TnpBmax.

When tested on a large library of target sites inserted into mammalian cells, TnpBmax performed remarkably, causing insertions and deletions of DNA sequences at a rate of about 70 percent. But any change in a key region of five bases upstream of the DNA target caused efficiency to drop dramatically. This short region containing the sequence 5’TTGAT 3′, called the transposon adjacent motif (TAM), is uncommon and limits where TnpBmax can work its magic.

To relax these limits, Schwank and team tested the interactions between different versions of TnpBmax and TAM variants. Replace lysine with alanine at 76^e position allowed TnpBmax to recognize TAMs that had cytosine or thymine at the second position and guanine or thymine at the third position. This sequence is four times more common in the genome than the original TAM. The changes in TnpBmax or TAM did not affect the machining efficiency.

Schwank wanted to add an extra function to the tool. He and his team built a model that can predict whether a guide RNA sequence can edit a given DNA sequence with an efficiency of at least 70 percent. “It used to be more or less a gamble. Having a TAM site meant that the site had to be targetable in principle, but you didn’t really know if there would be substantial efficiency,” said Schwank. “Now with this model it becomes a lot easier to figure out whether you can use the tool or not.”

“Having this model so that people can design their own guides will improve the usability of the system,” he said Omar Abudayyeha genome engineer at Harvard Medical School who was not involved in the study.

Finally, to put the tool to the ultimate test, the authors focused on genes in the brain and liver of mice. They observed machining efficiency of 65 percent and 75 percent, respectively. In the liver, the team edited a gene involved in cholesterol metabolism and observed a resulting drop in blood cholesterol levels in the mice.

“At the time of their discovery, there was a big question about how effective TnpBs would be as genome editing enzymes,” said Jonathan Gootenberga genome engineer at Harvard Medical School who was not involved in the study. “With this technology they are really competitive.”