Engineers at the University of Pennsylvania and Rice University have developed a more precise gene-editing technology aimed at improving treatments for genetic diseases, including cystic fibrosis. The research, detailed in a new paper in Molecular Therapy, describes advances that could make therapies safer and more reliable by minimizing unintended genetic changes.
Xue "Sherry" Gao, Presidential Penn Compact Associate Professor in Chemical and Biomolecular Engineering and Bioengineering at Penn Engineering, highlighted the complexity of treating cystic fibrosis. "More than a thousand different genetic mutations can cause cystic fibrosis," Gao said. "The fact that different mutations require distinct corrective tools highlights the importance of precision medicine."
Current gene-editing methods can unintentionally alter DNA near the targeted mutation, raising safety concerns due to so-called "bystander" mutations. Gao explained this challenge: "It's a bit like editing a document. We can already identify and replace a particular letter in a specific word. How do we change only that one letter without accidentally altering the letters next to it?"
Cystic fibrosis is often caused by the substitution of one nucleotide base for another in human DNA. Tyler C. Daniel, a doctoral candidate at Penn Engineering and co-first author of the study, described how such errors lead to disease: "In some cases, the letter should be a T. Instead, it's a C, which can impair or completely abolish the function of the gene, leading to disease."
While existing editors can change these letters—including tools developed by this research team in 2020—problems arise when multiple cytosines are clustered together within short DNA sequences. About three-quarters of known disease-causing mutations addressable by these editors involve such clusters.
To address this issue, researchers modified both the physical connection (the linker) between components of their editor and its interaction with DNA itself. By shortening and stiffening the linker, they limited how far the enzyme responsible for editing could reach within DNA strands. Daniel explained: "We essentially tightened the leash to ensure only our target was edited." They also weakened how strongly their editor acted on neighboring bases.
Laboratory tests using human cells showed significant reductions in unwanted edits; one variant reduced bystander mutations by over 80% while maintaining effectiveness at intended sites.
Cystic fibrosis is caused by mutations affecting salt and water movement in lung cells, resulting in mucus buildup and increased infection risk. Although recent drugs like Trikafta have improved outcomes for many patients, these medications require daily use and are expensive. For patients whose cystic fibrosis results from single-letter DNA changes—and who may not benefit from current drugs—the new base-pair editors could offer an alternative if off-target effects are minimized.
Gang Bao, Foyt Family Professor of Bioengineering at Rice University and co-senior author of the study, commented on their approach: "We were able to introduce specific cystic-fibrosis mutations into human epithelial cells relevant to the disease, generating cell models that will improve our understanding," he said. "We were also able to reverse those mutations and show improved cellular functions using the same editor, demonstrating the level of pinpoint gene-editing control this technology now offers and the potential of base-pair editors to treat the disease."
Although still at an early stage before clinical trials begin, experiments showed that unintended edits dropped from up to 60% down to less than 1% at certain cystic fibrosis-related sites while preserving desired changes.
Bao emphasized future possibilities: "The more precise we can make these tools," he said, "the greater their potential to change how we treat genetic disease with a high level of efficacy and safety."
Beyond cystic fibrosis, this refined editor could help scientists investigate other diseases caused by single-letter DNA changes by enabling them to model rare variants more accurately in laboratory settings rather than relying solely on large-scale clinical trials.
Gao noted: "The ability to precisely model disease-causing mutations gives us a much clearer window into how those mutations behave, including how they might respond to different therapies," she said. "That kind of insight is essential for moving toward more personalized approaches to treating genetic disease."
