Researchers at Harvard University have unveiled a groundbreaking tool called Helicase-Assisted Continuous Editing (HACE) that promises to transform therapeutic discovery and genomic research. This innovation allows for rapid mutations in specific genes while preserving the integrity of the rest of the genome, a significant advancement given the complexity of human DNA, which consists of approximately 3 billion base pairs.
HACE enables precise targeting within the genome, as explained by lead researcher Xi Dawn Chen, a student in synthetic biology. "Tools like this significantly improve our ability to work with evolution directly inside human cells," Chen stated. This method opens new pathways for creating enzymes and therapies that were previously challenging to develop.
Unlike traditional mutagenesis techniques that alter multiple genes or insert additional copies, HACE acts like a precise address for specific genomic locations. The process involves combining a helicase, an enzyme that unwinds DNA, with a gene-editing enzyme, utilizing CRISPR-Cas9 technology to guide the complex to the desired gene. The helicase unwinds the DNA and introduces mutations at the targeted site.
Demonstrating its potential, researchers identified drug resistance mutations in the MEK1 gene, a common culprit in cancer treatment failures. HACE facilitated the sequencing of mutated MEK1 genes, revealing unique mutations associated with resistance to drugs such as trametinib and selumetinib.
Furthermore, the team investigated mutations in the SF3B1 gene, which plays a crucial role in RNA splicing. HACE clarified which mutations contribute to splicing defects, a frequent issue in blood cancers. Collaborations with other laboratories allowed them to examine how changes in regulatory DNA regions influence protein production in immune cells, vital targets for cancer therapies.
According to Bradley Bernstein, the potential of HACE extends beyond current applications. He noted that the tool could facilitate extensive edits to gene regulatory sequences, especially when combined with deep learning for analysis. This could lead to innovative therapeutic options by precisely modifying these sequences to enhance gene activity and mitigate disease effects.
This research received funding from the National Institutes of Health, the Broad Institute, and the Harvard Stem Cell Institute.