DNA break-mapping platform demonstrates changes to specificity of CRISPR-Cas9 gene editing

Published: 20-Oct-2023

Broken String Biosciences' INDUCE-seq platform has been used in a peer-reviewed research paper

Broken String Biosciences, a genomics company building a technology platform to drive the development of cell and gene therapies that are safer by design, has announced that its INDUCE-seq (trademarked) had been used in a peer-reviewed research paper to characterise off-target effects of CRISPR-Cas9 gene editing resulting from changes in DNA topology.

Published in Molecular Cell, the research highlights DNA topology as a key regulator to CRISPR targeting specificity that must be carefully considered during development of CRISPR-based therapies.

Since its discovery, the CRISPR-Cas9 system has enabled researchers to elicit precise DNA edits at virtually any site across the genome. However, the full potential of this system as a clinical tool has been constrained by off-target effects.

The new study was co-authored by a team of scientists, including Broken String co-founders Professor Simon Reed and Patrick van Eijk, PhD.

As part of the research, the team analysed the impact of alterations to DNA structure, in the form of negative supercoiling, on off-target effects of Cas9. Using an adapted cell-free off-target measuring approach, the team identified that negative supercoiling induced up to 10,000 genome-wide off-target events that were formed as a result of increased mismatch tolerance. INDUCE-seq confirmed these findings in gene-edited cells, demonstrating that sites of increased superhelical torsion were more susceptible to off-target induction in live cells.

Professor Simon Reed, Chief Scientific Officer, Broken String Biosciences, said: "This study demonstrates the importance of measuring off-target gene editing activity directly in the cells that are being edited. Evidently, there are factors affecting off-target activity in cells, such as superhelical torsion in the DNA structure, that cannot be predicted in silico using DNA sequence analysis alone."

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