A Toolkit for Analysis of Gene Editing and Off-Target Effects of Engineered Nucleases

The targeted modification of genomes by engineered nucleases has applications ranging from improving industrial production to creating new cell and animal models to treating human disease.  Over 2500 diseases have been traced to mutations in single genes, and a growing alternative to viral gene therapy is the in situ correction of the underlying defect in the genome.  While proof-of-concept studies to correct different diseases have been performed by many laboratories, obstacles such as increasing the correction efficiency and accurately measuring safety profiles are impeding transfer to clinical settings.

This work provided new methods for analysis of engineered nucleases that can be performed without specialized laboratory equipment or training.  The objectives achieved in this work were: 1) to develop bioinformatic search algorithms that predict nuclease off-target activity, 2) to perform extensive off-target analysis of many nucleases to generate a larger training dataset and improve the algorithmic models of off-target activity through machine learning techniques, 3) to develop a highly sensitive method to measure targeted DNA modifications.

Understanding off-target activity—when nucleases cut locations in the genome other than their intended target site—and improving the efficiency of creating the desired DNA modifications in primary cell types are critical to moving engineered nucleases forward as clinical gene therapy treatments, but currently only a few laboratories have the expertise to utilize the available methods.  This work provided the field with readily usable tools that any lab can employ to investigate the application of engineered nucleases as treatments for their disease of interest.  Additionally, to demonstrate the utility of these tools, they were applied in this work to engineered nucleases for the treatment of sickle cell anemia.