Our doctoral researcher, Masako Kauffmann, and our PI, Toni Cathomen, were involved in a research article recently published in the Nature Methods journal titled: “Effective genome editing with an enhanced ISDra2 TnpB system and deep learning-predicted ωRNAs”.
In this groundbreaking work, the research team optimized the design of a compact endonuclease (ISDra2 TnpB), originally found in the bacterium Deinococcus radiodurans, in order to improve its gene editing activity in mammalian cells, effectively leading to an average 4.4-fold increase.
Furthermore, one of the big limitations that this endonuclease has is that, in order to start the process of gene editing, it requires a specific strand of DNA (5′-TTGAT) for target recognition and cleavage. In this work, the team managed to succesfully develop mutated variants that recognize alternative target-adjacent motifs (TAMs), effectively expanding the targeting range of ISDra2 TnpB.
Another aspect of the research was the development of a deep learning model termed “TnpB editing efficiency predictor” (TEEP; https://www.tnpb.app), to predict the guiding activity of the strands of RNA within the endonuclease TnpB in charge of DNA targeting, also known as ωRNAs. The model was trained on an extensive dataset of 10,211 target sites and was able predict the editing efficiency of ωRNAs with high performance (correlation coefficient r > 0.8).
Employing TEEP, the team ultimately achieved editing efficiencies of up to 75.3% and 65.9% in the mouse liver and brain, respectively.
Check out the report here.
Institute of Medical Biometry and Statistics,
Faculty of Medicine and Medical Center –
University of Freiburg
