Researchers at Rice University have developed a new approach that improves the avoidance of gene-editing errors in base editing strategies.
Gene editing
The development of genome editing tools with CRISPR systems has revolutionised the biomedical field. Not only has it advanced research, but it also holds great potential for the treatment of genetic diseases. Experts have developed several precise genome-editing tools based on CRISPR-Cas9, such as homology-directed repair (HDR) based systems. This method, however, requires double-stranded DNA breaks (DSBs) and can result in unpredictable editing outcomes.
Alternatively, base editors can enable more precise modifications without generating DSBs. Nonetheless, one of the major challenges in base editing is the discrimination of multiple identical bases within the activity window of 4-10 nucleotides. The target base and other bystander bases are subsequently modified, which negatively impacts the precision outcomes.
Engineering of base editors
Previous work has involved the introduction of beneficial mutations in deaminase to advance base editors. For example, an engineered APOBEC3A cytosine base editor with the mutation N57G maintained high editing activity at the target C in the TCR motif with greatly reduced activity against bystanders. Despite its successes, there is no current framework to guide the design of mutations that can yield high editing activity at the target base and low activity at bystanders.
In a recent study, published in Nature Communications, researchers sought to eliminate bystander edits. To do this, the team developed a framework that evaluates the probabilities of editing the target base and bystanders. The new framework eliminates trial and error in the design of a library of editors, which would better target mutations that cause disease and avoid bystanders. The approach combined molecular dynamic simulations and stochastic models that pinpointed the binding energies between molecules that are needed to achieve maximum editing selectivity.
The team then applied these principles to design a series of point mutations in APOBEC3G base editors to further reduce bystander editing. These mutations were then verified experimentally and yielded varying levels of stringency on reducing bystander effects, which was consistent with the theoretical model.
Overall, this study presents a computational platform that could assist researchers in designing base editors with reduced bystander effects.
Rice chemical and biomolecular engineer, Xue Sherry Gao, said:
“Because the number of mutants could be in the thousands, it’s unrealistic for experimentalists alone to verify individual base editors.
Only this multidisciplinary approach will allow us to build a huge library of editors computationally, then narrow the numbers down to the most promising candidates for further experimental verifications. That’s what we’re working toward.”
Image credit: canva
