CRISPR single base-editing: in silico predictions to variant clonal cell lines

Area of Science:

• CRISPR gene editing
• Molecular biology
• Bioinformatics

Background:

• CRISPR base editing technologies, including cytidine (CBEs) and adenine base editors (ABEs), enable precise nucleotide changes.
• Predicting the efficacy of specific base editor and sgRNA combinations remains a challenge.
• The TP53 tumor suppressor gene is frequently mutated in various cancers, including ovarian cancer.

Purpose of the Study:

• To develop and validate a computational tool (BE-Hive) for predicting and optimizing CRISPR base editing strategies.
• To engineer specific TP53 mutations found in ovarian cancer using base editing.
• To create streamlined plasmid constructs for efficient base editing experiments.

Main Methods:

• Utilized the BE-Hive machine learning algorithm to predict optimal sgRNA and base editor pairings for targeting TP53 mutations.
• Developed an automated ranking system for sgRNA selection based on PAM compatibility, bystander edits, efficiency, and target change.
• Generated single-plasmid constructs encoding ABE or CBE, sgRNA backbone, and EGFP reporter for simplified transfection.
• Engineered specific p53 mutants (Y220C, R282W, R248Q) in wild-type p53 cells.

Main Results:

• BE-Hive successfully predicted effective base editing strategies for TP53 mutations.
• The developed ranking system facilitated the selection of high-quality sgRNAs.
• Engineered p53 mutants Y220C, R282W, and R248Q exhibited impaired transactivation of p53 target genes, mirroring endogenous mutations.
• Novel single-plasmid constructs simplified the base editing workflow.

Conclusions:

• BE-Hive and the associated ranking system provide a powerful approach for designing efficient CRISPR base editing experiments.
• This strategy enables the precise engineering of cancer-associated mutations, facilitating functional studies.
• The developed tools and constructs accelerate research in base editing and cancer genomics.