High-throughput evaluation of genetic variants with prime editing sensor libraries.

Area of Science:

• Genomics
• Molecular Biology
• Cancer Research

Background:

• Tumor genomes contain genetic alterations that affect protein function.
• Prime editing is a tool for studying genetic variants, but its efficiency can vary.
• Evaluating the functional impact of endogenous cancer variants requires robust methods.

Purpose of the Study:

• To develop a high-throughput prime editing sensor strategy to assess the functional impact of endogenous genetic variants.
• To screen over 1,000 cancer-associated TP53 variants and identify those affecting p53 function.
• To understand the role of gene dosage in shaping protein function and interactions.

Main Methods:

• Developed a high-throughput prime editing sensor strategy.
• Coupled prime editing guide RNAs with synthetic target sites.
• Screened over 1,000 endogenous cancer-associated TP53 variants.
• Quantitatively assessed the functional impact of these variants on p53.

Main Results:

• Identified TP53 variants with mechanistically diverse impacts on p53 function.
• Found that certain TP53 variants show opposite phenotypes in different expression systems.
• Demonstrated the importance of gene dosage in native protein stoichiometry and interactions.

Conclusions:

• The developed prime editing sensor provides a framework for studying genetic variants at scale in their endogenous context.
• Gene dosage significantly influences protein function and interactions in vivo.
• Understanding endogenous variant function is crucial for cancer research.