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

Samuel I Gould; Alexandra N Wuest; Kexin Dong; Grace A Johnson; Alvin Hsu; Varun K Narendra; Ondine Atwa; Stuart S Levine; David R Liu; Francisco J Sánchez Rivera

doi:10.1038/s41587-024-02172-9

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

Nat Biotechnol. 2024 Mar 12. doi: 10.1038/s41587-024-02172-9. Online ahead of print.

Authors

Samuel I Gould^{1

2}, Alexandra N Wuest^{1

2}, Kexin Dong^{2

3}, Grace A Johnson^{1

2}, Alvin Hsu^{4

5

6}, Varun K Narendra⁷, Ondine Atwa^{1

2}, Stuart S Levine^{1

2}, David R Liu^{4

5

6}, Francisco J Sánchez Rivera^{8

9}

Affiliations

¹ Department of Biology, Massachusetts Institute of Technology, Cambridge, MA, USA.
² David H. Koch Institute for Integrative Cancer Research, Massachusetts Institute of Technology, Cambridge, MA, USA.
³ University of Chinese Academy of Sciences, Beijing, China.
⁴ Merkin Institute of Transformative Technologies in Healthcare, Broad Institute of MIT and Harvard, Cambridge, MA, USA.
⁵ Department of Chemistry and Chemical Biology, Harvard University, Cambridge, MA, USA.
⁶ Howard Hughes Medical Institute, Harvard University, Cambridge, MA, USA.
⁷ Cancer Biology and Genetics Program, Memorial Sloan Kettering Cancer Center, New York, NY, USA.
⁸ Department of Biology, Massachusetts Institute of Technology, Cambridge, MA, USA. fsr@mit.edu.
⁹ David H. Koch Institute for Integrative Cancer Research, Massachusetts Institute of Technology, Cambridge, MA, USA. fsr@mit.edu.

PMID: 38472508
DOI: 10.1038/s41587-024-02172-9

Abstract

Tumor genomes often harbor a complex spectrum of single nucleotide alterations and chromosomal rearrangements that can perturb protein function. Prime editing has been applied to install and evaluate genetic variants, but previous approaches have been limited by the variable efficiency of prime editing guide RNAs. Here we present a high-throughput prime editing sensor strategy that couples prime editing guide RNAs with synthetic versions of their cognate target sites to quantitatively assess the functional impact of endogenous genetic variants. We screen over 1,000 endogenous cancer-associated variants of TP53-the most frequently mutated gene in cancer-to identify alleles that impact p53 function in mechanistically diverse ways. We find that certain endogenous TP53 variants, particularly those in the p53 oligomerization domain, display opposite phenotypes in exogenous overexpression systems. Our results emphasize the physiological importance of gene dosage in shaping native protein stoichiometry and protein-protein interactions, and establish a framework for studying genetic variants in their endogenous sequence context at scale.

Abstract

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