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Helicase-assisted continuous editing for programmable mutagenesis of endogenous genomes

Affiliations
  • 1Gene Regulation Observatory, Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA.
  • 2Department of Stem Cell and Regenerative Biology, Harvard University, Cambridge, MA 02138, USA.
  • 3Systems, Synthetic, and Quantitative Biology PhD Program, Harvard University, Cambridge, MA 02138, USA.
  • 4Department of Cancer Biology, Dana-Farber Cancer Institute, Boston, MA 02215, USA.
  • 5Department of Cell Biology and Pathology, Harvard Medical School, Boston, MA 02115, USA.
  • 6Department of Chemistry and Chemical Biology, Harvard University, Cambridge, MA 02138, USA.
  • 7Cancer Program, Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA.
  • 8Cecil H. and Ida Green Center for Reproductive Biology Sciences, University of Texas Southwestern Medical Center, Dallas, TX 75390, USA.
  • 9Department of Obstetrics and Gynecology, University of Texas Southwestern Medical Center, Dallas, TX 75390, USA.
  • 10The Novo Nordisk Foundation Center for Genomic Mechanisms of Disease, Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA.

Published on:

October 10, 2024

Abstract

Deciphering the context-specific relationship between sequence and function is a major challenge in genomics. Existing tools for inducing locus-specific hypermutation and evolution in the native genome context are limited. Here we present a programmable platform for long-range, locus-specific hypermutation called helicase-assisted continuous editing (HACE). HACE leverages CRISPR-Cas9 to target a processive helicase-deaminase fusion that incurs mutations across large (>1000-base pair) genomic intervals. We applied HACE to identify mutations in mitogen-activated protein kinase kinase 1 (MEK1) that confer kinase inhibitor resistance, to dissect the impact of individual variants in splicing factor 3B subunit 1 (SF3B1)-dependent missplicing, and to evaluate noncoding variants in a stimulation-dependent immune enhancer of CD69. HACE provides a powerful tool for investigating coding and noncoding variants, uncovering combinatorial sequence-to-function relationships, and evolving new biological functions.

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