Precise, predictable genome integrations by deep-learning-assisted design of microhomology-based templates
- Author
- Thomas Naert (UGent) , Taiyo Yamamoto, Shuting Han, Ruth Röck, Melanie Horn, Philipp Bethge, Nikita Vladimirov, Fabian F. Voigt, Joana Figueiro-Silva, Ruxandra Bachmann-Gagescu, Kris Vleminckx (UGent) , Fritjof Helmchen and Soeren S. Lienkamp
- Organization
- Project
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- Morphological, molecular and functional insights into the cardiovascular complications of autosomal dominant polycystic kidney disease
- Precision medicine in inherited blindness using integrated omics in human and animal models
- Morphological, molecular and functional insights into the cardiovascular complications of autosomal dominant polycystic kidney disease
- Abstract
- Precise CRISPR-based DNA integration and editing remain challenging, largely because of insufficient control of the repair process. We find that repair at the genome-cargo interface is predictable by deep learning models and adheres to sequence-context-specific rules. On the basis of in silico predictions, we devised a strategy of base-pair tandem repeat repair arms matching microhomologies at double-strand breaks. These repeat homology arms promote frame-retentive cassette integration and reduce deletions both at the target site and within the transgene. We demonstrate precise integrations at 32 loci in HEK293T cells. Germline-transmissible transgene integration and endogenous protein tagging in Xenopus and adult mouse brains demonstrated precise integration during early embryonic cleavage and in nondividing, differentiated cells. Optimized repair arms also facilitated small edits for scarless single-nucleotide or double-nucleotide changes using oligonucleotide templates in vitro and in vivo. We provide the design tool Pythia to facilitate precise genomic integration and editing for experimental and therapeutic purposes for a wide range of target cell types and applications.
- Keywords
- DNA-REPAIR, KNOCK-IN, CRISPR-CAS9, GENE, TRANSGENESIS, ZEBRAFISH, PLATFORM, OUTCOMES, TOOL
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Citation
Please use this url to cite or link to this publication: http://hdl.handle.net/1854/LU-01KA1DEQKKSXEBEE7YQGTECV9Z
- MLA
- Naert, Thomas, et al. “Precise, Predictable Genome Integrations by Deep-Learning-Assisted Design of Microhomology-Based Templates.” NATURE BIOTECHNOLOGY, 2025, doi:10.1038/s41587-025-02771-0.
- APA
- Naert, T., Yamamoto, T., Han, S., Röck, R., Horn, M., Bethge, P., … Lienkamp, S. S. (2025). Precise, predictable genome integrations by deep-learning-assisted design of microhomology-based templates. NATURE BIOTECHNOLOGY. https://doi.org/10.1038/s41587-025-02771-0
- Chicago author-date
- Naert, Thomas, Taiyo Yamamoto, Shuting Han, Ruth Röck, Melanie Horn, Philipp Bethge, Nikita Vladimirov, et al. 2025. “Precise, Predictable Genome Integrations by Deep-Learning-Assisted Design of Microhomology-Based Templates.” NATURE BIOTECHNOLOGY. https://doi.org/10.1038/s41587-025-02771-0.
- Chicago author-date (all authors)
- Naert, Thomas, Taiyo Yamamoto, Shuting Han, Ruth Röck, Melanie Horn, Philipp Bethge, Nikita Vladimirov, Fabian F. Voigt, Joana Figueiro-Silva, Ruxandra Bachmann-Gagescu, Kris Vleminckx, Fritjof Helmchen, and Soeren S. Lienkamp. 2025. “Precise, Predictable Genome Integrations by Deep-Learning-Assisted Design of Microhomology-Based Templates.” NATURE BIOTECHNOLOGY. doi:10.1038/s41587-025-02771-0.
- Vancouver
- 1.Naert T, Yamamoto T, Han S, Röck R, Horn M, Bethge P, et al. Precise, predictable genome integrations by deep-learning-assisted design of microhomology-based templates. NATURE BIOTECHNOLOGY. 2025;
- IEEE
- [1]T. Naert et al., “Precise, predictable genome integrations by deep-learning-assisted design of microhomology-based templates,” NATURE BIOTECHNOLOGY, 2025.
@article{01KA1DEQKKSXEBEE7YQGTECV9Z,
abstract = {{Precise CRISPR-based DNA integration and editing remain challenging, largely because of insufficient control of the repair process. We find that repair at the genome-cargo interface is predictable by deep learning models and adheres to sequence-context-specific rules. On the basis of in silico predictions, we devised a strategy of base-pair tandem repeat repair arms matching microhomologies at double-strand breaks. These repeat homology arms promote frame-retentive cassette integration and reduce deletions both at the target site and within the transgene. We demonstrate precise integrations at 32 loci in HEK293T cells. Germline-transmissible transgene integration and endogenous protein tagging in Xenopus and adult mouse brains demonstrated precise integration during early embryonic cleavage and in nondividing, differentiated cells. Optimized repair arms also facilitated small edits for scarless single-nucleotide or double-nucleotide changes using oligonucleotide templates in vitro and in vivo. We provide the design tool Pythia to facilitate precise genomic integration and editing for experimental and therapeutic purposes for a wide range of target cell types and applications.}},
author = {{Naert, Thomas and Yamamoto, Taiyo and Han, Shuting and Röck, Ruth and Horn, Melanie and Bethge, Philipp and Vladimirov, Nikita and Voigt, Fabian F. and Figueiro-Silva, Joana and Bachmann-Gagescu, Ruxandra and Vleminckx, Kris and Helmchen, Fritjof and Lienkamp, Soeren S.}},
issn = {{1087-0156}},
journal = {{NATURE BIOTECHNOLOGY}},
keywords = {{DNA-REPAIR,KNOCK-IN,CRISPR-CAS9,GENE,TRANSGENESIS,ZEBRAFISH,PLATFORM,OUTCOMES,TOOL}},
language = {{eng}},
pages = {{28}},
title = {{Precise, predictable genome integrations by deep-learning-assisted design of microhomology-based templates}},
url = {{http://doi.org/10.1038/s41587-025-02771-0}},
year = {{2025}},
}
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