An experimental census of retrons for DNA production and genome editing.


Journal

Nature biotechnology
ISSN: 1546-1696
Titre abrégé: Nat Biotechnol
Pays: United States
ID NLM: 9604648

Informations de publication

Date de publication:
17 Sep 2024
Historique:
received: 21 12 2023
accepted: 12 08 2024
medline: 18 9 2024
pubmed: 18 9 2024
entrez: 17 9 2024
Statut: aheadofprint

Résumé

Retrons are bacterial immune systems that use reverse-transcribed DNA (RT-DNA) to detect phage infection. They are also deployed for genome editing, where they are modified so that the RT-DNA encodes an editing donor. Retrons are common in bacterial genomes, and thousands of unique retrons have been predicted bioinformatically. However, few have been characterized experimentally. We add to the corpus of experimentally studied retrons, finding 62 empirically determined, natural RT-DNAs that are not predictable from the retron sequence alone. We synthesize >100 previously untested retrons to identify the natural sequence of RT-DNA they produce, quantify their RT-DNA production and test the relative efficacy of editing using retron-derived donors to edit bacterial, phage and human genomes. We observe large diversity in RT-DNA production and editing rates across retrons, finding that top-performing editors are drawn from a subset of the retron phylogeny and outperform those used in previous studies, reaching precise editing rates of up to 40% in human cells.

Identifiants

pubmed: 39289529
doi: 10.1038/s41587-024-02384-z
pii: 10.1038/s41587-024-02384-z
doi:

Types de publication

Journal Article

Langues

eng

Sous-ensembles de citation

IM

Subventions

Organisme : National Science Foundation (NSF)
ID : MCB 2137692
Organisme : U.S. Department of Health & Human Services | NIH | National Institute of Biomedical Imaging and Bioengineering (NIBIB)
ID : R21EB031393
Organisme : U.S. Department of Health & Human Services | NIH | National Institute of Biomedical Imaging and Bioengineering (NIBIB)
ID : 1DP2GM140917

Informations de copyright

© 2024. The Author(s), under exclusive licence to Springer Nature America, Inc.

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Auteurs

Asim G Khan (AG)

Gladstone Institute of Data Science and Biotechnology, San Francisco, CA, USA.

Matías Rojas-Montero (M)

Gladstone Institute of Data Science and Biotechnology, San Francisco, CA, USA.

Alejandro González-Delgado (A)

Gladstone Institute of Data Science and Biotechnology, San Francisco, CA, USA.

Santiago C Lopez (SC)

Gladstone Institute of Data Science and Biotechnology, San Francisco, CA, USA.
Graduate Program in Bioengineering, University of California, San Francisco, San Francisco and University of California, Berkeley, Berkeley, CA, USA.

Rebecca F Fang (RF)

Gladstone Institute of Data Science and Biotechnology, San Francisco, CA, USA.
Graduate Program in Neuroscience, University of California, San Francisco, San Francisco, CA, USA.

Kate D Crawford (KD)

Gladstone Institute of Data Science and Biotechnology, San Francisco, CA, USA.
Graduate Program in Bioengineering, University of California, San Francisco, San Francisco and University of California, Berkeley, Berkeley, CA, USA.

Seth L Shipman (SL)

Gladstone Institute of Data Science and Biotechnology, San Francisco, CA, USA. seth.shipman@gladstone.ucsf.edu.
Department of Bioengineering and Therapeutic Sciences, University of California, San Francisco, San Francisco, CA, USA. seth.shipman@gladstone.ucsf.edu.
Chan Zuckerberg Biohub San Francisco, San Francisco, CA, USA. seth.shipman@gladstone.ucsf.edu.

Classifications MeSH