Evidence map›Paper›PMID 35076284›Full record

ArticleThe CRISPR journal2022

Bacterial Retrons Enable Precise Gene Editing in Human Cells.

Bin Zhao, Shi-An A Chen, Jiwoo Lee, Hunter B Fraser

Open access · greenAbstract read
In one paragraph

Article in The CRISPR journal, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 38 papers.

0numbers the graph read from it
0cells of the map it votes in
38citing papers in PubMed
3.9field-weighted citation impact, top 5% of its field
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

38 citing papers in PubMed, 47 citations in OpenAlex.

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  14. Current trends in gene therapy to treat inherited disorders of the brain.Molecular therapy : the journal of the American Society of Gene Therapy · 2025
    Review
  15. Review
  16. Article
  17. Article
  18. Article
  19. Gene Therapy Techniques and Delivery Methods (Review).Sovremennye tekhnologii v meditsine · 2025
    Review
  20. Review
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

4 authors at 1 institution in 1 country.

Bin ZhaoDepartment of Biology, Stanford University, Stanford, California, USA.
Shi-An A ChenDepartment of Biology, Stanford University, Stanford, California, USA.
Jiwoo LeeDepartment of Biology, Stanford University, Stanford, California, USA.
Hunter B FraserDepartment of Biology, Stanford University, Stanford, California, USA.
Stanford University · US

Funding

High-throughput precision genome editing to characterize natural genetic variantsR01GM134228 · NIGMS · STANFORD UNIVERSITY · PI FRASER, HUNTER B · 2019 to 2022
$1.8M
NIGMS NIH HHS R01 GM134228
6 · The paper itself

Abstract

Retrons are bacterial genetic elements involved in anti-phage defense. They have the unique ability to reverse transcribe RNA into multicopy single-stranded DNA (msDNA) that remains covalently linked to their template RNA. Retrons coupled with CRISPR-Cas9 in yeast have been shown to improve the efficiency of precise genome editing via homology-directed repair (HDR). In human cells, HDR editing efficiency has been limited by challenges associated with delivering extracellular donor DNA encoding the desired mutation. In this study, we tested the ability of retrons to produce msDNA as donor DNA and facilitate HDR by tethering msDNA to guide RNA in HEK293T and K562 cells. Through heterologous reconstitution of retrons from multiple bacterial species with the CRISPR-Cas9 system, we demonstrated HDR rates of up to 11.4%. Overall, our findings represent the first step in extending retron-based precise gene editing to human cells.

Indexed as

Gene EditingRNA-Directed DNA PolymeraseCRISPR-Cas SystemsDNA, BacterialHEK293 CellsHumansDNA, BacterialRNA-Directed DNA Polymerase

Identifiers

PMID35076284
PMCPMC8892976
OpenAlexW4220989737

What OpenQuestion holds

Textmetadata
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.