Evidence map›Paper›PMID 23612303›Full record

ArticleNature communications2013

Compact designer TALENs for efficient genome engineering.

Marine Beurdeley, Fabian Bietz, Jin Li, Severine Thomas, Thomas Stoddard, Alexandre Juillerat, Feng Zhang, Daniel F Voytas, Philippe Duchateau, George H Silva

Abstract read
In one paragraph

Article in Nature communications, 2013. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 46 papers.

0numbers the graph read from it
0cells of the map it votes in
46citing papers in PubMed
–field-weighted citation impact
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

46 citing papers in PubMed.

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  14. Development and clinical translation ofComputational and structural biotechnology journal · 2022
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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

10 authors.

Marine BeurdeleyCellectis, Research and Development, 8 rue de la Croix Jarry, 75013 Paris, France.
Fabian Bietz
Jin Li
Severine Thomas
Thomas Stoddard
Alexandre Juillerat
Feng Zhang
Daniel F Voytas
Philippe Duchateau
George H Silva

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Transcription activator-like effector nucleases are readily targetable 'molecular scissors' for genome engineering applications. These artificial nucleases offer high specificity coupled with simplicity in design that results from the ability to serially chain transcription activator-like effector repeat arrays to target individual DNA bases. However, these benefits come at the cost of an appreciably large multimeric protein complex, in which DNA cleavage is governed by the nonspecific FokI nuclease domain. Here we report a significant improvement to the standard transcription activator-like effector nuclease architecture by leveraging the partially specific I-TevI catalytic domain to create a new class of monomeric, DNA-cleaving enzymes. In vivo yeast, plant and mammalian cell assays demonstrate that the half-size, single-polypeptide compact transcription activator-like effector nucleases exhibit overall activity and specificity comparable to currently available designer nucleases. In addition, we harness the catalytic mechanism of I-TevI to generate novel compact transcription activator-like effector nuclease-based nicking enzymes that display a greater than 25-fold increase in relative targeted gene correction efficacy.

Indexed as

AnimalsBase SequenceCatalytic DomainCHO CellsCricetinaeCricetulusDeoxyribonuclease IEndodeoxyribonucleasesEndonucleasesGenome, FungalMolecular Sequence DataNicotianaProtein EngineeringSaccharomyces cerevisiaeTrans-ActivatorsDeoxyribonuclease IEndodeoxyribonucleasesEndonucleasesI-TEVI endonucleaseTrans-Activators

Identifiers

PMID23612303
PMCPMC3644105

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC-ND
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.