Evidence map›Paper›PMID 26051838›Full record

ArticleNature communications2015

Effective heritable gene knockdown in zebrafish using synthetic microRNAs.

Jean Giacomotto, Silke Rinkwitz, Thomas S Becker

Open access · goldAbstract read
In one paragraph

Article in Nature communications, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 29 papers.

0numbers the graph read from it
0cells of the map it votes in
29citing papers in PubMed
2.6field-weighted citation impact, top 11% 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

29 citing papers in PubMed, 49 citations in OpenAlex.

  1. Article
  2. Article
  3. Article
  4. Article
  5. Review
  6. Article
  7. Article
  8. The progress inSmall GTPases · 2022
    Review
  9. Modeling neuromuscular diseases in zebrafish.Frontiers in molecular neuroscience · 2022
    Review
  10. Article
  11. Article
  12. Review
  13. Review
  14. Article
  15. Article
  16. Review
  17. ModellingBiomedicines · 2020
    Review
  18. Article
  19. Article
  20. The αScientific reports · 2019
    Article
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

3 authors at 1 institution in 1 country.

Jean GiacomottoBrain and Mind Research Institute, Sydney Medical School, University of Sydney, Camperdown, New South Wales 2050, Australia.
Silke RinkwitzBrain and Mind Research Institute, Sydney Medical School, University of Sydney, Camperdown, New South Wales 2050, Australia.
Thomas S BeckerBrain and Mind Research Institute, Sydney Medical School, University of Sydney, Camperdown, New South Wales 2050, Australia.
University of Sydney · AU

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Although zebrafish is used to model human diseases through mutational and morpholino-based knockdown approaches, there are currently no robust transgenic knockdown tools. Here we investigate the knockdown efficiency of three synthetic miRNA-expressing backbones and show that these constructs can downregulate a sensor transgene with different degrees of potency. Using this approach, we reproduce spinal muscular atrophy (SMA) in zebrafish by targeting the smn1 gene. We also generate different transgenic lines, with severity and age of onset correlated to the level of smn1 inhibition, recapitulating for the first time the different forms of SMA in zebrafish. These lines are proof-of-concept that miRNA-based approaches can be used to generate potent heritable gene knockdown in zebrafish.

Indexed as

Gene Knockdown Techniques3' Untranslated RegionsAnimalsAnimals, Genetically ModifiedMicroRNAsZebrafishZebrafish Proteins3' Untranslated RegionsMicroRNAsZebrafish Proteins

Identifiers

PMID26051838
PMCPMC4468906
OpenAlexW1138747448

What OpenQuestion holds

Textmetadata
LicenceCC BY
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.