Evidence map›Paper›PMID 27866656›Full record

ArticleNeuropeptides2017

An analysis of possible off target effects following CAS9/CRISPR targeted deletions of neuropeptide gene enhancers from the mouse genome.

Elizabeth Anne Hay, Abdulla Razak Khalaf, Pietro Marini, Andrew Brown, Karyn Heath, Darrin Sheppard, Alasdair MacKenzie

Abstract read
In one paragraph

Article in Neuropeptides, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 19 papers.

0numbers the graph read from it
0cells of the map it votes in
19citing 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

19 citing papers in PubMed.

  1. Trial
  2. Article
  3. CRISPR Activation in Mouse Trophoblast Stem Cells.Methods in molecular biology (Clifton, N.J.) · 2024
    Article
  4. Article
  5. Article
  6. Review
  7. Review
  8. Article
  9. New insights on CRISPR/Cas9-based therapy for breast Cancer.Genes and environment : the official journal of the Japanese Environmental Mutagen Society · 2021
    Review
  10. Article
  11. Article
  12. Article
  13. Article
  14. Article
  15. Zoological research · 2018
    Review
  16. Review
  17. Article
  18. Article
  19. 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

7 authors.

Elizabeth Anne HaySchool of Medicine, Medical Science and Nutrition, Institute of Medical Sciences, University of Aberdeen, Foresterhill, Aberdeen, AB25 2ZD, Scotland, UK.
Abdulla Razak KhalafSchool of Medicine, Medical Science and Nutrition, Institute of Medical Sciences, University of Aberdeen, Foresterhill, Aberdeen, AB25 2ZD, Scotland, UK.
Pietro MariniSchool of Medicine, Medical Science and Nutrition, Institute of Medical Sciences, University of Aberdeen, Foresterhill, Aberdeen, AB25 2ZD, Scotland, UK.
Andrew BrownSchool of Medicine, Medical Science and Nutrition, Institute of Medical Sciences, University of Aberdeen, Foresterhill, Aberdeen, AB25 2ZD, Scotland, UK.
Karyn HeathSchool of Medicine, Medical Science and Nutrition, Institute of Medical Sciences, University of Aberdeen, Foresterhill, Aberdeen, AB25 2ZD, Scotland, UK.
Darrin SheppardSchool of Medicine, Medical Science and Nutrition, Institute of Medical Sciences, University of Aberdeen, Foresterhill, Aberdeen, AB25 2ZD, Scotland, UK.
Alasdair MacKenzieSchool of Medicine, Medical Science and Nutrition, Institute of Medical Sciences, University of Aberdeen, Foresterhill, Aberdeen, AB25 2ZD, Scotland, UK. Electronic address: mbi167@abdn.ac.uk.

Funding

Medical Research Council G0701003Wellcome Trust
6 · The paper itself

Abstract

We have successfully used comparative genomics to identify putative regulatory elements within the human genome that contribute to the tissue specific expression of neuropeptides such as galanin and receptors such as CB1. However, a previous inability to rapidly delete these elements from the mouse genome has prevented optimal assessment of their function in-vivo. This has been solved using CAS9/CRISPR genome editing technology which uses a bacterial endonuclease called CAS9 that, in combination with specifically designed guide RNA (gRNA) molecules, cuts specific regions of the mouse genome. However, reports of "off target" effects, whereby the CAS9 endonuclease is able to cut sites other than those targeted, limits the appeal of this technology. We used cytoplasmic microinjection of gRNA and CAS9 mRNA into 1-cell mouse embryos to rapidly generate enhancer knockout mouse lines. The current study describes our analysis of the genomes of these enhancer knockout lines to detect possible off-target effects. Bioinformatic analysis was used to identify the most likely putative off-target sites and to design PCR primers that would amplify these sequences from genomic DNA of founder enhancer deletion mouse lines. Amplified DNA was then sequenced and blasted against the mouse genome sequence to detect off-target effects. Using this approach we were unable to detect any evidence of off-target effects in the genomes of three founder lines using any of the four gRNAs used in the analysis. This study suggests that the problem of off-target effects in transgenic mice have been exaggerated and that CAS9/CRISPR represents a highly effective and accurate method of deleting putative neuropeptide gene enhancer sequences from the mouse genome.

Indexed as

AnimalsBase SequenceComputational BiologyCRISPR-Cas SystemsDNAEndonucleasesGene DeletionGene ExpressionGenomeMiceNeuropeptidesRNA, Guide, CRISPR-Cas SystemsDNAEndonucleasesNeuropeptidesRNA, Guide, CRISPR-Cas Systems1-cell mouse embryoCAS9/CRISPRCAS9 mRNACRISPR design toolCytoplasmic microinjectiongRNANeuropeptide, enhancerOff-target effectsPCRSanger sequencingTransgenic mouseUCSC browser BLAT

Identifiers

PMID27866656
PMCPMC5529291

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.