Evidence map›Paper›PMID 28038787›Full record

ReviewNeuropeptides2017

Using the CRISPR/Cas9 system to understand neuropeptide biology and regulation.

Elizabeth A Hay, Christopher Knowles, Andreas Kolb, Alasdair MacKenzie

Open access · hybridAbstract readReview
In one paragraph

Review in Neuropeptides, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
0.3field-weighted citation impact, top 30% 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

1 citing paper in PubMed, 4 citations in OpenAlex.

  1. 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

4 authors at 1 institution in 1 country.

Elizabeth A HayUniversity of Aberdeen, Scotland. Electronic address: elizabeth.hay@abdn.ac.uk.
Christopher KnowlesUniversity of Aberdeen, Scotland.
Andreas KolbUniversity of Aberdeen, Scotland.
Alasdair MacKenzieUniversity of Aberdeen, Scotland.
University of Aberdeen · GB

Funding

Medical Research Council G0701003Wellcome Trust
6 · The paper itself

Abstract

Neuropeptides and their receptors play a role in physiological responses such as appetite, stress and inflammatory pain. With neuropeptides having such diverse and important physiological roles, knocking-out the genes encoding them, their receptors, parts of their regulatory sequences, or reproducing disease associated polymorphic variants are important steps in studying neuropeptides and how they may contribute to disease. Previously, knock-outs were generated using methods such as targeted homologous recombination in embryonic stem cells but this method is costly and time-consuming. The CRISPR/Cas9 system has rapidly taken over the genome editing field and will advance our understanding of neuropeptide genes and their regulation. With CRISPR/Cas9 technology, the time and costs involved in producing transgenic animal models, is greatly reduced. In this review, we describe how the system can be used to manipulate genomic sequences by "knock-out" or "knock-in" mutations in cell lines or in animal models. We also discuss the specificity of the system and methods to limit off-target effects. When combined with the availability of genome sequences, CRISPR/Cas9 directed genome editing in vitro and in vivo, promises to provide a deeper understanding of the biology of the neuropeptides in health and disease than has ever been available before.

Indexed as

CRISPR-Cas SystemsGene Knock-In TechniquesGene Knockout TechniquesAnimalsGene ExpressionHumansMutationNeuropeptidesNeuropeptidesGenome editingHomology-directed repairNon-homologous end-joiningOff-target effects

Identifiers

PMID28038787
PMCPMC5645574
OpenAlexW2558243226

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.