Evidence map›Paper›PMID 34302019›Full record

ArticleScientific reports2021

Manipulation of the Tyrosinase gene permits improved CRISPR/Cas editing and neural imaging in cichlid fish.

Cheng-Yu Li, Joshua R Steighner, Garrett Sweatt, Tod R Thiele, Scott A Juntti

Abstract read
In one paragraph

Article in Scientific reports, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers.

0numbers the graph read from it
0cells of the map it votes in
14citing 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

14 citing papers in PubMed.

  1. Article
  2. Article
  3. Advances in Fish Gene Editing.Animals : an open access journal from MDPI · 2026
    Review
  4. Cichlid fishes.Nature methods · 2025
    Article
  5. Article
  6. Article
  7. Article
  8. 3 Biotech · 2024
    Review
  9. Review
  10. Review
  11. Article
  12. Review
  13. Royal Society open science · 2022
    Article
  14. 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

5 authors.

Cheng-Yu Li *Department of Biology, University of Maryland, College Park, MD, USA.
Joshua R Steighner *Department of Biology, University of Maryland, College Park, MD, USA.
Garrett SweattDepartment of Biology, University of Maryland, College Park, MD, USA.
Tod R ThieleDepartment of Biological Sciences, University of Toronto, Scarborough, ON, Canada.
Scott A JunttiDepartment of Biology, University of Maryland, College Park, MD, USA. sjuntti@umd.edu.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Direct tests of gene function have historically been performed in a limited number of model organisms. The CRISPR/Cas system is species-agnostic, offering the ability to manipulate genes in a range of models, enabling insights into evolution, development, and physiology. Astatotilapia burtoni, a cichlid fish from the rivers and shoreline around Lake Tanganyika, has been extensively studied in the laboratory to understand evolution and the neural control of behavior. Here we develop protocols for the creation of CRISPR-edited cichlids and create a broadly useful mutant line. By manipulating the Tyrosinase gene, which is necessary for eumelanin pigment production, we describe a fast and reliable approach to quantify and optimize gene editing efficiency. Tyrosinase mutants also remove a major obstruction to imaging, enabling visualization of subdermal structures and fluorophores in situ. These protocols will facilitate broad application of CRISPR/Cas9 to studies of cichlids as well as other non-traditional model aquatic species.

Indexed as

AnimalsCichlidsCRISPR-Cas SystemsEvolution, MolecularGene EditingLakesMonophenol MonooxygenasePhenotypeTanzaniaMonophenol Monooxygenase

Identifiers

PMID34302019
PMCPMC8302579

What OpenQuestion holds

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LicenceCC BY
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Registered trials

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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.