Evidence map›Paper›PMID 37660212›Full record

ArticleScientific reports2023

Reversible suppression of circadian-driven locomotor rhythms in mice using a gradual fragmentation of the day-night cycle.

Melissa E S Richardson, Chérie-Akilah Browne, Citlali I Huerta Mazariegos

Abstract read
In one paragraph

Article in Scientific reports, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing 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

2 citing papers in PubMed.

  1. Article
  2. 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.

Melissa E S Richardson *Department of Biological Sciences, Oakwood University, 7000 Adventist Blvd., Huntsville, AL, 35896, USA. mrichardson@oakwood.edu.
Chérie-Akilah Browne *Department of Biological Sciences, Oakwood University, 7000 Adventist Blvd., Huntsville, AL, 35896, USA.
Citlali I Huerta MazariegosDepartment of Biological Sciences, Oakwood University, 7000 Adventist Blvd., Huntsville, AL, 35896, USA.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Circadian rhythms are regulated by molecular clockwork and drive 24-h behaviors such as locomotor activity, which can be rendered non-functional through genetic knockouts of clock genes. Circadian rhythms are robust in constant darkness (DD) but are modulated to become exactly 24 h by the external day-night cycle. Whether ill-timed light and dark exposure can render circadian behaviors non-functional to the extent of genetic knockouts is less clear. In this study, we discovered an environmental approach that led to a reduction or lack in rhythmic 24-h-circadian wheel-running locomotor behavior in mice (referred to as arrhythmicity). We first observed behavioral circadian arrhythmicity when mice were gradually exposed to a previously published disruptive environment called the fragmented day-night cycle (FDN-G), while maintaining activity alignment with the four dispersed fragments of darkness. Remarkably, upon exposure to constant darkness (DD) or constant light (LL), FDN-G mice lost any resemblance to the FDN-G-only phenotype and instead, exhibited sporadic activity bursts. Circadian rhythms are maintained in control mice with sudden FDN exposure (FDN-S) and fully restored in FDN-G mice either spontaneously in DD or after 12 h:12 h light-dark exposure. This is the first study to generate a light-dark environment that induces reversible suppression of circadian locomotor rhythms in mice.

Indexed as

Circadian RhythmLocomotionAnimalsCLOCK ProteinsGene Knockout TechniquesMicePhenotypeCLOCK Proteins

Identifiers

PMID37660212
PMCPMC10475134

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.