Evidence map›Paper›PMID 23433184›Full record

ArticleGenes, brain, and behavior2013

Transcriptome analysis of Inbred Long Sleep and Inbred Short Sleep mice.

T M Darlington, M A Ehringer, C Larson, T L Phang, R A Radcliffe

Open access · bronzeAbstract read
In one paragraph

Article in Genes, brain, and behavior, 2013. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.

0numbers the graph read from it
0cells of the map it votes in
9citing papers in PubMed
1.9field-weighted citation impact, top 15% 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

9 citing papers in PubMed, 17 citations in OpenAlex.

  1. Article
  2. Article
  3. Article
  4. Genome characterization of the selected long- and short-sleep mouse lines.Mammalian genome : official journal of the International Mammalian Genome Society · 2016
    Article
  5. Article
  6. Article
  7. Exome sequencing and arrayCGH detection of gene sequence and copy number variation between ILS and ISS mouse strains.Mammalian genome : official journal of the International Mammalian Genome Society · 2014
    Article
  8. Article
  9. 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 at 3 institutions in 1 country.

T M DarlingtonInstitute for Behavioral Genetics, University of Colorado, Boulder, Boulder, CO 80309, USA. todd.darlington@colorado.edu
M A Ehringer
C Larson
T L Phang
R A Radcliffe
University of Colorado Boulder · USInstitute for Behavioral Medicine · USUniversity of Colorado Health · US

Funding

Research Training-Genetics of Substance AbuseT32DA017637 · NIDA · UNIVERSITY OF COLORADO AT BOULDER · PI STITZEL, JERRY A · 2004 to 2023
$5.3M
Translational studies of nicotinic receptor genes: alcohol and nicotine behaviorsR01AA017889 · NIAAA · UNIVERSITY OF COLORADO · PI EHRINGER, MARISSA A · 2009 to 2013
$2.6M
Genetic Studies of Alcohol ToleranceR01AA016957 · NIAAA · UNIVERSITY OF COLORADO DENVER · PI RADCLIFFE, RICHARD A · 2009 to 2013
$2.5M
NIAAA NIH HHS R01 AA016957NIAAA NIH HHS R01 AA017889NIDA NIH HHS T32 DA017637
6 · The paper itself

Abstract

Many studies have utilized the Inbred Long Sleep and Inbred Short Sleep mouse strains to model the genetic influence on initial sensitivity to ethanol. The mechanisms underlying this divergent phenotype are still not completely understood. In this study, we attempt to identify genes that are differentially expressed between these two strains and to identify baseline networks of co-expressed genes, which may provide insight regarding their phenotypic differences. We examined the whole brain and striatal transcriptomes of both strains, using next generation RNA sequencing techniques. Many genes were differentially expressed between strains, including several in chromosomal regions previously shown to influence initial sensitivity to ethanol. These results are in concordance with a similar sample of striatal transcriptomes measured using microarrays. In addition to the higher dynamic range, RNA-Seq is not hindered by high background noise or polymorphisms in probesets as with microarray technology, and we are able to analyze exome sequence of abundant genes. Furthermore, utilizing Weighted Gene Co-expression Network Analysis, we identified several modules of co-expressed genes corresponding to strain differences. Several candidate genes were identified, including protein phosphatase 1 regulatory unit 1b (Ppp1r1b), prodynorphin (Pdyn), proenkephalin (Penk), ras association (RalGDS/AF-6) domain family member 2 (Rassf2), myosin 1d (Myo1d) and transthyretin (Ttr). In addition, we propose a role for potassium channel activity as well as map kinase signaling in the observed phenotypic differences between the two strains.

Indexed as

TranscriptomeAnimalsBrainDopamine and cAMP-Regulated Phosphoprotein 32EnkephalinsEthanolExomeGene Expression ProfilingGene Regulatory NetworksHigh-Throughput Nucleotide SequencingMaleMiceMice, Inbred StrainsMyosinsPolymorphism, GeneticPrealbuminDopamine and cAMP-Regulated Phosphoprotein 32EnkephalinsEthanolMyo1d protein, mouseMyosinsPpp1r1b protein, mousePrealbuminpreproenkephalinProtein PrecursorsRASSF2 protein, mouseTumor Suppressor Proteins

Identifiers

PMID23433184
PMCPMC3581861
OpenAlexW1603716715

What OpenQuestion holds

Textmetadata
LicenceTDM
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.