Evidence map›Paper›PMID 29390069›Full record

ArticleThe international journal of neuropsychopharmacology2018

Long Noncoding RNA-Associated Transcriptomic Changes in Resiliency or Susceptibility to Depression and Response to Antidepressant Treatment.

Bhaskar Roy, Qingzhong Wang, Yogesh Dwivedi

Open access · goldAbstract read
In one paragraph

Article in The international journal of neuropsychopharmacology, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 19 papers, 3 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
19citing papers in PubMed, 3 pooled it
1.3field-weighted citation impact, top 24% 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

19 citing papers in PubMed, 3 syntheses or guidelines pooled it, 39 citations in OpenAlex.

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  18. The future of rodent models in depression research.Nature reviews. Neuroscience · 2019
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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 at 1 institution in 1 country.

Bhaskar RoyDepartment of Psychiatry and Behavioral Neurobiology, University of Alabama at Birmingham, Birmingham, Alabama.
Qingzhong WangDepartment of Psychiatry and Behavioral Neurobiology, University of Alabama at Birmingham, Birmingham, Alabama.
Yogesh DwivediDepartment of Psychiatry and Behavioral Neurobiology, University of Alabama at Birmingham, Birmingham, Alabama.
University of Alabama at Birmingham · US

Funding

Plasma Exosomal MicroRNAs as Promising Novel Biomarkers for Suicidality and Treatment OutcomeR01MH107183 · NIMH · UNIVERSITY OF ALABAMA AT BIRMINGHAM · PI DWIVEDI, YOGESH, SHELTON, RICHARD CHARLES · 2015 to 2019
$3.5M
MicroRNA Mapping in Major DepressionR01MH100616 · NIMH · UNIVERSITY OF ALABAMA AT BIRMINGHAM · PI DWIVEDI, YOGESH · 2014 to 2019
$1.8M
Perturbed cell signaling network and suicide neurobiologyR01MH101890 · NIMH · UNIVERSITY OF ALABAMA AT BIRMINGHAM · PI DWIVEDI, YOGESH · 2013 to 2017
$1.6M
Calcium Sensing Proteins in DepressionR01MH082802 · NIMH · UNIVERSITY OF ILLINOIS AT CHICAGO · PI DWIVEDI, YOGESH · 2009 to 2013
$1.4M
NIMH NIH HHS R01 MH082802NIMH NIH HHS R01 MH100616NIMH NIH HHS R01 MH101890NIMH NIH HHS R01 MH107183
6 · The paper itself

Abstract

Background: Recent emergence of long noncoding RNAs in regulating gene expression and thereby modulating physiological functions in brain has manifested their possible role in psychiatric disorders. In this study, the roles of long noncoding RNAs in susceptibility and resiliency to develop stress-induced depression and their response to antidepressant treatment were examined. Methods: Microarray-based transcriptome-wide changes in long noncoding RNAs were determined in hippocampus of male Holtzman rats who showed susceptibility (learned helplessness) or resiliency (nonlearned helplessness) to develop depression. Changes in long noncoding RNA expression were also ascertained after subchronic administration of fluoxetine to learned helplessness rats. Bioinformatic and target prediction analyses (cis- and trans-acting) and qPCR-based assays were performed to decipher the functional role of altered long noncoding RNAs. Results: Group-wise comparison showed an overrepresented class of long noncoding RNAs that were uniquely associated with nonlearned helplessness or learned helplessness behavior. Chromosomal mapping within the 5-kbp flank region of the top 20 dysregulated long noncoding RNAs in the learned helplessness group showed several target genes that were regulated through cis- or trans-actions, including Zbtb20 and Zfp385b from zinc finger binding protein family. Genomic context of differentially expressed long noncoding RNAs showed an overall blunted response in the learned helplessness group regardless of the long noncoding RNA classes analyzed. Gene ontology exhibited the functional clustering for anatomical structure development, cellular architecture modulation, protein metabolism, and cellular communications. Fluoxetine treatment reversed learned helplessness-induced changes in many long noncoding RNAs and target genes. Conclusions: The involvement of specific classes of long noncoding RNAs with distinctive roles in modulating target gene expression could confer the role of long noncoding RNAs in resiliency or susceptibility to develop depression with a reciprocal response to antidepressant treatment.

Indexed as

Behavior, AnimalDepressionGene ExpressionHelplessness, LearnedHippocampusResilience, PsychologicalRNA, Long NoncodingTranscriptomeAnimalsAntidepressive Agents, Second-GenerationDisease Models, AnimalFluoxetineGenetic Predisposition to DiseaseMaleRatsRats, Sprague-DawleyAntidepressive Agents, Second-GenerationFluoxetineRNA, Long Noncoding

Identifiers

PMID29390069
PMCPMC5932471
OpenAlexW2791525190

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC
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.