Literature DB >> 27438688

Predisposition to treatment response in major depressive episode: A peripheral blood gene coexpression network analysis.

Raoul Belzeaux1, Chien-Wei Lin2, Ying Ding2, Aurélie Bergon3, El Chérif Ibrahim4, Gustavo Turecki5, George Tseng2, Etienne Sibille6.   

Abstract

Antidepressant efficacy is insufficient, unpredictable and poorly understood in major depressive episode (MDE). Gene expression studies allow for the identification of significantly dysregulated genes but can limit the exploration of biological pathways. In the present study, we proposed a gene coexpression analysis to investigate biological pathways associated with treatment response predisposition and their regulation by microRNAs (miRNAs) in peripheral blood samples of MDE and healthy control subjects. We used a discovery cohort that included 34 MDE patients that were given 12-week treatment with citalopram and 33 healthy controls. Two replication cohorts with similar design were also analyzed. Expression-based gene network was built to define clusters of highly correlated sets of genes, called modules. Association between each module's first principal component of the expression data and clinical improvement was tested in the three cohorts. We conducted gene ontology analysis and miRNA prediction based on the module gene list. Nine of the 59 modules from the gene coexpression network were associated with clinical improvement. The association was partially replicated in other cohorts. Gene ontology analysis demonstrated that 4 modules were associated with cytokine production, acute inflammatory response or IL-8 functions. Finally, we found 414 miRNAs that may regulate one or several modules associated with clinical improvement. By contrast, only 12 miRNAs were predicted to specifically regulate modules unrelated to clinical improvement. Our gene coexpression analysis underlines the importance of inflammation-related pathways and the involvement of a large miRNA program as biological processes predisposing associated with antidepressant response.
Copyright © 2016 Elsevier Ltd. All rights reserved.

Entities:  

Keywords:  Antidepressant; Gene network; Inflammation; MicroRNA; Mood disorder

Mesh:

Substances:

Year:  2016        PMID: 27438688     DOI: 10.1016/j.jpsychires.2016.07.009

Source DB:  PubMed          Journal:  J Psychiatr Res        ISSN: 0022-3956            Impact factor:   4.791


  12 in total

Review 1.  Potential Use of MicroRNA for Monitoring Therapeutic Response to Antidepressants.

Authors:  Raoul Belzeaux; Rixing Lin; Gustavo Turecki
Journal:  CNS Drugs       Date:  2017-04       Impact factor: 5.749

2.  Sex-specific transcriptional signatures in human depression.

Authors:  Benoit Labonté; Olivia Engmann; Immanuel Purushothaman; Caroline Menard; Junshi Wang; Chunfeng Tan; Joseph R Scarpa; Gregory Moy; Yong-Hwee E Loh; Michael Cahill; Zachary S Lorsch; Peter J Hamilton; Erin S Calipari; Georgia E Hodes; Orna Issler; Hope Kronman; Madeline Pfau; Aleksandar L J Obradovic; Yan Dong; Rachael L Neve; Scott Russo; Andrew Kazarskis; Carol Tamminga; Naguib Mechawar; Gustavo Turecki; Bin Zhang; Li Shen; Eric J Nestler
Journal:  Nat Med       Date:  2017-08-21       Impact factor: 53.440

3.  Differential Peripheral Proteomic Biosignature of Fluoxetine Response in a Mouse Model of Anxiety/Depression.

Authors:  Indira Mendez-David; Céline Boursier; Valérie Domergue; Romain Colle; Bruno Falissard; Emmanuelle Corruble; Alain M Gardier; Jean-Philippe Guilloux; Denis J David
Journal:  Front Cell Neurosci       Date:  2017-08-16       Impact factor: 5.505

4.  Investigation of miR-1202, miR-135a, and miR-16 in Major Depressive Disorder and Antidepressant Response.

Authors:  Laura M Fiori; Juan Pablo Lopez; Stéphane Richard-Devantoy; Marcelo Berlim; Eduardo Chachamovich; Fabrice Jollant; Jane Foster; Susan Rotzinger; Sidney H Kennedy; Gustavo Turecki
Journal:  Int J Neuropsychopharmacol       Date:  2017-08-01       Impact factor: 5.176

Review 5.  Recent advances in predicting responses to antidepressant treatment.

Authors:  Thomas Frodl
Journal:  F1000Res       Date:  2017-05-03

6.  Co-expression network modeling identifies key long non-coding RNA and mRNA modules in altering molecular phenotype to develop stress-induced depression in rats.

Authors:  Qingzhong Wang; Bhaskar Roy; Yogesh Dwivedi
Journal:  Transl Psychiatry       Date:  2019-04-03       Impact factor: 6.222

Review 7.  miRNAs in depression vulnerability and resilience: novel targets for preventive strategies.

Authors:  Nicola Lopizzo; Valentina Zonca; Nadia Cattane; Carmine Maria Pariante; Annamaria Cattaneo
Journal:  J Neural Transm (Vienna)       Date:  2019-07-26       Impact factor: 3.575

8.  Predicting therapy outcome in a digital mental health intervention for depression and anxiety: A machine learning approach.

Authors:  Silvan Hornstein; Valerie Forman-Hoffman; Albert Nazander; Kristian Ranta; Kevin Hilbert
Journal:  Digit Health       Date:  2021-11-29

9.  Global long non-coding RNA expression in the rostral anterior cingulate cortex of depressed suicides.

Authors:  Yi Zhou; Pierre-Eric Lutz; Yu Chang Wang; Jiannis Ragoussis; Gustavo Turecki
Journal:  Transl Psychiatry       Date:  2018-10-18       Impact factor: 6.222

10.  Time Course of Changes in Peripheral Blood Gene Expression During Medication Treatment for Major Depressive Disorder.

Authors:  Ian A Cook; Eliza Congdon; David E Krantz; Aimee M Hunter; Giovanni Coppola; Steven P Hamilton; Andrew F Leuchter
Journal:  Front Genet       Date:  2019-09-18       Impact factor: 4.599

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