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Metabolomic identification of biochemical changes induced by fluoxetine and imipramine in a chronic mild stress mouse model of depression.


ABSTRACT: Metabolomics was applied to a C57BL/6N mouse model of chronic unpredictable mild stress (CMS). Such mice were treated with two antidepressants from different categories: fluoxetine and imipramine. Metabolic profiling of the hippocampus was performed using gas chromatography-mass spectrometry analysis on samples prepared under optimized conditions, followed by principal component analysis, partial least squares-discriminant analysis, and pair-wise orthogonal projections to latent structures discriminant analyses. Body weight measurement and behavior tests including an open field test and the forced swimming test were completed with the mice as a measure of the phenotypes of depression and antidepressive effects. As a result, 23 metabolites that had been differentially expressed among the control, CMS, and antidepressant-treated groups demonstrated that amino acid metabolism, energy metabolism, adenosine receptors, and neurotransmitters are commonly perturbed by drug treatment. Potential predictive markers for treatment effect were identified: myo-inositol for fluoxetine and lysine and oleic acid for imipramine. Collectively, the current study provides insights into the molecular mechanisms of the antidepressant effects of two widely used medications.

SUBMITTER: Zhao J 

PROVIDER: S-EPMC4352870 | biostudies-literature | 2015

REPOSITORIES: biostudies-literature

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Metabolomic identification of biochemical changes induced by fluoxetine and imipramine in a chronic mild stress mouse model of depression.

Zhao Jing J   Jung Yang-Hee YH   Jang Choon-Gon CG   Chun Kwang-Hoon KH   Kwon Sung Won SW   Lee Jeongmi J  

Scientific reports 20150309


Metabolomics was applied to a C57BL/6N mouse model of chronic unpredictable mild stress (CMS). Such mice were treated with two antidepressants from different categories: fluoxetine and imipramine. Metabolic profiling of the hippocampus was performed using gas chromatography-mass spectrometry analysis on samples prepared under optimized conditions, followed by principal component analysis, partial least squares-discriminant analysis, and pair-wise orthogonal projections to latent structures discr  ...[more]

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