2. Academic Validation
  3. Metabolomics Analysis of the Prefrontal Cortex in a Rat Chronic Unpredictable Mild Stress Model of Depression

Metabolomics Analysis of the Prefrontal Cortex in a Rat Chronic Unpredictable Mild Stress Model of Depression

  • Front Psychiatry. 2022 Mar 15:13:815211. doi: 10.3389/fpsyt.2022.815211.
Lihua Duan 1 2,  Rong Fan 1 2,  Teng Li 1 2,  Zhaoyu Yang 1 2,  En Hu 1 2,  Zhe Yu 1 2,  Jing Tian 1 2,  Weikang Luo 1 2,  Chunhu Zhang 1 2
Affiliations

Affiliations

  • 1 Department of Integrated Traditional Chinese and Western Medicine, Institute of Integrative Medicine, Xiangya Hospital, Central South University, Changsha, China.
  • 2 National Clinical Research Center for Geriatric Disorders, Xiangya Hospital, Central South University, Changsha, China.
PMID: 35370823 DOI: 10.3389/fpsyt.2022.815211
Abstract

Background: Depressive disorder is the leading cause of disability and suicidality worldwide. Metabolites are considered indicators and regulators of depression. However, the pathophysiology of the prefrontal cortex (PFC) in depression remains unclear.

Methods: A chronic unpredictable mild stress (CUMS) model and a maturation rodent model of depression was used to investigate metabolic changes in the PFC. Eighteen male Sprague-Dawley rats were randomly divided into CUMS and control groups. The sucrose preference test (SPT) and forced swimming test (FST) were employed to evaluate and record depression-associated behaviors and changes in body weight (BW). High-performance liquid chromatography-tandem mass spectrometry was applied to test metabolites in rat PFC. Furthermore, principal component analysis and orthogonal partial least-squares discriminant analysis were employed to identify differentially abundant metabolites. Metabolic pathways were analyzed using MetaboAnalyst. Finally, a metabolite-protein interaction network was established to illustrate the function of differential metabolites.

Results: SPT and FST results confirmed successful establishment of the CUMS-induced depression-like behavior model in rats. Five metabolites, including 1-methylnicotinamide, 3-methylhistidine, acetylcholine, glycerophospho-N-palmitoyl ethanolamine, α-D-mannose 1-phosphate, were identified as potential biomarkers of depression. Four pathways changed in the CUMS group. Metabolite-protein interaction analysis revealed that 10 pathways play roles in the metabolism of depression.

Conclusion: Five potential biomarkers were identified in the PFC and metabolite-protein interactions associated with metabolic pathophysiological processes were explored using the CUMS model. The results of this study will assist physicians and scientists in discovering potential diagnostic markers and novel therapeutic targets for depression.

Keywords

LC-MS/MS; chronic unpredictable mild stress; depression; metabolite-protein interaction; metabolomics; prefrontal cortex.

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