Abstract

The incorporation of peripheral biomarkers in the treatment of major depressive disorders (MDD) could improve the efficiency of treatments and increase remission rate. Peripheral blood mononuclear cells (PBMCs) represent an attractive biological substrate allowing the identification of a drug response signature. Using a proteomic approach with high-resolution mass spectrometry, the present study aimed to identify a biosignature of antidepressant response (fluoxetine, a Selective Serotonin Reuptake Inhibitor) in PBMCs in a mouse model of anxiety/depression. Following determination of an emotionality score, using complementary behavioral analysis of anxiety/depression across three different tests (Elevated Plus Maze, Novelty Suppressed Feeding, Splash Test), we showed that a 4-week corticosterone treatment (35 μg/ml, CORT model) in C57BL/6NTac male mice induced an anxiety/depressive-like behavior. Then, chronic fluoxetine treatment (18 mg/kg/day for 28 days in the drinking water) reduced corticosterone-induced increase in emotional behavior. However, among 46 fluoxetine-treated mice, only 30 of them presented a 50% decrease in emotionality score, defining fluoxetine responders (CORT/Flx-R). To determine a peripheral biological signature of fluoxetine response, proteomic analysis was performed from PBMCs isolated from the “most” affected corticosterone/vehicle (CORT/V), corticosterone/fluoxetine responders and non-responders (CORT/Flx-NR) animals. In comparison to CORT/V, a total of 263 proteins were differently expressed after fluoxetine exposure. Expression profile of these proteins showed a strong similarity between CORT/Flx-R and CORT/Flx-NR (R = 0.827, p < 1e-7). Direct comparison of CORT/Flx-R and CORT/Flx-NR groups revealed 100 differently expressed proteins, representing a combination of markers associated either with the maintenance of animals in a refractory state, or associated with behavioral improvement. Finally, 19 proteins showed a differential direction of expression between CORT/Flx-R and CORT/Flx-NR that drove them away from the CORT-treated profile. Among them, eight upregulated proteins (RPN2, HSPA9, NPTN, AP2B1, UQCRC2, RACK-1, TOLLIP) and one downregulated protein, TLN2, were previously associated with MDD or antidepressant drug response in the literature. Future preclinical studies will be required to validate whether proteomic changes observed in PBMCs from CORT/Flx-R mice mirror biological changes in brain tissues.

Highlights

  • Major Depressive Disorders (MDD) are the most frequent mental disorders worldwide (15% lifetime prevalence, 5% 1-year prevalence)

  • Using a proteomic approach with high-resolution mass spectrometry technique, this study aimed to identify an indicative biosignature of fluoxetine response, which is commonly used as an antidepressant medication in Peripheral blood mononuclear cells (PBMCs), and its isolated from a well-validated mouse model of anxiety/depression based on elevation of blood levels of glucocorticoids (David et al, 2009)

  • We demonstrated that CORT model of anxiety/depression in mice allows the study of response/nonresponse to chronic fluoxetine treatment

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Summary

Introduction

Major Depressive Disorders (MDD) are the most frequent mental disorders worldwide (15% lifetime prevalence, 5% 1-year prevalence). The main risk with MDD is suicide-related mortality. About 6–20% of patients suffering from MDD die by suicide (Isometsa, 2014). Selective Serotonin and/or Norepinephrine Reuptake Inhibitors are the most commonly prescribed drugs for the treatment of MDD. Despite recent advances in the pharmacological treatment of MDD, antidepressant drugs are only partially effective, with a 47% response rate and a 30% remission rate after the first-line treatment (Trivedi et al, 2006; Scarr et al, 2015). As most patients fail to enter remission with the first treatment, the incorporation of peripheral biomarkers in the treatment of MDD could supplement clinical observation and increase the remission rate (Breitenstein et al, 2014; Domenici, 2014; Chan et al, 2016a; Lam et al, 2016)

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