Chanjuan Zhou

Meta-analyses of comparative efficacy of antidepressant medications on peripheral BDNF concentration in patients with depression

Background Brain derived neurotrophic factor (BDNF) is one of the most important regulatory proteins in the pathophysiology of major depressive disorder (MDD). Increasing numbers of studies have reported the relationship between serum/plasma BDNF and antidepressants (ADs). However, the potential effects of several classes of antidepressants on BDNF concentrations are not well known. Hence, our meta-analyses aims to review the effects of differential antidepressant drugs on peripheral BDNF levels in MDD and make some recommendations for future research. Methods Electronic databases including PubMed, EMBASE, the Cochrane Library, Web of Science, and PsycINFO were searched from 1980 to June 2016. The change in BDNF levels were compared between baseline and post-antidepressants treatment by use of the standardized mean difference (SMD) with 95% confidence intervals (CIs). All statistical tests were two-sided. Results We identified 20 eligible trials of antidepressants treatments for BDNF in MDD. The overall effect size for all drug classes showed that BDNF levels were elevated following a course of antidepressants use. For between-study heterogeneity by stratification analyses, we detect that length of treatment and blood samples are significant effect modifiers for BDNF levels during antidepressants treatment. While both SSRIs and SNRIs could increase the BDNF levels after a period of antidepressant medication treatment, sertraline was superior to other three drugs (venlafaxine, paroxetine or escitalopram) in the early increase of BDNF concentrations with SMD 0.53(95% CI = 0.13–0.93; P = 0.009). Conclusions There is some evidence that treatment of antidepressants appears to be effective in the increase of peripheral BDNF levels. More robust evidence indicates that different types of antidepressants appear to induce differential effects on the BDNF levels. Since sertraline makes a particular effect on BDNF concentration within a short amount of time, there is potential value in exploring its relationship with BDNF and its pharmacological mechanism concerning peripheral blood BDNF. Further confirmatory trials are required for both observations.

Divergent Urinary Metabolic Phenotypes between Major Depressive Disorder and Bipolar Disorder Identified by a Combined GC-MS and NMR Spectroscopic Metabonomic Approach.

Bipolar disorder (BD) is a complex debilitating mental disorder that is often misdiagnosed as major depressive disorder (MDD). Therefore, a large percentage of BD subjects are incorrectly treated with antidepressants in clinical practice. To address this challenge, objective laboratory-based tests are needed to discriminate BD from MDD patients. Here, a combined gas chromatography-mass spectrometry (GC-MS)-based and nuclear magnetic resonance (NMR) spectroscopic-based metabonomic approach was performed to profile urine samples from 76 MDD and 43 BD subjects (training set) to identify the differential metabolites. Samples from 126 healthy controls were included as metabolic controls. A candidate biomarker panel was identified by further analyzing these differential metabolites. A testing set of, 50 MDD and 28 BD subjects was then used to independently validate the diagnostic efficacy of the identified panel using an area under the receiver operating characteristic curve (AUC). A total of 20 differential metabolites responsible for the discrimination between MDD and BD subjects were identified. A panel consisting of six candidate urinary metabolite biomarkers (propionate, formate, (R*,S*)2,3-dihydroxybutanoic acid, 2,4-dihydroxypyrimidine, phenylalanine, and β-alanine) was identified. This panel could distinguish BD from MDD subjects with an AUC of 0.913 and 0.896 in the training and testing sets, respectively. These results reveal divergent urinary metabolic phenotypes between MDD and BD. The identified urinary biomarkers can aid in the future development of an objective laboratory-based diagnostic test for distinguishing BD from MDD patients.

The identification of metabolic disturbances in the prefrontal cortex of the chronic restraint stress rat model of depression.

Major depressive disorder, with serious impairment in cognitive and social functioning, is a complex psychiatric disorder characterized by pervasive and persistent low mood and a loss of interest or pleasure. However, the underlying molecular mechanisms of depression remain largely unknown. In this study, we used a non-targeted metabolomics approach based on gas chromatography-mass spectrometry of the prefrontal cortex in chronic restraint stress (CRS)-treated rats. CRS was induced in the stress group by restraining rats in a plastic restrainer for 6h every day. This stress paradigm continued for 21 days. Body weight measurement and behavior tests were applied, including the sucrose preference test for anhedonia, the forced swimming test for despair-like behavior, and open field test and the elevated plus-maze to test for anxiety-like behaviors in rats after CRS. Differentially expressed metabolites associated with CRS-treated rats were identified by combining multivariate and univariate statistical analysis and corrected for multiple testing using the Benjamini-Hochberg procedure. A heat map of differential metabolites was constructed using Matlab. Ingenuity Pathways Analysis was applied to identify the predicted pathways and biological functions relevant to the bio-molecules of interest. Our findings showed that CRS induces depression-like behaviors and not anxiety-like behaviors. Thirty-six metabolites were identified as potential depression biomarkers involved in amino acid metabolism, energy metabolism and lipid metabolism, as well as a disturbance in neurotransmitters. Consequently, this study provides useful insights into the molecular mechanisms of depression.

Metabolomic analysis reveals metabolic disturbances in the prefrontal cortex of the lipopolysaccharide-induced mouse model of depression.

Major depressive disorder (MDD) is a debilitating illness. However, the underlying molecular mechanisms of depression remain largely unknown. Increasing evidence supports that inflammatory cytokine disturbances may be associated with the pathophysiology of depression in humans. Systemic administration of lipopolysaccharide (LPS) has been used to study inflammation-associated neurobehavioral changes in rodents, but no metabonomic study has been conducted to assess differential metabolites in the prefrontal cortex (PFC) of a LPS-induced mouse model of depression. Here, we employed a gas chromatography-mass spectrometry-based metabonomic approach in the LPS-induced mouse model of depression to investigate any significant metabolic changes in the PFC. Multivariate statistical analysis, including principal component analysis (PCA), partial least squares-discriminate analysis (PLS-DA), and pair-wise orthogonal projections to latent structures discriminant analysis (OPLS-DA), was implemented to identify differential PFC metabolites between LPS-induced depressed mice and healthy controls. A total of 20 differential metabolites were identified. Compared with control mice, LPS-treated mice were characterized by six lower level metabolites and 14 higher level metabolites. These molecular changes were closely related to perturbations in neurotransmitter metabolism, energy metabolism, oxidative stress, and lipid metabolism, which might be evolved in the pathogenesis of MDD. These findings provide insight into the pathophysiological mechanisms underlying MDD and could be of valuable assistance in the clinical diagnosis of MDD.

Urinary peptidomics identifies potential biomarkers for major depressive disorder.

Major depressive disorder (MDD) is a debilitating psychiatric illness with no available objective laboratory-based diagnostic test. In this study, matrix-assisted laser desorption ionization time-of-flight mass spectrometry (MALDI-TOF MS)-based peptidomics was applied to identify potential urinary diagnostic biomarkers for MDD. A training set of 42 first-episode drug-naive MDD patients and 28 age- and gender-matched healthy controls (HC) was used to develop a peptide diagnostic pattern. Then, the diagnostic efficacy of this pattern was assessed in an independent blinded test set consisting of 24 MDD patients and 13 age- and gender-matched HC. A combination of five potential biomarkers was identified, yielding a sensitivity of 91.7% and specificity of 84.6% in the test set. Moreover, the protein precursors of four of the five peptides were identified by tandem mass spectrometric analysis: serum albumin, apolipoprotein A-I, protein AMBP, and basement membrane-specific heparan sulfate proteoglycan core protein. Taken together, the peptide pattern may be valuable for establishing an objective laboratory-based diagnostic test for MDD.

Plasma lipidomics reveals potential lipid markers of major depressive disorder.

Major depressive disorder (MDD) is a grave debilitating mental disease with a high incidence and severely impairs quality of life. Therefore, its physiopathological basis study and diagnostic biomarker discovery are extremely valuable. In this study, a non-targeted lipidomics strategy using ultra performance liquid chromatography coupled with quadrupole time-of-flight mass spectrometry (UPLC-Q-TOF/MS) was performed to reveal differential lipids between MDD (n = 60) and healthy controls (HCs, n = 60). Validation of changed lipid species was performed in an independent batch including 75 MDD and 52 HC using the same lipidomic method. Pronouncedly changed lipid species in MDD were discovered, which mainly were lysophosphatidylcholine (LPC), lysophosphatidylethanolamine (LPE), phosphatidylcholine (PC), phosphatidylethanolamine (PE), phosphatidylinositol (PI), 1-O-alkyl-2-acyl-PE (PE O), 1-O-alkyl-2-acyl-PC (PC O), sphingomyelin (SM), diacylglycerol (DG), and triacylglycerol (TG). Among these lipid species, LPC, LPE, PC, PE, PI, TG, etc. remarkably increased in MDD and showed pronounced positive relationships with depression severity, while 1-O-alkyl-2-acyl-PE and SM with odd summed carbon number significantly decreased in MDD and demonstrated negative relationships with depression severity. A combinational lipid panel including LPE 20:4, PC 34:1, PI 40:4, SM 39:1, 2, and TG 44:2 was defined as potential diagnostic biomarker with a good sensitivity and specificity for distinguishing MDD from HCs. Our study brings insights into lipid metabolism disorder in MDD and provides a specific potential biomarker for MDD diagnose.

Identification of sex-specific urinary biomarkers for major depressive disorder by combined application of NMR- and GC-MS-based metabonomics.

Women are more vulnerable to major depressive disorder (MDD) than men. However, molecular biomarkers of sex differences are limited. Here we combined gas chromatography–mass spectrometry (GC–MS)- and nuclear magnetic resonance (NMR)-based metabonomics to investigate sex differences of urinary metabolite markers in MDD, and further explore their potential of diagnosing MDD. Consequently, the metabolite signatures of women and men MDD subjects were significantly different from of that in their respective healthy controls (HCs). Twenty seven women and 36 men related differentially expressed metabolites were identified in MDD. Fourteen metabolites were changed in both women and men MDD subjects. Significantly, the women-specific (m-Hydroxyphenylacetate, malonate, glycolate, hypoxanthine, isobutyrate and azelaic acid) and men-specific (tyrosine, N-acetyl-d-glucosamine, N-methylnicotinamide, indoxyl sulfate, citrate and succinate) marker panels were further identified, which could differentiate men and women MDD patients from their respective HCs with higher accuracy than previously reported sex-nonspecific marker panels. Our findings demonstrate that men and women MDD patients have distinct metabonomic signatures and sex-specific biomarkers have promising values in diagnosing MDD.

1H NMR-Based Metabolic Profiling Reveals the Effects of Fluoxetine on Lipid and Amino Acid Metabolism in Astrocytes

Fluoxetine, a selective serotonin reuptake inhibitor (SSRI), is a prescribed and effective antidepressant and generally used for the treatment of depression. Previous studies have revealed that the antidepressant mechanism of fluoxetine was related to astrocytes. However, the therapeutic mechanism underlying its mode of action in astrocytes remains largely unclear. In this study, primary astrocytes were exposed to 10 µM fluoxetine; 24 h post-treatment, a high-resolution proton nuclear magnetic resonance (1H NMR)-based metabolomic approach coupled with multivariate statistical analysis was used to characterize the metabolic variations of intracellular metabolites. The orthogonal partial least-squares discriminant analysis (OPLS-DA) score plots of the spectra demonstrated that the fluoxetine-treated astrocytes were significantly distinguished from the untreated controls. In total, 17 differential metabolites were identified to discriminate the two groups. These key metabolites were mainly involved in lipids, lipid metabolism-related molecules and amino acids. This is the first study to indicate that fluoxetine may exert antidepressant action by regulating the astrocyte’s lipid and amino acid metabolism. These findings should aid our understanding of the biological mechanisms underlying fluoxetine therapy.

Effects of gut microbiota on the microRNA and mRNA expression in the hippocampus of mice

Backgrounds Gut microbiota is increasingly recognized as an important environmental factor that could influence the brain function and behaviors through the microbiota‐gut‐brain axis. Method Here, we used the germ‐free (GF) mice to explore the effect of gut microbiota on hippocampal microRNA (miRNA) and messenger RNAs (mRNAs) expression. Results Behavioral tests showed that, compared to specific pathogen‐free (SPF) mice, the GF mice displayed more center time, center distance and less latency to familiar food. Colonization of the GF mice with gut microbiota from SPF mice did not reverse these behaviors. However, 7 differentially expressed miRNAs and 139 mRNAs were significantly restored. Through microRNA Target Filter analysis, 4 of 7 restored miRNAs had 2232 target mRNAs. Among these target mRNAs, 21 target mRNAs levels were decreased. Further analysis showed that the most significant GO terms were metabolic process (GO: 0008152), binding (GO: 0005488) and cell part (GO: 0044464) for biological process, molecular function and cellular component, respectively, and the most significantly altered pathway was axon guidance (mmu04360). Conclusions These findings indicated that colonization of gut microbiota to adolescent GF mice was not sufficient to reverse the behavioral alterations. Gut microbiota could significantly influence the expression levels of miRNAs and mRNAs in hippocampus. Our results could provide original and valuable data for researchers to further study the microbiota‐gut‐brain axis. Highlights7 miRNA in the hippocampus was related with gut microbiota.139 mRNA in the hippocampus was related with gut microbiota.4 miRNA and 21 target mRNAs resulted in 22 miRNA‐mRNA interactions.

Differential urinary metabolites related with the severity of major depressive disorder

&NA; Major depressive disorder (MDD) is a common mental disorder that affects a persons general health. However, there is still no objective laboratory test for diagnosing MDD. Here, an integrated analysis of data from our previous studies was performed to identify the differential metabolites in the urine of moderate and severe MDD patients. A dual platform approach (NMR spectroscopy and GC–MS) was used. Consequently, 14 and 22 differential metabolites responsible for separating moderate and severe MDD patients, respectively, from their respective healthy controls (HCs) were identified. Meanwhile, the moderate MDD‐specific panel (N‐Methylnicotinamide, Acetone, Choline, Citrate, vanillic acid and azelaic acid) and severe MDD‐specific panel (indoxyl sulphate, Taurine, Citrate, 3‐hydroxyphenylacetic acid, palmitic acid and Lactate) could discriminate moderate and severe MDD patients, respectively, from their respective HCs with high accuracy. Moreover, the differential metabolites in severe MDD were significantly involved in three metabolic pathways and some biofunctions. These results showed that there were divergent urinary metabolic phenotypes in moderate and severe MDD patients, and the identified potential urinary biomarkers might be useful for future developing objective diagnostic tests for MDD diagnosis. Our results could also be helpful for researchers to study the pathogenesis of MDD. HighlightsModerate MDD‐specific panel consisting of five metabolites was identified.Severe MDD‐specific panel consisting of five metabolites was identified.Moderate and severe MDD patients have divergent urinary metabolic phenotypes.