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.

Comparative efficacy and acceptability of electroconvulsive therapy versus repetitive transcranial magnetic stimulation for major depression: A systematic review and multiple-treatments meta-analysis.

BACKGROUNDS: The effects of electroconvulsive therapy (ECT) and bilateral, left prefrontal, and right prefrontal repetitive transcranial magnetic stimulation (rTMS) on major depressive disorder (MDD) have not been adequately addressed by previous studies. Here, a multiple-treatments meta-analysis, which incorporates evidence from direct and indirect comparisons from a network of trials, was performed to assess the efficacy and acceptability of these four treatment modalities on MDD. METHOD: The literature was searched for randomized controlled trials (RCTs) on ECT, bilateral rTMS, and unilateral rTMS for treating MDD up to May 2016. The main outcome measures were response and drop-out rates. RESULTS: Data were obtained from 25 studies consisting of 1288 individuals with MDD. ECT was non-significantly more efficacious than B-rTMS, R-rTMS, and L-rTMS. Left prefrontal rTMS was non -significantly less efficacious than all other treatment modalities. In terms of acceptability, R-rTMS was non-significantly better tolerated than ECT, B-rTMS, and L-rTMS. ECT was the most efficacious treatment with the cumulative probabilities of being the most efficacious treatment being: ECT (65%), B-rTMS (25%), R-rTMS (8%), and L-rTMS (2%). R-rTMS was the best-tolerated treatment with the cumulative probabilities of being the best-tolerated treatment being: R-rTMS (52%), B-rTMS (17%), L-rTMS (16%), and ECT (14%). Coherence analysis detected no statistically significant incoherence in any comparisons of direct with indirect evidence for the response rate and drop-out rate. CONCLUSIONS: ECT was the most efficacious, but least tolerated, treatment, while R-rTMS was the best tolerated treatment for MDD. B-rTMS appears to have the most favorable balance between efficacy and acceptability.

Combined Metabolomics and Proteomics Analysis of Major Depression in an Animal Model: Perturbed Energy Metabolism in the Chronic Mild Stressed Rat Cerebellum.

Major depressive disorder (MDD) is a highly prevalent, debilitating mental illness of importance for global health. However, its molecular pathophysiology remains poorly understood. Combined proteomics and metabolomics approaches should provide a comprehensive understanding of MDD's etiology. The present study reports novel "-omics" insights from a rodent model of MDD. Cerebellar samples from chronic mild stressed (CMS)-treated depressed rats and controls were compared with a focus on the differentially expressed proteins and metabolites using isobaric tags for relative and absolute quantitation (iTRAQ)-based proteomics and gas chromotography/mass spectrometry (GC-MS) metabolomics techniques, respectively. The combined analyses found significant alterations associated with cerebellar energy metabolism, as indicated by (1) abnormal amino acid metabolism accompanied by corresponding metabolic enzymatic alterations and disturbed protein turnover, (2) increased glycolytic and tricarboxylic acid (TCA) cycle enzyme levels paralleled by changes in the concentrations of associated metabolites, and (3) perturbation of ATP biosynthesis through adenosine accompanied by perturbation of the mitochondrial respiratory chain. To the best of our knowledge, this study is the first to integrate proteomics and metabolomics analyses to examine the pathophysiological mechanism(s) underlying MDD in a CMS rodent model of depression. These results can offer important insights into the pathogenesis of MDD.

Combined application of NMR- and GC-MS-based metabonomics yields a superior urinary biomarker panel for bipolar disorder.

Bipolar disorder (BD) is a debilitating mental disorder that cannot be diagnosed by objective laboratory-based modalities. Our previous studies have independently used nuclear magnetic resonance (NMR)-based and gas chromatography-mass spectrometry (GC-MS)-based metabonomic methods to characterize the urinary metabolic profiles of BD subjects and healthy controls (HC). However, the combined application of NMR spectroscopy and GC-MS may identify a more comprehensive metabolite panel than any single metabonomic platform alone. Therefore, here we applied a dual platform (NMR spectroscopy and GC-MS) that generated a panel of five metabolite biomarkers for BD-four GC-MS-derived metabolites and one NMR-derived metabolite. This composite biomarker panel could effectively discriminate BD subjects from HC, achieving an area under receiver operating characteristic curve (AUC) values of 0.974 in a training set and 0.964 in a test set. Moreover, the diagnostic performance of this panel was significantly superior to the previous single platform-derived metabolite panels. Thus, the urinary biomarker panel identified here shows promise as an effective diagnostic tool for BD. These findings also demonstrate the complementary nature of NMR spectroscopy and GC-MS for metabonomic analysis, suggesting that the combination of NMR spectroscopy and GC-MS can identify a more comprehensive metabolite panel than applying each platform in isolation.