Associations Between Disordered Microbial Metabolites and Changes of Neurotransmitters in Depressed Mice.

Backgrounds: Many pieces of evidence demonstrated that there were close relationships between gut microbiota and depression. However, the specific molecular mechanisms were still unknown. Here, using targeted metabolomics, this study was conducted to explore the relationships between microbial metabolites in feces and neurotransmitters in prefrontal cortex of depressed mice. Methods: Chronic unpredictable mild stress (CUMS) model of depression was built in this study. Targeted liquid chromatography-mass spectrometry analysis was used to detect the microbial metabolites in feces and neurotransmitters in prefrontal cortex of mice. Both univariate and multivariate statistical analyses were applied to identify the differential microbial metabolites and neurotransmitters and explore relationships between them. Results: Ninety-eight differential microbial metabolites (mainly belonged to amino acids, fatty acids, and bile acids) and 11 differential neurotransmitters (belonged to tryptophan pathway, GABAergic pathway, and catecholaminergic pathway) were identified. Five affected amino acid-related metabolic pathways were found in depressed mice. The 19 differential microbial metabolites and 10 differential neurotransmitters were found to be significantly correlated with depressive-like behaviors. The two differential neurotransmitters (tyrosine and glutamate) and differential microbial metabolites belonged to amino acids had greater contributions to the overall correlations between microbial metabolites and neurotransmitters. In addition, the significantly decreased L-tyrosine as microbial metabolites and tyrosine as neurotransmitter had the significantly positive correlation (r = 0.681, p = 0.0009). Conclusions: These results indicated that CUMS-induced disturbances of microbial metabolites (especially amino acids) might affect the levels of neurotransmitters in prefrontal cortex and then caused the onset of depression. Our findings could broaden the understanding of how gut microbiota was involved in the onset of depression.

MicroRNA Transcriptomics Analysis Identifies Dysregulated Hedgehog Signaling Pathway in a Mouse Model of Acute Intracerebral Hemorrhage Exposed to Hyperglycemia.

OBJECTIVE: Hyperglycemia is often observed in the patients after acute stroke. This study aims to elucidate the potential effect and mechanism of hyperglycemia by screening microRNAs expression in intracerebral hemorrhage mice. METHODS: We employed the collagenase model of intracerebral hemorrhage. Twenty male C57BL/6 mice were used and randomly divided in normo- and hyperglycemic. The hyperglycemia was induced by intraperitoneally injection of 50% of Dextrose (8 mL/kg) 3 hours after intracerebral hemorrhage. The neurologic impairment was investigated by neurologic deficit scale. To study the specific mechanisms of hyperglycemia, microRNAs expression in perihematomal area was investigated by RNA sequencing. MicroRNAs expression in hyperglycemic intracerebral hemorrhage animals were compared normoglycemic mice. Functional annotation analysis was used to indicate potential pathological pathway, underlying observed effects. Finally, polymerase chain reaction validation was administered. RESULTS: Intraperitoneal injection of dextrose significantly increased blood glucose level. That was associated with aggravation of neurological deficits in hyperglycemic compared to normoglycemic animals. A total of 73 differentially expressed microRNAs were identified via transcriptomics analysis. Bioinformatics analyses showed that these microRNAs were significantly altered in several signaling pathways, of which the hedgehog signaling pathway was regarded as the most potential pathway associated with the effect of hyperglycemia on acute intracerebral hemorrhage. Furthermore, polymerase chain reaction results validated the correlation between microRNAs and hedgehog signaling pathway. CONCLUSIONS: MicroRNA elevated in hyperglycemia group may be involved in worsening the neurological function via inhibiting the hedgehog signaling, which provides a novel molecular physiological mechanism and lays the foundation for treatment of intracerebral hemorrhage.

Circulating microRNA 134 sheds light on the diagnosis of major depressive disorder.

Major depressive disorder (MDD) is a prevalent and debilitating psychiatric mood disorder that lacks objective laboratory-based tests to support its diagnosis. A class of microRNAs (miRNAs) has been found to be centrally involved in regulating many molecular processes fundamental to central nervous system function. Among these miRNAs, miRNA-134 (miR-134) has been reported to be related to neurogenesis and synaptic plasticity. In this study, the hypothesis that plasma miR-134 can be used to diagnose MDD was tested. Perturbation of peripheral and central miR-134 in a depressive-like rat model was also examined. By reverse-transcription quantitative PCR, miR-134 was comparatively measured in a small set of plasma samples from MDD and healthy control (HC) subjects. To determine its diagnostic efficacy, plasma miR-134 levels were assessed in 100 MDD, 50 bipolar disorder (BD), 50 schizophrenic (SCZ), and 100 HC subjects. A chronic unpredictable mild stress (CUMS) rat model was also developed to evaluate miR-134 expression in plasma, hippocampus (HIP), prefrontal cortex (PFC), and olfactory bulb. We found that plasma miR-134 was significantly downregulated in MDD subjects. Diagnostically, plasma miR-134 levels could effectively distinguish MDD from HC with 79% sensitivity and 84% specificity, while distinguishing MDD from HC, BD, and SCZ subjects with 79% sensitivity and 76.5% specificity. Congruent with these clinical findings, CUMS significantly reduced miR-134 levels in the rat plasma, HIP, and PFC. Although limited by the relatively small sample size, these results demonstrated that plasma miR-134 displays potential ability as a biomarker for MDD.

Age-specific differential changes on gut microbiota composition in patients with major depressive disorder.

Emerging evidence has shown the age-related changes in gut microbiota, but few studies were conducted to explore the effects of age on the gut microbiota in patients with major depressive disorder (MDD). This study was performed to identify the age-specific differential gut microbiota in MDD patients. In total, 70 MDD patients and 71 healthy controls (HCs) were recruited and divided into two groups: young group (age 18-29 years) and middle-aged group (age 30-59 years). The 16S rRNA gene sequences were extracted from the collected fecal samples. Finally, we found that the relative abundances of Firmicutes and Bacteroidetes were significantly decreased and increased, respectively, in young MDD patients as compared with young HCs, and the relative abundances of Bacteroidetes and Actinobacteria were significantly decreased and increased, respectively, in middle-aged MDD patients as compared with middle-aged HCs. Meanwhile, six and 25 differentially abundant bacterial taxa responsible for the differences between MDD patients (young and middle-aged, respectively) and their respective HCs were identified. Our results demonstrated that there were age-specific differential changes on gut microbiota composition in patients with MDD. Our findings would provide a novel perspective to uncover the pathogenesis underlying MDD.

Absence of gut microbiota affects lipid metabolism in the prefrontal cortex of mice.

Objectives: Lipid metabolism is closely associated with many important biological functions. Here, we conducted this study to explore the effects of gut microbiota on the lipid metabolism in the prefrontal cortex of mice. Methods: Germ-free (GF) mice, specific pathogen-free (SPF) and colonized GF (CGF) mice were used in this study. The open field test (OFT), forced swimming test (FST) and novelty suppressed feeding test (NSFT) were conducted to assess the changes in general behavioral activity. The liquid Chromatography-Tandem Mass Spectrometry (LC-MS/MS) was used to obtain the lipid metabolites. Both one-way analysis of variance (one-way ANOVA) and orthogonal partial least-squares discriminant analysis (OPLS-DA) were used to obtain the key differential lipid metabolites. Results: The behavioral tests showed that compared to SPF mice, GF mice had more center distance, more center time, less immobility time and less latency to familiar food. Meanwhile, 142 key differential lipid metabolites between SPF mice and GF mice were identified. These lipid metabolites mainly belonged to glycerophospholipids, glycerolipids, sphingolipids, and saccharolipids. The gut microbiota colonization did not reverse these changed behavioral phenotypes, but could restore 25 key differential lipid metabolites. Discussion: These results showed that the absence of gut microbiota could influence host behaviors and lipid metabolism. Our findings could provide original and valuable data for future studies to further investigate the microbiota-gut-brain axis.

Age-specific urinary metabolite signatures and functions in patients with major depressive disorder.

Major depressive disorder (MDD) patients in different age ranges might have different urinary metabolic phenotypes, because age could significantly affect the physiological and psychological status of person. Therefore, it was very important to take age into consideration when studying MDD. Here, a dual platform metabolomic approach was performed to profile urine samples from young and middle-aged MDD patients. In total, 18 and 15 differential metabolites that separately discriminated young and middle-aged MDD patients, respectively, from their respective HC were identified. Only ten metabolites were significantly disturbed in both young and middle-aged MDD patients. Meanwhile, two different biomarker panels for diagnosing young and middle-aged MDD patients, respectively, were identified. Additionally, the TCA cycle was significantly affected in both young and middle-aged MDD patients, but the Glyoxylate and dicarboxylate metabolism and phenylalanine metabolism were only significantly affected in young and middle-aged MDD patients, respectively. Our results would be helpful for developing age-specific diagnostic method for MDD and further investigating the pathogenesis of this disease.

Urinary biomarker panel for diagnosing patients with depression and anxiety disorders.

Available data indicate that patients with depression and anxiety disorders are likely to be at greater risk for suicide. Therefore, it is important to correctly diagnose patients with depression and anxiety disorders. However, there are still no empirical laboratory methods to objectively diagnose these patients. In this study, the multiple metabolomics platforms were used to profile the urine samples from 32 healthy controls and 32 patients with depression and anxiety disorders for identifying differential metabolites and potential biomarkers. Then, 16 healthy controls and 16 patients with depression and anxiety disorders were used to independently validate the diagnostic performance of the identified biomarkers. Finally, a panel consisting of four biomarkers-N-methylnicotinamide, aminomalonic acid, azelaic acid and hippuric acid-was identified. This panel was capable of distinguishing patients with depression and anxiety disorders from healthy controls with an area under the receiver operating characteristic curve of 0.977 in the training set and 0.934 in the testing set. Meanwhile, we found that these identified differential metabolites were mainly involved in three metabolic pathways and five molecular and cellular functions. Our results could lay the groundwork for future developing a urine-based diagnostic method for patients with depression and anxiety disorders.

Biochemical effects of venlafaxine on astrocytes as revealed by 1H NMR-based metabolic profiling.

As a serotonin-norepinephrine reuptake inhibitor [SNRI], venlafaxine is one of the most commonly prescribed clinical antidepressants, with a broad range of antidepressant effects. Accumulating evidence shows that venlafaxine may target astrocytes to exert its antidepressant activity, although the underlying pharmacological mechanisms remained largely unknown. Here, we used a 1H nuclear magnetic resonance (NMR)-based metabonomics method coupled with multivariate statistical analysis to characterize the metabolic profiling of astrocytes treated with venlafaxine to explore the potential mechanism of its antidepressant effect. In total, 31 differential metabolites involved in energy, amino acid and lipid metabolism were identified. Ingenuity pathway analysis was used to identify the predicted pathways and biological functions with venlafaxine and fluoxetine. The most significantly altered network was "amino acid metabolism, cellular growth and proliferation", with a score above 20. Certain metabolites (lysine, tyrosine, glutamate, methionine, ethanolamine, fructose-6-phosphate, and phosphorylethanolamine) are involved in and play a central role in this network. Collectively, the biological effects of venlafaxine on astrocytes provide us with the further understanding of the mechanisms by which venlafaxine treats major depressive disorder.

Targeted Metabolomic Pathway Analysis and Validation Revealed Glutamatergic Disorder in the Prefrontal Cortex among the Chronic Social Defeat Stress Mice Model of Depression.

Major depressive disorder (MDD) is a severe psychiatric disease that has critically affected life quality for millions of people. Chronic stress is gradually recognized as a primary pathogenesis risk factor of MDD. Despite the remarkable progress in mechanism research, the pathogenesis mechanism of MDD is still not well understood. Therefore, we conducted a liquid chromatography-tandem mass spectrometry (LC-MS/MS) detection of 25 major metabolites of tryptophanic, GABAergic, and catecholaminergic pathways in the prefontal cortex (PFC) of mice in chronic social defeat stress (CSDS). The depressed mice exhibit significant reduction of glutamate in the GABAergic pathway and an increase of L-DOPA and vanillylmandelic acid in catecholaminergic pathways. The data of real-time-quantitative polymerase chain reaction (RT-qPCR) and Western blotting analysis revealed an altered level of glutamatergic circuitry. The metabolomic and molecular data reveal that the glutamatergic disorder in mice shed lights to reveal a mechanism on depression-like and stress resilient phenotype.

Identification and validation of argininosuccinate synthase as a candidate urinary biomarker for major depressive disorder.

BACKGROUND: Major depressive disorder (MDD) is a debilitating psychiatric mood disorder. However, no objective laboratory-based test is yet available to aid in the diagnosis of this disorder. METHODS: In order to identify urinary protein biomarker candidates for MDD, the differential proteomic analysis of urine samples from first-episode drug-naïve MDD subjects and healthy controls (HC) was carried out by using two-dimensional gel electrophoresis separation followed by MALDI-TOF/TOF-MS/MS identification. Then, the differential expression levels of some candidate proteins were further validated by immunoblot analysis. RESULTS: Through mass spectrometry and database searching, a total of 27 differential proteins were identified, primarily including enzymes, plasma proteins, serpins, and adhesion molecules. Five proteins were selected for subsequent validation by Western blotting. One arginine recycling enzyme - argininosuccinate synthase (ASS1) - was further confirmed to be significantly downregulated in the urine of 30 depressed subjects while remaining unchanged in the plasma. Importantly, receiver-operator curve analyses revealed that ASS1 displayed strong efficacy in distinguishing MDD subjects from HC. CONCLUSION: The present study provides a range of urinary protein biomarker candidates for MDD, and further demonstrates that ASS1 has a potential for clinical diagnosis of this disorder.

Severe disturbance of glucose metabolism in peripheral blood mononuclear cells of schizophrenia patients: a targeted metabolomic study.

BACKGROUND: Schizophrenia is a widespread and debilitating mental disorder. However, the underlying molecular mechanism of schizophrenia remains largely unknown and no objective laboratory tests are available to diagnose this disorder. The aim of the present study was to characterize the alternations of glucose metabolites and identify potential diagnostic biomarkers for schizophrenia. METHODS: Gas chromatography/mass spectrometry based targeted metabolomic method was used to quantify the levels of 13 glucose metabolites in peripheral blood mononuclear cells (PBMCs) derived from healthy controls, schizophrenia and major depression subjects (n = 55 for each group). RESULTS: The majority (84.6%) of glucose metabolites were significantly disturbed in schizophrenia subjects, while only two (15.4%) glucose metabolites were differently expressed in depression subjects relative to healthy controls in both training set (n = 35/group) and test set (n = 20/group). Antipsychotics had only a subtle effect on glucose metabolism pathway. Moreover, ribose 5-phosphate in PBMCs showed a high diagnostic performance for first-episode drug-naïve schizophrenia subjects. CONCLUSION: These findings suggested disturbance of glucose metabolism may be implicated in onset of schizophrenia and could aid in development of diagnostic tool for this disorder.