Differential Gut Microbiota Compositions Related With the Severity of Major Depressive Disorder.

Objective: Increasing evidence shows a close relationship between gut microbiota and major depressive disorder (MDD), but the specific mechanisms remain unknown. This study was conducted to explore differential gut microbiota compositions related to the severity of MDD. Methods: Healthy controls (HC) (n = 131) and MDD patients (n = 130) were included. MDD patients with Hamilton Depression Rating Scale (HDRS) score <25 and ≥25 were assigned into moderate (n = 72) and severe (n = 58) MDD groups, respectively. Univariate and multivariate analyses were used to analyze the gut microbiota compositions at the genus level. Results: Thirty-six and 27 differential genera were identified in moderate and severe MDD patients, respectively. The differential genera in moderate and severe MDD patients mainly belonged to three (Firmicutes, Actinobacteriota, and Bacteroidota) and two phyla (Firmicutes and Bacteroidota), respectively. One specific covarying network from phylum Actinobacteriota was identified in moderate MDD patients. In addition, five genera (Collinsella, Eggerthella, Alistipes, Faecalibacterium, and Flavonifractor) from the shared differential genera by two MDD groups had a fair efficacy in diagnosing MDD from HC (AUC = 0.786). Conclusions: Our results were helpful for further exploring the role of gut microbiota in the pathogenesis of depression and developing objective diagnostic methods for MDD.

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.

Associations between disordered gut microbiota and changes of neurotransmitters and short-chain fatty acids in depressed mice.

Mounting evidence suggests that gut microbiota can play an important role in pathophysiology of depression, but its specific molecular mechanisms are still unclear. This study was conducted to explore the associations between changes in neurotransmitters and short-chain fatty acids (SCFAs) and altered gut microbiota in depressed mice. Here, the chronic restraint stress (CRS) model of depression was built. The classical behavioral tests were conducted to assess the depressive-like behaviors of mice. The 16S rRNA gene sequence extracted from fecal samples was used to assess the gut microbial composition. Liquid and gas chromatography mass spectroscopy were used to identify neurotransmitters in hypothalamus and SCFAs in fecal samples, respectively. Finally, 29 differential bacteria taxa between depressed mice and control mice were identified, and the most differentially abundant bacteria taxa were genus Allobaculum and family Ruminococcaceae between the two groups. The acetic acid, propionic acid, pentanoic acid, norepinephrine, 5-HIAA and 5-HT were significantly decreased in depressed mice compared to control mice. Genus Allobaculum was found to be significantly positively correlated with acetic acid and 5-HT. Taken together, these results provided novel microbial and metabolic frameworks for understanding the role of microbiota-gut-brain axis in depression, and suggested new insights to pave the way for novel therapeutic methods.

Dual Metabolomic Platforms Identified a Novel Urinary Metabolite Signature for Hepatitis B Virus-Infected Patients with Depression.

OBJECTIVE: Depression could make the treatment outcome worse. However, up to now, no objective methods were developed to diagnose depression in hepatitis B virus (HBV)-infected patients. Therefore, the dual metabolomic platforms were used here to identify potential biomarkers for diagnosing HBV-infected patients with depression (dHB). METHODS: Both gas chromatography-mass spectrometry-based and nuclear magnetic resonance-based metabolomic platforms were used to conduct urine metabolic profiling of dHB subjects and HBV-infected patients without depression (HB). Orthogonal partial least-squares discriminant analysis was used to identify the differential metabolites between dHB subjects and HB subjects, and the step-wise logistic regression analysis was used to identify potential biomarkers. RESULTS: In total, 21 important metabolites responsible for distinguishing dHB subjects from HB subjects were identified. Meanwhile, seven potential biomarkers (α-ydroxyisobutyric acid, hippuric acid, azelaic acid, isobutyric acid, malonic acid, levulinic acid, and phenylacetylglycine) were viewed as potential biomarkers. The simplified biomarker panel consisting of these seven metabolites had an excellent diagnostic performance in discriminating dHB subjects from HB subjects. Moreover, this panel could yield a higher accuracy in separating dHB subjects from HB subjects than our previous panels (identified by single metabolomic platform) did. CONCLUSION: These results suggested that the dual metabolomic platforms could yield a better urinary biomarker panel for dHB subjects than any single metabolomic platform did, and our results could be helpful for developing an objective method in future to diagnose depression in HBV-infected patients.

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.

Fabrication of Latex-based Nanofibers by Electrospinning.

BACKGROUND: Natural latex has been widely used in medical gloves, gas masks and nipples characterized by high elasticity, good film-forming performance and flexible film, but it is seldom used in nanomaterials. Electrospinning is an effective technology for manufacturing microfibrous or nanofibrous membranes. Latex-based nanofibers can be fabricated by electrospinning. Few relevant patents to the topic have been reviewed and cited. METHODS: The natural rubber latex and PVA solution were prepared for electrospinning in this study. RESULTS: When the rubber tends to nano scales, the flexibility of natural rubber gets enhanced. Additionally, the latex fluid can be used as an additive to improve mechanical property of nanofibers. CONCLUSION: The electrospinning rubber nanofibers shed a new light on rubber industry.

Urinary metabolite signature in bipolar disorder patients during depressive episode.

The first few episodes of bipolar disorder (BD) are highly likely to be depressive. This phenomenon causes many BD patients to be misdiagnosed as having major depression. Therefore, it is very important to correctly diagnose BD patients during depressive episode. Here, we conducted this study to identify potential biomarkers for young and middle-aged BD patients during depressive episode. Both gas chromatography-mass spectroscopy (GC-MS) and nuclear magnetic resonance (NMR) spectroscopy were used to profile the urine samples from the recruited subjects. In total, 13 differential metabolites responsible for the discrimination between healthy controls (HCs) and patients were identified. Most differential metabolites had a close relationship with energy homeostasis. Meanwhile, a panel consisting of five differential metabolites was identified. This panel could effectively distinguish the patients from HCs with an AUC of 0.998 in the training set and 0.974 in the testing set. Our findings on one hand could be helpful in developing an objective diagnostic method for young and middle-aged BD patients during depressive episode; on the other hand could provide critical insight into the pathological mechanism of BD and the biological mechanisms responsible for the transformation of different episodes.

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.

Differential urinary metabolites related with the severity of major depressive disorder.

Major depressive disorder (MDD) is a common mental disorder that affects a person's 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.

Social defeat stress causes depression-like behavior with metabolite changes in the prefrontal cortex of rats.

Major depressive disorder is a serious mental disorder with high morbidity and mortality. The role of social stress in the development of depression remains unclear. Here, we used the social defeat stress paradigm to induce depression-like behavior in rats, then evaluated the behavior of the rats and measured metabolic changes in the prefrontal cortex using gas chromatography-mass spectrometry. Within the first week after the social defeat procedure, the sucrose preference test (SPT), open field test (OFT), elevated plus maze (EPM) and forced swim test (FST) were conducted to examine the depressive-like and anxiety-like behaviors. For our metabolite analysis, multivariate statistics were applied to observe the distribution of all samples and to differentiate the socially defeated group from the control group. Ingenuity pathway analysis was used to find the potential relationships among the differential metabolites. In the OFT and EPM, there were no significant differences between the two experimental groups. In the SPT and FST, socially defeated rats showed less sucrose intake and longer immobility time compared with control rats. Metabolic profiling identified 25 significant variables with good predictability. Ingenuity pathways analysis revealed that "Hereditary Disorder, Neurological Disease, Lipid Metabolism" was the most significantly altered network. Stress-induced alterations of low molecular weight metabolites were observed in the prefrontal cortex of rats. Particularly, lipid metabolism, amino acid metabolism, and energy metabolism were significantly perturbed. The results of this study suggest that repeated social defeat can lead to metabolic changes and depression-like behavior in rats.

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.

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.

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 ß-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.