Gut microbiota has an important role in the immune system, metabolism, and digestion, and has a significant effect on the nervous system. Recent studies have revealed that abnormal gut microbiota induces abnormal behaviors, which may be associated with the hypothalamic–pituitary–adrenal (HPA) axis. Therefore, we investigated the behavioral changes in germ-free (GF) mice by behavioral tests, quantified the basal serum cortisol levels, and examined glucocorticoid receptor pathway genes in hippocampus using microarray analysis followed by real-time PCR validation, to explore the molecular mechanisms by which the gut microbiota influences the host’s behaviors and brain function. Moreover, we quantified the basal serum cortisol levels and validated the differential genes in an Escherichia coli-derived lipopolysaccharide (LPS) treatment mouse model and fecal “depression microbiota” transplantation mouse model by real-time PCR. We found that GF mice showed antianxiety- and antidepressant-like behaviors, whereas E. coli LPS-treated mice showed antidepressant-like behavior, but did not show antianxiety-like behavior. However, “depression microbiota” recipient mice exhibited anxiety- and depressive-like behaviors. In addition, six glucocorticoid receptor pathway genes (Slc22a5, Aqp1, Stat5a, Ampd3, Plekhf1, and Cyb561) were upregulated in GF mice, and of these only two (Stat5a and Ampd3) were upregulated in LPS-treated mice, whereas the shared gene, Stat5a, was downregulated in “depression microbiota” recipient mice. Furthermore, basal serum cortisol levels were decreased in E. coli LPS-treated mice but not in GF mice and “depression microbiota” recipient mice. These results indicated that the gut microbiota may lead to behavioral abnormalities in mice through the downstream pathway of the glucocorticoid receptor. Herein, we proposed a new insight into the molecular mechanisms by which gut microbiota influence depressive-like behavior.

Background and Purpose-Early hematoma growth is not uncommon in patients with intracerebral hemorrhage and is an independent predictor of poor functional outcome. The purpose of our study was to report and validate the use of our newly identified computed tomographic (CT) blend sign in predicting early hematoma growth. Methods-Patients with intracerebral hemorrhage who underwent baseline CT scan within 6 hours after onset of symptoms were included. The follow-up CT scan was performed within 24 hours after the baseline CT scan. Significant hematoma growth was defined as an increase in hematoma volume of >33% or an absolute increase of hematoma volume of >12.5 mL. The blend sign on admission nonenhanced CT was defined as blending of hypoattenuating area and hyperattenuating region with a well-defined margin. Univariate and multivariable logistic regression analyses were performed to assess the relationship between the presence of the blend sign on nonenhanced admission CT and early hematoma growth. Results-A total of 172 patients were included in our study. Blend sign was observed in 29 of 172 (16.9%) patients with intracerebral hemorrhage on baseline nonenhanced CT scan. Of the 61 patients with hematoma growth, 24 (39.3%) had blend sign on admission CT scan. Interobserver agreement for identifying blend sign was excellent between the 2 readers (κ=0.957). The multivariate logistic regression analysis demonstrated that the time to baseline CT scan, initial hematoma volume, and presence of blend sign on baseline CT scan to be independent predictors of early hematoma growth. The sensitivity, specificity, positive and negative predictive values of blend sign for predicting hematoma growth were 39.3%, 95.5%, 82.7%, and 74.1%, respectively. Conclusions-The CT blend sign could be easily identified on regular nonenhanced CT and is highly specific for predicting hematoma growth.

Major depressive disorder (MDD) is a debilitating psychiatric illness. However, there is currently no objective laboratory-based diagnostic tests for this disorder. Although, perturbations in multiple neurotransmitter systems have been implicated in MDD, the biochemical changes underlying the disorder remain unclear, and a comprehensive global evaluation of neurotransmitters in MDD has not yet been performed. Here, using a GC-MS coupled with LC-MS/MS-based targeted metabolomics approach, we simultaneously quantified the levels of 19 plasma metabolites involved in GABAergic, catecholaminergic, and serotonergic neurotransmitter systems in 50 first-episode, antidepressant drug-naïve MDD subjects and 50 healthy controls to identify potential metabolite biomarkers for MDD (training set). Moreover, an independent sample cohort comprising 49 MDD patients, 30 bipolar disorder (BD) patients and 40 healthy controls (testing set) was further used to validate diagnostic generalizability and specificity of these candidate biomarkers. Among the 19 plasma neurotransmitter metabolites examined, nine were significantly changed in MDD subjects. These metabolites were mainly involved in GABAergic, catecholaminergic and serotonergic systems. The GABAergic and catecholaminergic had better diagnostic value than serotonergic pathway. A panel of four candidate plasma metabolite biomarkers (GABA, dopamine, tyramine, kynurenine) could distinguish MDD subjects from health controls with an AUC of 0.968 and 0.953 in the training and testing set, respectively. Furthermore, this panel distinguished MDD subjects from BD subjects with high accuracy. This study is the first to globally evaluate multiple neurotransmitters in MDD plasma. The altered plasma neurotransmitter metabolite profile has potential differential diagnostic value for MDD.

Major depressive disorder (MDD) is a prevalent and debilitating mental disorder. Yet, there are no objective biomarkers available to support diagnostic laboratory testing for this disease. Here, gas chromatography-mass spectrometry was applied to urine metabolic profiling of 126 MDD and 134 control subjects. Orthogonal partial least-squares discriminant analysis (OPLS-DA) was used to identify the differential metabolites in MDD subjects relative to healthy controls. The OPLS-DA analysis of data from training samples (82 first-episode, drug-naïve MDD subjects and 82 well-matched healthy controls) showed that the depressed group was significantly distinguishable from the control group. Totally, 23 differential urinary metabolites responsible for the discrimination between the two groups were identified. Postanalysis, 6 of the 23 metabolites (sorbitol, uric acid, azelaic acid, quinolinic acid, hippuric acid, and tyrosine) were defined as candidate diagnostic biomarkers for MDD. Receiver operating characteristic analysis of combined levels of these six biomarkers yielded an area under the receiver operating characteristic curve (AUC) of 0.905 in distinguishing training samples; this simplified metabolite signature classified blinded test samples (44 MDD subjects and 52 healthy controls) with an AUC of 0.837. Furthermore, a composite panel by the addition of previously identified urine biomarker (N-methylnicotinamide) to this biomarker panel achieved a more satisfactory accuracy, yielding an AUC of 0.909 in the training samples and 0.917 in the test samples. Taken together, these results suggest this composite urinary metabolite signature should facilitate development of a urine-based diagnostic test for MDD.

Major depressive disorder (MDD) is a common mood disorder. Gut microbiota may be involved in the pathogenesis of depression via the microbe–gut–brain axis. Liver is vulnerable to exposure of bacterial products translocated from the gut via the portal vein and may be involved in the axis. In this study, germ-free mice underwent fecal microbiota transplantation from MDD patients and healthy controls. Behavioral tests verified the depression model. Metabolomics using gas chromatography–mass spectrometry, nuclear magnetic resonance, and liquid chromatography–mass spectrometry determined the influence of microbes on liver metabolism. With multivariate statistical analysis, 191 metabolites were distinguishable in MDD mice from control (CON) mice. Compared with CON mice, MDD mice showed lower levels for 106 metabolites and higher levels for 85 metabolites. These metabolites are associated with lipid and energy metabolism and oxidative stress. Combined analyses of significantly changed proteins in livers from another depression model induced by chronic unpredictive mild stress returned a high score for the Lipid Metabolism, Free Radical Scavenging, and Molecule Transports network, and canonical pathways were involved in energy metabolism and tryptophan degradation. The two mouse models of depression suggest that changes in liver metabolism might be involved in the pathogenesis of MDD. Conjoint analyses of fecal, serum, liver, and hippocampal metabolites from fecal microbiota transplantation mice suggested that aminoacyl-tRNA biosynthesis significantly changed and fecal metabolites showed a close relationship with the liver. These findings may help determine the biological mechanisms of depression and provide evidence about “depression microbes” impacting on liver metabolism.

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Background and PurposeRecombinant tissue plasminogen activator (rtPA) is the only effective drug approved by US FDA to treat ischemic stroke, and it contains pleiotropic effects besides thrombolysis. We performed a meta-analysis to clarify effect of tissue plasminogen activator (tPA) on cerebral infarction besides its thrombolysis property in mechanical animal stroke.MethodsRelevant studies were identified by two reviewers after searching online databases, including Pubmed, Embase, and ScienceDirect, from 1979 to 2016. We identified 6, 65, 17, 12, 16, 12 and 13 comparisons reporting effect of endogenous tPA on infarction volume and effects of rtPA on infarction volume, blood-brain barrier, brain edema, intracerebral hemorrhage, neurological function and mortality rate in all 47 included studies. Standardized mean differences for continuous measures and risk ratio for dichotomous measures were calculated to assess the effects of endogenous tPA and rtPA on cerebral infarction in animals. The quality of included studies was assessed using the Stroke Therapy Academic Industry Roundtable score. Subgroup analysis, meta-regression and sensitivity analysis were performed to explore sources of heterogeneity. Funnel plot, Trim and Fill method and Egger’s test were obtained to detect publication bias.ResultsWe found that both endogenous tPA and rtPA had not enlarged infarction volume, or deteriorated neurological function. However, rtPA would disrupt blood-brain barrier, aggravate brain edema, induce intracerebral hemorrhage and increase mortality rate.ConclusionsThis meta-analysis reveals rtPA can lead to neurological side effects besides thrombolysis in mechanical animal stroke, which may account for clinical exacerbation for stroke patients that do not achieve vascular recanalization with rtPA.