Major depressive disorder (MDD) is a debilitating mental illness that can cause significant emotional disturbances and severe socioeconomic burdens. Rodent and nonhuman primate-based depression models have been studied, such as brain-derived neurotrophic factor (BDNF) and monoamine acid disorder hypotheses, as well as peripheral microbiota disturbances causing MDD; however, the pathogenesis is still largely unknown. This study aims to explore the relationship between ferritin and MDD. First, alterations in ferritin, including ferritin light chain (FTL) and ferritin heavy chain (FTH), in MDD patient plasma compared with healthy control (HC) plasma were detected using ELISA. Then, serum ferritin expression in cLPS-depressed mice was measured by ELISA. The existence of FTH in the hippocampus was validated by immunofluorescence, and the change in FTH levels in the hippocampus of mice injected with cLPS was detected by western blotting. FTL levels in MDD patients were decreased compared with those in HCs. In cLPS-depressed mice, serum ferritin was not different from that in the control group, while the expression of FTH in the hippocampus was significantly reduced in depressed mice. Our findings demonstrate the alteration of ferritin expression in MDD and provide new insight into the pathogenesis of MDD.

Carbonic Anhydrase 1 (CAR1) is a zinc-metalloenzyme that catalyzes the hydration of carbon dioxide, and the alteration of CAR1 has been implicated in neuropsychiatric disorders. However, the mechanism underlying the role of CAR1 in major depressive disorder (MDD) remains largely unknown. In this study, we report the decreased level of CAR1 in MDD patients and depression-like model rodents. We found that CAR1 is expressed in hippocampal astrocytes and CAR1 regulates extracellular bicarbonate concentration and pH value in the partial hilus. Ablation of the CAR1 gene increased the activity of granule cells via decreasing their miniature inhibitory postsynaptic currents (mIPSC), and caused depression-like behaviors in CAR1-knockout mice. Astrocytic CAR1 expression rescued the deficits in mIPSCs of granule cells and reduced depression-like behaviors in CAR1 deficient mice. Furthermore, pharmacological activation of CAR1 and overexpression of CAR1 in the ventral hippocampus of mice improved depressive behaviors. These findings uncover a critical role of CAR1 in the MDD pathogenesis and its therapeutic potential.

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.

The peripheral levels of vascular endothelial growth factor (VEGF) have been studied in major psychiatric diseases compared with healthy controls (HCs), but the results were inconsistent. Moreover, few studies have compared VEGF levels between these psychiatric diseases. The aim of the present study was to compare blood VEGF levels in major depressive disorder (MDD), schizophrenia (SCZ), bipolar disorder either in a manic episode, a depressive episode, or a euthymic state, and HC. We supposed that VEGF levels may be elevated in some of these diseases as a potential biomarker. In this study, forty-four studies with 6343 participants were included, and network meta-analysis was used to synthesize evidence from both direct and indirect comparisons. The main analysis showed that no significant differences were found between these groups. Subgroup analysis found that patients with MDD may have higher blood VEGF levels than patients with SCZ when the levels were measured through ELISA, and VEGF levels were increased in medication-treated MDD patients compared with HCs. Taken together, blood VEGF levels may be unaltered in these psychiatric disorders, while detection of VEGF in blood by ELISA may a feasible way to distinguish MDD and SCZ. Further replicated studies with larger samples are needed.

Our previous studies found that disturbances in gut microbiota might have a causative role in the onset of major depressive disorder (MDD). The aim of this study was to investigate whether there were sex differences in gut microbiota in patients with MDD.

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.

Major depressive disorder (MDD) as a dysfunction of neural circuits and brain networks has been established in modern neuroimaging sciences. However, the brain state transitions between MDD and health through external stimulation remain unclear, which limits translation to clinical contexts and demonstrable clinical utility. We propose a framework of the large-scale whole-brain network model for MDD linking the underlying anatomical connectivity with functional dynamics obtained from diffusion tensor imaging (DTI) and functional magnetic resonance imaging (fMRI). Then, we further explored the optimal brain regions to promote the transition of brain states between MDD and health through external stimulation of the model. Based on the whole-brain model successfully fitting the brain state space in MDD and the health, we demonstrated that the transition from MDD to health is achieved by the excitatory activation of the limbic system and from health to MDD by the inhibitory stimulation of the reward circuit. Our finding provides novel biophysical evidence for the neural mechanism of MDD and its recovery and allows the discovery of new stimulation targets for MDD recovery.

Major depressive disorder is associated with aberrant topological organizations of brain networks. However, whether this aberrance is shown in broader frequency bands or in a specific frequency band remains unknown. Fifty patients and fifty gender, age and education matched normal controls underwent resting state functional magnetic resonance imaging. Frequency dependent topological measures based on graph theory were calculated from wavelet decomposed resting state functional brain signals. In the specific frequency band of 0.03-0.06 Hz, the clustering coefficient and the global efficiency were reduced while the characteristic path length was increased. Furthermore, patients showed aberrant nodal centralities in the default mode network, executive network and occipital network. Network based statistical analysis revealed system-wise topological alterations in these networks. The finding provides the first systematic evidence that depression is associated with frequency specific global and local topological disruptions and highlights the importance of frequency information in investigating major depressive disorders.

Major depressive disorder (MDD) is mediated by chronic dysregulation of complex neural circuits, particularly the specific neurotransmitters or other neural substrates. Recently, both increases and decreases in resting-state functional connectivity have been observed in patients with MDD. However, previous research has only assessed the functional connectivity within a specific network or some regions of interests, without considering the modulatory effects of the entire brain regions. To fill in the research gap, this study employed PPI (physiophysiological interaction) to investigate the functional connectivity in the entire brain regions. Apart from the traditional PPI used for cognitive research, current PPI analysis is more suitable for exploring the neural mechanism in MDD patients. Besides, this PPI method does not require a new cognitive estimation task and can assess the modulatory effects on different part of brain without prior setting of regions of interest.

Although an association between major depressive disorder (MDD) and suicide exists, most depressed patients never attempt suicide. An improved understanding of the factors contributing to suicidal risk in MDD can provide direction for suicide predictor development. In MDD suicide attempters (MDD-SA), MDD non-attempters (MDD-NA), and healthy controls (HC) (n = 12 each group), complementary plasma proteomics identified 45 differential proteins mapped to coagulation and inflammation, 25 of which underwent Western blotting. In another cohort including antidepressant-treated patients (n = 49 each group), seven additional extrinsic pathway proteins were selected for ELISA. Two inflammatory proteins and eight coagulatory proteins demonstrated alterations in MDD-SA relative to MDD-NA and HC. Applying a relative mass-action ratio, MDD-SA subjects displayed a higher relative prothrombinase activity than MDD-NA subjects, while healthy controls displayed higher relative prothrombinase activity than both MDD-SA and MDD-NA subjects. Consistent with our human findings, we found that heparin treatment significantly increased forced swimming test (FST) immobility time in rodents. MDD, independent of suicidality, is associated with a proinflammatory state accompanied by a hypothrombotic state. Suicidal behavior in MDD is associated with a more pronounced proinflammatory and prothrombotic phenotype accompanied by extrinsic pathway activation, revealing an extrinsic pathway biomarker that can be applied in predicting and monitoring suicidal risk.

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.

Evidences have shown that both LRFN5 and OLFM4 can regulate neural development and synaptic function. Recent genome-wide association studies on major depressive disorder (MDD) have implicated LRFN5 and OLFM4, but their expressions and roles in MDD are still completely unclear. Here, we examined serum concentrations of LRFN5 and OLFM4 in 99 drug-naive MDD patients, 90 drug-treatment MDD patients, and 81 healthy controls (HCs) using ELISA methods. The results showed that both LRFN5 and OLFM4 levels were considerably higher in MDD patients compared to HCs, and were significantly lower in drug-treatment MDD patients than in drug-naive MDD patients. However, there were no significant differences between MDD patients who received a single antidepressant and a combination of antidepressants. Pearson correlation analysis showed that they were associated with the clinical data, including Hamilton Depression Scale score, age, duration of illness, fasting blood glucose, serum lipids, and hepatic, renal, or thyroid function. Moreover, these two molecules both yielded fairly excellent diagnostic performance in diagnosing MDD. In addition, a combination of LRFN5 and OLFM4 demonstrated a better diagnostic effectiveness, with an area under curve of 0.974 in the training set and 0.975 in the testing set. Taken together, our data suggest that LRFN5 and OLFM4 may be implicated in the pathophysiology of MDD and the combination of LRFN5 and OLFM4 may offer a diagnostic biomarker panel for MDD.

Psychotherapy has demonstrated comparable efficacy to antidepressant medication in the treatment of major depressive disorder. Metabolic alterations in the MDD state and in response to treatment have been detected by functional imaging methods, but the underlying white matter microstructural changes remain unknown. The goal of this study is to apply diffusion tensor imaging techniques to investigate psychotherapy-specific responses in the white matter.

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.

Major depressive disorder (MDD) is a highly prevalent mental disorder affecting millions of people worldwide. However, a clear causative etiology of MDD remains unknown. In this study, we aimed to identify critical protein alterations in plasma from patients with MDD and integrate our proteomics and previous metabolomics data to reveal significantly perturbed pathways in MDD. An isobaric tag for relative and absolute quantification (iTRAQ)-based quantitative proteomics approach was conducted to compare plasma protein expression between patients with depression and healthy controls (CON).

Major depressive disorder (MDD) is a serious mental illness, characterized by high morbidity, which has increased in recent decades. However, the molecular mechanisms underlying MDD remain unclear. Previous studies have identified altered metabolic profiles in peripheral tissues associated with MDD. Using curated metabolic characterization data from a large sample of MDD patients, we meta-analyzed the results of metabolites in peripheral blood. Pathway and network analyses were then performed to elucidate the biological themes within these altered metabolites. We identified 23 differentially expressed metabolites between MDD patients and controls from 46 studies. MDD patients were characterized by higher levels of asymmetric dimethylarginine, tyramine, 2-hydroxybutyric acid, phosphatidylcholine (32:1), and taurochenodesoxycholic acid and lower levels of L-acetylcarnitine, creatinine, L-asparagine, L-glutamine, linoleic acid, pyruvic acid, palmitoleic acid, L-serine, oleic acid, myo-inositol, dodecanoic acid, L-methionine, hypoxanthine, palmitic acid, L-tryptophan, kynurenic acid, taurine, and 25-hydroxyvitamin D compared with controls. L-tryptophan and kynurenic acid were consistently downregulated in MDD patients, regardless of antidepressant exposure. Depression rating scores were negatively associated with decreased levels of L-tryptophan. Pathway and network analyses revealed altered amino acid metabolism and lipid metabolism, especially for the tryptophan-kynurenine pathway and fatty acid metabolism, in the peripheral system of MDD patients. Taken together, our integrated results revealed that metabolic changes in the peripheral blood were associated with MDD, particularly decreased L-tryptophan and kynurenic acid levels, and alterations in the tryptophan-kynurenine and fatty acid metabolism pathways. Our findings may facilitate biomarker development and the elucidation of the molecular mechanisms that underly MDD.

Disturbed gut microbiota is a potential factor in the pathogenesis of major depressive disorder (MDD), yet whether gut microbiota dysbiosis is associated with the severity of MDD remains unclear. Here, we performed shotgun metagenomic profiling of cross-sectional stool samples from MDD (n = 138) and healthy controls (n = 155). The patients with MDD were divided into three groups according to Hamilton Depression Rating Scale 17 (HAMD-17), including mild (n = 24), moderate (n = 72) and severe (n = 42) individuals, respectively. We found that microbial diversity was closely related to the severity of MDD. Compared to HCs, the abundance of Bacteroides was significantly increased in both moderate and severe MDD, while Ruminococcus and Eubacterium depleted mainly in severe group. In addition, we identified 99 bacteria species specific to severity of depression. Furthermore, a panel of microbiota marker comprising of 37 bacteria species enabled to effectively distinguish MDD patients with different severity. Together, we identified different perturbation patterns of gut microbiota in mild-to-severe depression, and identified potential diagnostic and therapeutic targets.

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.

Accumulating evidence showed that major depressive disorder (MDD) is characterized by a dysfunction of serotonin neurotransmission. Raphe nuclei are the sources of most serotonergic neurons that project throughout the brain. Incorporating measurements of activity within the raphe nuclei into the analysis of connectivity characteristics may contribute to understanding how neurotransmitter synthesized centers are involved in thepathogenesisof MDD. Here, we analyzed the resting-state functional magnetic resonance imaging (RS-fMRI) dataset from 1,148 MDD patients and 1,079 healthy individuals recruited across nine centers. A seed-based analysis with the dorsal raphe and median raphe nuclei was performed to explore the functional connectivity (FC) alterations. Compared to controls, for dorsal raphe, the significantly decreased FC linking with the right precuneus and median cingulate cortex were found; for median raphe, the increased FC linking with right superior cerebellum (lobules V/VI) was found in MDD patients. In further exploratory analyzes, MDD-related connectivity alterations in dorsal and median raphe nuclei in different clinical factors remained highly similar to the main findings, indicating these abnormal connectivities are a disease-related alteration. Our study highlights a functional dysconnection pattern of raphe nuclei in MDD with multi-site big data. These findings help improve our understanding of the pathophysiology of depression and provide evidence of the theoretical foundation for the development of novel pharmacotherapies.

Major depressive disorder is associated with abnormal anatomical and functional connectivity, yet alterations in whole cortical thickness topology remain unknown. Here, we examined cortical thickness in medication-free adult depression patients (n = 76) and matched healthy controls (n = 116). Inter-regional correlation was performed to construct brain networks. By applying graph theory analysis, global (i.e., small-worldness) and regional (centrality) topology was compared between major depressive disorder patients and healthy controls. We found that in depression patients, topological organization of the cortical thickness network shifted towards randomness, and lower small-worldness was driven by a decreased clustering coefficient. Consistently, altered nodal centrality was identified in the isthmus of the cingulate cortex, insula, supra-marginal gyrus, middle temporal gyrus and inferior parietal gyrus, all of which are components within the default mode, salience and central executive networks. Disrupted nodes anchored in the default mode and executive networks were associated with depression severity. The brain systems involved sustain core symptoms in depression and implicate a structural basis for depression. Our results highlight the possibility that developmental and genetic factors are crucial to understand the neuropathology of depression.