A preliminary metabolomic analysis of older adults with and without depression

Luteolin alleviates depression-like behavior by modulating glycerophospholipid metabolism in the hippocampus and prefrontal cortex of LOD rats

BACKGROUND

Late-onset depression (LOD) is defined as primary depression that first manifests after the age of 65. Luteolin (LUT) is a natural flavonoid that has shown promising antidepressant effects and improvement in neurological function in previous studies.

AIMS

In this study, we utilized UPLC-MS/MS non-targeted metabolomics techniques, along with molecular docking technology and experimental validation, to explore the mechanism of LUT in treating LOD from a metabolomics perspective.

RESULTS

The behavioral results of our study demonstrate that LUT significantly ameliorated anxiety and depression-like behaviors while enhancing cognitive function in LOD rats. Metabolomic analysis revealed that the effects of LUT on LOD rats were primarily mediated through the glycerophospholipid metabolic pathway in the hippocampus and prefrontal cortex. The levels of key lipid metabolites, phosphatidylserine (PS), phosphatidylcholine (PC), and phosphatidylethanolamine (PE), in the glycerophospholipid metabolic pathway were significantly altered by LUT treatment, with PC and PE showing significant correlations with behavioral indices. Molecular docking analysis indicated that LUT had strong binding activity with phosphatidylserine synthase 1 (PTDSS1), phosphatidylserine synthase 2 (PTDSS2), and phosphatidylserine decarboxylase (PISD), which are involved in the transformation and synthesis of PC, PE, and PS. Lastly, our study explored the reasons for the opposing trends of PC, PE, and PS in the hippocampus and prefrontal cortex from the perspective of autophagy, which may be attributable to the bidirectional regulation of autophagy in distinct brain regions.

CONCLUSIONS

Our results revealed significant alterations in the glycerophospholipid metabolism pathways in both the hippocampus and prefrontal cortex of LOD rats. Moreover, LUT appears to regulate autophagy disorders by specifically modulating glycerophospholipid metabolism in different brain regions of LOD rats, consequently alleviating depression-like behavior in these animals.

Immune Dysregulation in Treatment-Resistant Depression: Precision Approaches to Treatment Selection and Development of Novel Treatments

Owing to the link between immune dysfunction and treatment-resistant depression (TRD) and the overwhelming evidence that the immune dysregulation and major depressive disorder (MDD) are associated with each other, using immune profiles to identify the biological distinct subgroup may be the step forward to understanding MDD and TRD. This report aims to briefly review the role of inflammation in the pathophysiology of depression (and TRD in particular), the role of immune dysfunction to guide precision medicine, tools used to understand immune function, and novel statistical techniques.

Potential Plasma Metabolic Biomarkers of Tourette Syndrome Discovery Based on Integrated Nontargeted and Targeted Metabolomics Screening Plasma Metabolic Biomarkers of TS

Objective

Tourette syndrome (TS) is a chronic neuropsychiatric disorder characterized by abnormal movements, phonations, and tics, but an accurate TS diagnosis remains challenging and indeed depends on its description of clinical symptoms. Our study was conducted to discover and verify some metabolite biomarkers based on nontargeted and targeted metabolomics.

Methods

We conducted untargeted ultrahigh-performance liquid chromatography-quadrupole time-of-flight mass spectrometry (UHPLC-Q-TOF/MS) for preliminary screening of potential biomarkers on 30 TS patients and 10 healthy controls and then performed validation experiments based on targeted ultrahigh-performance liquid chromatography triple quadrupole-MS (UHPLC/MS/MS) on 35 TS patients and 14 healthy controls.

Results

1775 differentially expressed metabolites were identified by partial least squares discriminant analysis (PLS-DA), fold-change analysis, T-test, and hierarchical clustering analysis (adjusted p value <0.05 and |logFC| > 1). TS plasma samples were found to be differentiated from healthy samples in our approach. Furthermore, aspartate and asparagine metabolism pathways were considered to be a significant enrichment pathway in TS progression based on metabolite pathway enrichment analysis. For the 8 metabolites involved in this pathway that we detected, we then performed validation experiments based on targeted UHPLC/MS/MS. The t-test, Mann-Whitney U test, and receiver operating characteristic (ROC) curve analysis were used to determine potential biomarkers. Ultimately, L-arginine and L-pipecolic acid were validated as significantly differentiated metabolites (p < 0.05), with an AUC of 70.0% and 80.3%, respectively.

Conclusion

L-pipecolic acid was defined as a potential biomarker for TS diagnosis by the combined application of nontargeted and targeted metabolomic analysis.

Genomics-based identification of a potential causal role for acylcarnitine metabolism in depression

BACKGROUND

Altered metabolism of acylcarnitines - transporting fatty acids to mitochondria - may link cellular energy dysfunction to depression. We examined the potential causal role of acylcarnitine metabolism in depression by leveraging genomics and Mendelian randomization.

METHODS

Summary statistics were obtained from large GWAS: the Fenland Study (N = 9363), and the Psychiatric Genomics Consortium (246,363 depression cases and 561,190 controls). Two-sample Mendelian randomization analyses tested the potential causal link of 15 endogenous acylcarnitines with depression.

RESULTS

In univariable analyses, genetically-predicted lower levels of short-chain acylcarnitines C2 (odds ratio [OR] 0.97, 95% confidence intervals [CIs] 0.95-1.00) and C3 (OR 0.97, 95%CIs 0.96-0.99) and higher levels of medium-chain acylcarnitines C8 (OR 1.04, 95%CIs 1.01-1.06) and C10 (OR 1.04, 95%CIs 1.02-1.06) were associated with increased depression risk. No reverse potential causal role of depression genetic liability on acylcarnitines levels was found. Multivariable analyses showed that the association with depression was driven by the medium-chain acylcarnitines C8 (OR 1.04, 95%CIs 1.02-1.06) and C10 (OR 1.04, 95%CIs 1.02-1.06), suggesting a potential causal role in the risk of depression. Causal estimates for C8 (OR = 1.05, 95%CIs = 1.02-1.07) and C10 (OR = 1.05, 95%CIs = 1.02-1.08) were confirmed in follow-up analyses using genetic instruments derived from a GWAS meta-analysis including up to 16,841 samples.

DISCUSSION

Accumulation of medium-chain acylcarnitines is a signature of inborn errors of fatty acid metabolism and age-related metabolic conditions. Our findings point to a link between altered mitochondrial energy production and depression pathogenesis. Acylcarnitine metabolism represents a promising access point for the development of novel therapeutic approaches for depression.

A Preliminary Nuclear Magnetic Resonance Metabolomics Study Identifies Metabolites that Could Serve as Diagnostic Markers of Major Depressive Disorder

BACKGROUND

The evaluation of metabolites that are directly involved in the physiological process, few steps short of phenotypical manifestation, remains vital for unravelling the biological moieties involved in the development of the (MDD) and in predicting its treatment outcome.

METHODOLOGY

Eight (8) urine and serum samples each obtained from consenting healthy controls (HC), twenty-five (25) urine and serum samples each from first episode treatment naïve MDD (TNMDD) patients, and twenty (22) urine and serum samples each s from treatment naïve MDD patients 2 weeks after SSRI treatment (TWMDD) were analysed for metabolites using proton nuclear magnetic resonance (1HNMR) spectroscopy. The evaluation of patients' samples was carried out using Partial Least Squares Discriminant Analysis (PLS-DA) and Orthogonal Partial Least Square- Discriminant Analysis (OPLSDA) models.

RESULTS

In the serum, decreased levels of lactate, glucose, glutamine, creatinine, acetate, valine, alanine, and fatty acid and an increased level of acetone and choline in TNMDD or TWMDD irrespective of whether an OPLSDA or PLSDA evaluation was used were identified. A test for statistical validations of these models was successful.

CONCLUSION

Only some changes in serum metabolite levels between HC and TNMDD identified in this study have potential values in the diagnosis of MDD. These changes included decreased levels of lactate, glutamine, creatinine, valine, alanine, and fatty acid, as well as an increased level of acetone and choline in TNMDD. The diagnostic value of these changes in metabolites was maintained in samples from TWMDD patients, thus reaffirming the diagnostic nature of these metabolites for MDD.

Metabolomic Approaches to Investigate the Effect of Metformin: An Overview

Metformin is the first-line antidiabetic drug that is widely used in the treatment of type 2 diabetes mellitus (T2DM). Even though the various therapeutic potential of metformin treatment has been reported, as well as the improvement of insulin sensitivity and glucose homeostasis, the mechanisms underlying those benefits are still not fully understood. In order to explain the beneficial effects on metformin treatment, various metabolomics analyses have been applied to investigate the metabolic alterations in response to metformin treatment, and significant systemic metabolome changes were observed in biofluid, tissues, and cells. In this review, we compare the latest metabolomic research including clinical trials, animal models, and in vitro studies comprehensively to understand the overall changes of metabolome on metformin treatment.

Biomarkers of Post-COVID Depression

The COVID-19 pandemic is spreading around the world and 187 million people have already been affected. One of its after-effects is post-COVID depression, which, according to the latest data, affects up to 40% of people who have had SARS-CoV-2 infection. A very important issue for the mental health of the general population is to look for the causes of this complication and its biomarkers. This will help in faster diagnosis and effective treatment of the affected patients. In our work, we focused on the search for major depressive disorder (MDD) biomarkers, which are also present in COVID-19 patients and may influence the development of post-COVID depression. For this purpose, we searched PubMed, Scopus and Google Scholar scientific literature databases using keywords such as 'COVID-19', 'SARS-CoV-2', 'depression', 'post-COVID', 'biomarkers' and others. Among the biomarkers found, the most important that were frequently described are increased levels of interleukin 6 (IL-6), soluble interleukin 6 receptor (sIL-6R), interleukin 1 β (IL-1β), tumor necrosis factor α (TNF-α), interferon gamma (IFN-γ), interleukin 10 (IL-10), interleukin 2 (IL-2), soluble interleukin 2 receptor (sIL-2R), C-reactive protein (CRP), Monocyte Chemoattractant Protein-1 (MCP-1), serum amyloid a (SAA1) and metabolites of the kynurenine pathway, as well as decreased brain derived neurotrophic factor (BDNF) and tryptophan (TRP). The biomarkers identified by us indicate the etiopathogenesis of post-COVID depression analogous to the leading inflammatory hypothesis of MDD.

Altered Fecal Metabolites and Colonic Glycerophospholipids Were Associated With Abnormal Composition of Gut Microbiota in a Depression Model of Mice

The microbiota–gut–brain axis has been considered to play an important role in the development of depression, but the underlying mechanism remains unclear. The gastrointestinal tract is home to trillions of microbiota and the colon is considered an important site for the interaction between microbiota and host, but few studies have been conducted to evaluate the alterations in the colon. Accordingly, in this study, we established a chronic social defeated stress (CSDS) mice model of depression. We applied 16S rRNA gene sequencing to assess the gut microbial composition and gas and liquid chromatography–mass spectroscopy to identify fecal metabolites and colonic lipids, respectively. Meanwhile, we used Spearman’s correlation analysis method to evaluate the associations between the gut microbiota, fecal metabolites, colonic lipids, and behavioral index. In total, there were 20 bacterial taxa and 18 bacterial taxa significantly increased and decreased, respectively, in the CSDS mice. Further, microbial functional prediction demonstrated a disturbance of lipid, carbohydrate, and amino acid metabolism in the CSDS mice. We also found 20 differential fecal metabolites and 36 differential colonic lipids (in the category of glycerolipids, glycerophospholipids, and sphingolipids) in the CSDS mice. Moreover, correlation analysis showed that fecal metabolomic signature was associated with the alterations in the gut microbiota composition and colonic lipidomic profile. Of note, three lipids [PC(16:0/20:4), PG(22:6/22:6), and PI(18:0/20:3), all in the category of glycerophospholipids] were significantly associated with anxiety- and depression-like phenotypes in mice. Taken together, our results indicated that the gut microbiota might be involved in the pathogenesis of depression via influencing fecal metabolites and colonic glycerophospholipid metabolism.

Associations of depression status with plasma levels of candidate lipid and amino acid metabolites: a meta-analysis of individual data from three independent samples of US postmenopausal women

Recent animal and small clinical studies have suggested depression is related to altered lipid and amino acid profiles. However, this has not been examined in a population-based sample, particularly in women. We identified multiple metabolites associated with depression as potential candidates from prior studies. Cross-sectional data from three independent samples of postmenopausal women were analyzed, including women from the Women's Health Initiative-Observational Study (WHI-OS, n = 926), the WHI-Hormone Trials (WHI-HT; n = 1,325), and the Nurses' Health Study II Mind-Body Study (NHSII-MBS; n = 218). Positive depression status was defined as having any of the following: elevated depressive symptoms, antidepressant use, or depression history. Plasma metabolites were measured using liquid chromatography-tandem mass spectrometry (21 phosphatidylcholines (PCs), 7 lysophosphatidylethanolamines, 5 ceramides, 3 branched chain amino acids, and 9 neurotransmitters). Associations between depression status and metabolites were evaluated using multivariable linear regression; results were pooled by random-effects meta-analysis with multiple testing adjustment using the false discovery rate (FDR). Prevalence rates of positive depression status were 24.4% (WHI-OS), 25.7% (WHI-HT), and 44.7% (NHSII-MBS). After multivariable adjustment, positive depression status was associated with higher levels of glutamate and PC 36 : 1/38 : 3, and lower levels of tryptophan and GABA-to-glutamate and GABA-to-glutamine ratio (FDR-p < 0.05). Positive associations with LPE 18 : 0/18 : 1 and inverse associations with valine and serotonin were also observed, although these associations did not survive FDR adjustment. Associations of positive depression status with several candidate metabolites including PC 36 : 1/38 : 3 and amino acids involved in neurotransmission suggest potential depression-related metabolic alterations in postmenopausal women, with possible implications for later chronic disease.

Alterations in acylcarnitines, amines, and lipids inform about the mechanism of action of citalopram/escitalopram in major depression

Selective serotonin reuptake inhibitors (SSRIs) are the first-line treatment for major depressive disorder (MDD), yet their mechanisms of action are not fully understood and their therapeutic benefit varies among individuals. We used a targeted metabolomics approach utilizing a panel of 180 metabolites to gain insights into mechanisms of action and response to citalopram/escitalopram. Plasma samples from 136 participants with MDD enrolled into the Mayo Pharmacogenomics Research Network Antidepressant Medication Pharmacogenomic Study (PGRN-AMPS) were profiled at baseline and after 8 weeks of treatment. After treatment, we saw increased levels of short-chain acylcarnitines and decreased levels of medium-chain and long-chain acylcarnitines, suggesting an SSRI effect on β-oxidation and mitochondrial function. Amines-including arginine, proline, and methionine sulfoxide-were upregulated while serotonin and sarcosine were downregulated, suggesting an SSRI effect on urea cycle, one-carbon metabolism, and serotonin uptake. Eighteen lipids within the phosphatidylcholine (PC aa and ae) classes were upregulated. Changes in several lipid and amine levels correlated with changes in 17-item Hamilton Rating Scale for Depression scores (HRSD₁₇). Differences in metabolic profiles at baseline and post-treatment were noted between participants who remitted (HRSD₁₇≤ 7) and those who gained no meaningful benefits (<30% reduction in HRSD₁₇). Remitters exhibited (a) higher baseline levels of C3, C5, alpha-aminoadipic acid, sarcosine, and serotonin; and (b) higher week-8 levels of PC aa C34:1, PC aa C34:2, PC aa C36:2, and PC aa C36:4. These findings suggest that mitochondrial energetics-including acylcarnitine metabolism, transport, and its link to β-oxidation-and lipid membrane remodeling may play roles in SSRI treatment response.

A metabolome-wide association study in the general population reveals decreased levels of serum laurylcarnitine in people with depression

Depression constitutes a leading cause of disability worldwide. Despite extensive research on its interaction with psychobiological factors, associated pathways are far from being elucidated. Metabolomics, assessing the final products of complex biochemical reactions, has emerged as a valuable tool for exploring molecular pathways. We conducted a metabolome-wide association analysis to investigate the link between the serum metabolome and depressed mood (DM) in 1411 participants of the KORA (Cooperative Health Research in the Augsburg Region) F4 study (discovery cohort). Serum metabolomics data comprised 353 unique metabolites measured by Metabolon. We identified 72 (5.1%) KORA participants with DM. Linear regression tests were conducted modeling each metabolite value by DM status, adjusted for age, sex, body-mass index, antihypertensive, cardiovascular, antidiabetic, and thyroid gland hormone drugs, corticoids and antidepressants. Sensitivity analyses were performed in subcohorts stratified for sex, suicidal ideation, and use of antidepressants. We replicated our results in an independent sample of 968 participants of the SHIP-Trend (Study of Health in Pomerania) study including 52 (5.4%) individuals with DM (replication cohort). We found significantly lower laurylcarnitine levels in KORA F4 participants with DM after multiple testing correction according to Benjamini/Hochberg. This finding was replicated in the independent SHIP-Trend study. Laurylcarnitine remained significantly associated (p value < 0.05) with depression in samples stratified for sex, suicidal ideation, and antidepressant medication. Decreased blood laurylcarnitine levels in depressed individuals may point to impaired fatty acid oxidation and/or mitochondrial function in depressive disorders, possibly representing a novel therapeutic target.

A Load to Find Clinically Useful Biomarkers for Depression

Depression is heterogeneous and complex disease with diverse symptoms. Its neurobiological underpinning is still not completely understood. For now, there are still no validated, easy obtainable, clinically useful noninvasive biomarker(s) or biomarker panel that will be able to confirm a diagnosis of depression, its subtypes and improve diagnostic procedures. Future multimodal preclinical and clinical research that involves (epi)genetic, molecular, cellular, imaging, and other studies is necessary to advance our understanding of the role of monoamines, GABA, HPA axis, neurotrophins, metabolome, and glycome in the pathogenesis of depression and their potential as diagnostic, prognostic, and treatment response biomarkers. These studies should be focused to include the first-episode depression and antidepressant drug-naïve patients with large sample sizes to reduce variability in different biological and clinical parameters. At present, metabolomics study revealed with high precision that a neurometabolite panel consisting of plasma metabolite biomarkers (GABA, dopamine, tyramine, kynurenine) might represent clinically useful biomarkers of MDD.

Peripheral Markers of Depression

Major Depressive Disorder (MDD) is a leading cause of disability worldwide, creating a high medical and socioeconomic burden. There is a growing interest in the biological underpinnings of depression, which are reflected by altered levels of biological markers. Among others, enhanced inflammation has been reported in MDD, as reflected by increased concentrations of inflammatory markers-C-reactive protein, interleukin-6, tumor necrosis factor-α and soluble interleukin-2 receptor. Oxidative and nitrosative stress also plays a role in the pathophysiology of MDD. Notably, increased levels of lipid peroxidation markers are characteristic of MDD. Dysregulation of the stress axis, along with increased cortisol levels, have also been reported in MDD. Alterations in growth factors, with a significant decrease in brain-derived neurotrophic factor and an increase in fibroblast growth factor-2 and insulin-like growth factor-1 concentrations have also been found in MDD. Finally, kynurenine metabolites, increased glutamate and decreased total cholesterol also hold promise as reliable biomarkers for MDD. Research in the field of MDD biomarkers is hindered by insufficient understanding of MDD etiopathogenesis, substantial heterogeneity of the disorder, common co-morbidities and low specificity of biomarkers. The construction of biomarker panels and their evaluation with use of new technologies may have the potential to overcome the above mentioned obstacles.

Kynurenic acid is a potential overlapped biomarker between diagnosis and treatment response for depression from metabolome analysis

Since optimal treatment at an early stage leads to remission of symptoms and recovery of function, putative biomarkers leading to early diagnosis and prediction of therapeutic responses are desired. The current study aimed to use a metabolomic approach to extract metabolites involved in both the diagnosis of major depressive disorder (MDD) and the prediction of therapeutic response for escitalopram. We compared plasma metabolites of MDD patients (n = 88) with those in healthy participants (n = 88) and found significant differences in the concentrations of 20 metabolites. We measured the Hamilton Rating Scale for Depression (HRSD) on 62 patients who completed approximately six-week treatment with escitalopram before and after treatment and found that kynurenic acid and kynurenine were significantly and negatively associated with HRSD reduction. Only one metabolite, kynurenic acid, was detected among 73 metabolites for overlapped biomarkers. Kynurenic acid was lower in MDD, and lower levels showed a better therapeutic response to escitalopram. Kynurenic acid is a metabolite in the kynurenine pathway that has been widely accepted as being a major mechanism in MDD. Overlapping biomarkers that facilitate diagnosis and prediction of the treatment response may help to improve disease classification and reduce the exposure of patients to less effective treatments in MDD.

Ketamine as an antidepressant: overview of its mechanisms of action and potential predictive biomarkers

Ketamine, a drug introduced in the 1960s as an anesthetic agent and still used for that purpose, has garnered marked interest over the past two decades as an emerging treatment for major depressive disorder. With increasing evidence of its efficacy in treatment-resistant depression and its potential anti-suicidal action, a great deal of investigation has been conducted on elucidating ketamine's effects on the brain. Of particular interest and therapeutic potential is the ability of ketamine to exert rapid antidepressant properties as early as several hours after administration. This is in stark contrast to the delayed effects observed with traditional antidepressants, often requiring several weeks of therapy for a clinical response. Furthermore, ketamine appears to have a unique mechanism of action involving glutamate modulation via actions at the N-methyl-D-aspartate (NMDA) and α -amino-3-hydroxy-5-methyl-4-isoxazolepropionic acid (AMPA) receptors, as well as downstream activation of brain-derived neurotrophic factor (BDNF) and mechanistic target of rapamycin (mTOR) signaling pathways to potentiate synaptic plasticity. This paper provides a brief overview of ketamine with regard to pharmacology/pharmacokinetics, toxicology, the current state of clinical trials on depression, postulated antidepressant mechanisms and potential biomarkers (biochemical, inflammatory, metabolic, neuroimaging sleep-related and cognitive) for predicting response to and/or monitoring of therapeutic outcome with ketamine.

Plasma lipid profile associates with the improvement of psychological well-being in individuals with perceived stress symptoms

Psychological stress is a suggested risk factor of metabolic disorders, but molecular mediators are not well understood. We investigated the association between the metabolic profiles of fasting plasma and the improvement of psychological well-being using non-targeted liquid chromatography-mass spectrometry (LC-MS) platform. The metabolic profiles of volunteers participating in the face-to-face intervention group (n = 60) in a randomised lifestyle intervention were compared to ones of controls (n = 64) between baseline and 36-week follow-up. Despite modest differences in metabolic profile between groups, we found associations between phosphatidylcholines (PCs) and several parameters indicating stress, adiposity, relaxation, and recovery. The relief of heart-rate-variability-based stress had positive, while improved indices of recovery and relaxation in the intervention group had an inverse association with the reduction of e.g. lysophosphatidylcholines (LPC). Interleukin-1 receptor antagonist and adiposity correlated positively with the suppressed PCs and negatively with the elevated plasmalogens PC(P-18:0/22:6) and PC(P-18:0/20:4). Also, we found changes in an unknown class of lipids over time regardless of the intervention groups, which also correlated with physiological and psychological markers of stress. The associations between lipid changes with some markers of psychological wellbeing and body composition may suggest the involvement of these lipids in the shared mechanisms between psychological and metabolic health.

Metabolome and microbiome profiling of a stress-sensitive rat model of gut-brain axis dysfunction

Stress negatively impacts gut and brain health. Individual differences in response to stress have been linked to genetic and environmental factors and more recently, a role for the gut microbiota in the regulation of stress-related changes has been demonstrated. However, the mechanisms by which these factors influence each other are poorly understood, and there are currently no established robust biomarkers of stress susceptibility. To determine the metabolic and microbial signatures underpinning physiological stress responses, we compared stress-sensitive Wistar Kyoto (WKY) rats to the normo-anxious Sprague Dawley (SD) strain. Here we report that acute stress-induced strain-specific changes in brain lipid metabolites were a prominent feature in WKY rats. The relative abundance of Lactococcus correlated with the relative proportions of many brain lipids. In contrast, plasma lipids were significantly elevated in response to stress in SD rats, but not in WKY rats. Supporting these findings, we found that the greatest difference between the SD and WKY microbiomes were the predicted relative abundance of microbial genes involved in lipid and energy metabolism. Our results provide potential insights for developing novel biomarkers of stress vulnerability, some of which appear genotype specific.

MetSigDis: a manually curated resource for the metabolic signatures of diseases

Complex diseases cannot be understood only on the basis of single gene, single mRNA transcript or single protein but the effect of their collaborations. The combination consequence in molecular level can be captured by the alterations of metabolites. With the rapidly developing of biomedical instruments and analytical platforms, a large number of metabolite signatures of complex diseases were identified and documented in the literature. Biologists' hardship in the face of this large amount of papers recorded metabolic signatures of experiments' results calls for an automated data repository. Therefore, we developed MetSigDis aiming to provide a comprehensive resource of metabolite alterations in various diseases. MetSigDis is freely available at http://www.bio-annotation.cn/MetSigDis/. By reviewing hundreds of publications, we collected 6849 curated relationships between 2420 metabolites and 129 diseases across eight species involving Homo sapiens and model organisms. All of these relationships were used in constructing a metabolite disease network (MDN). This network displayed scale-free characteristics according to the degree distribution (power-law distribution with R2 = 0.909), and the subnetwork of MDN for interesting diseases and their related metabolites can be visualized in the Web. The common alterations of metabolites reflect the metabolic similarity of diseases, which is measured using Jaccard index. We observed that metabolite-based similar diseases are inclined to share semantic associations of Disease Ontology. A human disease network was then built, where a node represents a disease, and an edge indicates similarity of pair-wise diseases. The network validated the observation that linked diseases based on metabolites should have more overlapped genes.

Metabolomic signature of exposure and response to citalopram/escitalopram in depressed outpatients

Metabolomics provides valuable tools for the study of drug effects, unraveling the mechanism of action and variation in response due to treatment. In this study we used electrochemistry-based targeted metabolomics to gain insights into the mechanisms of action of escitalopram/citalopram focusing on a set of 31 metabolites from neurotransmitter-related pathways. Overall, 290 unipolar patients with major depressive disorder were profiled at baseline, after 4 and 8 weeks of drug treatment. The 17-item Hamilton Depression Rating Scale (HRSD17) scores gauged depressive symptom severity. More significant metabolic changes were found after 8 weeks than 4 weeks post baseline. Within the tryptophan pathway, we noted significant reductions in serotonin (5HT) and increases in indoles that are known to be influenced by human gut microbial cometabolism. 5HT, 5-hydroxyindoleacetate (5HIAA), and the ratio of 5HIAA/5HT showed significant correlations to temporal changes in HRSD17 scores. In the tyrosine pathway, changes were observed in the end products of the catecholamines, 3-methoxy-4-hydroxyphenylethyleneglycol and vinylmandelic acid. Furthermore, two phenolic acids, 4-hydroxyphenylacetic acid and 4-hydroxybenzoic acid, produced through noncanconical pathways, were increased with drug exposure. In the purine pathway, significant reductions in hypoxanthine and xanthine levels were observed. Examination of metabolite interactions through differential partial correlation networks revealed changes in guanosine-homogentisic acid and methionine-tyrosine interactions associated with HRSD17. Genetic association studies using the ratios of these interacting pairs of metabolites highlighted two genetic loci harboring genes previously linked to depression, neurotransmission, or neurodegeneration. Overall, exposure to escitalopram/citalopram results in shifts in metabolism through noncanonical pathways, which suggest possible roles for the gut microbiome, oxidative stress, and inflammation-related mechanisms.

A comprehensive metabolomics investigation of hippocampus, serum, and feces affected by chronic fluoxetine treatment using the chronic unpredictable mild stress mouse model of depression

A metabolomic investigation of depression and chronic fluoxetine treatment was conducted using a chronic unpredictable mild stress model with C57BL/6N mice. Establishment of the depressive model was confirmed by body weight measurement and behavior tests including the forced swim test and the tail suspension test. Behavioral despair by depression was reversed by four week-treatment with fluoxetine. Hippocampus, serum, and feces samples collected from four groups (control + saline, control + fluoxetine, model + saline, and model + fluoxetine) were subjected to metabolomic profiling based on ultra-high performance liquid chromatography-quadrupole-time-of-flight mass spectrometry. Alterations in the metabolic patterns were evident in all sample types. The antidepressant effects of fluoxetine appeared to involve various metabolic pathways including energy metabolism, neurotransmitter synthesis, tryptophan metabolism, fatty acid metabolism, lipid metabolism, and bile acid metabolism. Predictive marker candidates of depression were identified, including β-citryl-L-glutamic acid (BCG) and docosahexaenoic acid (DHA) in serum and chenodeoxycholic acid and oleamide in feces. This study suggests that treatment effects of fluoxetine might be differentiated by altered levels of tyramine and BCG in serum, and that DHA is a potential serum marker for depression with positive association with hippocampal DHA. Collectively, our comprehensive study provides insights into the biochemical perturbations involved in depression and the antidepressant effects of fluoxetine.

Metabolomic analysis of male combat veterans with post traumatic stress disorder

Posttraumatic stress disorder (PTSD) is associated with impaired major domains of psychology and behavior. Individuals with PTSD also have increased co-morbidity with several serious medical conditions, including autoimmune diseases, cardiovascular disease, and diabetes, raising the possibility that systemic pathology associated with PTSD might be identified by metabolomic analysis of blood. We sought to identify metabolites that are altered in male combat veterans with PTSD. In this case-control study, we compared metabolomic profiles from age-matched male combat trauma-exposed veterans from the Iraq and Afghanistan conflicts with PTSD (n = 52) and without PTSD (n = 51) (‘Discovery group’). An additional group of 31 PTSD-positive and 31 PTSD-negative male combat-exposed veterans was used for validation of these findings (‘Test group’). Plasma metabolite profiles were measured in all subjects using ultrahigh performance liquid chromatography/tandem mass spectrometry and gas chromatography/mass spectrometry. We identified key differences between PTSD subjects and controls in pathways related to glycolysis and fatty acid uptake and metabolism in the initial ‘Discovery group’, consistent with mitochondrial alterations or dysfunction, which were also confirmed in the ‘Test group’. Other pathways related to urea cycle and amino acid metabolism were different between PTSD subjects and controls in the ‘Discovery’ but not in the smaller ‘Test’ group. These metabolic differences were not explained by comorbid major depression, body mass index, blood glucose, hemoglobin A1c, smoking, or use of analgesics, antidepressants, statins, or anti-inflammatories. These data show replicable, wide-ranging changes in the metabolic profile of combat-exposed males with PTSD, with a suggestion of mitochondrial alterations or dysfunction, that may contribute to the behavioral and somatic phenotypes associated with this disease.

Can targeted metabolomics predict depression recovery? Results from the CO-MED trial

Metabolomics is a developing and promising tool for exploring molecular pathways underlying symptoms of depression and predicting depression recovery. The AbsoluteIDQ™ p180 kit was used to investigate whether plasma metabolites (sphingomyelins, lysophosphatidylcholines, phosphatidylcholines, and acylcarnitines) from a subset of participants in the Combining Medications to Enhance Depression Outcomes (CO-MED) trial could act as predictors or biologic correlates of depression recovery. Participants in this trial were assigned to one of three pharmacological treatment arms: escitalopram monotherapy, bupropion-escitalopram combination, or venlafaxine-mirtazapine combination. Plasma was collected at baseline in 159 participants and again 12 weeks later at study exit in 83 of these participants. Metabolite concentrations were measured and combined with clinical and sociodemographic variables using the hierarchical lasso to simultaneously model whether specific metabolites are particularly informative of depressive recovery. Increased baseline concentrations of phosphatidylcholine C38:1 showed poorer outcome based on change in the Quick Inventory of Depressive Symptoms (QIDS). In contrast, an increased ratio of hydroxylated sphingomyelins relative to non-hydroxylated sphingomyelins at baseline and a change from baseline to exit suggested a better reduction of symptoms as measured by QIDS score. All metabolite-based models performed superior to models only using clinical and sociodemographic variables, suggesting that metabolomics may be a valuable tool for predicting antidepressant outcomes.

Plasma metabolomic patterns in patients with exhaustion disorder

Exhaustion disorder (ED) is a stress-related disorder that often implies a great burden on the individual patient as well as on society. Previous studies have shown that ED is associated with metabolic deviations, such as lowered fasting glucose. Several mechanisms have been discussed as a plausible explanation of the lack of energy described by these patients. Metabolic processes and reduced ability to mobilize energy have been suggested as important factors. This study investigated metabolomics in 20 patients diagnosed with ED and compared them with 21 healthy controls. Plasma metabolic profiles were examined in both fasting and nonfasting (postprandial) conditions. Blood plasma samples were analyzed for metabolite content using gas chromatography mass spectrometry. A total of 62 different metabolites were simultaneously detected in each of the samples. Multivariate models indicated systematic differences between patients with ED and healthy controls in both their fasting and nonfasting plasma metabolite levels. Lysine and octadecenoic acid were more abundant and glutamine, glycine, serine and gluconic acid were less abundant in the patients across both conditions. In the present study, we comprehensively and simultaneously screen for changes in a large number of metabolites. Our results show a difference in systemic metabolites between patients with exhaustion disorder and healthy controls both in the fasting and in the postprandial states. Here, we present new potential biomarkers mirroring exhaustion disorder metabolism. Lay summary Exhaustion disorder (ED) patients suffer from stress-related symptoms including a reduced energy level. This study investigates the body's metabolism in patients with ED, both fasting and after a meal. New potential markers that may help future investigations on ED were identified.

Metabolomics Biomarkers for Precision Psychiatry

The treatment of psychiatric disorders remains a significant challenge in part due to imprecise diagnostic criteria and incomplete understanding of the molecular pathology involved. Current diagnostic and pharmacological treatment guidelines use a uniform approach to address each disorder even though psychiatric clinical presentation and prognosis within a disorder are known to be heterogeneous. Limited therapeutic success highlights the need for a precision medicine approach in psychiatry, termed precision psychiatry. To practice precision psychiatry, it is essential to research and develop multiple omics-based biomarkers that consider environmental factors and careful phenotype determination. Metabolomics, which lies at the endpoint of the "omics cascade," allows for detection of alterations in systems-level metabolites within biological pathways, thereby providing insights into the mechanisms that underlie various physiological conditions and pathologies. The eicosanoids, a family of metabolites derived from oxygenated polyunsaturated fatty acids, play a key role in inflammatory mechanisms and have been implicated in psychiatric disorders such as anorexia nervosa and depression. This review (1) provides background on the current clinical challenges of psychiatric disorders, (2) gives an overview of metabolomics application as a tool to develop improved biomarkers for precision psychiatry, and (3) summarizes current knowledge on metabolomics and lipidomic findings in common psychiatric disorders, with a focus on eicosanoids. Metabolomics is a promising tool for precision psychiatry. This research has great potential for both discovering biomarkers and elucidating molecular mechanisms underlying psychiatric disorders.

The Metabolome and Osteoarthritis: Possible Contributions to Symptoms and Pathology

Osteoarthritis (OA) is a progressive, deteriorative disease of articular joints. Although traditionally viewed as a local pathology, biomarker exploration has shown that systemic changes can be observed. These include changes to cytokines, microRNAs, and more recently, metabolites. The metabolome is the set of metabolites within a biological sample and includes circulating amino acids, lipids, and sugar moieties. Recent studies suggest that metabolites in the synovial fluid and blood could be used as biomarkers for OA incidence, prognosis, and response to therapy. However, based on clinical, demographic, and anthropometric factors, the local synovial joint and circulating metabolomes may be patient specific, with select subsets of metabolites contributing to OA disease. This review explores the contribution of the local and systemic metabolite changes to OA, and their potential impact on OA symptoms and disease pathogenesis.

Reduced GABAergic cortical inhibition in aging and depression

The neurobiology underlying depression in older adults is less extensively evaluated than in younger adults, despite the putative influence of aging on depression neuropathology. Studies using transcranial magnetic stimulation (TMS), a neurophysiological tool capable of probing inhibitory and excitatory cortical neurotransmission, have identified dysfunctional GABAergic inhibitory activity in younger adults with depression. However, GABAergic and glutamatergic cortical neurotransmission have not yet been studied in late-life depression (LLD). Here, we used single- and paired-pulse TMS to measure cortical inhibition and excitation in 92 LLD patients and 41 age-matched healthy controls. To differentiate the influence of age and depression, we also compared these TMS indices to those of 30 younger depressed adults and 30 age- and sex-matched younger healthy adults. LLD patients, older healthy adults, and younger depressed adults demonstrated significantly lower GABA_A receptor-mediated cortical inhibition than younger healthy controls. By contrast, no significant differences in cortical inhibition were observed between older adults with and without depression. No significant differences in GABA_B receptor-mediated inhibition or cortical excitation were found between the groups. Altogether, these findings suggest that reduced cortical inhibition may be associated with both advancing age and depression, which (i) supports the model of depression as a disease of accelerated aging, and (ii) prompts future investigation into diminished GABAergic neurotransmission in late-life as a biological predisposing factor to the development of depression. Given that cortical neurophysiology was similar in depressed and healthy older adults, future prospective studies need to establish the relative influence of age and depression on cortical inhibition deficits.

Peripheral biomarkers of major depression and antidepressant treatment response: Current knowledge and future outlooks

BACKGROUND

In recent years, we have accomplished a deeper understanding about the pathophysiology of major depressive disorder (MDD). Nevertheless, this improved comprehension has not translated to improved treatment outcome, as identification of specific biologic markers of disease may still be crucial to facilitate a more rapid, successful treatment. Ongoing research explores the importance of screening biomarkers using neuroimaging, neurophysiology, genomics, proteomics, and metabolomics measures.

RESULTS

In the present review, we highlight the biomarkers that are differentially expressed in MDD and treatment response and place a particular emphasis on the most recent progress in advancing technology which will continue the search for blood-based biomarkers.

LIMITATIONS

Due to space constraints, we are unable to detail all biomarker platforms, such as neurophysiological and neuroimaging markers, although their contributions are certainly applicable to a biomarker review and valuable to the field.

CONCLUSIONS

Although the search for reliable biomarkers of depression and/or treatment outcome is ongoing, the rapidly-expanding field of research along with promising new technologies may provide the foundation for identifying key factors which will ultimately help direct patients toward a quicker and more effective treatment for MDD.

Identifying diseases-related metabolites using random walk

Background

Metabolites disrupted by abnormal state of human body are deemed as the effect of diseases. In comparison with the cause of diseases like genes, these markers are easier to be captured for the prevention and diagnosis of metabolic diseases. Currently, a large number of metabolic markers of diseases need to be explored, which drive us to do this work.

Methods

The existing metabolite-disease associations were extracted from Human Metabolome Database (HMDB) using a text mining tool NCBO annotator as priori knowledge. Next we calculated the similarity of a pair-wise metabolites based on the similarity of disease sets of them. Then, all the similarities of metabolite pairs were utilized for constructing a weighted metabolite association network (WMAN). Subsequently, the network was utilized for predicting novel metabolic markers of diseases using random walk.

Results

Totally, 604 metabolites and 228 diseases were extracted from HMDB. From 604 metabolites, 453 metabolites are selected to construct the WMAN, where each metabolite is deemed as a node, and the similarity of two metabolites as the weight of the edge linking them. The performance of the network is validated using the leave one out method. As a result, the high area under the receiver operating characteristic curve (AUC) (0.7048) is achieved. The further case studies for identifying novel metabolites of diabetes mellitus were validated in the recent studies.

Conclusion

In this paper, we presented a novel method for prioritizing metabolite-disease pairs. The superior performance validates its reliability for exploring novel metabolic markers of diseases.

Stereochemical and structural effects of (2R,6R)-hydroxynorketamine on the mitochondrial metabolome in PC-12 cells

BACKGROUND

Impairment in mitochondrial biogenesis and function plays a key role in depression and anxiety, both of which being associated with changes in fatty acid and phospholipid metabolism. The antidepressant effects of (R,S)-ketamine have been linked to its conversion into (2S,6S;2R,6R)-hydroxynorketamine (HNK); however, the connection between structure and stereochemistry of ketamine and HNK in the mitochondrial homeostatic response has not yet been fully elucidated at a metabolic level.

METHODS

We used a multi-platform, non-targeted metabolomics approach to study the change in mitochondrial metabolome of PC-12 cells treated with ketamine and HNK enantiomers. The identified metabolites were grouped into pathways in order to assess global responses.

RESULTS

Treatment with (2R,6R)-HNK elicited the significant change in 49 metabolites and associated pathways implicated in fundamental mitochondrial functions such as TCA cycle, branched-chain amino acid biosynthetic pathway, glycoxylate metabolic pathway, and fatty acid β-oxidation. The affected metabolites included glycerate, citrate, leucine, N,N-dimethylglycine, 3-hexenedioic acid, and carnitine and attenuated signals associated with 9 fatty acids and elaidic acid. Important metabolites involved in the purine and pyrimidine pathways were also affected by (2R-6R)-HNK. This global metabolic profile was not as strongly impacted by treatment with (2S,6S)-HNK, (R)- and (S)-ketamine and in some instances opposite effects were observed.

CONCLUSIONS

The present data provide an overall view of the metabolic changes in mitochondrial function produced by (2R,6R)-HNK and related ketamine compounds and offer an insight into the source of the observed variance in antidepressant response elicited by the compounds.

Metabolomics of Major Depressive Disorder and Bipolar Disorder: Overview and Future Perspective

Major depressive disorder (MDD) and bipolar disorder (BD) are the most common mood disorders. They are etiologically related, but clinically distinct psychiatric illnesses. Their shared clinical features result in high rates of misdiagnosis due to a lack of biomarkers that allow their differentiation. BD is more frequently misdiagnosed as MDD because of overlapping symptomology, often later onset of mania, and frequent occurrence of depressive episodes in patients with BD. Misdiagnosis is also increased when patients with BD present symptoms indicative of a clinically significant depressive episode, but are premorbid for manic symptoms, or previous manic states not recognized. Therefore, the development of specific biomarkers for these disorders would be invaluable for establishing the correct diagnosis and treatment of MDD and BD. This chapter presents an overview and future perspective of the identification of biomarkers for mood disorders using metabolomics.

Metabolite identification in fecal microbiota transplantation mouse livers and combined proteomics with chronic unpredictive mild stress mouse livers

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.

Meta-analysis of central and peripheral γ-aminobutyric acid levels in patients with unipolar and bipolar depression

BACKGROUND

Many studies have measured central and peripheral γ-aminobutyric acid (GABA) levels in patients with depression. We performed a meta-analysis to provide an objective overview of GABA changes in those with unipolar or bipolar depression.

METHODS

After a systematic database search, original data were extracted with the help of seminal authors to calculate standardized mean differences. We compared GABA levels between patients with current major depressive episodes and controls, between euthymic patients and controls, and in patients before and after treatment. We performed meta-regressions to explore the influence of demographic and clinical variables on GABA significant mean differences.

RESULTS

For unipolar depression, central and peripheral GABA levels were diminished in currently depressed patients, but normal in euthymic patients, compared with the healthy controls. For bipolar disorder, GABA levels were diminished in medication-free patients, but seemed to be normalized in medicated patients, compared with the healthy controls. We found no significant association with demographic or clinical variables.

LIMITATIONS

There was a great heterogeneity across studies, probably because of the substantial variation of clinical characteristics in the included samples. Many subanalyses were performed to assess how the diagnosis, medications, or the type of measurements of peripheral or central GABA levels may affect the main results.

CONCLUSION

The GABA levels evolved differentially in patients with unipolar and bipolar disorders. Our results suggest that GABA levels could represent a biomarker of symptomatic states in patients with unipolar disorder and would be normalized by mood stabilizers in those with bipolar disorder.

Metabolomic profiles of plasma, exhaled breath condensate, and saliva are correlated with potential for air toxics detection

INTRODUCTION

Advances in the development of high-resolution metabolomics (HRM) have provided new opportunities for their use in characterizing exposures to environmental air pollutants and air pollution-related disease etiologies. Exposure assessment studies have considered blood, breath, and saliva as biological matrices suitable for measuring responses to air pollution exposures. The current study examines comparability among these three matrices using HRM and explores their potential for measuring mobile-source air toxics.

METHODS

Four participants provided saliva, exhaled breath concentrate (EBC), and plasma before and after a 2 h road traffic exposure. Samples were analyzed on a Thermo Scientific QExactive MS system in positive electrospray ionization mode and resolution of 70 000 full-width at half-maximum with C18 chromatography. Data were processed using an apLCMS and xMSanalyzer on the R statistical platform.

RESULTS

The analysis yielded 7110, 6019, and 7747 reproducible features in plasma, EBC, and saliva, respectively. Correlations were moderate-to-strong (R = 0.41-0.80) across all pairwise comparisons of feature intensity within profiles, with the strongest between EBC and saliva. The associations of mean intensities between matrix pairs were positive and significant, controlling for subject and sampling time effects. Six out of 20 features shared in all three matrices putatively matched a list of known mobile-source air toxics.

CONCLUSIONS

Plasma, saliva, and EBC have largely comparable metabolic profiles measurable through HRM. These matrices have the potential to be used in identification and measurement of exposures to mobile-source air toxics, though further, targeted study is needed.

Investigation of the derivatization conditions for GC-MS metabolomics of biological samples

AIM

Metabolomics applications represent an emerging field where significant efforts are directed. Derivatization consists prerequisite for GC-MS metabolomics analysis.

METHODS

Common silylation agents were tested for the derivatization of blood plasma. Optimization of methoxyamination and silylation reactions was performed on a mixture of reference standards, consisting of 46 different metabolites. Stability of derivatized metabolites was tested at 4°C.

RESULTS

Optimum results were achieved using N-methyl-N-(trimethylsilyl)trifluoroacetamide. Methoxyamination at room temperature for 24 h followed by 2-h silylation at high temperature lead to efficient derivatization.

CONCLUSION

Formation and stability of derivatives among metabolites differ greatly, so derivatization should be studied before application in metabolomics studies.

Predictive diagnosis of major depression using NMR-based metabolomics and least-squares support vector machine

BACKGROUND

Major depressive (MD) disorder is a serious psychiatric disorder that can result in suicidal behavior if not treated. The MD diagnosis using a standardized instrument instead of a structured interview will be advantageous for treatment and management of the MD, but so far no such technique exists. We developed an integrated analytical method of NMR-based metabolomics and least squares-support vector machine (LS-SVM) for predictive diagnosis of the MD.

METHODS

The metabolite profiles in clinical plasma samples obtained from 72 depressive patients and 54 healthy subjects were analyzed by NMR spectroscopy. Then, LS-SVM models with different kernels were trained and tested using 80% and 20% of samples, respectively.

RESULTS

We found that the best performance for the MD prediction was achieved by LS-SVM equipped with RBF kernel. Moreover, the predictive performance of the MD using multi-biomarkers was largely improved as compared with that using a single biomarker. In this study, the LS-SVM-RBF using glucose-lipid signaling can achieve the MD prediction with the AUC values of 0.94 (0.89-0.99) in the training set and 0.96 (0.92-1.00) in the test set.

CONCLUSION

The LS-SVM-RBF using glucose-lipid signaling obtained from NMR spectroscopy can be used as an auxiliary diagnostic tool for the MD.

Circulating biosignatures of late-life depression (LLD): Towards a comprehensive, data-driven approach to understanding LLD pathophysiology

There is scarce information about the pathophysiological processes underlying Late-Life Depression (LLD). We aimed to determine the neurobiological abnormalities related to LLD through a multi-modal biomarker approach combining a large, unbiased peripheral proteomic panel and structural brain imaging. We examined data from 44 LLD and 31 control participants. Plasma proteomic analysis was performed using a multiplex immunoassay. We evaluated the differential protein expression between groups with random intercept models. We carried out enrichment pathway analyses (EPA) to uncover biological pathways and processes related to LLD. Machine learning analysis was applied to the combined dataset to determine the accuracy with which specific proteins could correctly discriminate LLD versus control participants. Sixty-one proteins were differentially expressed in LLD (p < 0.05 and FDR < 0.01). EPA showed that these proteins were related to abnormal immune-inflammatory control, cell survival and proliferation, proteostasis control, lipid metabolism, intracellular signaling. Machine learning analysis showed that a panel of three proteins (C-peptide, FABP-liver, ApoA-IV) discriminated LLD and control participants with 100% accuracy. The plasma proteomic profile in LLD revealed dysregulation in biological processes essential to the maintenance of homeostasis at cellular and systemic levels. These abnormalities increase brain and systemic allostatic load leading to the downstream negative outcomes of LLD, including increased risk of medical comorbidities and dementia. The peripheral biosignature of LLD has predictive power and may suggest novel putative therapeutic targets for prevention, treatment, and neuroprotection in LLD.

Discovery of biochemical biomarkers for aggression: A role for metabolomics in psychiatry

Human aggression encompasses a wide range of behaviors and is related to many psychiatric disorders. We introduce the different classification systems of aggression and related disorders as a basis for discussing biochemical biomarkers and then present an overview of studies in humans (published between 1990 and 2015) that reported statistically significant associations of biochemical biomarkers with aggression, DSM-IV disorders involving aggression, and their subtypes. The markers are of different types, including inflammation markers, neurotransmitters, lipoproteins, and hormones from various classes. Most studies focused on only a limited portfolio of biomarkers, frequently a specific class only. When integrating the data, it is clear that compounds from several biological pathways have been found to be associated with aggressive behavior, indicating complexity and the need for a broad approach. In the second part of the paper, using examples from the aggression literature and psychiatric metabolomics studies, we argue that a better understanding of aggression would benefit from a more holistic approach such as provided by metabolomics. © 2016 Wiley Periodicals, Inc.

Advantages and Pitfalls of Mass Spectrometry Based Metabolome Profiling in Systems Biology

Mass spectrometry-based metabolome profiling became the method of choice in systems biology approaches and aims to enhance biological understanding of complex biological systems. Genomics, transcriptomics, and proteomics are well established technologies and are commonly used by many scientists. In comparison, metabolomics is an emerging field and has not reached such high-throughput, routine and coverage than other omics technologies. Nevertheless, substantial improvements were achieved during the last years. Integrated data derived from multi-omics approaches will provide a deeper understanding of entire biological systems. Metabolome profiling is mainly hampered by its diversity, variation of metabolite concentration by several orders of magnitude and biological data interpretation. Thus, multiple approaches are required to cover most of the metabolites. No software tool is capable of comprehensively translating all the data into a biologically meaningful context yet. In this review, we discuss the advantages of metabolome profiling and main obstacles limiting progress in systems biology.

Altered Monoamine and Acylcarnitine Metabolites in HIV-Positive and HIV-Negative Subjects With Depression

BACKGROUND

Depression is a frequent comorbidity in HIV infection that has been associated with worse treatment outcomes and increased mortality. Recent studies suggest that increased innate immune activation and tryptophan catabolism are associated with higher risk of depression in HIV infection and other chronic inflammatory diseases, but the mechanisms leading to depression remain poorly understood.

METHODS

The severity of depressive symptoms was assessed by Beck Depression Inventory or Center for Epidemiological Studies Depression Scale. Untargeted metabolomic profiling of plasma from 104 subjects (68 HIV-positive and 36 HIV-negative) across 3 independent cohorts was performed using liquid or gas chromatography followed by mass spectrometry. Cytokine profiling was by Bioplex array. Bioinformatic analysis was performed in Metaboanalyst and R.

RESULTS

Decreased monoamine metabolites (phenylacetate, 4-hydroxyphenylacetate) and acylcarnitines (propionylcarnitine, isobutyrylcarnitine, isovalerylcarnitine, 2-methylbutyrylcarnitine) in plasma distinguished depressed subjects from controls in HIV-positive and HIV-negative cohorts, and these alterations correlated with the severity of depressive symptoms. In HIV-positive subjects, acylcarnitines and other markers of mitochondrial function correlated inversely with tryptophan catabolism, a marker of interferon responses, suggesting interrelationships between inflammatory pathways, tryptophan catabolism, and metabolic alterations associated with depression. Altered metabolites mapped to pathways involved in monoamine metabolism, mitochondrial function, and inflammation, suggesting a model in which complex relationships between monoamine metabolism and mitochondrial bioenergetics contribute to biological mechanisms involved in depression that may be augmented by inflammation during HIV infection.

CONCLUSIONS

Integrated approaches targeting inflammation, monoamine metabolism, and mitochondrial pathways may be important for prevention and treatment of depression in people with and without HIV.

What Have Mass Spectrometry-Based Proteomics and Metabolomics (Not) Taught Us about Psychiatric Disorders?

Understanding the molecular causes and finding appropriate therapies for psychiatric disorders are challenging tasks for research; -omics technologies are used to elucidate the molecular mechanisms underlying brain dysfunction in a hypothesis-free manner. In this review, we will focus on mass spectrometry-based proteomics and metabolomics and address how these approaches have contributed to our understanding of psychiatric disorders. Specifically, we will discuss what we have learned from mass spectrometry-based proteomics and metabolomics studies in rodent models and human cohorts, outline current limitations and discuss the potential of these methods for future applications in psychiatry.

Metabolomics analysis reveals insights into biochemical mechanisms of mental stress-induced left ventricular dysfunction

Mental stress induced left ventricular dysfunction (LVD) has been associated with a greater risk of adverse events in coronary heart disease (CHD) patients independent of conventional risk indicators. The underlying biochemical mechanisms of this cardiovascular condition are poorly understood. Our objective was to use metabolomics technology to identify biochemical changes that co-occur with mental stress-induced LVD in patients with clinically stable CHD. Participants were adult CHD patients who were recruited for mental stress-induced myocardial ischemia screening. For this study, we randomly selected 30 patients representing the extremes of the mental stress-induced left ventricular ejection fraction (LVEF) change distribution; 15 who showed LVD (i.e. LVEF reduction ≥5) and 15 who showed a normal left ventricular response (NLVR; i.e. a LVEF increase of ≥5) to three mental stressors. An electrochemistry based metabolomics platform was used to profile pre- and post-stress serum samples yielding data for 22 known compounds, primarily within the tyrosine, tryptophan, purine and methionine pathways. There were significant stress-induced changes in several compounds. A comparison between the NLVR and LVD groups showed significant effects for kynurenine (p = .036, N-acetylserotonin (p = .054), uric acid (p = .015), tyrosine (p = .019) and a trend for methionine (p = .065); the NLVR group showed a significantly greater stress-induced reduction in all of those compounds compared to the LVD group. Many of these biochemicals have been implicated in other stress-related phenomena and are plausible candidates for mechanisms underlying LVD in response to mental stress.

Proteomic analysis of serum from patients with major depressive disorder to compare their depressive and remission statuses

OBJECTIVE

Currently, there are a few biological markers to aid in the diagnosis and treatment of depression. However, it is not sufficient for diagnosis. We attempted to identify differentially expressed proteins during depressive moods as putative diagnostic biomarkers by using quantitative proteomic analysis of serum.

METHODS

Blood samples were collected twice from five patients with major depressive disorder (MDD) at depressive status before treatment and at remission status during treatment. Samples were individually analyzed by liquid chromatography-tandem mass spectrometry for protein profiling. Differentially expressed proteins were analyzed by label-free quantification. Enzyme-linked immunosorbent assay (ELISA) results and receiver-operating characteristic (ROC) curves were used to validate the differentially expressed proteins. For validation, 8 patients with MDD including 3 additional patients and 8 matched normal controls were analyzed.

RESULTS

The quantitative proteomic studies identified 10 proteins that were consistently upregulated or downregulated in 5 MDD patients. ELISA yielded results consistent with the proteomic analysis for 3 proteins. Expression levels were significantly different between normal controls and MDD patients. The 3 proteins were ceruloplasmin, inter-alpha-trypsin inhibitor heavy chain H4 and complement component 1qC, which were upregulated during the depressive status. The depressive status could be distinguished from the euthymic status from the ROC curves for these proteins, and this discrimination was enhanced when all 3 proteins were analyzed together.

CONCLUSION

This is the first proteomic study in MDD patients to compare intra-individual differences dependent on mood. This technique could be a useful approach to identify MDD biomarkers, but requires additional proteomic studies for validation.

Macaques exhibit a naturally-occurring depression similar to humans

Rodent models have dominated preclinical investigations into the mechanisms of depression. However, these models-which rely on subjecting individual rodents to physical stressors - do not realistically resemble the etiopathological development of depression, which occurs naturally in a social context. A non-human primate model that better reflects the social ethological aspects of depression would be more advantageous to investigating pathophysiological mechanisms and developing antidepressant therapeutics. Here, we describe and model a naturally-occurring depressive state in a non-human primate species, the cynomolgus monkey (Macaca fascicularis), in a realistic social ethological context and associate the depressed behavioral phenotype with significant serum metabolic perturbations. One to two subjects per stable social colony (17–22 subjects) manifested a depressive phenotype that may be attributed to psychosocial stress. In accordance with rodent and human studies, the serum metabolic phenotype of depressed and healthy subjects significantly differed, supporting the model's face validity. However, application of the fast-acting antidepressant ketamine failed to demonstrate predictive validity. This study proposes a non-human primate depression model in a realistic social ethological context that can better approximate the psychosocial stressors underlying depression.

Metabolomic identification of biochemical changes induced by fluoxetine and imipramine in a chronic mild stress mouse model of depression

Metabolomics was applied to a C57BL/6N mouse model of chronic unpredictable mild stress (CMS). Such mice were treated with two antidepressants from different categories: fluoxetine and imipramine. Metabolic profiling of the hippocampus was performed using gas chromatography-mass spectrometry analysis on samples prepared under optimized conditions, followed by principal component analysis, partial least squares-discriminant analysis, and pair-wise orthogonal projections to latent structures discriminant analyses. Body weight measurement and behavior tests including an open field test and the forced swimming test were completed with the mice as a measure of the phenotypes of depression and antidepressive effects. As a result, 23 metabolites that had been differentially expressed among the control, CMS, and antidepressant-treated groups demonstrated that amino acid metabolism, energy metabolism, adenosine receptors, and neurotransmitters are commonly perturbed by drug treatment. Potential predictive markers for treatment effect were identified: myo-inositol for fluoxetine and lysine and oleic acid for imipramine. Collectively, the current study provides insights into the molecular mechanisms of the antidepressant effects of two widely used medications.

Metabolic Profiling and Phenotyping of Central Nervous System Diseases: Metabolites Bring Insights into Brain Dysfunctions

Metabolic phenotyping corresponds to the large-scale quantitative and qualitative analysis of the metabolome i.e., the low-molecular weight <1 KDa fraction in biological samples, and provides a key opportunity to advance neurosciences. Proton nuclear magnetic resonance and mass spectrometry are the main analytical platforms used for metabolic profiling, enabling detection and quantitation of a wide range of compounds of particular neuro-pharmacological and physiological relevance, including neurotransmitters, secondary messengers, structural lipids, as well as their precursors, intermediates and degradation products. Metabolic profiling is therefore particularly indicated for the study of central nervous system by probing metabolic and neurochemical profiles of the healthy or diseased brain, in preclinical models or in human samples. In this review, we introduce the analytical and statistical requirements for metabolic profiling. Then, we focus on key studies in the field of metabolic profiling applied to the characterization of animal models and human samples of central nervous system disorders. We highlight the potential of metabolic profiling for pharmacological and physiological evaluation, diagnosis and drug therapy monitoring of patients affected by brain disorders. Finally, we discuss the current challenges in the field, including the development of systems biology and pharmacology strategies improving our understanding of metabolic signatures and mechanisms of central nervous system diseases.

Advances in biomarkers of major depressive disorder

Major depressive disorder (MDD) is characterized by mood, vegetative, cognitive, and even psychotic symptoms and signs that can cause substantial impairments in quality of life and functioning. Biomarkers are measurable indicators that could help diagnosing MDD or predicting treatment response. In this chapter, lipid profiles, immune/inflammation, and neurotrophic factor pathways that have long been implicated in the pathogenesis of MDD are discussed. Then, pharmacogenetics and epigenetics of serotonin transport and its metabolism pathway, brain-derived neurotrophic factor, and abnormality of hypothalamo-pituitary-adrenocortical axis also revealed new biomarkers. Lastly, new techniques, such as proteomics and metabolomics, which allow researchers to approach the studying of MDD with new directions and make new discoveries are addressed. In the future, more data are needed regarding pathophysiology of MDD, including protein levels, single nucleotide polymorphism, epigenetic regulation, and clinical data in order to better identify reliable and consistent biomarkers for diagnosis, treatment choice, and outcome prediction.

A pilot study of plasma metabolomic patterns from patients treated with ketamine for bipolar depression: evidence for a response-related difference in mitochondrial networks

BACKGROUND AND PURPOSE

(R,S)-ketamine produces rapid and significant antidepressant effects in approximately 65% of patients suffering from treatment-resistant bipolar depression (BD). The genetic, pharmacological and biochemical differences between ketamine responders and non-responders have not been identified. The purpose of this study was to employ a metabolomics approach, a global, non-targeted determination of endogenous metabolic patterns, to identify potential markers of ketamine response and non-response.

EXPERIMENTAL APPROACH

Plasma samples from 22 BD patients were analyzed to produce metabolomic patterns. The patients had received ketamine in a placebo-controlled crossover study and the samples were obtained 230 min post-administration at which time the patients were categorized as responders or non-responders. Matching plasma samples from the placebo arm of the study were also analysed. During the study, the patients were maintained on either lithium or valproate.

KEY RESULTS

The metabolomic patterns were significantly different between the patients maintained on lithium and those maintained on valproate, irrespective of response to ketamine. In the patients maintained on lithium, 18 biomarkers were identified. In responders, lysophosphatidylethanolamines (4) and lysophosphatidylcholines (9) were increased relative to non-responders.

CONCLUSIONS AND IMPLICATIONS

The results indicate that the differences between patients who respond to ketamine and those who do not are due to alterations in the mitochondrial β-oxidation of fatty acids. These differences were not produced by ketamine administration. The data indicate that pretreatment metabolomics screening may be a guide to the prediction of response and a potential approach to the individualization of ketamine therapy.

Proteomics, metabolomics, and protein interactomics in the characterization of the molecular features of major depressive disorder

Omics technologies emerged as complementary strategies to genomics in the attempt to understand human illnesses. In general, proteomics technologies emerged earlier than those of metabolomics for major depressive disorder (MDD) research, but both are driven by the identification of proteins and/or metabolites that can delineate a comprehensive characterization of MDD's molecular mechanisms, as well as lead to the identification of biomarker candidates of all types—prognosis, diagnosis, treatment, and patient stratification. Also, one can explore protein and metabolite interactomes in order to pinpoint additional molecules associated with the disease that had not been picked up initially. Here, results and methodological aspects of MDD research using proteomics, metabolomics, and protein interactomics are reviewed, focusing on human samples.

Identifying individual differences of fluoxetine response in juvenile rhesus monkeys by metabolite profiling

Fluoxetine is the only psychopharmacological agent approved for depression by the US Food and Drug Administration for children and is commonly used therapeutically in a variety of neurodevelopmental disorders. Therapeutic response shows high individual variability, and severe side effects have been observed. In the current study we set out to identify biomarkers of response to fluoxetine as well as biomarkers that correlate with impulsivity, a measure of reward delay behavior and potential side effect of the drug, in juvenile male rhesus monkeys. The study group was also genotyped for polymorphisms of monoamine oxidase A (MAOA), a gene that has been associated with psychiatric disorders. We used peripheral metabolite profiling of blood and cerebrospinal fluid (CSF) from animals treated daily with fluoxetine or vehicle for one year. Fluoxetine response metabolite profiles and metabolite/reward delay behavior associations were evaluated using multivariate analysis. Our analyses identified a set of plasma and CSF metabolites that distinguish fluoxetine- from vehicle-treated animals and metabolites that correlate with impulsivity. Some metabolites displayed an interaction between fluoxetine and MAOA genotype. The identified metabolite biomarkers belong to pathways that have important functions in central nervous system physiology. Biomarkers of response to fluoxetine in the normally functioning brain of juvenile nonhuman primates may aid in finding predictors of response to treatment in young psychiatric populations and in progress toward the realization of a precision medicine approach in the area of neurodevelopmental disorders.