GC-MS based metabolomics identification of possible novel biomarkers for schizophrenia in peripheral blood mononuclear cells

Metabolomic analysis of swainsonine poisoning in renal tubular epithelial cells

Locoweed is a poisonous plant widely present in grasslands around the world. Swainsonine (SW), an indole alkaloid that, is the main toxic component of the locoweed. To understand the mechanism of SW-induced toxicity and to delineate the metabolic profile of locoweed poisoning we performed the LC-MS/MS untargeted metabolomic study to analyze metabolites in SW-treated renal tubular epithelial cells (0.8 mg/mL, 12 h) and in order to identify the SW-induced metabolomic changes. The analysis identified 2,563 metabolites in positive ion mode and 1,990 metabolites in negative ion mode. Our results showed that the metabolites were mainly benzenoids, lipids and lipid-like molecules, nucleosides, nucleotides, and analogs, organic acids, and derivatives. The differential metabolites were primarily enriched in pathways involving bile secretion, primary bile acid biosynthesis, riboflavin metabolism, ferroptosis, drug metabolism-cytochrome P450, and primidine metabolism. We have screened out substances such as swainsonine, 3alpha,7alpha-Dihydroxy-5beta-cholestanate, 2-Hydroxyiminostilbene, and glycochenodeoxycholate, which may have the potential to serve as biomarkers for swainsonine poisoning. This study provides insights into the types of metabolomic alteration in renal tubular epithelial cells induced by swainsonine.

The relationship between alterations in plasma metabolites and treatment responses in antipsychotic-naïve female patients with schizophrenia

This study aimed to explore the relationship between alterations in plasma metabolites and treatment responses amongst antipsychotic-naïve female patients with schizophrenia. A total of 38 antipsychotic-naïve female schizophrenia patients (ANS) and 19 healthy female controls (HC) were recruited. Plasma samples were obtained from all participants, and targeted metabolomics were measured with FIA-MS/MS and LC-MS/MS. The positive and negative syndrome scale (PANSS) was used to assess the severity of psychotic symptoms before and after eight weeks of treatment. Receiver operator characteristics (ROC) curves were used to predict diagnostic and therapeutic responses. A total of 186 metabolites passed quality control procedures and were used in statistical analysis to identify potential biomarkers. Before treatment, the ANS patients had lower levels of γ -Aminobutyric Acid (GABA) and higher levels of Cholesteryl esters (CE) (20:3), Cholic Acid (CA) and Glycocholic Acid (GCA) compared to the HCs. These four differential metabonomic markers were synthesised into a combinatorial biomarker panel. This panel significantly distinguished ANS from HC. Moreover, this biomarker panel was able to effectively predict therapeutic responses. Our results suggest that plasma CE (20:3), CA, GCA, and GABA levels may be useful for diagnosing and predicting antipsychotic efficacy amongst female schizophrenia patients.

The relationship between oxidative stress and psychotic disorders in 22q11.2 deletion syndrome

BACKGROUND

22q11.2 Deletion syndrome (22q11.2DS) is the most common microdeletion syndrome in humans. This condition is associated with a wide range of symptoms including immune and neuropsychiatric disorders. Notably, psychotic disorders including schizophrenia have a prevalence of ∼ 30%. A growing body of evidence indicates that neuroinflammation and oxidative stress (OS) play a role in the pathophysiology of schizophrenia. In this study, we aim to assess the interaction between 22q11.2DS, OS and schizophrenia.

METHODS

Blood samples were collected from 125 participants (including individuals with 22q11.2DS [n = 73] and healthy controls [n = 52]) from two sites: Sheba Medical Center in Israel, and University Hospital Gasthuisberg in Belgium. Baseline OS levels were evaluated by measuring Myeloperoxidase (MPO) activity. A sub-sample of the Israeli sample (n = 50) was further analyzed to examine survival of Peripheral Blood Mononuclear Cells (PBMCs) following induction of OS using vitamin K3.

RESULTS

The levels of MPO were significantly higher in all individuals with 22q11.2DS, compared to healthy controls (0.346 ± 0.256 vs. 0.252 ± 0.238, p =.004). In addition, when comparing to healthy controls, the PBMCs of individuals with 22q11.2DS were less resilient to induced OS, specifically the group diagnosed with psychotic disorder (0.233 ± 0.206 for the 22q11.2DS individuals with psychotic disorders, 0.678 ± 1.162 for the 22q11.2DS individuals without psychotic disorders, and 1.428 ± 1.359 for the healthy controls, p =.003, η2 = 0.207).

CONCLUSIONS

Our results suggest that dysregulation of OS mechanisms may play a role in the pathophysiology of the 22q11.2DS phenotype. The 22q11.2DS individuals with psychotic disorders were more sensitive to induction of OS, but did not present significantly different levels of OS at baseline. These results may be due to the effect of antipsychotic treatment administered to this sup-group. By elucidating novel molecular pathways, early identification of biochemical risk markers for 22q11.2DS and psychotic disorders can be detected. This can ultimately pave the way to the design of early and more precise interventions of individuals with 22q11.2DS.

Downregulation of mGluR1-mediated signaling underlying autistic-like core symptoms in Shank1 P1812L-knock-in mice

Autism spectrum disorder (ASD) is a neurodevelopmental disorder characterized by core symptoms that consist of social deficits and repetitive behaviors. Unfortunately, no effective medication is available thus far to target the core symptoms of ASD, since the pathogenesis remains largely unknown. To investigate the pathogenesis of the core symptoms in ASD, we constructed Shank1 P1812L-knock-in (KI) mice corresponding to a recurrent ASD-related mutation, SHANK1 P1806L, to achieve construct validity and face validity. Shank1 P1812L-KI heterozygous (HET) mice presented with social deficits and repetitive behaviors without the presence of confounding comorbidities. HET mice also exhibited downregulation of metabotropic glutamate receptor (mGluR1) and associated signals, along with structural abnormalities in the dendritic spines and postsynaptic densities. Combined with findings from Shank1 R882H-KI mice, our study confirms that mGluR1-mediated signaling dysfunction is a pivotal mechanism underlying the core symptoms of ASD. Interestingly, Shank1 P1812L-KI homozygous (HOM) mice manifested behavioral signs of impaired long-term memory rather than autistic-like core traits; thus, their phenotype was markedly different from that of Shank1 P1812L-KI HET mice. Correspondingly, at the molecular level, Shank1 P1812L-KI HOM displayed upregulation of AMPA receptor (GluA2)-related signals. The different patterns of protein changes in HOM and HET mice may explain the differences in behaviors. Our study emphasizes the universality of mGluR1-signaling hypofunction in the pathogenesis of the core symptoms in ASD, providing a potential target for therapeutic drugs. The precise correspondence between genotype and phenotype, as shown in HOM and HET mice, indicates the importance of reproducing disease-related genotypes in mouse models.

The Impact of Mechanical Cues on the Metabolomic and Transcriptomic Profiles of Human Dermal Fibroblasts Cultured in Ultrashort Self-Assembling Peptide 3D Scaffolds

Cells' interactions with their microenvironment influence their morphological features and regulate crucial cellular functions including proliferation, differentiation, metabolism, and gene expression. Most biological data available are based on in vitro two-dimensional (2D) cellular models, which fail to recapitulate the three-dimensional (3D) in vivo systems. This can be attributed to the lack of cell-matrix interaction and the limitless access to nutrients and oxygen, in contrast to in vivo systems. Despite the emergence of a plethora of 3D matrices to address this challenge, there are few reports offering a proper characterization of these matrices or studying how the cell-matrix interaction influences cellular metabolism in correlation with gene expression. In this study, two tetrameric ultrashort self-assembling peptide sequences, FFIK and FIIK, were used to create in vitro 3D models using well-described human dermal fibroblast cells. The peptide sequences are derived from naturally occurring amino acids that are capable of self-assembling into stable hydrogels without UV or chemical cross-linking. Our results showed that 2D cultured fibroblasts exhibited distinct metabolic and transcriptomic profiles compared to 3D cultured cells. The observed changes in the metabolomic and transcriptomic profiles were closely interconnected and influenced several important metabolic pathways including the TCA cycle, glycolysis, MAPK signaling cascades, and hemostasis. Data provided here may lead to clearer insights into the influence of the surrounding microenvironment on human dermal fibroblast metabolic patterns and molecular mechanisms, underscoring the importance of utilizing efficient 3D in vitro models to study such complex mechanisms.

Metabolic Footprinting-Based DNA-AuNP Encoders for Extracellular Metabolic Response Profiling

Metabolic footprinting as a convenient and non-invasive cell metabolomics strategy relies on monitoring the whole extracellular metabolic process. It covers nutrient consumption and metabolite secretion of in vitro cell culture, which is hindered by low universality owing to pre-treatment of the cell medium and special equipment. Here, we report the design and a variety of applicability, for quantifying extracellular metabolism, of fluorescently labeled single-stranded DNA (ssDNA)-AuNP encoders, whose multi-modal signal response is triggered by extracellular metabolites. We constructed metabolic response profiling of cells by detecting extracellular metabolites in different tumor cells and drug-induced extracellular metabolites. We further assessed the extracellular metabolism differences using a machine learning algorithm. This metabolic response profiling based on the DNA-AuNP encoder strategy is a powerful complement to metabolic footprinting, which significantly applies potential non-invasive identification of tumor cell heterogeneity.

Identification of a Hypoxia-Related Signature as Candidate Detector for Schizophrenia Based on Genome-Wide Gene Expression

INTRODUCTION

Schizophrenia (SCZ), a severe neuropsychiatric disorder with high genetic susceptibility, has high rates of misdiagnosis due to the unavoidably subjective factors and heterogeneous clinical presentations. Hypoxia has been identified as an importantly risk factor that participates in the development of SCZ. Therefore, development of a hypoxia-related biomarker for SCZ diagnosis is promising. Therefore, we dedicated to develop a biomarker that could contribute to distinguishing healthy controls and SCZ patients.

METHODS

GSE17612, GSE21935, and GSE53987 datasets, consisting of 97 control samples and 99 SCZ samples, were involved in our study. The hypoxia score was calculated based on the single-sample gene-set enrichment analysis using the hypoxia-related differentially expressed genes to quantify the expression levels of these genes for each SCZ patient. Patients in high-score groups were defined if their hypoxia score was in the upper half of all hypoxia scores and patients in low-score groups if their hypoxia score was in the lower half. GSEA was applied to detect the functional pathway of these differently expressed genes. CIBERSORT algorithm was utilized to evaluate the tumor-infiltrating immune cells of SCZ patients.

RESULTS

In this study, we developed and validated a biomarker consisting of 12 hypoxia-related genes that could distinguish healthy controls and SCZ patients robustly. We found that the metabolism reprogramming might be activated in the patient with high hypoxia score. Finally, CIBERSORT analysis illustrated that lower composition of naive B cells and higher composition of memory B cells might be observed in low-score groups of SCZ patients.

CONCLUSION

These findings revealed that the hypoxia-related signature was acceptable as a detector for SCZ, providing further insight into effective diagnosis and treatment strategies for SCZ.

Qualitative and Quantitative Mass Spectrometry in Salivary Metabolomics and Proteomics

The metabolomics and proteomics analysis of saliva, an excellent biofluid that is a rich source of biological compounds, allows for the safe and frequent screening of drugs, their metabolites, and molecular biomarkers of various diseases. One of the most frequently used analytical methods in saliva analysis is liquid chromatography coupled with mass spectrometry (LC-MS) and tandem mass spectrometry. The low ionisation efficiency of some compounds and a complex matrix makes their identification by MS difficult. Furthermore, quantitative analysis by LC-MS frequently cannot be performed without isotopically labelled standards, which usually have to be specially synthesised. This review presented reports on qualitative and quantitative approaches in salivary metabolomics and proteomics. The purpose of this manuscript was to present the challenges, advances, and future prospects of mass spectrometry, both in the analysis of salivary metabolites and proteins. The presented review should appeal to those interested in the recent advances and trends in qualitative and quantitative mass spectrometry in salivary metabolomics and proteomics, which may facilitate a diagnostic accuracy, the evaluation of treatment efficacy, the early diagnosis of disease, and a forensic investigation of some unapproved drugs for any medical or dietary administration.

Experiences and Perspectives of GC-MS Application for the Search of Low Molecular Weight Discriminants of Schizophrenia

Schizophrenia is one of the most severe chronic mental disorders that is currently diagnosed and categorized through subjective clinical assessment of complex symptoms. At present, there is a recognized need for an objective, unbiased clinical test for schizophrenia diagnosis at an early stage and categorization of the disease. This can be achieved by assaying low-molecular-weight biomarkers of the disease. Here we give an overview of previously conducted research on the discovery of biomarkers of schizophrenia and focus on the studies implemented with the use of GC-MS and the least invasiveness of biological samples acquisition. The presented data demonstrate that GC-MS is a powerful instrumental platform for investigating dysregulated biochemical pathways implicated in schizophrenia pathogenesis. With this platform, different research groups suggested a number of low molecular weight biomarkers of schizophrenia. However, we recognize an inconsistency between the biomarkers or biomarkers patterns revealed by different groups even in the same matrix. Moreover, despite the importance of the problem, the number of relevant studies is limited. The intensification of the research, as well as the harmonization of the analytical procedures to overcome the observed inconsistencies, can be indicated as future directions in the schizophrenia bio-markers quest.

Patient-Derived In Vitro Models of Microglial Function and Synaptic Engulfment in Schizophrenia

Multiple lines of evidence implicate dysregulated microglia-mediated synaptic pruning in the pathophysiology of schizophrenia. In vitro human cellular studies represent a promising means of pursuing this hypothesis, complementing efforts with animal models and postmortem human data while addressing their limitations. The challenges in culturing homogeneous populations of cells derived from postmortem or surgical biopsy brain material from patients, and their limited availability, has led to a focus on differentiation of induced pluripotent stem cells. These methods too have limitations, in that they disrupt the epigenome and can demonstrate line-to-line variability due in part to extended time in culture, partial reprogramming, and/or residual epigenetic memory from the cell source, yielding large technical artifacts. Yet another strategy uses direct transdifferentiation of peripheral mononuclear blood cells, or umbilical cord blood cells, to microglia-like cells. Any of these approaches can be paired with patient-derived synaptosomes from differentiated neurons as a simpler alternative to co-culture. Patient-derived microglia models may facilitate identification of novel modulators of synaptic pruning and identification of biomarkers that may allow more targeted early interventions.

Metabolomic and proteomic profiling in bipolar disorder patients revealed potential molecular signatures related to hemostasis

INTRODUCTION

Bipolar disorder (BD) is a mood disorder characterized by the occurrence of depressive episodes alternating with episodes of elevated mood (known as mania). There is also an increased risk of other medical comorbidities.

OBJECTIVES

This work uses a systems biology approach to compare BD treated patients with healthy controls (HCs), integrating proteomics and metabolomics data using partial correlation analysis in order to observe the interactions between altered proteins and metabolites, as well as proposing a potential metabolic signature panel for the disease.

METHODS

Data integration between proteomics and metabolomics was performed using GC-MS data and label-free proteomics from the same individuals (N = 13; 5 BD, 8 HC) using generalized canonical correlation analysis and partial correlation analysis, and then building a correlation network between metabolites and proteins. Ridge-logistic regression models were developed to stratify between BD and HC groups using an extended metabolomics dataset (N = 28; 14 BD, 14 HC), applying a recursive feature elimination for the optimal selection of the metabolites.

RESULTS

Network analysis demonstrated links between proteins and metabolites, pointing to possible alterations in hemostasis of BD patients. Ridge-logistic regression model indicated a molecular signature comprising 9 metabolites, with an area under the receiver operating characteristic curve (AUROC) of 0.833 (95% CI 0.817-0.914).

CONCLUSION

From our results, we conclude that several metabolic processes are related to BD, which can be considered as a multi-system disorder. We also demonstrate the feasibility of partial correlation analysis for integration of proteomics and metabolomics data in a case-control study setting.

Metabolomic biomarkers related to non-suicidal self-injury in patients with bipolar disorder

BACKGROUND

Non-suicidal self-injury (NSSI) is an important symptom of bipolar disorder (BD) and other mental disorders and has attracted the attention of researchers lately. It is of great significance to study the characteristic markers of NSSI. Metabolomics is a relatively new field that can provide complementary insights into data obtained from genomic, transcriptomic, and proteomic analyses of psychiatric disorders. The aim of this study was to identify the metabolic pathways associated with BD with NSSI and assess important diagnostic and predictive indices of NSSI in BD.

METHOD

Nuclear magnetic resonance spectrometry was performed to evaluate the serum metabolic profiles of patients with BD with NSSI (n = 31), patients with BD without NSSI (n = 46), and healthy controls (n = 10). Data were analyzed using an Orthogonal Partial Least Square Discriminant Analysis and a t-test. Differential metabolites were identified (VIP > 1 and p < 0.05), and further analyzed using Metabo Analyst 3.0 to identify associated metabolic pathways.

RESULTS

Eight metabolites in the serum and two important metabolic pathways, the urea and glutamate metabolism cycles, were found to distinguish patients with BD with NSSI from healthy controls. Eight metabolites in the serum, glycine and serine metabolism pathway, and the glucose-alanine cycle were found to distinguish patients with BD without NSSI from healthy controls. Five metabolites in the serum and the purine metabolism pathway were found to distinguish patients with BD with NSSI from those with BD without NSSI.

CONCLUSIONS

Abnormalities in the urea cycle, glutamate metabolism, and purine metabolism played important roles in the pathogenesis of BD with NSSI.

Fluxomics - New Metabolomics Approaches to Monitor Metabolic Pathways

Fluxomics is an innovative -omics research field that measures the rates of all intracellular fluxes in the central metabolism of biological systems. Fluxomics gathers data from multiple different -omics fields, portraying the whole picture of molecular interactions. Recently, fluxomics has become one of the most relevant approaches to investigate metabolic phenotypes. Metabolic flux using ¹³C-labeled molecules is increasingly used to monitor metabolic pathways, to probe the corresponding gene-RNA and protein-metabolite interaction networks in actual time. Thus, fluxomics reveals the functioning of multi-molecular metabolic pathways and is increasingly applied in biotechnology and pharmacology. Here, we describe the main fluxomics approaches and experimental platforms. Moreover, we summarize recent fluxomic results in different biological systems.

Recent Reports on Redox Stress-Induced Mitochondrial DNA Variations, Neuroglial Interactions, and NMDA Receptor System in Pathophysiology of Schizophrenia

Schizophrenia (SZ) is a chronic psychiatric disorder affecting several people worldwide. Mitochondrial DNA (mtDNA) variations could invoke changes in the OXPHOS system, calcium buffering, and ROS production, which have significant implications for glial cell survival during SZ. Oxidative stress has been implicated in glial cells-mediated pathogenesis of SZ; the brain comparatively more prone to oxidative damage through NMDAR. A confluence of scientific evidence points to mtDNA alterations, Nrf2 signaling, dynamic alterations in dorsolateral prefrontal cortex (DLPFC), and provocation of oxidative stress that enhance pathophysiology of SZ. Furthermore, the alterations in excitatory signaling related to NMDAR signaling were particularly reported for SZ pathophysiology. Current review reported the recent evidence for the role of mtDNA variations and oxidative stress in relation to pathophysiology of SZ, NMDAR hypofunction, and glutathione deficiency. NMDAR system is influenced by redox dysregulation in oxidative stress, inflammation, and antioxidant mediators. Several studies have demonstrated the relationship of these variables on severity of pathophysiology in SZ. An extensive literature search was conducted using Medline, PubMed, PsycINFO, CINAHL PLUS, BIOSIS Preview, Google scholar, and Cochrane databases. We summarize consistent evidence pointing out a plausible model that may elucidate the crosstalk between mtDNA alterations in glial cells and redox dysregulation during oxidative stress and the perturbation of NMDA neurotransmitter system during current therapeutic modalities for the SZ treatment. This review can be beneficial for the development of promising novel diagnostics, and therapeutic modalities by ascertaining the mtDNA variations, redox state, and efficacy of pharmacological agents to mitigate redox dysregulation and augment NMDAR function to treat cognitive and behavioral symptoms in SZ.

Searching for biomarkers in schizophrenia and psychosis: Case-control study using capillary electrophoresis and liquid chromatography time-of-flight mass spectrometry and systematic review for biofluid metabolites

Metabolomics has been attracting attention in recent years as an objective method for diagnosing schizophrenia. In this study, we analyzed 378 metabolites in the serum of schizophrenia patients using capillary electrophoresis‐ and liquid chromatography‐time‐of‐flight mass spectrometry. Using multivariate analysis with the orthogonal partial least squares method, we observed significantly higher levels of alanine, glutamate, lactic acid, ornithine, and serine and significantly lower levels of urea, in patients with chronic schizophrenia compared to healthy controls. Additionally, levels of fatty acids (15:0), (17:0), and (19:1), cis‐11‐eicosenoic acid, and thyroxine were significantly higher in patients with acute psychosis than in those in remission. Moreover, we conducted a systematic review of comprehensive metabolomics studies on schizophrenia over the last 20 years and observed consistent trends of increase in some metabolites such as glutamate and glucose, and decrease in citrate in schizophrenia patients across several studies. Hence, we provide substantial evidence for metabolic biomarkers in schizophrenia patients through our metabolomics study.

In this study, we analyzed 378 metabolites in the serum of schizophrenia patients using CE and LC‐TOFMS. With a systematic review, here we provide substantial evidence for metabolic biomarkers in schizophrenia patients through our metabolomics study.

A system biology approach based on metabolic biomarkers and protein-protein interactions for identifying pathways underlying schizophrenia and bipolar disorder

Mental disorders (MDs), including schizophrenia (SCZ) and bipolar disorder (BD), have attracted special attention from scientists due to their high prevalence and significantly debilitating clinical features. The diagnosis of MDs is still essentially based on clinical interviews, and intensive efforts to introduce biochemical based diagnostic methods have faced several difficulties for implementation in clinics, due to the complexity and still limited knowledge in MDs. In this context, aiming for improving the knowledge in etiology and pathophysiology, many authors have reported several alterations in metabolites in MDs and other brain diseases. After potentially fishing all metabolite biomarkers reported up to now for SCZ and BD, we investigated here the proteins related to these metabolites in order to construct a protein-protein interaction (PPI) network associated with these diseases. We determined the statistically significant clusters in this PPI network and, based on these clusters, we identified 28 significant pathways for SCZ and BDs that essentially compose three groups representing three major systems, namely stress response, energy and neuron systems. By characterizing new pathways with potential to innovate the diagnosis and treatment of psychiatric diseases, the present data may also contribute to the proposal of new intervention for the treatment of still unmet aspects in MDs.

Metabolomics and metabolites in ischemic stroke

Stroke is a major reason for disability and the second highest cause of death in the world. When a patient is admitted to a hospital, it is necessary to identify the type of stroke, and the likelihood for development of a recurrent stroke, vascular dementia, and depression. These factors could be determined using different biomarkers. Metabolomics is a very promising strategy for identification of biomarkers. The advantage of metabolomics, in contrast to other analytical techniques, resides in providing low molecular weight metabolite profiles, rather than individual molecule profiles. Technically, this approach is based on mass spectrometry and nuclear magnetic resonance. Furthermore, variations in metabolite concentrations during brain ischemia could alter the principal neuronal functions. Different markers associated with ischemic stroke in the brain have been identified including those contributing to risk, acute onset, and severity of this pathology. In the brain, experimental studies using the ischemia/reperfusion model (IRI) have shown an impaired energy and amino acid metabolism and confirmed their principal roles. Literature data provide a good basis for identifying markers of ischemic stroke and hemorrhagic stroke and understanding metabolic mechanisms of these diseases. This opens an avenue for the successful use of identified markers along with metabolomics technologies to develop fast and reliable diagnostic tools for ischemic and hemorrhagic stroke.

Alteration of lipids and amino acids in plasma distinguish schizophrenia patients from controls: A targeted metabolomics study

BACKGROUND

Schizophrenia (SCZ) is a serious psychiatric disorder. Metabolite disturbance is an important pathogenic factor in schizophrenic patients. In this study, we aim to identify plasma lipid and amino acid biomarkers for SCZ using targeted metabolomics.

METHODS

Plasma from 76 SCZ patients and 50 matched controls were analyzed using the LC/MS-based multiple reaction monitoring (MRM) metabolomics approach. A total of 182 targeted metabolites, including 22 amino acids and 160 lipids or lipid-related metabolites were observed. We used binary logistic regression analysis to determine whether the lipid and amino acid biomarkers could discriminate SCZ patients from controls. The area under the curve (AUC) from receiver operation characteristic (ROC) curve analysis was conducted to evaluate the diagnostic performance of the biomarkers panel.

RESULTS

We identified 19 significantly differentially expressed metabolites between the SCZ patients and the controls (false discovery rate < 0.05), including one amino acid and 18 lipids or lipid-related metabolites. The binary logistic regression-selected panel showed good diagnostic performance in the drug-naïve group (AUC = 0.936) and all SCZ patients (AUC = 0.948), especially in the drug-treated group (AUC = 0.963).

CONCLUSIONS

Plasma lipids and amino acids showed significant dysregulation in SCZ, which could effectively discriminate SCZ patients from controls. The LC/MS/MS-based approach provides reliable data for the objective diagnosis of SCZ.

CD36 deficiency affects depressive-like behaviors possibly by modifying gut microbiota and the inflammasome pathway in mice

Both inflammatory processes and gut microbiota have been implicated in the pathophysiology of depressive disorders. The class B scavenger receptor CD36 is involved in the cytotoxicity associated with inflammation. However, its role in depression has not yet been examined. In this study, we investigated whether CD36 affects depression by modulating the microbiota-gut-inflammasome-brain axis. We used CD36^-/- (knockout) mice subjected to chronic social defeat stress, and measured the expression of CD36 in these depressed mice and in patients with depression. The hippocampus of CD36^-/- mice was used to investigate changes in the NLRP3 inflammasome signaling pathway. The 16S rRNA gene sequence-based approach was used to compare the cecal microbial communities in CD36^-/- and WT mice. The CD36 deficiency in CD36^-/- mice alleviated chronic stress-induced depression-like behaviors. CD36 was upregulated in depressed mice as well as in depressed patients. Furthermore, the NLRP3 inflammasome signaling pathway was downregulated in the hippocampus of CD36^-/- mice. The Simpson Diversity Index revealed increased cecal bacterial alpha-diversity in the CD36^-/- mice. Among genera, Bacteroides, Rikenella, and Alloprevotella were significantly more abundant in the CD36^-/- mice, whereas Allobaculum was less abundant, consistent with the attenuated inflammation in the hippocampus of CD36^-/- mice. Our findings suggest that CD36 deficiency changes the gut microbiota composition, which in turn may impact depressive-like behaviors by affecting the inflammasome pathway.

Transcriptomics Analysis Reveals Shared Pathways in Peripheral Blood Mononuclear Cells and Brain Tissues of Patients With Schizophrenia

Background: Schizophrenia is a serious mental disorder with complicated biological mechanisms. Few studies explore the transcriptional features that are shared in brain tissue and peripheral blood. In the present study, we aimed to explore the biological pathways with similar expression patterns in both peripheral blood mononuclear cells (PBMCs) and brain tissues. Methods: The present study used transcriptomics technology to detect mRNA expression of PBMCs of 10 drug-naïve patients with schizophrenia and 20 healthy controls. Transcriptome data sets of brain tissue of patients with schizophrenia downloaded from public databases were also analyzed in our study. The biological pathways with similar expression patterns in the PBMCs and brain tissues were uncovered by differential expression analysis, weighted gene co-expression network analysis (WGCNA), and pathway analysis. Finally, the expression levels of differential expressed genes (DEGs) were validated by real-time fluorescence quantitative polymerase chain reaction (qPCR) in another 12 drug-naïve patients with schizophrenia and 12 healthy controls. Results: We identified 542 DEGs, 51 DEGs, 732 DEGs, and 104 DEGs in PBMCs, dorsolateral prefrontal cortex, anterior cingulate gyrus, and nucleus accumbent, respectively. Five DEG clusters were recognized as having similar gene expression patterns in PBMCs and brain tissues by WGCNA. The pathway analysis illustrates that these DEG clusters are mainly enriched in several biological pathways that are related to phospholipid metabolism, ribosome signal transduction, and mitochondrial oxidative phosphorylation. The differential significance of PLAAT3, PLAAT4, PLD2, RPS29, RPL30, COX7C, COX7A2, NDUFAF2, and ATP5ME were confirmed by qPCR. Conclusions: This study finds that the pathways associated with phospholipid metabolism, ribosome signal transduction, and energy metabolism have similar expression patterns in PBMCs and brain tissues of patients with schizophrenia. Our results supply a novel insight for revealing the pathogenesis of schizophrenia and might offer a new approach to explore potential biological markers of peripheral blood in schizophrenia.

Associations of C-Reactive Protein, Free Triiodothyronine, Thyroid Stimulating Hormone and Creatinine Levels with Agitation in Patients with Schizophrenia: A Comparative Cross-Sectional Study

PURPOSE

Agitation is prevalent among inpatients with schizophrenia. The aim of this study was to investigate whether biochemical parameters are associated with agitation in schizophrenia.

PATIENTS AND METHODS

Agitation was evaluated by the Positive and Negative Syndrome Scale-Excited Component questionnaire (PANSS-EC). Fasting serum levels of C-reactive protein (CRP), free triiodothyronine (FT3), free thyroxine (FT4), thyroid-stimulating hormone (TSH), uric acid (UA), creatinine, glucose and lipids were measured.

RESULTS

The analysis included 154 inpatients with schizophrenia (71 with agitation, 83 without agitation) and 75 healthy control subjects. Patients with schizophrenia and agitation had higher serum levels of CRP, FT3, FT4 and UA as well as lower levels of serum TSH and creatinine than patients without agitation (all P < 0.05). Multivariate logistic regression analysis indicated that serum CRP (odds ratio [OR] = 1.470, P = 0.001), FT3 (OR = 13.026, P < 0.001), TSH (OR = 0.758, P = 0.033) and creatinine (OR = 0.965, P = 0.004) were significantly associated with agitation in schizophrenia. CRP, FT3, TSH and creatinine achieved an area under the ROC curve of 0.626, 0.728, 0.620 and 0.663 respectively in discriminating schizophrenia with or without agitation.

CONCLUSION

Increased serum CRP and FT3 levels and decreased serum TSH and creatinine levels are independent risk factors for agitation in hospitalized patients with schizophrenia. Inflammation, thyroid hormones and renal function may be involved in the pathogenesis of agitation in schizophrenia.

Metabolomic Identification of Exosome-Derived Biomarkers for Schizophrenia: A Large Multicenter Study

Exosomes have been suggested as promising targets for the diagnosis and treatment of neurological diseases, including schizophrenia (SCZ), but the potential role of exosome-derived metabolites in these diseases was rarely studied. Using ultra-performance liquid chromatography-tandem mass spectrometry, we performed the first metabolomic study of serum-derived exosomes from patients with SCZ. Our sample comprised 385 patients and 332 healthy controls recruited from 3 clinical centers and 4 independent cohorts. We identified 25 perturbed metabolites in patients that can be used to classify samples from patients and control participants with 95.7% accuracy (95% CI: 92.6%-98.9%) in the training samples (78 patients and 66 controls). These metabolites also showed good to excellent performance in differentiating between patients and controls in the 3 test sets of participants, with accuracies 91.0% (95% CI: 85.7%-96.3%; 107 patients and 62 controls), 82.7% (95% CI: 77.6%-87.9%; 104 patients and 142 controls), and 99.0% (95% CI: 97.7%-100%; 96 patients and 62 controls), respectively. Bioinformatic analysis suggested that these metabolites were enriched in pathways implicated in SCZ, such as glycerophospholipid metabolism. Taken together, our findings support a role for exosomal metabolite dysregulation in the pathophysiology of SCZ and indicate a strong potential for exosome-derived metabolites to inform the diagnosis of SCZ.

Delayed Motor Milestones Achievement in Infancy Associates with Perturbations of Amino Acids and Lipid Metabolic Pathways

The relationship between developmental milestone achievement in infancy and later cognitive function and mental health is well established, but underlying biochemical mechanisms are poorly described. Our study aims to discover pathways connected to motor milestone achievement during infancy by using untargeted plasma metabolomic profiles from 571 six-month-old children in connection with age of motor milestones achievement (Denver Developmental Index) in the Copenhagen Prospective Studies on Asthma in Childhood 2010 (COPSAC2010) mother–child cohort. We used univariate regression models and multivariate modelling (Partial Least Squares Discriminant Analysis: PLS-DA) to examine the associations and the VDAART (Vitamin D Antenatal Asthma Reduction Trial) cohort for validation. The univariate analyses showed 62 metabolites associated with gross-motor milestone achievement (p < 0.05) as well as the PLS-DA significantly differentiated between slow and fast milestone achievers (AUC = 0.87, p = 0.01). Higher levels of tyramine-O-sulfate in the tyrosine pathway were found in the late achievers in COPSAC (p = 0.0002) and in VDAART (p = 0.02). Furthermore, we observed that slow achievers were characterized by higher levels of fatty acids and products of fatty acids metabolism including acyl carnitines. Finally, we also observed changes in the lysine, histidine, glutamate, creatine and tryptophan pathways. Observing these metabolic changes in relation to gross-motor milestones in the first year of life, may be of importance for later cognitive function and mental health.

Urinary Amine and Organic Acid Metabolites Evaluated as Markers for Childhood Aggression: The ACTION Biomarker Study

Biomarkers are of interest as potential diagnostic and predictive instruments in personalized medicine. We present the first urinary metabolomics biomarker study of childhood aggression. We aim to examine the association of urinary metabolites and neurotransmitter ratios involved in key metabolic and neurotransmitter pathways in a large cohort of twins (N = 1,347) and clinic-referred children (N = 183) with an average age of 9.7 years. This study is part of ACTION (Aggression in Children: Unraveling gene-environment interplay to inform Treatment and InterventiON strategies), in which we developed a standardized protocol for large-scale collection of urine samples in children. Our analytical design consisted of three phases: a discovery phase in twins scoring low or high on aggression (N = 783); a replication phase in twin pairs discordant for aggression (N = 378); and a validation phase in clinical cases and matched twin controls (N = 367). In the discovery phase, 6 biomarkers were significantly associated with childhood aggression, of which the association of O-phosphoserine (β = 0.36; SE = 0.09; p = 0.004), and gamma-L-glutamyl-L-alanine (β = 0.32; SE = 0.09; p = 0.01) remained significant after multiple testing. Although non-significant, the directions of effect were congruent between the discovery and replication analyses for six biomarkers and two neurotransmitter ratios and the concentrations of 6 amines differed between low and high aggressive twins. In the validation analyses, the top biomarkers and neurotransmitter ratios, with congruent directions of effect, showed no significant associations with childhood aggression. We find suggestive evidence for associations of childhood aggression with metabolic dysregulation of neurotransmission, oxidative stress, and energy metabolism. Although replication is required, our findings provide starting points to investigate causal and pleiotropic effects of these dysregulations on childhood aggression.

Dysregulation of amino acids and lipids metabolism in schizophrenia with violence

BACKGROUND

Many studies have related biochemical characteristics to violence and have reported schizophrenia could elevated the risk of violent behaviour. However, the metabolic characteristics of schizophrenia patients with violence (V.SC) are unclear.

METHODS

To explore the metabolic characteristics of schizophrenia with violence and to identify potential biomarkers, untargeted metabolomics was performed by using gas chromatography time-of-flight mass spectrometry to analyse the plasma metabolites of fifty-three V.SC and twenty-four schizophrenia patients without violence (NV.SC). Multivariate and univariate analyses were performed to identify differential metabolites and biomarkers. Violence was assessed by the MacArthur Violence Assessment Study method. Psychiatric symptoms were assessed by the Positive and Negative Syndrome Scale.

RESULTS

Multivariate analysis was unable to distinguish V.SC from NV.SC. Glycerolipid metabolism and phenylalanine, tyrosine and tryptophan biosynthesis were the differential metabolic pathways between V.SC and NV.SC. We confirmed ten metabolites and five metabolites as metabolic biomarkers of V.SC by random forest and support vector machine analysis, respectively. The biomarker panel, including the ratio of L-asparagine to L-aspartic acid, vanillylmandelic acid and glutaric acid, yielded an area under the receiver operating characteristic curve of 0.808.

CONCLUSIONS

This study gives a holistic view of the metabolic phenotype of schizophrenia with violence which is characterized by the dysregulation of lipids and amino acids. These results might provide information for the aetiological understanding and management of violence in schizophrenia; however, this is a preliminary metabolomics study about schizophrenia with violence, which needs to be repeated in future studies.

Metabolic dysfunctions in the kynurenine pathway, noradrenergic and purine metabolism in schizophrenia and bipolar disorders

BACKGROUND

We aimed at exploring potential pathophysiological processes across psychotic disorders, applying metabolomics in a large and well-characterized sample of patients and healthy controls.

METHODS

Patients with schizophrenia and bipolar disorders (N = 212) and healthy controls (N = 68) had blood sampling with subsequent metabolomics analyses using electrochemical coulometric array detection. Concentrations of 52 metabolites including tyrosine, tryptophan and purine pathways were compared between patients and controls while controlling for demographic and clinical characteristics. Significant findings were further tested in medication-free subsamples.

RESULTS

Significantly decreased plasma concentrations in patients compared to healthy controls were found for 3-hydroxykynurenine (3OHKY, p = 0.0008), xanthurenic acid (XANU, p = 1.5×10-5), vanillylmandelic acid (VMA, p = 4.5×10-5) and metanephrine (MN, p = 0.0001). Plasma concentration of xanthine (XAN) was increased in the patient group (p = 3.5×10-5). Differences of 3OHKY, XANU, VMA and XAN were replicated across schizophrenia spectrum disorders and bipolar disorders subsamples of medication-free individuals.

CONCLUSIONS

Although prone to residual confounding, the present results suggest the kynurenine pathway of tryptophan metabolism, noradrenergic and purinergic system dysfunction as trait factors in schizophrenia spectrum and bipolar disorders. Of special interest is XANU, a metabolite previously not found to be associated with bipolar disorders.

A five-year follow-up study of antioxidants, oxidative stress and polyunsaturated fatty acids in schizophrenia

OBJECTIVE

Oxidative stress and dysregulated antioxidant defence may be involved in the pathophysiology of schizophrenia. In the present study, we investigated changes in antioxidants and oxidative stress from an acute to a later stable phase. We hypothesised that the levels of oxidative markers are increased in schizophrenia compared with healthy controls; change from the acute to the stable phase; and are associated with the levels of membrane polyunsaturated fatty acids (PUFAs) and symptom severity.

METHODS

Fifty-five patients with schizophrenia spectrum disorders, assessed during an acute phase and 5 years later during a stable phase, and 51 healthy controls were included. We measured antioxidants (α-tocopherol, uric acid, albumin and bilirubin), markers of oxidative stress (F2-isoprostane and reactive oxygen metabolites) and membrane fatty acids. Antioxidants and oxidative stress markers were compared in schizophrenia versus healthy controls, adjusting for differences in sex, age and smoking, and changes over time. Associations between symptoms and PUFA were also investigated.

RESULTS

In the acute phase, α-tocopherol was significantly higher (p &lt; 0.001), while albumin was lower (p &lt; 0.001) compared with the stable phase. Changes in α-tocopherol were associated with PUFA levels in the acute phase. In the stable phase, schizophrenia patients had higher uric acid (p = 0.009) and lower bilirubin (p = 0.046) than healthy controls. CRP was higher in patients in the stable phase (p &lt; 0.001), and there was no significant change from the acute phase.

CONCLUSION

The present findings of change in antioxidant levels in the acute versus stable phase of schizophrenia the present findings suggest that redox regulation is dynamic and changes during different phases of the disorder.

Advances and challenges in development of precision psychiatry through clinical metabolomics on mood and psychotic disorders

Metabolomics is defined as the study of the global metabolite profile in a system under a given set of conditions. The objective of this review is to comprehensively assess the literature on metabolomics in mood disorders and schizophrenia and provide data for mental health researchers about the challenges and potentials of metabolomics. The majority of studies in metabolomics in Psychiatry uses peripheral blood or urine. The most widely used analytical techniques in metabolomics research are nuclear magnetic resonance (NMR) and mass spectrometry (MS). They are multiparametric and provide extensive structural and conformational information on multiple chemical classes. NMR is useful in untargeted analysis, which focuses on biosignatures or 'metabolic fingerprints' of illnesses. MS targeted metabolomics approach focuses on the identification and quantification of selected metabolites known to be involved in a particular metabolic pathway. The available studies of metabolomics in Schizophrenia, Bipolar Disorder and Major Depressive Disorder suggest a potential in investigating metabolic pathways involved in these diseases' pathophysiology and response to treatment, as well as its potential in biomarkers identification.

NMR Spectroscopy for Metabolomics Research

Over the past two decades, nuclear magnetic resonance (NMR) has emerged as one of the three principal analytical techniques used in metabolomics (the other two being gas chromatography coupled to mass spectrometry (GC-MS) and liquid chromatography coupled with single-stage mass spectrometry (LC-MS)). The relative ease of sample preparation, the ability to quantify metabolite levels, the high level of experimental reproducibility, and the inherently nondestructive nature of NMR spectroscopy have made it the preferred platform for long-term or large-scale clinical metabolomic studies. These advantages, however, are often outweighed by the fact that most other analytical techniques, including both LC-MS and GC-MS, are inherently more sensitive than NMR, with lower limits of detection typically being 10 to 100 times better. This review is intended to introduce readers to the field of NMR-based metabolomics and to highlight both the advantages and disadvantages of NMR spectroscopy for metabolomic studies. It will also explore some of the unique strengths of NMR-based metabolomics, particularly with regard to isotope selection/detection, mixture deconvolution via 2D spectroscopy, automation, and the ability to noninvasively analyze native tissue specimens. Finally, this review will highlight a number of emerging NMR techniques and technologies that are being used to strengthen its utility and overcome its inherent limitations in metabolomic applications.

Advances in biomarker detection: Alternative approaches for blood-based biomarker detection

In the clinical setting, a blood sample is typically the starting point for biomarker search and discovery. Mass spectrometry (MS) is a highly sensitive and informative method for characterizing a very wide range of metabolites and proteins and is therefore a potentially powerful tool for biomarker discovery. However, the physicochemical characteristics of blood coupled with very large ranges of protein and metabolite concentrations present a significant technical obstacle for resolving and quantifying putative biomarkers by MS. Blood fractionation procedures are being developed to reduce the proteome/metabolome complexity and concentration ranges, allowing a greater diversity of analytes, including those at very low concentrations, to be quantified. In this chapter, several strategies for enriching and/or isolating specific blood components are summarized, including methods for the analysis of low and high molecular weight compounds, usually neglected in this type of assays, extracellular vesicles, and peripheral blood mononuclear cells (PBMCs). For each method, relevant practical information is presented for effective implementation.

Sensory perception of dead conspecifics induces aversive cues and modulates lifespan through serotonin in Drosophila

Sensory perception modulates health and aging across taxa. Understanding the nature of relevant cues and the mechanisms underlying their action may lead to novel interventions that improve the length and quality of life. We found that in the vinegar fly, Drosophila melanogaster, exposure to dead conspecifics in the environment induced cues that were aversive to other flies, modulated physiology, and impaired longevity. The effects of exposure to dead conspecifics on aversiveness and lifespan required visual and olfactory function in the exposed flies. Furthermore, the sight of dead flies was sufficient to produce aversive cues and to induce changes in the head metabolome. Genetic and pharmacologic attenuation of serotonergic signaling eliminated the effects of exposure on aversiveness and lifespan. Our results indicate that Drosophila have an ability to perceive dead conspecifics in their environment and suggest conserved mechanistic links between neural state, health, and aging; the roots of which might be unearthed using invertebrate model systems.

Metabolomic Biomarkers in Mental Disorders: Bipolar Disorder and Schizophrenia

Psychiatric disorders are some of the most impairing human diseases. Among them, bipolar disorder and schizophrenia are the most common. Both have complicated diagnostics due to their phenotypic, biological, and genetic heterogeneity, unknown etiology, and the underlying biological pathways, and molecular mechanisms are still not completely understood. Given the multifactorial complexity of these disorders, identification and implementation of metabolic biomarkers would assist in their early detection and diagnosis and facilitate disease monitoring and treatment responses. To date, numerous studies have utilized metabolomics to better understand psychiatric disorders, and findings from these studies have begun to converge. In this chapter, we briefly describe some of the metabolomic biomarkers found in these two disorders.

Metabolomics and Biomarker Discovery

Recently, metabolomics-the study of metabolite profiles within biological samples-has found a wide range of applications. This chapter describes the different techniques available for metabolomic analysis, the various samples that can be utilised for analysis and applications of both global and targeted metabolomic analysis to biomarker discovery in medicine.

A systematic review of metabolite biomarkers of schizophrenia

Current diagnosis of schizophrenia relies exclusively on the potentially subjective interpretation of clinical symptoms and social functioning as more objective biological measurement and medical diagnostic tests are not presently available. The use of metabolomics in the discovery of disease biomarkers has grown in recent years. Metabolomic methods could aid in the discovery of diagnostic biomarkers of schizophrenia. This systematic review focuses on biofluid metabolites associated with schizophrenia. A systematic search of Web of Science and Ovid Medline databases was conducted and 63 studies investigating metabolite biomarkers of schizophrenia were included. A review of these studies revealed several potential metabolite signatures of schizophrenia including reduced levels of essential polyunsaturated fatty acids (EPUFAs), vitamin E and creatinine; and elevated levels of lipid peroxidation metabolites and glutamate. Further research is needed to validate these biomarkers and would benefit from large cohort studies and more homogeneous and well-defined subject groups.

Mutations of the glycine cleavage system genes possibly affect the negative symptoms of schizophrenia through metabolomic profile changes

AIM

Hypofunction of N-methyl-D-aspartate receptors (NMDAR) may contribute to the pathophysiology of schizophrenia (SCZ). Recently, the glycine cleavage system (GCS) was shown to affect NMDAR function in the brain. GCS functional defects cause nonketotic hyperglycinemia, the atypical phenotype of which presents psychiatric symptoms similar to SCZ. Here, we examined the involvement of GCS in SCZ.

METHODS

First, to identify the rare variants and the exonic deletions, we resequenced all the coding exons and the splice sites of four GCS genes (GLDC, AMT, GCSH, and DLD) in 474 patients with SCZ and 475 controls and performed multiplex ligation-dependent probe amplification analysis in SCZ. Next, we performed metabolome analysis using plasma of patients harboring GCS variants (n = 5) and controls (n = 5) by capillary electrophoresis time-of-flight mass spectrometry. The correlation between plasma metabolites and Positive and Negative Syndrome Scale score was further examined.

RESULTS

Possibly damaging variants were observed in SCZ: A203V, S801N in GLDC, near the atypical nonketotic hyperglycinemia causative mutations (A202V, A802V); G825D in GLDC, a potential neural tube defect causative mutation; and R253X in AMT. Marked elevation of plasma 5-oxoproline (pyroglutamic acid), aspartate, and glutamate, which might affect NMDAR function, was observed in patients harboring GCS variants. The aspartate level inversely correlated with negative symptoms (r = -0.942, P = 0.0166).

CONCLUSION

These results suggest that GCS rare variants possibly contribute to the pathophysiology of SCZ by affecting the negative symptoms through elevation of aspartate.

Novel serum metabolomics-based approach by gas chromatography/triple quadrupole mass spectrometry for detection of human skin cancers: Candidate biomarkers

Skin cancer incidence rates are continuing to rise; however, if detected at an early stage, they can be cured with minimally invasive treatment. Therefore, the identification of novel and robust biomarkers for the early detection of skin cancer is required to improve the quality of life of the patient after treatment. In the present study, we aimed to identify novel biomarkers of skin cancers. We carried out serum metabolomics using gas chromatography/triple quadrupole mass spectrometry for two types of skin cancer: squamous cell carcinoma and melanoma. The changes in the expression of metabolites compared with healthy volunteers were analyzed by principal component analysis. Among all 118 metabolites, 27 in patients with squamous cell carcinoma and 33 in patients with melanoma showed significant changes in comparison with healthy volunteers. Principal component analysis showed that both skin cancer groups could be distinguished from the healthy volunteers group. We further investigated the specific metabolites most useful for these distinctions. In the squamous cell carcinoma group, these metabolites were glycerol, 4-hydroxybenzoic acid, sebacic acid, fucose and suberic acid. In the melanoma group, these metabolites were glutamic acid, sebacic acid, suberic acid, 4-hydroxybenzoic acid and phenylalanine. The present study identified several metabolites that were distinct for certain skin cancer types, which could potentially be used as diagnostic biomarkers leading to novel clinical management strategies.

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

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

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

As a serotonin–norepinephrine reuptake inhibitor [SNRI], venlafaxine is one of the most commonly prescribed clinical antidepressants, with a broad range of antidepressant effects.

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.

Metabolite signature for diagnosing major depressive disorder in peripheral blood mononuclear cells

BACKGROUND

Major depressive disorder (MDD) is a serious debilitating psychiatric disorder. However, the molecular mechanisms of MDD remain largely unknown, and no objective laboratory-based tests are available to diagnose this disorder.

METHODS

A gas chromatography-mass spectrometry (GC-MS) based metabolomic approach was used to compare peripheral blood mononuclear cells (PBMC) metabolic profiling of 50 first onset drug-naïve MDD subjects and 50 healthy controls (training samples), to identify potential metabolite biomarkers for MDD. An independent sample cohort including 58 MDD patients, 40 schizophrenia (SCZ) patients and 56 healthy controls (test samples) was used to validate diagnostic generalizability and specificity of identified biomarkers.

RESULTS

17 PBMC metabolites responsible for discriminating MDD group from healthy control group were identified. These metabolites were mainly involved in disturbances of energy and neurotransmitter metabolism. This PBMC metabolite signature could effectively discriminate MDD subjects from the healthy controls with an AUC of 0.926 in training samples and 0.870 in test samples. Moreover, this metabolite signature enabled distinguishing MDD subjects from schizophrenia subjects with an AUC of 0.899.

LIMITATIONS

This study was limited by potential confounding effects of different drug treatments in some MDD and schizophrenia subjects, and lack of animal studies to further validate the identified metabolite pathways in MDD.

CONCLUSION

These findings suggest that early disturbances of PBMC energy and neurotransmitter metabolism may be associated with the onset of MDD. This PBMC metabolite signature may facilitate development of a laboratory-based diagnostic test for MDD.

Recommendations and Standardization of Biomarker Quantification Using NMR-Based Metabolomics with Particular Focus on Urinary Analysis

NMR-based metabolomics has shown considerable promise in disease diagnosis and biomarker discovery because it allows one to nondestructively identify and quantify large numbers of novel metabolite biomarkers in both biofluids and tissues. Precise metabolite quantification is a prerequisite to move any chemical biomarker or biomarker panel from the lab to the clinic. Among the biofluids commonly used for disease diagnosis and prognosis, urine has several advantages. It is abundant, sterile, and easily obtained, needs little sample preparation, and does not require invasive medical procedures for collection. Furthermore, urine captures and concentrates many "unwanted" or "undesirable" compounds throughout the body, providing a rich source of potentially useful disease biomarkers; however, incredible variation in urine chemical concentrations makes analysis of urine and identification of useful urinary biomarkers by NMR challenging. We discuss a number of the most significant issues regarding NMR-based urinary metabolomics with specific emphasis on metabolite quantification for disease biomarker applications and propose data collection and instrumental recommendations regarding NMR pulse sequences, acceptable acquisition parameter ranges, relaxation effects on quantitation, proper handling of instrumental differences, sample preparation, and biomarker assessment.

Omics-Based Biomarkers: Application of Metabolomics in Neuropsychiatric Disorders

One of the major concerns of modern society is to identify putative biomarkers that serve as a valuable early diagnostic tool to identify a subset of patients with increased risk to develop neuropsychiatric disorders. Biomarker identification in neuropsychiatric disorders is proposed to offer a number of important benefits to patient well-being, including prediction of forthcoming disease, diagnostic precision, and a level of disease description that would guide treatment choice. Nowadays, the metabolomics approach has unlocked new possibilities in diagnostics of devastating disorders like neuropsychiatric disorders. Metabolomics-based technologies have the potential to map early biochemical changes in disease and hence provide an opportunity to develop predictive biomarkers that can be used as indicators of pathological abnormalities prior to development of clinical symptoms of neuropsychiatric disorders. This review highlights different -omics strategies for biomarker discovery in neuropsychiatric disorders. We also highlight initial outcomes from metabolomics studies in psychiatric disorders such as schizophrenia, bipolar disorder, and addictive disorders. This review will also present issues and challenges regarding the implementation of the metabolomics approach as a routine diagnostic tool in the clinical laboratory in context with neuropsychiatric disorders.