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

Protein associations and protein-metabolite interactions with depressive symptoms and the p-factor

Despite increasing mental health problems among young people, few studies have examined associations between plasma proteins and mental health. Interactions between proteins and metabolites in association with mental health problems remain underexplored. In 730 twins, we quantified associations between plasma proteins measured at age 22 with 21 indicators of either depressive symptoms or the p-factor and tested for interactions with metabolites. Symptoms were collected from questionnaires and interviews completed by different raters (e.g., self-report, teachers) through adolescence to young adulthood (12 to 22 years). We found 47 proteins associated with depressive symptoms or the p-factor (FDR < 0.2), 9 being associated with both. Two proteins, contactin-1 and mast/stem cell growth factor receptor kit, positively interacted with valine levels in explaining p-factor variability. Our study demonstrates strong associations between plasma proteins and mental health and provides evidence for proteome-metabolome interactions in explaining higher levels of mental health problems.

The role of microbiome in gut-brain-axis dysbiosis causing depression: From mechanisms to treatment

Gut microbiota not only affects the function of the gastrointestinal tract but also the function of other organs, including the brain. The microbiota-gut-brain axis reflects the constant bidirectional communication between the central nervous system and the gastrointestinal tract. Gut microbiota metabolites can cross brain barriers, the blood-brain barrier (BBB) and the blood-cerebrospinal fluid barrier (BCSF) and influence neuropsychiatric disorders, including depression. In recent years, the communication between the microbiome and brain in depression has been extensively studied in humans and animal models. In this chapter, we summarise the current literature on the role of gut microbiota in depression, focusing in particular on brain barriers and bidirectional gut-brain communication.

The Restoration of Energy Pathways Indicates the Efficacy of Ketamine Treatment in Depression: A Metabolomic Analysis

AIMS

Despite the clinical benefits of ketamine in treating major depressive disorder (MDD), some patients exhibit drug resistance, and the intricate mechanisms underlying this await comprehensive explication. We used metabolomics to find biomarkers for ketamine efficacy and uncover its mechanisms of action.

METHODS

The study included 40 MDD patients treated with ketamine in the discovery cohort and 24 patients in the validation cohort. Serum samples from the discovery cohort receiving ketamine were analyzed using ultra performance liquid chromatography-mass spectrometry to study metabolomic changes and identify potential biomarkers. Metabolic alterations were evaluated pre- and post-ketamine treatment. Spearman correlation was applied to examine the relationship between metabolite alterations and depressive symptom changes. In addition, potential biomarkers, particularly thyroxine, were investigated through quantitative measurements in the validation cohort.

RESULTS

We found that energy metabolite changes (adenosine triphosphate, adenosine diphosphate [ADP], pyruvate) were different in responders versus non-responders. The magnitude of the ADP shift was strongly correlated with the rate of reduction in Montgomery-Asberg Depression Rating Scale (MADRS) scores (Rho = 0.48, pFDR = 0.018). Additionally, baseline free triiodothyronine (FT3) levels are inversely associated with the rate of MADRS reduction (Rho = -0.645, p = 0.017).

CONCLUSIONS

Ketamine ameliorates depressive symptoms by modulating metabolic pathways linked to energy metabolism. Low baseline FT3 levels appear to predict a positive response in MDD patients, suggesting FT3 has potential as a biological marker for clinical ketamine treatment.

TRIAL REGISTRATION

ChiCTR-OOC-17012239.

Plasma proteomics identifies proteins and pathways associated with incident depression in 46,165 adults

Proteomic alterations preceding the onset of depression offer valuable insights into its development and potential interventions. Leveraging data from 46,165 UK Biobank participants and 2920 plasma proteins profiled at baseline, we conducted a longitudinal analysis with a median follow-up of 14.5 years to explore the relationship between plasma proteins and incident depression. Linear regression was then used to assess associations between depression-related proteins and brain structures, genetic factors, and stress-related events. Our analysis identified 157 proteins associated with incident depression (P <1.71 × 10-5), including novel associations with proteins such as GAST, PLAUR, LRRN1, BCAN, and ITGA11. Notably, higher expression levels of GDF15 (P = 6.18 × 10-26) and PLAUR (P = 2.88 × 10-14) were linked to an increased risk of depression, whereas higher levels of LRRN1 (P = 4.28 × 10-11) and ITGA11 (P = 3.68 × 10-9) were associated with a decreased risk. Dysregulation of the 157 proteins is correlated with brain regions implicated in depression, including the hippocampus and middle temporal gyrus. Additionally, these protein alterations were strongly correlated with stress-related events, including self-harm events, adult, and childhood trauma. Biological pathway enrichment analysis highlighted the critical roles of the immune response. EGFR and TNF emerged as key proteins in the protein-protein interaction network. BTN3A2, newly linked to incident depression (P = 4.35 × 10-10), was confirmed as a causal factor through Mendelian randomization analysis. In summary, our research identified the proteomic signatures associated with the onset of depression, highlighting its potential for early intervention and tailored therapeutic avenues.

Medwakh smoking induces alterations in salivary proteins and cytokine expression: a clinical exploratory proteomics investigation

BACKGROUND

Medwakh smoking has radically expanded among youth in the Middle East and around the world. The rising popularity of medwakh/dokha usage is linked to the onset of several chronic illnesses including cardiovascular diseases and cancers. Medwakh smoking is reported to increase the risk of inflammation in the lower respiratory tract owing to oxidative burden. To date, there are no reported studies investigating the impact of medwakh smoking on salivary protein profile. The current study aims to elucidate alterations in the salivary proteome profile of medwakh smokers.

METHODS

Saliva samples collected from 33 medwakh smokers and 30 non-smokers were subjected to proteomic analysis using UHPLC-ESI-QTOF-MS. Saliva samples were further subjected to validatory experiments involving analysis of inflammatory cytokine profile using LEGENDplex™ Human Essential Immune Response Panel.

RESULTS

Statistical analysis revealed alterations in the abundance of 74 key proteins including immune mediators and inflammatory markers in medwakh smokers (Accession: PXD045901). Proteins involved in building oxidative stress, alterations in cell anchorage, and cell metabolic processes were enhanced in medwakh smokers. Salivary immune response evaluation further validated the proteome findings, revealing significantly higher levels of IL-1β, IL-12p70, IL-23, IFN-γ (Th1 cytokines), IL-6 (Th2 cytokine), and MCP-1 (chemokine) in medwakh smokers. In addition, a substantial increase in abundance of involucrin suggesting a plausible stratified squamous cell differentiation and increased cell lysis in the oral cavity of medwakh smokers akin to chronic obstructive pulmonary diseases (COPD). The protein-metabolite joint pathway analysis further showed significantly enriched differentially expressed proteins and metabolites of glycolysis/gluconeogenesis, pentose phosphate, fructose and mannose, nicotinate and nicotinamide, and glutathione metabolism pathways among medwakh smokers.

CONCLUSIONS

The findings of the study provide valuable insights on potential perturbations in various key immune molecules, cytokines, and signaling pathways among medwakh smokers. Medwakh smokers displayed elevated inflammation, increased oxidative stress and defective antioxidant responses, dysregulated energy metabolism, and alterations in proteins related to cell adhesion, migration, differentiation, and proliferation. The findings of study underscore the urgent need for comprehensive public health interventions among youth by raising awareness, implementing effective smoking cessation programs, and promoting healthy lifestyle to safeguard the well-being of individuals and communities worldwide.

The triggering pathway, the metabolic amplifying pathway, and cellular transduction in regulation of glucose-dependent biphasic insulin secretion

INTRODUCTION

Insulin secretion is a highly regulated process critical for maintaining glucose homeostasis. This abstract explores the intricate interplay between three essential pathways: The Triggering Pathway, The Metabolic Amplifying Pathway, and Cellular Transduction, in orchestrating glucose-dependent biphasic insulin secretion.

MECHANISM

During the triggering pathway, glucose metabolism in pancreatic beta-cells leads to ATP production, closing ATP-sensitive potassium channels and initiating insulin exocytosis. The metabolic amplifying pathway enhances insulin secretion via key metabolites like NADH and glutamate, enhancing calcium influx and insulin granule exocytosis. Additionally, the cellular transduction pathway involves G-protein coupled receptors and cyclic AMP, modulating insulin secretion.

RESULT AND CONCLUSION

These interconnected pathways ensure a dynamic insulin response to fluctuating glucose levels, with the initial rapid phase and the subsequent sustained phase. Understanding these pathways' complexities provides crucial insights into insulin dysregulation in diabetes and highlights potential therapeutic targets to restore glucose-dependent insulin secretion.

LC-HRMS Lipidomic Fingerprints in Serbian Cohort of Schizophrenia Patients

Schizophrenia (SCH) is a major mental illness that causes impaired cognitive function and long-term disability, so the requirements for reliable biomarkers for early diagnosis and therapy of SCH are essential. The objective of this work was an untargeted lipidomic study of serum samples from a Serbian cohort including 30 schizophrenia (SCH) patients and 31 non-psychiatric control (C) individuals by applying liquid chromatography (LC) coupled with high-resolution mass spectrometry (HRMS) and chemometric analyses. Principal component analysis (PCA) of all samples indicated no clear separation between SCH and C groups but indicated clear gender separation in the C group. Multivariate statistical analyses (PCA and orthogonal partial least squares discriminant analysis (OPLS-DA)) of gender-differentiated SCH and C groups established forty-nine differential lipids in the differentiation of male SCH (SCH-M) patients and male controls (C-M), while sixty putative biomarkers were identified in the differentiation of female SCH patients (SCH-F) and female controls (C-F). Lipidomic study of gender-differentiated groups, between SCH-M and C-M and between SCH-F and C-F groups, confirmed that lipids metabolism was altered and the content of the majority of the most affected lipid classes, glycerophospholipids (GP), sphingolipids (SP), glycerolipids (GL) and fatty acids (FA), was decreased compared to controls. From differential lipid metabolites with higher content in both SCH-M and SCH-F patients groups compared to their non-psychiatric controls, there were four common lipid molecules: ceramides Cer 34:2, and Cer 34:1, lysophosphatidylcholine LPC 16:0 and triacylglycerol TG 48:2. Significant alteration of lipids metabolism confirmed the importance of metabolic pathways in the pathogenesis of schizophrenia.

Common protein networks for various drug regimens of major depression are associated with complement and immunity

BACKGROUND

Major depressive disorder (MDD) can present a variety of clinical presentations and has high inter-individual heterogeneity. Multiple studies have suggested various subtype models related to symptoms, etiology, sex, and treatment response. Employing different regimens is common when treating MDD, and identifying effective therapeutics requires time. Frequent treatment attempts and failures can lead to a diagnosis of treatment resistance, and the heterogeneity of treatment responses among individuals makes it difficult to understand and interpret the biological mechanisms underlying MDD.

AIM

This study explored the differentially expressed proteins and commonly altered protein networks across drug treatments by comparing the serum proteomes of patients with MDD treated with drug regimens (T-MDD, n = 20) and untreated patients (NT-MDD, n = 20).

METHODS

Differentially expressed proteins were profiled in non-drug-treated and drug-treated patients with depression using liquid chromatography-mass spectrometry. The common protein networks affected by different medications were studied.

RESULTS

Of the proteins profiled, 12 were significantly differentially expressed between the T-MDD and NT-MDD groups. Commonly altered proteins and networks of various drug treatments for depression were related to the complement system and immunity.

CONCLUSIONS

Our results provide information on common biological changes across different pharmacological treatments employed for depression and provide an alternative perspective for improving our understanding of the biological mechanisms of drug response in MDD with great heterogeneity in the background of the disease.

Psychobiotics for Mitigation of Neuro-Degenerative Diseases: Recent Advancements

Ageing is inevitable and poses a universal challenge for all living organisms, including humans. The human body experiences rapid cell division and metabolism until approximately 25 years of age, after which the accumulation of metabolic by-products and cellular damage leads to age-related diseases. Neurodegenerative diseases are of concern due to their irreversible nature, lack of effective treatment, and impact on society and the economy. Researchers are interested in finding drugs that can effectively alleviate ageing and age-related diseases without side-effects. Psychobiotics are a novel class of probiotic organisms and prebiotic interventions that confer mental health benefits to the host when taken appropriately. Psychobiotic strains affect functions related to the central nervous system (CNS) and behaviors mediated by the Gut-Brain-Axis (GBA) through various pathways. There is an increasing interest in researchers of these microbial-based psychopharmaceuticals. Psychobiotics have been reported to reduce neuronal ageing, inflammation, oxidative stress, and cortisol levels; increase synaptic plasticity and levels of neurotransmitters and antioxidants. The present review focuses on the manifestation of elderly neurodegenerative and mental disorders, particularly Alzheimer's disease (AD), Parkinson's disease (PD), and depression, and the current status of their potential alleviation through psychobiotic interventions, highlighting their possible mechanisms of action.

Microalgal multiomics-based approaches in bioremediation of hazardous contaminants

The enhanced industrial growth and higher living standards owing to the incessant population growth have caused heightened production of various chemicals in different manufacturing sectors globally, resulting in pollution of aquatic systems and soil with hazardous chemical contaminants. The bioremediation of such hazardous pollutants through microalgal processes is a viable and sustainable approach. Accomplishing microalgal-based bioremediation of polluted wastewater requires a comprehensive understanding of microalgal metabolic and physiological dynamics. Microalgae-bacterial consortia have emerged as a sustainable agent for synergistic bioremediation and metabolite production. Effective bioremediation involves proper consortium functioning and dynamics. The present review highlights the mechanistic processes employed through microalgae in reducing contaminants present in wastewater. It discusses the multi-omics approaches and their advantages in understanding the biological processes, monitoring, and dynamics among the partners in consortium through metagenomics. Transcriptomics, proteomics, and metabolomics enable an understanding of microalgal cell response toward the contaminants in the wastewater. Finally, the challenges and future research endeavors are summarised to provide an outlook on microalgae-based bioremediation.

Vitamin D-binding protein in plasma microglia-derived extracellular vesicles as a potential biomarker for major depressive disorder

No well-established biomarkers are available for the clinical diagnosis of major depressive disorder (MDD). Vitamin D-binding protein (VDBP) is altered in plasma and postmortem dorsolateral prefrontal cortex (DLPFC) tissues of MDD patients. Thereby, the role of VDBP as a potential biomarker of MDD diagnosis was further assessed. Total extracellular vesicles (EVs) and brain cell-derived EVs (BCDEVs) were isolated from the plasma of first-episode drug-naïve or drug-free MDD patients and well-matched healthy controls (HCs) in discovery (20 MDD patients and 20 HCs) and validation cohorts (88 MDD patients and 38 HCs). VDBP level in the cerebrospinal fluid (CSF) from chronic glucocorticoid-induced depressed rhesus macaques or prelimbic cortex from lipopolysaccharide (LPS)-induced depressed mice and wild control groups was measured to evaluate its relationship with VDBP in plasma microglia-derived extracellular vesicles (MDEVs). VDBP was significantly decreased in MDD plasma MDEVs compared to HCs, and negatively correlated with HAMD-24 score with the highest diagnostic accuracy among BCDEVs. VDBP in plasma MDEVs was decreased both in depressed rhesus macaques and mice. A positive correlation of VDBP in MDEVs with that in CSF was detected in depressed rhesus macaques. VDBP levels in prelimbic cortex microglia were negatively correlated with those in plasma MDEVs in depressed mice. The main results suggested that VDBP in plasma MDEVs might serve as a prospective candidate biomarker for MDD diagnosis.

Translational bioinformatics and data science for biomarker discovery in mental health: an analytical review

Translational bioinformatics and data science play a crucial role in biomarker discovery as it enables translational research and helps to bridge the gap between the bench research and the bedside clinical applications. Thanks to newer and faster molecular profiling technologies and reducing costs, there are many opportunities for researchers to explore the molecular and physiological mechanisms of diseases. Biomarker discovery enables researchers to better characterize patients, enables early detection and intervention/prevention and predicts treatment responses. Due to increasing prevalence and rising treatment costs, mental health (MH) disorders have become an important venue for biomarker discovery with the goal of improved patient diagnostics, treatment and care. Exploration of underlying biological mechanisms is the key to the understanding of pathogenesis and pathophysiology of MH disorders. In an effort to better understand the underlying mechanisms of MH disorders, we reviewed the major accomplishments in the MH space from a bioinformatics and data science perspective, summarized existing knowledge derived from molecular and cellular data and described challenges and areas of opportunities in this space.

The contribution of polyamine pathway to determinations of diagnosis for treatment-resistant depression: A metabolomic analysis

OBJECTIVES

Approximately one-third of major depressive disorder (MDD) patients do not respond to standard antidepressants and develop treatment-resistant depression (TRD). We aimed to reveal metabolic differences and discover promising potential biomarkers in TRD.

METHODS

Our study recruited 108 participants including healthy controls (n = 40) and patients with TRD (n = 35) and first-episode drug-naive MDD (DN-MDD) (n = 33). Plasma samples were presented to ultra performance liquid chromatography-tandem mass spectrometry. Then, a machine-learning algorithm was conducted to facilitate the selection of potential biomarkers.

RESULTS

TRD appeared to be a distinct metabolic disorder from DN-MDD and healthy controls (HCs). Compared to HCs, 199 and 176 differentially expressed metabolites were identified in TRD and DN-MDD, respectively. Of all the metabolites that were identified, spermine, spermidine, and carnosine were considered the most promising biomarkers for diagnosing TRD and DN-MDD patients, with the resulting area under the ROC curve of 0.99, 0.99, and 0.93, respectively. Metabolic pathway analysis yielded compelling evidence of marked changes or imbalances in both polyamine metabolism and energy metabolism, which could potentially represent the primary altered pathways associated with MDD. Additionally, L-glutamine, Beta-alanine, and spermine were correlated with HAMD score.

CONCLUSIONS

A more disordered metabolism structure is found in TRD than in DN-MDD and HCs. Future investigations should prioritize the comprehensive analysis of potential roles played by these differential metabolites and disturbances in polyamine pathways in the pathophysiology of TRD and depression.

Untargeted Lipidomics Study of Bipolar Disorder Patients in Serbia

The Lipidomic profiles of serum samples from patients with bipolar disorder (BD) and healthy controls (C) were explored and compared. The sample cohort included 31 BD patients and 31 control individuals. An untargeted lipidomics study applying liquid chromatography (LC) coupled with high-resolution mass spectrometry (HRMS) was conducted to achieve the lipid profiles. Multivariate statistical analyses (principal component analysis and partial least squares discriminant analysis) were performed, and fifty-six differential lipids were confirmed in BD and controls. Our results pointed to alterations in lipid metabolism, including pathways of glycerophospholipids, sphingolipids, glycerolipids, and sterol lipids, in BD patient sera. This study emphasized the role of lipid pathways in BD, and comprehensive research using the LC-HRMS platform is necessary for future application in the diagnosis and improvement of BD treatments.

Advances in Algomics technology: Application in wastewater treatment and biofuel production

Advanced sustainable bioremediation is gaining importance with rising global pollution. This review examines microalgae's potential for sustainable bioremediation and process enhancement using multi-omics approaches. Recently, microalgae-bacterial consortia have emerged for synergistic nutrient removal, allowing complex metabolite exchanges. Advanced bioremediation requires effective consortium design or pure culture based on the treatment stage and specific roles. The strain potential must be screened using modern omics approaches aligning wastewater composition. The review highlights crucial research gaps in microalgal bioremediation. It discusses multi-omics advantages for understanding microalgal fitness concerning wastewater composition and facilitating the design of microalgal consortia based on bioremediation skills. Metagenomics enables strain identification, thereby monitoring microbial dynamics during the treatment process. Transcriptomics and metabolomics encourage the algal cell response toward nutrients and pollutants in wastewater. Multi-omics role is also summarized for product enhancement to make algal treatment sustainable and fit for sustainable development goals and growing circular bioeconomy scenario.

Approach strategies and application of metabolomics to biotechnology in plants

Metabolomics refers to the technology for the comprehensive analysis of metabolites and low-molecular-weight compounds in a biological system, such as cells or tissues. Metabolites play an important role in biological phenomena through their direct involvement in the regulation of physiological mechanisms, such as maintaining cell homeostasis or signal transmission through protein-protein interactions. The current review aims provide a framework for how the integrated analysis of metabolites, their functional actions and inherent biological information can be used to understand biological phenomena related to the regulation of metabolites and how this information can be applied to safety assessments of crops created using biotechnology. Advancement in technology and analytical instrumentation have led new ways to examine the convergence between biology and chemistry, which has yielded a deeper understanding of complex biological phenomena. Metabolomics can be utilized and applied to safety assessments of biotechnology products through a systematic approach using metabolite-level data processing algorithms, statistical techniques, and database development. The integration of metabolomics data with sequencing data is a key step towards improving additional phenotypical evidence to elucidate the degree of environmental affects for variants found in genome associated with metabolic processes. Moreover, information analysis technology such as big data, machine learning, and IT investment must be introduced to establish a system for data extraction, selection, and metabolomic data analysis for the interpretation of biological implications of biotechnology innovations. This review outlines the integrity of metabolomics assessments in determining the consequences of genetic engineering and biotechnology in plants.

Cell-Type-Specific Neuroproteomics of Synapses

In the last two decades, our knowledge of synaptic proteomes and their relationship to normal brain function and neuropsychiatric disorders has been expanding rapidly through the use of more powerful neuroproteomic approaches. However, mass spectrometry (MS)-based neuroproteomic studies of synapses still require cell-type, spatial, and temporal proteome information. With the advancement of sample preparation and MS techniques, we have just begun to identify and understand proteomes within a given cell type, subcellular compartment, and cell-type-specific synapse. Here, we review the progress and limitations of MS-based neuroproteomics of synapses in the mammalian CNS and highlight the recent applications of these approaches in studying neuropsychiatric disorders such as major depressive disorder and substance use disorders. Combining neuroproteomic findings with other omics studies can generate an in-depth, comprehensive map of synaptic proteomes and possibly identify new therapeutic targets and biomarkers for several central nervous system disorders.

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.

Connecting Dots between Mitochondrial Dysfunction and Depression

Mitochondria are the prime source of cellular energy, and are also responsible for important processes such as oxidative stress, apoptosis and Ca2+ homeostasis. Depression is a psychiatric disease characterized by alteration in the metabolism, neurotransmission and neuroplasticity. In this manuscript, we summarize the recent evidence linking mitochondrial dysfunction to the pathophysiology of depression. Impaired expression of mitochondria-related genes, damage to mitochondrial membrane proteins and lipids, disruption of the electron transport chain, higher oxidative stress, neuroinflammation and apoptosis are all observed in preclinical models of depression and most of these parameters can be altered in the brain of patients with depression. A deeper knowledge of the depression pathophysiology and the identification of phenotypes and biomarkers with respect to mitochondrial dysfunction are needed to help early diagnosis and the development of new treatment strategies for this devastating disorder.

Consolidation of metabolomic, proteomic, and GWAS data in connective model of schizophrenia

Despite of multiple systematic studies of schizophrenia based on proteomics, metabolomics, and genome-wide significant loci, reconstruction of underlying mechanism is still a challenging task. Combination of the advanced data for quantitative proteomics, metabolomics, and genome-wide association study (GWAS) can enhance the current fundamental knowledge about molecular pathogenesis of schizophrenia. In this study, we utilized quantitative proteomic and metabolomic assay, and high throughput genotyping for the GWAS study. We identified 20 differently expressed proteins that were validated on an independent cohort of patients with schizophrenia, including ALS, A1AG1, PEDF, VTDB, CERU, APOB, APOH, FASN, GPX3, etc. and almost half of them are new for schizophrenia. The metabolomic survey revealed 18 group-specific compounds, most of which were the part of transformation of tyrosine and steroids with the prevalence to androgens (androsterone sulfate, thyroliberin, thyroxine, dihydrotestosterone, androstenedione, cholesterol sulfate, metanephrine, dopaquinone, etc.). The GWAS assay mostly failed to reveal significantly associated loci therefore 52 loci with the smoothened p < 10^-5 were fractionally integrated into proteome-metabolome data. We integrated three omics layers and powered them by the quantitative analysis to propose a map of molecular events associated with schizophrenia psychopathology. The resulting interplay between different molecular layers emphasizes a strict implication of lipids transport, oxidative stress, imbalance in steroidogenesis and associated impartments of thyroid hormones as key interconnected nodes essential for understanding of how the regulation of distinct metabolic axis is achieved and what happens in the conditioned proteome and metabolome to produce a schizophrenia-specific pattern.

Discovery and replication of blood-based proteomic signature of PTSD in 9/11 responders

Proteomics provides an opportunity to develop biomarkers for the early detection and monitoring of post-traumatic stress disorder (PTSD). However, research to date has been limited by small sample sizes and a lack of replication. This study performed Olink Proseek Multiplex Platform profiling of 81 proteins involved in neurological processes in 936 responders to the 9/11 disaster (mean age at blood draw = 55.41 years (SD = 7.93), 94.1% white, all men). Bivariate correlations and elastic net regressions were used in a discovery subsample to identify concurrent associations between PTSD symptom severity and the profiled proteins, and to create a multiprotein composite score. In hold-out subsamples, nine bivariate associations between PTSD symptoms and differentially expressed proteins were replicated: SKR3, NCAN, BCAN, MSR1, PVR, TNFRSF21, DRAXIN, CLM6, and SCARB2 (|r| = 0.08-0.17, p < 0.05). There were three replicated bivariate associations between lifetime PTSD diagnosis and differentially expressed proteins: SKR3, SIGLEC, and CPM (OR = 1.38-1.50, p < 0.05). The multiprotein composite score retained 38 proteins, including 10/11 proteins that replicated in bivariate tests. The composite score was significantly associated with PTSD symptom severity (β = 0.27, p < 0.001) and PTSD diagnosis (OR = 1.60, 95% CI: 1.17-2.19, p = 0.003) in the hold-out subsample. Overall, these findings suggest that PTSD is characterized by altered expression of several proteins implicated in neurological processes. Replicated associations with TNFRSF21, CLM6, and PVR support the neuroinflammatory signature of PTSD. The multiprotein composite score substantially increased associations with PTSD symptom severity over individual proteins. If generalizable to other populations, the current findings may inform the development of PTSD biomarkers.

Branched-chain amino acids linked to depression in young adults

Depression is a heterogeneous mental health problem affecting millions worldwide, but a majority of individuals with depression do not experience relief from initial treatments. Therefore, we need to improve our understanding of the biology of depression. Metabolomic approaches, especially untargeted ones, can suggest new hypotheses for further exploring biological mechanisms. Using the FinnTwin12 cohort, a longitudinal Finnish population-based twin cohort, with data collected in adolescence and young adulthood including 725 blood plasma samples, we investigated associations between depression and 11 low–molecular weight metabolites (amino acids and ketone bodies). In linear regression models with the metabolite (measured at age 22) as the dependent variable and depression ratings (measured at age 12, 14, 17, or 22 from multiple raters) as independent variables [adjusted first for age, sex, body mass index (BMI), and additional covariates (later)], we initially identified a significant negative association of valine with depression. Upon further analyses, valine remained significantly negatively associated with depression cross-sectionally and over time [meta-analysis beta = −13.86, 95% CI (−18.48 to −9.25)]. Analyses of the other branched-chain amino acids showed a significant negative association of leucine with depression [meta-analysis beta = −9.24, 95% CI (−14.53 to −3.95)], while no association was observed between isoleucine and depression [meta-analysis beta = −0.95, 95% CI (−6.00 to 4.11)]. These exploratory epidemiologic findings support further investigations into the role of branched-chain amino acids in depression.

The PROVIT Study-Effects of Multispecies Probiotic Add-on Treatment on Metabolomics in Major Depressive Disorder-A Randomized, Placebo-Controlled Trial

The gut-brain axis plays a role in major depressive disorder (MDD). Gut-bacterial metabolites are suspected to reduce low-grade inflammation and influence brain function. Nevertheless, randomized, placebo-controlled probiotic intervention studies investigating metabolomic changes in patients with MDD are scarce. The PROVIT study (registered at clinicaltrials.com NCT03300440) aims to close this scientific gap. PROVIT was conducted as a randomized, single-center, double-blind, placebo-controlled multispecies probiotic intervention study in individuals with MDD (n = 57). In addition to clinical assessments, metabolomics analyses (1H Nuclear Magnetic Resonance Spectroscopy) of stool and serum, and microbiome analyses (16S rRNA sequencing) were performed. After 4 weeks of probiotic add-on therapy, no significant changes in serum samples were observed, whereas the probiotic groups' (n = 28) stool metabolome shifted towards significantly higher concentrations of butyrate, alanine, valine, isoleucine, sarcosine, methylamine, and lysine. Gallic acid was significantly decreased in the probiotic group. In contrast, and as expected, no significant changes resulted in the stool metabolome of the placebo group. Strong correlations between bacterial species and significantly altered stool metabolites were obtained. In summary, the treatment with multispecies probiotics affects the stool metabolomic profile in patients with MDD, which sets the foundation for further elucidation of the mechanistic impact of probiotics on depression.

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

Objective

Increasing evidence shows a close relationship between gut microbiota and major depressive disorder (MDD), but the specific mechanisms remain unknown. This study was conducted to explore differential gut microbiota compositions related to the severity of MDD.

Methods

Healthy controls (HC) (n = 131) and MDD patients (n = 130) were included. MDD patients with Hamilton Depression Rating Scale (HDRS) score <25 and ≥25 were assigned into moderate (n = 72) and severe (n = 58) MDD groups, respectively. Univariate and multivariate analyses were used to analyze the gut microbiota compositions at the genus level.

Results

Thirty-six and 27 differential genera were identified in moderate and severe MDD patients, respectively. The differential genera in moderate and severe MDD patients mainly belonged to three (Firmicutes, Actinobacteriota, and Bacteroidota) and two phyla (Firmicutes and Bacteroidota), respectively. One specific covarying network from phylum Actinobacteriota was identified in moderate MDD patients. In addition, five genera (Collinsella, Eggerthella, Alistipes, Faecalibacterium, and Flavonifractor) from the shared differential genera by two MDD groups had a fair efficacy in diagnosing MDD from HC (AUC = 0.786).

Conclusions

Our results were helpful for further exploring the role of gut microbiota in the pathogenesis of depression and developing objective diagnostic methods for MDD.

Gut Microbiota Metabolites in Major Depressive Disorder-Deep Insights into Their Pathophysiological Role and Potential Translational Applications

The gut microbiota is a complex and dynamic ecosystem essential for the proper functioning of the organism, affecting the health and disease status of the individuals. There is continuous and bidirectional communication between gut microbiota and the host, conforming to a unique entity known as "holobiont". Among these crosstalk mechanisms, the gut microbiota synthesizes a broad spectrum of bioactive compounds or metabolites which exert pleiotropic effects on the human organism. Many of these microbial metabolites can cross the blood-brain barrier (BBB) or have significant effects on the brain, playing a key role in the so-called microbiota-gut-brain axis. An altered microbiota-gut-brain (MGB) axis is a major characteristic of many neuropsychiatric disorders, including major depressive disorder (MDD). Significative differences between gut eubiosis and dysbiosis in mental disorders like MDD with their different metabolite composition and concentrations are being discussed. In the present review, the main microbial metabolites (short-chain fatty acids -SCFAs-, bile acids, amino acids, tryptophan -trp- derivatives, and more), their signaling pathways and functions will be summarized to explain part of MDD pathophysiology. Conclusions from promising translational approaches related to microbial metabolome will be addressed in more depth to discuss their possible clinical value in the management of MDD patients.

Potential biomarkers of major depression diagnosis and chronicity

BACKGROUND

Molecular biomarkers are promising tools to be routinely used in clinical psychiatry. Among psychiatric diseases, major depression disorder (MDD) has gotten attention due to its growing prevalence and morbidity.

METHODS

We tested some peripheral molecular parameters such as serum mature Brain-Derived Neurotrophic Factor (mBDNF), plasma C-Reactive Protein (CRP), serum cortisol (SC), and the salivary Cortisol Awakening Response (CAR), as well as the Pittsburgh sleep quality inventory (PSQI), as part of a multibiomarker panel for potential use in MDD diagnosis and evaluation of disease's chronicity using regression models, and ROC curve.

RESULTS

For diagnosis model, two groups were analyzed: patients in the first episode of major depression (MD: n = 30) and a healthy control (CG: n = 32). None of those diagnosis models tested had greater power than Hamilton Depression Rating Scale-6. For MDD chronicity, a group of patients with treatment-resistant major depression (TRD: n = 28) was tested across the MD group. The best chronicity model (p < 0.05) that discriminated between MD and TRD included four parameters, namely PSQI, CAR, SC, and mBDNF (AUC ROC = 0.99), with 96% of sensitivity and 93% of specificity.

CONCLUSION

These results indicate that changes in specific biomarkers (CAR, SC, mBDNF and PSQI) have potential on the evaluation of MDD chronicity, but not for its diagnosis. Therefore, these findings can contribute for further studies aiming the development of a stronger model to be commercially available and used in psychiatry clinical practice.

Big data, integrative omics and network biology

A cell integrates various signals through a network of biomolecules that crosstalk to synergistically regulate the replication, transcription, translation and other metabolic activities of a cell. These networks regulate signal perception and processing that drives biological functions. The biological complexity cannot be fully captured by a single -omics discipline. The holistic study of an organism-in health, perturbation, exposure to environment and disease, is studied under systems biology. The bottom-up molecular approaches (genes, mRNA, protein, metabolite, etc.) have laid the foundation of current biological knowledge covering the horizon from viruses, bacteria, fungi, plants and animals. Yet, these techniques provide a rather myopic view of biology at the molecular level. To understand how the interconnected molecular components are formed and rewired in disease or exposure to environmental stimuli is the holy grail of modern biology. The omics era was heralded by the genomics revolution but advanced sequencing techniques are now also ubiquitous in transcriptomics, proteomics, metabolomics and lipidomics. Multi-omics data analysis and integration techniques are driving the quest for deeper insights into how the different layers of biomolecules talk to each other in diverse contexts.

Clinical serum metabolomics study on fluoxetine hydrochloride for depression

BACKGROUND

Fluoxetine hydrochloride is one of the familiar antidepressants of the second generation and has the effect of inhibiting the reuptake of 5-hydroxytryptamine by central nervous system. Both clinical trials and animal experiments show that it has good antidepressant effect, but there are few reports on its clinical efficacy in treating depression patients from the perspective of metabolomics. This study aimed at evaluating the antidepressant effect of fluoxetine hydrochloride by metabolomics, so that to find out its specific biomarkers and related metabolic characteristics of depression in the treatment of depression and analyze the intervention mechanism of fluoxetine hydrochloride in depression.

METHOD

Twenty depression patients and twenty healthy volunteers were recruited in clinical. Using ultra-performance liquid chromatography quadrupole time-of-flight mass spectrometry (UPLC-Q-TOF/MS) to analyze serum metabolites of depression patients pretherapy and post-treatment and compared with healthy people.

RESULT

Finally, we have detected 16 specific biomarkers of depression. Compared with the healthy group, the level of 10 biomarkers in the depression group was significantly increased (P < 0.05) and 6 biomarkers were significantly decreased (P < 0.01). After 8 weeks of fluoxetine hydrochloride treatment, all the biomarkers have showed a tendency of callback. The metabolic pathways involved amino acid metabolism, energy metabolism and lipid metabolism.

CONCLUSION

In our study, the antidepressant effect of fluoxetine hydrochloride in clinic was proved by metabolomics and provided basis for clinical use of fluoxetine hydrochloride. At the same time, the biomarkers that may be related to the occurrence of depression are determined to provide objective basis for the diagnosis of depression.

Neuroendocrine Response to Psychosocial Stressors, Inflammation Mediators and Brain-periphery Pathways of Adaptation

Threats, challenging events, adverse experiences, predictable or unpredictable, namely stressors, characterize life, being unavoidable for humans. The hypothalamus-pituitary-adrenal axis (HPA) and the sympathetic nervous system (SNS) are well-known to underlie adaptation to psychosocial stress in the context of other interacting systems, signals and mediators. However, much more effort is necessary to elucidate these modulatory cues for a better understanding of how and why the "brain-body axis" acts for resilience or, on the contrary, cannot cope with stress from a biochemical and biological point of view. Indeed, failure to adapt increases the risk of developing and/or relapsing mental illnesses such as burnout, post-traumatic stress disorder (PTSD), and at least some types of depression, even favoring/worsening neurodegenerative and somatic comorbidities, especially in the elderly. We will review here the current knowledge on this area, focusing on works presenting the main brain centers responsible for stressor interpretation and processing, together with those underscoring the physiology/biochemistry of endogenous stress responses. Autonomic and HPA patterns, inflammatory cascades and energy/redox metabolic arrays will be presented as allostasis promoters, leading towards adaptation to psychosocial stress and homeostasis, but also as possible vulnerability factors for allostatic overload and non-adaptive reactions. Besides, the existence of allostasis buffering systems will be treated. Finally, we will suggest promising lines of future research, particularly the use of animal and cell culture models together with human studies by means of high-throughput multi-omics technologies, which could entangle the biochemical signature of resilience or stress-related illness, a considerably helpful facet for improving patients' treatment and monitoring.

Bacterial Metabolites of Human Gut Microbiota Correlating with Depression

Depression is a global threat to mental health that affects around 264 million people worldwide. Despite the considerable evolution in our understanding of the pathophysiology of depression, no reliable biomarkers that have contributed to objective diagnoses and clinical therapy currently exist. The discovery of the microbiota-gut-brain axis induced scientists to study the role of gut microbiota (GM) in the pathogenesis of depression. Over the last decade, many of studies were conducted in this field. The productions of metabolites and compounds with neuroactive and immunomodulatory properties among mechanisms such as the mediating effects of the GM on the brain, have been identified. This comprehensive review was focused on low molecular weight compounds implicated in depression as potential products of the GM. The other possible mechanisms of GM involvement in depression were presented, as well as changes in the composition of the microbiota of patients with depression. In conclusion, the therapeutic potential of functional foods and psychobiotics in relieving depression were considered. The described biomarkers associated with GM could potentially enhance the diagnostic criteria for depressive disorders in clinical practice and represent a potential future diagnostic tool based on metagenomic technologies for assessing the development of depressive disorders.

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.

Molecular correlates of mitochondrial dysfunctions in major depression: Evidence from clinical and rodent studies

Major depressive disorder (MDD) is one of the most prevalent stress-related mental disorders worldwide. Several biological mechanisms underlying the pathophysiology of MDD have been proposed, including endocrine disturbances, neurotransmitter deficits, impaired neuronal plasticity, and more recently, mitochondrial dysfunctions. In this review, we provide an overview of relevant molecular correlates of mitochondrial dysfunction in MDD, based on findings from clinical studies and stress-induced rodent models. We also compare differences and similarities between the phenotypes of MDD patients and animal models. Our analysis of the literature reveals that both MDD and stress are associated, in humans and animals, with changes in mitochondrial biogenesis, redox imbalance, increased oxidative damages of cellular macromolecules, and apoptosis. Yet, a considerable amount of conflicting data exist and therefore, the translation of findings from clinical and preclinical research to novel therapies for MDD remains complex. Further studies are needed to advance our understanding of the molecular networks and biological mechanisms involving mitochondria in the pathophysiology of MDD.

Interleukin-6 Gene Expression Changes after a 4-Week Intake of a Multispecies Probiotic in Major Depressive Disorder-Preliminary Results of the PROVIT Study

Major depressive disorder (MDD) is a prevalent disease, in which one third of sufferers do not respond to antidepressants. Probiotics have the potential to be well-tolerated and cost-efficient treatment options. However, the molecular pathways of their effects are not fully elucidated yet. Based on previous literature, we assume that probiotics can positively influence inflammatory mechanisms. We aimed at analyzing the effects of probiotics on gene expression of inflammation genes as part of the randomized, placebo-controlled, multispecies probiotics PROVIT study in Graz, Austria. Fasting blood of 61 inpatients with MDD was collected before and after four weeks of probiotic intake or placebo. We analyzed the effects on gene expression of tumor necrosis factor (TNF), nuclear factor kappa B subunit 1 (NFKB1) and interleukin-6 (IL-6). In IL-6 we found no significant main effects for group (F(1,44) = 1.33, p = ns) nor time (F(1,44) = 0.00, p = ns), but interaction was significant (F(1,44) = 5.67, p < 0.05). The intervention group showed decreasing IL-6 gene expression levels while the placebo group showed increasing gene expression levels of IL-6. Probiotics could be a useful additional treatment in MDD, due to their anti-inflammatory effects. Results of the current study are promising, but further studies are required to investigate the beneficial effects of probiotic interventions in depressed individuals.

Identifying adaptive alleles in the human genome: from selection mapping to functional validation

The suite of phenotypic diversity across geographically distributed human populations is the outcome of genetic drift, gene flow, and natural selection throughout human evolution. Human genetic variation underlying local biological adaptations to selective pressures is incompletely characterized. With the emergence of population genetics modeling of large-scale genomic data derived from diverse populations, scientists are able to map signatures of natural selection in the genome in a process known as selection mapping. Inferred selection signals further can be used to identify candidate functional alleles that underlie putative adaptive phenotypes. Phenotypic association, fine mapping, and functional experiments facilitate the identification of candidate adaptive alleles. Functional investigation of candidate adaptive variation using novel techniques in molecular biology is slowly beginning to unravel how selection signals translate to changes in biology that underlie the phenotypic spectrum of our species. In addition to informing evolutionary hypotheses of adaptation, the discovery and functional annotation of adaptive alleles also may be of clinical significance. While selection mapping efforts in non-European populations are growing, there remains a stark under-representation of diverse human populations in current public genomic databases, of both clinical and non-clinical cohorts. This lack of inclusion limits the study of human biological variation. Identifying and functionally validating candidate adaptive alleles in more global populations is necessary for understanding basic human biology and human disease.

Improved metabolomic data-based prediction of depressive symptoms using nonlinear machine learning with feature selection

To solve major limitations in algorithms for the metabolite-based prediction of psychiatric phenotypes, a novel prediction model for depressive symptoms based on nonlinear feature selection machine learning, the Hilbert-Schmidt independence criterion least absolute shrinkage and selection operator (HSIC Lasso) algorithm, was developed and applied to a metabolomic dataset with the largest sample size to date. In total, 897 population-based subjects were recruited from the communities affected by the Great East Japan Earthquake; 306 metabolite features (37 metabolites identified by nuclear magnetic resonance measurements and 269 characterized metabolites based on the intensities from mass spectrometry) were utilized to build prediction models for depressive symptoms as evaluated by the Center for Epidemiologic Studies-Depression Scale (CES-D). The nested fivefold cross-validation was used for developing and evaluating the prediction models. The HSIC Lasso-based prediction model showed better predictive power than the other prediction models, including Lasso, support vector machine, partial least squares, random forest, and neural network. L-leucine, 3-hydroxyisobutyrate, and gamma-linolenyl carnitine frequently contributed to the prediction. We have demonstrated that the HSIC Lasso-based prediction model integrating nonlinear feature selection showed improved predictive power for depressive symptoms based on metabolome data as well as on risk metabolites based on nonlinear statistics in the Japanese population. Further studies should use HSIC Lasso-based prediction models with different ethnicities to investigate the generality of each risk metabolite for predicting depressive symptoms.

A Metabolomic Approach for Predicting Diurnal Changes in Cortisol

Introduction: The dysregulation of cortisol secretion has been associated with a number of mental health and mood disorders. However, diagnostics for mental health and mood disorders are behavioral and lack biological contexts. Objectives: The goal of this work is to identify volatile metabolites capable of predicting changes in total urinary cortisol across the diurnal cycle for long-term stress monitoring in psychological disorders. Methods: We applied comprehensive two-dimensional gas chromatography coupled with time-of-flight mass spectrometry to sample the urinary volatile metabolome using an untargeted approach across three time points in a single day for 60 subjects. Results: The finalized multiple regression model includes 14 volatile metabolites and 7 interaction terms. A review of the selected metabolites suggests pyrrole, 6-methyl-5-hepten-2-one and 1-iodo-2-methylundecane may originate from endogenous metabolic mechanisms influenced by glucocorticoid signaling mechanisms. Conclusion: This analysis demonstrated the feasibility of using specific volatile metabolites for the prediction of secreted cortisol across time.

Metabolomics Profile in Depression: A Pooled Analysis of 230 Metabolic Markers in 5283 Cases With Depression and 10,145 Controls

BACKGROUND

Depression has been associated with metabolic alterations, which adversely impact cardiometabolic health. Here, a comprehensive set of metabolic markers, predominantly lipids, was compared between depressed and nondepressed persons.

METHODS

Nine Dutch cohorts were included, comprising 10,145 control subjects and 5283 persons with depression, established with diagnostic interviews or questionnaires. A proton nuclear magnetic resonance metabolomics platform provided 230 metabolite measures: 51 lipids, fatty acids, and low-molecular-weight metabolites; 98 lipid composition and particle concentration measures of lipoprotein subclasses; and 81 lipid and fatty acids ratios. For each metabolite measure, logistic regression analyses adjusted for gender, age, smoking, fasting status, and lipid-modifying medication were performed within cohort, followed by random-effects meta-analyses.

RESULTS

Of the 51 lipids, fatty acids, and low-molecular-weight metabolites, 21 were significantly related to depression (false discovery rate q < .05). Higher levels of apolipoprotein B, very-low-density lipoprotein cholesterol, triglycerides, diglycerides, total and monounsaturated fatty acids, fatty acid chain length, glycoprotein acetyls, tyrosine, and isoleucine and lower levels of high-density lipoprotein cholesterol, acetate, and apolipoprotein A1 were associated with increased odds of depression. Analyses of lipid composition indicators confirmed a shift toward less high-density lipoprotein and more very-low-density lipoprotein and triglyceride particles in depression. Associations appeared generally consistent across gender, age, and body mass index strata and across cohorts with depressive diagnoses versus symptoms.

CONCLUSIONS

This large-scale meta-analysis indicates a clear distinctive profile of circulating lipid metabolites associated with depression, potentially opening new prevention or treatment avenues for depression and its associated cardiometabolic comorbidity.

Plasma metabolomics of depressed patients and treatment with Xiaoyaosan based on mass spectrometry technique

ETHNOPHARMACOLOGICAL RELEVANCE

Xiaoyaosan (XYS), a famous and classic traditional Chinese prescription, has been used for long time in treating depressive disorders. XYS consists of Radix Bupleuri (Bupleurum chinense DC.), Radix Angelicae Sinensis (Angelica sinensis (Oliv.) Diels), Radix PaeoniaeAlba (Paeonia lactiflora Pall.), Rhizoma Atractylodis Macrocepha lae (Atractylodes macrocephala Koidz.), Poria (Poria cocos (Schw.)Wolf), Radix Glycyrrhizae (Glycyrrhiza uralensis Fisch.), Herba Menthae Haplocalycis (Mentha haplocalyx Briq.), and Rhizoma Zin-giberis Recens (Zingiber officinale Rosc.).

AIM OF THE STUDY

A GC-MS based metabolomics approach was applied to discover the potential biomarkers that were related to metabolic differences between healthy volunteers and depression cohort diagnosed by HAMD and CGI, and to demonstrate the potential utility of these biomarkers in the diagnosis of depression and pharmaceutical efficacy of XYS.

MATERIALS AND METHODS

A total of 17 depressed patients and the 17 age- and gender-matched healthy subjects were served as the primary cohort. The depressed patients were screened according to the Chinese Classification of Mental Disorder (CCMD-3) and the Hamilton Depression Scale (HAMD). In addition, five other depressed patients were also enrolled as the primary cohort when the final step of sample collection was conducted. Plasma samples were analyzed by Gas Chromatography-Mass Spectrometry (GC-MS). Clinical and metabolomics data were analyzed by multivariate statistics analysis, Receiver Operating Characteristic (ROC) curve and MetaboAnalyst.

RESULTS

We observed significant differences between depression cohort and healthy volunteers, and between patients before and after the treatment of XYS. The method was then clinically validated in an independent validation cohort. Levels of oxalic and stearic acids significantly increased in depressed patients' plasma while valine and urea significantly decreased, as compared with healthy controls. Of note, XYS reversed these metabolite changes in terms of regulating dysfunctions in glyoxylate and dicarboxylate metabolism, fatty acid biosynthesis, valine, leucine and isoleucine biosynthesis, and arginine and proline metabolism. Importantly, the combination of oxalic and stearic acids is in prospect as diagnose biomarkers.

CONCLUSIONS

This study highlights the clinical application of metabolomics in disease diagnose and therapy evaluation, which will help in improving our understanding of depression and will lay solid foundation for the clinic application of TCMs. In addition, it suggests that the combination of the two potential biomarkers had also achieved a high diagnostic value, which consequently could be used a diagnose biomarkers.

Biomarkers in Psychiatry: Concept, Definition, Types and Relevance to the Clinical Reality

During the last years, an extraordinary effort has been made to identify biomarkers as potential tools for improving prevention, diagnosis, drug response and drug development in psychiatric disorders. Contrary to other diseases, mental illnesses are classified by diagnostic categories with a broad variety list of symptoms. Consequently, patients diagnosed from the same psychiatric illness present a great heterogeneity in their clinical presentation. This fact together with the incomplete knowledge of the neurochemical alterations underlying mental disorders, contribute to the limited efficacy of current pharmacological options. In this respect, the identification of biomarkers in psychiatry is becoming essential to facilitate diagnosis through the developing of markers that allow to stratify groups within the syndrome, which in turn may lead to more focused treatment options. In order to shed light on this issue, this review summarizes the concept and types of biomarkers including an operational definition for therapeutic development. Besides, the advances in this field were summarized and sorted into five categories, which include genetics, transcriptomics, proteomics, metabolomics, and epigenetics. While promising results were achieved, there is a lack of biomarker investigations especially related to treatment response to psychiatric conditions. This review includes a final conclusion remarking the future challenges required to reach the goal of developing valid, reliable and broadly-usable biomarkers for psychiatric disorders and their treatment. The identification of factors predicting treatment response will reduce trial-and-error switches of medications facilitating the discovery of new effective treatments, being a crucial step towards the establishment of greater personalized medicine.

Metabolomic evidence for the therapeutic effect of gentiopicroside in a corticosterone-induced model of depression

BACKGROUND

Depression is a disease that seriously threatens the quality of human life. To explore the effect of gentiopicroside on depression, this study investigated the therapeutic effect of gentiopicroside on corticosterone-induced depressionin vivo and in vitro by using metabolomic methods.

METHODS

A total of 36 rats were randomly assigned to three groups: a normal group, model group (depression), and treatment group (depression + gentiopicroside). Corticosterone was administrated to induce depression-like model rats. Morris water maze test was used to validated the behavior performance. The hippocampus of rats was obtained for metabolomic detection. Metabolites that were differentially expressed between the groups were extracted for Heatmap, Go, and pathway enrichment analyses. Finally, neuronal cells were cultured and examined to validated the effect of gentiopicroside.

RESULTS

Corticosterone injured rats learning capacity, and decreased the levels of 5-HT, and reversed by gentiopicroside delivery. Metabolites obtained from the hippocampus of rats in the three groups were subjected to a principal component analysis (PCA). Go and pathway enrichment analyses revealed the involvement of sphingolipid metabolism et al. Gentiopicroside could inhibit apoptosis caused by corticosterone, and also decrease neuronal cell proliferation and BDNF levels in vitro. Arachidonic acid (ARA) reversed the protective effect of gentiopicroside on neuronal cells.

CONCLUSION

These findings suggest that gentiopicroside reduces apoptosis and increases the proliferation of hippocampus cells in depressed animals by regulating metabolites. Moreover, our study provides a new basis for the clinical treatment of depression and demonstrates the potential efficacy of gentiopicroside in this area of pathology.

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.

Brain IGFBP-5 modifies the relation of depressive symptoms to decline in cognition in older persons

BACKGROUND

Brain proteins, including Insulin-like Growth Factor Binding Protein 5 (IGFBP-5), have been associated with cognitive dysfunction in aging. Mechanisms linking depression with cognition are poorly understood. We hypothesize that the association of depressive symptoms with cognition is mediated or modified by brain proteins.

METHODS

IGFBP-5, HSPB2, AK4, ITPK1 and PLXNB1 were measured in dorsolateral prefrontal cortex in 1057 deceased participants, who underwent annual assessments of depressive symptoms and cognition for a mean of 8.9 years. The average number of depressive symptoms per year before a dementia diagnosis was calculated for each person.

RESULTS

A one standard deviation above the mean IGFBP-5 was associated with a 14% higher odds of having more depressive symptoms (p < 0.031). Higher IGFBP-5 was associated with faster decline in global cognition (p < 0.001) and five cognitive domains (p < 0.008), controlling for depressive symptoms. IGFBP-5 moderated the association of depressive symptoms with decline in global cognition (p = 0.045). IGFBP-5 mediated ten percent or less of the total effect of depressive symptoms on decline in global cognition and the cognitive domains (p > 0.070).

LIMITATIONS

Participants were volunteers and self-selection bias limits the generalizability of our findings. In addition, we used self-reported data on depressive symptoms. However, we also used data on depression medications as sensitivity analyses to confirm findings.

CONCLUSIONS

In old age, brain IGFBP-5 is associated with depressive symptoms and cognition. The association of depressive symptoms with cognitive decline is conditional on IGFBP-5.

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.

iTRAQ-Based Quantitative Proteomics Suggests Synaptic Mitochondrial Dysfunction in the Hippocampus of Rats Susceptible to Chronic Mild Stress

Mounting studies show that hippocampal synaptic transmission and plasticity are abnormal in depression. It has been suggested that impairment of synaptic mitochondrial functions potentially occurs in the hippocampus. Thus, the synaptic mitochondria may be a crucial therapeutic target in the course of depression. Here, we investigated the potential dysregulation of synaptic mitochondrial proteins in the hippocampus of a chronic mild stress (CMS) rat model. Proteomic changes of hippocampal synaptosomes containing synaptic mitochondria were quantitatively examined using the isobaric tag for relative and absolute quantitation labeling combined with tandem mass spectrometry. 45 Proteins were identified to be differentially expressed, of which 21 were found to be putative synaptic mitochondrial proteins based on gene ontology component and SynaptomeDB analyses. Detailed investigations of protein functions and disease relevance support the importance of hippocampal synaptic mitochondria as a key substrate contributing to impairment in synaptic plasticity of stress-related disorders. Interestingly, eight synaptic mitochondrial proteins were specifically associated to the susceptible group, and might represent part of molecular basis of depression. Further analysis indicated that the synaptic mitochondrial oxidative phosphorylation (OXPHOS) system was heavily affected by CMS in the susceptible rats. The present results provide novel insights into the disease mechanism underlying the abnormal OXPHOS that is responsible for energy-demanding synaptic plasticity, and thereby increase our understanding of the role of hippocampal synaptic mitochondrial dysfunction in depression.

A preliminary study of bipolar disorder type I by mass spectrometry-based serum lipidomics

The present study aimed at investigating possible alterations in the serum lipid profile of euthymic patients with bipolar disorder type I (BD) compared to healthy controls (HC). Thirty-five individuals from both genders were recruited, with 14 diagnosed and treated as BD patients (BD group) and 21 healthy subjects (HC group). Clinical assessment was based on the Structured Clinical Interview for DSM-IV Axis I Disorders (SCID-I), Young Mania Rating Scale (YMRS), and 17-items of Hamilton Depression Rating Scale (HDRS-17) data, which were used to confirm diagnosis, to verify psychiatric comorbidities, and to estimate the severity of manic and depressive symptoms. Ultra-high performance liquid chromatography (UHPLC) coupled to high resolution mass spectrometry (HRMS) was applied to analyze the lipids extracted from all serum samples from both studied groups. In this pioneer and exploratory study, we observed different serum lipid profiles for BD and HC groups, especially regarding glycerophospholipid, glycerolipid, and sphingolipid distribution. Multivariate statistical analyses indicated that 121 lipids were significantly different between BD and HC. Phosphatidylinositols were identified as the most altered lipids in BD patient sera. The results of this preliminary study reinforce the role of lipid abnormalities in BD and offer additional methodological possibilities for investigation in the field.

Analytical approaches for lipidomics and its potential applications in neuropsychiatric disorders

OBJECTIVES

In this review, the authors discuss an overview of lipidomics followed by in-depth discussion of its application to the study of human diseases, including extraction methods of lipids, analytical techniques and clinical research in neuropsychiatric disorders.

METHODS

Lipidomics is a lipid-targeted metabolomics approach aiming at the comprehensive analysis of lipids in biological systems. Recent technological advancements in mass spectrometry and chromatography have greatly enhanced the development and applications of metabolic profiling of diverse lipids in complex biological samples.

RESULTS

An effective evaluation of the clinical course of diseases requires the application of very precise diagnostic and assessment approaches as early as possible. In order to achieve this, "omics" strategies offer new opportunities for biomarker identification and/or discovery in complex diseases and may provide pathological pathways understanding for diseases beyond traditional methodologies.

CONCLUSIONS

This review highlights the importance of lipidomics for the future perspectives as a tool for biomarker identification and discovery and its clinical application.