Multiplatform metabolome and proteome profiling identifies serum metabolite and protein signatures as prospective biomarkers for schizophrenia

A scoping review of protein biomarkers for schizophrenia: State of progress, underlying biology, and methodological considerations

Schizophrenia is characterized by symptoms such as delusions, hallucinations, and avolition. The diagnosis is clinical, based on interviews and the main treatment involves antipsychotics. Currently, given the lack of clinically applicable biomarkers for schizophrenia, there is no molecular test based on its biological mechanisms to assist psychiatrists either in the prediction or diagnosis of the disorder, nor to measure medication efficacy. This scoping review assessed original articles in English about protein biomarkers for schizophrenia with samples that could be used in a clinical context, classifying them into diagnosis, prognosis, therapeutics, risk for psychosis, and side-effects. The search was conducted on PubMed and key findings were inserted on a summary table. We discussed the methodologies used in these papers, suggested protein panels for validation in longitudinal research, and proposed a hypothesis to explain the observed variability in results. This heterogeneity is explored in light of the debated validity of this construct, applying recent discussions and the disorder's history. Our data suggest that there is insufficient evidence to integrate protein biomarkers into clinical psychiatry for schizophrenia, not due to study quality, but possibly due to flaws in the current diagnostic system. We propose exploring alternative categorization systems.

Metabolomic Profile Modification in the Cerebellum of Mice Repeatedly Exposed to Khat and Treated with β-Lactamase Inhibitor, Clavulanic Acid

BACKGROUND/OBJECTIVES

Catha edulis, commonly known as khat, is used for its psychoactive effects and is considered a natural amphetamine. The current study investigated the metabolomic profile in the cerebellum of mice after repeated exposure to khat and evaluated the effects of clavulanic acid on the metabolomic profile in the cerebellum in khat-treated mice.

METHODS

Male C67BL/6 mice that were 6-9 weeks old were recruited and divided into three groups: the control group was treated with 0.9% normal saline for 17 days; the khat group was given khat extract at a dose of 360 mg/kg via the intraperitoneal (i.p) route for 17 days; and another khat group was treated with khat for 17 days and clavulanic acid at a dose of 5 mg/kg for the last 7 days (days 11-17). At the end of the 17th day, the animals were sacrificed, and their brains were immediately collected and stored at -80 °C. The cerebellum region of the brain was isolated in each group by micropuncture using cryostat and underwent a metabolomics study via Gas Chromatography/Mass Spectroscopy (GC/MS). The total peak area ratios of the selected metabolites in the cerebellum after repeated exposure to the khat extract were significantly reduced (p < 0.05) and treatment of the khat group with clavulanic acid significantly increased (all p < 0.05) the total peak areas ratios of the selected metabolites when compared to their corresponding areas in the alternative khat group. These levels of selected metabolites were further confirmed by observing the metabolite peak area ratios and performing a heat map analysis and a principal compartment analysis of the samples in the cerebellum.

RESULTS

A network analysis of altered metabolites in the cerebellum showed a strong correlation between the different metabolites, which showed that an increase in one metabolite can modulate the levels of others. An analysis using the MetaboAnalyst software revealed the involvement of selected altered metabolites like lactic acid in many signaling pathways, like gluconeogenesis, while enrichment analysis data showed altered pathways for pyruvate metabolism and disease pathogenesis. Finally, a network analysis showed that selected metabolites were linked with other metabolites, indicating drug-drug interactions.

CONCLUSIONS

The present study showed that repeated exposure of mice to khat altered the levels of various metabolites in the cerebellum which are involved in the pathogenesis of different diseases, signaling pathways, and interactions with the pharmacokinetic profile of other therapeutic drugs. The treatment of khat-treated mice with clavulanic acid positively modified the metabolomics profile in the cerebellum and increased the levels of the altered metabolites.

Peripheral Lipid Signatures, Metabolic Dysfunction, and Pathophysiology in Schizophrenia Spectrum Disorders

Metabolic dysfunction is commonly observed in schizophrenia spectrum disorders (SSDs). The causes of metabolic comorbidity in SSDs are complex and include intrinsic or biological factors linked to the disorder, which are compounded by antipsychotic (AP) medications. The exact mechanisms underlying SSD pathophysiology and AP-induced metabolic dysfunction are unknown, but dysregulated lipid metabolism may play a role. Lipidomics, which detects lipid metabolites in a biological sample, represents an analytical tool to examine lipid metabolism. This systematic review aims to determine peripheral lipid signatures that are dysregulated among individuals with SSDs (1) with minimal exposure to APs and (2) during AP treatment. To accomplish this goal, we searched MEDLINE, Embase, and PsychINFO databases in February 2024 to identify all full-text articles written in English where the authors conducted lipidomics in SSDs. Lipid signatures reported to significantly differ in SSDs compared to controls or in relation to AP treatment and the direction of dysregulation were extracted as outcomes. We identified 46 studies that met our inclusion criteria. Most of the lipid metabolites that significantly differed in minimally AP-treated patients vs. controls comprised glycerophospholipids, which were mostly downregulated. In the AP-treated group vs. controls, the significantly different metabolites were primarily fatty acyls, which were dysregulated in conflicting directions between studies. In the pre-to-post AP-treated patients, the most impacted metabolites were glycerophospholipids and fatty acyls, which were found to be primarily upregulated and conflicting, respectively. These lipid metabolites may contribute to SSD pathophysiology and metabolic dysfunction through various mechanisms, including the modulation of inflammation, cellular membrane permeability, and metabolic signaling pathways.

Neuroinflammation and Neurometabolomic Profiling in Fentanyl Overdose Mouse Model Treated with Novel β-Lactam, MC-100093, and Ceftriaxone

Opioid-related deaths are attributed to overdoses, and fentanyl overdose has been on the rise in many parts of the world, including the USA. Glutamate transporter 1 (GLT-1) has been identified as a therapeutic target in several preclinical models of substance use disorders, and β-lactams effectively enhance its expression and function. In the current study, we characterized the metabolomic profile of the nucleus accumbens (NAc) in fentanyl-overdose mouse models, and we evaluated the protective effects of the functional enhancement of GLT-1 using β-lactams, ceftriaxone, and MC-100093. BALB/c mice were divided into four groups: control, fentanyl, fentanyl/ceftriaxone, and fentanyl/MC-100093. While the control group was intraperitoneally (i.p.) injected with normal saline simultaneously with other groups, all fentanyl groups were i.p. injected with 1 mg/kg of fentanyl as an overdose after habituation with four repetitive non-consecutive moderate doses (0.05 mg/kg) of fentanyl for a period of seven days. MC-100093 (50 mg/kg) and ceftriaxone (200 mg/kg) were i.p. injected from days 5 to 9. Gas chromatography-mass spectrometry (GC-MS) was used for metabolomics, and Western blotting was performed to determine the expression of target proteins. Y-maze spontaneous alternation performance and the open field activity monitoring system were used to measure behavioral manifestations. Fentanyl overdose altered the abundance of about 30 metabolites, reduced the expression of GLT-1, and induced the expression of inflammatory mediators IL-6 and TLR-4 in the NAc. MC-100093 and ceftriaxone attenuated the effects of fentanyl-induced downregulation of GLT-1 and upregulation of IL-6; however, only ceftriaxone attenuated fentanyl-induced upregulation of TRL4 expression. Both of the β-lactams attenuated the effects of fentanyl overdose on locomotor activities but did not induce significant changes in the overall metabolomic profile. Our findings revealed that the exposure to a high dose of fentanyl causes alterations in key metabolic pathways in the NAc. Pretreatment with ceftriaxone and MC-100093 normalized fentanyl-induced downregulation of GLT-1 expression with subsequent attenuation of neuroinflammation as well as the hyperactivity, indicating that β-lactams may be promising drugs for treating fentanyl use disorder.

Metabolomics, Lipidomics, and Antipsychotics: A Systematic Review

Antipsychotics are an important pharmacotherapy option for the treatment of many mental illnesses. Unfortunately, selecting antipsychotics is often a trial-and-error process due to a lack of understanding as to which medications an individual patient will find most effective and best tolerated. Metabolomics, or the study of small molecules in a biosample, is an increasingly used omics platform that has the potential to identify biomarkers for medication efficacy and toxicity. This systematic review was conducted to identify metabolites and metabolomic pathways associated with antipsychotic use in humans. Ultimately, 42 studies were identified for inclusion in this review, with all but three studies being performed in blood sources such as plasma or serum. A total of 14 metabolite classes and 12 lipid classes were assessed across studies. Although the studies were highly heterogeneous in approach and mixed in their findings, increases in phosphatidylcholines, decreases in carboxylic acids, and decreases in acylcarnitines were most consistently noted as perturbed in patients exposed to antipsychotics. Furthermore, for the targeted metabolomic and lipidomic studies, seven metabolites and three lipid species had findings that were replicated. The most consistent finding for targeted studies was an identification of a decrease in aspartate with antipsychotic treatment. Studies varied in depth of detail provided for their study participants and in study design. For example, in some cases, there was a lack of detail on specific antipsychotics used or concomitant medications, and the depth of detail on sample handling and analysis varied widely. The conclusions here demonstrate that there is a large foundation of metabolomic work with antipsychotics that requires more complete reporting so that an objective synthesis such as a meta-analysis can take place. This will then allow for validation and clinical application of the most robust findings to move the field forward. Future studies should be carefully controlled to take advantage of the sensitivity of metabolomics while limiting potential confounders that may result from participant heterogeneity and varied analysis approaches.

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.

BST-1 as a serum protein biomarker involved in neutrophil infiltration in schizophrenia

OBJECTIVES

Schizophrenia is a serious mental illness. The serum protein biomarkers of schizophrenia were explored using isobaric tags for relative and absolute quantitation (iTRAQ) technology. The underlying function of the identified protein biomarker was also investigated.

METHODS

We first collected serum samples from 12 schizophrenia patients and 12 healthy control (HC) subjects, followed by global screening with iTRAQ and tandem mass spectrometry (LC-MS/MS). In total, 691 serum proteins were detected and eight proteins, including ZYX, OSCAR, TPM4, SDPR, BST1, ARGHDB, ITIH5 and SH3BGRL3, were selected for further specific validation with enzyme-linked immunosorbent assay (ELISA) on the serum samples from 52 schizophrenia patients and 50 HC subjects.

RESULTS

Schizophrenia patients had significantly lower serum level of BST1 and higher ITIH5 level than the HC subjects did. Using the levels of BST1, ITIH5 and OSCAR combined with machine learning algorithm, we developed a prediction model of schizophrenia with an auROC value 0.78. Moreover, in vitro cell assay confirmed that BST1 significantly repressed neutrophil infiltration through endothelial layer, highlighted the anti-inflammation nature of BST1.

CONCLUSIONS

Four novel protein markers (BST1, ITIH5, SDPR, and OSCAR) of schizophrenia were identified, and BST-1 could serve as a serum protein biomarker involved in neutrophil infiltration in schizophrenia.

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.

Analysis of color vision and cognitive function in first-episode schizophrenia before and after antipsychotic treatment

BACKGROUND

A large body of recent research has demonstrated that patients with schizophrenia exhibit significant changes in visual function and ocular tissue structure in the early stages of onset. It is therefore possible to explore a novel scientific breakthrough in the etiology of schizophrenia by transforming the traditional study of brain structure and function with a view to examining the potential field of eye tissue and function. However, few studies have investigated the correlation between iris characteristics and schizophrenia, and evidence is lacking in this regard. Thus, further exploration is needed.

PURPOSE

This study was designed to analyze the characteristics of iris structure, color vision function and cognitive function, as well as the changes therein in patients with the first-episode drug-free schizophrenia before and after antipsychotic treatment. It aimed to preliminarily identify easily-measurable biomarkers for early clinical screening and diagnosis of schizophrenia.

METHODS

This study recruited 61 patients (22 males) with first-episode schizophrenia. Prior to the commencement of treatment with antipsychotic drugs, the Montreal Cognitive Assessment (MoCA) and Farnsworth-Munsell Dichotomous (D-15 Hue Test) were used as assessment tools to evaluate cognitive function and color vision function, respectively. Over a 6-week period, patients received a second-generation antipsychotic treatment (all converted to olanzapine equivalent dose) as prescribed by the doctor, and the Positive and Negative Syndrome Scale (PANSS) was applied to evaluate the clinical treatment effects before treatment (baseline), as well as at the 2nd, 4th, and 6th weeks after drug treatment. On the basis of iris characteristics, the patients were divided into groups. The observed differences in drug treatment effects between the groups were then compared and analyzed to further clarify the relationship between treatment efficacy and iris characteristics. Finally, changes in the cognitive function and color vision function of patients at baseline and at the 6th week after drug treatment were compared, and the effects of antipsychotic drug treatment on the above-mentioned functions were analyzed.

RESULTS

On the basis of structural iris characteristics, 61 patients were classified as follows: 28 patients without iris crypts and 33 with iris crypts; 35 without iris pigment dots and 26 with iris pigment dots; 42 without iris wrinkles and 19 with iris wrinkles. No significant difference was observed in the PANSS scores of all of the patients at baseline; however, significant differences were found in patients with iris crypts and iris pigment dots at each follow-up timepoint (i.e., at the 2nd, 4th, and 6th week). Moreover, it is noteworthy that, compared with other patients, the PANSS scores of patients without specific iris structure characteristics (iris crypts and pigment dots) decreased significantly (P<0.05), which indicated that the drug therapy was highly effective. Excluding the interference of drug factors, a significant correlation was found between the results of the D-15 (color vision function) and MoCA (cognitive function) in first-episode untreated patients (r = -0.401, P < 0.05). In addition, the MoCA scores (mean difference = 2.36, t = 10.05, P ˂ 0.01) were significantly higher after 6 weeks of antipsychotic drug treatment compared to conditions at baseline.

CONCLUSIONS

The findings of this study demonstrated that color vision function of patients with schizophrenia improved with the improvement of cognitive function. The structural characteristics of the iris with crypts and pigment dots could have a significant impact on the drug treatment effect of schizophrenia and could be considered as a potential biomarker for detecting and recognizing schizophrenia.

Systematic Review and Meta-Analysis of Mass Spectrometry Proteomics Applied to Human Peripheral Fluids to Assess Potential Biomarkers of Schizophrenia

Mass spectrometry (MS)-based techniques can be a powerful tool to identify neuropsychiatric disorder biomarkers, improving prediction and diagnosis ability. Here, we evaluate the efficacy of MS proteomics applied to human peripheral fluids of schizophrenia (SCZ) patients to identify disease biomarkers and relevant networks of biological pathways. Following PRISMA guidelines, a search was performed for studies that used MS proteomics approaches to identify proteomic differences between SCZ patients and healthy control groups (PROSPERO database: CRD42021274183). Nineteen articles fulfilled the inclusion criteria, allowing the identification of 217 differentially expressed proteins. Gene ontology analysis identified lipid metabolism, complement and coagulation cascades, and immune response as the main enriched biological pathways. Meta-analysis results suggest the upregulation of FCN3 and downregulation of APO1, APOA2, APOC1, and APOC3 in SCZ patients. Despite the proven ability of MS proteomics to characterize SCZ, several confounding factors contribute to the heterogeneity of the findings. In the future, we encourage the scientific community to perform studies with more extensive sampling and validation cohorts, integrating omics with bioinformatics tools to provide additional comprehension of differentially expressed proteins. The produced information could harbor potential proteomic biomarkers of SCZ, contributing to individualized prognosis and stratification strategies, besides aiding in the differential diagnosis.

Different patterns of association between white matter microstructure and plasma unsaturated fatty acids in those with high risk for psychosis and healthy participants

Background

Disrupted white matter (WM) microstructure has been commonly identified in youth at clinical high risk (CHR) for psychosis. Several lines of evidence suggest that fatty acids, especially unsaturated fatty acids (UFAs), might play a crucial role in the WM pathology of early onset psychosis. However, evidence linking UFA and WM microstructure in CHR is quite sparse.

Aims

We investigated the relationship between the plasma UFA level and WM microstructure in CHR participants and healthy controls (HC).

Methods

Plasma fatty acids were assessed and diffusion tensor imaging (DTI) data were performed with tract-based spatial statistics (TBSS) analysis for 66 individuals at CHR for psychosis and 70 HC.

Results

Both the global and regional diffusion measures showed significant between-group differences, with decreased fractional anisotropy (FA) but increased mean diffusivity (MD) and radial diffusivity (RD) found in the CHR group compared with the HC group. On top of that, we found that in the HC group, plasma arachidic acid showed obvious trend-level associations with higher global FA, lower global MD and lower global RD, which regionally spread over the corpus callosum, right anterior and superior corona radiata, bilateral anterior and posterior limb of the internal capsule, and bilateral superior longitudinal fasciculus. However, there were no associations between global WM measures and any UFA in the CHR group. Conversely, we even found negative associations between arachidic acid levels and regional FA values in the right superior longitudinal fasciculus and right retrolenticular part of the internal capsule in the CHR group.

Conclusions

Compared with the HC group, CHR subjects exhibited a different pattern of association between WM microstructure and plasma UFA, with a neuroprotective effect found in the HC group but not in the CHR group. Such discrepancy could be due to the excessively upregulated UFAs accumulated in the plasma of the CHR group, highlighting the role of balanced plasma-membrane fatty acids homeostasis in WM development.

Multi-Omics Analysis Reveals Aberrant Gut-Metabolome-Immune Network in Schizophrenia

Schizophrenia (SCZ) is associated with several immune dysfunctions, including elevated levels of pro-inflammatory cytokines. Microorganisms and their metabolites have been found to regulate the immune system, and that intestinal microbiota is significantly disturbed in schizophrenic patients. To systematically investigate aberrant gut-metabolome-immune network in schizophrenia, we performed an integrative analysis of intestinal microbiota, serum metabolome, and serum inflammatory cytokines in 63 SCZ patients and 57 healthy controls using a multi-omics strategy. Eighteen differentially abundant metabolite clusters were altered in patients displayed higher cytokine levels, with a significant increase in pro-inflammatory metabolites and a significant decrease in anti-inflammatory metabolites (such as oleic acid and linolenic acid). The bacterial co-abundance groups in the gut displayed more numerous and stronger correlations with circulating metabolites than with cytokines. By integrating these data, we identified that certain bacteria might affect inflammatory cytokines by modulating host metabolites, such as amino acids and fatty acids. A random forest model was constructed based on omics data, and seven serum metabolites significantly associated with cytokines and α-diversity of intestinal microbiota were able to accurately distinguish the cases from the controls with an area under the receiver operating characteristic curve of 0.99. Our results indicated aberrant gut-metabolome-immune network in SCZ and gut microbiota may influence immune responses by regulating host metabolic processes. These findings suggest a mechanism by which microbial-derived metabolites regulated inflammatory cytokines and insights into the diagnosis and treatment of mental disorders from the microbial-immune system in the future.

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.

Lipidomic signatures from physically frail and robust older adults at hospital admission

Identifying serum biomarkers that can predict physical frailty in older adults would have tremendous clinical value for primary care, as this condition is inherently related to poor quality of life and premature mortality. We compared the serum lipid profile of physically frail and robust older adults to identify specific lipid biomarkers that could be used to assess physical frailty in older patients at hospital admission. Forty-three older adults (58.1% male), mean (range) age 86.4 (78–100 years) years, were classified as physically frail (n = 18) or robust (n = 25) based on scores from the Short Physical Performance Battery (≤ 6 points). Non-targeted metabolomic study by ultra-high performance liquid chromatography coupled to mass spectrometry (UHPLC-MS) analysis with later bioinformatics data analysis. Once the significantly different metabolites were identified, the KEGG database was used on them to establish which were the metabolic pathways mainly involved. Area under receiver-operating curve (AUROC) analysis was used to test the discriminatory ability of lipid biomarkers for frailty based on the Short Physical Performance Battery. We identified a panel of five metabolites including ceramides Cer (40:2), Cer (d18:1/20:0), Cer (d18:1/23:0), cholesterol, and phosphatidylcholine (PC) (14:0/20:4) that were significantly increased in physically frail older adults compared with robust older adults at hospital admission. The most interesting in the physically frail metabolome study found with the KEGG database were the metabolic pathways, vitamin digestion and absorption, AGE-RAGE signaling pathway in diabetic complications, and insulin resistance. In addition, Cer (40:2) (AUROC 0.747), Cer (d18:1/23:0) (AUROC 0.720), and cholesterol (AUROC 0.784) were identified as higher values of physically frail at hospital admission. The non-targeted metabolomic study can open a wide view of the physically frail features changes at the plasma level, which would be linked to the physical frailty phenotype at hospital admission. Also, we propose that metabolome analysis will have a suitable niche in personalized medicine for physically frail older adults.

Causal Inference Methods to Integrate Omics and Complex Traits

Major biotechnological advances have facilitated a tremendous boost to the collection of (gen-/transcript-/prote-/methyl-/metabol-)omics data in very large sample sizes worldwide. Coordinated efforts have yielded a deluge of studies associating diseases with genetic markers (genome-wide association studies) or with molecular phenotypes. Whereas omics-disease associations have led to biologically meaningful and coherent mechanisms, the identified (non-germline) disease biomarkers may simply be correlates or consequences of the explored diseases. To move beyond this realm, Mendelian randomization provides a principled framework to integrate information on omics- and disease-associated genetic variants to pinpoint molecular traits causally driving disease development. In this review, we show the latest advances in this field, flag up key challenges for the future, and propose potential solutions.

Candidate metabolic biomarkers for schizophrenia in CNS and periphery: Do any possible associations exist?

Due to the limitations of analytical techniques and the complicity of schizophrenia, nowadays it is still a challenge to diagnose and stratify schizophrenia patients accurately. Many attempts have been made to identify and validate available biomarkers for schizophrenia from CSF and/or peripheral blood in clinical studies with consideration to disease stages, antipsychotic effects and even gender differences. However, conflicting results handicap the validation and application of biomarkers for schizophrenia. In view of availability and feasibility, peripheral biomarkers have superior advantages over biomarkers in CNS. Meanwhile, schizophrenia is considered to be a devastating neuropsychiatric disease mainly taking place in CNS featured by widespread defects in multiple metabolic pathways whose dynamic interactions, until recently, have been difficult to difficult to investigate. Evidence for these alterations has been collected piecemeal, limiting the potential to inform our understanding of the interactions among relevant biochemical pathways. Taken these points together, it will be interesting to investigate possible associations of biomarkers between CNS and periphery. Numerous studies have suggested putative correlations within peripheral and CNS systems especially for dopaminergic and glutamatergic metabolic biomarkers. In addition, it has been demonstrated that blood concentrations of BDNF protein can also reflect its changes in the nervous system. In turn, BDNF also interacts with glutamatergic, dopaminergic and serotonergic systems. Therefore, this review will summarize metabolic biomarkers identified both in the CNS (brain tissues and CSF) and peripheral blood. Further, more attentions will be paid to discussing possible physical and functional associations between CNS and periphery, especially with respect to BDNF.

Plasma Metabolite Profiles in First Episode Psychosis: Exploring Symptoms Heterogeneity/Severity in Schizophrenia and Bipolar Disorder Cohorts

INTRODUCTION

The first symptoms of psychosis are frequently shared amongst several neuropsychiatry disorders, which makes the differentiation by clinical diagnosis challenging. Early recognition of symptoms is important in the management of psychosis. Therefore, the implementation of molecular biomarkers will be crucial for transforming the currently used diagnostic and therapeutic approach, improving insights into the underlying biological processes and clinical management.

OBJECTIVES

To define a set of metabolites that supports diagnosis or prognosis of schizophrenia (SCZ) and bipolar disorder (BD) at first onset psychosis.

METHODS

Plasma samples from 55 drug-naïve patients, 28 SCZ and 27 BD, and 42 healthy controls (HC). All participants underwent a seminaturalistic treatment regimen, clinically evaluated on a weekly basis until achieving clinical remission. All clinical or sociodemographic aspects considered for this study were equivalent between the groups at first-onset psychosis time point. The plasma samples were analyzed by untargeted liquid chromatography-tandem mass spectrometry (LC-MS/MS) using reversed-phase and hydrophilic interaction chromatography. The acquired molecular features were analyzed with MetaboAnalyst.

RESULTS

We identified two patient groups with different metabolite profiles. Both groups are composed of SCZ and BD patients. We found differences between these two groups regarding general symptoms of PANSS score after remission (p = 0.008), and the improvement of general symptoms (delta of the score at remission minus the baseline) (-0.50 vs. -0.33, p = 0.019).

CONCLUSION

Our results suggest that plasma metabolite profiles cluster clinical remission phenotypes based on PANSS general psychopathology scores.

Proteomics in Schizophrenia: A Gateway to Discover Potential Biomarkers of Psychoneuroimmune Pathways

Schizophrenia is a severe and disabling psychiatric disorder with a complex and multifactorial etiology. The lack of consensus regarding the multifaceted dysfunction of this ailment has increased the need to explore new research lines. This research makes use of proteomics data to discover possible analytes associated with psychoneuroimmune signaling pathways in schizophrenia. Thus, we analyze plasma of 45 patients [10 patients with first-episode schizophrenia (FES) and 35 patients with chronic schizophrenia] and 43 healthy subjects by label-free liquid chromatography-tandem mass spectrometry. The analysis revealed a significant reduction in the levels of glia maturation factor beta (GMF-β), the brain-derived neurotrophic factor (BDNF), and the 115-kDa isoform of the Rab3 GTPase-activating protein catalytic subunit (RAB3GAP1) in patients with schizophrenia as compared to healthy volunteers. In conclusion, GMF-β, BDNF, and 115-kDa isoform of RAB3GAP1 showed significantly reduced levels in plasma of patients with schizophrenia, thus making them potential biomarkers in schizophrenia.

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.

Role of Endogenous Metabolite Alterations in Neuropsychiatric Disease

The potential role in neuropsychiatric disease risk arising from alterations and derangements of endogenous small-molecule metabolites remains understudied. Alterations of endogenous metabolite concentrations can arise in multiple ways. Marked derangements of single endogenous small-molecule metabolites are found in a large group of rare genetic human diseases termed "inborn errors of metabolism", many of which are associated with prominent neuropsychiatric symptomology. Whether such metabolites act neuroactively to directly lead to distinct neural dysfunction has been frequently hypothesized but rarely demonstrated unequivocally. Here we discuss this disease concept in the context of our recent findings demonstrating that neural dysfunction arising from accumulation of the schizophrenia-associated metabolite l-proline is due to its structural mimicry of the neurotransmitter GABA that leads to alterations in GABA-ergic short-term synaptic plasticity. For cases in which a similar direct action upon neurotransmitter binding sites is suspected, we lay out a systematic approach that can be extended to assessing the potential disruptive action of such candidate disease metabolites. To address the potentially important and broader role in neuropsychiatric disease, we also consider whether the more subtle yet more ubiquitous variations in endogenous metabolites arising from natural allelic variation may likewise contribute to disease risk but in a more complex and nuanced manner.

Proteomics for blood biomarker exploration of severe mental illness: pitfalls of the past and potential for the future

Recent improvements in high-throughput proteomic approaches are likely to constitute an essential advance in biomarker discovery, holding promise for improved personalized care and drug development. These methodologies have been applied to study multivariate protein patterns and provide valuable data of peripheral tissues. To highlight findings of the last decade for three of the most common psychiatric disorders, namely schizophrenia (SZ), bipolar disorder (BD), and major depressive disorder (MDD), we queried PubMed. Here we delve into the findings from thirty studies, which used proteomics and multiplex immunoassay approaches for peripheral blood biomarker exploration. In an explorative approach, we ran enrichment analyses in peripheral blood according to these results and ascertained the overlap between proteomic findings and genetic loci identified in genome-wide association studies (GWAS). The studies we appraised demonstrate that proteomics for psychiatric research has been heterogeneous in aims and methods and limited by insufficient sample sizes, poorly defined case definitions, methodological inhomogeneity, and confounding results constraining the conclusions that can be extracted from them. Here, we discuss possibilities for overcoming methodological challenges for the implementation of proteomic signatures in psychiatric diagnosis and offer an outlook for future investigations. To fulfill the promise of proteomics in mental disease diagnostics, future research will need large, well-defined cohorts in combination with state-of-the-art technologies.

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.

Biomarkers in schizophrenia: A focus on blood based diagnostics and theranostics

Identifying biomarkers that can be used as diagnostics or predictors of treatment response (theranostics) in people with schizophrenia (Sz) will be an important step towards being able to provide personalized treatment. Findings from the studies in brain tissue have not yet been translated into biomarkers that are practical in clinical use because brain biopsies are not acceptable and neuroimaging techniques are expensive and the results are inconclusive. Thus, in recent years, there has been search for blood-based biomarkers for Sz as a valid alternative. Although there are some encouraging preliminary data to support the notion of peripheral biomarkers for Sz, it must be acknowledged that Sz is a complex and heterogeneous disorder which needs to be further dissected into subtype using biological based and clinical markers. The scope of this review is to critically examine published blood-based biomarker of Sz, focusing on possible uses for diagnosis, treatment response, or their relationship with schizophrenia-associated phenotype. We sorted the studies into six categories which include: (1) brain-derived neurotrophic factor; (2) inflammation and immune function; (3) neurochemistry; (4) oxidative stress response and metabolism; (5) epigenetics and microRNA; and (6) transcriptome and proteome studies. This review also summarized the molecules which have been conclusively reported as potential blood-based biomarkers for Sz in different blood cell types. Finally, we further discusses the pitfall of current blood-based studies and suggest that a prediction model-based, Sz specific, blood oriented study design as well as standardize blood collection conditions would be useful for Sz biomarker development.

Integrative Analysis of Multi-omics Data for Discovery and Functional Studies of Complex Human Diseases

Complex and dynamic networks of molecules are involved in human diseases. High-throughput technologies enable omics studies interrogating thousands to millions of makers with similar biochemical properties (eg, transcriptomics for RNA transcripts). However, a single layer of "omics" can only provide limited insights into the biological mechanisms of a disease. In the case of genome-wide association studies, although thousands of single nucleotide polymorphisms have been identified for complex diseases and traits, the functional implications and mechanisms of the associated loci are largely unknown. Additionally, the genomic variants alone are not able to explain the changing disease risk across the life span. DNA, RNA, protein, and metabolite often have complementary roles to jointly perform a certain biological function. Such complementary effects and synergistic interactions between omic layers in the life course can only be captured by integrative study of multiple molecular layers. Building upon the success in single-omics discovery research, population studies started adopting the multi-omics approach to better understanding the molecular function and disease etiology. Multi-omics approaches integrate data obtained from different omic levels to understand their interrelation and combined influence on the disease processes. Here, we summarize major omics approaches available in population research, and review integrative approaches and methodologies interrogating multiple omic layers, which enhance the gene discovery and functional analysis of human diseases. We seek to provide analytical recommendations for different types of multi-omics data and study designs to guide the emerging multi-omic research, and to suggest improvement of the existing analytical methods.

Physical Exercise Alleviates Health Defects, Symptoms, and Biomarkers in Schizophrenia Spectrum Disorder

Schizophrenia spectrum disorders are characterized by symptom profiles consisting of positive and negative symptoms, cognitive impairment, and a plethora of genetic, epigenetic, and phenotypic biomarkers. Assorted animal models of these disorders and clinical neurodevelopmental indicators have implicated neurodegeneration as an element in the underlying pathophysiology. Physical exercise or activity regimes--whether aerobic, resistance, or endurance--ameliorate regional brain and functional deficits not only in affected individuals but also in animal models of the disorder. Cognitive deficits, often linked to regional deficits, were alleviated by exercise, as were quality-of-life, independent of disorder staging and risk level. Apoptotic processes intricate to the etiopathogenesis of schizophrenia were likewise attenuated by physical exercise. There is also evidence of manifest benefits endowed by physical exercise in preserving telomere length and integrity. Not least, exercise improves overall health and quality-of-life. The notion of scaffolding as the outcome of physical exercise implies the "buttressing" of regional network circuits, neurocognitive domains, anti-inflammatory defenses, maintenance of telomeric integrity, and neuro-reparative and regenerative processes.

The proteome of schizophrenia

On observing schizophrenia from a clinical point of view up to its molecular basis, one may conclude that this is likely to be one of the most complex human disorders to be characterized in all aspects. Such complexity is the reflex of an intricate combination of genetic and environmental components that influence brain functions since pre-natal neurodevelopment, passing by brain maturation, up to the onset of disease and disease establishment. The perfect function of tissues, organs, systems, and finally the organism depends heavily on the proper functioning of cells. Several lines of evidence, including genetics, genomics, transcriptomics, neuropathology, and pharmacology, have supported the idea that dysfunctional cells are causative to schizophrenia. Together with the above-mentioned techniques, proteomics have been contributing to understanding the biochemical basis of schizophrenia at the cellular and tissue level through the identification of differentially expressed proteins and consequently their biochemical pathways, mostly in the brain tissue but also in other cells. In addition, mass spectrometry-based proteomics have identified and precisely quantified proteins that may serve as biomarker candidates to prognosis, diagnosis, and medication monitoring in peripheral tissue. Here, we review all data produced by proteomic investigation in the last 5 years using tissue and/or cells from schizophrenic patients, focusing on postmortem brain tissue and peripheral blood serum and plasma. This information has provided integrated pictures of the biochemical systems involved in the pathobiology, and has suggested potential biomarkers, and warrant potential targets to alternative treatment therapies to schizophrenia.