[Membrane lipids in schizophrenia and early phases of psychosis: Potential biomarkers and therapeutic targets?]

Advances in Mass Spectrometry-Based Blood Metabolomics Profiling for Non-Cancer Diseases: A Comprehensive Review

Blood metabolomics profiling using mass spectrometry has emerged as a powerful approach for investigating non-cancer diseases and understanding their underlying metabolic alterations. Blood, as a readily accessible physiological fluid, contains a diverse repertoire of metabolites derived from various physiological systems. Mass spectrometry offers a universal and precise analytical platform for the comprehensive analysis of blood metabolites, encompassing proteins, lipids, peptides, glycans, and immunoglobulins. In this comprehensive review, we present an overview of the research landscape in mass spectrometry-based blood metabolomics profiling. While the field of metabolomics research is primarily focused on cancer, this review specifically highlights studies related to non-cancer diseases, aiming to bring attention to valuable research that often remains overshadowed. Employing natural language processing methods, we processed 507 articles to provide insights into the application of metabolomic studies for specific diseases and physiological systems. The review encompasses a wide range of non-cancer diseases, with emphasis on cardiovascular disease, reproductive disease, diabetes, inflammation, and immunodeficiency states. By analyzing blood samples, researchers gain valuable insights into the metabolic perturbations associated with these diseases, potentially leading to the identification of novel biomarkers and the development of personalized therapeutic approaches. Furthermore, we provide a comprehensive overview of various mass spectrometry approaches utilized in blood metabolomics research, including GC-MS, LC-MS, and others discussing their advantages and limitations. To enhance the scope, we propose including recent review articles supporting the applicability of GC×GC-MS for metabolomics-based studies. This addition will contribute to a more exhaustive understanding of the available analytical techniques. The Integration of mass spectrometry-based blood profiling into clinical practice holds promise for improving disease diagnosis, treatment monitoring, and patient outcomes. By unraveling the complex metabolic alterations associated with non-cancer diseases, researchers and healthcare professionals can pave the way for precision medicine and personalized therapeutic interventions. Continuous advancements in mass spectrometry technology and data analysis methods will further enhance the potential of blood metabolomics profiling in non-cancer diseases, facilitating its translation from the laboratory to routine clinical application.

Combined effects of lead and manganese on locomotor activity and microbiota in zebrafish

Exposure to lead (Pb) and manganese (Mn) during early life influences neurodevelopment and increases the risk of neurodegenerative disorders. However, the level of developmental neurotoxicity due to combined exposure to the two metals remains unclear. Although the microbiota plays an essential part in the development of the nervous system via the gut-brain axis, there is a paucity of information regarding the interactions between exposure to Pb and Mn, the destruction of the microbiome, and neurodevelopmental impacts. To fill in this knowledge gap, we investigated the developmental neurotoxicity and effects on the microbiota of Pb (0.05 mg·L-1) alone and in combination with Mn (0.3 mg·L-1) in zebrafish larvae. Our results revealed that combined exposure precipitated higher malformation rates and lower locomotor activity levels than exposure to either Pb or Mn alone. Additionally, when we separated the combined exposure group from the other groups by applying unsupervised principal coordinates analysis (PCoA) and linear discriminant analysis (LEfSe) of microflora sequencing results, we observed extensive alterations in microbial abundances under combined-exposure conditions. Functional prediction analysis showed that combined exposure contributed to altered amino acid and lipid metabolism, and also that combined exposure to Pb and Mn reflected the greatest number of differentially activated biological pathways compared to the other three groups. ATP-binding cassette G (ABCG) genes and genes related to serotonin signaling and metabolism were altered following combined Pb and Mn exposure and exhibited disparate trends vis-à-vis Pb or Mn exposure alone. According to the results, the combined exposure to Pb and Mn led to more severe effects on both zebrafish locomotor activity and gut microbial composition. We suggest that the microbiota contributes to the combined neurotoxicity by increasing ABCG5 and ABCG8 gene expression.

Membrane Lipids in Ultra-High-Risk Patients: Potential Predictive Biomarkers of Conversion to Psychosis

Alterations in membrane lipids are reported in schizophrenia. However, no conclusion can be drawn regarding the extended and predictive value of these alterations in persons at ultra-high risk of psychosis (UHR). Recent studies suggested that sterols' impact on psychiatric disorders was underestimated. Here, we simultaneously explored sterols, fatty acids (FA), and phospholipids (PL) in UHR persons for the first time. We analysed erythrocyte membrane lipids in 61 UHR persons, including 29 who later converted to psychosis (UHR-C) and 32 who did not (UHC-NC). We used gas chromatography for FA and liquid chromatography tandem with mass spectrometry for sterols and phospholipids. Among UHR individuals, elevated baseline membrane linoleic acid level was associated with conversion to psychosis (26.1% vs. 60.5%, p = 0.02). Combining sterols, FA, and PL membrane composition improved the prediction of psychosis onset (AUC = 0.73). This is the first report showing that membrane sterol participates, with other membrane lipids, in modulating the risk of psychosis. It suggests that membrane lipids could be used as biomarkers for personalised medicine in UHR patients.

Shared Biological Pathways between Antipsychotics and Omega-3 Fatty Acids: A Key Feature for Schizophrenia Preventive Treatment?

Schizophrenia typically emerges during adolescence, with progression from an ultra-high risk state (UHR) to the first episode of psychosis (FEP) followed by a chronic phase. The detailed pathophysiology of schizophrenia and the factors leading to progression across these stages remain relatively unknown. The current treatment relies on antipsychotics, which are effective for FEP and chronic schizophrenia but ineffective for UHR patients. Antipsychotics modulate dopaminergic and glutamatergic neurotransmission, inflammation, oxidative stress, and membrane lipids pathways. Many of these biological pathways intercommunicate and play a role in schizophrenia pathophysiology. In this context, research of preventive treatment in early stages has explored the antipsychotic effects of omega-3 supplementation in UHR and FEP patients. This review summarizes the action of omega-3 in various biological systems involved in schizophrenia. Similar to antipsychotics, omega-3 supplementation reduces inflammation and oxidative stress, improves myelination, modifies the properties of cell membranes, and influences dopamine and glutamate pathways. Omega-3 supplementation also modulates one-carbon metabolism, the endocannabinoid system, and appears to present neuroprotective properties. Omega-3 has little side effects compared to antipsychotics and may be safely prescribed for UHR patients and as an add-on for FEP patients. This could to lead to more efficacious individualised treatments, thus contributing to precision medicine in psychiatry.

Secondary Ion Mass Spectrometry Imaging Reveals Changes in the Lipid Structure of the Plasma Membranes of Hippocampal Neurons following Drugs Affecting Neuronal Activity

The cellular functions of lipids in the neuronal plasma membranes have been increasingly acknowledged, particularly their association to neuronal processes and synaptic plasticity. However, the knowledge of their regulatory mechanisms in neuronal cells remains sparse. To address this, we investigated the lipid organization of the plasma membranes of hippocampal neurons in relation to neuronal activity using secondary ion mass spectrometry imaging. The neurons were treated with drugs, particularly tetrodotoxin (TTX) and bicuculline (BIC), to induce chronic activation and silencing. Distinct lipid organization was found in the plasma membrane of the cell body and the neurites. Moreover, significant alterations of the levels of the membrane lipids, especially ceramides, phosphatidylserines, phosphatidic acids, and triacylglycerols, were observed under the TTX and BIC treatments. We suggest that many types of membrane lipids are affected by, and may be involved in, the regulation of neuronal function.

A Metabolomics Study of Serum in Hospitalized Patients With Chronic Schizophrenia

Purpose: Metabolomics has attracted attention as a new method for understanding the molecular mechanisms of psychiatric disorders. Current metabolomics technology allows us to measure over hundreds of metabolites at a time and is a useful indicator of the consequences of complex and continuous changes in metabolic profiles due to the execution of genomic information and external factors of biological activity. Therefore, metabolomics is imperative to the discovery of biomarkers and mechanisms associated with pathophysiological processes. In this study, we investigated metabolites changes in hospitalized patients with chronic schizophrenia compared to that in healthy controls, and examined the correlations between the metabolites and psychiatric symptoms. Patients and Methods: Thirty patients with schizophrenia and ten healthy controls participated in this study between September 2019 and June 2020. The mean duration of disease in patients with schizophrenia was 26 years. Clinical and neuropsychiatric symptoms of patients with schizophrenia were assessed using the Positive and Negative Syndrome Scale (PANSS). Metabolomics was conducted using Capillary Electrophoresis Fourier Transform Mass Spectrometry (CE-FTMS), using serum samples from patients with schizophrenia and healthy controls. Metabolomics assigned a candidate compound to the 446 (cation 279, anion 167) peaks. Hierarchical cluster analysis (HCA), principal component analysis (PCA), logistic regression analysis, receiver operating characteristic (ROC) analysis, and linear regression analysis were used to analyze the metabolites changes, identifying the disease and the relationship between metabolites and psychiatric symptoms. Results: HCA showed that approximately 60% of metabolites had lower peak values in patients with schizophrenia than in healthy controls. Glutamate metabolism and the urea cycle had the highest proportions in the metabolic pathway, which decreased in patients with schizophrenia. PCA showed a clear separation between patients with schizophrenia and healthy controls in the first principal component (the contribution ratio of the first principal component was 15.9%). Logistic regression analysis suggested that the first principal component was a predictor of disease (odds = 1.36, 95%CI = 1.11-1.67, p = 0.0032). ROC analysis showed a sensitivity of 93% and a specificity of 100% for the diagnosis of schizophrenia with a cut-off value of the first principal component; -3.33 (AUC = 0.95). We extracted the high factor loading for the first principal component. Gamma-glutamyl-valine (γ-Glu-Val) was significantly negatively correlated with PANSS total scores (r = -0.45, p = 0.012) and PANSS general scores (r = -0.49, p = 0.0055). Gamma-glutamyl-phenylalanine (γ-Glu-Phe) was significantly negatively correlated with PANSS total score (r = -0.40, p = 0.031) and PANSS general score (r = -0.41, p = 0.025). Tetrahydrouridine was significantly positively correlated with PANSS negative scores (r = 0.53, p = 0.0061). Conclusion: Metabolites changes in hospitalized patients with chronic schizophrenia showed extensive and generalized declines. Glutamate metabolism and the urea cycle had the highest proportions in the metabolic pathway, which decreased in the schizophrenia group. Metabolomic analysis was useful to identify chronic schizophrenia. Some glutamate compound metabolites had a relationship with psychiatric symptoms.