Machine Learning algorithm unveils glutamatergic alterations in the post-mortem schizophrenia brain

Dysregulated Transcript Expression but Not Function of the Glutamate Transporter EAAT2 in the Dorsolateral Prefrontal Cortex in Schizophrenia

BACKGROUND

Schizophrenia (SCZ) is a serious mental illness with complex pathology, including abnormalities in the glutamate system. Glutamate is rapidly removed from the synapse by excitatory amino acid transporters (EAATs). Changes in the expression and localization of the primary glutamate transporter EAAT2 are found in the brain in central nervous system (CNS) disorders including SCZ. We hypothesize that neuronal expression and function of EAAT2 are increased in the frontal cortex in subjects diagnosed with SCZ.

STUDY DESIGN

EAAT2 protein expression and glutamate transporter function were assayed in synaptosome preparations from the dorsolateral prefrontal cortex (DLPFC) of SCZ subjects and age- and sex-matched nonpsychiatrically ill controls. EAAT2 splice variant transcript expression was assayed in enriched populations of neurons and astrocytes from the DLPFC. Pathway analysis of publicly available transcriptomic datasets was carried out to identify biological changes associated with EAAT2 perturbation in different cell types.

RESULTS

We found no significant changes in EAAT2 protein expression or glutamate uptake in the DLPFC in SCZ subjects compared with controls (n = 10/group). Transcript expression of EAAT2 and signaling molecules associated with EAAT2b trafficking (CaMKIIa and DLG1) were significantly altered in enriched populations of astrocytes and pyramidal neurons (P < .05) in SCZ (n = 16/group). These changes were not associated with antipsychotic medications. Pathway analysis also identified cell-type-specific enrichment of biological pathways associated with perturbation of astrocyte (immune pathways) and neuronal (metabolic pathways) EAAT2 expression.

CONCLUSIONS

Overall, these data support the growing body of evidence for the role of dysregulation of the glutamate system in the pathophysiology of SCZ.

Interpretable machine learning to evaluate relationships between DAO/DAOA (pLG72) protein data and features in clinical assessments, functional outcome, and cognitive function in schizophrenia patients

Machine learning has been proposed to utilize D-amino acid oxidase (DAO) and DAO activator (DAOA [or pLG72]) protein levels to ascertain disease status in schizophrenia. However, it remains unclear whether machine learning can effectively evaluate clinical features in relation to DAO and DAOA in schizophrenia patients. We employed an interpretable machine learning (IML) framework including linear regression, least absolute shrinkage and selection operator (Lasso) models, and generalized additive models (GAMs) to analyze DAO/DAOA levels using 380 Taiwanese schizophrenia patients. Additionally, we incorporated 27 parameters encompassing demographic variables, clinical assessments, functional outcomes, and cognitive function as features. The IML framework facilitated linear and non-linear relationships between features and DAO/DAOA. DAO levels demonstrated significant associations with the 17-item Hamilton Depression Rating Scale (HAMD17) based on linear regression. The Lasso model identified four features-HAMD17, age, working memory, and overall cognitive function (OCF)-and highlighted HAMD17 as the most significant feature, using DAO from chronically stable patients. Utilizing DAOA from acutely exacerbated patients, the Lasso model also identified four features-OCF, Scale for the Assessment of Negative Symptoms 20-item, quality of life scale (QLS), and category fluency-and emphasized OCF as the most significant feature. Furthermore, GAMs revealed a non-linear relationship between category fluency and DAO in chronically stable patients, as well as between QLS and DAOA in acutely exacerbated patients. The study suggests that an IML framework holds promise for assessing linear and non-linear relationships between DAO/DAOA and various features in clinical assessments, functional outcomes, and cognitive function in patients with schizophrenia.

Variations of blood D-serine and D-aspartate homeostasis track psychosis stages

Schizophrenia (SCZ) is a severe psychotic disorder characterized by a disruption in glutamatergic NMDA receptor (NMDAR)-mediated neurotransmission. Compelling evidence has revealed that NMDAR activation is not limited to L-glutamate, L-aspartate, and glycine since other free amino acids (AAs) in the atypical D-configuration, such as D-aspartate and D-serine, also modulate this class of glutamatergic receptors. Although dysregulation of AAs modulating NMDARs has been previously reported in SCZ, it remains unclear whether distinct variations of these biomolecules occur during illness progression from at-risk premorbid to clinically manifest stage. To probe this issue, we used High-Performance Liquid Chromatography (HPLC) to measure serum levels of D- and L-AAs that stimulate NMDARs across four groups of individuals diagnosed with (a) At-Risk Mental State (ARMS) for psychosis, (b) First Episode of Psychosis (FEP), (c) full-blown SCZ and (d) Healthy Donors (HD). We examined how diagnosis, demographic features, and antipsychotic treatment influence the variation of AA levels throughout psychosis progression. Finally, we explored the potential association between AA blood concentrations and clinical and cognitive measures related to psychosis. Our findings identified inter-group differences in serum AA composition, highlighting that the upregulation of D-serine/total serine and D-aspartate/total aspartate ratios represent a peculiar blood biochemical signature of early stages of psychosis progression, while increased L-glutamate, L-aspartate and glycine associate with chronic SCZ diagnosis. The present findings provide direct evidence for early dysregulation of D-AA metabolism and have potential implications for the identification of biomarkers for the early detection and staging of psychosis.

Addressing brain metabolic connectivity in treatment-resistant schizophrenia: a novel graph theory-driven application of 18F-FDG-PET with antipsychotic dose correction

Few studies using Positron Emission Tomography with 18F-fluorodeoxyglucose (18F-FDG-PET) have examined the neurobiological basis of antipsychotic resistance in schizophrenia, primarily focusing on metabolic activity, with none investigating connectivity patterns. Here, we aimed to explore differential patterns of glucose metabolism between patients and controls (CTRL) through a graph theory-based approach and network comparison tests. PET scans with 18F-FDG were obtained by 70 subjects, 26 with treatment-resistant schizophrenia (TRS), 28 patients responsive to antipsychotics (nTRS), and 16 CTRL. Relative brain glucose metabolism maps were processed in the automated anatomical labeling (AAL)-Merged atlas template. Inter-subject connectivity matrices were derived using Gaussian Graphical Models and group networks were compared through permutation testing. A logistic model based on machine-learning was employed to estimate the association between the metabolic signals of brain regions and treatment resistance. To account for the potential influence of antipsychotic medication, we incorporated chlorpromazine equivalents as a covariate in the network analysis during partial correlation calculations. Additionally, the machine-learning analysis employed medication dose-stratified folds. Global reduced connectivity was detected in the nTRS (p-value = 0.008) and TRS groups (p-value = 0.001) compared to CTRL, with prominent alterations localized in the frontal lobe, Default Mode Network, and dorsal dopamine pathway. Disruptions in frontotemporal and striatal-cortical connectivity were detected in TRS but not nTRS patients. After adjusting for antipsychotic doses, alterations in the anterior cingulate, frontal and temporal gyri, hippocampus, and precuneus also emerged. The machine-learning approach demonstrated an accuracy ranging from 0.72 to 0.8 in detecting the TRS condition.

Sex- and dose-dependent catalase increase and its clinical impact in a benzoate dose-finding, randomized, double-blind, placebo-controlled trial for Alzheimer's disease

BACKGROUND

Sex differences in Alzheimer's disease (AD) are gaining increasing attention. Previously research has shown that sodium benzoate treatment can improve cognitive function in AD patients, particularly in the female patients; and 1000 mg/day of benzoate appears more efficacious than lower doses. Catalase is a crucial endogenous antioxidant; and deficiency of catalase is regarded to be related to the pathogenesis of AD. The current study aimed to explore the role of sex and benzoate dose in the change of catalase activity among benzoate-treated AD patients.

METHODS

This secondary analysis used data from a double-blind trial, in which 149 CE patients were randomized to receive placebo or one of three benzoate doses (500, 750, or 1000 mg/day) and measured with Alzheimer's disease assessment scale-cognitive subscale. Plasma catalase was assayed before and after treatment.

RESULTS

Benzoate treatment, particularly at 1000 mg/day, increased catalase among female patients, but not among male. The increases in the catalase activity among the benzoate-treated women were correlated with their cognitive improvements. In addition, higher baseline catalase activity was associated with more cognitive improvement after benzoate treatment among both female and male patients.

CONCLUSIONS

Supporting the oxidative stress theory and sex difference in AD, the finding suggest that sex (female) and benzoate dose co-determine catalase increase in benzoate-treated AD patients and the catalase increment contributes to cognitive improvement of benzoate-treated women.

TRIAL REGISTRATION

ClinicalTrials.gov Identifier: NCT03752463.

Exploring the Intersection of Schizophrenia, Machine Learning, and Genomics: Scoping Review

BACKGROUND

An increasing body of literature highlights the integration of machine learning with genomic data in psychiatry, particularly for complex mental health disorders such as schizophrenia. These advanced techniques offer promising potential for uncovering various facets of these disorders. A comprehensive review of the current applications of machine learning in conjunction with genomic data within this context can significantly enhance our understanding of the current state of research and its future directions.

OBJECTIVE

This study aims to conduct a systematic scoping review of the use of machine learning algorithms with genomic data in the field of schizophrenia.

METHODS

To conduct a systematic scoping review, a search was performed in the electronic databases MEDLINE, Web of Science, PsycNet (PsycINFO), and Google Scholar from 2013 to 2024. Studies at the intersection of schizophrenia, genomic data, and machine learning were evaluated.

RESULTS

The literature search identified 2437 eligible articles after removing duplicates. Following abstract screening, 143 full-text articles were assessed, and 121 were subsequently excluded. Therefore, 21 studies were thoroughly assessed. Various machine learning algorithms were used in the identified studies, with support vector machines being the most common. The studies notably used genomic data to predict schizophrenia, identify schizophrenia features, discover drugs, classify schizophrenia amongst other mental health disorders, and predict the quality of life of patients.

CONCLUSIONS

Several high-quality studies were identified. Yet, the application of machine learning with genomic data in the context of schizophrenia remains limited. Future research is essential to further evaluate the portability of these models and to explore their potential clinical applications.

Schizophrenia diagnosis based on diverse epoch size resting-state EEG using machine learning

Schizophrenia is a severe mental disorder that impairs a person's mental, social, and emotional faculties gradually. Detection in the early stages with an accurate diagnosis is crucial to remedying the patients. This study proposed a new method to classify schizophrenia disease in the rest state based on neurologic signals achieved from the brain by electroencephalography (EEG). The datasets used consisted of 28 subjects, 14 for each group, which are schizophrenia and healthy control. The data was collected from the scalps with 19 EEG channels using a 250 Hz frequency. Due to the brain signal variation, we have decomposed the EEG signals into five sub-bands using a band-pass filter, ensuring the best signal clarity and eliminating artifacts. This work was performed with several scenarios: First, traditional techniques were applied. Secondly, augmented data (additive white Gaussian noise and stretched signals) were utilized. Additionally, we assessed Minimum Redundancy Maximum Relevance (MRMR) as the features reduction method. All these data scenarios are applied with three different window sizes (epochs): 1, 2, and 5 s, utilizing six algorithms to extract features: Fast Fourier Transform (FFT), Approximate Entropy (ApEn), Log Energy entropy (LogEn), Shannon Entropy (ShnEn), and kurtosis. The L2-normalization method was applied to the derived features, positively affecting the results. In terms of classification, we applied four algorithms: K-nearest neighbor (KNN), support vector machine (SVM), quadratic discriminant analysis (QDA), and ensemble classifier (EC). From all the scenarios, our evaluation showed that SVM had remarkable results in all evaluation metrics with LogEn features utilizing a 1-s window size, impacting the diagnosis of Schizophrenia disease. This indicates that an accurate diagnosis of schizophrenia can be achieved through the right features and classification model selection. Finally, we contrasted our results to recently published works using the same and a different dataset, where our method showed a notable improvement.

Decreased free D-aspartate levels in the blood serum of patients with schizophrenia

Introduction

Schizophrenia (SCZ) and autism spectrum disorder (ASD) are neurodevelopmental diseases characterized by different psychopathological manifestations and divergent clinical trajectories. Various alterations at glutamatergic synapses have been reported in both disorders, including abnormal NMDA and metabotropic receptor signaling.

Methods

We conducted a bicentric study to assess the blood serum levels of NMDA receptors-related glutamatergic amino acids and their precursors, including L-glutamate, L-glutamine, D-aspartate, L-aspartate, L-asparagine, D-serine, L-serine and glycine, in ASD, SCZ patients and their respective control subjects. Specifically, the SCZ patients were subdivided into treatment-resistant and non-treatment-resistant SCZ patients, based on their responsivity to conventional antipsychotics.

Results

D-serine and D-aspartate serum reductions were found in SCZ patients compared to controls. Conversely, no significant differences between cases and controls were found in amino acid concentrations in the two ASD cohorts analyzed.

Discussion

This result further encourages future research to evaluate the predictive role of selected D-amino acids as peripheral markers for SCZ pathophysiology and diagnosis.

Longitudinal trajectories of anterior cingulate glutamate and subclinical psychotic experiences in early adolescence: the impact of bullying victimization

Previous studies reported decreased glutamate levels in the anterior cingulate cortex (ACC) in non-treatment-resistant schizophrenia and first-episode psychosis. However, ACC glutamatergic changes in subjects at high-risk for psychosis, and the effects of commonly experienced environmental emotional/social stressors on glutamatergic function in adolescents remain unclear. In this study, adolescents recruited from the general population underwent proton magnetic resonance spectroscopy (MRS) of the pregenual ACC using a 3-Tesla scanner. We explored longitudinal data on the association of combined glutamate-glutamine (Glx) levels, measured by MRS, with subclinical psychotic experiences. Moreover, we investigated associations of bullying victimization, a risk factor for subclinical psychotic experiences, and help-seeking intentions, a coping strategy against stressors including bullying victimization, with Glx levels. Finally, path analyses were conducted to explore multivariate associations. For a contrast analysis, gamma-aminobutyric acid plus macromolecule (GABA+) levels were also analyzed. Negative associations were found between Glx levels and subclinical psychotic experiences at both Times 1 (n = 219, mean age 11.5 y) and 2 (n = 211, mean age 13.6 y), as well as for over-time changes (n = 157, mean interval 2.0 y). Moreover, effects of bullying victimization and bullying victimization × help-seeking intention interaction effects on Glx levels were found (n = 156). Specifically, bullying victimization decreased Glx levels, whereas help-seeking intention increased Glx levels only in bullied adolescents. Finally, associations among bullying victimization, help-seeking intention, Glx levels, and subclinical psychotic experiences were revealed. GABA+ analysis revealed no significant results. This is the first adolescent study to reveal longitudinal trajectories of the association between glutamatergic function and subclinical psychotic experiences and to elucidate the effect of commonly experienced environmental emotional/social stressors on glutamatergic function. Our findings may deepen the understanding of how environmental emotional/social stressors induce impaired glutamatergic neurotransmission that could be the underpinning of liability for psychotic experiences in early adolescence.

Identifying frequency-dependent imaging genetic associations via hypergraph-structured multi-task sparse canonical correlation analysis

Identifying complex associations between genetic variations and imaging phenotypes is a challenging task in the research of brain imaging genetics. The previous study has proved that neuronal oscillations within distinct frequency bands are derived from frequency-dependent genetic modulation. Thus it is meaningful to explore frequency-dependent imaging genetic associations, which may give important insights into the pathogenesis of brain disorders. In this work, the hypergraph-structured multi-task sparse canonical correlation analysis (HS-MTSCCA) was developed to explore the associations between multi-frequency imaging phenotypes and single-nucleotide polymorphisms (SNPs). Specifically, we first created a hypergraph for the imaging phenotypes of each frequency and the SNPs, respectively. Then, a new hypergraph-structured constraint was proposed to learn high-order relationships among features in each hypergraph, which can introduce biologically meaningful information into the model. The frequency-shared and frequency-specific imaging phenotypes and SNPs could be identified using the multi-task learning framework. We also proposed a useful strategy to tackle this algorithm and then demonstrated its convergence. The proposed method was evaluated on four simulation datasets and a real schizophrenia dataset. The experimental results on synthetic data showed that HS-MTSCCA outperforms the other competing methods according to canonical correlation coefficients, canonical weights, and cosine similarity. And the results on real data showed that HS-MTSCCA could obtain superior canonical coefficients and canonical weights. Furthermore, the identified frequency-shared and frequency-specific biomarkers could provide more interesting and meaningful information, demonstrating that HS-MTSCCA is a powerful method for brain imaging genetics.

A powerful machine learning approach to identify interactions of differentially abundant gut microbial subsets in patients with metastatic and non-metastatic pancreatic cancer

Pancreatic cancer has a dismal prognosis, as it is often diagnosed at stage IV of the disease and is characterized by metastatic spread. Gut microbiota and its metabolites have been suggested to influence the metastatic spread by modulating the host immune system or by promoting angiogenesis. To date, the gut microbial profiles of metastatic and non-metastatic patients need to be explored. Taking advantage of the 16S metagenomic sequencing and the PEnalized LOgistic Regression Analysis (PELORA) we identified clusters of bacteria with differential abundances between metastatic and non-metastatic patients. An overall increase in Gram-negative bacteria in metastatic patients compared to non-metastatic ones was identified using this method. Furthermore, to gain more insight into how gut microbes can predict metastases, a machine learning approach (iterative Random Forest) was performed. Iterative Random Forest analysis revealed which microorganisms were characterized by a different level of relative abundance between metastatic and non-metastatic patients and established a functional relationship between the relative abundance and the probability of having metastases. At the species level, the following bacteria were found to have the highest discriminatory power: Anaerostipes hadrus, Coprobacter secundus, Clostridium sp. 619, Roseburia inulinivorans, Porphyromonas and Odoribacter at the genus level, and Rhodospirillaceae, Clostridiaceae and Peptococcaceae at the family level. Finally, these data were intertwined with those from a metabolomics analysis on fecal samples of patients with or without metastasis to better understand the role of gut microbiota in the metastatic process. Artificial intelligence has been applied in different areas of the medical field. Translating its application in the field of gut microbiota analysis may help fully exploit the potential information contained in such a large amount of data aiming to open up new supportive areas of intervention in the management of cancer.

The Neurobiological Underpinnings of Obsessive-Compulsive Symptoms in Psychosis, Translational Issues for Treatment-Resistant Schizophrenia

Almost 25% of schizophrenia patients suffer from obsessive-compulsive symptoms (OCS) considered a transdiagnostic clinical continuum. The presence of symptoms pertaining to both schizophrenia and obsessive-compulsive disorder (OCD) may complicate pharmacological treatment and could contribute to lack or poor response to the therapy. Despite the clinical relevance, no reviews have been recently published on the possible neurobiological underpinnings of this comorbidity, which is still unclear. An integrative view exploring this topic should take into account the following aspects: (i) the implication for glutamate, dopamine, and serotonin neurotransmission as demonstrated by genetic findings; (ii) the growing neuroimaging evidence of the common brain regions and dysfunctional circuits involved in both diseases; (iii) the pharmacological modulation of dopaminergic, serotoninergic, and glutamatergic systems as current therapeutic strategies in schizophrenia OCS; (iv) the recent discovery of midbrain dopamine neurons and dopamine D1- and D2-like receptors as orchestrating hubs in repetitive and psychotic behaviors; (v) the contribution of N-methyl-D-aspartate receptor subunits to both psychosis and OCD neurobiology. Finally, we discuss the potential role of the postsynaptic density as a structural and functional hub for multiple molecular signaling both in schizophrenia and OCD pathophysiology.

The influence of tissue pH and RNA integrity number on gene expression of human postmortem brain

Background

Evaluating and controlling confounders are necessary when investigating molecular pathogenesis using human postmortem brain tissue. Particularly, tissue pH and RNA integrity number (RIN) are valuable indicators for controlling confounders. However, the influences of these indicators on the expression of each gene in postmortem brain have not been fully investigated. Therefore, we aimed to assess these effects on gene expressions of human brain samples.

Methods

We isolated total RNA from occipital lobes of 13 patients with schizophrenia and measured the RIN and tissue pH. Gene expression was analyzed and gene sets affected by tissue pH and RIN were identified. Moreover, we examined the functions of these genes by enrichment analysis and upstream regulator analysis.

Results

We identified 2,043 genes (24.7%) whose expressions were highly correlated with pH; 3,004 genes (36.3%) whose expressions were highly correlated with RIN; and 1,293 genes (15.6%) whose expressions were highly correlated with both pH and RIN. Genes commonly affected by tissue pH and RIN were highly associated with energy production and the immune system. In addition, genes uniquely affected by tissue pH were highly associated with the cell cycle, whereas those uniquely affected by RIN were highly associated with RNA processing.

Conclusion

The current study elucidated the influence of pH and RIN on gene expression profiling and identified gene sets whose expressions were affected by tissue pH or RIN. These findings would be helpful in the control of confounders for future postmortem brain studies.

Insulin effects on core neurotransmitter pathways involved in schizophrenia neurobiology: a meta-analysis of preclinical studies. Implications for the treatment

Impairment of insulin action and metabolic dysregulation have traditionally been associated with schizophrenia, although the molecular basis of such association remains still elusive. The present meta-analysis aims to assess the impact of insulin action manipulations (i.e., hyperinsulinemia, hypoinsulinemia, systemic or brain insulin resistance) on glutamatergic, dopaminergic, γ-aminobutyric acid (GABA)ergic, and serotonergic pathways in the central nervous system. More than one hundred outcomes, including transcript or protein levels, kinetic parameters, and other components of the neurotransmitter pathways, were collected from cultured cells, animals, or humans, and meta-analyzed by applying a random-effects model and adopting Hedges'g to compare means. Two hundred fifteen studies met the inclusion criteria, of which 180 entered the quantitative synthesis. Significant impairments in key regulators of synaptic plasticity processes were detected as the result of insulin handlings. Specifically, protein levels of N-methyl-D-aspartate receptor (NMDAR) subunits including type 2A (NR2A) (Hedges' g = -0.95, 95%C.I. = -1.50, -0.39; p = 0.001; I2 = 47.46%) and 2B (NR2B) (Hedges'g = -0.69, 95%C.I. = -1.35, -0.02; p = 0.043; I2 = 62.09%), and Postsynaptic density protein 95 (PSD-95) (Hedges'g = -0.91, 95%C.I. = -1.51, -0.32; p = 0.003; I2 = 77.81%) were found reduced in insulin-resistant animal models. Moreover, insulin-resistant animals showed significantly impaired dopamine transporter activity, whereas the dopamine D2 receptor mRNA expression (Hedges'g = 3.259; 95%C.I. = 0.497, 6.020; p = 0.021; I2 = 90.61%) increased under insulin deficiency conditions. Insulin action modulated glutamate and GABA release, as well as several enzymes involved in GABA and serotonin synthesis. These results suggest that brain neurotransmitter systems are susceptible to insulin signaling abnormalities, resembling the discrete psychotic disorders' neurobiology and possibly contributing to the development of neurobiological hallmarks of treatment-resistant schizophrenia.

Bilirubin-Induced Transcriptomic Imprinting in Neonatal Hyperbilirubinemia

Recent findings indicated aberrant epigenetic control of the central nervous system (CNS) development in hyperbilirubinemic Gunn rats as an additional cause of cerebellar hypoplasia, the landmark of bilirubin neurotoxicity in rodents. Because the symptoms in severely hyperbilirubinemic human neonates suggest other regions as privileged targets of bilirubin neurotoxicity, we expanded the study of the potential impact of bilirubin on the control of postnatal brain development to regions correlating with human symptoms. Histology, transcriptomic, gene correlation, and behavioral studies were performed. The histology revealed widespread perturbation 9 days after birth, restoring in adulthood. At the genetic level, regional differences were noticed. Bilirubin affected synaptogenesis, repair, differentiation, energy, extracellular matrix development, etc., with transient alterations in the hippocampus (memory, learning, and cognition) and inferior colliculi (auditory functions) but permanent changes in the parietal cortex. Behavioral tests confirmed the presence of a permanent motor disability. The data correlate well both with the clinic description of neonatal bilirubin-induced neurotoxicity, as well as with the neurologic syndromes reported in adults that suffered neonatal hyperbilirubinemia. The results pave the way for better deciphering the neurotoxic features of bilirubin and evaluating deeply the efficacy of new therapeutic approaches against the acute and long-lasting sequels of bilirubin neurotoxicity.

Schizophrenia Synaptic Pathology and Antipsychotic Treatment in the Framework of Oxidative and Mitochondrial Dysfunction: Translational Highlights for the Clinics and Treatment

Schizophrenia is a worldwide mental illness characterized by alterations at dopaminergic and glutamatergic synapses resulting in global dysconnectivity within and between brain networks. Impairments in inflammatory processes, mitochondrial functions, energy expenditure, and oxidative stress have been extensively associated with schizophrenia pathophysiology. Antipsychotics, the mainstay of schizophrenia pharmacological treatment and all sharing the common feature of dopamine D2 receptor occupancy, may affect antioxidant pathways as well as mitochondrial protein levels and gene expression. Here, we systematically reviewed the available evidence on antioxidants' mechanisms in antipsychotic action and the impact of first- and second-generation compounds on mitochondrial functions and oxidative stress. We further focused on clinical trials addressing the efficacy and tolerability of antioxidants as an augmentation strategy of antipsychotic treatment. EMBASE, Scopus, and Medline/PubMed databases were interrogated. The selection process was conducted in respect of the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) criteria. Several mitochondrial proteins involved in cell viability, energy metabolism, and regulation of oxidative systems were reported to be significantly modified by antipsychotic treatment with differences between first- and second-generation drugs. Finally, antioxidants may affect cognitive and psychotic symptoms in patients with schizophrenia, and although the evidence is only preliminary, the results indicate that further studies are warranted.

Inhibition of Vesicular Glutamate Transporters (VGLUTs) with Chicago Sky Blue 6B Before Focal Cerebral Ischemia Offers Neuroprotection

Brain ischemia is one of the leading causes of death and long-term disability in the world. Interruption of the blood supply to the brain is a direct stimulus for many pathological events. The massive vesicular release of glutamate (Glu) after ischemia onset induces excitotoxicity, which is a potent stress on neurons. Loading of presynaptic vesicles with Glu is the first step of glutamatergic neurotransmission. Vesicular glutamate transporters 1, 2, and 3 (VGLUT1, 2, and 3) are the main players involved in filling presynaptic vesicles with Glu. VGLUT1 and VGLUT2 are expressed mainly in glutamatergic neurons. Therefore, the possibility of pharmacological modulation to prevent ischemia-related brain damage is attractive. In this study, we aimed to determine the effect of focal cerebral ischemia on the spatiotemporal expression of VGLUT1 and VGLUT2 in rats. Next, we investigated the influence of VGLUT inhibition with Chicago Sky Blue 6B (CSB6B) on Glu release and stroke outcome. The effect of CSB6B pretreatment on infarct volume and neurological deficit was compared with a reference model of ischemic preconditioning. The results of this study indicate that ischemia upregulated the expression of VGLUT1 in the cerebral cortex and in the dorsal striatum 3 days after ischemia onset. The expression of VGLUT2 was elevated in the dorsal striatum and in the cerebral cortex 24 h and 3 days after ischemia, respectively. Microdialysis revealed that pretreatment with CSB6B significantly reduced the extracellular Glu concentration. Altogether, this study shows that inhibition of VGLUTs might be a promising therapeutic strategy for the future.

Dysregulated Signaling at Postsynaptic Density: A Systematic Review and Translational Appraisal for the Pathophysiology, Clinics, and Antipsychotics' Treatment of Schizophrenia

Emerging evidence from genomics, post-mortem, and preclinical studies point to a potential dysregulation of molecular signaling at postsynaptic density (PSD) in schizophrenia pathophysiology. The PSD that identifies the archetypal asymmetric synapse is a structure of approximately 300 nm in diameter, localized behind the neuronal membrane in the glutamatergic synapse, and constituted by more than 1000 proteins, including receptors, adaptors, kinases, and scaffold proteins. Furthermore, using FASS (fluorescence-activated synaptosome sorting) techniques, glutamatergic synaptosomes were isolated at around 70 nm, where the receptors anchored to the PSD proteins can diffuse laterally along the PSD and were stabilized by scaffold proteins in nanodomains of 50-80 nm at a distance of 20-40 nm creating "nanocolumns" within the synaptic button. In this context, PSD was envisioned as a multimodal hub integrating multiple signaling-related intracellular functions. Dysfunctions of glutamate signaling have been postulated in schizophrenia, starting from the glutamate receptor's interaction with scaffolding proteins involved in the N-methyl-D-aspartate receptor (NMDAR). Despite the emerging role of PSD proteins in behavioral disorders, there is currently no systematic review that integrates preclinical and clinical findings addressing dysregulated PSD signaling and translational implications for antipsychotic treatment in the aberrant postsynaptic function context. Here we reviewed a critical appraisal of the role of dysregulated PSD proteins signaling in the pathophysiology of schizophrenia, discussing how antipsychotics may affect PSD structures and synaptic plasticity in brain regions relevant to psychosis.

Antipsychotics-Induced Changes in Synaptic Architecture and Functional Connectivity: Translational Implications for Treatment Response and Resistance

Schizophrenia is a severe mental illness characterized by alterations in processes that regulate both synaptic plasticity and functional connectivity between brain regions. Antipsychotics are the cornerstone of schizophrenia pharmacological treatment and, beyond occupying dopamine D2 receptors, can affect multiple molecular targets, pre- and postsynaptic sites, as well as intracellular effectors. Multiple lines of evidence point to the involvement of antipsychotics in sculpting synaptic architecture and remodeling the neuronal functional unit. Furthermore, there is an increasing awareness that antipsychotics with different receptor profiles could yield different interregional patterns of co-activation. In the present systematic review, we explored the fundamental changes that occur under antipsychotics' administration, the molecular underpinning, and the consequences in both acute and chronic paradigms. In addition, we investigated the relationship between synaptic plasticity and functional connectivity and systematized evidence on different topographical patterns of activation induced by typical and atypical antipsychotics.

Relevance of interactions between dopamine and glutamate neurotransmission in schizophrenia

Dopamine (DA) and glutamate neurotransmission are strongly implicated in schizophrenia pathophysiology. While most studies focus on contributions of neurons that release only DA or glutamate, neither DA nor glutamate models alone recapitulate the full spectrum of schizophrenia pathophysiology. Similarly, therapeutic strategies limited to either system cannot effectively treat all three major symptom domains of schizophrenia: positive, negative, and cognitive symptoms. Increasing evidence suggests extensive interactions between the DA and glutamate systems and more effective treatments may therefore require the targeting of both DA and glutamate signaling. This offers the possibility that disrupting DA-glutamate circuitry between these two systems, particularly in the striatum and forebrain, culminate in schizophrenia pathophysiology. Yet, the mechanisms behind these interactions and their contributions to schizophrenia remain unclear. In addition to circuit- or system-level interactions between neurons that solely release either DA or glutamate, here we posit that functional alterations involving a subpopulation of neurons that co-release both DA and glutamate provide a novel point of integration between DA and glutamate systems, offering a key missing link in our understanding of schizophrenia pathophysiology. Better understanding of mechanisms underlying DA/glutamate co-release from these neurons may therefore shed new light on schizophrenia pathophysiology and lead to more effective therapeutics.

D-aspartate oxidase gene duplication induces social recognition memory deficit in mice and intellectual disabilities in humans

The D-aspartate oxidase (DDO) gene encodes the enzyme responsible for the catabolism of D-aspartate, an atypical amino acid enriched in the mammalian brain and acting as an endogenous NMDA receptor agonist. Considering the key role of NMDA receptors in neurodevelopmental disorders, recent findings suggest a link between D-aspartate dysmetabolism and schizophrenia. To clarify the role of D-aspartate on brain development and functioning, we used a mouse model with constitutive Ddo overexpression and D-aspartate depletion. In these mice, we found reduced number of BrdU-positive dorsal pallium neurons during corticogenesis, and decreased cortical and striatal gray matter volume at adulthood. Brain abnormalities were associated with social recognition memory deficit at juvenile phase, suggesting that early D-aspartate occurrence influences neurodevelopmental related phenotypes. We corroborated this hypothesis by reporting the first clinical case of a young patient with severe intellectual disability, thought disorders and autism spectrum disorder symptomatology, harboring a duplication of a chromosome 6 region, including the entire DDO gene.

Rational and Translational Implications of D-Amino Acids for Treatment-Resistant Schizophrenia: From Neurobiology to the Clinics

Schizophrenia has been conceptualized as a neurodevelopmental disorder with synaptic alterations and aberrant cortical–subcortical connections. Antipsychotics are the mainstay of schizophrenia treatment and nearly all share the common feature of dopamine D2 receptor occupancy, whereas glutamatergic abnormalities are not targeted by the presently available therapies. D-amino acids, acting as N-methyl-D-aspartate receptor (NMDAR) modulators, have emerged in the last few years as a potential augmentation strategy in those cases of schizophrenia that do not respond well to antipsychotics, a condition defined as treatment-resistant schizophrenia (TRS), affecting almost 30–40% of patients, and characterized by serious cognitive deficits and functional impairment. In the present systematic review, we address with a direct and reverse translational perspective the efficacy of D-amino acids, including D-serine, D-aspartate, and D-alanine, in poor responders. The impact of these molecules on the synaptic architecture is also considered in the light of dendritic spine changes reported in schizophrenia and antipsychotics’ effect on postsynaptic density proteins. Moreover, we describe compounds targeting D-amino acid oxidase and D-aspartate oxidase enzymes. Finally, other drugs acting at NMDAR and proxy of D-amino acids function, such as D-cycloserine, sarcosine, and glycine, are considered in the light of the clinical burden of TRS, together with other emerging molecules.

Pathological oligodendrocyte precursor cells revealed in human schizophrenic brains and trigger schizophrenia-like behaviors and synaptic defects in genetic animal model

Although the link of white matter to pathophysiology of schizophrenia is documented, loss of myelin is not detected in patients at the early stages of the disease, suggesting that pathological evolution of schizophrenia may occur before significant myelin loss. Disrupted-in-schizophrenia-1 (DISC1) protein is highly expressed in oligodendrocyte precursor cells (OPCs) and regulates their maturation. Recently, DISC1-Δ3, a major DISC1 variant that lacks exon 3, has been identified in schizophrenia patients, although its pathological significance remains unknown. In this study, we detected in schizophrenia patients a previously unidentified pathological phenotype of OPCs exhibiting excessive branching. We replicated this phenotype by generating a mouse strain expressing DISC1-Δ3 gene in OPCs. We further demonstrated that pathological OPCs, rather than myelin defects, drive the onset of schizophrenic phenotype by hyperactivating OPCs' Wnt/β-catenin pathway, which consequently upregulates Wnt Inhibitory Factor 1 (Wif1), leading to the aberrant synaptic formation and neuronal activity. Suppressing Wif1 in OPCs rescues synaptic loss and behavioral disorders in DISC1-Δ3 mice. Our findings reveal the pathogenetic role of OPC-specific DISC1-Δ3 variant in the onset of schizophrenia and highlight the therapeutic potential of Wif1 as an alternative target for the treatment of this disease.