The cell cycle-related genes as biomarkers for schizophrenia

Patient iPSC-derived neural progenitor cells display aberrant cell cycle control, p53, and DNA damage response protein expression in schizophrenia

BACKGROUND

Schizophrenia (SCZ) is a severe psychiatric disorder associated with alterations in early brain development. Details of underlying pathomechanisms remain unclear, despite genome and transcriptome studies providing evidence for aberrant cellular phenotypes and pathway deregulation in developing neuronal cells. However, mechanistic insight at the protein level is limited.

METHODS

Here, we investigate SCZ-specific protein expression signatures of neuronal progenitor cells (NPC) derived from patient iPSC in comparison to healthy controls using high-throughput Western Blotting (DigiWest) in a targeted proteomics approach.

RESULTS

SCZ neural progenitors displayed altered expression and phosphorylation patterns related to Wnt and MAPK signaling, protein synthesis, cell cycle regulation and DNA damage response. Consistent with impaired cell cycle control, SCZ NPCs also showed accumulation in the G2/M cell phase and reduced differentiation capacity. Furthermore, we correlated these findings with elevated p53 expression and phosphorylation levels in SCZ patient-derived cells, indicating a potential implication of p53 in hampering cell cycle progression and efficient neurodevelopment in SCZ.

CONCLUSIONS

Through targeted proteomics we demonstrate that SCZ NPC display coherent mechanistic alterations in regulation of DNA damage response, cell cycle control and p53 expression. These findings highlight the suitability of iPSC-based approaches for modeling psychiatric disorders and contribute to a better understanding of the disease mechanisms underlying SCZ, particularly during early development.

Unraveling epigenomic signatures and effectiveness of electroconvulsive therapy in treatment-resistant depression patients: a prospective longitudinal study

BACKGROUND

Electroconvulsive therapy (ECT) benefits patients with treatment-resistant depression (TRD), but the underlying biological processes are unclear. We conducted an epigenome-wide association study in 32 TRD patients undergoing ECT to depict ECT-associated methylation changes. Illness severity and ECT outcomes were assessed with the Montgomery-Åsberg Depression Rating Scale at baseline (T0) and 1 month after its end (T1). Methylation was profiled at T0 and T1 with the Illumina Infinium Methylation EPIC BeadChip array.

RESULTS

Longitudinal T0-T1 analyses showed 3 differentially methylated probes (DMPs) with nominal p values ≤ 10-5, with 2 annotated in the genes CYB5B and PVRL4. Including covariates, we found 4 DMPs for symptoms variation, annotated in FAM20C, EPB41, OTUB1 and ADARB1, and 3 DMPs for response status, with 2 annotated in IQCE and FAM20C. Regional analysis revealed 54 differentially methylated regions (DMRs) with nominal p value area ≤ 0.05, with 9 presenting adjusted p-value area ≤ 0.10, annotated in MCF2L, SLC25A24, RUNX3, MIR637, FOXK2, FAM180B, POU6F1, ALS2CL and CCRL2. Considering covariates, we found 21 DMRs for symptoms variation and 26 DMRs for response (nominal p value area ≤ 0.05), with 4 presenting adjusted p-value area ≤ 0.10 for response, annotated in SNORD34, NLRP6, GALNT2 and SFT2D3. None remained significant after false discovery rate correction. Notably, ADARB1 variants are associated with suicide attempt in patients with psychiatric disorders, and SLC25A24 relates to conduct disorder. Several DMPs and DMRs are annotated in genes associated with inflammatory/immune processes. Longitudinal analyses on females (n = 22) revealed statistically significant DMRs (adjusted p value area ≤ 0.05) and trend-significant DMRs (adjusted p value area ≤ 0.07) for symptoms variation and response status, annotated in genes related to psychiatric disorders (ZFP57, POLD4, TRIM10, GAS7, ADORA2A, TOLLIP), trauma exposure (RIPOR2) and inflammatory/immune responses (LAT, DLX4, POLD4, FAM30A, H19). Pathway analysis on females revealed enrichment for transcriptional activity, growth factors, DNA maintenance, and immune pathways including IRF7 and IRF2.

CONCLUSION

Although no significant results were found for the whole cohort, the study provides insights into ECT-associated methylation changes, highlighting DMPs and DMRs related to ECT outcomes. Analyses on females revealed significant DMRs and pathways related to psychiatric disorders and inflammatory/immune processes.

Ensemble Learning for Higher Diagnostic Precision in Schizophrenia Using Peripheral Blood Gene Expression Profile

Introduction

Stigma contributes to a significant part of the burden of schizophrenia (SCZ), therefore reducing false positives from the diagnosis would be liberating for the individuals with SCZ and desirable for the clinicians. The stigmatization associated with schizophrenia advocates the need for high-precision diagnosis. In this study, we present an ensemble learning-based approach for high-precision diagnosis of SCZ using peripheral blood gene expression profiles.

Methodology

The machine learning (ML) models, support vector machines (SVM), and prediction analysis for microarrays (PAM) were developed using differentially expressed genes (DEGs) as features. The SCZ samples were classified based on a voting ensemble classifier of SVM and PAM. Further, microarray-based learning was used to classify RNA sequencing (RNA-Seq) samples from our case-control study (Pune-SCZ) to assess cross-platform compatibility.

Results

Ensemble learning using ML models resulted in a significantly higher precision of 80.41% (SD: 0.04) when compared to the individual models (SVM-radial: 71.69%, SD: 0.04 and PAM 77.20%, SD: 0.02). The RNA sequencing samples from our case-control study (Pune-SCZ) resulted in a moderate precision (59.92%, SD: 0.05). The feature genes used for model building were enriched for biological processes such as response to stress, regulation of the immune system, and metabolism of organic nitrogen compounds. The network analysis identified RBX1, CUL4B, DDB1, PRPF19, and COPS4 as hub genes.

Conclusion

In summary, this study developed robust models for higher diagnostic precision in psychiatric disorders. Future efforts will be directed towards multi-omic integration and developing "explainable" diagnostic models.

Comparison of Oncogenes, Tumor Suppressors, and MicroRNAs Between Schizophrenia and Glioma: The Balance of Power

The risk of cancer in schizophrenia has been controversial. Confounders of the issue are cigarette smoking in schizophrenia, and antiproliferative effects of antipsychotic medications. The author has previously suggested comparison of a specific cancer like glioma to schizophrenia might help determine a more accurate relationship between cancer and schizophrenia. To accomplish this goal, the author performed three comparisons of data; the first a comparison of conventional tumor suppressors and oncogenes between schizophrenia and cancer including glioma. This comparison determined schizophrenia has both tumor-suppressive and tumor-promoting characteristics. A second, larger comparison between brain-expressed microRNAs in schizophrenia with their expression in glioma was then performed. This identified a core carcinogenic group of miRNAs in schizophrenia offset by a larger group of tumor-suppressive miRNAs. This proposed "balance of power" between oncogenes and tumor suppressors could cause neuroinflammation. This was assessed by a third comparison between schizophrenia, glioma and inflammation in asbestos-related lung cancer and mesothelioma (ALRCM). This revealed that schizophrenia shares more oncogenic similarity to ALRCM than glioma.

Biomarker prediction in autism spectrum disorder using a network-based approach

BACKGROUND

Autism is a neurodevelopmental disorder that is usually diagnosed in early childhood. Timely diagnosis and early initiation of treatments such as behavioral therapy are important in autistic people. Discovering critical genes and regulators in this disorder can lead to early diagnosis. Since the contribution of miRNAs along their targets can lead us to a better understanding of autism, we propose a framework containing two steps for gene and miRNA discovery.

METHODS

The first step, called the FA_gene algorithm, finds a small set of genes involved in autism. This algorithm uses the WGCNA package to construct a co-expression network for control samples and seek modules of genes that are not reproducible in the corresponding co-expression network for autistic samples. Then, the protein-protein interaction network is constructed for genes in the non-reproducible modules and a small set of genes that may have potential roles in autism is selected based on this network. The second step, named the DMN_miRNA algorithm, detects the minimum number of miRNAs related to autism. To do this, DMN_miRNA defines an extended Set Cover algorithm over the mRNA-miRNA network, consisting of the selected genes and corresponding miRNA regulators.

RESULTS

In the first step of the framework, the FA_gene algorithm finds a set of important genes; TP53, TNF, MAPK3, ACTB, TLR7, LCK, RAC2, EEF2, CAT, ZAP70, CD19, RPLP0, CDKN1A, CCL2, CDK4, CCL5, CTSD, CD4, RACK1, CD74; using co-expression and protein-protein interaction networks. In the second step, the DMN_miRNA algorithm extracts critical miRNAs, hsa-mir-155-5p, hsa-mir-17-5p, hsa-mir-181a-5p, hsa-mir-18a-5p, and hsa-mir-92a-1-5p, as signature regulators for autism using important genes and mRNA-miRNA network. The importance of these key genes and miRNAs is confirmed by previous studies and enrichment analysis.

CONCLUSION

This study suggests FA_gene and DMN_miRNA algorithms for biomarker discovery, which lead us to a list of important players in ASD with potential roles in the nervous system or neurological disorders that can be experimentally investigated as candidates for ASD diagnostic tests.

A novel multi-class classification model for schizophrenia, bipolar disorder and healthy controls using comprehensive transcriptomic data

Two common psychiatric disorders, schizophrenia (SCZ) and bipolar disorder (BP), confer lifelong disability and collectively affect 2% of the world population. Because the diagnosis of psychiatry is based only on symptoms, developing more effective methods for the diagnosis of psychiatric disorders is a major international public health priority. Furthermore, SCZ and BP overlap considerably in terms of symptoms and risk genes. Therefore, the clarity of the underlying etiology and pathology remains lacking for these two disorders. Although many studies have been conducted, a classification model with higher accuracy and consistency was found to still be necessary for accurate diagnoses of SCZ and BP. In this study, a comprehensive dataset was combined from five independent transcriptomic studies. This dataset comprised 120 patients with SCZ, 101 patients with BP, and 149 healthy subjects. The partial least squares discriminant analysis (PLS-DA) method was applied to identify the gene signature among multiple groups, and 341 differentially expressed genes (DEGs) were identified. Then, the disease relevance of these DEGs was systematically performed, including (α) the great disease relevance of the identified signature, (β) the hub genes of the protein-protein interaction network playing a key role in psychiatric disorders, and (γ) gene ontology terms and enriched pathways playing a key role in psychiatric disorders. Finally, a popular multi-class classifier, support vector machine (SVM), was applied to construct a novel multi-class classification model using the identified signature for SCZ and BP. Using the independent test sets, the classification capacity of this multi-class model was assessed, which showed this model had a strong classification ability.

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.

Immune Response Is Key to Genetic Mechanisms of SARS-CoV-2 Infection With Psychiatric Disorders Based on Differential Gene Expression Pattern Analysis

Existing evidence demonstrates that coronavirus disease 2019 (COVID-19) leads to psychiatric illness, despite its main clinical manifestations affecting the respiratory system. People with mental disorders are more susceptible to COVID-19 than individuals without coexisting mental health disorders, with significantly higher rates of severe illness and mortality in this population. The incidence of new psychiatric diagnoses after infection with severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is also remarkably high. SARS-CoV-2 has been reported to use angiotensin-converting enzyme-2 (ACE2) as a receptor for infecting susceptible cells and is expressed in various tissues, including brain tissue. Thus, there is an urgent need to investigate the mechanism linking psychiatric disorders to COVID-19. Using a data set of peripheral blood cells from patients with COVID-19, we compared this to data sets of whole blood collected from patients with psychiatric disorders and used bioinformatics and systems biology approaches to identify genetic links. We found a large number of overlapping immune-related genes between patients infected with SARS-CoV-2 and differentially expressed genes of bipolar disorder (BD), schizophrenia (SZ), and late-onset major depressive disorder (LOD). Many pathways closely related to inflammatory responses, such as MAPK, PPAR, and TGF-β signaling pathways, were observed by enrichment analysis of common differentially expressed genes (DEGs). We also performed a comprehensive analysis of protein-protein interaction network and gene regulation networks. Chemical-protein interaction networks and drug prediction were used to screen potential pharmacologic therapies. We hope that by elucidating the relationship between the pathogenetic processes and genetic mechanisms of infection with SARS-CoV-2 with psychiatric disorders, it will lead to innovative strategies for future research and treatment of psychiatric disorders linked to COVID-19.

Peripheral Blood-Based Gene Expression Studies in Schizophrenia: A Systematic Review

Schizophrenia is a disorder that is characterized by delusions, hallucinations, disorganized speech or behavior, and socio-occupational impairment. The duration of observation and variability in symptoms can make the accurate diagnosis difficult. Identification of biomarkers for schizophrenia (SCZ) can help in early diagnosis, ascertaining the diagnosis, and development of effective treatment strategies. Here we review peripheral blood-based gene expression studies for identification of gene expression biomarkers for SCZ. A literature search was carried out in PubMed and Web of Science databases for blood-based gene expression studies in SCZ. A list of differentially expressed genes (DEGs) was compiled and analyzed for overlap with genetic markers, differences based on drug status of the participants, functional enrichment, and for effect of antipsychotics. This literature survey identified 61 gene expression studies. Seventeen out of these studies were based on expression microarrays. A comparative analysis of the DEGs (n = 227) from microarray studies revealed differences between drug-naive and drug-treated SCZ participants. We found that of the 227 DEGs, 11 genes (ACOT7, AGO2, DISC1, LDB1, RUNX3, SIGIRR, SLC18A1, NRG1, CHRNB2, PRKAB2, and ZNF74) also showed genetic and epigenetic changes associated with SCZ. Functional enrichment analysis of the DEGs revealed dysregulation of proline and 4-hydroxyproline metabolism. Also, arginine and proline metabolism was the most functionally enriched pathway for SCZ in our analysis. Follow-up studies identified effect of antipsychotic treatment on peripheral blood gene expression. Of the 27 genes compiled from the follow-up studies AKT1, DISC1, HP, and EIF2D had no effect on their expression status as a result of antipsychotic treatment. Despite the differences in the nature of the study, ethnicity of the population, and the gene expression analysis method used, we identified several coherent observations. An overlap, though limited, of genetic, epigenetic and gene expression changes supports interplay of genetic and environmental factors in SCZ. The studies validate the use of blood as a surrogate tissue for biomarker analysis. We conclude that well-designed cohort studies across diverse populations, use of high-throughput sequencing technology, and use of artificial intelligence (AI) based computational analysis will significantly improve our understanding and diagnostic capabilities for this complex disorder.

A Potential Autophagy-Related Competing Endogenous RNA Network and Corresponding Diagnostic Efficacy in Schizophrenia

Competing endogenous RNA (ceRNA) and autophagy were related to neurological diseases. But the relationship among ceRNA, autophagy and Schizophrenia (SZ) was not clear. In this study, we obtained gene expression profile of SZ patients (GSE38484, GSE54578, and GSE16930) from Gene Expression Omnibus (GEO) database. Then we screened the autophagy-related differentially expressed lncRNA, miRNA, and mRNA (DElncRNA, DEmiRNA, and DEmRNA) combined with Gene database from The National Center for Biotechnology Information (NCBI). In addition, we performed enrichment analysis. The result showed that biological processes (BPs) mainly were associated with cellular responses to oxygen concentration. The enriched pathways mainly included ErbB, AMPK, mTOR signaling pathway and cell cycle. Furthermore, we constructed autophagy-related ceRNA network based on the TargetScan database. Moreover, we explored the diagnostic efficiency of lncRNA, miRNA and mRNA in ceRNA, through gene set variation analysis (GSVA). The result showed that the diagnostic efficiency was robust, especially miRNA (AUC = 0.884). The miRNA included hsa-miR-423-5p, hsa-miR-4532, hsa-miR-593-3p, hsa-miR-618, hsa-miR-4723-3p, hsa-miR-4640-3p, hsa-miR-296-5p, and hsa-miR-3943. The result of this study may be helpful for deepening the pathophysiology of SZ. In addition, our finding may provide a guideline for the clinical diagnosis of SZ.

Epigenetic mechanisms in schizophrenia and other psychotic disorders: a systematic review of empirical human findings

Schizophrenia and other psychotic disorders are highly debilitating psychiatric conditions that lack a clear etiology and exhibit polygenic inheritance underlain by pleiotropic genes. The prevailing explanation points to the interplay between predisposing genes and environmental exposure. Accumulated evidence suggests that epigenetic regulation of the genome may mediate dynamic gene-environment interactions at the molecular level by modulating the expression of psychiatric phenotypes through transcription factors. This systematic review summarizes the current knowledge linking schizophrenia and other psychotic disorders to epigenetics, based on PubMed and Web of Science database searches conducted according to the PRISMA guidelines. Three groups of mechanisms in case-control studies  of human tissue (i.e., postmortem brain and bio-fluids) were considered: DNA methylation, histone modifications, and non-coding miRNAs. From the initial pool of 3,204 records, 152 studies met our inclusion criteria (11,815/11,528, 233/219, and 2,091/1,827 cases/controls for each group, respectively). Many of the findings revealed associations with epigenetic modulations of genes regulating neurotransmission, neurodevelopment, and immune function, as well as differential miRNA expression (e.g., upregulated miR-34a, miR-7, and miR-181b). Overall, actual evidence moderately supports an association between epigenetics and schizophrenia and other psychotic disorders. However, heterogeneous results and cross-tissue extrapolations call for future work. Integrating epigenetics into systems biology may critically enhance research on psychosis and thus our understanding of the disorder. This may have implications for psychiatry in risk stratification, early recognition, diagnostics, precision medicine, and other interventional approaches targeting epigenetic fingerprints.

Platelet Serotonin Concentration Is Associated with Illness Duration in Schizophrenia and Chronological Age in Depression

OBJECTIVE

Impaired serotonergic neurotransmission has been implicated in the pathogenesis of depression and schizophrenia. Blood platelets have been used for years as a peripheral model of neuronal serotonin dynamics. The objective was to investigate platelet count and serotonin concentration in patients with depression and schizophrenia, in an attempt to ascertain their clinical usefulness.

METHODS

953 participants were included in the study, 329 patients with depression, 339 patients with schizophrenia and 285 healthy controls. ELISA was used to assess platelet serotonin concentrations.

RESULTS

There were no statistically significant differences among groups regarding age, total platelet count and serotonin concentration. Linear regression analyses revealed inverse correlations between platelet serotonin concentration and age of patients with depression and healthy individuals, as well as between platelet serotonin concentration and illness duration in patients with schizophrenia. In other words, longer illness duration in patients with schizophrenia, and higher age in patients with depression and healthy individuals was associated with lower platelet serotonin concentrations.

CONCLUSION

Platelet count and serotonin concentration did not prove to be of diagnostic value in differentiating patients and healthy individuals. However, illness duration in patients with schizophrenia may be associated with reduced concentrations of platelet serotonin.

Ketamine Affects the Expression of ErbB4 in the Hippocampus and Prefrontal Cortex of Rats

Schizophrenia is a severe chronic neuropsychiatric disorder, and its exact pathogenesis remains unclear. This study investigated the effect of ketamine on the expression of ErbB4 (considered a schizophrenia candidate gene) in the hippocampus and prefrontal cortex of rats. Rats were randomly divided into four groups: control, low-dose, medium-dose and high-dose groups. The low-dose, medium-dose and high-dose groups were intraperitoneally injected with 15 mg/kg, 30 mg/kg and 60 mg/kg ketamine, respectively, twice a day (9:00 a.m. and 9:00 p.m.); the control group was administered normal saline. The treatment lasted 7 days. After treatment, rats were euthanized, and their brain tissues were collected and then analyzed by immunohistochemistry. The results of immunohistochemistry staining demonstrated that the ErbB4 protein was expressed exclusively in the CA3 region of the hippocampus and the Cg1 region of the prefrontal cortex. Ketamine administration significantly decreased the expression of ErbB4 in a dose-dependent manner. The high-dose ketamine treatment was found to be optimal for establishing a rat model for schizophrenia. Ketamine induced symptoms similar to schizophrenia in humans. The ketamine-induced rat model for schizophrenia constructed in this study provides novel insights to better understand the pathogenic mechanisms of schizophrenia and aid in drug discovery.

Schizophrenia and gut-flora related epigenetic factors

BACKGROUND

Schizophrenia (SZ) is a complex psychiatric disorder and the exact mechanisms that underpin SZ remain poorly understood despite decades of research. Genetic, epigenetic, and environmental factors are all considered to play a role. The importance of gut flora and its influence on the central nervous system has been recognized in recent years. We hypothesize that gut flora may be a converging point where environmental factors interact with epigenetic factors and contribute to SZ pathogenesis.

AIM

To summarize the current understanding of genetic and epigenetic factors and the possible involvement of gut flora in the pathogenesis of schizophrenia.

RESULTS

We searched PubMed and Medline with a combination of the key words schizophrenia, microbiome, epigenetic factors to identify studies of genetic and epigenetic factors in the pathogenesis of schizophrenia. Numerous genes that encode key proteins in neuronal signaling pathways have been linked to SZ. Epigenetic modifications, particularly, methylation and acetylation profiles, have been found to differ in individuals that present with SZ from those that don't. Gut flora may affect epigenetic modifications by regulation of key metabolic pathway molecules, including methionine, florate, biotin, and metabolites that are acetyl group donors. Despite a lack of direct studies on the subject, it is possible that gut flora may influence genetic and epigenetic expression and thereby contribute to the pathogenesis of SZ.

CONCLUSION

Gut flora is sensitive to both internal and environmental stimuli and the synthesis of some key molecules that participate in the epigenetic modulation of gene expression. Therefore, it is possible that gut flora is a converging point where environmental factors interact with genetic and epigenetic factors in the pathogenesis of SZ.

Epigenetic clock analysis of blood samples from Japanese schizophrenia patients

The accelerated aging hypothesis of schizophrenia (SCZ) has been proposed. DNA methylation profiles were developed for determining “epigenetic age.” Here, we assessed intrinsic and extrinsic epigenetic age acceleration (IEAA and EEAA, respectively) in SCZ. We examined two independent cohorts of Japanese ancestry. The first cohort consisted of 80 patients with SCZ under long-term or repeated hospitalization and 40 controls, with the economical DNA pooling technique. The second cohort consisted of 24 medication-free patients with SCZ and 23 controls. Blood of SCZ subjects exhibited decreased EEAA in the first cohort (p = 0.0162), but not in the second cohort. IEAA did not differ in either cohort. We performed replication analyses using publicly available datasets from European ancestry (three blood and one brain datasets). One blood dataset showed increased EEAA in SCZ (p = 0.0228). Overall, our results provide evidence for decreased EEAA in SCZ associated with hospitalization in the Japanese population.

Increased serum levels and promoter polymorphisms of macrophage migration inhibitory factor in schizophrenia

BACKGROUND

Numerous studies have suggested that an immune system imbalance plays an important role in schizophrenia. Macrophage migration inhibitory factor (MIF) is a pleiotropic cytokine. It plays multiple roles in various biological processes, including inflammation and neurogenesis. Furthermore, several exhaustive serum proteomic profiling studies have identified MIF as a potential biomarker of schizophrenia. Here, we investigate MIF protein levels in serum and postmortem prefrontal cortex in patients with schizophrenia and controls. Moreover, we investigate the association of two functional polymorphisms in the MIF gene promoter region (MIF-794CATT_5-8 microsatellite and MIF-173G/C single-nucleotide polymorphism [SNP]) with schizophrenia.

METHODS

We measured serum MIF levels with an enzyme-linked immunosorbent assay (ELISA) (51 patients vs. 86 controls) and postmortem brain MIF levels with a western blotting assay (18 patients vs. 22 controls). Subsequently, we genotyped the MIF-794CATT_5-8 microsatellite with a fluorescence-based fragment assay and the MIF-173G/C SNP with a TaqMan SNP genotyping assay (1483 patients vs. 1454 controls).

RESULTS

Serum MIF levels were significantly higher in patients with schizophrenia than in controls (p=0.00118), and were positively correlated with antipsychotic dose (Spearman's r=0.222, p=0.0402). In addition, an earlier age of onset was observed in patients with a high serum MIF level (≥40ng/mL) than those with a low serum MIF level (<40ng/mL) (p=0.0392). However, postmortem brain MIF levels did not differ between patients with schizophrenia and controls. The association study revealed that the CATT₆-G haplotype was nominally significantly associated with schizophrenia (p=0.0338), and that the CATT₆ allele and CATT₆-G haplotype were significantly associated with female adolescent-onset schizophrenia (AsOS) (corrected p=0.0222 and p=0.0147, respectively).

CONCLUSIONS

These results suggest that serum MIF level is a potential pharmacodynamic and/or monitoring marker of schizophrenia, and is related to a novel antipsychotic effect beyond dopamine antagonism. Furthermore, the MIF gene polymorphisms are associated with the risk for schizophrenia especially in adolescent females, and are potential stratification markers of schizophrenia. Further studies of MIF are warranted to elucidate the pathophysiology of schizophrenia and the effects of antipsychotics.

Investigation of chromosome Y loss in men with schizophrenia

BACKGROUND

Life expectancy is 10-20 years lower in patients with schizophrenia than in the general population. In addition, men with schizophrenia have an earlier age at onset, more pronounced deficit symptoms, poorer course, and poorer response to antipsychotic medications than women. Recent studies have indicated that loss of chromosome Y (LOY) in peripheral blood is associated with an increased risk of all-cause mortality. In order to elucidate the pathophysiology of male-specific features, we investigated the association between LOY and schizophrenia.

MATERIALS AND METHODS

The present study included 360 Japanese men (146 patients with schizophrenia vs 214 controls). The relative amount of Y chromosome was defined as the ratio of chromosome Y to chromosome X (Y/X ratio) based on the fluorescent signal of co-amplified short sequences from the Y-X homologous amelogenin genes (AMELY and AMELX).

RESULTS

There was no significant difference in the frequency of LOY between the schizophrenia and control groups. However, longer duration of illness was associated with LOY after controlling for age and smoking status in the schizophrenia group (P=0.007, OR =1.11 [95% CI =1.03-1.19]).

CONCLUSION

According to our results, schizophrenia may not have a remarkable effect on blood LOY; however, LOY may be associated with disease course in patients with schizophrenia.

Cellular models to study schizophrenia: A systematic review

BACKGROUND

Advancements in cellular reprogramming techniques have made it possible to directly study brain cells from patients with neuropsychiatric disorders. We have systematically reviewed the applications of induced pluripotent stem cells (IPSCs) and their neural derivatives in understanding the biological basis of schizophrenia.

METHOD

We searched the scientific literature published in MEDLINE with the following search strategy: (Pluripotent) AND (Schizophrenia OR Antipsychotic OR Psychosis). Studies written in English that used IPSCs derived from patients with schizophrenia were included.

RESULTS

Out of 23 articles, which had used IPSCs from patients with schizophrenia, neurons or neural stem cells had been derived from them in a majority. Several parameters had been studied; the key cellular phenotypes identified included those of synaptic pathology, neural migration/proliferation deficits, and abnormal oxidative phosphorylation.

CONCLUSION

Cellular modelling using IPSCs could improve the biological understanding of schizophrenia. Emerging findings are consistent with those of other study designs (post-mortem brain expression, animal studies, genome-wide association, brain imaging). Future studies should focus on refined study designs (family-based, pharmacogenomics, gene editing) and a combination of cellular studies with deep clinical phenotyping.