A study of single nucleotide polymorphisms of GRIN2B in schizophrenia from Chinese Han population

Single nucleotide polymorphisms of ANKK1, DDR4, and GRIN2B genes predict behavior in a prospective cohort of Mexican children and adolescents

Numerous studies have established associations between single nucleotide polymorphisms (SNPs) and various behavioral and neurodevelopmental conditions. This study explores the links between SNPs in candidate genes involved in central nervous system (CNS) physiology and their implications for the behavioral and emotional aspects in children and teenagers. A total of 590 participants, aged 7-15 years, from the Early Life Exposures In Mexico To Environmental Toxicants (ELEMENT) cohort study in Mexico City, underwent genotyping for at least one of 15 CNS gene-related SNPs at different timepoints. We employed multiple linear regression models to assess the potential impact of genetic variations on behavioral and cognitive traits, as measured by the Behavioral Assessment System for Children (BASC) and Conners parent rating scales. Significant associations were observed, including the rs1800497 TC genotype (ANKK1) with the Cognitive Problems/Inattention variable (p value = 0.003), the rs1800955 CT genotype (DDR4) with the Emotional Lability Global index variable (p value = 0.01), and the rs10492138 GA and rs7970177 TC genotypes (GRIN2B) with the Depression variable (p values 0.007 and 0.012, respectively). These finds suggest potential genetic profiles associated with "risk" and "protective" behaviors for these SNPs. Our results provide valuable insights into the role of genetic variations in neurobehavior and highlight the need for further research in the early identification and intervention in individuals at risk for these conditions.

Disclosing common biological signatures and predicting new therapeutic targets in schizophrenia and obsessive-compulsive disorder by integrated bioinformatics analysis

Schizophrenia (SCZ) is a severe mental illness mainly characterized by a number of psychiatric symptoms. Obsessive-compulsive disorder (OCD) is a long-lasting and devastating mental disorder. SCZ has high co-occurrence with OCD resulting in the emergence of a concept entitled "schizo-obsessive disorder" as a new specific clinical entity with more severe psychiatric symptoms. Many studies have been done on SCZ and OCD, but the common pathogenesis between them is not clear yet. Therefore, this study aimed to identify shared genetic basis, potential biomarkers and therapeutic targets between these two disorders. Gene sets were extracted from the Geneweaver and Harmonizome databases for each disorder. Interestingly, the combination of both sets revealed 89 common genes between SCZ and OCD, the most important of which were BDNF, SLC6A4, GAD1, HTR2A, GRIN2B, DRD2, SLC6A3, COMT, TH and DLG4. Then, we conducted a comprehensive bioinformatics analysis of the common genes. Receptor activity as the molecular functions, neuron projection and synapse as the cellular components as well as serotonergic synapse, dopaminergic synapse and alcoholism as the pathways were the most significant commonalities in enrichment analyses. In addition, transcription factor (TFs) analysis predicted significant TFs such as HMGA1, MAPK14, HINFP and TEAD2. Hsa-miR-3121-3p and hsa-miR-495-3p were the most important microRNAs (miRNAs) associated with both disorders. Finally, our study predicted 19 existing drugs (importantly, Haloperidol, Fluoxetine and Melatonin) that may have a potential influence on this co-occurrence. To summarize, this study may help us to better understand and handle the co-occurrence of SCZ and OCD by identifying potential biomarkers and therapeutic targets.

A role for endothelial NMDA receptors in the pathophysiology of schizophrenia

Numerous genetic and postmortem studies link N-methyl-d-aspartate receptor (NMDAR) dysfunction with schizophrenia, forming the basis of the popular glutamate hypothesis. Neuronal NMDAR abnormalities are consistently reported from both basic and clinical experiments, however, non-neuronal cells also contain NMDARs, and are rarely, if ever, considered in the discussion of glutamate action in schizophrenia. We offer an examination of recent discoveries elucidating the actions and consequences of NMDAR activation in the neuroendothelium. While there has been mixed literature regarding blood flow alterations in the schizophrenia brain, in this review, we posit that some common findings may be explained by neuroendothelial NMDAR dysfunction. In particular, we emphasize that endothelial NMDARs are key mediators of neurovascular coupling, where increased neuronal activity leads to increased blood flow. Based on the broad conclusions that hypoperfusion is a neuroanatomical finding in schizophrenia, we discuss potential mechanisms by which endothelial NMDARs contribute to this disorder. We propose that endothelial NMDAR dysfunction can be a primary cause of neurovascular abnormalities in schizophrenia. Importantly, functional MRI studies using BOLD signal as a proxy for neuron activity should be considered in a new light if neurovascular coupling is impaired in schizophrenia. This review is the first to propose that NMDARs in non-excitable cells play a role in schizophrenia.

Prenatal methylmercury exposure and DNA methylation in seven-year-old children in the Seychelles Child Development Study

BACKGROUND

Methylmercury (MeHg) is present in fish and is a neurotoxicant at sufficiently high levels. One potential mechanism of MeHg toxicity early in life is epigenetic dysregulation that may affect long-term neurodevelopment. Altered DNA methylation of nervous system-related genes has been associated with adult mental health outcomes.

OBJECTIVE

To assess associations between prenatal MeHg exposure and DNA methylation (at the cytosine of CG dinucleotides, CpGs) in three nervous system-related genes, encoding brain-derived neurotropic factor (BDNF), glutamate receptor subunit NR2B (GRIN2B), and the glucocorticoid receptor (NR3C1), in children who were exposed to MeHg in utero.

METHODS

We tested 406 seven-year-old Seychellois children participating in the Seychelles Child Development Study (Nutrition Cohort 2), who were prenatally exposed to MeHg from maternal fish consumption. Total mercury in maternal hair (prenatal MeHg exposure measure) collected during pregnancy was measured using atomic absorption spectroscopy. Methylation in DNA from the children's saliva was measured by pyrosequencing. To assess associations between prenatal MeHg exposure and CpG methylation at seven years of age, we used multivariable linear regression models adjusted for covariates.

RESULTS

We identified associations with prenatal MeHg exposure for DNA methylation of one GRIN2B CpG and two NR3C1 CpGs out of 12 total CpG sites. Higher prenatal MeHg was associated with higher methylation for each CpG site. For example, NR3C1 CpG3 had an expected increase of 0.03-fold for each additional 1 ppm of prenatal MeHg (B = 0.030, 95% CI 0.001, 0.059; p = 0.047). Several CpG sites associated with MeHg are located in transcription factor binding sites and the observed methylation changes are predicted to lead to lower gene expression.

CONCLUSIONS

In a population of people who consume large amounts of fish, we showed that higher prenatal MeHg exposure was associated with differential DNA methylation at seven years of age at specific CpG sites that may influence neurodevelopment and mental health.

Childhood adversity increases methylation in the GRIN2B gene

Childhood adversity is an early life stressor associated with increased risk of several psychiatric disorders such as depression. Epigenetic changes, primarily DNA methylation, can be affected by early life stress, which in turn might contribute to altered disease susceptibility later in life. One plausible biomarker of early life stress is methylation of the ionotropic glutamate receptor NMDA type subunit 2B (GRIN2B) gene, which has been previously shown to be epigenetically affected by prenatal environmental stressors. Here, we set out to investigate if stress-inducing adversity during childhood is associated with changes in methylation of GRIN2B in adulthood. We studied 186 individuals from a Swedish naturalistic population-based cohort who had provided saliva samples (DNA) as well as information regarding both childhood adversity (CA) and depressive symptoms (dep) (n_CA,dep = 41, n_CA,no-dep = 56, n_no-CA,dep = 40, N_no-CA,no-dep = 49). Methylation at four CpG sites in a regulatory region of GRIN2B was analysed using bisulfite pyrosequencing. Associations for methylation status to childhood adversity and to depression status were investigated using linear regression models. Our study shows that childhood adversity is associated with increased methylation levels of GRIN2B in adulthood, for three of the measured CpGs (p = 0.007, 0.006 and 5 × 10^-14). This indicates that GRIN2B methylation is susceptible to early life stress, and that methylation at this gene is persistent over time. No association was found between GRIN2B methylation and depression status. Yet, this does not rule out a role for alterations in GRIN2B methylation for other neuropsychological outcomes not studied here.

Epigenetic-mediated N-methyl-D-aspartate receptor changes in the brain of isolated reared rats

Aim: We investigated: Grin1, Grin2a, Grin2b DNA methylation; NR1 and NR2 mRNA/protein in the prefrontal cortex (PFC); and hippocampus of male Wistar rats exposed to isolation rearing. Materials & methods: Animals were kept isolated or grouped (n = 10/group) from weaning for 10 weeks. Tissues were dissected for RNA/DNA extraction and N-methyl-D-aspartate receptor subunits were analyzed using quantitative reverse transcription (RT)-PCR, ELISA and pyrosequencing. Results: Isolated-reared animals had: decreased mRNA in PFC for all markers, increased NR1 protein in hippocampus and hypermethylation of Grin1 in PFC and Grin2b in hippocampus, compared with grouped rats. Associations between mRNA/protein and DNA methylation were found for both brain areas. Conclusion: This study indicates that epigenetic DNA methylation may underlie N-methyl-D-aspartate receptor mRNA/protein expression alterations caused by isolation rearing.

A Deep Learning Approach for Predicting Antidepressant Response in Major Depression Using Clinical and Genetic Biomarkers

In the wake of recent advances in scientific research, personalized medicine using deep learning techniques represents a new paradigm. In this work, our goal was to establish deep learning models which distinguish responders from non-responders, and also to predict possible antidepressant treatment outcomes in major depressive disorder (MDD). To uncover relationships between the responsiveness of antidepressant treatment and biomarkers, we developed a deep learning prediction approach resulting from the analysis of genetic and clinical factors such as single nucleotide polymorphisms (SNPs), age, sex, baseline Hamilton Rating Scale for Depression score, depressive episodes, marital status, and suicide attempt status of MDD patients. The cohort consisted of 455 patients who were treated with selective serotonin reuptake inhibitors (treatment-response rate = 61.0%; remission rate = 33.0%). By using the SNP dataset that was original to a genome-wide association study, we selected 10 SNPs (including ABCA13 rs4917029, BNIP3 rs9419139, CACNA1E rs704329, EXOC4 rs6978272, GRIN2B rs7954376, LHFPL3 rs4352778, NELL1 rs2139423, NUAK1 rs2956406, PREX1 rs4810894, and SLIT3 rs139863958) which were associated with antidepressant treatment response. Furthermore, we pinpointed 10 SNPs (including ARNTL rs11022778, CAMK1D rs2724812, GABRB3 rs12904459, GRM8 rs35864549, NAALADL2 rs9878985, NCALD rs483986, PLA2G4A rs12046378, PROK2 rs73103153, RBFOX1 rs17134927, and ZNF536 rs77554113) in relation to remission. Then, we employed multilayer feedforward neural networks (MFNNs) containing 1-3 hidden layers and compared MFNN models with logistic regression models. Our analysis results revealed that the MFNN model with 2 hidden layers (area under the receiver operating characteristic curve (AUC) = 0.8228 ± 0.0571; sensitivity = 0.7546 ± 0.0619; specificity = 0.6922 ± 0.0765) performed maximally among predictive models to infer the complex relationship between antidepressant treatment response and biomarkers. In addition, the MFNN model with 3 hidden layers (AUC = 0.8060 ± 0.0722; sensitivity = 0.7732 ± 0.0583; specificity = 0.6623 ± 0.0853) achieved best among predictive models to predict remission. Our study indicates that the deep MFNN framework may provide a suitable method to establish a tool for distinguishing treatment responders from non-responders prior to antidepressant therapy.