Genome-wide investigation of schizophrenia associated plasma Ndel1 enzyme activity

Investigation of Genetic Variants Associated with Tryptophan Metabolite Levels via Serotonin and Kynurenine Pathways in Patients with Bipolar Disorder

The kynurenine pathway (KP) may play a role in the pathophysiology of bipolar disorder (BD). We conducted a genome-wide association study (GWAS) to identify genetic variants associated with the plasma levels of the metabolites of tryptophan (TRP) via the serotonin (5-HT) and kynurenine (KYN) pathways in 44 patients with BD and 45 healthy controls. We assessed whether variants that were differentially associated with metabolite levels based on the diagnostic status improved the prediction accuracy of BD using penalized regression approaches. We identified several genetic variants that were significantly associated with metabolites (5-HT, 5-hydroxytryptophan (5-HTP), TRP, and quinolinic acid (QA) or metabolite ratios (5-HTP/TRP and KYN/TRP) and for which the diagnostic status exerted a significant effect. The inclusion of genetic variants led to increased accuracy in the prediction of the BD diagnostic status. Specifically, we obtained an accuracy of 0.77 using Least Absolute Shrinkage and Selection Operator (LASSO) regression. The predictors retained as informative in this model included body mass index (BMI), the levels of TRP, QA, and 5-HT, the 5-HTP/TRP ratio, and genetic variants associated with the levels of QA (rs6827515, rs715692, rs425094, rs4645874, and rs77048355) and TRP (rs292212) or the 5-HTP/TRP ratio (rs7902231). In conclusion, our study identified statistically significant associations between metabolites of TRP via the 5-HT and KYN pathways and genetic variants at the genome-wide level. The discriminative performance of penalized regression models incorporating clinical, genetic, and metabolic predictors warrants a follow-up analysis of this panel of determinants.

Behavioral Deficits in Mice with Postnatal Disruption of Ndel1 in Forebrain Excitatory Neurons: Implications for Epilepsy and Neuropsychiatric Disorders

Dysfunction of nuclear distribution element-like 1 (Ndel1) is associated with schizophrenia, a neuropsychiatric disorder characterized by cognitive impairment and with seizures as comorbidity. The levels of Ndel1 are also altered in human and models with epilepsy, a chronic condition whose hallmark feature is the occurrence of spontaneous recurrent seizures and is typically associated with comorbid conditions including learning and memory deficits, anxiety, and depression. In this study, we analyzed the behaviors of mice postnatally deficient for Ndel1 in forebrain excitatory neurons (Ndel1 CKO) that exhibit spatial learning and memory deficits, seizures, and shortened lifespan. Ndel1 CKO mice underperformed in species-specific tasks, that is, the nest building, open field, Y maze, forced swim, and dry cylinder tasks. We surveyed the expression and/or activity of a dozen molecules related to Ndel1 functions and found changes that may contribute to the abnormal behaviors. Finally, we tested the impact of Reelin glycoprotein that shows protective effects in the hippocampus of Ndel1 CKO, on the performance of the mutant animals in the nest building task. Our study highlights the importance of Ndel1 in the manifestation of species-specific animal behaviors that may be relevant to our understanding of the clinical conditions shared between neuropsychiatric disorders and epilepsy.

Insular functional alterations in emotional processing of schizophrenia patients revealed by Multivariate Pattern Analysis fMRI

Emotion perception is impaired in schizophrenia patients (SP) and related to reduced social skills performance. There is a remarkable variability across subjects for functional neuroimaging alterations related to this phenomenon. In contrast to the univariate approaches of fMRI, Multivariate Pattern Analysis (MVPA) maintains the within-subject voxel-level variability. The purpose of this study was to assess emotion processing in SP, in previously identified ROIs -i.e. amygdala, hippocampus, insula, and thalamus-, while retaining the functional heterogeneity that may exist between subjects. We evaluated 23 SP and 23 healthy controls (HC). Happy, sad, and neutral faces were presented. A single trial fMRI model was applied. Patterns of activation within each ROI were classified at the subject level. Within each group, stimuli classification scores were tested against random label classification scores. In ROIs with significant results, a whole ROI classification was performed, to test whether en bloc stimuli discrimination was present. A between-group analysis was conducted also. For the classification of stimuli above chance, in the HC results were significant in the left insula in all of the stimuli dichotomies, but were non-significant in SP for happy vs. sad. In whole ROI classification, SP had significant results in bilateral insular cortex for happy vs. neutral. The left amygdala showed diminished stimuli classification scores in SP for sad vs. neutral. In conclusion, MVPA seems useful to study emotional processing in schizophrenia. In SP, either en bloc or no stimuli discrimination was seen in the insula, and reduced stimuli discrimination was seen in the left amygdala.

Evaluation of NDEL1 oligopeptidase activity in blood and brain in an animal model of schizophrenia: effects of psychostimulants and antipsychotics

Nuclear distribution element-like 1 (NDEL1) enzyme activity is important for neuritogenesis, neuronal migration, and neurodevelopment. We reported previously lower NDEL1 enzyme activity in blood of treated first episode psychosis and chronic schizophrenia (SCZ) compared to healthy control subjects, with even lower activity in treatment resistant chronic SCZ patients, implicating NDEL1 activity in SCZ. Herein, higher NDEL1 activity was observed in the blood and several brain regions of a validated animal model for SCZ at baseline. In addition, long-term treatment with typical or atypical antipsychotics, under conditions in which SCZ-like phenotypes were reported to be reversed in this animal model for SCZ, showed a significant NDEL1 activity reduction in blood and brain regions which is in line with clinical data. Importantly, these results support measuring NDEL1 enzyme activity in the peripheral blood to predict changes in NDEL1 activity in the CNS. Also, acute administration of psychostimulants, at levels reported to induce SCZ-like phenotype in normal rat strains, increased NDEL1 enzyme activity in blood. Therefore, alterations in NDEL1 activity after treatment with antipsychotics or psychostimulants may suggest a possible modulation of NDEL1 activity secondary to neurotransmission homeostasis and provide new insights into the role of NDEL1 in SCZ pathophysiology.

ACE activity in blood and brain axis in an animal model for schizophrenia: Effects of dopaminergic manipulation with antipsychotics and psychostimulants

Objectives: Angiotensin I-converting enzyme (ACE) was initially correlated with schizophrenia (SCZ) in studies showing a correlation of ACE increased enzyme activity with memory impairments. Possible role for ACE in SCZ was also suggested by ACE activity interaction with dopaminergic mechanisms to modulate abnormalities of sensorimotor gating. In addition, we have demonstrated higher ACE activity in blood of SCZ subjects, its implication in cognitive performance in SCZ and its power as a predictor for SCZ diagnosis.Methods: ACE activity was determined in the serum and in selected brain regions of an animal model presenting SCZ-like behaviour, before and after the treatment with typical and atypical antipsychotics, and also in the serum of animals receiving the psychostimulants amphetamine/lisdexamphetamine.Results: Dopaminergic manipulations with antipsychotics and psychostimulants influenced the ACE activity, but with no correlation with the animal blood pressure.Conclusions: The validity of measuring ACE activity in animal blood to predict activity in the CNS, as well as the lack of correlation between the activity and blood pressure, before and after the treatment with antipsychotics, were confirmed here. Correlations of the present findings with data from clinical studies also strengthen the value of this animal model for studying several aspects of SCZ.

Sequential phosphorylation of NDEL1 by the DYRK2-GSK3β complex is critical for neuronal morphogenesis

Neuronal morphogenesis requires multiple regulatory pathways to appropriately determine axonal and dendritic structures, thereby to enable the functional neural connectivity. Yet, however, the precise mechanisms and components that regulate neuronal morphogenesis are still largely unknown. Here, we newly identified the sequential phosphorylation of NDEL1 critical for neuronal morphogenesis through the human kinome screening and phospho-proteomics analysis of NDEL1 from mouse brain lysate. DYRK2 phosphorylates NDEL1 S336 to prime the phosphorylation of NDEL1 S332 by GSK3β. TARA, an interaction partner of NDEL1, scaffolds DYRK2 and GSK3β to form a tripartite complex and enhances NDEL1 S336/S332 phosphorylation. This dual phosphorylation increases the filamentous actin dynamics. Ultimately, the phosphorylation enhances both axonal and dendritic outgrowth and promotes their arborization. Together, our findings suggest the NDEL1 phosphorylation at S336/S332 by the TARA-DYRK2-GSK3β complex as a novel regulatory mechanism underlying neuronal morphogenesis.

Transcriptome profiles of hypothalamus and adrenal gland linked to haplotype related to coping behavior in pigs

The hypothalamic-pituitary-adrenal (HPA) axis is an important component of neuroendocrine stress regulation and coping behavior. Transcriptome profiles of the hypothalamus and adrenal gland were assessed to identify molecular pathways and candidate genes for coping behavior in pigs. Ten each of high- (HR) and low- (LR) reactive pigs (n = 20) were selected for expression profiling based haplotype information of a prominent QTL-region on SSC12 discovered in our previous genome-wide association study (GWAS) on coping behavior. Comparing the HR and LR pigs showed 692 differentially expressed genes (DEGs) in the adrenal gland and 853 DEGs in the hypothalamus, respectively. Interestingly, 47% (17 out of 36) of DEGs found in both tissues were located in GWAS regions identified on SSC12, indicating that there are significant functional positional candidate genes for coping behaviour. Pathway analysis assigned DEGs to glucocorticoid receptor signaling in the adrenal gland. Furthermore, oxidative phosphorylation, mitochondrial dysfunction, and NGF signaling as well as cholecystokinin/Gastrin-mediated were identified in the hypothalamus. We narrowed the list of candidate genes in GWAS regions by analyzing their DEGs in the HPA axis. The top identified transcripts, including ATP1B2, AURKB, MPDU1 and NDEL1 provide evidence for molecular correlates of coping behavior in GWAS regions.

Multivariate analysis of genome-wide data to identify potential pleiotropic genes for five major psychiatric disorders using MetaCCA

BACKGROUND

Genome-wide association studies have been extensively applied in identifying SNP associated with major psychiatric disorders. However, the SNPs identified by the prevailing univariate approach only explain a small percentage of the genetic variance of traits, and the extensive data have shown the major psychiatric disorders have common biological mechanisms and the overlapping pathophysiological pathways.

METHODS

We applied the genetic pleiotropy-informed metaCCA method on summary statistics data from the Psychiatric Genomics Consortium Cross-Disorder Group to examine the overlapping genetic relations between the five major psychiatric disorders. Furthermore, to refine all genes, we performed gene-based association analyses for the five disorders respectively using VEGAS2. Gene enrichment analysis was applied to explore the potential functional significance of the identified genes.

RESULTS

After metaCCA analysis, 1147 SNPs reached the Bonferroni corrected threshold (p < 1.06 × 10-6) in the univariate SNP-multivariate phenotype analysis, and 246 genes with a significance threshold (p < 3.85 × 10-6) were identified as potentially pleiotropic genes in the multivariate SNP-multivariate phenotype analysis. By screening the results of gene-based p-values, we identified 37 putative pleiotropic genes which achieved significance threshold in metaCCA analyses and were also associated with at least one disorder in the VEGAS2 analyses.

LIMITATIONS

Alternative approaches and experimental studies may be applied to check whether novel genes could still be identified/substantiated with these methods.

CONCLUSIONS

The metaCCA method identified novel variants associated with psychiatric disorders by effectively incorporating information from different GWAS datasets. Our analyses may provide insights for some common therapeutic approaches of these five major psychiatric disorders based on the pleiotropic genes and common mechanisms identified.

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.

NDE1 and NDEL1 from genes to (mal)functions: parallel but distinct roles impacting on neurodevelopmental disorders and psychiatric illness

NDE1 (Nuclear Distribution Element 1, also known as NudE) and NDEL1 (NDE-Like 1, also known as NudEL) are the mammalian homologues of the fungus nudE gene, with important and at least partially overlapping roles for brain development. While a large number of studies describe the various properties and functions of these proteins, many do not directly compare the similarities and differences between NDE1 and NDEL1. Although sharing a high degree structural similarity and multiple common cellular roles, each protein presents several distinct features that justify their parallel but also unique functions. Notably both proteins have key binding partners in dynein, LIS1 and DISC1, which impact on neurodevelopmental and psychiatric illnesses. Both are implicated in schizophrenia through genetic and functional evidence, with NDE1 also strongly implicated in microcephaly, as well as other neurodevelopmental and psychiatric conditions through copy number variation, while NDEL1 possesses an oligopeptidase activity with a unique potential as a biomarker in schizophrenia. In this review, we aim to give a comprehensive overview of the various cellular roles of these proteins in a "bottom-up" manner, from their biochemistry and protein-protein interactions on the molecular level, up to the consequences for neuronal differentiation, and ultimately to their importance for correct cortical development, with direct consequences for the pathophysiology of neurodevelopmental and mental illness.