Diverse types of genetic variation converge on functional gene networks involved in schizophrenia

Heterogeneity in functional connectivity: Dimensional predictors of individual variability during rest and task fMRI in psychosis

BACKGROUND

Individuals with schizophrenia spectrum disorders (SSD) often demonstrate cognitive impairments, associated with poor functional outcomes. While neurobiological heterogeneity has posed challenges when examining social cognition in SSD, it provides a unique opportunity to explore brain-behavior relationships. The aim of this study was to investigate the relationship between individual variability in functional connectivity during resting state and the performance of a social task and social and non-social cognition in a large sample of controls and individuals diagnosed with SSD.

METHODS

Neuroimaging and behavioral data were analyzed for 193 individuals with SSD and 155 controls (total n = 348). Individual variability was quantified through mean correlational distance (MCD) of functional connectivity between participants; MCD was defined as a global 'variability score'. Pairwise correlational distance was calculated as 1 - the correlation coefficient between a given pair of participants, and averaging distance from one participant to all other participants provided the mean correlational distance metric. Hierarchical regressions were performed on variability scores derived from resting state and Empathic Accuracy (EA) task functional connectivity data to determine potential predictors (e.g., age, sex, neurocognitive and social cognitive scores) of individual variability.

RESULTS

Group comparison between SSD and controls showed greater SSD MCD during rest (p = 0.00038), while no diagnostic differences were observed during task (p = 0.063). Hierarchical regression analyses demonstrated the persistence of a significant diagnostic effect during rest (p = 0.008), contrasting with its non-significance during the task (p = 0.50), after social cognition was added to the model. Notably, social cognition exhibited significance in both resting state and task conditions (both p = 0.01).

CONCLUSIONS

Diagnostic differences were more prevalent during unconstrained resting scans, whereas the task pushed participants into a more common pattern which better emphasized transdiagnostic differences in cognitive abilities. Focusing on variability may provide new opportunities for interventions targeting specific cognitive impairments to improve functional outcomes.

De Novo Variants Found in Three Distinct Schizophrenia Populations Hit a Common Core Gene Network Related to Microtubule and Actin Cytoskeleton Gene Ontology Classes

Schizophrenia (SZ) is a heterogeneous and debilitating psychiatric disorder with a strong genetic component. To elucidate functional networks perturbed in schizophrenia, we analysed a large dataset of whole-genome studies that identified SNVs, CNVs, and a multi-stage schizophrenia genome-wide association study. Our analysis identified three subclusters that are interrelated and with small overlaps: GO:0007017~Microtubule-Based Process, GO:00015629~Actin Cytoskeleton, and GO:0007268~SynapticTransmission. We next analysed three distinct trio cohorts of 75 SZ Algerian, 45 SZ French, and 61 SZ Japanese patients. We performed Illumina HiSeq whole-exome sequencing and identified de novo mutations using a Bayesian approach. We validated 88 de novo mutations by Sanger sequencing: 35 in French, 21 in Algerian, and 32 in Japanese SZ patients. These 88 de novo mutations exhibited an enrichment in genes encoding proteins related to GO:0051015~actin filament binding (p = 0.0011) using David, and enrichments in GO: 0003774~transport (p = 0.019) and GO:0003729~mRNA binding (p = 0.010) using Amigo. One of these de novo variant was found in CORO1C coding sequence. We studied Coro1c haploinsufficiency in a Coro1c+/- mouse and found defects in the corpus callosum. These results could motivate future studies of the mechanisms surrounding genes encoding proteins involved in transport and the cytoskeleton, with the goal of developing therapeutic intervention strategies for a subset of SZ cases.

Mitochondrial, cell cycle control and neuritogenesis alterations in an iPSC-based neurodevelopmental model for schizophrenia

Schizophrenia is a severe psychiatric disorder of neurodevelopmental origin that affects around 1% of the world's population. Proteomic studies and other approaches have provided evidence of compromised cellular processes in the disorder, including mitochondrial function. Most of the studies so far have been conducted on postmortem brain tissue from patients, and therefore, do not allow the evaluation of the neurodevelopmental aspect of the disorder. To circumvent that, we studied the mitochondrial and nuclear proteomes of neural stem cells (NSCs) and neurons derived from induced pluripotent stem cells (iPSCs) from schizophrenia patients versus healthy controls to assess possible alterations related to energy metabolism and mitochondrial function during neurodevelopment in the disorder. Our results revealed differentially expressed proteins in pathways related to mitochondrial function, cell cycle control, DNA repair and neuritogenesis and their possible implication in key process of neurodevelopment, such as neuronal differentiation and axonal guidance signaling. Moreover, functional analysis of NSCs revealed alterations in mitochondrial oxygen consumption in schizophrenia-derived cells and a tendency of higher levels of intracellular reactive oxygen species (ROS). Hence, this study shows evidence that alterations in important cellular processes are present during neurodevelopment and could be involved with the establishment of schizophrenia, as well as the phenotypic traits observed in adult patients. Neural stem cells (NSCs) and neurons were derived from induced pluripotent stem cells (iPSCs) from schizophrenia patients and controls. Proteomic analyses were performed on the enriched mitochondrial and nuclear fractions of NSCs and neurons. Whole-cell proteomic analysis was also performed in neurons. Our results revealed alteration in proteins related to mitochondrial function, cell cycle control, among others. We also performed energy pathway analysis and reactive oxygen species (ROS) analysis of NSCs, which revealed alterations in mitochondrial oxygen consumption and a tendency of higher levels of intracellular ROS in schizophrenia-derived cells.

Systems biology approach discovers comorbidity interaction of Parkinson's disease with psychiatric disorders utilizing brain transcriptome

Several studies found that most patients with Parkinson's disorder (PD) appear to have psychiatric symptoms such as depression, anxiety, hallucination, delusion, and cognitive dysfunction. Therefore, recognizing these psychiatrically symptoms of PD patients is crucial for both symptomatic therapy and better knowledge of the pathophysiology of PD. In order to address this issue, we created a bioinformatics framework to determine the effects of PD mRNA expression on understanding its relationship with psychiatric symptoms in PD patients. We have discovered a significant overlap between the sets of differentially expressed genes from PD exposed tissue and psychiatric disordered tissues using RNA-seq datasets. We have chosen Bipolar disorder and Schizophrenia as psychiatric disorders in our study. A number of significant correlations between PD and the occurrence of psychiatric diseases were also found by gene set enrichment analysis, investigations of the protein-protein interaction network, gene regulatory network, and protein-chemical agent interaction network. We anticipate that the results of this pathogenetic study will provide crucial information for understanding the intricate relationship between PD and psychiatric diseases.

A single-nucleus transcriptome-wide association study implicates novel genes in depression pathogenesis

Background

Depression is a common psychiatric illness and global public health problem. However, our limited understanding of the biological basis of depression has hindered the development of novel treatments and interventions.

Methods

To identify new candidate genes for therapeutic development, we examined single-nucleus RNA sequencing (snucRNAseq) data from the dorsolateral prefrontal cortex (N=424) in relation to ante-mortem depressive symptoms. To complement these direct analyses, we also used genome-wide association study (GWAS) results for depression (N=500,199) along with genetic tools for inferring the expression of 22,159 genes in 7 cell types and 55 cell subtypes to perform transcriptome-wide association studies (TWAS) of depression followed by Mendelian randomization (MR).

Results

Our single-nucleus TWAS analysis identified 71 causal genes in depression that have a role in specific neocortical cell subtypes; 59 of 71 genes were novel compared to previous studies. Depression TWAS genes showed a cell type specific pattern, with the greatest enrichment being in both excitatory and inhibitory neurons as well as astrocytes. Gene expression in different neuron subtypes have different directions of effect on depression risk. Compared to lower genetically correlated traits (e.g. body mass index) with depression, higher correlated traits (e.g., neuroticism) have more common TWAS genes with depression. In parallel, we performed differential gene expression analysis in relation to depression in 55 cortical cell subtypes, and we found that genes such as ANKRD36, MADD, TAOK3, SCAI and CHUK are associated with depression in specific cell subtypes.

Conclusions

These two sets of analyses illustrate the utility of large snucRNAseq data to uncover both genes whose expression is altered in specific cell subtypes in the context of depression and to enhance the interpretation of well-powered GWAS so that we can prioritize specific susceptibility genes for further analysis and therapeutic development.

Netrin-1 regulates the balance of synaptic glutamate signaling in the adult ventral tegmental area

The axonal guidance cue netrin-1 serves a critical role in neural circuit development by promoting growth cone motility, axonal branching, and synaptogenesis. Within the adult mouse brain, expression of the gene encoding (Ntn1) is highly enriched in the ventral midbrain where it is expressed in both GABAergic and dopaminergic neurons, but its function in these cell types in the adult system remains largely unknown. To address this, we performed viral-mediated, cell-type specific CRISPR-Cas9 mutagenesis of Ntn1 in the ventral tegmental area (VTA) of adult mice. Ntn1 loss-of-function in either cell type resulted in a significant reduction in excitatory postsynaptic connectivity. In dopamine neurons, the reduced excitatory tone had a minimal phenotypic behavioral outcome; however, reduced glutamatergic tone on VTA GABA neurons induced behaviors associated with a hyperdopaminergic phenotype. Simultaneous loss of Ntn1 function in both cell types largely rescued the phenotype observed in the GABA-only mutagenesis. These findings demonstrate an important role for Ntn1 in maintaining excitatory connectivity in the adult midbrain and that a balance in this connectivity within two of the major cell types of the VTA is critical for the proper functioning of the mesolimbic system.

Identification of a Hypoxia-Related Signature as Candidate Detector for Schizophrenia Based on Genome-Wide Gene Expression

INTRODUCTION

Schizophrenia (SCZ), a severe neuropsychiatric disorder with high genetic susceptibility, has high rates of misdiagnosis due to the unavoidably subjective factors and heterogeneous clinical presentations. Hypoxia has been identified as an importantly risk factor that participates in the development of SCZ. Therefore, development of a hypoxia-related biomarker for SCZ diagnosis is promising. Therefore, we dedicated to develop a biomarker that could contribute to distinguishing healthy controls and SCZ patients.

METHODS

GSE17612, GSE21935, and GSE53987 datasets, consisting of 97 control samples and 99 SCZ samples, were involved in our study. The hypoxia score was calculated based on the single-sample gene-set enrichment analysis using the hypoxia-related differentially expressed genes to quantify the expression levels of these genes for each SCZ patient. Patients in high-score groups were defined if their hypoxia score was in the upper half of all hypoxia scores and patients in low-score groups if their hypoxia score was in the lower half. GSEA was applied to detect the functional pathway of these differently expressed genes. CIBERSORT algorithm was utilized to evaluate the tumor-infiltrating immune cells of SCZ patients.

RESULTS

In this study, we developed and validated a biomarker consisting of 12 hypoxia-related genes that could distinguish healthy controls and SCZ patients robustly. We found that the metabolism reprogramming might be activated in the patient with high hypoxia score. Finally, CIBERSORT analysis illustrated that lower composition of naive B cells and higher composition of memory B cells might be observed in low-score groups of SCZ patients.

CONCLUSION

These findings revealed that the hypoxia-related signature was acceptable as a detector for SCZ, providing further insight into effective diagnosis and treatment strategies for SCZ.

Impact of schizophrenia GWAS loci converge onto distinct pathways in cortical interneurons vs glutamatergic neurons during development

Remarkable advances have been made in schizophrenia (SCZ) GWAS, but gleaning biological insight from these loci is challenging. Genetic influences on gene expression (e.g., eQTLs) are cell type-specific, but most studies that attempt to clarify GWAS loci's influence on gene expression have employed tissues with mixed cell compositions that can obscure cell-specific effects. Furthermore, enriched SCZ heritability in the fetal brain underscores the need to study the impact of SCZ risk loci in specific developing neurons. MGE-derived cortical interneurons (cINs) are consistently affected in SCZ brains and show enriched SCZ heritability in human fetal brains. We identified SCZ GWAS risk genes that are dysregulated in iPSC-derived homogeneous populations of developing SCZ cINs. These SCZ GWAS loci differential expression (DE) genes converge on the PKC pathway. Their disruption results in PKC hyperactivity in developing cINs, leading to arborization deficits. We show that the fine-mapped GWAS locus in the ATP2A2 gene of the PKC pathway harbors enhancer marks by ATACseq and ChIPseq, and regulates ATP2A2 expression. We also generated developing glutamatergic neurons (GNs), another population with enriched SCZ heritability, and confirmed their functionality after transplantation into the mouse brain. Then, we identified SCZ GWAS risk genes that are dysregulated in developing SCZ GNs. GN-specific SCZ GWAS loci DE genes converge on the ion transporter pathway, distinct from those for cINs. Disruption of the pathway gene CACNA1D resulted in deficits of Ca2+ currents in developing GNs, suggesting compromised neuronal function by GWAS loci pathway deficits during development. This study allows us to identify cell type-specific and developmental stage-specific mechanisms of SCZ risk gene function, and may aid in identifying mechanism-based novel therapeutic targets.

DeepND: Deep multitask learning of gene risk for comorbid neurodevelopmental disorders

Autism spectrum disorder and intellectual disability are comorbid neurodevelopmental disorders with complex genetic architectures. Despite large-scale sequencing studies, only a fraction of the risk genes was identified for both. We present a network-based gene risk prioritization algorithm, DeepND, that performs cross-disorder analysis to improve prediction by exploiting the comorbidity of autism spectrum disorder (ASD) and intellectual disability (ID) via multitask learning. Our model leverages information from human brain gene co-expression networks using graph convolutional networks, learning which spatiotemporal neurodevelopmental windows are important for disorder etiologies and improving the state-of-the-art prediction in single- and cross-disorder settings. DeepND identifies the prefrontal and motor-somatosensory cortex (PFC-MFC) brain region and periods from early- to mid-fetal and from early childhood to young adulthood as the highest neurodevelopmental risk windows for ASD and ID. We investigate ASD- and ID-associated copy-number variation (CNV) regions and report our findings for several susceptibility gene candidates. DeepND can be generalized to analyze any combinations of comorbid disorders.

How Variation in Risk Allele Output and Gene Interactions Shape the Genetic Architecture of Schizophrenia

Schizophrenia is a highly heritable polygenic psychiatric disorder. Characterization of its genetic architecture may lead to a better understanding of the overall burden of risk variants and how they determine susceptibility to disease. A major goal of this project is to develop a modeling approach to compare and quantify the relative effects of single nucleotide polymorphisms (SNPs), copy number variants (CNVs) and other factors. We derived a mathematical model for the various genetic contributions based on the probability of expressing a combination of risk variants at a frequency that matched disease prevalence. The model included estimated risk variant allele outputs (VAOs) adjusted for population allele frequency. We hypothesized that schizophrenia risk genes would be more interactive than random genes and we confirmed this relationship. Gene–gene interactions may cause network ripple effects that spread and amplify small individual effects of risk variants. The modeling revealed that the number of risk alleles required to achieve the threshold for susceptibility will be determined by the average functional locus output (FLO) associated with a risk allele, the risk allele frequency (RAF), the number of protective variants present and the extent of gene interactions within and between risk loci. The model can account for the quantitative impact of protective variants as well as CNVs on disease susceptibility. The fact that non-affected individuals must carry a non-trivial burden of risk alleles suggests that genetic susceptibility will inevitably reach the threshold for schizophrenia at a recurring frequency in the population.

Regulatory Variant rs2535629 in ITIH3 Intron Confers Schizophrenia Risk By Regulating CTCF Binding and SFMBT1 Expression

Genome-wide association studies have identified 3p21.1 as a robust risk locus for schizophrenia. However, the underlying molecular mechanisms remain elusive. Here a functional regulatory variant (rs2535629) is identified that disrupts CTCF binding at 3p21.1. It is confirmed that rs2535629 is also significantly associated with schizophrenia in Chinese population and the regulatory effect of rs2535629 is validated. Expression quantitative trait loci analysis indicates that rs2535629 is associated with the expression of three distal genes (GLT8D1, SFMBT1, and NEK4) in the human brain, and CRISPR-Cas9-mediated genome editing confirmed the regulatory effect of rs2535629 on GLT8D1, SFMBT1, and NEK4. Interestingly, differential expression analysis of GLT8D1, SFMBT1, and NEK4 suggested that rs2535629 may confer schizophrenia risk by regulating SFMBT1 expression. It is further demonstrated that Sfmbt1 regulates neurodevelopment and dendritic spine density, two key pathological characteristics of schizophrenia. Transcriptome analysis also support the potential role of Sfmbt1 in schizophrenia pathogenesis. The study identifies rs2535629 as a plausibly causal regulatory variant at the 3p21.1 risk locus and demonstrates the regulatory mechanism and biological effect of this functional variant, indicating that this functional variant confers schizophrenia risk by altering CTCF binding and regulating expression of SFMBT1, a distal gene which plays important roles in neurodevelopment and synaptic morphogenesis.

Unveiling the Pathogenesis of Psychiatric Disorders Using Network Models

Psychiatric disorders are complex brain disorders with a high degree of genetic heterogeneity, affecting millions of people worldwide. Despite advances in psychiatric genetics, the underlying pathogenic mechanisms of psychiatric disorders are still largely elusive, which impedes the development of novel rational therapies. There has been accumulating evidence suggesting that the genetics of complex disorders can be viewed through an omnigenic lens, which involves contextualizing genes in highly interconnected networks. Thus, applying network-based multi-omics integration methods could cast new light on the pathophysiology of psychiatric disorders. In this review, we first provide an overview of the recent advances in psychiatric genetics and highlight gaps in translating molecular associations into mechanistic insights. We then present an overview of network methodologies and review previous applications of network methods in the study of psychiatric disorders. Lastly, we describe the potential of such methodologies within a multi-tissue, multi-omics approach, and summarize the future directions in adopting diverse network approaches.

Altered Parvalbumin Basket Cell Terminals in the Cortical Visuospatial Working Memory Network in Schizophrenia

BACKGROUND

Visuospatial working memory (vsWM), which is commonly impaired in schizophrenia, involves information processing across the primary visual cortex, association visual cortex, posterior parietal cortex, and dorsolateral prefrontal cortex (DLPFC). Within these regions, vsWM requires inhibition from parvalbumin-expressing basket cells (PVBCs). Here, we analyzed indices of PVBC axon terminals across regions of the vsWM network in schizophrenia.

METHODS

For 20 matched pairs of subjects with schizophrenia and unaffected comparison subjects, tissue sections from the primary visual cortex, association visual cortex, posterior parietal cortex, and DLPFC were immunolabeled for PV, the 65- and 67-kDa isoforms of glutamic acid decarboxylase (GAD65 and GAD67) that synthesize GABA (gamma-aminobutyric acid), and the vesicular GABA transporter. The density of PVBC terminals and of protein levels per terminal was quantified in layer 3 of each cortical region using fluorescence confocal microscopy.

RESULTS

In comparison subjects, all measures, except for GAD65 levels, exhibited a caudal-to-rostral decline across the vsWM network. In subjects with schizophrenia, the density of detectable PVBC terminals was significantly lower in all regions except the DLPFC, whereas PVBC terminal levels of PV, GAD67, and GAD65 proteins were lower in all regions. A composite measure of inhibitory strength was lower in subjects with schizophrenia, although the magnitude of the diagnosis effect was greater in the primary visual, association visual, and posterior parietal cortices than in the DLPFC.

CONCLUSIONS

In schizophrenia, alterations in PVBC terminals across the vsWM network suggest the presence of a shared substrate for cortical dysfunction during vsWM tasks. However, regional differences in the magnitude of the disease effect on an index of PVBC inhibitory strength suggest region-specific alterations in information processing during vsWM tasks.

Distinct effect of prenatal and postnatal brain expression across 20 brain disorders and anthropometric social traits: a systematic study of spatiotemporal modularity

Different spatiotemporal abnormalities have been implicated in different neuropsychiatric disorders and anthropometric social traits, yet an investigation in the temporal network modularity with brain tissue transcriptomics has been lacking. We developed a supervised network approach to investigate the genome-wide association study (GWAS) results in the spatial and temporal contexts and demonstrated it in 20 brain disorders and anthropometric social traits. BrainSpan transcriptome profiles were used to discover significant modules enriched with trait susceptibility genes in a developmental stage-stratified manner. We investigated whether, and in which developmental stages, GWAS-implicated genes are coordinately expressed in brain transcriptome. We identified significant network modules for each disorder and trait at different developmental stages, providing a systematic view of network modularity at specific developmental stages for a myriad of brain disorders and traits. Specifically, we observed a strong pattern of the fetal origin for most psychiatric disorders and traits [such as schizophrenia (SCZ), bipolar disorder, obsessive-compulsive disorder and neuroticism], whereas increased co-expression activities of genes were more strongly associated with neurological diseases [such as Alzheimer's disease (AD) and amyotrophic lateral sclerosis] and anthropometric traits (such as college completion, education and subjective well-being) in postnatal brains. Further analyses revealed enriched cell types and functional features that were supported and corroborated prior knowledge in specific brain disorders, such as clathrin-mediated endocytosis in AD, myelin sheath in multiple sclerosis and regulation of synaptic plasticity in both college completion and education. Our study provides a landscape view of the spatiotemporal features in a myriad of brain-related disorders and traits.

Transcriptome-wide association study identifies new susceptibility genes and pathways for depression

Depression is the most prevalent mental disorder with substantial morbidity and mortality. Although genome-wide association studies (GWASs) have identified multiple risk variants for depression, due to the complicated gene regulatory mechanisms and complexity of linkage disequilibrium (LD), the biological mechanisms by which the risk variants exert their effects on depression remain largely unknown. Here, we perform a transcriptome-wide association study (TWAS) of depression by integrating GWAS summary statistics from 807,553 individuals (246,363 depression cases and 561,190 controls) and summary-level gene-expression data (from the dorsolateral prefrontal cortex (DLPFC) of 1003 individuals). We identified 53 transcriptome-wide significant (TWS) risk genes for depression, of which 23 genes were not implicated in risk loci of the original GWAS. Seven out of 53 risk genes (B3GALTL, FADS1, TCTEX1D1, XPNPEP3, ZMAT2, ZNF501 and ZNF502) showed TWS associations with depression in two independent brain expression quantitative loci (eQTL) datasets, suggesting that these genes may represent promising candidates. We further conducted conditional analyses and identified the potential risk genes that driven the TWAS association signal in each locus. Finally, pathway enrichment analysis revealed biologically pathways relevant to depression. Our study identified new depression risk genes whose expression dysregulation may play a role in depression. More importantly, we translated the GWAS associations into risk genes and relevant pathways. Further mechanistic study and functional characterization of the TWS depression risk genes will facilitate the diagnostics and therapeutics for depression.

Exons as units of phenotypic impact for truncating mutations in autism

Autism spectrum disorders (ASD) are a group of related neurodevelopmental diseases displaying significant genetic and phenotypic heterogeneity. Despite recent progress in understanding ASD genetics, the nature of phenotypic heterogeneity across probands remains unclear. Notably, likely gene-disrupting (LGD) de novo mutations affecting the same gene often result in substantially different ASD phenotypes. Nevertheless, we find that truncating mutations affecting the same exon frequently lead to strikingly similar intellectual phenotypes in unrelated ASD probands. Analogous patterns are observed for two independent proband cohorts and several other important ASD-associated phenotypes. We find that exons biased toward prenatal and postnatal expression preferentially contribute to ASD cases with lower and higher IQ phenotypes, respectively. These results suggest that exons, rather than genes, often represent a unit of effective phenotypic impact for truncating mutations in autism. The observed phenotypic patterns are likely mediated by nonsense-mediated decay (NMD) of splicing isoforms, with autism phenotypes usually triggered by relatively mild (15-30%) decreases in overall gene dosage. We find that each ASD gene with recurrent mutations can be characterized by a parameter, phenotype dosage sensitivity (PDS), which quantifies the relationship between changes in a gene's dosage and changes in a given disease phenotype. We further demonstrate analogous relationships between exon LGDs and gene expression changes in multiple human tissues. Therefore, similar phenotypic patterns may be also observed in other human genetic disorders.

Clinical and Neurobiological Aspects of TAO Kinase Family in Neurodevelopmental Disorders

Despite the complexity of neurodevelopmental disorders (NDDs), from their genotype to phenotype, in the last few decades substantial progress has been made in understanding their pathophysiology. Recent accumulating evidence shows the relevance of genetic variants in thousand and one (TAO) kinases as major contributors to several NDDs. Although it is well-known that TAO kinases are a highly conserved family of STE20 kinase and play important roles in multiple biological processes, the emerging roles of TAO kinases in neurodevelopment and NDDs have yet to be intensively discussed. In this review article, we summarize the potential roles of the TAO kinases based on structural and biochemical analyses, present the genetic data from clinical investigations, and assess the mechanistic link between the mutations of TAO kinases, neuropathology, and behavioral impairment in NDDs. We then offer potential perspectives from basic research to clinical therapies, which may contribute to fully understanding how TAO kinases are involved in NDDs.

Mental Health, Mitochondria, and the Battle of the Sexes

This paper presents a broad perspective on how mental disease relates to the different evolutionary strategies of men and women and to growth, metabolism, and mitochondria—the enslaved bacteria in our cells that enable it all. Several mental disorders strike one sex more than the other; yet what truly matters, regardless of one’s sex, is how much one’s brain is “female” and how much it is “male”. This appears to be the result of an arms race between the parents over how many resources their child ought to extract from the mother, hence whether it should grow a lot or stay small and undemanding. An uneven battle alters the child’s risk of developing not only insulin resistance, diabetes, or cancer, but a mental disease as well. Maternal supremacy increases the odds of a psychosis-spectrum disorder; paternal supremacy, those of an autism-spectrum one. And a particularly lopsided struggle may invite one or the other of a series of syndromes that come in pairs, with diametrically opposite, excessively “male” or “female” characteristics. By providing the means for this tug of war, mitochondria take center stage in steadying or upsetting the precarious balance on which our mental health is built.

Predicting regulatory variants using a dense epigenomic mapped CNN model elucidated the molecular basis of trait-tissue associations

Assessing the causal tissues of human complex diseases is important for the prioritization of trait-associated genetic variants. Yet, the biological underpinnings of trait-associated variants are extremely difficult to infer due to statistical noise in genome-wide association studies (GWAS), and because >90% of genetic variants from GWAS are located in non-coding regions. Here, we collected the largest human epigenomic map from ENCODE and Roadmap consortia and implemented a deep-learning-based convolutional neural network (CNN) model to predict the regulatory roles of genetic variants across a comprehensive list of epigenomic modifications. Our model, called DeepFun, was built on DNA accessibility maps, histone modification marks, and transcription factors. DeepFun can systematically assess the impact of non-coding variants in the most functional elements with tissue or cell-type specificity, even for rare variants or de novo mutations. By applying this model, we prioritized trait-associated loci for 51 publicly-available GWAS studies. We demonstrated that CNN-based analyses on dense and high-resolution epigenomic annotations can refine important GWAS associations in order to identify regulatory loci from background signals, which yield novel insights for better understanding the molecular basis of human complex disease. We anticipate our approaches will become routine in GWAS downstream analysis and non-coding variant evaluation.

Genomic Variation, Evolvability, and the Paradox of Mental Illness

Twentieth-century genetics was hard put to explain the irregular behavior of neuropsychiatric disorders. Autism and schizophrenia defy a principle of natural selection; they are highly heritable but associated with low reproductive success. Nevertheless, they persist. The genetic origins of such conditions are confounded by the problem of variable expression, that is, when a given genetic aberration can lead to any one of several distinct disorders. Also, autism and schizophrenia occur on a spectrum of severity, from mild and subclinical cases to the overt and disabling. Such irregularities reflect the problem of missing heritability; although hundreds of genes may be associated with autism or schizophrenia, together they account for only a small proportion of cases. Techniques for higher resolution, genomewide analysis have begun to illuminate the irregular and unpredictable behavior of the human genome. Thus, the origins of neuropsychiatric disorders in particular and complex disease in general have been illuminated. The human genome is characterized by a high degree of structural and behavioral variability: DNA content variation, epistasis, stochasticity in gene expression, and epigenetic changes. These elements have grown more complex as evolution scaled the phylogenetic tree. They are especially pertinent to brain development and function. Genomic variability is a window on the origins of complex disease, neuropsychiatric disorders, and neurodevelopmental disorders in particular. Genomic variability, as it happens, is also the fuel of evolvability. The genomic events that presided over the evolution of the primate and hominid lineages are over-represented in patients with autism and schizophrenia, as well as intellectual disability and epilepsy. That the special qualities of the human genome that drove evolution might, in some way, contribute to neuropsychiatric disorders is a matter of no little interest.

Convergence and Divergence in the Genetics of Psychiatric Disorders From Pathways to Developmental Stages

In the past decade, the identification of susceptibility genes for psychiatric disorders has become routine, but understanding the biology underlying these discoveries has proven extremely difficult. The large number of potential risk genes and the genetic overlap between disorders are major obstacles for studying the etiology of these conditions. Systems biology approaches relying on gene ontologies, gene coexpression, and protein-protein interactions are used to identify convergence of the genes in relation to biological processes, cell types, brain areas, and developmental stages. Across psychiatric disorders, there is a clear enrichment for genes expressed in the brain and especially in the cortex, but a higher resolution is vastly dependent on sample size and statistical power. There is indication that susceptibility genes tend to be expressed in the brain during periods preceding the typical onset of the disorders. Thus, the role of genes in prenatal brain development is more pronounced for childhood-onset disorders, such as autism spectrum disorder and attention-deficit/hyperactivity disorder, but is much less so for bipolar disorder and depression. One of the most consistent findings across multiple disorders and classes of genetic variants is the role of genes intolerant to mutations in psychiatric disorders, yet this association is more pronounced for disorders with a clear neurodevelopmental component. Notwithstanding, a detailed understanding of the neurobiology of psychiatric disorders is still lacking. It is possible that it will only be revealed by studying the risk genes at the level of the development and function of neuronal networks and circuits.

Characterization of genome-wide association study data reveals spatiotemporal heterogeneity of mental disorders

Background

Psychiatric disorders such as schizophrenia (SCZ), bipolar disorder (BIP), major depressive disorder (MDD), attention deficit-hyperactivity disorder (ADHD), and autism spectrum disorder (ASD) are often related to brain development. Both shared and unique biological and neurodevelopmental processes have been reported to be involved in these disorders.

Methods

In this work, we developed an integrative analysis framework to seek for the sensitive spatiotemporal point during brain development underlying each disorder. Specifically, we first identified spatiotemporal gene co-expression modules for four brain regions three developmental stages (prenatal, birth to 11 years old, and older than 13 years), totaling 12 spatiotemporal sites. By integrating GWAS summary statistics and the spatiotemporal co-expression modules, we characterized the risk genes and their co-expression partners for five disorders.

Results

We found that SCZ and BIP, ASD and ADHD tend to cluster with each other and keep a distance from other psychiatric disorders. At the gene level, we identified several genes that were shared among the most significant modules, such as CTNNB1 and LNX1, and a hub gene, ATF2, in multiple modules. Moreover, we pinpointed two spatiotemporal points in the prenatal stage with active expression activities and highlighted one postnatal point for BIP. Further functional analysis of the disorder-related module highlighted the apoptotic signaling pathway for ASD and the immune-related and cell-cell adhesion function for SCZ, respectively.

Conclusion

Our study demonstrated the dynamic changes of disorder-related genes at the network level, shedding light on the spatiotemporal regulation during brain development.

Gene expression imputation and cell-type deconvolution in human brain with spatiotemporal precision and its implications for brain-related disorders

As the most complex organ of the human body, the brain is composed of diverse regions, each consisting of distinct cell types and their respective cellular interactions. Human brain development involves a finely tuned cascade of interactive events. These include spatiotemporal gene expression changes and dynamic alterations in cell-type composition. However, our understanding of this process is still largely incomplete owing to the difficulty of brain spatiotemporal transcriptome collection. In this study, we developed a tensor-based approach to impute gene expression on a transcriptome-wide level. After rigorous computational benchmarking, we applied our approach to infer missing data points in the widely used BrainSpan resource and completed the entire grid of spatiotemporal transcriptomics. Next, we conducted deconvolutional analyses to comprehensively characterize major cell-type dynamics across the entire BrainSpan resource to estimate the cellular temporal changes and distinct neocortical areas across development. Moreover, integration of these results with GWAS summary statistics for 13 brain-associated traits revealed multiple novel trait–cell-type associations and trait-spatiotemporal relationships. In summary, our imputed BrainSpan transcriptomic data provide a valuable resource for the research community and our findings help further studies of the transcriptional and cellular dynamics of the human brain and related diseases.

Pogz deficiency leads to transcription dysregulation and impaired cerebellar activity underlying autism-like behavior in mice

Several genes implicated in autism spectrum disorder (ASD) are chromatin regulators, including POGZ. The cellular and molecular mechanisms leading to ASD impaired social and cognitive behavior are unclear. Animal models are crucial for studying the effects of mutations on brain function and behavior as well as unveiling the underlying mechanisms. Here, we generate a brain specific conditional knockout mouse model deficient for Pogz, an ASD risk gene. We demonstrate that Pogz deficient mice show microcephaly, growth impairment, increased sociability, learning and motor deficits, mimicking several of the human symptoms. At the molecular level, luciferase reporter assay indicates that POGZ is a negative regulator of transcription. In accordance, in Pogz deficient mice we find a significant upregulation of gene expression, most notably in the cerebellum. Gene set enrichment analysis revealed that the transcriptional changes encompass genes and pathways disrupted in ASD, including neurogenesis and synaptic processes, underlying the observed behavioral phenotype in mice. Physiologically, Pogz deficiency is associated with a reduction in the firing frequency of simple and complex spikes and an increase in amplitude of the inhibitory synaptic input in cerebellar Purkinje cells. Our findings support a mechanism linking heterochromatin dysregulation to cerebellar circuit dysfunction and behavioral abnormalities in ASD.

Identifying Drug Sensitivity Subnetworks with NETPHIX

Phenotypic heterogeneity in cancer is often caused by different patterns of genetic alterations. Understanding such phenotype-genotype relationships is fundamental for the advance of personalized medicine. We develop a computational method, named NETPHIX (NETwork-to-PHenotype association with eXclusivity) to identify subnetworks of genes whose genetic alterations are associated with drug response or other continuous cancer phenotypes. Leveraging interaction information among genes and properties of cancer mutations such as mutual exclusivity, we formulate the problem as an integer linear program and solve it optimally to obtain a subnetwork of associated genes. Applied to a large-scale drug screening dataset, NETPHIX uncovered gene modules significantly associated with drug responses. Utilizing interaction information, NETPHIX modules are functionally coherent and can thus provide important insights into drug action. In addition, we show that modules identified by NETPHIX together with their association patterns can be leveraged to suggest drug combinations.

Implications of germline copy-number variations in psychiatric disorders: review of large-scale genetic studies

Copy number variants (CNVs), defined as genome sequences of ≥50 bp that differ in copy number from that in a reference genome, are a common form of structural variation. Germline CNVs account for some of the missing heritability that single nucleotide polymorphisms could not account for. Recent technological advances have had a huge impact on CNV research. Microarray technology enables relatively low-cost, high-throughput, genome-wide measurements, and short-read sequencing technology enables the detection of short CNVs that cannot be detected by microarrays. As a result, large-scale genetic studies have been able to identify a variety of common and rare germline CNVs and their associations with diseases. Rare germline CNVs have been reported to be associated with neuropsychiatric disorders. In this review, we focused on germline CNVs and briefly described their functional characteristics, formation mechanisms, detection methods, related databases, and the latest findings. Finally, we introduced recent large-scale genetic studies to assess associations of CNVs with diseases, especially psychiatric disorders, and discussed the use of CNV-based animal models to investigate the molecular and cellular mechanisms underlying these disorders. The development and implementation of improved detection methods, such as long-read single-molecule sequencing, are expected to provide additional insight into the molecular basis of psychiatric disorders and other complex diseases, thus facilitating basic and clinical research on CNVs.

Polygenic risk for autism spectrum disorder affects left amygdala activity and negative emotion in schizophrenia

Although the diagnoses based on phenomenology have many practical advantages, accumulating evidence shows that schizophrenia and autism spectrum disorder (ASD) share some overlap in genetics and clinical presentation. It remains largely unknown how ASD-associated polygenetic risk contributes to the pathogenesis of schizophrenia. In the present study, we calculated high-resolution ASD polygenic risk scores (ASD PRSs) and selected optimal ten ASD PRS with minimal P values in the association analysis of PRSs, with schizophrenia to assess the effect of ASD PRS on brain neural activity in schizophrenia cases and controls. We found that amplitude of low-frequency fluctuation in left amygdala was positively associated with ASD PRSs in our cohort. Correlation analysis of ASD PRSs with facial emotion recognition test identified the negative correlation of ASD PRSs with negative emotions in schizophrenia cases and controls. Finally, functional enrichment analysis of PRS genes revealed that neural system function and development, as well as signal transduction, were mainly enriched in PRS genes. Our results provide empirical evidence that polygenic risk for ASD contributes to schizophrenia by the intermediate phenotypes of left amygdala function and emotion recognition. It provides a promising strategy to understand the relationship between phenotypes and genotypes shared in mental disorders.

An Overview of Bioinformatics Methods for Analyzing Autism Spectrum Disorders

Autism Spectrum Disorders (ASD) are a group of neurodevelopmental disorders and are well recognized to be biologically heterogeneous in which various factors are associated, including genetic, metabolic, and environmental ones. Despite its high prevalence, only a few drugs have been approved for the treatment of ASD. Therefore, extensive studies have been conducted to identify ASD risk genes and novel drug targets. Since many genes and many other factors are associated with ASD, various bioinformatics methods have also been developed for the analysis of ASD. In this paper, we review bioinformatics methods for analyzing ASD data with the focus on computational aspects. We classify existing methods into two categories: (i) methods based on genomic variants and gene expression data, and (ii) methods using biological networks, which include gene co-expression networks and protein-protein interaction networks. Next, for each method, we provide an overall flow and elaborate on the computational techniques used. We also briefly review other approaches and discuss possible future directions and strategies for developing bioinformatics approaches to analyze ASD.

Identification of de novo mutations in prenatal neurodevelopment-associated genes in schizophrenia in two Han Chinese patient-sibling family-based cohorts

Schizophrenia (SCZ) is a severe psychiatric disorder with a strong genetic component. High heritability of SCZ suggests a major role for transmitted genetic variants. Furthermore, SCZ is also associated with a marked reduction in fecundity, leading to the hypothesis that alleles with large effects on risk might often occur de novo. In this study, we conducted whole-genome sequencing for 23 families from two cohorts with unaffected siblings and parents. Two nonsense de novo mutations (DNMs) in GJC1 and HIST1H2AD were identified in SCZ patients. Ten genes (DPYSL2, NBPF1, SDK1, ZNF595, ZNF718, GCNT2, SNX9, AACS, KCNQ1, and MSI2) were found to carry more DNMs in SCZ patients than their unaffected siblings by burden test. Expression analyses indicated that these DNM implicated genes showed significantly higher expression in prefrontal cortex in prenatal stage. The DNM in the GJC1 gene is highly likely a loss function mutation (pLI = 0.94), leading to the dysregulation of ion channel in the glutamatergic excitatory neurons. Analysis of rare variants in independent exome sequencing dataset indicates that GJC1 has significantly more rare variants in SCZ patients than in unaffected controls. Data from genome-wide association studies suggested that common variants in the GJC1 gene may be associated with SCZ and SCZ-related traits. Genes co-expressed with GJC1 are involved in SCZ, SCZ-associated pathways, and drug targets. These evidences suggest that GJC1 may be a risk gene for SCZ and its function may be involved in prenatal and early neurodevelopment, a vulnerable period for developmental disorders such as SCZ.

Integrin adhesion in brain assembly: From molecular structure to neuropsychiatric disorders

Integrins are extracellular matrix receptors that mediate biochemical and mechanical bi-directional signals between the extracellular and intracellular environment of a cell thanks to allosteric conformational changes. In the brain, they are found in both neurons and glial cells, where they play essential roles in several aspects of brain development and function, such as cell migration, axon guidance, synaptogenesis, synaptic plasticity and neuro-inflammation. Although there are many successful examples of how regulating integrin adhesion and signaling can be used for therapeutic purposes, for example for halting tumor progression, this is not the case for the brain, where the growing evidence of the importance of integrins for brain pathophysiology has not translated yet into medical applications. Here, we review recent literature showing how alterations in integrin structure, expression and signaling may be involved in the etiology of autism spectrum disorder, epilepsy, schizophrenia, addiction, depression and Alzheimer's disease. We focus on common mechanisms and recurrent signaling pathways, trying to bridge studies on the genetics and molecular structure of integrins with those on synaptic physiology and brain pathology. Further, we discuss integrin-targeting strategies and their potential benefits for therapeutic purposes in neuropsychiatric disorders.

ST-Steiner: a spatio-temporal gene discovery algorithm

MOTIVATION

Whole exome sequencing (WES) studies for autism spectrum disorder (ASD) could identify only around six dozen risk genes to date because the genetic architecture of the disorder is highly complex. To speed the gene discovery process up, a few network-based ASD gene discovery algorithms were proposed. Although these methods use static gene interaction networks, functional clustering of genes is bound to evolve during neurodevelopment and disruptions are likely to have a cascading effect on the future associations. Thus, approaches that disregard the dynamic nature of neurodevelopment are limited.

RESULTS

Here, we present a spatio-temporal gene discovery algorithm, which leverages information from evolving gene co-expression networks of neurodevelopment. The algorithm solves a prize-collecting Steiner forest-based problem on co-expression networks, adapted to model neurodevelopment and transfer information from precursor neurodevelopmental windows. The decisions made by the algorithm can be traced back, adding interpretability to the results. We apply the algorithm on ASD WES data of 3871 samples and identify risk clusters using BrainSpan co-expression networks of early- and mid-fetal periods. On an independent dataset, we show that incorporation of the temporal dimension increases the predictive power: predicted clusters are hit more and show higher enrichment in ASD-related functions compared with the state-of-the-art.

AVAILABILITY AND IMPLEMENTATION

The code is available at http://ciceklab.cs.bilkent.edu.tr/st-steiner.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Expression of Genes Involved in Axon Guidance: How Much Have We Learned?

Neuronal axons are guided to their target during the development of the brain. Axon guidance allows the formation of intricate neural circuits that control the function of the brain, and thus the behavior. As the axons travel in the brain to find their target, they encounter various axon guidance cues, which interact with the receptors on the tip of the growth cone to permit growth along different signaling pathways. Although many scientists have performed numerous studies on axon guidance signaling pathways, we still have an incomplete understanding of the axon guidance system. Lately, studies on axon guidance have shifted from studying the signal transduction pathways to studying other molecular features of axon guidance, such as the gene expression. These new studies present evidence for different molecular features that broaden our understanding of axon guidance. Hence, in this review we will introduce recent studies that illustrate different molecular features of axon guidance. In particular, we will review literature that demonstrates how axon guidance cues and receptors regulate local translation of axonal genes and how the expression of guidance cues and receptors are regulated both transcriptionally and post-transcriptionally. Moreover, we will highlight the pathological relevance of axon guidance molecules to specific diseases.

miRNA-Coordinated Schizophrenia Risk Network Cross-Talk With Cardiovascular Repair and Opposed Gliomagenesis

Background

Schizophrenia risk genes are widely investigated, but a systemic analysis of miRNAs contributing to schizophrenia is lacking.

Methods

Schizophrenia-associated genetic loci profiles were derived from a genome-wide association study (GWAS) from the Schizophrenia Working Group of the Psychiatric Genomics Consortium (PGC) dataset. Experimentally confirmed relationships between miRNAs and their target genes were retrieved from a miRTarBase. A competitive gene set association analysis for miRNA-target regulations was conducted by the Multi-marker Analysis of GenoMic Annotation (MAGMA) and further validated by literature-based functional pathway analysis using Pathway Studio. The association between the targets of three miRNAs and schizophrenia was further validated using a GWAS of antipsychotic treatment responses.

Results

Three novel schizophrenia-risk miRNAs, namely, miR-208b-3p, miR-208a-3p, and miR-494-5p, and their targetomes converged on calcium voltage-gated channel subunit alpha1 C (CACNA1C) and B-cell lymphoma 2 (BCL2), and these are well-known contributors to schizophrenia. Both miR-208a-3p and miR-208b-3p reduced the expression of the RNA-binding protein Quaking (QKI), whose suppression commonly contributes to demyelination of the neurons and to ischemia/reperfusion injury. On the other hand, both QKI and hsa-miR-494-5p were involved in gliomagenesis.

Conclusion

Presented results point at an orchestrating role of miRNAs in the pathophysiology of schizophrenia. The sharing of regulatory networks between schizophrenia and other pathologies may explain higher cardiovascular mortality and lower odds of glioma previously reported in psychiatric patients.

The abiding relevance of mouse models of rare mutations to psychiatric neuroscience and therapeutics

Studies using powerful family-based designs aided by large scale case-control studies, have been instrumental in cracking the genetic complexity of the disease, identifying rare and highly penetrant risk mutations and providing a handle on experimentally tractable model systems. Mouse models of rare mutations, paired with analysis of homologous cognitive and sensory processing deficits and state-of-the-art neuroscience methods to manipulate and record neuronal activity have started providing unprecedented insights into pathogenic mechanisms and building the foundation of a new biological framework for understanding mental illness. A number of important principles are emerging, namely that degradation of the computational mechanisms underlying the ordered activity and plasticity of both local and long-range neuronal assemblies, the building blocks necessary for stable cognition and perception, might be the inevitable consequence and the common point of convergence of the vastly heterogeneous genetic liability, manifesting as defective internally- or stimulus-driven neuronal activation patterns and triggering the constellation of schizophrenia symptoms. Animal models of rare mutations have the unique potential to help us move from "which" (gene) to "how", "where" and "when" computational regimes of neural ensembles are affected. Linking these variables should improve our understanding of how symptoms emerge and how diagnostic boundaries are established at a circuit level. Eventually, a better understanding of pathophysiological trajectories at the level of neural circuitry in mice, aided by basic human experimental biology, should guide the development of new therapeutics targeting either altered circuitry itself or the underlying biological pathways.

Maximum Entropy Framework for Predictive Inference of Cell Population Heterogeneity and Responses in Signaling Networks

Predictive models of signaling networks are essential for understanding cell population heterogeneity and designing rational interventions in disease. However, using computational models to predict heterogeneity of signaling dynamics is often challenging because of the extensive variability of biochemical parameters across cell populations. Here, we describe a maximum entropy-based framework for inference of heterogeneity in dynamics of signaling networks (MERIDIAN). MERIDIAN estimates the joint probability distribution over signaling network parameters that is consistent with experimentally measured cell-to-cell variability of biochemical species. We apply the developed approach to investigate the response heterogeneity in the EGFR/Akt signaling network. Our analysis demonstrates that a significant fraction of cells exhibits high phosphorylated Akt (pAkt) levels hours after EGF stimulation. Our findings also suggest that cells with high EGFR levels predominantly contribute to the subpopulation of cells with high pAkt activity. We also discuss how MERIDIAN can be extended to accommodate various experimental measurements.

Receptor-based mechanism of relative sensing and cell memory in mammalian signaling networks

Detecting relative rather than absolute changes in extracellular signals enables cells to make decisions in constantly fluctuating environments. It is currently not well understood how mammalian signaling networks store the memories of past stimuli and subsequently use them to compute relative signals, that is perform fold change detection. Using the growth factor-activated PI3K-Akt signaling pathway, we develop here computational and analytical models, and experimentally validate a novel non-transcriptional mechanism of relative sensing in mammalian cells. This mechanism relies on a new form of cellular memory, where cells effectively encode past stimulation levels in the abundance of cognate receptors on the cell surface. The surface receptor abundance is regulated by background signal-dependent receptor endocytosis and down-regulation. We show the robustness and specificity of relative sensing for two physiologically important ligands, epidermal growth factor (EGF) and hepatocyte growth factor (HGF), and across wide ranges of background stimuli. Our results suggest that similar mechanisms of cell memory and fold change detection may be important in diverse signaling cascades and multiple biological contexts.

Chromatin remodeling dysfunction extends the etiological spectrum of schizophrenia: a case report

BACKGROUND

The role of deleterious copy number variations in schizophrenia is well established while data regarding pathogenic variations remain scarce. We report for the first time a case of schizophrenia in a child with a pathogenic mutation of the chromodomain helicase DNA binding protein 2 (CHD2) gene.

CASE PRESENTATION

The proband was the second child of unrelated parents. Anxiety and sleep disorders appeared at the age of 10 months. He presented febrile seizures and, at the age of 8, two generalized tonic-clonic seizures. At the age of 10, emotional withdrawal emerged, along with a flat affect, disorganization and paranoid ideation, without seizures. He began to talk and giggle with self. Eventually, the patient presented daily auditory and visual hallucinations. The diagnosis of childhood onset schizophrenia (DSM V) was then evoked. Brain imaging was unremarkable. Wakefulness electroencephalography showed a normal background and some bilateral spike-wave discharges that did not explain the psychosis features. A comparative genomic hybridization array (180 K, Agilent, Santa Clara, CA, USA) revealed an 867-kb 16p13.3 duplication, interpreted as a variant of unknown significance confirmed by a quantitative PCR that also showed its maternal inheritance. Risperidone (1,5 mg per day), led to clinical improvement. At the age of 11, an explosive relapse of epilepsy occurred with daily seizures of various types. The sequencing of a panel for monogenic epileptic disorders and Sanger sequencing revealed a de novo pathogenic heterozygous transition in CHD2 (NM_001271.3: c.4003G > T).

CONCLUSIONS

This case underlines that schizophrenia may be, sometimes, underpinned by a Mendelian disease. It addresses the question of systematic genetic investigations in the presence of warning signs such as a childhood onset of the schizophrenia or a resistant epilepsy. It points that, in the absence of pathogenic copy number variation, the investigations should also include a search for pathogenic variations, which means that some of the patients with schizophrenia should benefit from Next Generation Sequencing tools. Last but not least, CHD2 encodes a member of the chromodomain helicase DNA-binding (CHD) family involved in chromatin remodeling. This observation adds schizophrenia to the phenotypic spectrum of chromodomain remodeling disorders, which may lead to innovative therapeutic approaches.

Kinesin Kif3b mutation reduces NMDAR subunit NR2A trafficking and causes schizophrenia-like phenotypes in mice

The transport of N-methyl-d-aspartate receptors (NMDARs) is crucial for neuronal plasticity and synapse formation. Here, we show that KIF3B, a member of the kinesin superfamily proteins (KIFs), supports the transport of vesicles simultaneously containing NMDAR subunit 2A (NR2A) and the adenomatous polyposis coli (APC) complex. Kif3b+/- neurons exhibited a reduction in dendritic levels of both NR2A and NR2B due to the impaired transport of NR2A and increased degradation of NR2B. In Kif3b+/- hippocampal slices, electrophysiological NMDAR response was found decreased and synaptic plasticity was disrupted, which corresponded to a common feature of schizophrenia (SCZ). The histological features of Kif3b+/- mouse brain also mimicked SCZ features, and Kif3b+/- mice exhibited behavioral defects in prepulse inhibition (PPI), social interest, and cognitive flexibility. Indeed, a mutation of KIF3B was specifically identified in human SCZ patients, which was revealed to be functionally defective in a rescue experiment. Therefore, we propose that KIF3B transports NR2A/APC complex and that its dysfunction is responsible for SCZ pathogenesis.

Association Between FoxO1, A2M, and TGF-β1, Environmental Factors, and Major Depressive Disorder

INTRODUCTION

Investigations of gene-environment (G×E) interactions in major depressive disorder (MDD) have been limited to hypothesis testing of candidate genes while poly-gene-environmental causation has not been adequately address. To this end, the present study analyzed the association between three candidate genes, two environmental factors, and MDD using a hypothesis-free testing approach.

METHODS

A logistic regression model was used to analyze interaction effects; a hierarchical regression model was used to evaluate the effects of different genotypes and the dose-response effects of the environment; genetic risk score (GRS) was used to estimate the cumulative contribution of genetic factors to MDD; and protein-protein interaction (PPI) analyses were carried out to evaluate the relationship between candidate genes and top MDD susceptibility genes.

RESULTS

Allelic association analyses revealed significant effects of the interaction between the candidate genes Forkhead box (Fox)O1, α2-macroglobulin (A2M), and transforming growth factor (TGF)-β1 genes and the environment on MDD. Gene-gene (G×G) and gene-gene-environment (G×G×E) interactions in MDD were also included in the model. Hierarchical regression analysis showed that the effect of environmental factors on MDD was greater in homozygous than in heterozygous mutant genotypes of the FoxO1 and TGF-β1 genes; a dose-response effect between environment and MDD on genotypes was also included in this model. Haplotype analyses revealed significant global and individual effects of haplotypes on MDD in the whole sample as well as in subgroups. There was a significant association between GRS and MDD (P = 0.029) and a GRS and environment interaction effect on MDD (P = 0.009). Candidate and top susceptibility genes were connected in PPI networks.

CONCLUSIONS

FoxO1, A2M, and TGF-β1 interact with environmental factors and with each other in MDD. Multi-factorial G×E interactions may be responsible for a higher explained variance and may be associated with causal factors and mechanisms that could inform new diagnosis and therapeutic strategies, which can contribute to the personalized medicine of MDD.

Comparative Analyses of Copy-Number Variation in Autism Spectrum Disorder and Schizophrenia Reveal Etiological Overlap and Biological Insights

Compelling evidence in Caucasian populations suggests a role for copy-number variations (CNVs) in autism spectrum disorder (ASD) and schizophrenia (SCZ). We analyzed 1,108 ASD cases, 2,458 SCZ cases, and 2,095 controls in a Japanese population and confirmed an increased burden of rare exonic CNVs in both disorders. Clinically significant (or pathogenic) CNVs, including those at 29 loci common to both disorders, were found in about 8% of ASD and SCZ cases, which was significantly higher than in controls. Phenotypic analysis revealed an association between clinically significant CNVs and intellectual disability. Gene set analysis showed significant overlap of biological pathways in both disorders including oxidative stress response, lipid metabolism/modification, and genomic integrity. Finally, based on bioinformatics analysis, we identified multiple disease-relevant genes in eight well-known ASD/SCZ-associated CNV loci (e.g., 22q11.2, 3q29). Our findings suggest an etiological overlap of ASD and SCZ and provide biological insights into these disorders.

Spatial genome exploration in the context of cognitive and neurological disease

The 'non-linear' genome, or the spatial proximity of non-contiguous sequences, emerges as an important regulatory layer for genome organization and function, including transcriptional regulation. Here, we review recent genome-scale chromosome conformation mappings ('Hi-C') in developing and adult human and mouse brain. Neural differentiation is associated with widespread remodeling of the chromosomal contact map, reflecting dynamic changes in cell-type-specific gene expression programs, with a massive (estimated 20-50%) net loss of chromosomal contacts that is specific for the neuronal lineage. Hi-C datasets provided an unexpected link between locus-specific abnormal expansion of repeat sequences positioned at the boundaries of self-associating topological chromatin domains, and monogenic neurodevelopmental and neurodegenerative disease. Furthermore, integrative cell-type-specific Hi-C and transcriptomic analysis uncovered an expanded genomic risk space for sequences conferring liability for schizophrenia and other cognitive disease. We predict that spatial genome exploration will deliver radically new insights into the brain nucleome in health and disease.

Genetic Control of Expression and Splicing in Developing Human Brain Informs Disease Mechanisms

Tissue-specific regulatory regions harbor substantial genetic risk for disease. Because brain development is a critical epoch for neuropsychiatric disease susceptibility, we characterized the genetic control of the transcriptome in 201 mid-gestational human brains, identifying 7,962 expression quantitative trait loci (eQTL) and 4,635 spliceQTL (sQTL), including several thousand prenatal-specific regulatory regions. We show that significant genetic liability for neuropsychiatric disease lies within prenatal eQTL and sQTL. Integration of eQTL and sQTL with genome-wide association studies (GWAS) via transcriptome-wide association identified dozens of novel candidate risk genes, highlighting shared and stage-specific mechanisms in schizophrenia (SCZ). Gene network analysis revealed that SCZ and autism spectrum disorder (ASD) affect distinct developmental gene co-expression modules. Yet, in each disorder, common and rare genetic variation converges within modules, which in ASD implicates superficial cortical neurons. More broadly, these data, available as a web browser and our analyses, demonstrate the genetic mechanisms by which developmental events have a widespread influence on adult anatomical and behavioral phenotypes.

Multi-scale analysis of schizophrenia risk genes, brain structure, and clinical symptoms reveals integrative clues for subtyping schizophrenia patients

Analysis linking directly genomics, neuroimaging phenotypes and clinical measurements is crucial for understanding psychiatric disorders, but remains rare. Here, we describe a multi-scale analysis using genome-wide SNPs, gene expression, grey matter volume (GMV), and the positive and negative syndrome scale scores (PANSS) to explore the etiology of schizophrenia. With 72 drug-naive schizophrenic first episode patients (FEPs) and 73 matched heathy controls, we identified 108 genes, from schizophrenia risk genes, that correlated significantly with GMV, which are highly co-expressed in the brain during development. Among these 108 candidates, 19 distinct genes were found associated with 16 brain regions referred to as hot clusters (HCs), primarily in the frontal cortex, sensory-motor regions and temporal and parietal regions. The patients were subtyped into three groups with distinguishable PANSS scores by the GMV of the identified HCs. Furthermore, we found that HCs with common GMV among patient groups are related to genes that mostly mapped to pathways relevant to neural signaling, which are associated with the risk for schizophrenia. Our results provide an integrated view of how genetic variants may affect brain structures that lead to distinct disease phenotypes. The method of multi-scale analysis that was described in this research, may help to advance the understanding of the etiology of schizophrenia.

Biomarker Identification Through Integrating fMRI and Epigenetics

OBJECTIVE

Integration of multiple datasets is a hot topic in many fields. When studying complex mental disorders, great effort has been dedicated to fusing genetic and brain imaging data. However, an increasing number of studies have pointed out the importance of epigenetic factors in the cause of psychiatric diseases. In this study, we endeavor to fill the gap by combining epigenetics (e.g., DNA methylation) with imaging data (e.g., fMRI) to identify biomarkers for schizophrenia (SZ).

METHODS

We propose to combine linear regression with canonical correlation analysis (CCA) in a relaxed yet coupled manner to extract discriminative features for SZ that are co-expressed in the fMRI and DNA methylation data.

RESULT

After validation through simulations, we applied our method to real imaging epigenetics data of 184 subjects from the Mental Illness and Neuroscience Discovery Clinical Imaging Consortium. After significance test, we identified 14 brain regions and 44 cytosine-phosphate-guanine(CpG) sites. Average classification accuracy is [Formula: see text]. By linking the CpG sites to genes, we identified pathways Guanosine ribonucleotides de novo biosynthesis and Guanosine nucleotides de novo biosynthesis, and a GO term Perikaryon.

CONCLUSION

This imaging epigenetics study has identified both brain regions and genes that are associated with neuron development and memory processing. These biomarkers contribute to a good understanding of the mechanism underlying SZ but are overlooked by previous imaging genetics studies.

SIGNIFICANCE

Our study sheds light on the understanding and diagnosis of SZ with a imaging epigenetics approach, which is demonstrated to be effective in extracting novel biomarkers associated with SZ.

Characterization of intellectual disability and autism comorbidity through gene panel sequencing

Intellectual disability (ID) and autism spectrum disorder (ASD) are clinically and genetically heterogeneous diseases. Recent whole exome sequencing studies indicated that genes associated with different neurological diseases are shared across disorders and converge on common functional pathways. Using the Ion Torrent platform, we developed a low-cost next-generation sequencing gene panel that has been transferred into clinical practice, replacing single disease-gene analyses for the early diagnosis of individuals with ID/ASD. The gene panel was designed using an innovative in silico approach based on disease networks and mining data from public resources to score disease-gene associations. We analyzed 150 unrelated individuals with ID and/or ASD and a confident diagnosis has been reached in 26 cases (17%). Likely pathogenic mutations have been identified in another 15 patients, reaching a total diagnostic yield of 27%. Our data also support the pathogenic role of genes recently proposed to be involved in ASD. Although many of the identified variants need further investigation to be considered disease-causing, our results indicate the efficiency of the targeted gene panel on the identification of novel and rare variants in patients with ID and ASD.

Genetic Associations between Voltage-Gated Calcium Channels and Psychiatric Disorders

Psychiatric disorders are mental, behavioral or emotional disorders. These conditions are prevalent, one in four adults suffer from any type of psychiatric disorders world-wide. It has always been observed that psychiatric disorders have a genetic component, however, new methods to sequence full genomes of large cohorts have identified with high precision genetic risk loci for these conditions. Psychiatric disorders include, but are not limited to, bipolar disorder, schizophrenia, autism spectrum disorder, anxiety disorders, major depressive disorder, and attention-deficit and hyperactivity disorder. Several risk loci for psychiatric disorders fall within genes that encode for voltage-gated calcium channels (CaVs). Calcium entering through CaVs is crucial for multiple neuronal processes. In this review, we will summarize recent findings that link CaVs and their auxiliary subunits to psychiatric disorders. First, we will provide a general overview of CaVs structure, classification, function, expression and pharmacology. Next, we will summarize tools to study risk loci associated with psychiatric disorders. We will examine functional studies of risk variations in CaV genes when available. Finally, we will review pharmacological evidence of the use of CaV modulators to treat psychiatric disorders. Our review will be of interest for those studying pathophysiological aspects of CaVs.

A gene co-expression network-based analysis of multiple brain tissues reveals novel genes and molecular pathways underlying major depression

Major depression is a common and severe psychiatric disorder with a highly polygenic genetic architecture. Genome-wide association studies have successfully identified multiple independent genetic loci that harbour variants associated with major depression, but the exact causal genes and biological mechanisms are largely unknown. Tissue-specific network approaches may identify molecular mechanisms underlying major depression and provide a biological substrate for integrative analyses. We provide a framework for the identification of individual risk genes and gene co-expression networks using genome-wide association summary statistics and gene expression information across multiple human brain tissues and whole blood. We developed a novel gene-based method called eMAGMA that leverages tissue-specific eQTL information to identify 99 biologically plausible risk genes associated with major depression, of which 58 are novel. Among these novel associations is Complement Factor 4A (C4A), recently implicated in schizophrenia through its role in synaptic pruning during postnatal development. Major depression risk genes were enriched in gene co-expression modules in multiple brain tissues and the implicated gene modules contained genes involved in synaptic signalling, neuronal development, and cell transport pathways. Modules enriched with major depression signals were strongly preserved across brain tissues, but were weakly preserved in whole blood, highlighting the importance of using disease-relevant tissues in genetic studies of psychiatric traits. We identified tissue-specific genes and gene co-expression networks associated with major depression. Our novel analytical framework can be used to gain fundamental insights into the functioning of the nervous system in major depression and other brain-related traits.

Although genome-wide association studies have identified genetic risk variants associated with major depression, our understanding of the mechanisms through which they influence disease susceptibility remains largely unknown. Genetic risk variants are highly enriched in non-coding regions of the genome and affect gene expression. Genes are co-expressed and regulate the activity of one another to form highly organized networks. In this study, we generate tissue-specific gene co-expression networks, each containing groups of functionally related genes or “modules”, to delineate gene co-expression and thereby facilitate the identification of gene processes in major depression. We developed and applied a novel research methodology (called “eMAGMA”) which integrates genetic and transcriptomic information in a tissue-specific analysis to identify risk genes and test for their enrichment in gene co-expression modules. Using this novel approach, we identified gene modules in multiple tissues that are both enriched with major depression genetic association signals and biologically meaningful pathways. We also show the disease implicated gene modules are strongly preserved across brain regions, but not in whole blood, suggesting unique patterns of gene co-expression within the two tissue types. Our novel analytical framework provides important insights into the functional genetics major depression and can be applied to other neuropsychiatric disorders.

De novo Mutations From Whole Exome Sequencing in Neurodevelopmental and Psychiatric Disorders: From Discovery to Application

Neurodevelopmental and psychiatric disorders are a highly disabling and heterogeneous group of developmental and mental disorders, resulting from complex interactions of genetic and environmental risk factors. The nature of multifactorial traits and the presence of comorbidity and polygenicity in these disorders present challenges in both disease risk identification and clinical diagnoses. The genetic component has been firmly established, but the identification of all the causative variants remains elusive. The development of next-generation sequencing, especially whole exome sequencing (WES), has greatly enriched our knowledge of the precise genetic alterations of human diseases, including brain-related disorders. In particular, the extensive usage of WES in research studies has uncovered the important contribution of de novo mutations (DNMs) to these disorders. Trio and quad familial WES are a particularly useful approach to discover DNMs. Here, we review the major WES studies in neurodevelopmental and psychiatric disorders and summarize how genes hit by discovered DNMs are shared among different disorders. Next, we discuss different integrative approaches utilized to interrogate DNMs and to identify biological pathways that may disrupt brain development and shed light on our understanding of the genetic architecture underlying these disorders. Lastly, we discuss the current state of the transition from WES research to its routine clinical application. This review will assist researchers and clinicians in the interpretation of variants obtained from WES studies, and highlights the need to develop consensus analytical protocols and validated lists of genes appropriate for clinical laboratory analysis, in order to reach the growing demands.

Defining the Genetic, Genomic, Cellular, and Diagnostic Architectures of Psychiatric Disorders

Studies of the genetics of psychiatric disorders have become one of the most exciting and fast-moving areas in human genetics. A decade ago, there were few reproducible findings, and now there are hundreds. In this review, we focus on the findings that have illuminated the genetic architecture of psychiatric disorders and the challenges of using these findings to inform our understanding of pathophysiology. The evidence is now overwhelming that psychiatric disorders are "polygenic"-that many genetic loci contribute to risk. With the exception of a subset of those with ASD, few individuals with a psychiatric disorder have a single, deterministic genetic cause; rather, developing a psychiatric disorder is influenced by hundreds of different genetic variants, consistent with a polygenic model. As progressively larger studies have uncovered more about their genetic architecture, the need to elucidate additional architectures has become clear. Even if we were to have complete knowledge of the genetic architecture of a psychiatric disorder, full understanding requires deep knowledge of the functional genomic architecture-the implicated loci impact regulatory processes that influence gene expression and the functional coordination of genes that control biological processes. Following from this is cellular architecture: of all brain regions, cell types, and developmental stages, where and when are the functional architectures operative? Given that the genetic architectures of different psychiatric disorders often strongly overlap, we are challenged to re-evaluate and refine the diagnostic architectures of psychiatric disorders using fundamental genetic and neurobiological data.