Puzzling over schizophrenia: schizophrenia as a pathway disease

Elevated Extracellular Free Water in the Brain Predicts Clinical Improvement in First-Episode Psychosis

BACKGROUND

Despite the diverse nature of clinical trajectories after a first episode of psychosis, few baseline characteristics have been predictive of clinical improvement, and the neurobiological underpinnings of this heterogeneity remain largely unknown. Elevated extracellular free water (FW) in the brain is a diffusion imaging measure that has been consistently reported in different phases of psychosis that may indicate a neuroinflammatory state. However, its predictive capacity in terms of clinical outcomes is unknown.

METHODS

We used diffusion imaging to determine FW and tissue-specific fractional anisotropy (FA-t) in first-episode psychosis. Forty-seven participants were categorized as clinical improvers (n = 26) if they achieved a 20% decrease in total Brief Psychiatric Rating Scale score at 12 months. To determine the predictive capacity of FW and FA-t, these measures were introduced in a stepwise logistic regression model to predict clinical improvement. For measures that survived the model, regional between-group differences were also investigated in cortical surface or white matter tracts, as applicable.

RESULTS

Both higher gray matter FW (odds ratio 1.698; 95% CI, 1.134-2.542) and FA-t (odds ratio, 1.358; 95% CI, 0.905-2.038) predicted improver status. FW in gray matter was also linearly correlated with the Brief Psychiatric Rating Scale total score at the 12-month follow-up. When we examined regional specificity, we found that improvers showed greater FW predominantly in temporal regions and higher FA-t values in several white matter tracts, including the bilateral longitudinal superior fasciculus.

CONCLUSIONS

Our results show that elevated FW in gray matter and FA-t predict further clinical improvement during the initial phases of psychosis. The potential roles of brain inflammatory processes in predicting clinical improvement are discussed.

GABAergic neuron dysregulation in a human neurodevelopmental model for major psychiatric disorders

"GABA dysfunction" is a major hypothesis for the biological basis of schizophrenia with indirect supporting evidence from human post-mortem brain and genetic studies. Patient-derived induced pluripotent stem cells (iPSCs) have emerged as a valuable platform for modeling psychiatric disorders, and previous modeling has revealed glutamatergic synapse deficits. Whether GABAergic synapse properties are affected in patient-derived human neurons and how this impacts neuronal network activity remain poorly understood. Here we optimized a protocol to differentiate iPSCs into highly enriched ganglionic eminence-like neural progenitors and GABAergic neurons. Using a collection of iPSCs derived from patients of psychiatric disorders carrying a Disrupted-in-Schizophrenia 1 ( DISC1 ) mutation and their unaffected family member, together with respective isogenic lines, we identified mutation-dependent deficits in GABAergic synapse formation and function, a phenotype similar to that of mutant glutamatergic neurons. However, mutant glutamatergic and GABAergic neurons contribute differentially to neuronal network excitability and synchrony deficits. Finally, we showed that GABAergic synaptic transmission is also defective in neurons derived from several idiopathic schizophrenia patient iPSCs. Transcriptome analysis further showed some shared gene expression dysregulation, which is more prominent in DISC1 mutant neurons. Together, our study supports a functional GABAergic synaptic deficit in major psychiatric disorders.

Decoding frontotemporal and cell-type-specific vulnerabilities to neuropsychiatric disorders and psychoactive drugs

Frontotemporal lobe abnormalities are linked to neuropsychiatric disorders and cognition, but the role of cellular heterogeneity between temporal lobe (TL) and frontal lobe (FL) in the vulnerability to genetic risk factors remains to be elucidated. We integrated single-nucleus transcriptome analysis in 'fresh' human FL and TL with genetic susceptibility, gene dysregulation in neuropsychiatric disease and psychoactive drug response data. We show how intrinsic differences between TL and FL contribute to the vulnerability of specific cell types to both genetic risk factors and psychoactive drugs. Neuronal populations, specifically PVALB neurons, were most highly vulnerable to genetic risk factors for psychiatric disease. These psychiatric disease-associated genes were mostly upregulated in the TL, and dysregulated in the brain of patients with obsessive-compulsive disorder, bipolar disorder and schizophrenia. Among these genes, GRIN2A and SLC12A5, implicated in schizophrenia and bipolar disorder, were significantly upregulated in TL PVALB neurons and in psychiatric disease patients' brain. PVALB neurons from the TL were twofold more vulnerable to psychoactive drugs than to genetic risk factors, showing the influence and specificity of frontotemporal lobe differences on cell vulnerabilities. These studies provide a cell type resolved map of the impact of brain regional differences on cell type vulnerabilities in neuropsychiatric disorders.

Unlocking Neural Function with 3D In Vitro Models: A Technical Review of Self-Assembled, Guided, and Bioprinted Brain Organoids and Their Applications in the Study of Neurodevelopmental and Neurodegenerative Disorders

Understanding the complexities of the human brain and its associated disorders poses a significant challenge in neuroscience. Traditional research methods have limitations in replicating its intricacies, necessitating the development of in vitro models that can simulate its structure and function. Three-dimensional in vitro models, including organoids, cerebral organoids, bioprinted brain models, and functionalized brain organoids, offer promising platforms for studying human brain development, physiology, and disease. These models accurately replicate key aspects of human brain anatomy, gene expression, and cellular behavior, enabling drug discovery and toxicology studies while providing insights into human-specific phenomena not easily studied in animal models. The use of human-induced pluripotent stem cells has revolutionized the generation of 3D brain structures, with various techniques developed to generate specific brain regions. These advancements facilitate the study of brain structure development and function, overcoming previous limitations due to the scarcity of human brain samples. This technical review provides an overview of current 3D in vitro models of the human cortex, their development, characterization, and limitations, and explores the state of the art and future directions in the field, with a specific focus on their applications in studying neurodevelopmental and neurodegenerative disorders.

Effect of polygenic risk for schizophrenia on cardiac structure and function: a UK Biobank observational study

BACKGROUND

Cardiovascular disease is a major cause of excess mortality in people with schizophrenia. Several factors are responsible, including lifestyle and metabolic effects of antipsychotics. However, variations in cardiac structure and function are seen in people with schizophrenia in the absence of cardiovascular disease risk factors and after accounting for lifestyle and medication. Therefore, we aimed to explore whether shared genetic causes contribute to these cardiac variations.

METHODS

For this observational study, we used data from the UK Biobank and included White British or Irish individuals without diagnosed schizophrenia with variable polygenic risk scores for the condition. To test the association between polygenic risk score for schizophrenia and cardiac phenotype, we used principal component analysis and regression. Robust regression was then used to explore the association between the polygenic risk score for schizophrenia and individual cardiac phenotypes. We repeated analyses with fibro-inflammatory pathway-specific polygenic risk scores for schizophrenia. Last, we investigated genome-wide sharing of common variants between schizophrenia and cardiac phenotypes using linkage disequilibrium score regression. The primary outcome was principal component regression.

FINDINGS

Of 33 353 individuals recruited, 32 279 participants had complete cardiac MRI data and were included in the analysis, of whom 16 625 (51·5%) were female and 15 654 (48·5%) were male. 1074 participants were excluded on the basis of incomplete cardiac MRI data (for all phenotypes). A model regressing polygenic risk scores for schizophrenia onto the first five cardiac principal components of the principal components analysis was significant (F=5·09; p=0·00012). Principal component 1 captured a pattern of increased cardiac volumes, increased absolute peak diastolic strain rates, and reduced ejection fractions; polygenic risk scores for schizophrenia and principal component 1 were negatively associated (β=-0·01 [SE 0·003]; p=0·017). Similar to the principal component analysis results, for individual cardiac phenotypes, we observed negative associations between polygenic risk scores for schizophrenia and indexed right ventricular end-systolic volume (β=-0·14 [0·04]; p=0·0013, pFDR=0·015), indexed right ventricular end-diastolic volume (β=-0·17 [0·08]); p=0·025; pFDR=0·082), and absolute longitudinal peak diastolic strain rates (β=-0·01 [0·003]; p=0·0024, pFDR=0·015), and a positive association between polygenic risk scores for schizophrenia and right ventricular ejection fraction (β=0·09 [0·03]; p=0·0041, pFDR=0·015). Models examining the transforming growth factor-β (TGF-β)-specific and acute inflammation-specific polygenic risk scores for schizophrenia found significant associations with the first five principal components (F=2·62, p=0·022; F=2·54, p=0·026). Using linkage disequilibrium score regression, we observed genetic overlap with schizophrenia for right ventricular end-systolic volume and right ventricular ejection fraction (p=0·0090, p=0·0077).

INTERPRETATION

High polygenic risk scores for schizophrenia are associated with decreased cardiac volumes, increased ejection fractions, and decreased absolute peak diastolic strain rates. TGF-β and inflammatory pathways might be implicated, and there is evidence of genetic overlap for some cardiac phenotypes. Reduced absolute peak diastolic strain rates indicate increased myocardial stiffness and diastolic dysfunction, which increases risk of cardiac disease. Thus, genetic risk for schizophrenia is associated with cardiac structural changes that can worsen cardiac outcomes. Further work is required to determine whether these associations are specific to schizophrenia or are also seen in other psychiatric conditions.

FUNDING

National Institute for Health Research, Maudsley Charity, Wellcome Trust, Medical Research Council, Academy of Medical Sciences, Edmond J Safra Foundation, British Heart Foundation.

Artificial intelligence and machine-learning approaches in structure and ligand-based discovery of drugs affecting central nervous system

CNS disorders are indications with a very high unmet medical needs, relatively smaller number of available drugs, and a subpar satisfaction level among patients and caregiver. Discovery of CNS drugs is extremely expensive affair with its own unique challenges leading to extremely high attrition rates and low efficiency. With explosion of data in information age, there is hardly any aspect of life that has not been touched by data driven technologies such as artificial intelligence (AI) and machine learning (ML). Drug discovery is no exception, emergence of big data via genomic, proteomic, biological, and chemical technologies has driven pharmaceutical giants to collaborate with AI oriented companies to revolutionise drug discovery, with the goal of increasing the efficiency of the process. In recent years many examples of innovative applications of AI and ML techniques in CNS drug discovery has been reported. Research on therapeutics for diseases such as schizophrenia, Alzheimer's and Parkinsonism has been provided with a new direction and thrust from these developments. AI and ML has been applied to both ligand-based and structure-based drug discovery and design of CNS therapeutics. In this review, we have summarised the general aspects of AI and ML from the perspective of drug discovery followed by a comprehensive coverage of the recent developments in the applications of AI/ML techniques in CNS drug discovery.

Artificial intelligence and machine learning-aided drug discovery in central nervous system diseases: State-of-the-arts and future directions

Neurological disorders significantly outnumber diseases in other therapeutic areas. However, developing drugs for central nervous system (CNS) disorders remains the most challenging area in drug discovery, accompanied with the long timelines and high attrition rates. With the rapid growth of biomedical data enabled by advanced experimental technologies, artificial intelligence (AI) and machine learning (ML) have emerged as an indispensable tool to draw meaningful insights and improve decision making in drug discovery. Thanks to the advancements in AI and ML algorithms, now the AI/ML-driven solutions have an unprecedented potential to accelerate the process of CNS drug discovery with better success rate. In this review, we comprehensively summarize AI/ML-powered pharmaceutical discovery efforts and their implementations in the CNS area. After introducing the AI/ML models as well as the conceptualization and data preparation, we outline the applications of AI/ML technologies to several key procedures in drug discovery, including target identification, compound screening, hit/lead generation and optimization, drug response and synergy prediction, de novo drug design, and drug repurposing. We review the current state-of-the-art of AI/ML-guided CNS drug discovery, focusing on blood-brain barrier permeability prediction and implementation into therapeutic discovery for neurological diseases. Finally, we discuss the major challenges and limitations of current approaches and possible future directions that may provide resolutions to these difficulties.

Polygenic risk scores differentiate schizophrenia patients with toxoplasma gondii compared to toxoplasma seronegative patients

Schizophrenia (SCZ) is an etiologically heterogeneous disease with genetic and environmental risk factors (e.g., Toxoplasma gondii infection) differing among affected individuals. Distinguishing such risk factors may point to differences in pathophysiological pathways and facilitate the discovery of individualized treatments. Toxoplasma gondii (TOXO) has been implicated in increasing the risk of schizophrenia. To determine whether TOXO-positive individuals with SCZ have a different polygenic risk burden than uninfected people, we applied the SCZ polygenic risk score (SCZ-PRS) derived from the Psychiatric GWAS Consortium separately to the TOXO-positive and TOXO-negative subjects with the diagnosis of SCZ as the outcome variable. The SCZ-PRS does not include variants in the major histocompatibility complex. Of 790 subjects assessed for TOXO, the 662 TOXO-negative subjects (50.8% with SCZ) reached a Bonferroni corrected significant association (p = 0.00017, R2 = 0.023). In contrast, the 128 TOXO-positive individuals (53.1% with SCZ) showed no significant association (p = 0.354) for SCZ-PRS and had a much lower R2 (R2 = 0.007). To account for Type-2 error in the TOXO-positive dataset, we performed a random sampling of the TOXO-negative subpopulation (n = 130, repeated 100 times) to simulate equivalent power between groups: the p-value was <0.05 for SCZ-PRS 55% of the time but was rarely (6% of the time) comparable to the high p-value of the seropositive group at p > 0.354. We found intriguing evidence that the SCZ-PRS predicts SCZ in TOXO-negative subjects, as expected, but not in the TOXO-positive individuals. This result highlights the importance of considering environmental risk factors to distinguish a subgroup with independent or different genetic components involved in the development of SCZ.

Pathway analysis for genome-wide genetic variation data: Analytic principles, latest developments, and new opportunities

Pathway analysis, also known as gene-set enrichment analysis, is a multilocus analytic strategy that integrates a priori, biological knowledge into the statistical analysis of high-throughput genetics data. Originally developed for the studies of gene expression data, it has become a powerful analytic procedure for in-depth mining of genome-wide genetic variation data. Astonishing discoveries were made in the past years, uncovering genes and biological mechanisms underlying common and complex disorders. However, as massive amounts of diverse functional genomics data accrue, there is a pressing need for newer generations of pathway analysis methods that can utilize multiple layers of high-throughput genomics data. In this review, we provide an intellectual foundation of this powerful analytic strategy, as well as an update of the state-of-the-art in recent method developments. The goal of this review is threefold: (1) introduce the motivation and basic steps of pathway analysis for genome-wide genetic variation data; (2) review the merits and the shortcomings of classic and newly emerging integrative pathway analysis tools; and (3) discuss remaining challenges and future directions for further method developments.

Functional patient-derived cellular models for neuropsychiatric drug discovery

Mental health disorders are a leading cause of disability worldwide. Challenges such as disease heterogeneity, incomplete characterization of the targets of existing drugs and a limited understanding of functional interactions of complex genetic risk loci and environmental factors have compromised the identification of novel drug candidates. There is a pressing clinical need for drugs with new mechanisms of action which address the lack of efficacy and debilitating side effects of current medications. Here we discuss a novel strategy for neuropsychiatric drug discovery which aims to address these limitations by identifying disease-related functional responses ('functional cellular endophenotypes') in a variety of patient-derived cells, such as induced pluripotent stem cell (iPSC)-derived neurons and organoids or peripheral blood mononuclear cells (PBMCs). Disease-specific alterations in cellular responses can subsequently yield novel drug screening targets and drug candidates. We discuss the potential of this approach in the context of recent advances in patient-derived cellular models, high-content single-cell screening of cellular networks and changes in the diagnostic framework of neuropsychiatric disorders.

Exploring the neuropsychiatric spectrum using high-content functional analysis of single-cell signaling networks

Neuropsychiatric disorders overlap in symptoms and share genetic risk factors, challenging their current classification into distinct diagnostic categories. Novel cross-disorder approaches are needed to improve our understanding of the heterogeneous nature of neuropsychiatric diseases and overcome existing bottlenecks in their diagnosis and treatment. Here we employ high-content multi-parameter phospho-specific flow cytometry, fluorescent cell barcoding and automated sample preparation to characterize ex vivo signaling network responses (n = 1764) measured at the single-cell level in B and T lymphocytes across patients diagnosed with four major neuropsychiatric disorders: autism spectrum condition (ASC), bipolar disorder (BD), major depressive disorder (MDD), and schizophrenia (SCZ; n = 25 each), alongside matched healthy controls (n = 100). We identified 25 nodes (individual cell subtype-epitope-ligand combinations) significantly altered relative to the control group, with variable overlap between different neuropsychiatric diseases and heterogeneously expressed at the level of each individual patient. Reconstruction of the diagnostic categories from the altered nodes revealed an overlapping neuropsychiatric spectrum extending from MDD on one end, through BD and SCZ, to ASC on the other end. Network analysis showed that although the pathway structure of the epitopes was broadly preserved across the clinical groups, there were multiple discrete alterations in network connectivity, such as disconnections within the antigen/integrin receptor pathway and increased negative regulation within the Akt1 pathway in CD4⁺ T cells from ASC and SCZ patients, in addition to increased correlation of Stat1 (pY701) and Stat5 (pY694) responses in B cells from BD and MDD patients. Our results support the "dimensional" approach to neuropsychiatric disease classification and suggest potential novel drug targets along the neuropsychiatric spectrum.

Brain-based ranking of cognitive domains to predict schizophrenia

Schizophrenia is a devastating brain disorder that disturbs sensory perception, motor action, and abstract thought. Its clinical phenotype implies dysfunction of various mental domains, which has motivated a series of theories regarding the underlying pathophysiology. Aiming at a predictive benchmark of a catalog of cognitive functions, we developed a data-driven machine-learning strategy and provide a proof of principle in a multisite clinical dataset (n = 324). Existing neuroscientific knowledge on diverse cognitive domains was first condensed into neurotopographical maps. We then examined how the ensuing meta-analytic cognitive priors can distinguish patients and controls using brain morphology and intrinsic functional connectivity. Some affected cognitive domains supported well-studied directions of research on auditory evaluation and social cognition. However, rarely suspected cognitive domains also emerged as disease relevant, including self-oriented processing of bodily sensations in gustation and pain. Such algorithmic charting of the cognitive landscape can be used to make targeted recommendations for future mental health research.

Disease comorbidity-guided drug repositioning: a case study in schizophrenia

The key to any computational drug repositioning is the availability of relevant data in machine-understandable format. While large amount of genetic, genomic and chemical data are publicly available, large-scale higher-level disease and drug phenotypic data are limited. We recently constructed a large-scale disease-comorbidity relationship knowledge base (dCombKB) and a comprehensive drug-treatment relationship knowledge base (TreatKB) from 21 million biomedical research articles and other resources. In this study, we demonstrated the potential of dCombKB and TreatKB in drug repositioning for schizophrenia, one of the top ten illnesses contributing to the global burden of disease. dCombKB contains 121,359 unique disease-disease comorbidity pairs for 23,041 diseases. TreatKB contains 208,330 unique drug-disease treatment pairs for 2,484 drugs and 24,511 diseases. We constructed a phenotypic comorbidity disease network (PDN) of 14,645 disease nodes and 101,275 edges based on dCombKB. We applied standard network-based ranking algorithm to find diseases that are phenotypically related to SCZ. We developed a drug prioritization system, PhenoPredict-CDN, to systematically reposition drugs for SCZ from diseases phenotypically related to SCZ. PhenoPredict-CDN found all 18 FDA-approved SCZ drugs and ranked them highly as tested in a de-novo validation setting (recall: 1.0, mean ranking: top 6.05%, median ranking: top 1.65%). When compared to PREDICT, a comprehensive drug repositioning system, for novel predictions, Pheno-Predict-CDN outperformed PREDICT in Precision-Recall (PR) curves across three different evaluation datasets. Compared to PREDICT, PhenoPredict-CDN showed a significant 110.0-230.0% improvements in mean average precision. In summary, large-scale higher-level disease-comorbidity relationships data extracted from biomedical literature has potential in drug discovery for SCZ, a complex disease with unknown pathophysiological mechanisms. All the data are publicly available: dCombKB at http://nlp.

CASE

edu/public/data/dCombKB, TreatKB at http://nlp.

CASE

edu/public/data/treatKB/, and predictions for SCZ at http://nlp.

CASE

edu/public/data/SCZ_CDN/.

Factor structure of therapist fidelity to individual resiliency training in the Recovery After an Initial Schizophrenia Episode Early Treatment Program

BACKGROUND

Evidence-based approaches and early intervention have improved the long-term prognosis of individuals with schizophrenia. However, little is known about the therapeutic processes involved in individual therapy in first-episode psychosis. A comprehensive psychosocial/psychiatric programme for this population, NAVIGATE, includes an individual therapy component, individual resiliency training (IRT). Fidelity of clinicians' adherence to the IRT protocol has been collected to ensure proper implementation of this manual-based intervention. These data can provide insight into the elements of the therapeutic process in this intervention.

MATERIALS AND METHODS

To achieve this goal, we first examined the factor structure of the IRT fidelity scale with exploratory factor analysis. Second, we explored the relationships among the IRT fidelity ratings with clinician years of experience and years of education, as well as client's baseline symptom severity and duration of untreated psychosis.

RESULTS AND CONCLUSIONS

Results supported a 2-factor structure of the IRT fidelity scale. Correlations between clinician years of education and fidelity ratings were statistically significant.

Analysis of Gene Expression Variance in Schizophrenia Using Structural Equation Modeling

Schizophrenia (SCZ) is a psychiatric disorder of unknown etiology. There is evidence suggesting that aberrations in neurodevelopment are a significant attribute of schizophrenia pathogenesis and progression. To identify biologically relevant molecular abnormalities affecting neurodevelopment in SCZ we used cultured neural progenitor cells derived from olfactory neuroepithelium (CNON cells). Here, we tested the hypothesis that variance in gene expression differs between individuals from SCZ and control groups. In CNON cells, variance in gene expression was significantly higher in SCZ samples in comparison with control samples. Variance in gene expression was enriched in five molecular pathways: serine biosynthesis, PI3K-Akt, MAPK, neurotrophin and focal adhesion. More than 14% of variance in disease status was explained within the logistic regression model (C-value = 0.70) by predictors accounting for gene expression in 69 genes from these five pathways. Structural equation modeling (SEM) was applied to explore how the structure of these five pathways was altered between SCZ patients and controls. Four out of five pathways showed differences in the estimated relationships among genes: between KRAS and NF1, and KRAS and SOS1 in the MAPK pathway; between PSPH and SHMT2 in serine biosynthesis; between AKT3 and TSC2 in the PI3K-Akt signaling pathway; and between CRK and RAPGEF1 in the focal adhesion pathway. Our analysis provides evidence that variance in gene expression is an important characteristic of SCZ, and SEM is a promising method for uncovering altered relationships between specific genes thus suggesting affected gene regulation associated with the disease. We identified altered gene-gene interactions in pathways enriched for genes with increased variance in expression in SCZ. These pathways and loci were previously implicated in SCZ, providing further support for the hypothesis that gene expression variance plays important role in the etiology of SCZ.

Epigenetics and gene expression profile in first-episode psychosis: The role of childhood trauma

Childhood Trauma (CT) mediation of the epigenome and its impact on gene expression profile could provide a mechanism for the gene-environment interaction underling psychosis. We reviewed the evidence concerning epigenetic and gene expression modifications associated with CT in both First-Episode Psychosis (FEP) and healthy subjects. In order to explore the relative role of psychosis itself in determining these modifications, evidence about FEP and epigenetics/gene expression was also summarized. We performed a systematic search on PubMed, last updated in December 2016. Out of 2966 potentially relevant records, only 41 studies were included. CT resulted associated: in FEP subjects, with global DNA hypo-methylation and reduced BDNF gene-expression; in healthy subjects, with hyper-methylation of SLC6A4, NR3C1, KITLG, and OXTR; hypo-methylation of FKBP5, IL-6, and BDNF; increased IL1B, IL8, and PTGS gene expression; and decreased SLC6A4 gene expression. FEP showed global DNA hypo-methylation; increased methylation and reduced gene expression of GCH1; hyper-expression of MPB, NDEL1, AKT1, and DICER1; and hypo-expression of DROSHA, COMT, and DISC1 in comparison with healthy controls.

Microvascular anomaly conditions in psychiatric disease. Schizophrenia - angiogenesis connection

Schizophrenia (SZ) is a severe mental disorder with unknown etiology and elusive neuropathological and neurobiological features have been a focus of many theoretical hypotheses and empirical studies. Current genetic and neurobiology information relevant to SZ implicates neuronal developmental and synaptic plasticity abnormalities, and neurotransmitter, microglial and oligodendrocytes dysfunction. Several recent theories have highlighted the neurovascular unit as a potential contributor to the pathophysiology of SZ. We explored the biological plausibility of a link between SZ and the neurovascular system by examining insights gained from genetic, neuroimaging and postmortem studies, which include gene expression and neuropathology analyses. We also reviewed information from animal models of cerebral angiogenesis in order to understand better the complex interplay between angiogenic and neurotrophic factors in development, vascular endothelium/blood brain barrier remodeling and maintenance, all of which contribute to sustaining adequate regional blood flow and safeguarding normal brain function. Microvascular and hemodynamic alterations in SZ highlight the importance of further research and reveal the neurovascular unit as a potential therapeutic target in SZ.

Microvascular anomaly conditions in psychiatric disease. Schizophrenia - angiogenesis connection

Schizophrenia (SZ) is a severe mental disorder with unknown etiology and elusive neuropathological and neurobiological features have been a focus of many theoretical hypotheses and empirical studies. Current genetic and neurobiology information relevant to SZ implicates neuronal developmental and synaptic plasticity abnormalities, and neurotransmitter, microglial and oligodendrocytes dysfunction. Several recent theories have highlighted the neurovascular unit as a potential contributor to the pathophysiology of SZ. We explored the biological plausibility of a link between SZ and the neurovascular system by examining insights gained from genetic, neuroimaging and postmortem studies, which include gene expression and neuropathology analyses. We also reviewed information from animal models of cerebral angiogenesis in order to understand better the complex interplay between angiogenic and neurotrophic factors in development, vascular endothelium/blood brain barrier remodeling and maintenance, all of which contribute to sustaining adequate regional blood flow and safeguarding normal brain function. Microvascular and hemodynamic alterations in SZ highlight the importance of further research and reveal the neurovascular unit as a potential therapeutic target in SZ.

Pathway-wide association study identifies five shared pathways associated with schizophrenia in three ancestral distinct populations

Genome-wide association studies have confirmed the polygenic nature of schizophrenia and suggest that there are hundreds or thousands of alleles associated with increased liability for the disorder. However, the generalizability of any one allelic marker of liability is remarkably low and has bred the notion that schizophrenia may be better conceptualized as a pathway(s) disorder. Here, we empirically tested this notion by conducting a pathway-wide association study (PWAS) encompassing 255 experimentally validated Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways among 5033 individuals diagnosed with schizophrenia and 5332 unrelated healthy controls across three distinct ethnic populations; European-American (EA), African-American (AA) and Han Chinese (CH). We identified 103, 74 and 87 pathways associated with schizophrenia liability in the EA, CH and AA populations, respectively. About half of these pathways were uniquely associated with schizophrenia liability in each of the three populations. Five pathways (serotonergic synapse, ubiquitin mediated proteolysis, hedgehog signaling, adipocytokine signaling and renin secretion) were shared across all three populations and the single-nucleotide polymorphism sets representing these five pathways were enriched for single-nucleotide polymorphisms with regulatory function. Our findings provide empirical support for schizophrenia as a pathway disorder and suggest schizophrenia is not only a polygenic but likely also a poly-pathway disorder characterized by both genetic and pathway heterogeneity.

Unravelling socio-motor biomarkers in schizophrenia

We present novel, low-cost and non-invasive potential diagnostic biomarkers of schizophrenia. They are based on the 'mirror-game', a coordination task in which two partners are asked to mimic each other's hand movements. In particular, we use the patient's solo movement, recorded in the absence of a partner, and motion recorded during interaction with an artificial agent, a computer avatar or a humanoid robot. In order to discriminate between the patients and controls, we employ statistical learning techniques, which we apply to nonverbal synchrony and neuromotor features derived from the participants' movement data. The proposed classifier has 93% accuracy and 100% specificity. Our results provide evidence that statistical learning techniques, nonverbal movement coordination and neuromotor characteristics could form the foundation of decision support tools aiding clinicians in cases of diagnostic uncertainty.

Altered CSNK1E, FABP4 and NEFH protein levels in the dorsolateral prefrontal cortex in schizophrenia

Schizophrenia constitutes a complex disease. Negative and cognitive symptoms are enduring and debilitating components of the disorder, highly associated to disability and burden. Disrupted neurotransmission circuits in dorsolateral prefrontal cortex (DLPFC) have been related to these symptoms. To identify candidates altered in schizophrenia, we performed a pilot proteomic analysis on postmortem human DLPFC tissue from patients with schizophrenia (n=4) and control (n=4) subjects in a pool design using differential isotope peptide labelling followed by liquid chromatography tandem mass spectrometry (LC-MS/MS). We quantified 1315 proteins with two or more unique peptides, 116 of which showed altered changes. Of these altered proteins, we selected four with potential roles on cell signaling, neuronal development and synapse functioning for further validation: casein kinase I isoform epsilon (CSNK1E), fatty acid-binding protein 4 (FABP4), neurofilament triplet H protein (NEFH), and retinal dehydrogenase 1 (ALDH1A1). Immunoblot validation confirmed our proteomic findings of these proteins being decreased in abundance in the schizophrenia samples. Additionally, we conducted immunoblot validation of these candidates on an independent sample cohort comprising 23 patients with chronic schizophrenia and 23 matched controls. In this second cohort, CSNK1E, FABP4 and NEFH were reduced in the schizophrenia group while ALDH1A1 did not significantly change. This study provides evidence indicating these proteins are decreased in schizophrenia: CSNK1E, involved in circadian molecular clock signaling, FABP4 with possible implication in synapse functioning, and NEFH, important for cytoarchitecture organization. Hence, these findings suggest the possible implication of these proteins in the cognitive and/or negative symptoms in schizophrenia.

Interplay between Schizophrenia Polygenic Risk Score and Childhood Adversity in First-Presentation Psychotic Disorder: A Pilot Study

A history of childhood adversity is associated with psychotic disorder, with an increase in risk according to number or severity of exposures. However, it is not known why only some exposed individuals go on to develop psychosis. One possibility is pre-existing genetic vulnerability. Research on gene-environment interaction in psychosis has primarily focused on candidate genes, although the genetic effects are now known to be polygenic. This pilot study investigated whether the effect of childhood adversity on psychosis is moderated by the polygenic risk score for schizophrenia (PRS). Data were utilised from the Genes and Psychosis (GAP) study set in South London, UK. The GAP sample comprises 285 first-presentation psychosis cases and 256 unaffected controls with information on childhood adversity. We studied only white subjects (80 cases and 110 controls) with PRS data, as the PRS has limited predictive ability in patients of African ancestry. The occurrence of childhood adversity was assessed with the Childhood Experience of Care and Abuse Questionnaire (CECA.Q) and the PRS was based on genome-wide meta-analysis results for schizophrenia from the Psychiatric Genomics Consortium. Higher schizophrenia PRS and childhood adversities each predicted psychosis status. Nevertheless, no evidence was found for interaction as departure from additivity, indicating that the effect of polygenic risk scores on psychosis was not increased in the presence of a history of childhood adversity. These findings are compatible with a multifactorial threshold model in which both genetic liability and exposure to environmental risk contribute independently to the etiology of psychosis.

G72 primate-specific gene: a still enigmatic element in psychiatric disorders

Numerous studies have demonstrated a link between genetic markers on chromosome 13 and schizophrenia, bipolar affective disorder, and other psychiatric phenotypes. The G72/G30 genes (transcribed in opposite directions) are located on chromosome 13q33, a region demonstrating strong evidence for linkage with various neuropsychiatric disorders. G72/G30 was identified in 2002 as a schizophrenia susceptibility locus; however, subsequent association studies did not reach consensus on single SNPs within the locus. Simultaneously, a new vision for the genetic architecture of psychiatric disorders suggested that schizophrenia was a quantitative trait, therefore ascribable to potentially hundreds of genes and subjected to the vagaries of the environment. The main protein product of G72 gene is named pLG72 or D-amino acid oxidase activator DAOA (153 amino acids) and its function is still debated. Functional analyses, also showing controversial results, indicate that pLG72 contributes to N-methyl-D-aspartate receptor modulation by affecting activity of the flavoprotein D-amino acid oxidase, the enzyme responsible for degrading the neuromodulator D-serine. In this review we, for the first time, summarize findings from molecular genetic linkage and association studies concerning G72 gene, cellular and molecular studies on pLG72, and investigations performed on G72/G30 transgenic mice. This will help elucidate the role of psychosis susceptibility genes, which will have a major impact on our understanding of disease pathophysiology and thus change classification and treatment.

Translation of Genotype to Phenotype by a Hierarchy of Cell Subsystems

Accurately translating genotype to phenotype requires accounting for the functional impact of genetic variation at many biological scales. Here we present a strategy for genotype-phenotype reasoning based on existing knowledge of cellular subsystems. These subsystems and their hierarchical organization are defined by the Gene Ontology or a complementary ontology inferred directly from previously published datasets. Guided by the ontology's hierarchical structure, we organize genotype data into an "ontotype," that is, a hierarchy of perturbations representing the effects of genetic variation at multiple cellular scales. The ontotype is then interpreted using logical rules generated by machine learning to predict phenotype. This approach substantially outperforms previous, non-hierarchical methods for translating yeast genotype to cell growth phenotype, and it accurately predicts the growth outcomes of two new screens of 2,503 double gene knockouts impacting DNA repair or nuclear lumen. Ontotypes also generalize to larger knockout combinations, setting the stage for interpreting the complex genetics of disease.

Focus on psychosis

The concept of psychosis has been shaped by traditions in the concepts of mental disorders during the last 170 years. The term "psychosis" still lacks a unified definition, but denotes a clinical construct composed of several symptoms. Delusions, hallucinations, and thought disorders are the core clinical features. The search for a common denominator of psychotic symptoms points toward combinations of neuropsychological mechanisms resulting in reality distortion. To advance the elucidation of the causes and the pathophysiology of the symptoms of psychosis, a deconstruction of the term into its component symptoms is therefore warranted. Current research is dealing with the delineation from "normality", the genetic underpinnings, and the causes and pathophysiology of the symptoms of psychosis.

New statistical approaches exploit the polygenic architecture of schizophrenia--implications for the underlying neurobiology

Schizophrenia is a complex disorder with high heritability. Recent findings from several large genetic studies suggest a large number of risk variants are involved (i.e. schizophrenia is a polygenic disorder) and analytic approaches could be tailored for this scenario. Novel statistical approaches for analyzing GWAS data have recently been developed to be more sensitive to polygenic traits. These approaches have provided intriguing new insights into neurobiological pathways and support for the involvement of regulatory mechanisms, neurotransmission (glutamate, dopamine, GABA), and immune and neurodevelopmental pathways. Integrating the emerging statistical genetics evidence with sound neurobiological experiments will be a crucial, and challenging, next step in deciphering the specific disease mechanisms of schizophrenia.

Contrasting genetic architectures of schizophrenia and other complex diseases using fast variance-components analysis

Heritability analyses of genome-wide association study (GWAS) cohorts have yielded important insights into complex disease architecture, and increasing sample sizes hold the promise of further discoveries. Here we analyze the genetic architectures of schizophrenia in 49,806 samples from the PGC and nine complex diseases in 54,734 samples from the GERA cohort. For schizophrenia, we infer an overwhelmingly polygenic disease architecture in which ≥71% of 1-Mb genomic regions harbor ≥1 variant influencing schizophrenia risk. We also observe significant enrichment of heritability in GC-rich regions and in higher-frequency SNPs for both schizophrenia and GERA diseases. In bivariate analyses, we observe significant genetic correlations (ranging from 0.18 to 0.85) for several pairs of GERA diseases; genetic correlations were on average 1.3 tunes stronger than the correlations of overall disease liabilities. To accomplish these analyses, we developed a fast algorithm for multicomponent, multi-trait variance-components analysis that overcomes prior computational barriers that made such analyses intractable at this scale.

An introductory review of parallel independent component analysis (p-ICA) and a guide to applying p-ICA to genetic data and imaging phenotypes to identify disease-associated biological pathways and systems in common complex disorders

Complex inherited phenotypes, including those for many common medical and psychiatric diseases, are most likely underpinned by multiple genes contributing to interlocking molecular biological processes, along with environmental factors (Owen et al., 2010). Despite this, genotyping strategies for complex, inherited, disease-related phenotypes mostly employ univariate analyses, e.g., genome wide association. Such procedures most often identify isolated risk-related SNPs or loci, not the underlying biological pathways necessary to help guide the development of novel treatment approaches. This article focuses on the multivariate analysis strategy of parallel (i.e., simultaneous combination of SNP and neuroimage information) independent component analysis (p-ICA), which typically yields large clusters of functionally related SNPs statistically correlated with phenotype components, whose overall molecular biologic relevance is inferred subsequently using annotation software suites. Because this is a novel approach, whose details are relatively new to the field we summarize its underlying principles and address conceptual questions regarding interpretation of resulting data and provide practical illustrations of the method.

Functional genomics indicate that schizophrenia may be an adult vascular-ischemic disorder

In search for the elusive schizophrenia pathway, candidate genes for the disorder from a discovery sample were localized within the energy-delivering and ischemia protection pathway. To test the adult vascular-ischemic (AVIH) and the competing neurodevelopmental hypothesis (NDH), functional genomic analyses of practically all available schizophrenia-associated genes from candidate gene, genome-wide association and postmortem expression studies were performed. Our results indicate a significant overrepresentation of genes involved in vascular function (P < 0.001), vasoregulation (that is, perivascular (P < 0.001) and shear stress (P < 0.01), cerebral ischemia (P < 0.001), neurodevelopment (P < 0.001) and postischemic repair (P < 0.001) among schizophrenia-associated genes from genetic association studies. These findings support both the NDH and the AVIH. The genes from postmortem studies showed an upregulation of vascular-ischemic genes (P = 0.020) combined with downregulated synaptic (P = 0.005) genes, and ND/repair (P = 0.003) genes. Evidence for the AVIH and the NDH is critically discussed. We conclude that schizophrenia is probably a mild adult vascular-ischemic and postischemic repair disorder. Adult postischemic repair involves ND genes for adult neurogenesis, synaptic plasticity, glutamate and increased long-term potentiation of excitatory neurotransmission (i-LTP). Schizophrenia might be caused by the cerebral analog of microvascular angina.

Functional genomics indicate that schizophrenia may be an adult vascular-ischemic disorder

In search for the elusive schizophrenia pathway, candidate genes for the disorder from a discovery sample were localized within the energy-delivering and ischemia protection pathway. To test the adult vascular-ischemic (AVIH) and the competing neurodevelopmental hypothesis (NDH), functional genomic analyses of practically all available schizophrenia-associated genes from candidate gene, genome-wide association and postmortem expression studies were performed. Our results indicate a significant overrepresentation of genes involved in vascular function (P < 0.001), vasoregulation (that is, perivascular (P < 0.001) and shear stress (P < 0.01), cerebral ischemia (P < 0.001), neurodevelopment (P < 0.001) and postischemic repair (P < 0.001) among schizophrenia-associated genes from genetic association studies. These findings support both the NDH and the AVIH. The genes from postmortem studies showed an upregulation of vascular-ischemic genes (P = 0.020) combined with downregulated synaptic (P = 0.005) genes, and ND/repair (P = 0.003) genes. Evidence for the AVIH and the NDH is critically discussed. We conclude that schizophrenia is probably a mild adult vascular-ischemic and postischemic repair disorder. Adult postischemic repair involves ND genes for adult neurogenesis, synaptic plasticity, glutamate and increased long-term potentiation of excitatory neurotransmission (i-LTP). Schizophrenia might be caused by the cerebral analog of microvascular angina.

PhenoPredict: A disease phenome-wide drug repositioning approach towards schizophrenia drug discovery

Schizophrenia (SCZ) is a common complex disorder with poorly understood mechanisms and no effective drug treatments. Despite the high prevalence and vast unmet medical need represented by the disease, many drug companies have moved away from the development of drugs for SCZ. Therefore, alternative strategies are needed for the discovery of truly innovative drug treatments for SCZ. Here, we present a disease phenome-driven computational drug repositioning approach for SCZ. We developed a novel drug repositioning system, PhenoPredict, by inferring drug treatments for SCZ from diseases that are phenotypically related to SCZ. The key to PhenoPredict is the availability of a comprehensive drug treatment knowledge base that we recently constructed. PhenoPredict retrieved all 18 FDA-approved SCZ drugs and ranked them highly (recall=1.0, and average ranking of 8.49%). When compared to PREDICT, one of the most comprehensive drug repositioning systems currently available, in novel predictions, PhenoPredict represented clear improvements over PREDICT in Precision-Recall (PR) curves, with a significant 98.8% improvement in the area under curve (AUC) of the PR curves. In addition, we discovered many drug candidates with mechanisms of action fundamentally different from traditional antipsychotics, some of which had published literature evidence indicating their treatment benefits in SCZ patients. In summary, although the fundamental pathophysiological mechanisms of SCZ remain unknown, integrated systems approaches to studying phenotypic connections among diseases may facilitate the discovery of innovative SCZ drugs.

The borderline of bipolar: opinions of patients and lessons for clinicians on the diagnostic conflict

Aims and method It has been observed that some individuals self-diagnose with a bipolar affective disorder and many are later diagnosed with a borderline personality disorder. There is a background context of clinical and neurobiological overlap between these conditions, and fundamental debates on the validity of current diagnostic systems. This qualitative study is the first work to explore the views of patients caught at this diagnostic interface. We predicted that media exposure, stigma and attribution of responsibility would be key factors affecting patient understanding and opinion. Results Six core illness-differentiating themes emerged: public information, diagnosis delivery, illness causes, illness management, stigma, and relationship with others. Individuals did not 'want' to be diagnosed with a bipolar disorder, but wished for informed care. Clinical implications Understanding patient perspectives will allow clinical staff to better appreciate the difficulties faced by those we seek to help, identify gaps in care provision, and should stimulate thought on our attitudes to care and how we facilitate provision of information, including information about diagnosis.

Genetics and Epigenetics of Eating Disorders

Eating disorders (EDs) are serious psychiatric conditions influenced by biological, psychological, and sociocultural factors. A better understanding of the genetics of these complex traits and the development of more sophisticated molecular biology tools have advanced our understanding of the etiology of EDs. The aim of this review is to critically evaluate the literature on the genetic research conducted on three major EDs: anorexia nervosa (AN), bulimia nervosa (BN), and binge eating disorder (BED). We will first review the diagnostic criteria, clinical features, prevalence, and prognosis of AN, BN, and BED, followed by a review of family, twin, and adoption studies. We then review the history of genetic studies of EDs covering linkage analysis, candidate gene association studies, genome-wide association studies, and the study of rare variants in EDs. Our review also incorporates a translational perspective by covering animal models of ED-related phenotypes. Finally, we review the nascent field of epigenetics of EDs and a look forward to future directions for ED genetic research.

Effects of the nicotinic α7 receptor partial agonist GTS-21 on NMDA-glutamatergic receptor related deficits in sensorimotor gating and recognition memory in rats

RATIONALE

Disturbances in information processing and cognitive function are key features of schizophrenia. Nicotinic α7 acetylcholine receptors (α7-nAChR) are involved in sensory gating and cognition, thereby representing a viable therapeutic strategy.

OBJECTIVES AND METHODS

We investigated the effects of GTS-21, an α7-nAChR partial agonist, on prepulse inhibition (PPI) of acoustic startle in two pharmacologic impairment models in Wistar male rats: NMDA-glutamate receptor antagonism by MK-801 and dopamine receptor agonism by apomorphine. The cognitive effects of GTS-21 were assessed using the object recognition task (ORT) at short (3 h) and long (48 h) delays in Sprague-Dawley male rats. Pharmacological specificity was assessed by methyllycaconitine (MLA) coadministration with GTS-21.

RESULTS

In the PPI task, GTS-21 (1-10 mg/kg) alone did not alter the PPI response or startle amplitude. Coadministration of GTS-21 with MK-801 (0.1 mg/kg) or apomorphine (0.5 mg/kg) abolished the pharmacologic-induced PPI impairment as did the antipsychotics clozapine (5.0 mg/kg) and haloperidol (0.3 mg/kg). MK-801 alone increased startle amplitude which was blocked by GTS-21. In the ORT, GTS-21 (0.1-10 mg/kg) reversed the MK-801 (0.08 mg/kg)-induced memory deficit at the 3 h delay and enhanced memory at the 48 h delay, an effect abolished by MLA (0.313-5 mg/kg).

CONCLUSIONS

The results extend our preclinical pharmacological understanding of GTS-21 to include the ability of GTS-21 to modulate NMDA-glutamate receptor function, in vivo. Given the role of NMDA-glutamate receptor involvement in schizophrenia, α7-nAChR agonists may represent a novel treatment strategy for the pathophysiological deficits of schizophrenia and other psychiatric disorders.

Technological advances for deciphering the complexity of psychiatric disorders: merging proteomics with cell biology

Proteomic studies have increased our understanding of the molecular pathways affected in psychiatric disorders. Mass spectrometry and two-dimensional gel electrophoresis analyses of post-mortem brain samples from psychiatric patients have revealed effects on synaptic, cytoskeletal, antioxidant and mitochondrial protein networks. Multiplex immunoassay profiling studies have found alterations in hormones, growth factors, transport and inflammation-related proteins in serum and plasma from living first-onset patients. Despite these advances, there are still difficulties in translating these findings into platforms for improved treatment of patients and for discovery of new drugs with better efficacy and side effect profiles. This review describes how the next phase of proteomic investigations in psychiatry should include stringent replication studies for validation of biomarker candidates and functional follow-up studies which can be used to test the impact on physiological function. All biomarker candidates should now be tested in series with traditional and emerging cell biological approaches. This should include investigations of the effects of post-translational modifications, protein dynamics and network analyses using targeted proteomic approaches. Most importantly, there is still an urgent need for development of disease-relevant cellular models for improved translation of proteomic findings into a means of developing novel drug treatments for patients with these life-altering disorders.

Sequencing and expression analyses of the synaptic lipid raft adapter gene PAG1 in schizophrenia

Disruption of synaptic networks has been advocated in the pathogenesis of psychiatric diseases like schizophrenia. The majority of synaptic proteins involved in neuronal communications are localized in lipid rafts. These rafts form the platform for coordinating neuronal signal transduction, by clustering interacting partners. The PAG1 protein is a transmembrane adaptor protein in the lipid raft signaling cluster that regulates Src family kinases (SFKs), a convergent point for multiple pathways regulating N-methyl-D-aspartate (NMDA) receptors. Reports of de novo missense mutations in PAG1 and SFK mediated reductions in tyrosine phosphorylation of NMDA receptor subunit proteins in schizophrenia patients, point to a putative role in schizophrenia pathogenesis. To evaluate this, we resequenced the entire coding region of PAG1 in Japanese schizophrenia patients (n = 1,140) and controls (n = 1,140). We identified eight missense variants, of which four were previously unreported. Case-control genetic association analysis of these variants in a larger cohort (n = 4,182) showed neither a statistically significant association of the individual variants with schizophrenia, nor any increased burden of the rare alleles in the patient group. Expression levels of PAG1 in post-mortem brain samples from schizophrenia patients and controls also showed no significant differences. To assess the precise role of PAG1 in schizophrenia, future studies with larger sample sizes are needed.

A genome-wide association analysis of a broad psychosis phenotype identifies three loci for further investigation

BACKGROUND

Genome-wide association studies (GWAS) have identified several loci associated with schizophrenia and/or bipolar disorder. We performed a GWAS of psychosis as a broad syndrome rather than within specific diagnostic categories.

METHODS

1239 cases with schizophrenia, schizoaffective disorder, or psychotic bipolar disorder; 857 of their unaffected relatives, and 2739 healthy controls were genotyped with the Affymetrix 6.0 single nucleotide polymorphism (SNP) array. Analyses of 695,193 SNPs were conducted using UNPHASED, which combines information across families and unrelated individuals. We attempted to replicate signals found in 23 genomic regions using existing data on nonoverlapping samples from the Psychiatric GWAS Consortium and Schizophrenia-GENE-plus cohorts (10,352 schizophrenia patients and 24,474 controls).

RESULTS

No individual SNP showed compelling evidence for association with psychosis in our data. However, we observed a trend for association with same risk alleles at loci previously associated with schizophrenia (one-sided p = .003). A polygenic score analysis found that the Psychiatric GWAS Consortium's panel of SNPs associated with schizophrenia significantly predicted disease status in our sample (p = 5 × 10(-14)) and explained approximately 2% of the phenotypic variance.

CONCLUSIONS

Although narrowly defined phenotypes have their advantages, we believe new loci may also be discovered through meta-analysis across broad phenotypes. The novel statistical methodology we introduced to model effect size heterogeneity between studies should help future GWAS that combine association evidence from related phenotypes. Applying these approaches, we highlight three loci that warrant further investigation. We found that SNPs conveying risk for schizophrenia are also predictive of disease status in our data.

Circadian rhythms and mood: opportunities for multi-level analyses in genomics and neuroscience: circadian rhythm dysregulation in mood disorders provides clues to the brain's organizing principles, and a touchstone for genomics and neuroscience

In the healthy state, both circadian rhythm and mood are stable against perturbations, yet they are capable of adjusting to altered internal cues or ongoing changes in external conditions. The dual demands of stability and flexibility are met by the collective properties of complex neural networks. Disruption of this balance underlies both circadian rhythm abnormality and mood disorders. However, we do not fully understand the network properties that govern the crosstalk between the circadian system and mood regulation. This puzzle reflects a challenge at the center of neurobiology, and its solution requires the successful integration of existing data across all levels of neural organization, from molecules, cells, circuits, network dynamics, to integrated mental function. This essay discusses several open questions confronting the cross-level synthesis, and proposes that circadian regulation, and its role in mood, stands as a uniquely tractable system to study the causal mechanisms of neural adaptation.

A blueprint for research on Shankopathies: a view from research on autism spectrum disorder

Autism spectrum disorders (ASD) are associated with mutations in a host of genes including a number that function in synaptic transmission. Phelan McDermid syndrome involves mutations in SHANK3 which encodes a protein that forms a scaffold for glutamate receptors at the synapse. SHANK3 is one of the genes that underpins the synaptic hypothesis for ASD. We discuss this hypothesis with a view to the broader context of ASD and with special emphasis on highly penetrant genetic disorders including Shankopathies. We propose a blueprint for near and longer-term goals for fundamental and translational research on Shankopathies.

Neurodevelopmental disorders: mechanisms and boundary definitions from genomes, interactomes and proteomes

Neurodevelopmental disorders such as intellectual disability, autism spectrum disorder and schizophrenia lack precise boundaries in their clinical definitions, epidemiology, genetics and protein-protein interactomes. This calls into question the appropriateness of current categorical disease concepts. Recently, there has been a rising tide to reformulate neurodevelopmental nosological entities from biology upward. To facilitate this developing trend, we propose that identification of unique proteomic signatures that can be strongly associated with patient's risk alleles and proteome-interactome-guided exploration of patient genomes could define biological mechanisms necessary to reformulate disorder definitions.

Allosteric modulators for the treatment of schizophrenia: targeting glutamatergic networks

Schizophrenia is a highly debilitating mental disorder which afflicts approximately 1% of the global population. Cognitive and negative deficits account for the lifelong disability associated with schizophrenia, whose symptoms are not effectively addressed by current treatments. New medicines are needed to treat these aspects of the disease. Neurodevelopmental, neuropathological, genetic, and behavioral pharmacological data indicate that schizophrenia stems from a dysfunction of glutamate synaptic transmission, particularly in frontal cortical networks. A number of novel pre- and postsynaptic mechanisms affecting glutamatergic synaptic transmission have emerged as viable targets for schizophrenia. While developing orthosteric glutamatergic agents for these targets has proven extremely difficult, targeting allosteric sites of these targets has emerged as a promising alternative. From a medicinal chemistry perspective, allosteric sites provide an opportunity of finding agents with better drug-like properties and greater target specificity. Furthermore, allosteric modulators are better suited to maintaining the highly precise temporal and spatial aspects of glutamatergic synaptic transmission. Herein, we review neuropathological and genomic/genetic evidence underscoring the importance of glutamate synaptic dysfunction in the etiology of schizophrenia and make a case for allosteric targets for therapeutic intervention. We review progress in identifying allosteric modulators of AMPA receptors, NMDA receptors, and metabotropic glutamate receptors, all with the aim of restoring physiological glutamatergic synaptic transmission. Challenges remain given the complexity of schizophrenia and the difficulty in studying cognition in animals and humans. Nonetheless, important compounds have emerged from these efforts and promising preclinical and variable clinical validation has been achieved.

Analysis of miR-137 expression and rs1625579 in dorsolateral prefrontal cortex

MicroRNAs (miRNAs) are small non-coding RNAs that act as potent regulators of gene expression. A recent GWAS reported the rs1625579 SNP, located downstream of miR-137, as the strongest new association with schizophrenia [Ripke S, Sanders AR, Kendler KS, Levinson DF, Sklar P, Holmans PA, et al. Genome-wide association study identifies five new schizophrenia loci. Nat Genet 2011;43:969-76.]. Prior to this GWAS finding, a schizophrenia imaging-genetic study found miR-137 target genes significantly enriched for association with activation in the dorsolateral prefrontal cortex (DLPFC) [Potkin SG, Macciardi F, Guffanti G, Fallon JH, Wang Q, Turner JA, et al. Identifying gene regulatory networks in schizophrenia. Neuroimage 2010;53:839-47.]. We investigated the expression levels of miR-137 and three candidate target genes (ZNF804A, CACNA1C, TCF4) in the DLPFC of postmortem brain tissue from 2 independent cohorts: (1) 26 subjects (10 control (CTR), 7 schizophrenia (SZ), 9 bipolar disorder (BD)) collected at the UCI brain bank; and (2) 99 subjects (33 CTR, 35 SZ, 31 BD) obtained from the Stanley Medical Research Institute (SMRI). MiR-137 expression in the DLPFC did not differ between diagnoses. We also explored the relationship between rs1625579 genotypes and miR-137 expression. Significantly lower miR-137 expression levels were observed in the homozygous TT subjects compared to TG and GG subjects in the control group (30% decrease, p-value = 0.03). Moreover, reduced miR-137 levels in TT subjects corresponded to increased levels of the miR-137 target gene TCF4. The miR-137 expression pattern in 9 brain regions was significant for regional effect (ANOVA p-value = 1.83E-12), with amygdala and hippocampus having the highest miR-137 expression level. In conclusion, decreased miR-137 expression is associated with the SZ risk allele of rs1625579, and potential regulation of TCF4, another SZ candidate gene. This study offers additional support for involvement of miR-137 and downstream targets as mechanisms of risk for psychiatric disorders.

Prioritization of genetic variants in the microRNA regulome as functional candidates in genome-wide association studies

Comprehensive analyses of results from genome-wide association studies (GWAS) have demonstrated that complex disease/trait-associated loci are enriched in gene regulatory regions of the genome. The search for causal regulatory variation has focused primarily on transcriptional elements, such as promoters and enhancers. microRNAs (miRNAs) are now widely appreciated as critical posttranscriptional regulators of gene expression and are thought to impart stability to biological systems. Naturally occurring genetic variation in the miRNA regulome is likely an important contributor to phenotypic variation in the human population. However, the extent to which polymorphic miRNA-mediated gene regulation underlies GWAS signals remains unclear. In this study, we have developed the most comprehensive bioinformatic analysis pipeline to date for cataloging and prioritizing variants in the miRNA regulome as functional candidates in GWAS. We highlight specific findings, including a variant in the promoter of the miRNA let-7 that may contribute to human height variation. We also provide a discussion of how our approach can be expanded in the future. Overall, we believe that the results of this study will be valuable for researchers interested in determining whether GWAS signals implicate the miRNA regulome in their disease/trait of interest.

Role of DISC1 interacting proteins in schizophrenia risk from genome-wide analysis of missense SNPs

A balanced translocation affecting DISC1 cosegregates with several psychiatric disorders, including schizophrenia, in a Scottish family. DISC1 is a hub protein of a network of protein-protein interactions involved in multiple developmental pathways within the brain. Gene set-based analysis has been proposed as an alternative to individual analysis of single nucleotide polymorphisms (SNPs) to get information from genome-wide association studies. In this work, we tested for an overrepresentation of the DISC1 interacting proteins within the top results of our ranked list of genes based on our previous genome-wide association study of missense SNPs in schizophrenia. Our data set consisted of 5100 common missense SNPs genotyped in 476 schizophrenic patients and 447 control subjects from Galicia, NW Spain. We used a modification of the Gene Set Enrichment Analysis adapted for SNPs, as implemented in the GenGen software. The analysis detected an overrepresentation of the DISC1 interacting proteins (permuted P-value=0.0158), indicative of the role of this gene set in schizophrenia risk. We identified seven leading-edge genes, MACF1, UTRN, DST, DISC1, KIF3A, SYNE1, and AKAP9, responsible for the overrepresentation. These genes are involved in neuronal cytoskeleton organization and intracellular transport through the microtubule cytoskeleton, suggesting that these processes may be impaired in schizophrenia.

Modulation of NMDA receptor function as a treatment for schizophrenia

Schizophrenia is a devastating mental illness that afflicts nearly 1% of the world's population. Currently available antipsychotics treat positive symptoms, but are largely ineffective at addressing negative symptoms and cognitive dysfunction. Thus, improved pharmacotherapies that treat all aspects of the disease remain a critical unmet need. There is mounting evidence that links NMDA receptor hypofunction and the expression of schizophrenia, and numerous drug discovery programs have developed agents that directly or indirectly potentiate NMDA receptor-mediated neurotransmission. Several compounds have emerged that show promise for treating all symptom sub-domains in both preclinical models and clinical studies, and we will review recent developments in many of these areas.