Mapping genomic loci implicates genes and synaptic biology in schizophrenia

Schizophrenia has a heritability of 60-80%1, much of which is attributable to common risk alleles. Here, in a two-stage genome-wide association study of up to 76,755 individuals with schizophrenia and 243,649 control individuals, we report common variant associations at 287 distinct genomic loci. Associations were concentrated in genes that are expressed in excitatory and inhibitory neurons of the central nervous system, but not in other tissues or cell types. Using fine-mapping and functional genomic data, we identify 120 genes (106 protein-coding) that are likely to underpin associations at some of these loci, including 16 genes with credible causal non-synonymous or untranslated region variation. We also implicate fundamental processes related to neuronal function, including synaptic organization, differentiation and transmission. Fine-mapped candidates were enriched for genes associated with rare disruptive coding variants in people with schizophrenia, including the glutamate receptor subunit GRIN2A and transcription factor SP4, and were also enriched for genes implicated by such variants in neurodevelopmental disorders. We identify biological processes relevant to schizophrenia pathophysiology; show convergence of common and rare variant associations in schizophrenia and neurodevelopmental disorders; and provide a resource of prioritized genes and variants to advance mechanistic studies

Altered Functional Subnetwork During Emotional Face Processing

Importance Although deficits in emotional processing are prominent in schizophrenia, it has been difficult to identify neural mechanisms related to the genetic risk for this highly heritable illness. Prior studies have not found consistent regional activation or connectivity alterations in first-degree relatives compared with healthy controls, suggesting that a more comprehensive search for connectomic biomarkers is warranted.Objectives To identify a potential systems-level intermediate phenotype linked to emotion processing in schizophrenia and to examine the psychological association, task specificity, test-retest reliability, and clinical validity of the identified phenotype.Design, Setting, and Participations The study was performed in university research hospitals from June 1, 2008, through December 31, 2013. We examined 58 unaffected first-degree relatives of patients with schizophrenia and 94 healthy controls with an emotional face-matching functional magnetic resonance imaging paradigm. Test-retest reliability was analyzed with an independent sample of 26 healthy participants. A clinical association study was performed in 31 patients with schizophrenia and 45 healthy controls. Data analysis was performed from January 1 to September 30, 2014.Main Outcomes and Measures Conventional amygdala activity and seeded connectivity measures, graph-based global and local network connectivity measures, Spearman rank correlation, intraclass correlation, and gray matter volumes.Results Among the 152 volunteers included in the relative-control sample, 58 were unaffected first-degree relatives of patients with schizophrenia (mean [SD] age, 33.29 [12.56]; 38 were women), and 94 were healthy controls without a first-degree relative with mental illness (mean [SD] age, 32.69 [10.09] years; 55 were women). A graph-theoretical connectivity approach identified significantly decreased connectivity in a subnetwork that primarily included the limbic cortex, visual cortex, and subcortex during emotional face processing (cluster-level P corrected for familywise error = .006) in relatives compared with controls. The connectivity of the same subnetwork was significantly decreased in patients with schizophrenia (F = 6.29, P = .01). Furthermore, we found that this subnetwork connectivity measure was negatively correlated with trait anxiety scores (P = .04), test-retest reliable (intraclass correlation coefficient = 0.57), specific to emotional face processing (F = 17.97, P < .001), and independent of gray matter volumes of the identified brain areas (F = 1.84, P = .18). Replicating previous results, no significant group differences were found in face-related amygdala activation and amygdala–anterior cingulate cortex connectivity (P corrected for familywise error =.37 and .11, respectively).Conclusions and Relevance Our results indicate that altered connectivity in a visual-limbic subnetwork during emotional face processing may be a functional connectomic intermediate phenotype for schizophrenia. The phenotype is reliable, task specific, related to trait anxiety, and associated with manifest illness. These data encourage the further investigation of this phenotype in clinical and pharmacologic studies

Reproducible grey matter patterns index a multivariate, global alteration of brain structure in schizophrenia and bipolar disorder

Schizophrenia is a severe mental disorder characterized by numerous subtle changes in brain structure and function. Machine learning allows exploring the utility of combining structural and functional brain magnetic resonance imaging (MRI) measures for diagnostic application, but this approach has been hampered by sample size limitations and lack of differential diagnostic data. Here, we performed a multi-site machine learning analysis to explore brain structural patterns of T1 MRI data in 2668 individuals with schizophrenia, bipolar disorder or attention-deficit/ hyperactivity disorder, and healthy controls. We found reproducible changes of structural parameters in schizophrenia that yielded a classification accuracy of up to 76% and provided discrimination from ADHD, through it lacked specificity against bipolar disorder. The observed changes largely indexed distributed grey matter alterations that could be represented through a combination of several global brain-structural parameters. This multi-site machine learning study identified a brain-structural signature that could reproducibly differentiate schizophrenia patients from controls, but lacked specificity against bipolar disorder. While this currently limits the clinical utility of the identified signature, the present study highlights that the underlying alterations index substantial global grey matter changes in psychotic disorders, reflecting the biological similarity of these conditions, and provide a roadmap for future exploration of brain structural alterations in psychiatric patients

Reproducible grey matter patterns index a multivariate, global alteration of brain structure in schizophrenia and bipolar disorder

Schizophrenia is a severe mental disorder characterized by numerous subtle changes in brain structure and function. Machine learning allows exploring the utility of combining structural and functional brain magnetic resonance imaging (MRI) measures for diagnostic application, but this approach has been hampered by sample size limitations and lack of differential diagnostic data. Here, we performed a multi-site machine learning analysis to explore brain structural patterns of T1 MRI data in 2668 individuals with schizophrenia, bipolar disorder or attention-deficit/ hyperactivity disorder, and healthy controls. We found reproducible changes of structural parameters in schizophrenia that yielded a classification accuracy of up to 76% and provided discrimination from ADHD, through it lacked specificity against bipolar disorder. The observed changes largely indexed distributed grey matter alterations that could be represented through a combination of several global brain-structural parameters. This multi-site machine learning study identified a brain-structural signature that could reproducibly differentiate schizophrenia patients from controls, but lacked specificity against bipolar disorder. While this currently limits the clinical utility of the identified signature, the present study highlights that the underlying alterations index substantial global grey matter changes in psychotic disorders, reflecting the biological similarity of these conditions, and provide a roadmap for future exploration of brain structural alterations in psychiatric patients

Possible repurposing of pyrvinium pamoate for the treatment of mesothelioma: A pre-clinical assessment

Malignant mesothelioma (MM) is a very aggressive asbestos-related cancer, whose incidence is increasing worldwide. Unfortunately, no effective therapies are currently available and the prognosis is extremely poor. Recently, the anti-helminthic drug pyrvinium pamoate has attracted a strong interest for its anti-cancer activity, which has been demonstrated in many cancer models. Considering the previously established inhibitory effect of pyrvinium pamoate on the Wnt/β-catenin pathway and given the important role of this pathway in MM, we investigated the potential anti-tumor activity of this drug in MM cell lines. We observed that pyrvinium pamoate significantly impairs MM cell proliferation, cloning efficiency, migration, and tumor spheroid formation. At the molecular level, our data show that pyrvinium pamoate down-regulates the expression of β-catenin and Wnt-regulates genes. Overall, our study suggests that the repurposing of pyrvinium pamoate for MM treatment could represent a new promising therapeutic approach

Time-resolved influences of functional DAT1 and COMT variants on visual perception and post-processing

BACKGROUND: Dopamine plays an important role in orienting and the regulation of selective attention to relevant stimulus characteristics. Thus, we examined the influences of functional variants related to dopamine inactivation in the dopamine transporter (DAT1) and catechol-O-methyltransferase genes (COMT) on the time-course of visual processing in a contingent negative variation (CNV) task. METHODS: 64-channel EEG recordings were obtained from 195 healthy adolescents of a community-based sample during a continuous performance task (A-X version). Early and late CNV as well as preceding visual evoked potential components were assessed. RESULTS: Significant additive main effects of DAT1 and COMT on the occipito-temporal early CNV were observed. In addition, there was a trend towards an interaction between the two polymorphisms. Source analysis showed early CNV generators in the ventral visual stream and in frontal regions. There was a strong negative correlation between occipito-temporal visual post-processing and the frontal early CNV component. The early CNV time interval 500-1000 ms after the visual cue was specifically affected while the preceding visual perception stages were not influenced. CONCLUSIONS: Late visual potentials allow the genomic imaging of dopamine inactivation effects on visual post-processing. The same specific time-interval has been found to be affected by DAT1 and COMT during motor post-processing but not motor preparation. We propose the hypothesis that similar dopaminergic mechanisms modulate working memory encoding in both the visual and motor and perhaps other systems

Abstracts from the 23rd Italian congress of Cystic Fibrosis and the 13th National congress of Cystic Fibrosis Italian Society

Cystic Fibrosis (CF) occurs most frequently in caucasian populations. Although less common, this disorder have been reported in all the ethnicities. Currently, there are more than 2000 described sequence variations in CFTR gene, uniformly distributed and including variants pathogenic and benign (CFTR1:www.genet.sickkids.on.ca/). To date,only a subset have been firmily established as variants annotated as disease-causing (CFTR2: www.cftr2.org). The spectrum and the frequency of individual CFTR variants, however, vary among specific ethnic groups and geographic areas. Genetic screening for CF with standard panels of CFTR mutations is widely used for the diagnosis of CF in newborns and symptomatic patients, and to diagnose CF carrier status. These screening panels have an high diagnostic sensitivity (around 85%) for CFTR mutations in caucasians populations but very low for non caucasians. Developed in the last decade, Next-Generation Sequencing (NGS) has been the last breakthrough technology in genetic studies with a substantial reduction in cost per sequenced base and a considerable enhancement of the sequence generation capabilities. Extended CFTR gene sequencing in NGS includes all the coding regions, the splicing sites and their flankig intronic regions, deep intronic regions where are localized known mutations,the promoter and the 5'-3' UTR regions. NGS allows the analysis of many samples concurrently in a shorter period of time compared to Sanger method . Moreover, NGS platforms are able to identify CFTR copy number variation (CNVs), not detected by Sanger sequencing. This technology has provided new and reliable approaches to molecular diagnosis of CF and CFTR-Related Disorders. It also allows to improve the diagnostic sensitivity of newborn and carrier screeningmolecular tests. In fact, bioinformatics tools suitable for all the NGS platforms can filter data generated from the gene sequencing, and analyze only mutations with well-established disease liability. This approach allows the development of targeted mutations panels with a higher number of frequent CF mutations for the target populationcompared to the standard panels and a consequent enhancement of the diagnostic sensitivity. Moreover, in the emerging challenge of diagnosing CF in non caucasians patients, the possibility of customize a NGS targeted mutations panel should increase the diagnostic sensitivity when the target population has different ethnicities