Evidence of an interaction between FXR1 and GSK3β polymorphisms on levels of Negative Symptoms of Schizophrenia and their response to antipsychotics

Introduction: Genome Wide Association Studies (GWAS) have identified several genes associated with schizophrenia (SCZ) and exponentially increased knowledge on the genetic basis of the disease. Additionally, products of GWAS genes interact with neuronal factors coded by genes lacking association, such that this interaction may confer risk for specific phenotypes of this brain disorder. In this regard, FXR1 (Fragile-X mental-retardation-syndrome-related 1) gene has been GWAS associated with SCZ. FXR1 protein is regulated by Glycogen Synthase Kinase-3 (GSK3), which has been implicated in pathophysiology of SCZ and response to Antipsychotics (APs). rs496250 and rs12630592, two eQTLs of FXR1 and GSK3 respectively, interact on emotion stability and amygdala/PFC activity during emotion processing. These two phenotypes are associated with Negative Symptoms (NS) of SCZ suggesting that the interaction between these SNPs may also affect NS severity and responsiveness to medication. Methods: To test this hypothesis, in two independent samples of patients with SCZ, we investigated rs496250 by rs12630592 interaction on NS severity and response to APs. We also tested a putative link between APs administration and fxr1 expression, as already reported for GSK3 expression. Results: We found that rs496250 and rs12630592 interact on NS severity. We also found evidence suggesting interaction of these polymorphisms also on response to APs. This interaction was not present when looking at positive and general psychopathology scores. Furthermore, chronic olanzapine administration led to a reduction of FXR1 expression in mouse frontal cortex. Discussion: Our findings suggest that, like GSK3 , FXR1 is affected by APs while shedding new light on the role of the FXR1/GSK3 pathway for NS of SCZ

Dopamine signaling enriched striatal gene set predicts striatal dopamine synthesis and physiological activity in vivo

The polygenic architecture of schizophrenia implicates several molecular pathways involved in synaptic function. However, it is unclear how polygenic risk funnels through these pathways to translate into syndromic illness. Using tensor decomposition, we analyze gene co-expression in the caudate nucleus, hippocampus, and dorsolateral prefrontal cortex of post-mortem brain samples from 358 individuals. We identify a set of genes predominantly expressed in the caudate nucleus and associated with both clinical state and genetic risk for schizophrenia that shows dopaminergic selectivity. A higher polygenic risk score for schizophrenia parsed by this set of genes predicts greater dopamine synthesis in the striatum and greater striatal activation during reward anticipation. These results translate dopamine-linked genetic risk variation into in vivo neurochemical and hemodynamic phenotypes in the striatum that have long been implicated in the pathophysiology of schizophrenia

Lessons Learned From Parsing Genetic Risk for Schizophrenia Into Biological Pathways

: The clinically heterogeneous presentation of schizophrenia is compounded by the heterogeneity of risk factors and neurobiological correlates of the disorder. Genome-wide association studies in schizophrenia have uncovered a remarkably high number of genetic variants, but the biological pathways they impact upon remain largely unidentified. Among the diverse methodological approaches employed to provide a more granular understanding of genetic risk for schizophrenia, the use of biological labels, such as gene ontologies, regulome approaches, and gene coexpression have all provided novel perspectives into how genetic risk translates into the neurobiology of schizophrenia. Here, we review the salient aspects of parsing polygenic risk for schizophrenia into biological pathways. We argue that parsed scores, compared to standard polygenic risk scores, may afford a more biologically plausible and accurate physiological modeling of the different dimensions involved in translating genetic risk into brain mechanisms, including multiple brain regions, cell types, and maturation stages. We discuss caveats, opportunities, and pitfalls inherent in the parsed risk approach

Prefrontal interneuron genes underlie neurobiological processes shared between psychiatric disorders

Previous reports on brain co-expression networks, mostly applied to tissue homogenates, have utilized cluster-based strategies for assigning genes to a unique co-expression cluster. This feature is incompatible with the diversity of cell populations and cellular components involved. Single-cell RNA-sequencing provides a finer-grained resolution than bulk tissue when interrogating brain cell types, dynamic states, and functional processes. We uncovered co-expression patterns across different brain cell types by applying tensor decomposition to single-nucleus transcriptomes from the prefrontal cortex of male patients with depression who died by suicide. We identified a gene set differentially co-expressed in inhibitory neurons between patients and controls and enriched for genes associated with major depression and schizophrenia

Dorsolateral Prefrontal Cortex Single Nuclei Tensor Decomposition Identifies Shared Genetic Risk for Major Depressive Disorder and Schizophrenia in Suicidal Subjects

Previous reports on brain co-expression networks have assigned genes to unique co-expression gene sets using cluster-based strategies. This feature is incompatible with the diversity of cell populations involved. We investigated gene co-expression in single nuclei RNA-sequencing from the dorsolateral prefrontal cortex of patients with Major Depressive Disorder (MDD) and neurotypical controls (NC) using Tensor Models to identify components with overlapping gene memberships. We associated co-expression components with diagnosis and risk for psychiatric disorders

Tumori stromali gastrici:su tre casi clinici trattati con resezioni videoassistite

In conclusione riteniamo che: 1) i GIST debbano essere trattati con ampia terapia resettiva in quanto la loro potenziale malignità è imprevedibile utilizzando i tradizionali parametri prognostici; 2) la loro exeresi è notevolmente semplificata dall’ausilio della assistenza videolaparoscopica. 3) lo studio istopatologico definitivo è cruciale per la verifica della adeguatezza della exeresi e per la definizine del successivo follow-up dei pazient

GENE CONNECTIVITY ANALYSIS OF CO-EXPRESSION NETWORKS PROVIDES INSIGHTS INTO THE OMNIGENIC MODEL AND IDENTIFIES NOVEL GENETIC HUBS OF SCHIZOPHRENIA RISK

The omnigenic model posits that genetic risk for a heritable disease arises from cumulative effects of many peripheral genes on “core genes” that mechanistically drive disease traits. A relevant viewpoint to observe the implications of the omnigenic model is offered by co-expression networks. Within a co-expressed gene set, the most central genes are best poised to connect to all genes, including potential core genes, as long as genetic risk converges into the gene set. We thus evaluated the relationship between genome wide association study (GWAS) statistics and gene-set connectivity to interrogate omnigenic architecture in schizophrenia (SCZ) and other disorders

Prefrontal Co-Expression of miR-137 Target Genes is Related With Prefrontal Activity During Emotion Recognition

Schizophrenia risk is associated with multiple genes. Co-expression is a possible mechanism mediating the orchestrated contribution of these genes to risk. As key regulators of co-expression, miRNAs with a proven involvement in Schizophrenia such as miRNA-137 can be nodes of convergence of risk on the disorder system-level phenotypes. We hypothesized that miRNA-137 target genes converge in a co-expression pathway associated with Schizophrenia risk and that co-expression of these genes is linked with system-level phenotypes previously related with miRNA-137

The interaction between early life complications and a polygenic risk score for schizophrenia is associated with brain activity during emotion processing in healthy participants

Background: Previous evidence suggests that early life complications (ELCs) interact with polygenic risk for schizophrenia (SCZ) in increasing risk for the disease. However, no studies have investigated this interaction on neurobiological phenotypes. Among those, anomalous emotion-related brain activity has been reported in SCZ, even if evidence of its link with SCZ-related genetic risk is not solid. Indeed, it is possible this relationship is influenced by non-genetic risk factors. Thus, this study investigated the interaction between SCZ-related polygenic risk and ELCs on emotion-related brain activity. Methods: 169 healthy participants (HP) in a discovery and 113 HP in a replication sample underwent functional magnetic resonance imaging (fMRI) during emotion processing, were categorized for history of ELCs and genome-wide genotyped. Polygenic risk scores (PRSs) were computed using SCZ-associated variants considering the most recent genome-wide association study. Furthermore, 75 patients with SCZ also underwent fMRI during emotion processing to verify consistency of their brain activity patterns with those associated with risk factors for SCZ in HP. Results: Results in the discovery and replication samples indicated no effect of PRSs, but an interaction between PRS and ELCs in left ventrolateral prefrontal cortex (VLPFC), where the greater the activity, the greater PRS only in presence of ELCs. Moreover, SCZ had greater VLPFC response than HP. Conclusions: These results suggest that emotion-related VLPFC response lies in the path from genetic and non-genetic risk factors to the clinical presentation of SCZ, and may implicate an updated concept of intermediate phenotype considering early non-genetic factors of risk for SCZ