Pasquale Di Carlo

Grey matter volume patterns in thalamic nuclei are associated with familial risk for schizophrenia.

Previous evidence suggests reduced thalamic grey matter volume (GMV) in patients with schizophrenia (SCZ). However, it is not considered an intermediate phenotype for schizophrenia, possibly because previous studies did not assess the contribution of individual thalamic nuclei and employed univariate statistics. Here, we hypothesized that multivariate statistics would reveal an association of GMV in different thalamic nuclei with familial risk for schizophrenia. We also hypothesized that accounting for the heterogeneity of thalamic GMV in healthy controls would improve the detection of subjects at familial risk for the disorder. We acquired MRI scans for 96 clinically stable SCZ, 55 non-affected siblings of patients with schizophrenia (SIB), and 249 HC. The thalamus was parceled into seven regions of interest (ROIs). After a canonical univariate analysis, we used GMV estimates of thalamic ROIs, together with total thalamic GMV and premorbid intelligence, as features in Random Forests to classify HC, SIB, and SCZ. Then, we computed a Misclassification Index for each individual and tested the improvement in SIB detection after excluding a subsample of HC misclassified as patients. Random Forests discriminated SCZ from HC (accuracy=81%) and SIB from HC (accuracy=75%). Left anteromedial thalamic volumes were significantly associated with both multivariate classifications (p<0.05). Excluding HC misclassified as SCZ improved greatly HC vs. SIB classification (Cohens d=1.39). These findings suggest that multivariate statistics identify a familial background associated with thalamic GMV reduction in SCZ. They also suggest the relevance of inter-individual variability of GMV patterns for the discrimination of individuals at familial risk for the disorder.

A complex network approach reveals a pivotal substructure of genes linked to schizophrenia

Research on brain disorders with a strong genetic component and complex heritability, such as schizophrenia, has led to the development of brain transcriptomics. This field seeks to gain a deeper understanding of gene expression, a key factor in exploring further research issues. Our study focused on how genes are associated amongst each other. In this perspective, we have developed a novel data-driven strategy for characterizing genetic modules, i.e., clusters of strongly interacting genes. The aim was to uncover a pivotal community of genes linked to a target gene for schizophrenia. Our approach combined network topological properties with information theory to highlight the presence of a pivotal community, for a specific gene, and to simultaneously assess the information content of partitions with the Shannon’s entropy based on betweenness. We analyzed the publicly available BrainCloud dataset containing post-mortem gene expression data and focused on the Dopamine D2 receptor, encoded by the DRD2 gene. We used four different community detection algorithms to evaluate the consistence of our approach. A pivotal DRD2 community emerged for all the procedures applied, with a considerable reduction in size, compared to the initial network. The stability of the results was confirmed by a Dice index ≥80% within a range of tested parameters. The detected community was also the most informative, as it represented an optimization of the Shannon entropy. Lastly, we verified the strength of connection of the DRD2 community, which was stronger than any other randomly selected community and even more so than the Weighted Gene Co-expression Network Analysis module, commonly considered the standard approach for such studies. This finding substantiates the conclusion that the detected community represents a more connected and informative cluster of genes for the DRD2 community, and therefore better elucidates the behavior of this module of strongly related DRD2 genes. Because this gene plays a relevant role in Schizophrenia, this finding of a more specific DRD2 community will improve the understanding of the genetic factors related with this disorder.

Association of functional genetic variation in PP2A with prefrontal working memory processing

HighlightsRs959627, a SNP in PPP2R2B modulates postmortem prefrontal cortex gene expression.The same SNP affects prefrontal cortex function during Working Memory processing.Putative mechanism relies on modulation on D2 cAMP‐independent dopamine signaling. ABSTRACT Variation in prefrontal dopaminergic signaling mediated by D2 receptor has been implicated in cognitive phenotypes of schizophrenia, including working memory. Molecular cascades downstream of D2 receptor include a cAMP‐dependent‐ and a cAMP‐independent‐pathway. Protein‐Phosphatase‐2A (PP2A) is a key partner of D2 receptor in cAMP‐independent signaling. This enzyme comprises a regulatory subunit that is coded by PPP2R2B gene. Given the molecular relationship between PP2A and D2 signaling, we hypothesized genetic variation in PPP2R2B affecting mRNA expression of this gene in prefrontal cortex to be associated with prefrontal processing during working memory. In order to probe such a hypothesis we investigated SNPs associated with PPP2R2B expression in two independent samples of human postmortem prefrontal cortex. Then, we tested SNPs for which association was replicated as predictors of prefrontal activity during WM as probed by functional magnetic resonance (fMRI) in a sample of healthy humans. We found that a SNP associated with PPP2R2B expression (rs959627) predicted prefrontal activity during the N‐Back working memory task. In particular, individuals carrying rs959627T allele, a condition associated with lower PPP2R2B expression in postmortem prefrontal cortex, showed greater activity in right inferior frontal gyrus (IFG) during N‐Back compared to CC subjects. Furthermore, such an activity was negatively correlated with behavioral performance at the task. Consistently with previous studies, these findings suggest reduced right IFG efficiency during working memory processing in rs959627 T‐carriers, as indexed by their greater need to activate this brain region in order to achieve similar levels of behavioral proficiency as compared to CC individuals.

Genetic Variation of a DRD2 Co-expression Network is Associated with Changes in Prefrontal Function After D2 Receptors Stimulation

&NA; Dopamine D2 receptors (D2Rs) contribute to the inverted U‐shaped relationship between dopamine signaling and prefrontal function. Genetic networks from post‐mortem human brain revealed 84 partner genes co‐expressed with DRD2. Moreover, eight functional single nucleotide polymorphisms combined into a polygenic co‐expression index (PCI) predicted co‐expression of this DRD2 network and were associated with prefrontal function in humans. Here, we investigated the non‐linear association of the PCI with behavioral and Working Memory (WM) related brain response to pharmacological D2Rs stimulation. Fifty healthy volunteers took part in a double‐blind, placebo‐controlled, functional MRI (fMRI) study with bromocriptine and performed the N‐Back task. The PCI by drug interaction was significant on both WM behavioral scores (P = 0.046) and related prefrontal activity (all corrected P < 0.05) using a polynomial PCI model. Non‐linear responses under placebo were reversed by bromocriptine administration. fMRI results on placebo were replicated in an independent sample of 50 participants who did not receive drug administration (P = 0.034). These results match earlier evidence in non‐human primates and confirm the physiological relevance of this DRD2 co‐expression network. Results show that in healthy subjects, different alleles evaluated as an ensemble are associated with non‐linear prefrontal responses. Therefore, brain response to a dopaminergic drug may depend on a complex system of allelic patterns associated with DRD2 co‐expression.

Topological Complex Networks Properties for Gene Community Detection Strategy: DRD2 Case Study

Gene interactions can suitably be modeled as communities through weighted complex networks. However, the problem to efficiently detect these communities , eventually gaining biological insight from them, is still an open question. This paper presents a novel data-driven strategy for community detection and tests it on the specific case study of DRD2 gene coding for the D2 dopamine receptor, which plays a prominent role in risk for Schizophrenia . We adopt a combined use of centrality and topological properties to detect an optimal network partition. We find that 21 genes belongs with our target community with probability \(P \ge 90\,\%\). The robustness of the partition is assessed with two independent methodologies: (i) fuzzy c-means and (ii) consensus analyses . We use the first one to measure how strong the membership of these genes to the DRD2 community is and the latter to confirm the stability of the detected partition. These results show an interesting reduction (\({\sim }80\,\%\)) of the target community size. Moreover, to allow this validation on different case studies, the proposed methodology is available on an open cloud infrastructure, according to the Software as a Service paradigm (SaaS).

A Polygenic Risk Score of glutamatergic SNPs associated with schizophrenia predicts attentional behavior and related brain activity in healthy humans

Multiple genetic variations impact on risk for schizophrenia. Recent analyses by the Psychiatric Genomics Consortium (PGC2) identified 128 SNPs genome-wide associated with the disorder. Furthermore, attention and working memory deficits are core features of schizophrenia, are heritable and have been associated with variation in glutamatergic neurotransmission. Based on this evidence, in a sample of healthy volunteers, we used SNPs associated with schizophrenia in PGC2 to construct a Polygenic-Risk-Score (PRS) reflecting the cumulative risk for schizophrenia, along with a Polygenic-Risk-Score including only SNPs related to genes implicated in glutamatergic signaling (Glu-PRS). We performed Factor Analysis for dimension reduction of indices of cognitive performance. Furthermore, both PRS and Glu-PRS were used as predictors of cognitive functioning in the domains of Attention, Speed of Processing and Working Memory. The association of the Glu-PRS on brain activity during the Variable Attention Control (VAC) task was also explored. Finally, in a second independent sample of healthy volunteers we sought to confirm the association between the Glu-PRS and both performance in the domain of Attention and brain activity during the VAC.We found that performance in Speed of Processing and Working Memory was not associated with any of the Polygenic-Risk-Scores. The Glu-PRS, but not the PRS was associated with Attention and brain activity during the VAC. The specific effects of Glu-PRS on Attention and brain activity during the VAC were also confirmed in the replication sample.Our results suggest a pathway specificity in the relationship between genetic risk for schizophrenia, the associated cognitive dysfunction and related brain processing.

Genetics of brain age suggest an overlap with common brain disorders

Numerous genetic and environmental factors contribute to psychiatric disorders and other brain disorders. Common risk factors likely converge on biological pathways regulating the optimization of brain structure and function across the lifespan. Here, using structural magnetic resonance imaging and machine learning, we estimated the gap between brain age and chronological age in 36,891 individuals aged 3 to 96 years, including individuals with different brain disorders. We show that several disorders are associated with accentuated brain aging, with strongest effects in schizophrenia, multiple sclerosis and dementia, and document differential regional patterns of brain age gaps between disorders. In 16,269 healthy adult individuals, we show that brain age gap is heritable with a polygenic architecture overlapping those observed in common brain disorders. Our results identify brain age gap as a genetically modulated trait that offers a window into shared and distinct mechanisms in different brain disorders.

Prefrontal co-expression of schizophrenia risk genes is associated with treatment response in patients

Gene co-expression networks are relevant to functional and clinical translation of schizophrenia (SCZ) risk genes. We hypothesized that SCZ risk genes may converge into coexpression pathways which may be associated with gene regulation mechanisms and with response to treatment in patients with SCZ. We identified gene co-expression networks in two prefrontal cortex post-mortem RNA sequencing datasets (total N=688) and replicated them in four more datasets (total N=227). We identified and replicated (all p-values<.001) a single module enriched for SCZ risk loci (13 risk genes in 10 loci). In silico screening of potential regulators of the SCZ risk module via bioinformatic analyses identified two transcription factors and three miRNAs associated with the risk module. To translate post-mortem information into clinical phenotypes, we identified polymorphisms predicting co-expression and combined them to obtain an index approximating module co-expression (Polygenic Co-expression Index: PCI). The PCI-co-expression association was successfully replicated in two independent brain transcriptome datasets (total N=131; all p-values<.05). Finally, we tested the association between the PCI and short-term treatment response in two independent samples of patients with SCZ treated with olanzapine (total N=167). The PCI was associated with treatment response in the positive symptom domain in both clinical cohorts (all p-values<.05). In summary, our findings in a large sample of human post-mortem prefrontal cortex show that coexpression of a set of genes enriched for schizophrenia risk genes is relevant to treatment response. This co-expression pathway may be co-regulated by transcription factors and miRNA associated with it.

T10. HERITABILITY OF AMYGDALA ACTIVITY AND ITS GENOME WIDE ASSOCIATION WITH THE SCHIZOPHRENIA RISK LOCUS OF MIR137

Abstract Background It is well known that heritability plays a prominent role in risk for schizophrenia, and that this brain disorder is crucially characterized by emotional symptoms. Less known is how heritability shapes brain activity during emotion processing and whether this brain phenotype is also associated with genetic variation increasing risk for schizophrenia. Here, we implemented a multi-step, data-driven approach in order to assess the relevance of the link between heritability, genetic variation, and schizophrenia for brain activity during emotion processing. Methods We investigated three samples of healthy individuals and one sample of schizophrenia (SCZ) patients: i) 28 healthy twin pairs (16 monozygotic and 12 dizygotic twin pairs); ii) 289 unrelated healthy participants (genome-wide association study - GWAS -discovery sample); iii) 90 unrelated healthy participants (replication sample); iv) 40 SCZ patients. During fMRI, participants approached or avoided threatening angry faces (explicit emotion processing). Intra-class correlations (ICC) between twin pairs and ACE models (A: additive genetics; C: common environment; E: unique environment) were used to identify regions of interest (ROIs) with heritable functional activity. Then, we extracted BOLD signal from these ROIs and conducted a GWAS on 565,137 single nucleotide polymorphisms (SNPs) (selected with the following criteria: minor allele frequency>0.15, Hardy–Weinberg equilibrium<0.001, linkage disequilibrium pruning r2>0.9) using robust linear models of allelic dosage corrected for multiple comparisons (Gao et al. 2008 Genetic Epidemiology). Finally, we assessed the effect of surviving SNPs in the replication sample of healthy individuals as well as in the sample of SCZ patients. Results In healthy twins, we identified bilateral amygdala as the brain region with the highest heritability during explicit emotion processing as evaluated with our task (ICC=.79; h2=0.54; p<.001). The subsequent GWAS in healthy non-twins indicated that bilateral amygdala activity during the task was associated with a polymorphism close to miR-137 (rs1198575) (p=1.5 × 10–7), with the C allele corresponding to lower activity than the t allele. A similar effect was found in the replication sample (p=.01) and in patients with SCZ (p=.03). Discussion Our data-driven approach revealed that amygdala activity as evaluated with our task is heritable. Furthermore, our results indicate that a polymorphism in miR-137 has genome wide association with amygdala response during emotion processing which is also replicated in two independent samples of healthy subjects and of patients with schizophrenia. Previous findings indicated that this polymorphism has genome-wide association with schizophrenia (Ripke et al. 2014). Other results reveal that miR-137 is a key regulatory neuronal factor linked to SCZ and involved in emotion processing (Cosgrove et al., 2017). Our findings are consistent with these previous findings and further highlight a crucial role for miR-137 in emotion processing and SCZ (Anticevic et al., 2012 Schizophr Bull).

T197. A DRD2 CO-EXPRESSION GENE SET ENRICHED FOR SCHIZOPHRENIA RISK GENES IS CHARACTERIZED BY A COMMON TRANSCRIPTIONAL REGULATION INVOLVING NURR1 TRANSCRIPTION FACTOR

Abstract Background Genome-wide association studies demonstrated that multiple genetic variants are associated with schizophrenia (SCZ). Additional evidence revealed that genes are prone to operate in functional molecular networks that subtend complex clinical phenotypes. This knowledge raises the need to investigate how genes linked to SCZ and their possible co-regulators are inserted into molecular networks with a key impact in disease pathogenesis. Using post-mortem brain mRNA data sets (Pergola et al. 2017), we have previously identified a co-expression network enriched for SCZ risk genes, including DRD2, the gene coding for the D2 dopamine receptor, and predicted cognitive and neuroimaging phenotypes of SCZ, as well as response to antipsychotic treatment. Given the relevance of DRD2 to the pathophysiology of SCZ, in the current study we sought to further our understanding of biological mechanisms underpinning co-expression of the DRD2 network. In detail, we aimed at probing the hypothesis that expression of genes within the DRD2-related co-expression network is modulated by a common transcriptional regulation involving one or more Transcription factors (TFs). Methods In order to identify TF binding sites (TFBSs) in the promoter region of the 85 genes belonging to the DRD2 co-expression network, we performed a motif enrichment analysis using Pscan and Genomatix MatInspector tools. Biological validation experiments were performed in primary mouse cortical neurons. By real-time PCR analysis we measured the mRNA transcript levels of a group of genes included in the DRD2 co-expression module in basal conditions and upon viral vector-mediated overexpression (OE) and knockdown (KD) of the predicted TFs. We studied expression of genes linked to either DRD2 in the co-expression gene set, such as Gpld1, Chit1, Btg4 and Osr1, or SCZ risk, such as Gatad2a and Slc28a1. We also analyzed transcript levels of Cnr1, which mediates cannabinoid-induced transmission and is relevant to SCZ. Moreover, we analyzed the transcript levels of D2 long splicing isoform (D2L), which was included in the co-expression network, and D2 short splicing isoform (D2S), to verify whether TF regulation was specific for D2L. Results Promoters of the DRD2 co-expression gene set were enriched for two TFBSs, recognized by Nur-Related Factor 1 (NURR1, FDR-adjusted p=0.03) and Estrogen-Related Receptor Alpha (ERR1, FDR-adjusted p=0.02), respectively. Validation experiments in mouse primary cortical neurons established that NURR1, and not ERR1, is a regulator of genes of the DRD2 co-expression module analyzed in this study. In detail, Nurr1 gain of function (OE) decreased Cnr1 transcript levels (p=0.0002), whereas it increased Gpld1 transcript levels (p=0.03). The transcript levels of these genes showed an opposite expression trend upon Nurr1 KD. D2L (p=0.008), but not D2S, Gatad2a (p=0.00001), Slc28a1 (p=0.0005) and Chit1 (p=0.02) showed significant expression profile changes only upon Nurr1 OE and vice versa. Discussion NURR1 participates in developmental, differentiation and survival processes of dopaminergic neurons. It is implicated in transcriptional modulation of genes involved in dopaminergic transmission, including DRD2, as well as in behavioral phenotypes related to dopaminergic anomalies and reminiscent of SCZ in animal models. Finally, NURR1 genetic variation has been associated with SCZ. Taken together, our results are consistent with previous literature and with the hypothesis that molecular mechanisms responsible for co-expression in DRD2 network involve transcriptional regulation by NURR1. They also suggest reliability of our DRD2 co-expression network, and add new insights on mechanisms linked to DRD2-related molecular ensembles and to SCZ.