Marco Papalino

DRD2 co-expression network and a related polygenic index predict imaging, behavioral and clinical phenotypes linked to schizophrenia

Genetic risk for schizophrenia (SCZ) is determined by many genetic loci whose compound biological effects are difficult to determine. We hypothesized that co-expression pathways of SCZ risk genes are associated with system-level brain function and clinical phenotypes of SCZ. We examined genetic variants related to the dopamine D2 receptor gene DRD2 co-expression pathway and associated them with working memory (WM) behavior, the related brain activity and treatment response. Using two independent post-mortem prefrontal messenger RNA (mRNA) data sets (total N=249), we identified a DRD2 co-expression pathway enriched for SCZ risk genes. Next, we identified non-coding single-nucleotide polymorphisms (SNPs) associated with co-expression of this pathway. These SNPs were associated with regulatory genetic loci in the dorsolateral prefrontal cortex (P<0.05). We summarized their compound effect on co-expression into a Polygenic Co-expression Index (PCI), which predicted DRD2 pathway co-expression in both mRNA data sets (all P<0.05). We associated the PCI with brain activity during WM performance in two independent samples of healthy individuals (total N=368) and 29 patients with SCZ who performed the n-back task. Greater predicted DRD2 pathway prefrontal co-expression was associated with greater prefrontal activity and longer WM reaction times (all corrected P<0.05), thus indicating inefficient WM processing. Blind prediction of treatment response to antipsychotics in two independent samples of patients with SCZ suggested better clinical course of patientswith greater PCI (total N=87; P<0.05). The findings on this DRD2 co-expression pathway are a proof of concept that gene co-expression can parse SCZ risk genes into biological pathways associated with intermediate phenotypes as well as with clinically meaningful information.

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.

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).

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).

Transcriptomic context of DRD1 is associated with prefrontal activity and behavior during working memory

Significance Dopamine D1 receptors in the prefrontal cortex (PFC) are critical for working memory (WM). However, it is unknown how D1-related genetic background mediates differences in WM performance between humans. Furthermore, previous studies did not consider that DRD1 is likely part of a coregulated molecular network, which may contribute to WM performance and its underlying neural correlates. The key of this research is the identification of a relationship between genetically predicted coexpression and WM processing. In particular, genetically predicted greater DRD1-related coexpression was associated with lower PFC activity and higher WM performance, indicating greater WM efficiency. Our findings may help to link gene expression with brain activity and to develop WM-enhancing drugs by differentiating individuals based on their genetic background. Dopamine D1 receptor (D1R) signaling shapes prefrontal cortex (PFC) activity during working memory (WM). Previous reports found higher WM performance associated with alleles linked to greater expression of the gene coding for D1Rs (DRD1). However, there is no evidence on the relationship between genetic modulation of DRD1 expression in PFC and patterns of prefrontal activity during WM. Furthermore, previous studies have not considered that D1Rs are part of a coregulated molecular environment, which may contribute to D1R-related prefrontal WM processing. Thus, we hypothesized a reciprocal link between a coregulated (i.e., coexpressed) molecular network including DRD1 and PFC activity. To explore this relationship, we used three independent postmortem prefrontal mRNA datasets (total n = 404) to characterize a coexpression network including DRD1. Then, we indexed network coexpression using a measure (polygenic coexpression index—DRD1-PCI) combining the effect of single nucleotide polymorphisms (SNPs) on coexpression. Finally, we associated the DRD1-PCI with WM performance and related brain activity in independent samples of healthy participants (total n = 371). We identified and replicated a coexpression network including DRD1, whose coexpression was correlated with DRD1-PCI. We also found that DRD1-PCI was associated with lower PFC activity and higher WM performance. Behavioral and imaging results were replicated in independent samples. These findings suggest that genetically predicted expression of DRD1 and of its coexpression partners stratifies healthy individuals in terms of WM performance and related prefrontal activity. They also highlight genes and SNPs potentially relevant to pharmacological trials aimed to test cognitive enhancers modulating DRD1 signaling.

Brain scans from 21,297 individuals reveal the genetic architecture of hippocampal subfield volumes

The hippocampus is a heterogeneous structure, comprising histologically distinguishable subfields. These subfields are differentially involved in memory consolidation, spatial navigation and pattern separation, complex functions often impaired in individuals with brain disorders characterized by reduced hippocampal volume, including Alzheimer’s disease (AD) and schizophrenia. Given the structural and functional heterogeneity of the hippocampal formation, we sought to characterize the subfields’ genetic architecture. T1-weighted brain scans (n = 21,297, 16 cohorts) were processed with the hippocampal subfields algorithm in FreeSurfer v6.0. We ran a genome-wide association analysis on each subfield, co-varying for whole hippocampal volume. We further calculated the single-nucleotide polymorphism (SNP)-based heritability of 12 subfields, as well as their genetic correlation with each other, with other structural brain features and with AD and schizophrenia. All outcome measures were corrected for age, sex and intracranial volume. We found 15 unique genome-wide significant loci across six subfields, of which eight had not been previously linked to the hippocampus. Top SNPs were mapped to genes associated with neuronal differentiation, locomotor behaviour, schizophrenia and AD. The volumes of all the subfields were estimated to be heritable (h2 from 0.14 to 0.27, all p < 1 × 10–16) and clustered together based on their genetic correlations compared with other structural brain features. There was also evidence of genetic overlap of subicular subfield volumes with schizophrenia. We conclude that hippocampal subfields have partly distinct genetic determinants associated with specific biological processes and traits. Taking into account this specificity may increase our understanding of hippocampal neurobiology and associated pathologies.