The interaction between cannabis use and a CB1-related polygenic co-expression index modulates dorsolateral prefrontal activity during working memory processing.

Convergent findings indicate that cannabis use and variation in the cannabinoid CB1 receptor coding gene (CNR1) modulate prefrontal function during working memory (WM). Other results also suggest that cannabis modifies the physiological relationship between genetically induced expression of CNR1 and prefrontal WM processing. However, it is possible that cannabis exerts its modifying effect on prefrontal physiology by interacting with complex molecular ensembles co-regulated with CB1. Since co-regulated genes are likely co-expressed, we investigated how genetically predicted co-expression of a molecular network including CNR1 interacts with cannabis use in modulating WM processing in humans. Using post-mortem human prefrontal data, we first computed a polygenic score (CNR1-PCI), combining the effects of single nucleotide polymorphisms (SNPs) on co-expression of a cohesive gene set including CNR1, and positively correlated with such co-expression. Then, in an in vivo study, we computed CNR1-PCI in 88 cannabis users and 147 non-users and investigated its interaction with cannabis use on brain activity during WM. Results revealed an interaction between cannabis use and CNR1-PCI in the dorsolateral prefrontal cortex (DLPFC), with a positive relationship between CNR1-PCI and DLPFC activity in cannabis users and a negative relationship in non-users. Furthermore, DLPFC activity in cannabis users was positively correlated with the frequency of cannabis use. Taken together, our results suggest that co-expression of a CNR1-related network predicts WM-related prefrontal activation as a function of cannabis use. Furthermore, they offer novel insights into the biological mechanisms associated with the use of cannabis.

Thalamic connectivity measured with fMRI is associated with a polygenic index predicting thalamo-prefrontal gene co-expression.

The functional connectivity between thalamic medio-dorsal nucleus (MD) and cortical regions, especially the dorsolateral prefrontal cortex (DLPFC), is implicated in attentional processing and is anomalous in schizophrenia, a brain disease associated with polygenic risk and attentional deficits. However, the molecular and genetic underpinnings of thalamic connectivity anomalies are unclear. Given that gene co-expression across brain areas promotes synchronous interregional activity, our aim was to investigate whether coordinated expression of genes relevant to schizophrenia in MD and DLPFC may reflect thalamic connectivity anomalies in an attention-related network including the DLPFC. With this aim, we identified in datasets of post-mortem prefrontal mRNA expression from healthy controls a gene module with robust overrepresentation of genes with coordinated MD-DLPFC expression and enriched for schizophrenia genes according to the largest genome-wide association study to date. To link this gene cluster with imaging phenotypes, we computed a Polygenic Co-Expression Index (PCI) combining single-nucleotide polymorphisms predicting module co-expression. Finally, we investigated the association between PCI and thalamic functional connectivity during attention through fMRI Independent Component Analysis in 265 healthy participants. We found that PCI was positively associated with connectivity strength of a thalamic region overlapping with the MD within an attention brain circuit. These findings identify a novel association between schizophrenia-related genes and thalamic functional connectivity. Furthermore, they highlight the association between gene expression co-regulation and brain connectivity, such that genes with coordinated MD-DLPFC expression are associated with coordinated activity between the same brain regions. We suggest that gene co-expression is a plausible mechanism underlying biological phenotypes of schizophrenia.

Shannon entropy approach reveals relevant genes in Alzheimer's disease.

Alzheimer's disease (AD) is the most common type of dementia and affects millions of people worldwide. Since complex diseases are often the result of combinations of gene interactions, microarray data and gene co-expression analysis can provide tools for addressing complexity. Our study aimed to find groups of interacting genes that are relevant in the development of AD. In this perspective, we implemented a method proposed in a previous work to detect gene communities linked to AD. Our strategy combined co-expression network analysis with the study of Shannon entropy of the betweenness. We analyzed the publicly available GSE1297 dataset, achieved from the GEO database in NCBI, containing hippocampal gene expression of 9 control and 22 AD human subjects. Co-expressed genes were clustered into different communities. Two communities of interest (composed by 72 and 39 genes) were found by calculating the correlation coefficient between communities and clinical features. The detected communities resulted stable, replicated on two independent datasets and mostly enriched in pathways closely associated with neuro-degenative diseases. A comparison between our findings and other module detection techniques showed that the detected communities were more related to AD phenotype. Lastly, the hub genes within the two communities of interest were identified by means of a centrality analysis and a bootstrap procedure. The communities of the hub genes presented even stronger correlation with clinical features. These findings and further explorations on the detected genes could shed light on the genetic aspects related with physiological aspects of Alzheimer's disease.

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

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.

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.

Deep inferior epigastric artery perforator flap donor-site closure with cannula-assisted, limited undermining, and progressive high-tension sutures versus standard abdominoplasty: complications, sensitivity, and cosmetic outcomes.

BACKGROUND: In deep inferior epigastric artery perforator (DIEP) flap breast reconstruction, abdominal donor-site cosmetic and sensibility outcomes and the closure technique have drawn little attention in the literature, with many surgeons still following the principles of standard abdominoplasty. In this article, the authors report their experience with the cannula-assisted, limited undermining, and progressive high-tension suture ("CALP") technique of DIEP donor-site closure compared with standard abdominoplasty. METHODS: Between December of 2008 and January of 2013, 137 consecutive women underwent DIEP flap breast reconstruction. Of these, 82 patients (between December of 2008 and November of 2011) underwent DIEP flap donor-site closure by means of standard abdominoplasty (control group) and 55 patients (from December of 2011 to January of 2013) by means of cannula-assisted, limited undermining, and progressive high-tension suture (study group). The abdominal drainage daily output, donor-site complications, abdominal skin sensitivity at 1-year follow-up, cosmetic outcomes, and patient satisfaction were recorded and analyzed statistically. RESULTS: Daily drainage output was significantly lower in the study group. Donor-site complications were significantly higher in the control group (37.8 percent versus 9 percent). Seroma and wound healing problems were experienced in the control group. Abdominal skin sensibility was better preserved in the study group. Overall, abdominal wall aesthetic outcomes were similar in both groups, except for scar quality (better in the study group). CONCLUSION: According to the authors' experience, cannula-assisted, limited undermining, and progressive high-tension suture should be always preferred to standard abdominoplasty for DIEP donor-site closure to reduce the complication rate to improve abdominal skin sensitivity and scar quality. CLINICAL QUESTION/LEVEL OF EVIDENCE: Therapeutic, II.