Distilling a visual network of Retinitis Pigmentosa gene-protein interactions to uncover new disease candidates

BACKGROUND: Retinitis pigmentosa (RP) is a highly heterogeneous genetic visual disorder with more than 70 known causative genes, some of them shared with other non-syndromic retinal dystrophies (e.g. Leber congenital amaurosis, LCA). The identification of RP genes has increased steadily during the last decade, and the 30% of the cases that still remain unassigned will soon decrease after the advent of exome/genome sequencing. A considerable amount of genetic and functional data on single RD genes and mutations has been gathered, but a comprehensive view of the RP genes and their interacting partners is still very fragmentary. This is the main gap that needs to be filled in order to understand how mutations relate to progressive blinding disorders and devise effective therapies. METHODOLOGY: We have built an RP-specific network (RPGeNet) by merging data from different sources: high-throughput data from BioGRID and STRING databases, manually curated data for interactions retrieved from iHOP, as well as interactions filtered out by syntactical parsing from up-to-date abstracts and full-text papers related to the RP research field. The paths emerging when known RP genes were used as baits over the whole interactome have been analysed, and the minimal number of connections among the RP genes and their close neighbors were distilled in order to simplify the search space. CONCLUSIONS: In contrast to the analysis of single isolated genes, finding the networks linking disease genes renders powerful etiopathological insights. We here provide an interactive interface, RPGeNet, for the molecular biologist to explore the network centered on the non-syndromic and syndromic RP and LCA causative genes. By integrating tissue-specific expression levels and phenotypic data on top of that network, a more comprehensive biological view will highlight key molecular players of retinal degeneration and unveil new RP disease candidates

RPGeNet v2 .0: expanding the universe of retinal disease gene interactions network

RPGeNet offers researchers a user-friendly queriable tool to visualize the interactome network of visual disorder genes, thus enabling the identification of new potential causative genes and the assignment of novel candidates to specific retinal or cellular pathways. This can be highly relevant for clinical applications as retinal dystrophies affect 1:3000 people worldwide, and the causative genes are still unknown for 30% of the patients. RPGeNet is a refined interaction network interface that limits its skeleton network to the shortest paths between each and every known causative gene of inherited syndromic and non-syndromic retinal dystrophies. RPGeNet integrates interaction information from STRING, BioGRID and PPaxe, along with retina-specific expression data and associated genetic variants, over a Cytoscape.js web interface. For the new version, RPGeNet v2.0, the database engine was migrated to Neo4j graph database manager, which speeds up the initial queries and can handle whole interactome data for new ways to query the network. Further, user facilities have been introduced as the capability of saving and restoring a researcher customized network layout or as novel features to facilitate navigation and data projection on the network explorer interface. Responsiveness has been further improved by transferring some functionality to the client side

SiNoPsis: single nucleotide polymorphisms selection and promoter profiling

Motivation: The selection of a single nucleotide polymorphism (SNP) using bibliographic methods can be a very time-consuming task. Moreover, a SNP selected in this way may not be easily visualized in its genomic context by a standard user hoping to correlate it with other valuable information. Here we propose a web form built on top of Circos that can assist SNP-centered screening, based on their location in the genome and the regulatory modules they can disrupt. Its use may allow researchers to prioritize SNPs in genotyping and disease studies. Results: SiNoPsis is bundled as a web portal. It focuses on the different structures involved in the genomic expression of a gene, especially those found in the core promoter upstream region. These structures include transcription factor binding sites (for promoter and enhancer signals), histones and promoter flanking regions. Additionally, the tool provides eQTL and linkage disequilibrium (LD) properties for a given SNP query, yielding further clues about other indirectly associated SNPs. Possible disruptions of the aforementioned structures affecting gene transcription are reported using multiple resource databases. SiNoPsis has a simple user-friendly interface, which allows single queries by gene symbol, genomic coordinates, Ensembl gene identifiers, RefSeq transcript identifiers and SNPs. It is the only portal providing useful SNP selection based on regulatory modules and LD with functional variants in both textual and graphic modes (by properly defining the arguments and parameters needed to run Circos). Availability and implementation: SiNoPsis is freely available at https://compgen.bio.ub.edu/SiNoPsis/ Supplementary information: Supplementary data are available at Bioinformatics online

Different modulation of RPS6 phosphorylation by risperidone in striatal cells sub populations: involvement of the mTOR pathway in antipsychotic-induced extrapyramidal symptoms in mice

Objective: Acute extrapyramidal symptoms (EPS) are frequent and serious adverse reactions to antipsychotic (AP) drugs. Although the proposed mechanism is an excessive blockade of dopamine D2 receptors in the striatopallidal pathway of the striatum, previous studies implicated the mTOR pathway in the susceptibility to EPS. The objective of the present study is to analyze the mTOR-mediated response to risperidone in subpopulations of striatal neurons and its relationship to risperidone-induced motor side effects. Methods: Two mouse strains (A/J and DBA/2J) with different susceptibility to developing EPS were treated with risperidone 1 mg/kg for three consecutive days. Here we monitored, by double labeling immunohistochemistry, ribosomal protein S6 (rpS6) phosphorylation (Ser235/236 and Ser244/247 sites), a marker of mTOR signaling, in the striatonigral pathway (D1-medium spiny neurons (MSNs)), the striatopallidal pathway (D2-MSNs) and striatal cholinergic interneurons. Results: We found that EPS-resistant DBA/2J mice show higher baseline levels of phosphoactivated rpS6 protein in striatal MSNs, compared with EPS-prone A/J mice. Moreover, risperidone differentially targeted rpS6 phosphorylation in direct and indirect pathway neurons in a strain-specific manner: a significant decrease in the phosphorylation of rpS6 at Ser235/236 and Ser240/244 in DRD1-MSNs EPS-resistant DBA/2J mice after; and a significant increase of phospho-Ser235/236-rpS6 in the striatopallidal pathway of the EPS-prone A/J mice in response to risperidone. Conclusions: Our results reveal the vital role of genetic background in the response to risperidone, and point to the mTOR pathway as an important factor in EPS susceptibility. Keywords: Schizophrenia, Antipsychotic, Risperidone, Extrapyramidal symptoms. mTOR pathway, Striatum, Medium spiny neuron

Inflammatory dysregulation of monocytes in pediatric patients with obsessive-compulsive disorder

BACKGROUND: Although the exact etiology of obsessive-compulsive disorder (OCD) is unknown, there is growing evidence of a role for immune dysregulation in the pathophysiology of the disease, especially in the innate immune system including the microglia. To test this hypothesis, we studied inflammatory markers in monocytes from pediatric patients with OCD and from healthy controls. METHODS: We determined the percentages of total monocytes, CD16+ monocytes, and classical (CD14highCD16-), intermediate (CD14highCD16low), and non-classical (CD14lowCD16high) monocyte subsets in 102 patients with early-onset OCD and in 47 healthy controls. Moreover, proinflammatory cytokine production (GM-CSF, IL-1β, IL-6, IL-8, and TNF-α) was measured by multiplex Luminex analysis in isolated monocyte cultures, in basal conditions, after exposure to lipopolysaccharide (LPS) to stimulate immune response or after exposure to LPS and the immunosuppressant dexamethasone. RESULTS: OCD patients had significantly higher percentages of total monocytes and CD16+ monocytes than healthy controls, mainly due to an increase in the intermediate subset but also in the non-classical monocytes. Monocytes from OCD patients released higher amounts of GM-CSF, IL-1β, IL-6, IL-8, and TNF-α than healthy controls after exposure to LPS. However, there were no significant differences in basal cytokine production or the sensitivity of monocytes to dexamethasone treatment between both groups. Based on monocyte subset distribution and cytokine production after LPS stimulation, patients receiving psychoactive medications seem to have an intermediate inflammatory profile, that is, lower than non-medicated OCD individuals and higher than healthy controls. CONCLUSIONS: These results strongly support the involvement of an enhanced proinflammatory innate immune response in the etiopathogenesis of early-onset OCD

Response to fluoxetine in children and adolescents: a weighted gene co-expression network analysis of peripheral blood

The inconclusive and non-replicated results of pharmacogenetic studies of antidepressant response could be related to the lack of acknowledgement of its mechanism of action. In this scenario, gene expression studies provide and interesting framework to reveal new candidate genes for pharmacogenetic studies or peripheral biomarkers of fluoxetine response. We propose a system biology approach to analyse changes in gene expression induced by eight weeks of treatment with fluoxetine in peripheral blood. 21 naïve child and adolescents participated in the present study. Our analysis include the identification of gene co-expression modules, using Weighted Gene Co-expression Network Analysis (WGCNA), followed by protein-protein interaction (PPi) network construction coupled with functional annotation. Our results revealed two modules of co-expression genes related to fluoxetine treatment. The constructed networks from these modules were enriched for biological processes related to cellular and metabolic processes, cell communication, immune system processes, cell death, response to stimulus and neurogenesis. Some of these processes, such as immune system, replicated previous findings in the literature, whereas, neurogenesis, a mechanism proposed to be involved in fluoxetine response, had been identified for first time using peripheral tissues. In conclusion, our study identifies several biological processes in relation to fluoxetine treatment in peripheral blood, offer new candidate genes for pharmacogenetic studies and valuable markers for peripheral moderator biomarkers discovery

Improving pharmacogenetic prediction of extrapyramidal symptoms induced by antipshycotics

In previous work we developed a pharmacogenetic predictor of antipsychotic (AP) induced extrapyramidal symptoms (EPS) based on four genes involved in mTOR regulation. The main objective is to improve this predictor by increasing its biological plausibility and replication. We re-sequence the four genes using next-generation sequencing. We predict functionality 'in silico' of all identified SNPs and test it using gene reporter assays. Using functional SNPs, we develop a new predictor utilizing machine learning algorithms (Discovery Cohort, N = 131) and replicate it in two independent cohorts (Replication Cohort 1, N = 113; Replication Cohort 2, N = 113). After prioritization, four SNPs were used to develop the pharmacogenetic predictor of AP-induced EPS. The model constructed using the Naive Bayes algorithm achieved a 66% of accuracy in the Discovery Cohort, and similar performances in the replication cohorts. The result is an improved pharmacogenetic predictor of AP-induced EPS, which is more robust and generalizable than the original

Distilling a visual network of Retinitis Pigmentosa gene-protein interactions to uncover new disease candidates

BACKGROUND: Retinitis pigmentosa (RP) is a highly heterogeneous genetic visual disorder with more than 70 known causative genes, some of them shared with other non-syndromic retinal dystrophies (e.g. Leber congenital amaurosis, LCA). The identification of RP genes has increased steadily during the last decade, and the 30% of the cases that still remain unassigned will soon decrease after the advent of exome/genome sequencing. A considerable amount of genetic and functional data on single RD genes and mutations has been gathered, but a comprehensive view of the RP genes and their interacting partners is still very fragmentary. This is the main gap that needs to be filled in order to understand how mutations relate to progressive blinding disorders and devise effective therapies. METHODOLOGY: We have built an RP-specific network (RPGeNet) by merging data from different sources: high-throughput data from BioGRID and STRING databases, manually curated data for interactions retrieved from iHOP, as well as interactions filtered out by syntactical parsing from up-to-date abstracts and full-text papers related to the RP research field. The paths emerging when known RP genes were used as baits over the whole interactome have been analysed, and the minimal number of connections among the RP genes and their close neighbors were distilled in order to simplify the search space. CONCLUSIONS: In contrast to the analysis of single isolated genes, finding the networks linking disease genes renders powerful etiopathological insights. We here provide an interactive interface, RPGeNet, for the molecular biologist to explore the network centered on the non-syndromic and syndromic RP and LCA causative genes. By integrating tissue-specific expression levels and phenotypic data on top of that network, a more comprehensive biological view will highlight key molecular players of retinal degeneration and unveil new RP disease candidates

Schema of the protocol used to derive the RP/LCA genes network.

<p>Driver (RP/LCA) genes were selected from RetNet, while interactions were gathered from a variety of sources. Main script combines such information to build the driver subnetwork that is the kernel of the web interface. Further information, such as gene expression or number of reported polymorphisms, is integrated when navigating through the selected paths.</p

A summary of the functional annotation for RP/LCA driver genes.

<p>* Mutations in this gene can cause both syndromic and non-syndromic forms of RP or LCA</p><p>^# Mutations in this gene can cause RP as well as other retinal dystrophies</p><p>A summary of the functional annotation for RP/LCA driver genes.</p