17 research outputs found
The continuity of effect of schizophrenia polygenic risk score and patterns of cannabis use on transdiagnostic symptom dimensions at first-episode psychosis: findings from the EU-GEI study
Abstract: Diagnostic categories do not completely reflect the heterogeneous expression of psychosis. Using data from the EU-GEI study, we evaluated the impact of schizophrenia polygenic risk score (SZ-PRS) and patterns of cannabis use on the transdiagnostic expression of psychosis. We analysed first-episode psychosis patients (FEP) and controls, generating transdiagnostic dimensions of psychotic symptoms and experiences using item response bi-factor modelling. Linear regression was used to test the associations between these dimensions and SZ-PRS, as well as the combined effect of SZ-PRS and cannabis use on the dimensions of positive psychotic symptoms and experiences. We found associations between SZ-PRS and (1) both negative (B = 0.18; 95%CI 0.03â0.33) and positive (B = 0.19; 95%CI 0.03â0.35) symptom dimensions in 617 FEP patients, regardless of their categorical diagnosis; and (2) all the psychotic experience dimensions in 979 controls. We did not observe associations between SZ-PRS and the general and affective dimensions in FEP. Daily and current cannabis use were associated with the positive dimensions in FEP (B = 0.31; 95%CI 0.11â0.52) and in controls (B = 0.26; 95%CI 0.06â0.46), over and above SZ-PRS. We provide evidence that genetic liability to schizophrenia and cannabis use map onto transdiagnostic symptom dimensions, supporting the validity and utility of the dimensional representation of psychosis. In our sample, genetic liability to schizophrenia correlated with more severe psychosis presentation, and cannabis use conferred risk to positive symptomatology beyond the genetic risk. Our findings support the hypothesis that psychotic experiences in the general population have similar genetic substrates as clinical disorders
The Application of the Open Pharmacological Concepts Triple Store (Open PHACTS) to Support Drug Discovery Research
<div><p>Integration of open access, curated, high-quality information from multiple disciplines in the Life and Biomedical Sciences provides a holistic understanding of the domain. Additionally, the effective linking of diverse data sources can unearth hidden relationships and guide potential research strategies. However, given the lack of consistency between descriptors and identifiers used in different resources and the absence of a simple mechanism to link them, gathering and combining relevant, comprehensive information from diverse databases remains a challenge. The Open Pharmacological Concepts Triple Store (Open PHACTS) is an Innovative Medicines Initiative project that uses semantic web technology approaches to enable scientists to easily access and process data from multiple sources to solve real-world drug discovery problems. The project draws together sources of publicly-available pharmacological, physicochemical and biomolecular data, represents it in a stable infrastructure and provides well-defined information exploration and retrieval methods. Here, we highlight the utility of this platform in conjunction with workflow tools to solve pharmacological research questions that require interoperability between target, compound, and pathway data. Use cases presented herein cover 1) the comprehensive identification of chemical matter for a dopamine receptor drug discovery program 2) the identification of compounds active against all targets in the Epidermal growth factor receptor (ErbB) signaling pathway that have a relevance to disease and 3) the evaluation of established targets in the Vitamin D metabolism pathway to aid novel Vitamin D analogue design. The example workflows presented illustrate how the Open PHACTS Discovery Platform can be used to exploit existing knowledge and generate new hypotheses in the process of drug discovery.</p></div
Use case C workflows 1 and 2.
<p>Open PHACTS v 1.3 API calls are shown in orange boxes along with the results obtained. Bioactivity filters and other data processing operations are shown in yellow boxes with results obtained in light grey boxes. Blue colored boxes show results included in the manuscript. Sample input URLs are shown in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0115460#pone.0115460.s005" target="_blank">S2 Table</a>. For workflow 1, a description of the pathway and targets contained were obtained using the âPathway informationâ and âPathway Information: Get targetsâ API calls. Other pathways where these targets are present were obtained using âPathways for Target: Listâ API call. Approved drugs against single protein targets were obtained using âTarget Informationâ API call by specifying target type - approved. Compounds tested against all targets in the pathway were retrieved using âTarget Pharmacology: Listâ API call. Approved drugs targeting protein complexes (containing any member of the pathway) were identified by filtering for protein complexes and âapprovedâ target type via the âCompound Informationâ API call. For workflow 2, compounds hitting CYP24A1 from the previous results were used as input to find additional targets using the âCompound Pharmacology: Listâ API. Additional pathways containing these new targets were obtained using âPathways for Target: Listâ API.</p
case B workflow.
<p>Open PHACTS v 1.3 API calls are shown in orange boxes along with the results obtained. Bioactivity filters and other data processing operations are shown in yellow boxes with results obtained in light grey boxes. Blue colored boxes show results included in the manuscript. Compound pharmacology at the pathway level was retrieved by consecutive execution of the API calls âPathway Information: Get targetsâ and âTarget Pharmacology: Listâ - the latter includes a filtering for desired activity endpoints and units - and other filtering, transformation, and normalization steps: transformation into â- logActivity values [molar]â, setting a threshold for binary representation, and subsequent filtering by keeping only the max. activity value for each compound/target pair. Retrieving GO annotations for a list of targets, and ChEBI annotations for compounds that have been tested against those targets was achieved by using the API calls âTarget Classificationsâ and âCompound Classificationsâ and subsequent restriction to terms of the type âbiological processâ and âhas roleâ, respectively.</p
Benefits of using the Open PHACTS Discovery Platform for drug discovery research.
<p>Benefits of using the Open PHACTS Discovery Platform for drug discovery research.</p
Regulators of Vitamin D signaling obtained from Workflow 3.
<p>Terms in bold are discussed in the text.</p><p>Regulators of Vitamin D signaling obtained from Workflow 3.</p
Use case A workflow.
<p>Schematic representation of the workflow for use case A. Starting with a free text search for the desired target(s), Uniprot AC identifiers, protein sequences and gene symbols are obtained using âFree Text to Conceptâ and âTarget Informationâ API calls. A gene symbol list is obtained for targets from the same family (based on GO) using a âTarget Classificationâ API call. Alternatively, UniProt ACs obtained for related protein sequences via a BLAST search are used to get corresponding gene symbols using the âTarget Informationâ API call. Using this gene list, corresponding pharmacology records in the public domain are obtained via the âPharmacology by Targetâ API. In parallel, the gene symbol list is used to retrieve target pharmacology information in Thomson Reuters Integrity, World Drug Index, PharmaProjects, GVKBio GOSTAR, and Janssen pharmacology proprietary databases. Public pharmacology records (additional targets) for the retrieved compounds are then obtained using the âPharmacology by compoundâ API call with equivalent searches in Janssen pharmacology proprietary databases. If required, a structure similarity search is performed with the retrieved compounds to identify additional compounds, followed by another round of searches in Open PHACTS and proprietary databases as before. A Pipeline Pilot script was developed to run the above steps and produce an integrated list of compounds, activity data and target information from all databases. Proprietary components developed at Janssen were used to parse Janssen pharmacology data. All data processing was performed within the Pipeline Pilot framework.</p
Number of DRD2-targeted compounds found in different databases.
<p>Active compounds have % activity values>50% or -log(IC<sub>50</sub>) values>6.</p><p>Number of DRD2-targeted compounds found in different databases.</p
Use case C workflows 3 and 4.
<p>Open PHACTS v 1.3 API calls are shown in orange boxes along with the results obtained. Bioactivity filters and other operations are shown in yellow boxes. Results obtained after these operations are shown in light grey boxes. Blue colored boxes show results included in the manuscript. Sample input URLs are shown in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0115460#pone.0115460.s005" target="_blank">S2 Table</a>. For workflow 3, Urls for all species orthologues of a given target were obtained using âFree Text to Concept for Semantic Tagâ API. Pharmacology data for these orthologues was obtained using âTarget Pharmacology: Listâ API. Data was limited to compounds tested in binding affinity assays from bovine, porcine and human in both VDR and DBP by applying appropriate filters in KNIME. For workflow 4, GO terms related to âRegulation of Vitamin Dâ were obtained using the âFree Text to Conceptâ API. Children of these GO terms were obtained using âHierarchies: Child Nodesâ API. The data were sorted by positive/negative regulation. Gene products associated with these GO terms were obtained using âTarget Class Member: Listâ API.</p
Open PHACTS v1.3 API calls (orange boxes) used to address use cases A, B and C, as described in Methods.
<p>Operations performed outside Open PHACTS, viz., sequence similarity searches via BLAST and access to proprietary databases (dark grey boxes) are facilitated by information derived from the platform. Sample input URIs for each API call is shown in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0115460#pone.0115460.s005" target="_blank">S2 Table</a>.</p