The Drug-Gene Interaction Database (DGIdb, https://dgidb.org) is a publicly accessible resource that aggregates genes or gene products, drugs and drug-gene interaction records to drive hypothesis generation and discovery for clinicians and researchers. DGIdb 5.0 is the latest release and includes substantial architectural and functional updates to support integration into clinical and drug discovery pipelines. The DGIdb service architecture has been split into separate client and server applications, enabling consistent data access for users of both the application programming interface (API) and web interface. The new interface was developed in ReactJS, and includes dynamic visualizations and consistency in the display of user interface elements. A GraphQL API has been added to support customizable queries for all drugs, genes, annotations and associated data. Updated documentation provides users with example queries and detailed usage instructions for these new features. In addition, six sources have been added and many existing sources have been updated. Newly added sources include ChemIDplus, HemOnc, NCIt (National Cancer Institute Thesaurus), Drugs@FDA, HGNC (HUGO Gene Nomenclature Committee) and RxNorm. These new sources have been incorporated into DGIdb to provide additional records and enhance annotations of regulatory approval status for therapeutics. Methods for grouping drugs and genes have been expanded upon and developed as independent modular normalizers during import. The updates to these sources and grouping methods have resulted in an improvement in FAIR (findability, accessibility, interoperability and reusability) data representation in DGIdb.
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http://dx.doi.org/10.1093/nar/gkad1040 | DOI Listing |
Mol Neurobiol
January 2025
Hebei Medical University-Galway University Stem Cell Research Center, Hebei Medical University, Shijiazhuang, 050017, Hebei Province, China.
This study utilises amyotrophic lateral sclerosis (ALS) and Parkinson's disease (PD) human brain samples from the GEO database and employs differential expression gene (DEG) analysis to identify genes that are pivotal in both neurodegenerative diseases. Through in depth GO and KEGG enrichment analyses, we elucidated the biological functions and potential pathways associated with these DEGs. Furthermore, by constructing protein‒protein interaction networks, we highlight the significance of shared DEGs in both cellular physiology and disease contexts.
View Article and Find Full Text PDFExpert Opin Drug Metab Toxicol
January 2025
Institute of Psychology, University of Innsbruck, Austria.
Introduction: The prevalence of polypharmacy and the increasing availability of pharmacogenetic information in clinical practice have raised the prospect of data-driven clinical decision making when addressing the issues of drug-drug interactions and genetic polymorphisms in metabolizing enzymes. Inhibition of metabolizing enzymes in drug interactions can lead to genotype-phenotype discrepancies (phenoconversion) that reduce the relevance of individual pharmacogenetic information.
Areas Covered: The aim of this review is to provide an overview on existing models of phenoconversion and we discuss how phenoconversion models may be developed to estimate joint drug-interactions and genetic effects.
Dermatitis
January 2025
From the Department of Orthopedics, The Second Affiliated Hospital of Chongqing Medical University, Chongqing, People's Republic of China.
Eczema and dermatitis are common inflammatory skin conditions with significant morbidity. Identifying drug-targetable genes can facilitate the development of effective treatments. This study analyzed data obtained by meta-analysis of 2 genome-wide association studies on eczema/dermatitis (57,311 cases and 896,779 controls, European ancestry).
View Article and Find Full Text PDFJ Cannabis Res
January 2025
Division of General Internal Medicine, Mayo Clinic College of Medicine and Science, 200 First St SW, Rochester, MN, 55905, USA.
Background: Differences in cannabinoid metabolism and patient responses can arise even with equivalent doses and formulations. Genetic polymorphisms in genes responsible for cannabinoid metabolism and medications that alter CYP450 pathways responsible for metabolism of cannabinoids may account for some of this variability.
Materials And Methods: A retrospective chart review was conducted on a cohort of unselected patients who had previously completed pharmacogenomic testing and reported oral cannabis use, as defined as "oral" or "by mouth" route of administration.
Discov Oncol
January 2025
Department of Hematology, The First Affiliated Hospital of Ningbo University, No.59 Liu-Ting Road, Ningbo, 315000, People's Republic of China.
Background: Chronic lymphocytic leukemia (CLL) is a common hematologic malignancy. Although previous research has explored associations between plasma proteins and CLL, the causal relationships remain unclear. This study used Mendelian randomization (MR) to investigate the causal relationship between 7156 plasma proteins and CLL risk.
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