Transport of Drugs and Endogenous Compounds Mediated by Human OCT1: Studies in Single- and Double-Transfected Cell Models

Organic Cation Transporter 1 (OCT1, gene symbol: SLC22A1) is predominately expressed in human liver, localized in the basolateral membrane of hepatocytes and facilitates the uptake of endogenous compounds (e.g. serotonin, acetylcholine, thiamine), and widely prescribed drugs (e.g. metformin, fenoterol, morphine). Furthermore, exogenous compounds such as MPP+, ASP+ and Tetraethylammonium can be used as prototypic substrates to study the OCT1-mediated transport in vitro. Single-transfected cell lines recombinantly overexpressing OCT1 (e.g., HEK-OCT1) were established to study OCT1-mediated uptake and to evaluate transporter-mediated drug-drug interactions in vitro. Furthermore, double-transfected cell models simultaneously overexpressing basolaterally localized OCT1 together with an apically localized export protein have been established. Most of these cell models are based on polarized grown MDCK cells and can be used to analyze transcellular transport, mimicking the transport processes e.g. during the hepatobiliary elimination of drugs. Multidrug and toxin extrusion protein 1 (MATE1, gene symbol: SLC47A1) and the ATP-driven efflux pump P-glycoprotein (P-gp, gene symbol: ABCB1) are both expressed in the canalicular membrane of human hepatocytes and are described as transporters of organic cations. OCT1 and MATE1 have an overlapping substrate spectrum, indicating an important interplay of both transport proteins during the hepatobiliary elimination of drugs. Due to the important role of OCT1 for the transport of endogenous compounds and drugs, in vitro cell systems are important for the determination of the substrate spectrum of OCT1, the understanding of the molecular mechanisms of polarized transport, and the investigation of potential drug-drug interactions. Therefore, the aim of this review article is to summarize the current knowledge on cell systems recombinantly overexpressing human OCT1.

Human islet expression levels of Prostaglandin E2 synthetic enzymes, but not prostaglandin EP3 receptor, are positively correlated with markers of β-cell function and mass in non-diabetic obesity

We and others previously reported increased signaling through the Prostaglandin E3 Receptor (EP3), a G protein-coupled receptor (GPCR) for the arachidonic acid metabolite, prostaglandin E2 (PGE2), is associated with β-cell dysfunction of type 2 diabetes (T2D). Yet, the relationship between PGE2 production and signaling and β-cell function during the progression to T2D remains unclear. In this work, we assessed gene expression from a panel of cadaveric human islets from 40 non-diabetic donors with BMI values spanning the spectrum from lean to high-risk obesity. Interleukin-6 (gene symbol: IL6) and cyclooxygenase-2 (COX-2) (gene symbol: PTGS2) mRNA levels were positively correlated with donor body mass index (BMI), while EP3 (gene symbol: PTGER3) was not. IL6 was itself strongly correlated with PTGS2 and all but one of the other PGE2 synthetic pathway genes tested. About half of the islet preparations were used in glucose-stimulated- and incretin-potentiated insulin secretion assays using an EP3-specific antagonist, confirming functionally-relevant up-regulation of PGE2 production. Islets from obese donors showed no inherent β-cell dysfunction and were at least equally as glucose- and incretin-responsive as islets from non-obese donors. Furthermore, insulin content, a marker of islet size known to be associated with donor BMI, was also significantly and positively correlated with islet PTGS2 expression. We conclude up-regulated islet PGE2 production and signaling may be a necessary part of the β-cell adaption response, compensating for obesity and insulin resistance. Analysis of plasma PGE2 metabolite levels from a clinical cohort reveal these findings are not in conflict with the concept of further elevations in PGE2 production contributing to T2D-related β-cell dysfunction where islet EP3 expression has also been up-regulated.Graphical Abstract

HGNChelper: identification and correction of invalid gene symbols for human and mouse

Gene symbols are recognizable identifiers for gene names but are unstable and error-prone due to aliasing, manual entry, and unintentional conversion by spreadsheets to date format. Official gene symbol resources such as HUGO Gene Nomenclature Committee (HGNC) for human genes and the Mouse Genome Informatics project (MGI) for mouse genes provide authoritative sources of valid, aliased, and outdated symbols, but lack a programmatic interface and correction of symbols converted by spreadsheets. We present HGNChelper, an R package that identifies known aliases and outdated gene symbols based on the HGNC human and MGI mouse gene symbol databases, in addition to common mislabeling introduced by spreadsheets, and provides corrections where possible. HGNChelper identified invalid gene symbols in the most recent Molecular Signatures Database (mSigDB 7.0) and in platform annotation files of the Gene Expression Omnibus, with prevalence ranging from ~3% in recent platforms to 30-40% in the earliest platforms from 2002-03. HGNChelper is installable from CRAN.

Avian Immunome DB: an example of a user-friendly interface for extracting genetic information

Background Genomic and genetic studies often require a target list of genes before conducting any hypothesis testing or experimental verification. With the ever-growing number of sequenced genomes and a variety of different annotation strategies, comes the potential for ambiguous gene symbols, making it cumbersome to capture the “correct” set of genes. In this article, we present and describe the Avian Immunome DB (Avimm) for easy gene property extraction as exemplified by avian immune genes. The avian immune system is characterised by a cascade of complex biological processes underlaid by more than 1000 different genes. It is a vital trait to study particularly in birds considering that they are a significant driver in spreading zoonotic diseases. With the completion of phase II of the B10K (“Bird 10,000 Genomes”) consortium’s whole-genome sequencing effort, we have included 363 annotated bird genomes in addition to other publicly available bird genome data which serve as a valuable foundation for Avimm. Construction and content A relational database with avian immune gene evidence from Gene Ontology, Ensembl, UniProt and the B10K consortium has been designed and set up. The foundation stone or the “seed” for the initial set of avian immune genes is based on the well-studied model organism chicken (Gallus gallus). Gene annotations, different transcript isoforms, nucleotide sequences and protein information, including amino acid sequences, are included. Ambiguous gene names (symbols) are resolved within the database and linked to their canonical gene symbol. Avimm is supplemented by a command-line interface and a web front-end to query the database. Utility and discussion The internal mapping of unique gene symbol identifiers to canonical gene symbols allows for an ambiguous gene property search. The database is organised within core and feature tables, which makes it straightforward to extend for future purposes. The database design is ready to be applied to other taxa or biological processes. Currently, the database contains 1170 distinct avian immune genes with canonical gene symbols and 612 synonyms across 363 bird species. While the command-line interface readily integrates into bioinformatics pipelines, the intuitive web front-end with download functionality offers sophisticated search functionalities and tracks the origin for each record. Avimm is publicly accessible at https://avimm.ab.mpg.de.

HGNChelper: identification and correction of invalid gene symbols for human and mouse

Gene symbols are recognizable identifiers for gene names but are unstable and error-prone due to aliasing, manual entry, and unintentional conversion by spreadsheets to date format. Official gene symbol resources such as HUGO Gene Nomenclature Committee (HGNC) for human genes and the Mouse Genome Informatics project (MGI) for mouse genes provide authoritative sources of valid, aliased, and outdated symbols, but lack a programmatic interface and correction of symbols converted by spreadsheets. We present HGNChelper, an R package that identifies known aliases and outdated gene symbols based on the HGNC human and MGI mouse gene symbol databases, in addition to common mislabeling introduced by spreadsheets, and provides corrections where possible. HGNChelper identified invalid gene symbols in the most recent Molecular Signatures Database (mSigDB 7.0) and in platform annotation files of the Gene Expression Omnibus, with prevalence ranging from ∼3% in recent platforms to 30-40% in the earliest platforms from 2002-03. HGNChelper is installable from CRAN, with open development and issue tracking on GitHub and an associated pkgdown site https://waldronlab.io/HGNChelper/.

Bibliome Variant Database: Automated Identification and Annotation of Genetic Variants in Primary Literature

ABSTRACTThe Bibliome Variant Database (BVdb) is a freely available reference database containing over 1 million human genetic variants mapped to the human genome that have been mined from primary literature. The BVdb is designed to facilitate variant interpretation in clinical and research contexts by reducing or eliminating the time required to search for literature describing a given variant. Users can search the database using gene symbols, HGVS variant nomenclature, genomic positions, or rsIDs. Each variant page lists references in the database that describe the variant, as well as the exact gene symbol and variant text description identified in each reference.AVAILABILITY AND IMPLEMENTATIONThe BVdb is freely available at http://bibliome.ai

Neuropeptide FF/neuropeptide AF receptors (version 2019.4) in the IUPHAR/BPS Guide to Pharmacology Database

The Neuropeptide FF receptor family contains two subtypes, NPFF1 and NPFF2 (provisional nomenclature [10]), which exhibit high affinities for neuropeptide FF (NPFF, O15130) and RFamide related peptides (RFRP: precursor gene symbol NPVF, Q9HCQ7). NPFF1 is broadly distributed in the central nervous system with the highest levels found in the limbic system and the hypothalamus. NPFF2 is present in high density in the superficial layers of the mammalian spinal cord where it is involved in nociception and modulation of opioid functions.

Validating the AMRFinder Tool and Resistance Gene Database by Using Antimicrobial Resistance Genotype-Phenotype Correlations in a Collection of Isolates

ABSTRACT Antimicrobial resistance (AMR) is a major public health problem that requires publicly available tools for rapid analysis. To identify AMR genes in whole-genome sequences, the National Center for Biotechnology Information (NCBI) has produced AMRFinder, a tool that identifies AMR genes using a high-quality curated AMR gene reference database. The Bacterial Antimicrobial Resistance Reference Gene Database consists of up-to-date gene nomenclature, a set of hidden Markov models (HMMs), and a curated protein family hierarchy. Currently, it contains 4,579 antimicrobial resistance proteins and more than 560 HMMs. Here, we describe AMRFinder and its associated database. To assess the predictive ability of AMRFinder, we measured the consistency between predicted AMR genotypes from AMRFinder and resistance phenotypes of 6,242 isolates from the National Antimicrobial Resistance Monitoring System (NARMS). This included 5,425 Salmonella enterica, 770 Campylobacter spp., and 47 Escherichia coli isolates phenotypically tested against various antimicrobial agents. Of 87,679 susceptibility tests performed, 98.4% were consistent with predictions. To assess the accuracy of AMRFinder, we compared its gene symbol output with that of a 2017 version of ResFinder, another publicly available resistance gene detection system. Most gene calls were identical, but there were 1,229 gene symbol differences (8.8%) between them, with differences due to both algorithmic differences and database composition. AMRFinder missed 16 loci that ResFinder found, while ResFinder missed 216 loci that AMRFinder identified. Based on these results, AMRFinder appears to be a highly accurate AMR gene detection system.