The active metabolite of leflunomide, A77 1726, inhibits the production of prostaglandin E2, matrix metalloproteinase 1 and interleukin 6 in human fibroblast‐like synoviocytes

Objectives. To investigate the effects of the active metabolite of leflunomide, A77 1726, on fibroblast‐like synoviocytes. In rheumatoid arthritis (RA) synoviocytes participate in tissue destruction by producing metalloproteinases (MMP), prostaglandin E2 (PGE2) and interleukin (IL) 6, which are involved in extracellular matrix degradation, resorption of the mineral phase and osteoclast‐mediated bone resorption. Methods. Human synoviocytes were stimulated with IL‐1α or tumour necrosis factor α (TNF‐α) in the presence of A77 1726. Culture supernatants were analysed for production of interstitial collagenase (MMP‐1), tissue‐inhibitor of metalloproteinases 1 (TIMP‐1), PGE2 and IL‐6. Total RNA was isolated and analysed for steady‐state levels of MMP‐1, cyclooxygenase‐2 (COX‐2) and IL‐6 mRNA. Results. A77 1726 inhibited the production of PGE2 in synoviocytes activated by TNF‐α and IL‐1α with median inhibitory concentrations (IC50) of 7 and 3 µm respectively. In contrast, MMP‐1 and IL‐6 production was inhibited at high A77 1726 concentrations (> 10 µm), whereas TIMP‐1 was not affected. The inhibition of MMP‐1 and IL‐6 production was due to the known inhibitory effect of A77 1726 on pyrimidine synthesis, as it was reversed by the addition of uridine. This did not apply to PGE2 production, which was inhibited via direct action of A77 1726 on COX‐2, as shown by the increasing amount of substrate (arachidonic acid) in the culture medium. Conclusion. This study shows that some of the beneficial effect of leflunomide in RA patients may be due to the inhibition of PGE2, IL‐6 and MMP‐1 production in synoviocytes. This effect, coupled with its multiple inhibitory effects on T lymphocyte functions, might account for the significant reduction in the rate of disease progression in RA patients treated with leflunomid

HDL Interfere with the Binding of T Cell Microparticles to Human Monocytes to Inhibit Pro-Inflammatory Cytokine Production

BACKGROUND: Direct cellular contact with stimulated T cells is a potent mechanism that induces cytokine production in human monocytes in the absence of an infectious agent. This mechanism is likely to be relevant to T cell-mediated inflammatory diseases such as rheumatoid arthritis and multiple sclerosis. Microparticles (MP) generated by stimulated T cells (MPT) display similar monocyte activating ability to whole T cells, isolated T cell membranes, or solubilized T cell membranes. We previously demonstrated that high-density lipoproteins (HDL) inhibited T cell contact- and MPT-induced production of IL-1beta but not of its natural inhibitor, the secreted form of IL-1 receptor antagonist (sIL-1Ra). METHODOLOGY/PRINCIPAL FINDINGS: Labeled MPT were used to assess their interaction with monocytes and T lymphocytes by flow cytometry. Similarly, interactions of labeled HDL with monocytes and MPT were assessed by flow cytometry. In parallel, the MPT-induction of IL-1beta and sIL-1Ra production in human monocytes and the effect of HDL were assessed in cell cultures. The results show that MPT, but not MP generated by activated endothelial cells, bond monocytes to trigger cytokine production. MPT did not bind T cells. The inhibition of IL-1beta production by HDL correlated with the inhibition of MPT binding to monocytes. HDL interacted with MPT rather than with monocytes suggesting that they bound the activating factor(s) of T cell surface. Furthermore, prototypical pro-inflammatory cytokines and chemokines such as TNF, IL-6, IL-8, CCL3 and CCL4 displayed a pattern of production induced by MPT and inhibition by HDL similar to IL-1beta, whereas the production of CCL2, like that of sIL-1Ra, was not inhibited by HDL. CONCLUSIONS/SIGNIFICANCE: HDL inhibit both MPT binding to monocytes and the MPT-induced production of some but not all cytokines, shedding new light on the mechanism by which HDL display their anti-inflammatory functions

The UniProt-GO Annotation database in 2011

The GO annotation dataset provided by the UniProt Consortium (GOA: http://www.ebi.ac.uk/GOA) is a comprehensive set of evidenced-based associations between terms from the Gene Ontology resource and UniProtKB proteins. Currently supplying over 100 million annotations to 11 million proteins in more than 360 000 taxa, this resource has increased 2-fold over the last 2 years and has benefited from a wealth of checks to improve annotation correctness and consistency as well as now supplying a greater information content enabled by GO Consortium annotation format developments. Detailed, manual GO annotations obtained from the curation of peer-reviewed papers are directly contributed by all UniProt curators and supplemented with manual and electronic annotations from 36 model organism and domain-focused scientific resources. The inclusion of high-quality, automatic annotation predictions ensures the UniProt GO annotation dataset supplies functional information to a wide range of proteins, including those from poorly characterized, non-model organism species. UniProt GO annotations are freely available in a range of formats accessible by both file downloads and web-based views. In addition, the introduction of a new, normalized file format in 2010 has made for easier handling of the complete UniProt-GOA data set

Perspectives on tracking data reuse across biodata resources

c The Author(s) 2024. Published by Oxford University Press.Motivation: Data reuse is a common and vital practice in molecular biology and enables the knowledge gathered over recent decades to drive discovery and innovation in the life sciences. Much of this knowledge has been collated into molecular biology databases, such as UniProtKB, and these resources derive enormous value from sharing data among themselves. However, quantifying and documenting this kind of data reuse remains a challenge. Results: The article reports on a one-day virtual workshop hosted by the UniProt Consortium in March 2023, attended by representatives from biodata resources, experts in data management, and NIH program managers. Workshop discussions focused on strategies for tracking data reuse, best practices for reusing data, and the challenges associated with data reuse and tracking. Surveys and discussions showed that data reuse is widespread, but critical information for reproducibility is sometimes lacking. Challenges include costs of tracking data reuse, tensions between tracking data and open sharing, restrictive licenses, and difficulties in tracking commercial data use. Recommendations that emerged from the discussion include: development of standardized formats for documenting data reuse, education about the obstacles posed by restrictive licenses, and continued recognition by funding agencies that data management is a critical activity that requires dedicated resources

The Gene Ontology knowledgebase in 2023

The Gene Ontology (GO) knowledgebase (http://geneontology.org) is a comprehensive resource concerning the functions of genes and gene products (proteins and noncoding RNAs). GO annotations cover genes from organisms across the tree of life as well as viruses, though most gene function knowledge currently derives from experiments carried out in a relatively small number of model organisms. Here, we provide an updated overview of the GO knowledgebase, as well as the efforts of the broad, international consortium of scientists that develops, maintains, and updates the GO knowledgebase. The GO knowledgebase consists of three components: (1) the GO-a computational knowledge structure describing the functional characteristics of genes; (2) GO annotations-evidence-supported statements asserting that a specific gene product has a particular functional characteristic; and (3) GO Causal Activity Models (GO-CAMs)-mechanistic models of molecular "pathways" (GO biological processes) created by linking multiple GO annotations using defined relations. Each of these components is continually expanded, revised, and updated in response to newly published discoveries and receives extensive QA checks, reviews, and user feedback. For each of these components, we provide a description of the current contents, recent developments to keep the knowledgebase up to date with new discoveries, and guidance on how users can best make use of the data that we provide. We conclude with future directions for the project

The Gene Ontology resource: enriching a GOld mine

The Gene Ontology Consortium (GOC) provides the most comprehensive resource currently available for computable knowledge regarding the functions of genes and gene products. Here, we report the advances of the consortium over the past two years. The new GO-CAM annotation framework was notably improved, and we formalized the model with a computational schema to check and validate the rapidly increasing repository of 2838 GO-CAMs. In addition, we describe the impacts of several collaborations to refine GO and report a 10% increase in the number of GO annotations, a 25% increase in annotated gene products, and over 9,400 new scientific articles annotated. As the project matures, we continue our efforts to review older annotations in light of newer findings, and, to maintain consistency with other ontologies. As a result, 20 000 annotations derived from experimental data were reviewed, corresponding to 2.5% of experimental GO annotations. The website (http://geneontology.org) was redesigned for quick access to documentation, downloads and tools. To maintain an accurate resource and support traceability and reproducibility, we have made available a historical archive covering the past 15 years of GO data with a consistent format and file structure for both the ontology and annotations

Gene Ontology annotations and resources.

The Gene Ontology (GO) Consortium (GOC, http://www.geneontology.org) is a community-based bioinformatics resource that classifies gene product function through the use of structured, controlled vocabularies. Over the past year, the GOC has implemented several processes to increase the quantity, quality and specificity of GO annotations. First, the number of manual, literature-based annotations has grown at an increasing rate. Second, as a result of a new 'phylogenetic annotation' process, manually reviewed, homology-based annotations are becoming available for a broad range of species. Third, the quality of GO annotations has been improved through a streamlined process for, and automated quality checks of, GO annotations deposited by different annotation groups. Fourth, the consistency and correctness of the ontology itself has increased by using automated reasoning tools. Finally, the GO has been expanded not only to cover new areas of biology through focused interaction with experts, but also to capture greater specificity in all areas of the ontology using tools for adding new combinatorial terms. The GOC works closely with other ontology developers to support integrated use of terminologies. The GOC supports its user community through the use of e-mail lists, social media and web-based resources

Gamma probes and their use in tumor detection in colorectal cancer

The purpose of this article is to summarize the role of gamma probes in intraoperative tumor detection in patients with colorectal cancer (CRC), as well as provide basic information about the physical and practical characteristics of the gamma probes, and the radiopharmaceuticals used in gamma probe tumor detection. In a significant portion of these studies, radiolabeled monoclonal antibodies (Mabs), particularly 125I labeled B72.3 Mab that binds to the TAG-72 antigen, have been used to target tumor. Studies have reported that intraoperative gamma probe radioimmunodetection helps surgeons to localize primary tumor, clearly delineate its resection margins and provide immediate intraoperative staging. Studies also have emphasized the value of intraoperative gamma probe radioimmunodetection in defining the extent of tumor recurrence and finding sub-clinical occult tumors which would assure the surgeons that they have completely removed the tumor burden. However, intraoperative gamma probe radioimmunodetection has not been widely adapted among surgeons because of some constraints associated with this technique. The main difficulty with this technique is the long period of waiting time between Mab injection and surgery. The technique is also laborious and costly. In recent years, Fluorine-18-2-fluoro-2-deoxy-D-glucose (18F-FDG) use in gamma probe tumor detection surgery has renewed interest among surgeons. Preliminary studies during surgery have demonstrated that use of FDG in gamma probe tumor detection during surgery is feasible and useful