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

Small-Molecule Immunosuppressive Drugs and Therapeutic Immunoglobulins Differentially Inhibit NK Cell Effector Functions in vitro

Small-molecule immunosuppressive drugs (ISD) prevent graft rejection mainly by inhibiting T lymphocytes. Therapeutic immunoglobulins (IVIg) are used for substitution, antibody-mediated rejection (AbMR) and HLA-sensitized recipients by targeting distinct cell types. Since the effect of ISD and IVIg on natural killer (NK) cells remains somewhat controversial in the current literature, the aim of this comparative study was to investigate healthy donor's human NK cell functions after exposure to ISD and IVIg, and to comprehensively review the current literature. NK cells were incubated overnight with IL2/IL12 and different doses and combinations of ISD and IVIg. Proliferation was evaluated by 3[H]-thymidine incorporation; phenotype, degranulation and interferon gamma (IFNγ) production by flow cytometry and ELISA; direct NK cytotoxicity by standard 51[Cr]-release and non-radioactive DELFIA assays using K562 as stimulator and target cells; porcine endothelial cells coated with human anti-pig antibodies were used as targets in antibody-dependent cellular cytotoxicity (ADCC) assays. We found that CD69, CD25, CD54, and NKG2D were downregulated by ISD. Proliferation was inhibited by methylprednisolone (MePRD), mycophenolic acid (MPA), and everolimus (EVE). MePRD and MPA reduced degranulation, MPA only of CD56bright NK cells. MePRD and IVIg inhibited direct cytotoxicity and ADCC. Combinations of ISD demonstrated cumulative inhibitory effects. IFNγ production was inhibited by MePRD and ISD combinations, but not by IVIg. In conclusion, IVIg, ISD and combinations thereof differentially inhibit NK cell functions. The most potent drug with an effect on all NK functions was MePRD. The fact that MePRD and IVIg significantly block NK cytotoxicity, especially ADCC, has major implications for AbMR as well as therapeutic strategies targeting cancer and immune cells with monoclonal antibodies

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

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