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CD69 is a transmembrane lectin that can be expressed on most hematopoietic cells. In monocytes, it has been functionally linked to the 5-lipoxygenase pathway in which the leukotrienes, a class of highly potent inflammatory mediators, are produced. However, regarding CD69 gene expression and its regulatory mechanisms in monocytes, only scarce data are available. Here, we report that CD69 mRNA expression, analogous to that of 5-lipoxygenase, is induced by the physiologic stimuli transforming growth factor-β (TGF-β) and 1α,25-dihydroxyvitamin D3 (1α,25(OH)2D3) in monocytic cells. Comparison with T- and B-cell lines showed that the effect was specific for monocytes. CD69 expression levels were increased in a concentration-dependent manner, and kinetic analysis revealed a rapid onset of mRNA expression, indicating that CD69 is a primary TGF-β/1α,25(OH)2D3 target gene. PCR analysis of different regions of the CD69 mRNA revealed that de novo transcription was initiated and proximal and distal parts were induced concomitantly. In common with 5-lipoxygenase, no activation of 0.7 kb or ~2.3 kb promoter fragments by TGF-β and 1α,25(OH)2D3 could be observed in transient reporter assays for CD69. Analysis of mRNA stability using a transcription inhibitor and a 3′UTR reporter construct showed that TGF-β and 1α,25(OH)2D3 do not influence CD69 mRNA stability. Functional knockdown of Smad3 clearly demonstrated that upregulation of CD69 mRNA, in contrast to 5-LO, depends on Smad3. Comparative studies with different inhibitors for mitogen activated protein kinases (MAPKs) revealed that MAPK signalling is involved in CD69 gene regulation, whereas 5-lipoxygenase gene expression was only partly affected. Mechanistically, we found evidence that CD69 gene upregulation depends on TAK1-mediated p38 activation. In summary, our data indicate that CD69 gene expression, conforming with 5-lipoxygenase, is regulated monocyte-specifically by the physiologic stimuli TGF-β and 1α,25(OH)2D3 on mRNA level, although different mechanisms account for the upregulation of each gene.
We present a computational method for the reaction-based de novo design of drug-like molecules. The software DOGS (Design of Genuine Structures) features a ligand-based strategy for automated ‘in silico’ assembly of potentially novel bioactive compounds. The quality of the designed compounds is assessed by a graph kernel method measuring their similarity to known bioactive reference ligands in terms of structural and pharmacophoric features. We implemented a deterministic compound construction procedure that explicitly considers compound synthesizability, based on a compilation of 25'144 readily available synthetic building blocks and 58 established reaction principles. This enables the software to suggest a synthesis route for each designed compound. Two prospective case studies are presented together with details on the algorithm and its implementation. De novo designed ligand candidates for the human histamine H4 receptor and γ-secretase were synthesized as suggested by the software. The computational approach proved to be suitable for scaffold-hopping from known ligands to novel chemotypes, and for generating bioactive molecules with drug-like properties.
Introduction: Despite the excellent anti-inflammatory and immunosuppressive action of glucocorticoids (GCs), their use for the treatment of inflammatory bowel disease (IBD) still carries significant risks in terms of frequently occurring severe side effects, such as the impairment of intestinal tissue repair. The recently-introduced selective glucocorticoid receptor (GR) agonists (SEGRAs) offer anti-inflammatory action comparable to that of common GCs, but with a reduced side effect profile.
Methods: The in vitro effects of the non-steroidal SEGRAs Compound A (CpdA) and ZK216348, were investigated in intestinal epithelial cells and compared to those of Dexamethasone (Dex). GR translocation was shown by immunfluorescence and Western blot analysis. Trans-repressive effects were studied by means of NF-κB/p65 activity and IL-8 levels, trans-activation potency by reporter gene assay. Flow cytometry was used to assess apoptosis of cells exposed to SEGRAs. The effects on IEC-6 and HaCaT cell restitution were determined using an in vitro wound healing model, cell proliferation by BrdU assay. In addition, influences on the TGF-β- or EGF/ERK1/2/MAPK-pathway were evaluated by reporter gene assay, Western blot and qPCR analysis.
Results: Dex, CpdA and ZK216348 were found to be functional GR agonists. In terms of trans-repression, CpdA and ZK216348 effectively inhibited NF-κB activity and IL-8 secretion, but showed less trans-activation potency. Furthermore, unlike SEGRAs, Dex caused a dose-dependent inhibition of cell restitution with no effect on cell proliferation. These differences in epithelial restitution were TGF-β-independent but Dex inhibited the EGF/ERK1/2/MAPK-pathway important for intestinal epithelial wound healing by induction of MKP-1 and Annexin-1 which was not affected by CpdA or ZK216348.
Conclusion: Collectively, our results indicate that, while their anti-inflammatory activity is comparable to Dex, SEGRAs show fewer side effects with respect to wound healing. The fact that SEGRAs did not have a similar effect on cell restitution might be due to a different modulation of EGF/ERK1/2 MAPK signalling.
Background: Threonine Aspartase 1 (Taspase1) mediates cleavage of the mixed lineage leukemia (MLL) protein and leukemia provoking MLL-fusions. In contrast to other proteases, the understanding of Taspase1's (patho)biological relevance and function is limited, since neither small molecule inhibitors nor cell based functional assays for Taspase1 are currently available. Methodology/Findings: Efficient cell-based assays to probe Taspase1 function in vivo are presented here. These are composed of glutathione S-transferase, autofluorescent protein variants, Taspase1 cleavage sites and rational combinations of nuclear import and export signals. The biosensors localize predominantly to the cytoplasm, whereas expression of biologically active Taspase1 but not of inactive Taspase1 mutants or of the protease Caspase3 triggers their proteolytic cleavage and nuclear accumulation. Compared to in vitro assays using recombinant components the in vivo assay was highly efficient. Employing an optimized nuclear translocation algorithm, the triple-color assay could be adapted to a high-throughput microscopy platform (Z'factor = 0.63). Automated high-content data analysis was used to screen a focused compound library, selected by an in silico pharmacophor screening approach, as well as a collection of fungal extracts. Screening identified two compounds, N-[2-[(4-amino-6-oxo-3H-pyrimidin-2-yl)sulfanyl]ethyl]benzenesulfonamideand 2-benzyltriazole-4,5-dicarboxylic acid, which partially inhibited Taspase1 cleavage in living cells. Additionally, the assay was exploited to probe endogenous Taspase1 in solid tumor cell models and to identify an improved consensus sequence for efficient Taspase1 cleavage. This allowed the in silico identification of novel putative Taspase1 targets. Those include the FERM Domain-Containing Protein 4B, the Tyrosine-Protein Phosphatase Zeta, and DNA Polymerase Zeta. Cleavage site recognition and proteolytic processing of these substrates were verified in the context of the biosensor. Conclusions: The assay not only allows to genetically probe Taspase1 structure function in vivo, but is also applicable for high-content screening to identify Taspase1 inhibitors. Such tools will provide novel insights into Taspase1's function and its potential therapeutic relevance.
Nep1 (Emg1) is a highly conserved nucleolar protein with an essential function in ribosome biogenesis. A mutation in the human Nep1 homolog causes Bowen–Conradi syndrome—a severe developmental disorder. Structures of Nep1 revealed a dimer with a fold similar to the SPOUT-class of RNA-methyltransferases suggesting that Nep1 acts as a methyltransferase in ribosome biogenesis. The target for this putative methyltransferase activity has not been identified yet. We characterized the RNA-binding specificity of Methanocaldococcus jannaschii Nep1 by fluorescence- and NMR-spectroscopy as well as by yeast three-hybrid screening. Nep1 binds with high affinity to short RNA oligonucleotides corresponding to nt 910–921 of M. jannaschii 16S rRNA through a highly conserved basic surface cleft along the dimer interface. Nep1 only methylates RNAs containing a pseudouridine at a position corresponding to a previously identified hypermodified N1-methyl-N3-(3-amino-3-carboxypropyl) pseudouridine (m1acp3-Psi) in eukaryotic 18S rRNAs. Analysis of the methylated nucleoside by MALDI-mass spectrometry, HPLC and NMR shows that the methyl group is transferred to the N1 of the pseudouridine. Thus, Nep1 is the first identified example of an N1-specific pseudouridine methyltransferase. This enzymatic activity is also conserved in human Nep1 suggesting that Nep1 is the methyltransferase in the biosynthesis of m1acp3-Psi in eukaryotic 18S rRNAs.
Poster presentation at 5th German Conference on Cheminformatics: 23. CIC-Workshop Goslar, Germany. 8-10 November 2009 We demonstrate the theoretical and practical application of modern kernel-based machine learning methods to ligand-based virtual screening by successful prospective screening for novel agonists of the peroxisome proliferator-activated receptor gamma (PPARgamma) [1]. PPARgamma is a nuclear receptor involved in lipid and glucose metabolism, and related to type-2 diabetes and dyslipidemia. Applied methods included a graph kernel designed for molecular similarity analysis [2], kernel principle component analysis [3], multiple kernel learning [4], and, Gaussian process regression [5]. In the machine learning approach to ligand-based virtual screening, one uses the similarity principle [6] to identify potentially active compounds based on their similarity to known reference ligands. Kernel-based machine learning [7] uses the "kernel trick", a systematic approach to the derivation of non-linear versions of linear algorithms like separating hyperplanes and regression. Prerequisites for kernel learning are similarity measures with the mathematical property of positive semidefiniteness (kernels). The iterative similarity optimal assignment graph kernel (ISOAK) [2] is defined directly on the annotated structure graph, and was designed specifically for the comparison of small molecules. In our virtual screening study, its use improved results, e.g., in principle component analysis-based visualization and Gaussian process regression. Following a thorough retrospective validation using a data set of 176 published PPARgamma agonists [8], we screened a vendor library for novel agonists. Subsequent testing of 15 compounds in a cell-based transactivation assay [9] yielded four active compounds. The most interesting hit, a natural product derivative with cyclobutane scaffold, is a full selective PPARgamma agonist (EC50 = 10 ± 0.2 microM, inactive on PPARalpha and PPARbeta/delta at 10 microM). We demonstrate how the interplay of several modern kernel-based machine learning approaches can successfully improve ligand-based virtual screening results.
Background: Nitric oxide (NO) is an essential vasodilator. In vascular diseases, oxidative stress attenuates NO signaling by both chemical scavenging of free NO and oxidation and down-regulation of its major intracellular receptor, the alpha/beta heterodimeric heme-containing soluble guanylate cyclase (sGC). Oxidation can also induce loss of sGC's heme and responsiveness to NO.
Results: sGC activators such as BAY 58-2667 bind to oxidized/heme-free sGC and reactivate the enzyme to exert disease-specific vasodilation. Here we show that oxidation-induced down-regulation of sGC protein extends to isolated blood vessels. Mechanistically, degradation was triggered through sGC ubiquitination and proteasomal degradation. The heme-binding site ligand, BAY 58-2667, prevented sGC ubiquitination and stabilized both alpha and beta subunits.
Conclusion: Collectively, our data establish oxidation-ubiquitination of sGC as a modulator of NO/cGMP signaling and point to a new mechanism of action for sGC activating vasodilators by stabilizing their receptor, oxidized/heme-free sGC.
We developed the Pharmacophore Alignment Search Tool (PhAST), a text-based technique for rapid hit and lead structure searching in large compound databases. For each molecule, a two-dimensional graph of potential pharmacophoric points (PPPs) is created, which has an identical topology as the original molecule with implicit hydrogen atoms. Each vertex is coloured by a symbol representing the corresponding PPP. The vertices of the graph are canonically labelled. The symbols associated with the vertices are combined to a so-called PhAST-Sequence beginning with the vertex with the lowest canonical label. Due to the canonical labelling the created PhAST-Sequence is characteristic for each molecule. For similarity assessment, PhAST-Sequences are compared using the sequence identity in their global pairwise alignment. The alignment score lies between 0 (no similarity) and 1 (identical PhAST-Sequences). In order to use global pairwise sequence alignment, a score matrix for pharmacophoric symbols was developed and gap penalties were optimized. PhAST performed comparably and sometimes superior to other similarity search tools (CATS2D, MOE pharmacophore quadruples) in retrospective virtual screenings using the COBRA collection of drugs and lead structures. Most importantly, the PhAST alignment technique allows for the computation of significance estimates that help prioritize a virtual hit list.