Glucocorticoids suppress Wnt16 expression in osteoblasts in vitro and in vivo

Glucocorticoid-induced osteoporosis is a frequent complication of systemic glucocorticoid (GC) therapy and mainly characterized by suppressed osteoblast activity. Wnt16 derived from osteogenic cells is a key determinant of bone mass. Here, we assessed whether GC suppress bone formation via inhibiting Wnt16 expression. GC treatment with dexamethasone (DEX) decreased Wnt16 mRNA levels in murine bone marrow stromal cells (mBMSCs) time- and dose-dependently. Similarly, Wnt16 expression was also suppressed after DEX treatment in calvarial organ cultures. Consistently, mice receiving GC-containing slow-release prednisolone pellets showed lower skeletal Wnt16 mRNA levels and bone mineral density than placebo-treated mice. The suppression of Wnt16 by GCs was GC-receptor-dependent as co-treatment of mBMSCs with DEX and the GR antagonist RU-486 abrogated the GC-mediated suppression of Wnt16. Likewise, DEX failed to suppress Wnt16 expression in GR knockout-mBMSCs. In addition, Wnt16 mRNA levels were unaltered in bone tissue of GC-treated GR dimerization-defective GRdim mice, suggesting that GCs suppress Wnt16 via direct DNA-binding mechanisms. Consistently, DEX treatment reduced Wnt16 promoter activity in MC3T3-E1 cells. Finally, recombinant Wnt16 restored DEX-induced suppression of bone formation in mouse calvaria. Thus, this study identifies Wnt16 as a novel target of GC action in GC-induced suppression of bone formation

Algorithmisches Programmieren (Numerische Algorithmen mit C++)

Dieser Kurs führt in die Programmiersprache C++ ein. Es werden die Grundlagen von C++, Kontrollstrukturen, Zahldarstellungen und Datentypen, Funktionen, Zeiger, objekt-orientierte Programmierung, Operatoren und deren Überladung, bishin zu Grundlagen der Vererbung und Klassentemplates, behandelt. Dieses Skriptum ist durch langjährige Erfahrungen der Autoren im Rahmen der gleichnamigen Vorlesung an der Leibniz Universität Hannover entstanden

Heightened immune response to autocitrullinated porphyromonas gingivalis peptidylarginine deiminase: a potential mechanism for breaching immunologic tolerance in rheumatoid arthritis

Background: Rheumatoid arthritis (RA) is characterised by autoimmunity to citrullinated proteins, and there is increasing epidemiologic evidence linking Porphyromonas gingivalis to RA. P gingivalis is apparently unique among periodontal pathogens in possessing a citrullinating enzyme, peptidylarginine deiminase (PPAD) with the potential to generate antigens driving the autoimmune response. Objectives: To examine the immune response to PPAD in patients with RA, individuals with periodontitis (PD) and controls (without arthritis), confirm PPAD autocitrullination and identify the modified arginine residues. Methods: PPAD and an inactivated mutant (C351A) were cloned and expressed and autocitrullination of both examined by immunoblotting and mass spectrometry. ELISAs using PPAD, C351A and another P gingivalis protein arginine gingipain (RgpB) were developed and antibody reactivities examined in patients with RA (n=80), individuals with PD (n=44) and controls (n=82). Results: Recombinant PPAD was a potent citrullinating enzyme. Antibodies to PPAD, but not to Rgp, were elevated in the RA sera (median 122 U/ml) compared with controls (median 70 U/ml; p<0.05) and PD (median 60 U/ml; p<0.01). Specificity of the anti-peptidyl citrullinated PPAD response was confirmed by the reaction of RA sera with multiple epitopes tested with synthetic citrullinated peptides spanning the PPAD molecule. The elevated antibody response to PPAD was abolished in RA sera if the C351A mutant was used on ELISA. Conclusions: The peptidyl citrulline-specific immune response to PPAD supports the hypothesis that, as a bacterial protein, it might break tolerance in RA, and could be a target for therapy

Reduced risk of myocardial infarct and revascularization following coronary artery bypass grafting compared with percutaneous coronary intervention in patients with chronic kidney disease

Coronary atherosclerotic disease is highly prevalent in chronic kidney disease (CKD). Although revascularization improves outcomes, procedural risks are increased in CKD and unbiased data comparing bypass surgery (CABG) and percutaneous intervention (PCI) in CKD are sparse. To compare outcomes of CABG and PCI in stage 3-5 CKD, we identified randomized trials comparing these procedures. Investigators were contacted to obtain individual, patient-level data. Ten of 27 trials meeting inclusion criteria provided data. These trials enrolled 3993 patients encompassing 526 patients with stage 3-5 CKD of which 137 were stage 3b-5 CKD. Among individuals with stage 3-5 CKD survival through 5-years was not different following CABG compared with PCI (hazard ratio 0.99, 95% confidence interval: 0.67, 1.46) or stage 3b-5 CKD (1.29: 0.68, 2.46). However, CKD modified the impact on survival free from myocardial infarction: it was not different between CABG and PCI for individuals with preserved kidney function (0.97: 0.80, 1.17), but was significantly lower following CABG in stage 3-5 CKD (0.49: 0.29, 0.82) and stage 3b-5 CKD (0.23: 0.09, 0.58). Repeat revascularization was reduced following CABG compared with PCI regardless of baseline kidney function. Results were limited by unavailability of data from several trials and paucity of enrolled patients with stage 4-5 CKD. Thus, our patient-level meta-analysis of individuals with CKD randomized to CABG versus PCI suggests that CABG significantly reduces the risk of subsequent myocardial infarction and revascularization without impacting survival in these patients

Automated pathway and reaction prediction facilitates in silico identification of unknown metabolites in human cohort studies

Identification of metabolites in non-targeted metabolomics continues to be a bottleneck in metabolomics studies in large human cohorts. Unidentified metabolites frequently emerge in the results of association studies linking metabolite levels to, for example, clinical phenotypes. For further analyses these unknown metabolites must be identified. Current approaches utilize chemical information, such as spectral details and fragmentation characteristics to determine components of unknown metabolites. Here, we propose a systems biology model exploiting the internal correlation structure of metabolite levels in combination with existing biochemical and genetic information to characterize properties of unknown molecules. Levels of 758 metabolites (439 known, 319 unknown) in human blood samples of 2279 subjects were measured using a non-targeted metabolomics platform (LC-MS and GC-MS). We reconstructed the structure of biochemical pathways that are imprinted in these metabolomics data by building an empirical network model based on 1040 significant partial correlations between metabolites. We further added associations of these metabolites to 134 genes from genome-wide association studies as well as reactions and functional relations to genes from the public database Recon 2 to the network model. From the local neighborhood in the network, we were able to predict the pathway annotation of 180 unknown metabolites. Furthermore, we classified 100 pairs of known and unknown and 45 pairs of unknown metabolites to 21 types of reactions based on their mass differences. As a proof of concept, we then looked further into the special case of predicted dehydrogenation reactions leading us to the selection of 39 candidate molecules for 5 unknown metabolites. Finally, we could verify 2 of those candidates by applying LC-MS analyses of commercially available candidate substances. The formerly unknown metabolites X-13891 and X-13069 were shown to be 2-dodecendioic acid and 9-tetradecenoic acid, respectively. Our data-driven approach based on measured metabolite levels and genetic associations as well as information from public resources can be used alone or together with methods utilizing spectral patterns as a complementary, automated and powerful method to characterize unknown metabolites