Background: Although enamel matrix derivative (EMD) can promote osteogenic differentiation of the pluripotent mesenchymal precursor cell line, C2C12, the molecular mechanism that underlies this phenomenon is unclear. The purpose of this study was to determine which molecules in EMD stimulate osteogenic differentiation.
Methods: C2C12 cells were cultured in 5% serum-containing medium to induce differentiation, either with or without the addition of EMD. The expression of core binding factor alpha1/runtrelated transcription factor-2 (Cbfa1/Runx2) was measured using Northern blot, Western blot, and/or real-time polymerase chain reaction (R-PCR) analysis. Phosphorylation of mothers against decapentaplegic homolog 1 (Smad1) and bone morphogenetic protein (BMP)-like molecules in EMD was determined by Western blot.
Results: EMD increased Cbfa1/Runx2 mRNA and protein expression substantially. EMD also induced phosphorylation of Smad1. Noggin inhibited the EMD-induced phosphorylation of Smad1 markedly, and also partially blocked EMD-induced Cbfa1/ Runx2 mRNA expression. In the Western blot analysis, single bands that corresponded to approximately 15 and approximately 17.5 kDa proteins were recognized in EMD by anti-BMP-2/4 and anti-BMP-7 antibodies, respectively.
Conclusions: Our study demonstrates that EMD stimulates Cbfa1/Runx2 expression and the phosphorylation of Smad1, and that both of these processes can be blocked by noggin. Therefore, the osteogenic activity of EMD may be mediated by BMPlike molecules in EMD.
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http://dx.doi.org/10.1902/jop.2005.76.2.244 | DOI Listing |
Lung Cancer (Auckl)
December 2024
University of California Irvine School of Medicine, Department of Medicine, Orange, CA, 92868, USA.
Mutations in KRAS G12C are among the more common oncogenic driver mutations in non-small cell lung cancer (NSCLC). In December 2022, the US Food and Drug Administration (FDA) granted accelerated approval to adagrasib, a small molecule covalent inhibitor of KRAS G12C, for the treatment of patients with locally advanced or metastatic KRAS G12C mutant NSCLC who received at least one prior systemic therapy based on promising results from phase 1 and 2 trials wherein adagrasib demonstrated a median PFS of 6.5 months.
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November 2024
Nestlé Institute of Health Sciences, Nestlé Research, Société des Produit Nestlé S.A., EPFL Innovation Park, 1015 Lausanne, Switzerland. Electronic address:
Lancet Rheumatol
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Idorsia Pharmaceuticals, Allschwil, Switzerland.
Background: Sphingosine-1-phosphate (S1P) is a signalling molecule that has an inhibitory role in atherosclerosis, inflammation, cell proliferation, and immunity. Cenerimod is a selective S1P receptor modulator under investigation for the treatment of systemic lupus erythematosus (SLE). We aimed to determine the efficacy, safety, and tolerability of four doses of cenerimod in adults with moderate-to-severe SLE receiving standard of care background therapy.
View Article and Find Full Text PDFTarget Oncol
November 2024
Sarah Cannon Research Institute, Nashville, TN, USA.
Background: Adavosertib (AZD1775) is a small-molecule Wee1 inhibitor. Durvalumab is a PD-L1 inhibitor.
Objective: The safety, tolerability, pharmacokinetics, and preliminary antitumor activity of adavosertib plus durvalumab were evaluated in patients with refractory solid tumors to define the maximum tolerated dose (MTD) and recommended phase II dose (RP2D).
J Chem Inf Model
December 2024
In Silico Toxicology and Structural Bioinformatics, Institute of Physiology, Charité - Universitätsmedizin Berlin, corporate member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Augustenburger Platz 1, 13353 Berlin, Germany.
In recent years, machine learning has transformed many aspects of the drug discovery process, including small molecule design, for which the prediction of bioactivity is an integral part. Leveraging structural information about the interactions between a small molecule and its protein target has great potential for downstream machine learning scoring approaches but is fundamentally limited by the accuracy with which protein-ligand complex structures can be predicted in a reliable and automated fashion. With the goal of finding practical approaches to generating useful kinase-inhibitor complex geometries for downstream machine learning scoring approaches, we present a kinase-centric docking benchmark assessing the performance of different classes of docking and pose selection strategies to assess how well experimentally observed binding modes are recapitulated in a realistic cross-docking scenario.
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