A novel negative regulatory mechanism of Smurf2 in BMP/Smad signaling in bone.

Transforming growth factor-ß (TGF-ß) and bone morphogenetic protein (BMP) play important roles in bone metabolism. Smad ubiquitination regulatory factors (Smurfs) regulate TGF-ß/BMP signaling via ubiquitination, resulting in degradation of signaling molecules to prevent excessive activation of TGF-ß/BMP signaling. Though Smurf2 has been shown to negatively regulate TGF-ß/Smad signaling, its involvement in BMP/Smad signaling in bone metabolism has not been thoroughly investigated. In the present study, we sought to evaluate the role of Smurf2 in BMP/Smad signaling in bone metabolism. Absorbable collagen sponges containing 3 µg of recombinant human BMP2 (rhBMP2) were implanted in the dorsal muscle pouches of wild type (WT) and Smurf2-/- mice. The rhBMP2-induced ectopic bone in Smurf2-/- mice showed greater bone mass, higher mineral apposition and bone formation rates, and greater osteoblast numbers than the ectopic bone in WT mice. In WT mice, the ectopic bone consisted of a thin discontinuous outer cortical shell and scant inner trabecular bone. In contrast, in Smurf2-/- mice, the induced bone consisted of a thick, continuous outer cortical shell and abundant inner trabecular bone. Additionally, rhBMP2-stimulated bone marrow stromal cells (BMSCs) from Smurf2-/- mice showed increased osteogenic differentiation. Smurf2 induced the ubiquitination of Smad1/5. BMP/Smad signaling was enhanced in Smurf2-/- BMSCs stimulated with rhBMP2, and the inhibition of BMP/Smad signaling suppressed osteogenic differentiation of these BMSCs. These findings demonstrate that Smurf2 negatively regulates BMP/Smad signaling, thereby identifying a new regulatory mechanism in bone metabolism.

Therapeutic effects of the PKR inhibitor C16 suppressing tumor proliferation and angiogenesis in hepatocellular carcinoma in vitro and in vivo.

The therapeutic effects of C16, which is an inhibitor of RNA-dependent protein kinase (PKR), on growth of hepatocellular carcinoma (HCC) cells and tumor progression in vitro and in vivo were evaluated. Huh7 cells, a human HCC cell line, were used. The effects of C16 on cell viability were evaluated with the MTT assay, and real-time RT-PCR was performed. Huh7 cells were grafted into immunodeficient mice, and the in vivo effects of C16 on tumorigenesis were examined. C16 suppressed proliferation of HCC cells in a dose-dependent manner in vitro. Mouse models with xenograft transplantation showed that the inhibitor suppressed the growth of HCC cells in vivo. Moreover, C16 decreased angiogenesis in HCC tissue in the xenograft model. Consistent with these results in mice, transcript levels of vascular endothelial growth factor-A and factor-B, platelet-derived growth factor-A and factor-B, fibroblast growth factor-2, epidermal growth factor, and hepatocyte growth factor, which are angiogenesis-related growth factors, were significantly decreased by C16 in vitro. In conclusion, the PKR inhibitor C16 blocked tumor cell growth and angiogenesis via a decrease in mRNA levels of several growth factors. C16 may be useful in the treatment of HCC.

Tissue Intrinsic Fluorescence Spectra-Based Digital Pathology of Liver Fibrosis by Marker-Controlled Segmentation.

Tissue intrinsic emission fluorescence provides useful diagnostic information for various diseases. Because of its unique feature of spectral profiles depending on tissue types, spectroscopic imaging is a promising tool for accurate evaluation of endogenous fluorophores. However, due to difficulties in discriminating those sources, quantitative analysis remains challenging. In this study, we quantitatively investigated spectral-spatial features of multi-photon excitation fluorescence in normal and diseased livers. For morphometrics of multi-photon excitation spectra, we examined a marker-controlled segmentation approach and its application to liver fibrosis assessment by employing a mouse model of carbon tetrachloride (CCl4)-induced liver fibrosis. We formulated a procedure of internal marker selection where markers were chosen to reflect typical biochemical species in the liver, followed by image segmentation and local morphological feature extraction. Image segmentation enabled us to apply mathematical morphology analysis, and the local feature was applied to the automated classification test based on a machine learning framework, both demonstrating highly accurate classifications. Through the analyses, we showed that spectral imaging of native fluorescence from liver tissues have the capability of differentiating not only between normal and diseased, but also between progressive disease states. The proposed approach provides the basics of spectroscopy-based digital histopathology of chronic liver diseases, and can be applied to a range of diseases associated with autofluorescence alterations.