To effectively treat bone diseases using bone regenerative medicine, there is an urgent need to develop safe and cheap drugs that can potently induce bone formation. Here, we demonstrate the osteogenic effects of icariin, the main active compound of Epimedium pubescens. Icariin induced osteogenic differentiation of preosteoblastic cells. The combination of icariin and a helioxanthin-derived small compound synergistically induced osteogenic differentiation of MC3T3-E1 cells to a similar extent to bone morphogenetic protein-2. Icariin enhanced the osteogenic induction activity of bone morphogenetic protein-2 in a fibroblastic cell line. Mineralization was enhanced by treatment with a combination of icariin and calcium-enriched medium. The in vivo anabolic effect of icariin was confirmed in a mouse calvarial defect model. Eight-week-old male C57BL/6N mice were transplanted with icariin-calcium phosphate cement (CPC) tablets or CPC tablets only (n = 5 for each), and bone regeneration was evaluated after 4 and 6 weeks. Significant new bone formation was observed in the icariin-CPC group at 4 weeks, and the new bone thickness had increased by 6 weeks. Obvious blood vessel formation was observed in the icariin-induced new bone. Treatment of senescence-accelerated mouse prone 1 and senescence-accelerated mouse prone 6 models further demonstrated that icariin was able to enhance bone formation in vivo. Therefore, icariin is a strong candidate for an osteogenic compound for use in bone tissue engineering.

The application of semantic technologies to the integration of biological data and the interoperability of bioinformatics analysis and visualization tools has been the common theme of a series of annual BioHackathons hosted in Japan for the past five years. Here we provide a review of the activities and outcomes from the BioHackathons held in 2011 in Kyoto and 2012 in Toyama. In order to efficiently implement semantic technologies in the life sciences, participants formed various sub-groups and worked on the following topics: Resource Description Framework (RDF) models for specific domains, text mining of the literature, ontology development, essential metadata for biological databases, platforms to enable efficient Semantic Web technology development and interoperability, and the development of applications for Semantic Web data. In this review, we briefly introduce the themes covered by these sub-groups. The observations made, conclusions drawn, and software development projects that emerged from these activities are discussed.

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Polymeric immunoglobulin receptor (pIgR) plays an important role in mucosal immune systems. Secretory immunoglobulin A, composed of secretory component of pIgR and a dimeric form of immunoglobulin A, is secreted on mucosal surfaces and serves as a biological defense factor. pIgR gene expression is reportedly induced by activation of the transcription factor nuclear factor (NF)-κB. On the other hand, secretory leukocyte protease inhibitor (SLPI) is a glycoprotein that functions as a serine protease inhibitor. In alveolar epithelial cells, SLPI increases the level of IκBβ, which indicates that it is an inhibitor of NF-κB at the protein level. Taken together, SLPI may regulate pIgR expression; however, the specific mechanism by which this occurs is unclear. Therefore, the aim of this study was to elucidatethe influence of SLPI on pIgR expression.SLPI and pIgR localized in goblet cells and ciliated epithelial cells of the gastrointestinal tract, respectively. No cells were detected in which SLPI and pIgR were co-expressed. In addition, recombinant human SLPI stimulation of an epithelial cell line (HT-29) decreased the pIgR expression. The pIgR expression was also higher in SLPI-deficient Ca9-22 cells than in wild-type Ca9-22 cells. Furthermore, a luciferase assay using a NF-κB reporter plasmid and real-time RT-PCR analysis indicated that when SLPI was present, the transcriptional activity of NF-κB protein was suppressed, which was accompanied by anincrease in the protein, but not the mRNA,expression of IκBβ. These results demonstrate that SLPI down-regulates pIgR expression through the NF-κB signaling pathway by inhibiting degradation of IκBβ protein.

Several methods have been proposed for protein-sugar binding site prediction using machine learning algorithms. However, they are not effective to learn various properties of binding site residues caused by various interactions between proteins and sugars. In this study, we classified sugars into acidic and nonacidic sugars and showed that their binding sites have different amino acid occurrence frequencies. By using this result, we developed sugar-binding residue predictors dedicated to the two classes of sugars: an acid sugar binding predictor and a nonacidic sugar binding predictor. We also developed a combination predictor which combines the results of the two predictors. We showed that when a sugar is known to be an acidic sugar, the acidic sugar binding predictor achieves the best performance, and showed that when a sugar is known to be a nonacidic sugar or is not known to be either of the two classes, the combination predictor achieves the best performance. Our method uses only amino acid sequences for prediction. Support vector machine was used as a machine learning algorithm and the position-specific scoring matrix created by the position-specific iterative basic local alignment search tool was used as the feature vector. We evaluated the performance of the predictors using five-fold cross-validation. We have launched our system, as an open source freeware tool on the GitHub repository (https://doi.org/10.5281/zenodo.61513).