Comparison of gene expression profiles of gingival carcinoma Ca9-22 cells and colorectal adenocarcinoma HT-29 cells to identify potentially important mediators of SLPI-induced cell migration.

Secretory leukocyte protease inhibitor (SLPI) is a serine protease inhibitor whose expression level is positively correlated with tumor aggressiveness and metastatic potential. However, the mechanism underlying SLPI-induced enhancement of malignant phenotype is not completely understood. The malignancy of cancer cells is highly dependent on cell migration activity. Our previous study revealed that gingival carcinoma Ca9-22 cells, but not colorectal adenocarcinoma HT-29 cells, expressed SLPI. Therefore, we investigated the migration activity of these two cell types to understand the nature of SLPI-mediated tumor aggressiveness and metastatic potential. In vitro wound healing assay indicated that HT-29 cells and SLPI-deleted Ca9-22 cells showed lower migration activity than wild-type Ca9-22 cells, suggesting that SLPI-induced cell migration plays an important role in tumor aggressiveness and metastatic potential. In addition, our gene expression profiling study based on microarray data for the three cell types identified a number of candidates, including LCP1 and GLI, that could be key molecules in the mechanism of SLPI-induced cell migration.

Development of a sugar-binding residue prediction system from protein sequences using support vector machine.

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).

Human uterus myoma and gene expression profiling: A novel in vitro model for studying secretory leukocyte protease inhibitor-mediated tumor invasion.

Secretory leukocyte protease inhibitor (SLPI) is a serine protease inhibitor that diminishes tissue destruction during inflammation. A recent report revealed high levels of SLPI expression in the oral carcinoma cell. In addition, overexpression of SLPI up-regulates metastasis in lung carcinoma cells. On the other hand, matrix metalloproteinases (MMPs) are proteinases that participate in extracellular matrix degradation. SLPI and MMPs are involved as accelerators of the tumor invasion process; however, their exact roles are not fully understood. Understanding the mechanism of tumor invasion requires models that take the effect of microenvironmental factors into account. In one such in vitro model, different carcinoma cells have been shown to invade myoma tissue in highly distinct patterns. We have used this myoma model, as it provides a more natural stroma-like environment, to investigate the role of SLPI in tumor invasion. Our results indicate that the model provides a relevant matrix for tumor invasion studies, and that SLPI is important for the invasion of oral carcinoma Ca9-22 cells in conjunction with MMPs. Furthermore, using bioinformatics analysis, we have identified candidates as key molecules involved in SLPI-mediated tumor invasion.

OpenTein: a database of digital whole-slide images of stem cell-derived teratomas.

Human stem cells are promising sources for regenerative therapy. To ensure safety of future therapeutic applications, the differentiation potency of stem cells has to be tested and be widely opened to the public. The potency is generally assessed by teratoma formation comprising differentiated cells from all three germ layers, and the teratomas can be inspected through high-quality digital images. The teratoma assay, however, lacks consistency in transplantation protocols and even in interpretation, which needs community-based efforts for improving the assay quality. Here, we have developed a novel database OpenTein (Open Teratoma Investigation, http://opentein.hgc.jp/) to archive and freely distribute high-resolution whole-slide images and relevant records. OpenTein has been designed as a searchable, zoomable and annotatable web-based repository system. We have deposited 468 images of teratomas derived by our transplantation of human stem cells, and users can freely access and process such digital teratoma images. Approximately, the current version of OpenTein responds within 11.2 min for processing 2.03 gigapixel teratoma images. Our system offers valuable tools and resources in the new era of stem cell biology.

Automatic generation of bioinformatics tools for predicting protein-ligand binding sites.

MOTIVATION: Predictive tools that model protein-ligand binding on demand are needed to promote ligand research in an innovative drug-design environment. However, it takes considerable time and effort to develop predictive tools that can be applied to individual ligands. An automated production pipeline that can rapidly and efficiently develop user-friendly protein-ligand binding predictive tools would be useful. RESULTS: We developed a system for automatically generating protein-ligand binding predictions. Implementation of this system in a pipeline of Semantic Web technique-based web tools will allow users to specify a ligand and receive the tool within 0.5-1 day. We demonstrated high prediction accuracy for three machine learning algorithms and eight ligands. AVAILABILITY AND IMPLEMENTATION: The source code and web application are freely available for download at http://utprot.net They are implemented in Python and supported on Linux. CONTACT: shimizu@bi.a.u-tokyo.ac.jp SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Tyrosine Kinase Ligand-Receptor Pair Prediction by Using Support Vector Machine.

Receptor tyrosine kinases are essential proteins involved in cellular differentiation and proliferation in vivo and are heavily involved in allergic diseases, diabetes, and onset/proliferation of cancerous cells. Identifying the interacting partner of this protein, a growth factor ligand, will provide a deeper understanding of cellular proliferation/differentiation and other cell processes. In this study, we developed a method for predicting tyrosine kinase ligand-receptor pairs from their amino acid sequences. We collected tyrosine kinase ligand-receptor pairs from the Database of Interacting Proteins (DIP) and UniProtKB, filtered them by removing sequence redundancy, and used them as a dataset for machine learning and assessment of predictive performance. Our prediction method is based on support vector machines (SVMs), and we evaluated several input features suitable for tyrosine kinase for machine learning and compared and analyzed the results. Using sequence pattern information and domain information extracted from sequences as input features, we obtained 0.996 of the area under the receiver operating characteristic curve. This accuracy is higher than that obtained from general protein-protein interaction pair predictions.

Secretory leukocyte protease inhibitor inhibits expression of polymeric immunoglobulin receptor via the NF-κB signaling pathway.

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 IκBß, 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 IκBß. These results demonstrate that SLPI down-regulates pIgR expression through the NF-κB signaling pathway by inhibiting degradation of IκBß protein.

Whole transcriptome analysis using next-generation sequencing of sterile-cultured Eisenia andrei for immune system research.

Recently, earthworms have become a useful model for research into the immune system, and it is expected that results obtained using this model will shed light on the sophisticated vertebrate immune system and the evolution of the immune response, and additionally help identify new biomolecules with therapeutic applications. However, for earthworms to be used as a genetic model of the invertebrate immune system, basic molecular and genetic resources, such as an expressed sequence tag (EST) database, must be developed for this organism. Next-generation sequencing technologies have generated EST libraries by RNA-seq in many model species. In this study, we used Illumina RNA-sequence technology to perform a comprehensive transcriptome analysis using an RNA sample pooled from sterile-cultured Eisenia andrei. All clean reads were assembled de novo into 41,423 unigenes using the Trinity program. Using this transcriptome data, we performed BLAST analysis against the GenBank non-redundant (NR) database and obtained a total of 12,285 significant BLAST hits. Furthermore, gene ontology (GO) analysis assigned 78 unigenes to 24 immune class GO terms. In addition, we detected a unigene with high similarity to beta-1,3-glucuronyltransferase 1 (GlcAT-P), which mediates a glucuronyl transfer reaction during the biosynthesis of the carbohydrate epitope HNK-1 (human natural killer-1, also known as CD57), a marker of NK cells. The identified transcripts will be used to facilitate future research into the immune system using E. andrei.

BioHackathon series in 2011 and 2012: penetration of ontology and linked data in life science domains.

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.

Enhancement of bone formation ex vivo and in vivo by a helioxanthin-derivative.

To effectively treat serious bone defects using bone-regenerative medicine, a small chemical compound that potently induces bone formation must be developed. We previously reported on the osteogenic effect of 4-(4-methoxyphenyl)pyrido[40,30:4,5]thieno[2,3-b]pyridine-2-carboxamide (TH), a helioxanthin-derivative, in vitro. Here, we report on TH's osteogenic effects ex vivo and in vivo. TH-induced new bone formation in both calvarial and metatarsal organ cultures. A novel monitoring system of osteoblastic differentiation using MC3T3-E1 cells revealed that TH was released from alpha-TCP bone cement and this release continued for more than one month. Lastly, the implantation of the alpha-TCP carrier containing TH into defects in mouse skull resulted in increased new bone areas within the defects after 4 weeks. A TH-containing scaffold may help establish a more efficient bone regeneration system.

Icariin: a potential osteoinductive compound for bone tissue engineering.

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.

A novel osteogenic helioxanthin-derivative acts in a BMP-dependent manner.

To effectively treat serious bone defects using bone regenerative medicine, there is a need for the development of a small chemical compound that potently induces bone formation. We now report a novel osteogenic helioxanthin-derivative, TH. TH induced osteogenic differentiation in MC3T3-E1 cells, mouse primary osteoblasts, and mouse embryonic stem cells. The combination of TH and bone morphogenetic protein (BMP) 2 induced the mRNA expression of osteoblast marker genes and calcification in primary fibroblasts. The TH induced the mRNA of the inhibitor of DNA-binding 1 (Id-1), and its osteogenic effect was inhibited by Smad6 or Noggin. Furthermore, TH induced the mRNA expression of Bmp4 and Bmp6. These data suggest that TH exerts its potent osteogenic effect in a BMP-dependent manner by enhancing the effects of the existing BMPs and/or increasing the expression of Bmp4 and Bmp6. TH may help establish a more efficient bone regeneration system.