Intra-platform repeatability and inter-platform comparability of microRNA microarray technology.

Over the last decade, DNA microarray technology has provided a great contribution to the life sciences. The MicroArray Quality Control (MAQC) project demonstrated the way to analyze the expression microarray. Recently, microarray technology has been utilized to analyze a comprehensive microRNA expression profiling. Currently, several platforms of microRNA microarray chips are commercially available. Thus, we compared repeatability and comparability of five different microRNA microarray platforms (Agilent, Ambion, Exiqon, Invitrogen and Toray) using 309 microRNAs probes, and the Taqman microRNA system using 142 microRNA probes. This study demonstrated that microRNA microarray has high intra-platform repeatability and comparability to quantitative RT-PCR of microRNA. Among the five platforms, Agilent and Toray array showed relatively better performances than the others. However, the current lineup of commercially available microRNA microarray systems fails to show good inter-platform concordance, probably because of lack of an adequate normalization method and severe divergence in stringency of detection call criteria between different platforms. This study provided the basic information about the performance and the problems specific to the current microRNA microarray systems

Synthetic Pre-miRNA-Based shRNA as Potent RNAi Triggers

RNA interference (RNAi) is a powerful tool for studying gene function owing to the ease with which it can selectively silence genes of interest, and it has also attracted attention because of its potential for therapeutic applications. Chemically synthesized small interfering RNAs (siRNAs) and DNA vector-based short hairpin RNAs (shRNAs) are now widely used as RNAi triggers. In contrast to expressed shRNAs, the use of synthetic shRNAs is limited. Here we designed shRNAs modeled on a precursor microRNA (pre-miRNA) and evaluated their biological activity. We demonstrated that chemically synthetic pre-miRNA-based shRNAs have more potent RNAi activity than their corresponding siRNAs and found that their antisense strands are more efficiently incorporated into the RNA-induced silencing complex. Although greater off-target effects and interferon responses were induced by shRNAs than by their corresponding siRNAs, these effects could be overcome by simply using a lower concentration or by optimizing and chemically modifying shRNAs similar to synthetic siRNAs. These are challenges for the future

The oral lipid sensor GPR120 is not indispensable for the orosensory detectionof dietary lipids in the mouse

International audienceImplication of the long-chain fatty acid (LCFA) receptor GPR120, also termed free fatty acid receptor 4 (FFAR4), in the taste-guided preference for lipids is a matter of debate. To further unravel the role of GPR120 in the "taste of fat", the present study was conducted on GPR120-null mice and their wild-type littermates. Using a combination of morphological (i.e. immunohistochemical staining of circumvallate papillae - CVP), behavioral (i.e. two-bottle preference tests, licking tests and conditioned taste aversion) and functional studies (i.e. calcium imaging in freshly isolated taste bud cells - TBC), we show that absence of GPR120 in oral cavity was not associated with changes in i) the gross anatomy of CVP, ii) the LCFA-mediated increases in [Ca2+]i, iii) the preference for oily and LCFA solutions and iv) the conditioned avoidance of LCFA solutions. In contrast, the rise in [Ca2+]i triggered by grifolic acid (GA), a specific GPR120 agonist, was dramatically curtailed when GPR120 gene was lacking. Taken together these data demonstrate that activation of lingual GPR120 and preference for fat are disconnected, suggesting that GPR120 expressed in TBC is not absolutely required for the oral fat detection in the mouse

Application of a New Probabilistic Model for Mining Implicit Associated Cancer Genes from OMIM and Medline

An important issue in current medical science research is to find the genes that are strongly related to an inherited disease. A particular focus is placed on cancer-gene relations, since some types of cancers are inherited. As biomedical databases have grown speedily in recent years, an informatics approach to predict such relations from currently available databases should be developed. Our objective is to find implicit associated cancer-genes from biomedical databases including the literature database. Co-occurrence of biological entities has been shown to be a popular and efficient technique in biomedical text mining. We have applied a new probabilistic model, called mixture aspect model (MAM) [48], to combine different types of co-occurrences of genes and cancer derived from Medline and OMIM (Online Mendelian Inheritance in Man). We trained the probability parameters of MAM using a learning method based on an EM (Expectation and Maximization) algorithm. We examined the performance of MAM by predicting associated cancer gene pairs. Through cross-validation, prediction accuracy was shown to be improved by adding gene-gene co-occurrences from Medline to cancer-gene cooccurrences in OMIM. Further experiments showed that MAM found new cancer-gene relations which are unknown in the literature. Supplementary information can be found at http://www.bic.kyotou.ac.jp/pathway/zhusf/CancerInformatics/Supplemental2006.htm

Genome-wide analysis of aberrant methylation in human breast cancer cells using methyl-DNA immunoprecipitation combined with high-throughput sequencing

<p>Abstract</p> <p>Background</p> <p>Cancer cells undergo massive alterations to their DNA methylation patterns that result in aberrant gene expression and malignant phenotypes. However, the mechanisms that underlie methylome changes are not well understood nor is the genomic distribution of DNA methylation changes well characterized.</p> <p>Results</p> <p>Here, we performed methylated DNA immunoprecipitation combined with high-throughput sequencing (MeDIP-seq) to obtain whole-genome DNA methylation profiles for eight human breast cancer cell (BCC) lines and for normal human mammary epithelial cells (HMEC). The MeDIP-seq analysis generated non-biased DNA methylation maps by covering almost the entire genome with sufficient depth and resolution. The most prominent feature of the BCC lines compared to HMEC was a massively reduced methylation level particularly in CpG-poor regions. While hypomethylation did not appear to be associated with particular genomic features, hypermethylation preferentially occurred at CpG-rich gene-related regions independently of the distance from transcription start sites. We also investigated methylome alterations during epithelial-to-mesenchymal transition (EMT) in MCF7 cells. EMT induction was associated with specific alterations to the methylation patterns of gene-related CpG-rich regions, although overall methylation levels were not significantly altered. Moreover, approximately 40% of the epithelial cell-specific methylation patterns in gene-related regions were altered to those typical of mesenchymal cells, suggesting a cell-type specific regulation of DNA methylation.</p> <p>Conclusions</p> <p>This study provides the most comprehensive analysis to date of the methylome of human mammary cell lines and has produced novel insights into the mechanisms of methylome alteration during tumorigenesis and the interdependence between DNA methylome alterations and morphological changes.</p

GEM-TREND: a web tool for gene expression data mining toward relevant network discovery

<p>Abstract</p> <p>Background</p> <p>DNA microarray technology provides us with a first step toward the goal of uncovering gene functions on a genomic scale. In recent years, vast amounts of gene expression data have been collected, much of which are available in public databases, such as the Gene Expression Omnibus (GEO). To date, most researchers have been manually retrieving data from databases through web browsers using accession numbers (IDs) or keywords, but gene-expression patterns are not considered when retrieving such data. The Connectivity Map was recently introduced to compare gene expression data by introducing gene-expression signatures (represented by a set of genes with up- or down-regulated labels according to their biological states) and is available as a web tool for detecting similar gene-expression signatures from a limited data set (approximately 7,000 expression profiles representing 1,309 compounds). In order to support researchers to utilize the public gene expression data more effectively, we developed a web tool for finding similar gene expression data and generating its co-expression networks from a publicly available database.</p> <p>Results</p> <p>GEM-TREND, a web tool for searching gene expression data, allows users to search data from GEO using gene-expression signatures or gene expression ratio data as a query and retrieve gene expression data by comparing gene-expression pattern between the query and GEO gene expression data. The comparison methods are based on the nonparametric, rank-based pattern matching approach of Lamb et al. (Science 2006) with the additional calculation of statistical significance. The web tool was tested using gene expression ratio data randomly extracted from the GEO and with in-house microarray data, respectively. The results validated the ability of GEM-TREND to retrieve gene expression entries biologically related to a query from GEO. For further analysis, a network visualization interface is also provided, whereby genes and gene annotations are dynamically linked to external data repositories.</p> <p>Conclusion</p> <p>GEM-TREND was developed to retrieve gene expression data by comparing query gene-expression pattern with those of GEO gene expression data. It could be a very useful resource for finding similar gene expression profiles and constructing its gene co-expression networks from a publicly available database. GEM-TREND was designed to be user-friendly and is expected to support knowledge discovery. GEM-TREND is freely available at <url>http://cgs.pharm.kyoto-u.ac.jp/services/network</url>.</p

Characterization of gene expression profiles for different types of mast cells pooled from mouse stomach subregions by an RNA amplification method

<p>Abstract</p> <p>Background</p> <p>Mast cells (MCs) play pivotal roles in allergy and innate immunity and consist of heterogenous subclasses. However, the molecular basis determining the different characteristics of these multiple MC subclasses remains unclear.</p> <p>Results</p> <p>To approach this, we developed a method of RNA extraction/amplification for intact <it>in vivo </it>MCs pooled from frozen tissue sections, which enabled us to obtain the global gene expression pattern of pooled MCs belonging to the same subclass. MCs were isolated from the submucosa (sMCs) and mucosa (mMCs) of mouse stomach sections, respectively, 15 cells were pooled, and their RNA was extracted, amplified and subjected to microarray analysis. Known marker genes specific for mMCs and sMCs showed expected expression trends, indicating accuracy of the analysis.</p> <p>We identified 1,272 genes showing significantly different expression levels between sMCs and mMCs, and classified them into clusters on the basis of similarity of their expression profiles compared with bone marrow-derived MCs, which are the cultured MCs with so-called 'immature' properties. Among them, we found that several key genes such as <it>Notch4 </it>had sMC-biased expression and <it>Ptgr1 </it>had mMC-biased expression. Furthermore, there is a difference in the expression of several genes including extracellular matrix protein components, adhesion molecules, and cytoskeletal proteins between the two MC subclasses, which may reflect functional adaptation of each MC to the mucosal or submucosal environment in the stomach.</p> <p>Conclusion</p> <p>By using the method of RNA amplification from pooled intact MCs, we characterized the distinct gene expression profiles of sMCs and mMCs in the mouse stomach. Our findings offer insight into possible unidentified properties specific for each MC subclass.</p

Crystallization and preliminary x-ray diffraction studies of DNA decamer d(CCAGGCCTGG) complexed with cobalt(III)-pepleomycin

Crystals of the self-complementary oligonucleotide d(CCAGG­CCTGG) [Heinemann & Alings (1989), J. Mol. Biol. 210, 369-381] complexed with the hydroperoxide of cobalt-pepleomycin (CoPEP) were obtained by the hanging-drop vapour-diffusion method at 298 K. An X-ray diffraction data set was collected to 2.8 Å at 100 K. The crystal belonged to space group P212121, with unit-cell parameters a = 34.52, b = 59.88, c = 72.93 Å

Analysis of multiple compound–protein interactions reveals novel bioactive molecules

The authors use machine learning of compound-protein interactions to explore drug polypharmacology and to efficiently identify bioactive ligands, including novel scaffold-hopping compounds for two pharmaceutically important protein families: G-protein coupled receptors and protein kinases