Biomedical subjects

Yasushi Okuno

Publications and source records attributed to Yasushi Okuno.

15 recordsLinked to original sources

PubMed2006

Altered gene expression of transcriptional regulatory factors in tumor marker-positive cells during chemically induced hepatocarcinogenesis.

Glutathione-S-transferase placental form (GST-P) is markedly and specifically inducible in rat chemical hepatocarcinogenesis and is a reliable marker protein for pre-neoplasia. To gain insights into the molecular mechanisms at the early stage of hepatocarcinogenesis and hepatotoxicity, we investigated the gene expression profile by DNA microarray analysis. We prepared RNA from GST-P-positive foci in three individual rats and compared with normal liver sections from three individual rats, and labeled RNA was individually hybridized onto Affymetrix GeneChip Rat Expression Array 230A. DNA microarray analysis showed distinctly different profiles of dysregulated gene expression and supported the previous finding that some enzymes involved in metabolism and detoxification are overexpressed and suppressed. Here we discovered that several DNA-binding transcription factors and cofactors, including sterol-regulatory-element binding protein 1 (SREBP1) and Wilms' tumour 1 (WT1)-interacting protein, and their target genes were dysregulated in GST-P-positive foci. Moreover, genes involved in chromatin components, histone modification enzymes, and centrosome duplication were highly expressed. These genes were not previously known to be up-regulated during chemically induced hepatocarcinogenesis. DNA microarray analysis using RNA prepared from tumor marker-positive foci and control tissues provided a candidate gene link to the early stage of carcinogenesis and hepatotoxicity.

Animals↗

PubMed2006

GLIDA: GPCR-ligand database for chemical genomic drug discovery.

G-protein coupled receptors (GPCRs) represent one of the most important families of drug targets in pharmaceutical development. GPCR-LIgand DAtabase (GLIDA) is a novel public GPCR-related chemical genomic database that is primarily focused on the correlation of information between GPCRs and their ligands. It provides correlation data between GPCRs and their ligands, along with chemical information on the ligands, as well as access information to the various web databases regarding GPCRs. These data are connected with each other in a relational database, allowing users in the field of GPCR-related drug discovery to easily retrieve such information from either biological or chemical starting points. GLIDA includes structure similarity search functions for the GPCRs and for their ligands. Thus, GLIDA can provide correlation maps linking the searched homologous GPCRs (or ligands) with their ligands (or GPCRs). By analyzing the correlation patterns between GPCRs and ligands, we can gain more detailed knowledge about their interactions and improve drug design efforts by focusing on inferred candidates for GPCR-specific drugs. GLIDA is publicly available at http://gdds.pharm.kyoto-u.ac.jp:8081/glida. We hope that it will prove very useful for chemical genomic research and GPCR-related drug discovery.

Animals↗

PubMed2006

MicroRNA: biogenetic and functional mechanisms and involvements in cell differentiation and cancer.

MicroRNAs (miRNAs) are endogenous small noncoding RNAs (20-23 nucleotides) that negatively regulate the gene expressions at the posttranscriptional level by base pairing to the 3' untranslated region of target messenger RNAs. Hundreds of miRNAs have been identified in humans and evolutionarily conserved from plants to animals. It is revealed that miRNAs regulate various physiological and pathological pathways such as cell differentiation, cell proliferation, and tumoriogenesis. By the computational analysis, it is predicted that 30% of protein-encoding genes are regulated by miRNAs. In this review, we discuss recent remarkable advances in the miRNA biogenetic and functional mechanisms and the involvements of miRNAs in cell differentiation, especially in hematopoietic lineages, and cancer. These evidences offer the possibility that miRNAs would be potentially useful for drug discovery.

Animals↗

PubMed2005

A probabilistic model for mining implicit 'chemical compound-gene' relations from literature.

MOTIVATION: The importance of chemical compounds has been emphasized more in molecular biology, and 'chemical genomics' has attracted a great deal of attention in recent years. Thus an important issue in current molecular biology is to identify biological-related chemical compounds (more specifically, drugs) and genes. Co-occurrence of biological entities in the literature is a simple, comprehensive and popular technique to find the association of these entities. Our focus is to mine implicit 'chemical compound and gene' relations from the co-occurrence in the literature. RESULTS: We propose a probabilistic model, called the mixture aspect model (MAM), and an algorithm for estimating its parameters to efficiently handle different types of co-occurrence datasets at once. We examined the performance of our approach not only by a cross-validation using the data generated from the MEDLINE records but also by a test using an independent human-curated dataset of the relationships between chemical compounds and genes in the ChEBI database. We performed experimentation on three different types of co-occurrence datasets (i.e. compound-gene, gene-gene and compound-compound co-occurrences) in both cases. Experimental results have shown that MAM trained by all datasets outperformed any simple model trained by other combinations of datasets with the difference being statistically significant in all cases. In particular, we found that incorporating compound-compound co-occurrences is the most effective in improving the predictive performance. We finally computed the likelihoods of all unknown compound-gene (more specifically, drug-gene) pairs using our approach and selected the top 20 pairs according to the likelihoods. We validated them from biological, medical and pharmaceutical viewpoints.

Artificial Intelligence↗

PubMed2005

Prostaglandin E2 stimulates granulocyte colony-stimulating factor production via the prostanoid EP2 receptor in mouse peritoneal neutrophils.

G-CSF is a hemopoietic growth factor involved in granulocytic differentiation of progenitor cells. In this study, we investigated the effects of PGE2 on G-CSF production in murine peritoneal neutrophils in vitro and in vivo. PGE2 augmented LPS-primed G-CSF release from peritoneal neutrophils. This augmentation was mimicked by a type E prostanoid receptor (EP)2-selective agonist but not by other EP-specific agonists. Indeed, the effect of PGE2 on G-CSF release was abolished in neutrophils isolated from EP2-deficient mice. PGE2 and an EP2 agonist have the ability to stimulate G-CSF gene expression even in the absence of LPS. In the casein-induced peritonitis model, the appearance of G-CSF in the casein-injected peritoneal cavity associated well with the timing of neutrophil infiltration as well as PGE2 levels in exudates, with a peak value at 6 h postinjection. Inhibition of endogenous PG synthesis by indomethacin resulted in a marked decrease in G-CSF content and neutrophil number in the peritoneal cavity. Moreover, EP2-deficient mice exhibited a strikingly reduced G-CSF content in peritoneal exudates with comparable responses in neutrophil migration and local PGE2 production at 6 h postinjection. These results suggest that the PGE2-EP2 system contributes to the local production of G-CSF during acute inflammation.

Animals↗

PubMed2005

Inhibition of protein kinase CK2 prevents the progression of glomerulonephritis.

Glomerulonephritis (GN) is a progressive inflammation that may be caused by a variety of underlying disorders. It is the primary cause of chronic renal failure and end-stage renal disease, which require dialysis and transplantation worldwide. Immunosuppressive therapy has been used to treat GN clinically, but this treatment has had insufficient therapeutic effects. Here, we show that protein kinase CK2 is a key molecule in the progression of GN. cDNA microarray analysis identified CK2alpha, the catalytic subunit of CK2, as a GN-related, differentially expressed gene. Overexpression of CK2alpha was noted in the proliferative glomerular lesions in rat GN models and in renal biopsy specimens from lupus nephritis or IgA nephropathy patients. Administration of either antisense oligodeoxynucleotide against CK2alpha or low molecular weight CK2-specific inhibitors effectively prevented the progression of renal pathology in the rat GN models. The resolution of GN by CK2 inhibition may result from its suppression of extracellular signal-regulated kinase-mediated cell proliferation, and its suppression of inflammatory and fibrotic processes that are enhanced in GN. Our results show that CK2 plays a critical role in the progression of immunogenic renal injury, and therefore, CK2 is a potential target for GN therapy.

Analysis of Variance↗

PubMed2005

Comprehensive analysis of the effect of phytoestrogen, daidzein, on a testicular cell line, using mRNA and protein expression profile.

In this study, we examined the effects of exposure to phytoestrogen (daidzein), 17beta-estradiol (E2), diethylstilbestrol (DES) and staurosporin on the TM4 testicular cell line, using comprehensive analysis, such as cDNA microarray and two-dimension polyacrylamide gel electropholesis (2D-PAGE) analysis, and we demonstrated if these toxicogenomic analyses could classify the chemical compounds. First, RNA was extracted from TM4 cells that had been treated with daidzein (80 microM), DES, E2 (40 microM) and stauroporin (100 nM) for 30 min. We performed cDNA microarray analysis, and the expression ratio data thus obtained were then analyzed using hierarchical clustering. This hierarchical clustering showed that daidzein exposure induced a different effect on gene expression change from that of E2, DES and staurosporin. Next, protein extracted from TM4 cells also underwent cDNA microarray analysis for 3 h. We performed 2D-PAGE analysis, and the spot intensity ratio data thus obtained were analyzed using hierarchical clustering. As with cDNA microarray, the hierarchical clustering of protein spot ratios showed that daidzein exposure induced a different effect on gene expression change from that of the other substances. In conclusion, we have demonstrated for the first time that classification of these chemicals can be performed by clustering analysis, using data from cDNA microarray and 2D-PAGE analyses, and that exposure to daidzein induces effects different from those of E2, DES and staurosporin.

Cell Line↗

PubMed2004

Computational assignment of the EC numbers for genomic-scale analysis of enzymatic reactions.

The EC (Enzyme Commission) numbers represent a hierarchical classification of enzymatic reactions, but they are also commonly utilized as identifiers of enzymes or enzyme genes in the analysis of complete genomes. This duality of the EC numbers makes it possible to link the genomic repertoire of enzyme genes to the chemical repertoire of metabolic pathways, the process called metabolic reconstruction. Unfortunately, there are numerous reactions known to be present in various pathways, but they will never get EC numbers because the EC number assignment requires published articles on full characterization of enzymes. Here we report a computerized method to automatically assign the EC numbers up to the sub-subclasses, i.e., without the fourth serial number for substrate specificity, given pairs of substrates and products. The method is based on a new classification scheme of enzymatic reactions, named the RC (reaction classification) number. Each reaction in the current dataset of the EC numbers is first decomposed into reactant pairs. Each pair is then structurally aligned to identify the reaction center, the matched region, and the difference region. The RC number represents the conversion patterns of atom types in these three regions. We examined the correspondence between computationally assigned RC numbers and manually assigned EC numbers by the jackknife cross-validation test and found that the EC sub-subclasses could be assigned with the accuracy of about 90%. Furthermore, we examined the correlation with genomic information as represented by the KEGG ortholog clusters (OC) and confirmed that the RC numbers are correlated not only with elementary reaction mechanisms but also with protein families.

Computers↗

PubMed2004

Microarray evaluation of EP4 receptor-mediated prostaglandin E2 suppression of 3T3-L1 adipocyte differentiation.

Prostaglandin E(2) (PGE(2)) has been shown to negatively regulate adipogenesis. To explore to what extent PGE(2) inhibits the differentiation of cells to adipocytes and to examine whether its effect could be due to EP4 receptor signaling, we used microarrays to analyze the gene expression profiles of 3T3-L1 cells exposed to a differentiation cocktail supplemented with PGE(2), AE1-329 (an EP4 agonist), or vehicle. The differentiation-associated responses in genes such as adipocytokines and enzymes related to lipid metabolism were largely weakened upon PGE(2) treatment. In particular, the expression of peroxisome proliferator activated receptor-gamma and CCAAT/enhancer binding protein-alpha, genes playing a central role in adipogenesis, was greatly suppressed. PGE(2) appears to be ineffective to a subclass of insulin target genes such as hexokinase 2 and phosphofructokinase. Similar responses were produced in the differentiation-associated genes upon AE1-329 treatment. These results suggest that PGE(2) inhibits a crucial step of the adipocyte differentiation process by acting on the EP4 receptor in 3T3-L1 cells.

3T3 Cells↗

PubMed2004

The KEGG resource for deciphering the genome.

A grand challenge in the post-genomic era is a complete computer representation of the cell and the organism, which will enable computational prediction of higher-level complexity of cellular processes and organism behavior from genomic information. Toward this end we have been developing a knowledge-based approach for network prediction, which is to predict, given a complete set of genes in the genome, the protein interaction networks that are responsible for various cellular processes. KEGG at http://www.genome.ad.jp/kegg/ is the reference knowledge base that integrates current knowledge on molecular interaction networks such as pathways and complexes (PATHWAY database), information about genes and proteins generated by genome projects (GENES/SSDB/KO databases) and information about biochemical compounds and reactions (COMPOUND/GLYCAN/REACTION databases). These three types of database actually represent three graph objects, called the protein network, the gene universe and the chemical universe. New efforts are being made to abstract knowledge, both computationally and manually, about ortholog clusters in the KO (KEGG Orthology) database, and to collect and analyze carbohydrate structures in the GLYCAN database.

Animals↗

PubMed2004

A hierarchical mixture of Markov models for finding biologically active metabolic paths using gene expression and protein classes.

With the recent development of experimental high-throughput techniques, the type and volume of accumulating biological data have extremely increased these few years. Mining from different types of data might lead us to find new biological insights. We present a new methodology for systematically combining three different datasets to find biologically active metabolic paths/patterns. This method consists of two steps: First it synthesizes metabolic paths from a given set of chemical reactions, which are already known and whose enzymes are co-expressed, in an efficient manner. It then represents the obtained metabolic paths in a more comprehensible way through estimating parameters of a probabilistic model by using these synthesized paths. This model is built upon an assumption that an entire set of chemical reactions corresponds to a Markov state transition diagram. Furthermore, this model is a hierarchical latent variable model, containing a set of protein classes as a latent variable, for clustering input paths in terms of existing knowledge of protein classes. We tested the performance of our method using a main pathway of glycolysis, and found that our method achieved higher predictive performance for the issue of classifying gene expressions than those obtained by other unsupervised methods. We further analyzed the estimated parameters of our probabilistic models, and found that biologically active paths were clustered into only two or three patterns for each expression experiment type, and each pattern suggested some new long-range relations in the glycolysis pathway.

Computer Simulation↗

PubMed2003

Development of a chemical structure comparison method for integrated analysis of chemical and genomic information in the metabolic pathways.

Cellular functions result from intricate networks of molecular interactions, which involve not only proteins and nucleic acids but also small chemical compounds. Here we present an efficient algorithm for comparing two chemical structures of compounds, where the chemical structure is treated as a graph consisting of atoms as nodes and covalent bonds as edges. On the basis of the concept of functional groups, 68 atom types (node types) are defined for carbon, nitrogen, oxygen, and other atomic species with different environments, which has enabled detection of biochemically meaningful features. Maximal common subgraphs of two graphs can be found by searching for maximal cliques in the association graph, and we have introduced heuristics to accelerate the clique finding and to detect optimal local matches (simply connected common subgraphs). Our procedure was applied to the comparison and clustering of 9383 compounds, mostly metabolic compounds, in the KEGG/LIGAND database. The largest clusters of similar compounds were related to carbohydrates, and the clusters corresponded well to the categorization of pathways as represented by the KEGG pathway map numbers. When each pathway map was examined in more detail, finer clusters could be identified corresponding to subpathways or pathway modules containing continuous sets of reaction steps. Furthermore, it was found that the pathway modules identified by similar compound structures sometimes overlap with the pathway modules identified by genomic contexts, namely, by operon structures of enzyme genes.

Algorithms↗

PubMed2003

Efficient tree-matching methods for accurate carbohydrate database queries.

One aspect of glycome informatics is the analysis of carbohydrate sugar chains, or glycans, whose basic structure is not a sequence, but a tree structure. Although there has been much work in the development of sequence databases and matching algorithms for sequences (for performing queries and analyzing similarity), the more complicated tree structure of glycans does not allow a direct implementation of such a database for glycans, and further, does not allow for the direct application of sequence alignment algorithms for performing searches or analyzing similarity. Therefore, we have utilized a polynomial-time dynamic programming algorithm for solving the maximum common subtree of two trees to implement an accurate and efficient tool for finding and aligning maximally matching glycan trees. The KEGG Glycan database for glycan structures released recently incorporates our tree-structure alignment algorithm with various parameters to adapt to the needs of a variety of users. Because we use similarity scores as opposed to a distance metric, our methods are more readily used to display trees of higher similarity. We present the two methods developed for this purpose and illustrate its validity.

Algorithms↗

PubMed2003

Heuristics for chemical compound matching.

We have developed an efficient algorithm for comparing two chemical compounds, where the chemical structure is treated as a 2D graph consisting of atoms as vertices and covalent bonds as edges. Based on the concept of functional groups in chemistry, 68 atom types (vertex types) are defined for carbon, nitrogen, oxygen, and other atomic species with different environments, which has enabled detection of biochemically meaningful features. Maximal common subgraphs of two graphs can be found by searching for maximal cliques in the association graph, and we have introduced heuristics to accelerate the clique finding. Our heuristic procedure is controlled by some adjustable parameters. Here we applied our procedure to the latest KEGG/LIGAND database with different sets of parameters, and demonstrated the correlation of parameters in our algorithm with the distribution of similarity scores and/or the execution time. Finally, we showed the effectiveness of our heuristics for compound pairs along metabolic pathways.

Algorithms↗

PubMed2002

LIGAND: database of chemical compounds and reactions in biological pathways.

LIGAND is a composite database comprising three sections: COMPOUND for the information about metabolites and other chemical compounds, REACTION for the collection of substrate-product relations representing metabolic and other reactions, and ENZYME for the information about enzyme molecules. The current release (as of September 7, 2001) includes 7298 compounds, 5166 reactions and 3829 enzymes. In addition to the keyword search provided by the DBGET/LinkDB system, a substructure search to the COMPOUND and REACTION sections is now available through the World Wide Web (http://www.genome.ad.jp/ligand/). LIGAND may be also downloaded by anonymous FTP (ftp://ftp.genome.ad.jp/pub/kegg/ligand/).

Animals↗