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Biomedical subjects

Satoru Kuhara

Publications and source records attributed to Satoru Kuhara.

16 recordsLinked to original sources
PubMed2004

Nucleotide substitutions in Staphylococcus aureus strains, Mu50, Mu3, and N315.

A specific phenotype of Staphylococcus aureus strains Mu50 and Mu3 is characterized by thickened cell wall and moderate resistance to vancomycin. The N315 strain is a prototype of methicillin-resistant S. aureus (MRSA), but it is methicillin susceptible, despite carrying the mecA resistance gene. Here, we revised differences in the sequences of Mu50 and N315, referencing that of Mu3 which were assumed to be of one lineage. The 362 ORFs diverse between Mu50 and N315 were picked up, and the corresponding ones in three strains were re-sequenced. This defined 213 ORFs diverse between Mu50 and N315, and 9 between Mu50 and Mu3. The fixed diversities of 174 ORFs (except for 39 silent ORFs from 213), including nucleotide substitution (NSs), frame shift, and truncation were grouped into three major functional categories, which were transport (14.9% in the 174 diverse ORFs), metabolism of carbohydrates (5.7%), and RNA synthesis (9.6%). The other gene categories had small diversities. These gene categories seemed to be functionally decisive for the Mu50-specific characters, the thickened cell wall and moderate vancomycin resistance. All of the diverse genes and the high quality sequence of Mu50 can be viewed at the web site (http://133.5.48.239/VRSA/).

Bacterial Proteins↗
PubMed2004

Combining microarrays and biological knowledge for estimating gene networks via bayesian networks.

We propose a statistical method for estimating a gene network based on Bayesian networks from microarray gene expression data together with biological knowledge including protein-protein interactions, protein-DNA interactions, binding site information, existing literature and so on. Microarray data do not contain enough information for constructing gene networks accurately in many cases. Our method adds biological knowledge to the estimation method of gene networks under a Bayesian statistical framework, and also controls the trade-off between microarray information and biological knowledge automatically. We conduct Monte Carlo simulations to show the effectiveness of the proposed method. We analyze Saccharomyces cerevisiae gene expression data as an application.

Algorithms↗
PubMed2003

Identification of BMP and activin membrane-bound inhibitor (BAMBI), an inhibitor of transforming growth factor-beta signaling, as a target of the beta-catenin pathway in colorectal tumor cells.

The Wnt signaling pathway is activated in most human colorectal tumors. Mutational inactivation in the tumor suppressor adenomatous polyposis coli (APC), as well as activation of beta-catenin, causes the accumulation of beta-catenin, which in turn associates with the T cell factor/lymphoid enhancer factor (TCF/LEF) family of transcription factors and activates transcription of their target genes. Here we show that beta-catenin activates transcription of the BMP and activin membrane-bound inhibitor (BAMBI)/NMA gene. The expression level of BAMBI was found to be aberrantly elevated in most colorectal and hepatocellular carcinomas relative to the corresponding non-cancerous tissues. Expression of BAMBI in colorectal tumor cell lines was repressed by a dominant-negative mutant of TCF-4 or by an inhibitor of beta-catenin-TCF interaction, suggesting that beta-catenin is responsible for the aberrant expression of BAMBI in colorectal tumor cells. Furthermore, overexpression of BAMBI inhibited the response of tumor cells to transforming growth factor-beta signaling. These results suggest that beta-catenin interferes with transforming growth factor-beta-mediated growth arrest by inducing the expression of BAMBI, and this may contribute to colorectal and hepatocellular tumorigenesis.

Adenoviridae↗
PubMed2003

Discovery of novel transcription control relationships with gene regulatory networks generated from multiple-disruption full genome expression libraries.

Gene regulatory networks elucidated from strategic, genome-wide experimental data can aid in the discovery of novel gene function information and expression regulation events from observation of transcriptional regulation among genes of known and unknown biological function. To create a reliable and comprehensive data set for the elucidation of transcription regulation networks, we conducted systematic genome-wide disruption expression experiments of yeast on 118 genes with known involvement in transcription regulation. We report several novel regulatory relationships between known transcription factors and other genes with previously unknown biological function discovered with this expression library. Here we report the downstream regulatory subnetworks for UME6 and MET28. The elucidated network topology among these genes demonstrates MET28's role as a nodal point between genes involved in cell division and those involved in DNA repair mechanisms.

Algorithms↗
PubMed2003

Use of gene networks from full genome microarray libraries to identify functionally relevant drug-affected genes and gene regulation cascades.

We developed an extensive yeast gene expression library consisting of full-genome cDNA array data for over 500 yeast strains, each with a single-gene disruption. Using this data, combined with dose and time course expression experiments with the oral antifungal agent griseofulvin, whose exact molecular targets were previously unknown, we used Boolean and Bayesian network discovery techniques to determine the gene expression regulatory cascades affected directly by this drug. Using this method we identified CIK1 as an important affected target gene related to the functional phenotype induced by griseofulvin. Cellular functional analysis of griseofulvin showed similar tubulin-specific morphological effects on mitotic spindle formation to those of the drug, in agreement with the known function of CIK1p. Further, using the nonparametric, nonlinear Bayesian gene networks we were able to identify alternative ligand-dependant transcription factors and G protein homologues upstream of CIK1 that regulate CIK1 expression and might therefore serve as alternative molecular targets to induce the same molecular response as griseofulvin.

Bayes Theorem↗
PubMed2003

Family 19 chitinase from rice (Oryza sativa L.): substrate-binding subsites demonstrated by kinetic and molecular modeling studies.

A family 19 chitinase (OsChia1c, class I) from rice, Oryza sativa L., and its chitin-binding domain-truncated mutant (OsChia1c deltaCBD, class II) were produced by the Pichia expression system, and the hydrolytic mechanism toward N-acetylglucosamine hexasaccharide [(GlcNAc)6] was investigated by HPLC analysis of the reaction products. The profile of the time-course of (GlcNAc)6 degradation obtained by OsChia1c was identical to that obtained by OsChia1c deltaCBD, indicating that the chitin-binding domain does not significantly participate in oligosaccharide hydrolysis. From the theoretical analysis of the reaction time-course of OsChia1c deltaCBD, the free energy changes of sugar residue binding were estimated to be -0.4, -4.7, +3.4, -0.5, -2.3, and -1.0 kcal/mol for the individual subsites of (-3), (-2), (-1), (+1), (+2), and (+3), respectively. The hexasaccharide substrate appears to bind to the enzyme through interactions at the high-affinity sites, (-2) and (+2), and the sugar residues at both ends more loosely bind to the corresponding subsites, (-3) and (+3). The docking study of (GlcNAc)6 with the modeled structure of OsChia1c deltaCBD supported the subsite structure estimated from the experimental time-course of hexasaccharide degradation. Since the class II chitinase from barley seeds was reported to possess a similar subsite structure from (-3) to (+3) and a similar free energy distribution, substrate-binding mode of plant chitinases of this class would be similar to each other.

Acetylglucosamine↗
PubMed2003

Comparative DNA sequence analysis of mouse and human CC chemokine gene clusters.

The CC chemokines are a closely related subfamily of the chemokine superfamily. Most of the CC chemokine genes form a cluster on chromosome 11 in mice and chromosome 17 in humans. To date, 11 and 16 functional genes have been localized within the mouse and human clusters, respectively. Notably, some of the genes within these clusters appear to have no counterparts between the two species, and the orthologous relationships of some of the genes are difficult to establish solely on the basis of amino acid similarity. In this study, we have taken a comparative genomic approach to reveal some of the features that may be involved in the dynamic evolution of these gene clusters. We sequenced a 122-kb region containing five chemokine genes of the mouse CC cluster. This mouse sequence was combined with those determined by the Mouse Genome Sequencing Project, and the entire sequence of the mouse CC cluster was compared with that of the corresponding cluster in the human genome by percent identity plot and dot-plot analyses. Although no additional chemokine genes have been found in these clusters, our analysis has revealed that numerous gene rearrangements have occurred even after the diversification of rodents and primates, resulting in several species-specific chemokine genes and pseudogenes. In addition, phylogenetic analysis and comparison of the genomic sequences unambiguously identified the orthologous relationships of some of the chemokine genes in the mouse and human CC gene clusters.

Animals↗
PubMed2003

Estimating gene networks from gene expression data by combining Bayesian network model with promoter element detection.

We present a statistical method for estimating gene networks and detecting promoter elements simultaneously. When estimating a network from gene expression data alone, a common problem is that the number of microarrays is limited compared to the number of variables in the network model, making accurate estimation a difficult task. Our method overcomes this problem by integrating the microarray gene expression data and the DNA sequence information into a Bayesian network model. The basic idea of our method is that, if a parent gene is a transcription factor, its children may share a consensus motif in their promoter regions of the DNA sequences. Our method detects consensus motifs based on the structure of the estimated network, then re-estimates the network using the result of the motif detection. We continue this iteration until the network becomes stable. To show the effectiveness of our method, we conducted Monte Carlo simulations and applied our method to Saccharomyces cerevisiae data as a real application.

Bayes Theorem↗
PubMed2003

Response of genes associated with mitochondrial function to mild heat stress in yeast Saccharomyces cerevisiae.

The genome-wide expression pattern of budding yeast Saccharomyces cerevisiae in response to mild heat treatment in a non-fermentable carbon source was analyzed using DNA microarrays. Of 5,870 open reading frames (nuclear genome transcripts) examined, 104 genes were upregulated and 287 genes were downregulated upon shifting of the cells from 25 degrees C to 37 degrees C. Forty upregulated genes and 235 downregulated genes encoded localization-assigned proteins. Of 113 heat-repressible genes (excluding 122 heat-repressible ribosomal genes), 36 were mitochondria-related genes, whereas only 2 of 40 heat-inducible genes were mitochondria-related. In particular, 9 genes involved in the mitochondrial respiratory chain and 7 genes involved in mitochondrial protein translocation were significantly repressed, suggesting that mitochondrial respiratory function and biogenesis were downregulated. Consistent with these findings, the growth of yeast cells in a non-fermentable carbon source was repressed at 37 degrees C and the mitochondria isolated from heat-stressed cells exhibited compromised preprotein-import activity compared with those from unstressed cells. In contrast, many genes involved in glycolysis and the metabolic pathway to produce glutamate via the tricarboxylic acid cycle, which is essential for biosynthetic reactions, were upregulated. Yeast cells might downregulate mitochondrial function to circumvent heat-induced oxidative stress, upregulate stress-related genes, and remodel genes for metabolic pathways in response to mild heat stress: an adaptive response at the expense of cell growth.

Down-Regulation↗
PubMed2003

Bayesian network and nonparametric heteroscedastic regression for nonlinear modeling of genetic network.

We propose a new statistical method for constructing a genetic network from microarray gene expression data by using a Bayesian network. An essential point of Bayesian network construction is the estimation of the conditional distribution of each random variable. We consider fitting nonparametric regression models with heterogeneous error variances to the microarray gene expression data to capture the nonlinear structures between genes. Selecting the optimal graph, which gives the best representation of the system among genes, is still a problem to be solved. We theoretically derive a new graph selection criterion from Bayes approach in general situations. The proposed method includes previous methods based on Bayesian networks. We demonstrate the effectiveness of the proposed method through the analysis of Saccharomyces cerevisiae gene expression data newly obtained by disrupting 100 genes.

Bayes Theorem↗
PubMed2003

Use of gene networks for identifying and validating drug targets.

We propose a new method for identifying and validating drug targets by using gene networks, which are estimated from cDNA microarray gene expression profile data. We created novel gene disruption and drug response microarray gene expression profile data libraries for the purpose of drug target elucidation. We use two types of microarray gene expression profile data for estimating gene networks and then identifying drug targets. The estimated gene networks play an essential role in understanding drug response data and this information is unattainable from clustering methods, which are the standard for gene expression analysis. In the construction of gene networks, we use the Bayesian network model. We use an actual example from analysis of the Saccharomyces cerevisiae gene expression profile data to express a concrete strategy for the application of gene network information to drug discovery.

Algorithms↗
PubMed2003

Amino acid residue substitution at T-cell determinant-flanking sites in beta-lactoglobulin modulates antigen presentation to T cells through subtle conformational change.

We compared T-cell responses to regions in residues 21-40 of A and B variants of bovine milk beta-lactoglobulin (beta-LG) that vary by two different amino acid residues at 64 and 118. Results showed that T cells from C57/BL6 and C3H/HeN mice immunized with peptide 21-40 or BALB/c mice immunized with peptide 21-32 or 25-40 responded more vigorously to beta-LG B than to beta-LG A. This difference in response to 25-40 in BALB/c mice was not observed when beta-LGs B and A were denatured, suggesting that the conformation difference affects display of the determinant 25-40. Reactivity of anti-beta-LG monoclonal antibodies and molecular modeling using molecular dynamics calculations revealed subtle differences in the three-dimensional structure of these two variants. Furthermore, substitution of two amino acid residues at sites distant from the T-cell determinant induced differential determinant display on antigen-presenting cells, possibly due to subtle conformational changes in beta-LG.

Amino Acid Substitution↗
PubMed2003

Mutational analysis of amino acid residues involved in catalytic activity of a family 18 chitinase from tulip bulbs.

We expressed chitinase-1 (TBC-1) from tulip bulbs (Tulipa bakeri) in E. coli cells and used site-directed mutagenesis to identify amino acid residues essential for catalytic activity. Mutations at Glu-125 and Trp-251 completely abolished enzyme activity, and activity decreased with mutations at Asp-123 and Trp-172 when glycolchitin was the substrate. Activity changed with the mutations of Trp-251 to one of several amino acids with side-chains of little hydrophobicity, suggesting that hydrophobic interaction of Trp-251 is important for the activity. Molecular dynamics (MD) simulation analysis with hevamine as the model compound showed that the distance between Asp-123 and Glu-125 was extended by mutation of Trp-251. Kinetic studies of Trp-251-mutated chitinases confirmed these various phenomena. The results suggested that Glu-125 and Trp-251 are essential for enzyme activity and that Trp-251 had a direct role in ligand binding.

Amino Acid Substitution↗
PubMed2002

Complete genome sequence of Clostridium perfringens, an anaerobic flesh-eater.

Clostridium perfringens is a Gram-positive anaerobic spore-forming bacterium that causes life-threatening gas gangrene and mild enterotoxaemia in humans, although it colonizes as normal intestinal flora of humans and animals. The organism is known to produce a variety of toxins and enzymes that are responsible for the severe myonecrotic lesions. Here we report the complete 3,031,430-bp sequence of C. perfringens strain 13 that comprises 2,660 protein coding regions and 10 rRNA genes, showing pronounced low overall G + C content (28.6%). The genome contains typical anaerobic fermentation enzymes leading to gas production but no enzymes for the tricarboxylic acid cycle or respiratory chain. Various saccharolytic enzymes were found, but many enzymes for amino acid biosynthesis were lacking in the genome. Twenty genes were newly identified as putative virulence factors of C. perfringens, and we found a total of five hyaluronidase genes that will also contribute to virulence. The genome analysis also proved an efficient method for finding four members of the two-component VirR/VirS regulon that coordinately regulates the pathogenicity of C. perfringens. Clearly, C. perfringens obtains various essential materials from the host by producing several degradative enzymes and toxins, resulting in massive destruction of the host tissues.

Amino Acids↗
PubMed2002

[Drug discovery based on microarray].

Gene regulatory networks developed from full genome expression libraries from gene perturbation variant cell lines can be used to quickly and efficiently identify the molecular mechanism of action of drugs or lead compound molecules. We developed an extensive yeast gene expression library consisting of full-genome cDNA array data for over 500 yeast strains each with a single gene disruption. Using this data, combined with dose and time course expression experiments with the oral antifungal agent, we used Boolean network discovery techniques to determine the genes whose expression was most profoundly affected by this drug. Our system identified the gene as the most significantly suppressed target molecule due to exposure to the antifungal agent. This process for network based drug discovery can significantly decrease the time and resources necessary to make rational drug targeting decisions.

Antifungal Agents↗