Biomedical subjects

Kousuke Tashiro

Publications and source records attributed to Kousuke Tashiro.

12 recordsLinked to original sources

PubMed2006

Complexity of the genomic diversity in enterohemorrhagic Escherichia coli O157 revealed by the combinational use of the O157 Sakai OligoDNA microarray and the Whole Genome PCR scanning.

Escherichia coli O157, an etiological agent of hemorrhagic colitis and hemolytic uremic syndrome, is one of the leading worldwide public health threats. Genome sequencing of two O157 strains have revealed that the chromosome is comprised of a 4.1 Mb backbone shared by K-12 and a total of 1.4 Mb O157-specific sequences. Most of the large O157-specific sequences are prophages and prophage-like elements, which have carried many virulence genes into the O157 genome. This suggests that bacteriophages have played the key roles in the emergence of O157. The Whole Genome PCR Scanning (WGPScanning) analysis of O157 strains, on the other hand, revealed a high level of genomic diversity in O157. Variation of prophages has also been suggested as a major factor generating such diversity. In this study, we analyzed the gene content of O157 strains, by an oligoDNA microarray, using the same set of strains as examined by the WGPScanning method. Although most of the strains were typical O157 : H7, they differed remarkably in gene composition, particularly in those on prophages, and we identified more than 400 'variably absent or present' genes which included virulence-related genes. This confirms the role of prophages in generating the genomic diversity, and raises a possibility that some level of variation in potential virulence is present among O157 strains. Fine comparison of the two datasets obtained by microarray and WGPScanning provided much further details on the O157 genome diversity than illustrated by each method alone, indicating the usefulness of this combinational approach in the genomic comparison of closely related strains.

Combinatorial Chemistry Techniques↗

PubMed2006

Computational strategy for discovering druggable gene networks from genome-wide RNA expression profiles.

We propose a computational strategy for discovering gene networks affected by a chemical compound. Two kinds of DNA microarray data are assumed to be used: One dataset is short time-course data that measure responses of genes following an experimental treatment. The other dataset is obtained by several hundred single gene knock-downs. These two datasets provide three kinds of information; (i) A gene network is estimated from time-course data by the dynamic Bayesian network model, (ii) Relationships between the knocked-down genes and their regulatees are estimated directly from knock-down microarrays and (iii) A gene network can be estimated by gene knock-down data alone using the Bayesian network model. We propose a method that combines these three kinds of information to provide an accurate gene network that most strongly relates to the mode-of-action of the chemical compound in cells. This information plays an essential role in pharmacogenomics. We illustrate this method with an actual example where human endothelial cell gene networks were generated from a novel time course of gene expression following treatment with the drug fenofibrate, and from 270 novel gene knock-downs. Finally, we succeeded in inferring the gene network related to PPAR-alpha, which is a known target of fenofibrate.

Bayes Theorem↗

PubMed2005

Identifying drug active pathways from gene networks estimated by gene expression data.

We present a computational method for identifying genes and their regulatory pathways influenced by a drug, using microarray gene expression data collected by single gene disruptions and drug responses. The automatic identification of such genes and pathways in organisms' cells is an important problem for pharmacogenomics and the tailor-made medication. Our method estimates regulatory relationships between genes as a gene network from microarray data of gene disruptions with a Bayesian network model, then identifies the drug affected genes and their regulatory pathways on the estimated network with time course drug response microarray data. Compared to the existing method, our proposed method can identify not only the drug affected genes and the druggable genes, but also the drug responses of the pathways. For evaluating the proposed method, we conducted simulated examples based on artificial networks and expression data. Our method succeeded in identifying the pseudo drug affected genes and pathways with the high coverage greater than 80 %. We also applied our method to Saccharomyces cerevisiae drug response microarray data. In this real example, we identified the genes and the pathways that are potentially influenced by a drug. These computational experiments indicate that our method successfully identifies the drug-activated genes and pathways, and is capable of predicting undesirable side effects of the drug, identifying novel drug target genes, and understanding the unknown mechanisms of the drug.

Bayes Theorem↗

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↗

PubMed2004

Use of segment-based microarray in the analysis of global gene expression in response to various environmental stresses in the cyanobacterium Anabaena sp. PCC 7120.

We prepared microarrays that contain genomic sequences of a heterocyst-forming filamentous cyanobacterium Anabaena sp. PCC 7120. The complete genome of this cyanobacterium codes for about 5,368 protein-coding genes in the main chromosome of 6.4 Mbp. In total, 2,407 DNA segments were selected from the sequencing clones, and amplified by PCR, then spotted on glass slides in duplicate. These microarrays differ from the widely used commercial or custom-made ones for other microorganisms in that each DNA segment was 3-4 kbp long, and contained about 3-4 predicted genes on average. This feature, however, did not decrease the usefulness of the microarrays, since we were able to detect a number of potentially novel genes that are induced in response to nitrogen deprivation, low temperature and drought. In addition, we found some genomic regions in which dozens of contiguous genes are simultaneously regulated. These results suggest that these segment-based microarrays are useful especially for such large genomes as Anabaena, for which the number of genes exceeds either technical or practical limitations.

Anabaena↗

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

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

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

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. Unfortunately, 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.

Bayes Theorem↗

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↗

PubMed2002

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

We propose a new statistical method for constructing genetic network from microarray gene expression data by using a Bayesian network. An essential point of Bayesian network construction is in 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. A problem still remains to be solved in selecting an optimal graph, which gives the best representation of the system among genes. 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.

Artificial Intelligence↗