Biomedical subjects

Satoru Miyano

Publications and source records attributed to Satoru Miyano.

At least 37 records · Page 2Linked to original sources

PubMed2004

Association of a single-nucleotide polymorphism in the immunoglobulin mu-binding protein 2 gene with immunoglobulin A nephropathy.

Immunoglobulin A (IgA) nephropathy is the most common form of primary glomerulonephritis worldwide. The pathogenesis of IgA nephropathy is unknown, but it is certain that some genetic factors are involved in susceptibility to the disease. Employing a large-scale, case-control association study using gene-based single-nucleotide polymorphism (SNP) markers, we previously reported four candidate genes. We report here an additional significant association between IgA nephropathy and an SNP located in the gene encoding immunoglobulin micro-binding protein 2 (IGHMBP2) at chromosome 11q13.2-q13.4. The association (chi2 =17.1, p = 0.00003; odds ratio of 1.85 with 95% confidence interval of 1.39-2.50 in a dominant association model) was found using DNA from 465 affected individuals and 634 controls. The SNP (G34448A) caused an amino acid substitution from glutamine to lysine (E928K). As the gene product is involved in immunoglobulin-class switching and patients with the A allele revealed higher serum levels of IgA (p = 0.048), the amino acid change might influence a class switch to increase serum IgA levels, resulting in a higher risk of IgA nephropathy.

Adult↗

PubMed2004

Identification of novel adrenomedullin in mammals: a potent cardiovascular and renal regulator.

We have identified cDNA encoding a new member of the adrenomedullin (AM) family, AM2, for the first time in mammals (mouse, rat and human). The predicted precursor carried mature AM2 in the C-terminus, which had an intramolecular ring formed by an S-S bond and a possibly amidated C-terminus. Phylogenetic analyses clustered AM2 and AM into two distinct but closely related groups. Similarity of exon-intron structure and synteny of neighboring genes showed that mammalian AM2 is an ortholog of pufferfish AM2 and a paralog of mammalian AM. AM2 mRNA was expressed in submaxillary gland, kidney, stomach, ovary, lymphoid tissues and pancreas of mice, but not in adrenal and testis. Intravenous injection of synthetic mature AM2 decreased arterial pressure more potently than AM, and induced antidiuresis and antinatriuresis in mice. These results show that at least two peptides, AM and AM2, comprise an adrenomedullin family in mammals, and that AM2 may play pivotal roles in cardiovascular and body fluid regulation.

Adrenomedullin↗

PubMed2004

Dynamic Bayesian network and nonparametric regression for nonlinear modeling of gene networks from time series gene expression data.

We propose a dynamic Bayesian network and nonparametric regression model for constructing a gene network from time series microarray gene expression data. The proposed method can overcome a shortcoming of the Bayesian network model in the sense of the construction of cyclic regulations. The proposed method can analyze the microarray data as a continuous data and can capture even nonlinear relations among genes. It can be expected that this model will give a deeper insight into complicated biological systems. We also derive a new criterion for evaluating an estimated network from Bayes approach. We conduct Monte Carlo experiments to examine the effectiveness of the proposed method. We also demonstrate the proposed method through the analysis of the Saccharomyces cerevisiae gene expression data.

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

Efficiently finding regulatory elements using correlation with gene expression.

We present an efficient algorithm for detecting putative regulatory elements in the upstream DNA sequences of genes, using gene expression information obtained from microarray experiments. Based on a generalized suffix tree, our algorithm looks for motif patterns whose appearance in the upstream region is most correlated with the expression levels of the genes. We are able to find the optimal pattern, in time linear in the total length of the upstream sequences. We implement and apply our algorithm to publicly available microarray gene expression data, and show that our method is able to discover biologically significant motifs, including various motifs which have been reported previously using the same data set. We further discuss applications for which the efficiency of the method is essential, as well as possible extensions to our algorithm.

Algorithms↗

PubMed2004

Kernel mixture survival models for identifying cancer subtypes, predicting patient's cancer types and survival probabilities.

One important application of microarray gene expression data is to study the relationship between the clinical phenotype of cancer patients and gene expression profiles on the whole-genome scale. The clinical phenotype includes several different types of cancers, survival times, relapse times, drug responses and so on. Under the situation that the subtypes of cancer have not been previously identified or known to exist, we develop a new kernel mixture modeling method that performs simultaneously identification of the subtype of cancer, prediction of the probabilities of both cancer type and patient's survival, and detection of a set of marker genes on which to base a diagnosis. The proposed method is successfully performed on real data analysis and simulation studies.

Artificial Intelligence↗

PubMed2004

A neural network method for identification of RNA-interacting residues in protein.

Identification of the most putative RNA-interacting residues in protein is an important and challenging problem in a field of molecular recognition. Structural analysis of protein-RNA complexes reveals a strong correlation between interaction residues and their structure. Building on this viewpoint, we have developed a neural network predictor to correctly identify residues involved in protein-RNA interactions from protein sequence and its structural information. The system has been exhaustedly cross-validated with various strategies differing in input encoding, amount of input information, and network architectures. In addition, we have evaluated performance among functional subsets of complexes. Finally, to reflect the properties of protein-RNA complexes in our dataset, two kinds of post-processing method are adopted. The experimental result shows that our system yields not-trivial performance although the residues in interaction sites are too scarce.

Binding Sites↗

PubMed2004

A versatile petri net based architecture for modeling and simulation of complex biological processes.

The research on modeling and simulation of complex biological systems is getting more important in Systems Biology. In this respect, we have developed Hybrid Function Petri net (HFPN) that was newly developed from existing Petri net because of their intuitive graphical representation and their capabilities for mathematical analyses. However, in the process of modeling metabolic, gene regulatory or signal transduction pathways with the architecture, we have realized three extensions of HFPN, (i) an entity should be extended to contain more than one value, (ii) an entity should be extended to handle other primitive types, e.g. boolean, string, (iii) an entity should be extended to handle more advanced type called object that consists of variables and methods, are necessary for modeling biological systems with Petri net based architecture. To deal with it, we define a new enhanced Petri net called hybrid functional Petri net with extension (HFPNe). To demonstrate the effectiveness of the enhancements, we model and simulate with HFPNe four biological processes that are diffcult to represent with the previous architecture HFPN.

Algorithms↗

PubMed2004

Constructing biological pathway models with hybrid functional Petri nets.

In many research projects on modeling and analyzing biological pathways, the Petri net has been recognized as a promising method for representing biological pathways. From the pioneering works by Reddy et al., 1993, and Hofestädt, 1994, that model metabolic pathways by traditional Petri net, several enhanced Petri nets such as colored Petri net, stochastic Petri net, and hybrid Petri net have been used for modeling biological phenomena. Recently, Matsuno et al., 2003b, introduced the hybrid functional Petri net (HFPN) in order to give a more intuitive and natural modeling method for biological pathways than these existing Petri nets. Although the paper demonstrates the effectiveness of HFPN with two examples of gene regulation mechanism for circadian rhythms and apoptosis signaling pathway, there has been no detailed explanation about the method of HFPN construction for these examples. The purpose of this paper is to describe method to construct biological pathways with the HFPN step-by-step. The method is demonstrated by the well-known glycolytic pathway controlled by the lac operon gene regulatory mechanism.

Adenosine Diphosphate↗

PubMed2003

Association of single-nucleotide polymorphisms in the polymeric immunoglobulin receptor gene with immunoglobulin A nephropathy (IgAN) in Japanese patients.

Immunoglobulin A nephropathy (IgAN) is a primary glomerulonephritis of common incidence world-wide whose etiology and pathogenesis remain unresolved, although genetic factors are assumed to be involved in the development and progression of this disease. To identify genetic variations that might confer susceptibility to IgAN, we performed a case-control association study involving 389 Japanese IgAN patients and 465 controls. Genome-wide analysis of approximately 80,000 single-nucleotide polymorphisms (SNPs) identified a significant association between IgAN and six SNPs located in the PIGR (polymeric immuoglobulin receptor) gene at chromosome 1q31-q41. One of them, PIGR-17, caused an amino-acid substitution from alanine to valine at codon 580 (chi(2)=13.05, P=0.0003, odds ratio [OR] =1.59, 95% confidence interval [95% CI] =1.24-2.05); the OR of minor homozygotes to others was 2.71 (95% CI=1.31-5.61). Another SNP, PIGR-2, could affect promoter activity (chi(2)=11.95, P=0.00055, OR=1.60, 95% CI=1.22-2.08); the OR of minor homozygotes to others was 2.08 (95% CI=0.94-4.60). Pairwise analyses demonstrated that all six SNPs were in almost complete linkage disequilibrium. Biopsy specimens from IgAN patients were positively stained by antibody against the secretory component of PIGR, but corresponding tissues from non-IgAN patients were not. Our results suggest that a gene associated with susceptibility to IgAN lies within or close to the PIGR gene locus on chromosome 1q in the Japanese population.

Adult↗

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

Inferring gene networks from time series microarray data using dynamic Bayesian networks.

Dynamic Bayesian networks (DBNs) are considered as a promising model for inferring gene networks from time series microarray data. DBNs have overtaken Bayesian networks (BNs) as DBNs can construct cyclic regulations using time delay information. In this paper, a general framework for DBN modelling is outlined. Both discrete and continuous DBN models are constructed systematically and criteria for learning network structures are introduced from a Bayesian statistical viewpoint. This paper reviews the applications of DBNs over the past years. Real data applications for Saccharomyces cerevisiae time series gene expression data are also shown.

Algorithms↗

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

Inferring gene regulatory networks from time-ordered gene expression data of Bacillus subtilis using differential equations.

We describe a new method to infer a gene regulatory network, in terms of a linear system of differential equations, from time course gene expression data. As biologically the gene regulatory network is known to be sparse, we expect most coefficients in such a linear system of differential equations to be zero. In previously proposed methods, the number of nonzero coefficients in the system was limited based on ad hoc assumptions. Instead, we propose to infer the degree of sparseness of the gene regulatory network from the data, where we use Akaike's Information Criterion to determine which coefficients are nonzero. We apply our method to MMGE time course data of Bacillus subtilis.

Bacillus subtilis↗

PubMed2003

Boundary formation by notch signaling in Drosophila multicellular systems: experimental observations and gene network modeling by Genomic Object Net.

The Delta-Notch signaling system plays an essential role in various morphogenetic systems of multicellular animal development. Here we analyzed the mechanism of Notch-dependent boundary formation in the Drosophila large intestine, by experimental manipulation of Delta expression and computational modeling and simulation by Genomic Object Net. Boundary formation representing the situation in normal large intestine was shown by the simulation. By manipulating Delta expression in the large intestine, a few types of disorder in boundary cell differentiation were observed, and similar abnormal patterns were generated by the simulation. Simulation results suggest that parameter values representing the strength of cell-autonomous suppression of Notch signaling by Delta are essential for generating two different modes of patterning: lateral inhibition and boundary formation, which could explain how a common gene regulatory network results in two different patterning modes in vivo. Genomic Object Net proved to be a useful and flexible biosimulation system that is suitable for analyzing complex biological phenomena such as patternings of multicellular systems as well as intracellular changes in cell states including metabolic activities, gene regulation, and enzyme reactions.

Animals↗

PubMed2003

Biopathways representation and simulation on hybrid functional Petri net.

The following two matters should be resolved in order for biosimulation tools to be accepted by users in biology/medicine: (1) remove issues which are irrelevant to biological importance, and (2) allow users to represent biopathways intuitively and understand/manage easily the details of representation and simulation mechanism. From these criteria, we firstly define a novel notion of Petri net called Hybrid Functional Petri Net (HFPN). Then, we introduce a software tool, Genomic Object Net, for representing and simulating biopathways, which we have developed by employing the architecture of HFPN. In order to show the usefulness of Genomic Object Net for representing and simulating biopathways, we show two HFPN representations of gene regulation mechanisms of Drosophila melanogaster (fruit fly) circadian rhythm and apoptosis induced by Fas ligand. The simulation results of these biopathways are also correlated with biological observations. The software is available to academic users from http://www.GenomicObject.Net/.

Animals↗