Kousuke Tashiro

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 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.

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.

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.

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.

Identification of a two-component VirR/VirS regulon in Clostridium perfringens

Clostridium perfringens, a Gram-positive anaerobic pathogen, is a causative agent of human gas gangrene that leads to severe rapid tissue destruction and can cause death within hours unless treated immediately. Production of several toxins is known to be controlled by the two-component VirR/VirS system involving a regulatory RNA (VR-RNA) in C. perfringens. To elucidate the precise regulatory network governed by VirR/VirS and VR-RNA, a series of microarray screening using VirR/VirS and VR-RNA-deficient mutants was performed. Finally, by qRT-PCR analysis, 147 genes (30 single genes and 21 putative operons) were confirmed to be under the control of the VirR/VirS-VR-RNA regulatory cascade. Several virulence-related genes for alpha-toxin, kappa-toxin, hyaluronidases, sialidase, and capsular polysaccharide synthesis were found. Furthermore, some genes for catalytic enzymes, various genes for transporters, and many genes for energy metabolism were also found to be controlled by the cascade. Our data indicate that the VirR/VirS-VR-RNA system is a global gene regulator that might control multiple cellular functions to survive and multiply in the host, which would turn out to be a lethal flesh-eating infection.

Periodic Gene Expression Patterns during the Highly Synchronized Cell Nucleus and Organelle Division Cycles in the Unicellular Red Alga Cyanidioschyzon merolae

Previous cell cycle studies have been based on cell-nuclear proliferation only. Eukaryotic cells, however, have double membranes-bound organelles, such as the cell nucleus, mitochondrion, plastids and single-membrane-bound organelles such as ER, the Golgi body, vacuoles (lysosomes) and microbodies. Organelle proliferations, which are very important for cell functions, are poorly understood. To clarify this, we performed a microarray analysis during the cell cycle of Cyanidioschyzon merolae. C. merolae cells contain a minimum set of organelles that divide synchronously. The nuclear, mitochondrial and plastid genomes were completely sequenced. The results showed that, of 158 genes induced during the S or G2-M phase, 93 were known and contained genes related to mitochondrial division, ftsZ1-1, ftsz1-2 and mda1, and plastid division, ftsZ2-1, ftsZ2-2 and cmdnm2. Moreover, three genes, involved in vesicle trafficking between the single-membrane organelles such as vps29 and the Rab family protein, were identified and might be related to partitioning of single-membrane-bound organelles. In other genes, 46 were hypothetical and 19 were hypothetical conserved. The possibility of finding novel organelle division genes from hypothetical and hypothetical conserved genes in the S and G2-M expression groups is discussed.

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.

Gene expression profiles in peripheral blood as a biomarker in cancer patients receiving peptide vaccination.

Because only a subset of patients show clinical responses to peptide‐based cancer vaccination, it is critical to identify biomarkers for selecting patients who would most likely benefit from this treatment.

Analysis of PPARα-dependent and PPARα-independent transcript regulation following fenofibrate treatment of human endothelial cells

Fenofibrate is a synthetic ligand for the nuclear receptor peroxisome proliferator-activated receptor (PPAR) alpha and has been widely used in the treatment of metabolic disorders, especially hyperlipemia, due to its lipid-lowering effect. The molecular mechanism of lipid-lowering is relatively well defined: an activated PPARα forms a PPAR–RXR heterodimer and this regulates the transcription of genes involved in energy metabolism by binding to PPAR response elements in their promoter regions, so-called “trans-activation”. In addition, fenofibrate also has anti-inflammatory and anti-athrogenic effects in vascular endothelial and smooth muscle cells. We have limited information about the anti-inflammatory mechanism of fenofibrate; however, “trans-repression” which suppresses production of inflammatory cytokines and adhesion molecules probably contributes to this mechanism. Furthermore, there are reports that fenofibrate affects endothelial cells in a PPARα-independent manner. In order to identify PPARα-dependently and PPARα-independently regulated transcripts, we generated microarray data from human endothelial cells treated with fenofibrate, and with and without siRNA-mediated knock-down of PPARα. We also constructed dynamic Bayesian transcriptome networks to reveal PPARα-dependent and -independent pathways. Our transcriptome network analysis identified growth differentiation factor 15 (GDF15) as a hub gene having PPARα-independently regulated transcripts as its direct downstream children. This result suggests that GDF15 may be PPARα-independent master-regulator of fenofibrate action in human endothelial cells.

Unraveling dynamic activities of autocrine pathways that control drug-response transcriptome networks

Some drugs affect secretion of secreted proteins (e.g. cytokines) released from target cells, but it remains unclear whether these proteins act in an autocrine manner and directly effect the cells on which the drugs act. In this study, we propose a computational method for testing a biological hypothesis: there exist autocrine signaling pathways that are dynamically regulated by drug response transcriptome networks and control them simultaneously. If such pathways are identified, they could be useful for revealing drug mode-of-action and identifying novel drug targets. By the node-set separation method proposed, dynamic structural changes can be embedded in transcriptome networks that enable us to find master-regulator genes or critical paths at each observed time. We then combine the protein-protein interaction network with the estimated dynamic transcriptome network to discover drug-affected autocrine pathways if they exist. The statistical significance (p-values) of the pathways are evaluated by the meta-analysis technique. The dynamics of the interactions between the transcriptome networks and the signaling pathways will be shown in this framework. We illustrate our strategy by an application using anti-hyperlipidemia drug, Fenofibrate. From over one million protein-protein interaction pathways, we extracted significant 23 autocrine-like pathways with the Bonferroni correction, including VEGF-NRP1-GIPC1-PRKCA-PPARalpha, that is one of the most significant ones and contains PPARalpha, a target of Fenofibrate.