Yoshinori Tamada

Iron-Catalyzed Suzuki−Miyaura Coupling of Alkyl Halides

In the presence of novel iron(II) chloride-diphosphine complexes and magnesium bromide, lithium arylborates react with primary and secondary alkyl halides to give the corresponding coupling products in good to excellent yields. High functional group compatibility is also demonstrated in the reactions of substrates possessing reactive substituents, such as alkoxycarbonyl, cyano, and carbonyl groups.

Gene network inference and visualization tools for biologists: application to new human transcriptome datasets

Gene regulatory networks inferred from RNA abundance data have generated significant interest, but despite this, gene network approaches are used infrequently and often require input from bioinformaticians. We have assembled a suite of tools for analysing regulatory networks, and we illustrate their use with microarray datasets generated in human endothelial cells. We infer a range of regulatory networks, and based on this analysis discuss the strengths and limitations of network inference from RNA abundance data. We welcome contact from researchers interested in using our inference and visualization tools to answer biological questions.

Electronic Structure of Four-Coordinate Iron(I) Complex Supported by a Bis(phosphaethenyl)pyridine Ligand

A 15-electron iron complex with a formal Fe(I) center, [FeBr(BPEP)] (BPEP = 2,6-bis(1-phenyl-2-phosphaethenyl)pyridine), was prepared by one-electron reduction of the dibromide precursor [FeBr(2)(BPEP)]. The single-crystal diffraction analysis revealed a distorted trigonal monopyramidal arrangement around the iron center, and SQUID magnetometry established the S = 3/2 ground state. The Mossbauer isomer shift value (delta = 0.59 mm/s) was consistent with a high-spin Fe(I) center of [FeBr(BPEP)]. DFT calculations for a model complex revealed two highly delocalized molecular orbitals formed by bonding and antibonding interactions between the d(z(2)) (Fe) and pi* (BPEP) orbitals. Orbital occupancy analysis demonstrated the electronic structure with a high-spin Fe(I) center. The effective dpi-ppi interaction between iron and BPEP was concluded to be responsible for the highly distorted structure of [FeBr(BPEP)], with its rather uncommon trigonal monopyramidal configuration.

Estimating Genome-Wide Gene Networks Using Nonparametric Bayesian Network Models on Massively Parallel Computers

We present a novel algorithm to estimate genome-wide gene networks consisting of more than 20,000 genes from gene expression data using nonparametric Bayesian networks. Due to the difficulty of learning Bayesian network structures, existing algorithms cannot be applied to more than a few thousand genes. Our algorithm overcomes this limitation by repeatedly estimating subnetworks in parallel for genes selected by neighbor node sampling. Through numerical simulation, we confirmed that our algorithm outperformed a heuristic algorithm in a shorter time. We applied our algorithm to microarray data from human umbilical vein endothelial cells (HUVECs) treated with siRNAs, to construct a human genome-wide gene network, which we compared to a small gene network estimated for the genes extracted using a traditional bioinformatics method. The results showed that our genome-wide gene network contains many features of the small network, as well as others that could not be captured during the small network estimation. The results also revealed master-regulator genes that are not in the small network but that control many of the genes in the small network. These analyses were impossible to realize without our proposed algorithm.

Utilizing evolutionary information and gene expression data for estimating gene networks with bayesian network models.

Since microarray gene expression data do not contain sufficient information for estimating accurate gene networks, other biological information has been considered to improve the estimated networks. Recent studies have revealed that highly conserved proteins that exhibit similar expression patterns in different organisms, have almost the same function in each organism. Such conserved proteins are also known to play similar roles in terms of the regulation of genes. Therefore, this evolutionary information can be used to refine regulatory relationships among genes, which are estimated from gene expression data. We propose a statistical method for estimating gene networks from gene expression data by utilizing evolutionarily conserved relationships between genes. Our method simultaneously estimates two gene networks of two distinct organisms, with a Bayesian network model utilizing the evolutionary information so that gene expression data of one organism helps to estimate the gene network of the other. We show the effectiveness of the method through the analysis on Saccharomyces cerevisiae and Homo sapiens cell cycle gene expression data. Our method was successful in estimating gene networks that capture many known relationships as well as several unknown relationships which are likely to be novel. Supplementary information is available at http://bonsai.ims.u-tokyo.ac.jp/~tamada/bayesnet/.

Well-ordered L10-FePt nanoparticles synthesized by improved SiO2-nanoreactor method

It was found that the well-ordered L10 structure is formed in the FePt nanoparticles synthesized by the improved “SiO2-nanoreactor” method, whereas the previously employed annealing condition has suffered from the presence of the unconverted fcc-FePt nanoparticles which are superparamagnetic at room temperature. The L10-FePt nanoparticles prepared by this method showed a smooth hysteresis loop with no kink, and the room temperature coercivity reaches an extremely large value of 28kOe, even though the particle size is 6.7nm in diameter. The Mossbauer hyperfine parameters of the nanoparticles are very close to those of the bulk L10-FePt alloy, indicating that they possess magnetic moments comparable to the bulk even at the particle surface.

Understanding endothelial cell apoptosis: what can the transcriptome, glycome and proteome reveal?

Endothelial cell (EC) apoptosis may play an important role in blood vessel development, homeostasis and remodelling. In support of this concept, EC apoptosis has been detected within remodelling vessels in vivo, and inactivation of EC apoptosis regulators has caused dramatic vascular phenotypes. EC apoptosis has also been associated with cardiovascular pathologies. Therefore, understanding the regulation of EC apoptosis, with the goal of intervening in this process, has become a current research focus. The protein-based signalling and cleavage cascades that regulate EC apoptosis are well known. However, the possibility that programmed transcriptome and glycome changes contribute to EC apoptosis has only recently been explored. Traditional bioinformatic techniques have allowed simultaneous study of thousands of molecular signals during the process of EC apoptosis. However, to progress further, we now need to understand the complex cause and effect relationships among these signals. In this article, we will first review current knowledge about the function and regulation of EC apoptosis including the roles of the proteome transcriptome and glycome. Then, we assess the potential for further bioinformatic analysis to advance our understanding of EC apoptosis, including the limitations of current technologies and the potential of emerging technologies such as gene regulatory networks.

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.

Oncogenic roles of TOPK and MELK, and effective growth suppression by small molecular inhibitors in kidney cancer cells

T–lymphokine-activated killer cell–originated protein kinase (TOPK) and maternal embryonic leucine zipper kinase (MELK) have been reported to play critical roles in cancer cell proliferation and maintenance of stemness. In this study, we investigated possible roles of TOPK and MELK in kidney cancer cells and found their growth promotive effect as well as some feedback mechanism between these two molecules. Interestingly, the blockade of either of these two kinases effectively caused downregulation of forkhead box protein M1 (FOXM1) activity which is known as an oncogenic transcriptional factor in various types of cancer cells. Small molecular compound inhibitors against TOPK (OTS514) and MELK (OTS167) effectively suppressed the kidney cancer cell growth, and the combination of these two compounds additively worked and showed the very strong growth suppressive effect on kidney cancer cells. Collectively, our results suggest that both TOPK and MELK are promising molecular targets for kidney cancer treatment and that dual blockade of OTS514 and OTS167 may bring additive anti-tumor effects with low risk of side effects.

Computational gene network analysis reveals TNF-induced angiogenesis

BackgroundTNF (Tumor Necrosis Factor-α) induces HUVEC (Human Umbilical Vein Endothelial Cells) to proliferate and form new blood vessels. This TNF-induced angiogenesis plays a key role in cancer and rheumatic disease. However, the molecular system that underlies TNF-induced angiogenesis is largely unknown.MethodsWe analyzed the gene expression changes stimulated by TNF in HUVEC over a time course using microarrays to reveal the molecular system underlying TNF-induced angiogenesis. Traditional k-means clustering analysis was performed to identify informative temporal gene expression patterns buried in the time course data. Functional enrichment analysis using DAVID was then performed for each cluster. The genes that belonged to informative clusters were then used as the input for gene network analysis using a Bayesian network and nonparametric regression method. Based on this TNF-induced gene network, we searched for sub-networks related to angiogenesis by integrating existing biological knowledge.Resultsk-means clustering of the TNF stimulated time course microarray gene expression data, followed by functional enrichment analysis identified three biologically informative clusters related to apoptosis, cellular proliferation and angiogenesis. These three clusters included 648 genes in total, which were used to estimate dynamic Bayesian networks. Based on the estimated TNF-induced gene networks, we hypothesized that a sub-network including IL6 and IL8 inhibits apoptosis and promotes TNF-induced angiogenesis. More particularly, IL6 promotes TNF-induced angiogenesis by inducing NF-κB and IL8, which are strong cell growth factors.ConclusionsComputational gene network analysis revealed a novel molecular system that may play an important role in the TNF-induced angiogenesis seen in cancer and rheumatic disease. This analysis suggests that Bayesian network analysis linked to functional annotation may be a powerful tool to provide insight into disease.