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Masanori Arita

Publications and source records attributed to Masanori Arita.

15 recordsLinked to original sources

Species-specific variation of alternative splicing and transcriptional initiation in six eukaryotes.

The genome-wide detection of alternative splicing and transcriptional initiation (ASTI) was conducted in six eukaryotes (human, mouse, fruit fly, nematode, cress and rice) whose genome sequencing has been completed or nearly completed. Transcriptional isoforms were collected by mapping a batch of full-length cDNA sequences onto the respective cognate genomic sequences. Isoforms mapped on the same gene locus were compared pair-wise, ASTI patterns were segmented into minimal spans, and then the minimal patterns (ASTI units) were classified into unique types, such as the cassette type or the alternative donor site. All these procedures were performed automatically under the same conditions so that the results obtained from different species could be compared directly. The fraction of loci that underwent ASTI of the total mapped loci was the largest for mammals and fruit fly, and the smallest for plants. Exactly the same trend was observed for the number of unique ASTI types found in each species. The observed fractional representations of the ASTI types were similar between evolutionarily close species, such as human and mouse or cress and rice. On the other hand, the relative orders of abundance in individual ASTI type were considerably different between evolutionarily distant species, such as between mammals and plants. In human and mouse, alternative splicing other than the retained introns tended to occur within the protein coding sequence (CDS) regions rather than within the untranslated regions (UTRs), whereas this tendency was obscure in the other four species. In all the species examined, the difference in alternative exon lengths was most likely in multiples of three, and this tendency was most prominent when the alternative exons were embedded within the CDSs. These observations are generally consistent with the idea that higher organisms utilize the ASTI mechanisms more extensively and in a more complicated manner than lower organisms, and that ASTI actively participates in the enhancement of the functional and structural diversity of products generated from a limited number of genes on a genome.

3' Untranslated Regions↗

Large-scale prediction of cationic metabolite identity and migration time in capillary electrophoresis mass spectrometry using artificial neural networks.

We developed a computational technique to assist in the large-scale identification of charged metabolites. The electrophoretic mobility of metabolites in capillary electrophoresis-mass spectrometry (CE-MS) was predicted from their structure, using an ensemble of artificial neural networks (ANNs). Comparison between relative migration times of 241 various cations measured by CE-MS and predicted by a trained ANN ensemble produced a correlation coefficient of 0.931. When we used our technique to characterize all metabolites listed in the KEGG ligand database, the correct compounds among the top three candidates were predicted in 78.0% of cases. We suggest that this approach can be used for the prediction of the migration time of any cation and that it represents a powerful method for the identification of uncharacterized CE-MS peaks in metabolome analysis.

Cations↗

All systems go: launching cell simulation fueled by integrated experimental biology data.

Biological simulation serves to unify the basic elements of systems biology, namely, model selection, experimentation and model refinement. To select biochemical models for simulation, metabolome analysis can be performed using capillary electrophoresis or liquid chromatography coupled with mass spectrometry. In this manner, selected models can be elaborated with temporal/spatial gene and protein expression data obtained from model organisms such as Escherichia coli. The E. coli single gene deletion mutant library (KO collection) and His-tag/GFP-fusion single open reading frame clone expression library (ASKA) are powerful resources for this task. The integration of parallel experimental datasets into dynamic simulation tools forms the remaining challenge for the systematic analysis and elucidation of biological networks and holds promise for biotechnological applications.

Cell Physiological Phenomena↗

Scale-freeness and biological networks.

The notion of scale-freeness and its prevalence in both natural and artificial networks have recently attracted much attention. The concept of scale-freeness is enthusiastically applied to almost any conceivable network, usually with affirmative conclusions. Well-known scale-free examples include the internet, electric lines among power plants, the co-starring of movie actors, the co-authorship of researchers, food webs, and neural, protein-protein interactional, genetic, and metabolic networks. The purpose of this review is to clarify the relationship between scale-freeness and power-law distribution, and to assess critically the previous related works, especially on biological networks. In addition, I will focus on the close relationship between power-law distribution and lognormal distribution to show that power-law distribution is not a special characteristic of natural selection.

Computational Biology↗

Evaluation of corneal endothelial pump function with a cold stress test.

PURPOSE: This study was designed to evaluate corneal endothelial pump function with a cold-stress test. METHODS: The study group was comprised of 16 healthy volunteers (20 eyes), 12 patients with penetrating keratoplasty (15 eyes), 9 patients with decreased corneal sensitivity (9 eyes), and 7 patients with corneal endothelial anomaly (10 eyes). After measuring central corneal sensitivity, endothelial cell density, temperature, and thickness, cold stress was applied for 10 minutes and corneal temperature and thickness were measured again. RESULTS: After cooling, the average corneal temperature decreased from 34.7 degrees C to 26.0 degrees C. The degree of corneal swelling (percent increase in thickness) for the control, penetrating keratoplasty, decreased corneal sensitivity, and corneal endothelial anomaly groups was 1.25 +/- 0.89, 3.37 +/- 2.44, 4.72 +/- 2.51, and 3.40 +/- 1.92 (% +/- standard deviation), respectively. There was a negative correlation between the degree of corneal swelling and corneal sensitivity, but not endothelial cell density, temperature, or thickness. CONCLUSION: Corneal sensitivity is an effective factor in assessing endothelial pump function.

Cell Count↗

Integration of transcriptomics and metabolomics for understanding of global responses to nutritional stresses in Arabidopsis thaliana.

Plant metabolism is a complex set of processes that produce a wide diversity of foods, woods, and medicines. With the genome sequences of Arabidopsis and rice in hands, postgenomics studies integrating all "omics" sciences can depict precise pictures of a whole-cellular process. Here, we present, to our knowledge, the first report of investigation for gene-to-metabolite networks regulating sulfur and nitrogen nutrition and secondary metabolism in Arabidopsis, with integration of metabolomics and transcriptomics. Transcriptome and metabolome analyses were carried out, respectively, with DNA macroarray and several chemical analytical methods, including ultra high-resolution Fourier transform-ion cyclotron MS. Mathematical analyses, including principal component analysis and batch-learning self-organizing map analysis of transcriptome and metabolome data suggested the presence of general responses to sulfur and nitrogen deficiencies. In addition, specific responses to either sulfur or nitrogen deficiency were observed in several metabolic pathways: in particular, the genes and metabolites involved in glucosinolate metabolism were shown to be coordinately modulated. Understanding such gene-to-metabolite networks in primary and secondary metabolism through integration of transcriptomics and metabolomics can lead to identification of gene function and subsequent improvement of production of useful compounds in plants.

Arabidopsis↗

The metabolic world of Escherichia coli is not small.

To elucidate the organizational and evolutionary principles of the metabolism of living organisms, recent studies have addressed the graph-theoretic analysis of large biochemical networks responsible for the synthesis and degradation of cellular building blocks [Jeong, H., Tombor, B., Albert, R., Oltvai, Z. N. & Barabási, A. L. (2000) Nature 407, 651-654; Wagner, A. & Fell, D. A. (2001) Proc. R. Soc. London Ser. B 268, 1803-1810; and Ma, H.-W. & Zeng, A.-P. (2003) Bioinformatics 19, 270-277]. In such studies, the global properties of the network are computed by considering enzymatic reactions as links between metabolites. However, the pathways computed in this manner do not conserve their structural moieties and therefore do not correspond to biochemical pathways on the traditional metabolic map. In this work, we reassessed earlier results by digitizing carbon atomic traces in metabolic reactions annotated for Escherichia coli. Our analysis revealed that the average path length of its metabolism is much longer than previously thought and that the metabolic world of this organism is not small in terms of biosynthesis and degradation.

Escherichia coli↗

Additional paper: computational resources for metabolomics.

Metabolomics, a comprehensive extension of traditional targeted metabolite analysis, has recently attracted much attention as the biological jigsaw puzzle's missing piece that can complement transcriptome and proteome analysis. This tutorial survey introduces practical web resources with special emphasis on the computational aspects involved in processing and navigating metabolome data. The introduced materials are also accessible from the author's web directory (Atomic Reconstruction of Metabolism or ARM).

Algorithms↗

Biosynthesis of camptothecin. In silico and in vivo tracer study from [1-13C]glucose.

Camptothecin derivatives are clinically used antitumor alkaloids that belong to monoterpenoid indole alkaloids. In this study, we investigated the biosynthetic pathway of camptothecin from [1-13C]glucose (Glc) by in silico and in vivo studies. The in silico study measured the incorporation of Glc into alkaloids using the Atomic Reconstruction of Metabolism software and predicted the labeling patterns of successive metabolites from [1-13C]Glc. The in vivo study followed incorporation of [1-13C]Glc into camptothecin with hairy roots of Ophiorrhiza pumila by 13C nuclear magnetic resonance spectroscopy. The 13C-labeling pattern of camptothecin isolated from the hairy roots clearly showed that the monoterpene-secologanin moiety was synthesized via the 2C-methyl-D-erythritol 4-phosphate pathway, not via the mevalonate pathway. This conclusion was supported by differential inhibition of camptothecin accumulation by the pathway-specific inhibitors (fosmidomycin and lovastatin). The quinoline moiety from tryptophan was also labeled as predicted by the Atomic Reconstruction of Metabolism program via the shikimate pathway. These results indicate that camptothecin is formed by the combination of the 2C-methyl-D-erythritol 4-phosphate pathway and the shikimate pathway. This study provides the innovative example for how a computer-aided comprehensive metabolic analysis will refine the experimental design to obtain more precise biological information.

Camptothecin↗

In silico atomic tracing by substrate-product relationships in Escherichia coli intermediary metabolism.

We present a software system that computationally reproduces biochemical radioisotope-tracer experiments. It consists of three main components: A mapping database of substrate-product atomic correspondents derived from known reaction formulas, a tracing engine that can compute all pathways between two given compounds by using the mapping database, and a graphical user interface. As the system can facilitate the display of all possible pathways between any two compounds and the tracing of every single carbon, nitrogen, or sulfur atom in the metabolism, it complements and bridges other metabolic databases and simulations on fixed models.

Carbon↗

Dynamic modeling of genetic networks using genetic algorithm and S-system.

MOTIVATION: The modeling of system dynamics of genetic networks, metabolic networks or signal transduction cascades from time-course data is formulated as a reverse-problem. Previous studies focused on the estimation of only network structures, and they were ineffective in inferring a network structure with feedback loops. We previously proposed a method to predict not only the network structure but also its dynamics using a Genetic Algorithm (GA) and an S-system formalism. However, it could predict only a small number of parameters and could rarely obtain essential structures. In this work, we propose a unified extension of the basic method. Notable improvements are as follows: (1) an additional term in its evaluation function that aims at eliminating futile parameters; (2) a crossover method called Simplex Crossover (SPX) to improve its optimization ability; and (3) a gradual optimization strategy to increase the number of predictable parameters. RESULTS: The proposed method is implemented as a C program called PEACE1 (Predictor by Evolutionary Algorithms and Canonical Equations 1). Its performance was compared with the basic method. The comparison showed that: (1) the convergence rate increased about 5-fold; (2) the optimization speed was raised about 1.5-fold; and (3) the number of predictable parameters was increased about 5-fold. Moreover, we successfully inferred the dynamics of a small genetic network constructed with 60 parameters for 5 network variables and feedback loops using only time-course data of gene expression.

Algorithms↗

Processing sequence annotation data using the Lua programming language.

The data processing language in a graphical software tool that manages sequence annotation data from genome databases should provide flexible functions for the tasks in molecular biology research. Among currently available languages we adopted the Lua programming language. It fulfills our requirements to perform computational tasks for sequence map layouts, i.e. the handling of data containers, symbolic reference to data, and a simple programming syntax. Upon importing a foreign file, the original data are first decomposed in the Lua language while maintaining the original data schema. The converted data are parsed by the Lua interpreter and the contents are stored in our data warehouse. Then, portions of annotations are selected and arranged into our catalog format to be depicted on the sequence map. Our sequence visualization program was successfully implemented, embedding the Lua language for processing of annotation data and layout script. The program is available at http://staff.aist.go.jp/yutaka.ueno/guppy/.

Computational Biology↗

Modeling splicing sites with pairwise correlations.

MOTIVATION: A new method for finding subtle patterns in sequences is introduced. It approximates the multiple correlations among residuals with pair-wise correlations, with the learning cost O(m(2)n) where n is the number of training sequences, each of length m. The method suits to model splicing sites in human DNA, which are reported to have higher-order dependencies. RESULTS: By computational experiments, the prediction accuracy of our model was shown to surpass that of previously reported Markov models for the prediction of acceptor sites in human. AVAILABILITY: The C++ source code is available on request from the authors.

Algorithms↗

Secret signatures inside genomic DNA.

A simple, practical method to watermark short trademarks or signatures into genomic DNA is introduced. Since the marking method is biologically innocuous, it can be applied to all commercialized bacteria to help establish brand names for the engineered strains and to resolve legal disputes regarding gene-related patents. The first such strain of Bacillus subtilis is engineered and is ready to be distributed.

Bacillus subtilis↗