Biomedical subjects

Yoko Shinbo

Publications and source records attributed to Yoko Shinbo.

2 recordsLinked to original sources

PubMed2006

Clarification of pathway-specific inhibition by Fourier transform ion cyclotron resonance/mass spectrometry-based metabolic phenotyping studies.

We have developed a metabolic profiling scheme based on direct-infusion Fourier transform ion cyclotron resonance mass spectrometry (FT-ICR/MS). The scheme consists of: (1) reproducible data collection under optimized FT-ICR/MS analytical conditions; (2) automatic mass-error correction and multivariate analyses for metabolome characterization using a newly developed metabolomics tool (DMASS software); (3) identification of marker metabolite candidates by searching a species-metabolite relationship database, KNApSAcK; and (4) structural analyses by an MS/MS method. The scheme was applied to metabolic phenotyping of Arabidopsis (Arabidopsis thaliana) seedlings treated with different herbicidal chemical classes for pathway-specific inhibitions. Arabidopsis extracts were directly infused into an electrospray ionization source on an FT-ICR/MS system. Acquired metabolomics data were comprised of mass-to-charge ratio values with ion intensity information subjected to principal component analysis, and metabolic phenotypes from the herbicide treatments were clearly differentiated from those of the herbicide-free treatment. From each herbicide treatment, candidate metabolites representing such metabolic phenotypes were found through the KNApSAcK database search. The database search and MS/MS analyses suggested dose-dependent accumulation patterns of specific metabolites including several flavonoid glycosides. The metabolic phenotyping scheme on the basis of FT-ICR/MS coupled with the DMASS program is discussed as a general tool for high throughput metabolic phenotyping studies.

Arabidopsis↗

PubMed2006

Development and implementation of an algorithm for detection of protein complexes in large interaction networks.

BACKGROUND: After complete sequencing of a number of genomes the focus has now turned to proteomics. Advanced proteomics technologies such as two-hybrid assay, mass spectrometry etc. are producing huge data sets of protein-protein interactions which can be portrayed as networks, and one of the burning issues is to find protein complexes in such networks. The enormous size of protein-protein interaction (PPI) networks warrants development of efficient computational methods for extraction of significant complexes. RESULTS: This paper presents an algorithm for detection of protein complexes in large interaction networks. In a PPI network, a node represents a protein and an edge represents an interaction. The input to the algorithm is the associated matrix of an interaction network and the outputs are protein complexes. The complexes are determined by way of finding clusters, i. e. the densely connected regions in the network. We also show and analyze some protein complexes generated by the proposed algorithm from typical PPI networks of Escherichia coli and Saccharomyces cerevisiae. A comparison between a PPI and a random network is also performed in the context of the proposed algorithm. CONCLUSION: The proposed algorithm makes it possible to detect clusters of proteins in PPI networks which mostly represent molecular biological functional units. Therefore, protein complexes determined solely based on interaction data can help us to predict the functions of proteins, and they are also useful to understand and explain certain biological processes.

Algorithms↗