Analysis of Common Substructures of Metabolic Compounds within the Different Organism Groups

Ai Muto (
Masahiro Hattori (
Minoru Kanehisa (

Bioinformatics Center, Institute for Chemical Research, Kyoto University, Gokasho, Uji, Kyoto 611-0011, Japan


With the increase in available post-genomic data and metabolic pathway information, we have been focusing on revealing the biological meaning of higher phenomena such as relationships of metabolic systems in different organisms. Metabolism plays an essential role in all cellular organisms, e.g. energy transportation, signal transduction and structural formation of cell components. The metabolic pathway of each organism has a different landscape from all others because of the different sets of enzymes encoded in the genome. The organisms that are incapable of producing their own essential chemical compounds should acquire them in some way from other organisms that can produce them. For example, several vitamins are required by animals to survive. In this manner we can assume that the different availabilities of metabolites may influence the relationship between organisms in nature. In this study, we focus on the differences in available metabolites among organisms. First, we divided 239 species with complete genomes into 9 organism groups in accordance with phylogeny and averaged out the annotation quality and the phylogenetic sparsity. Then, we calculated the commonly used chemical compounds between organism groups and the uniquely used chemical compounds in an organism group. The total number of metabolites we consider in this study is 1,074, which is about one-third of all metabolites that appear in the KEGG metabolic pathways. Finally we show the differences and the similarities between organism groups on every metabolic pathway map, illustrating the commonly observed substructures within the uniquely used metabolites. These results will help us to better comprehend the architecture of metabolic pathways and the relationships between organisms.

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Japanese Society for Bioinformatics