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KNApSAcK: A Comprehensive Species-Metabolite Relationship Database

  • Y. Shinbo
  • Y. Nakamura
  • M. Altaf-Ul-Amin
  • H. Asahi
  • K. Kurokawa
  • M. Arita
  • K. Saito
  • D. Ohta
  • D. Shibata
  • S. Kanaya
Part of the Biotechnology in Agriculture and Forestry book series (AGRICULTURE, volume 57)

Conclusion and Remarks

We prepared a database, KNApSAcK for accumulation and search of metabolite-species relationships. The power-law distribution observed in the present study is likely to be associated with research activity for finding novel metabolites from nature. In addition, it seems to be derived from searching rare metabolites from the organisms originally exhibiting power-law in the degree distribution of their metabolic networks. This suggests that the database contains chemical diversity of metabolites which occurred through evolution of species. Graph clustering is shown to be useful to extract taxonomic relationships on the basis of common metabolites. As we are continuously accumulating metabolite-species pairs in the database, we continue to advance our understanding of species-metabolite relations in taxonomic hierarchy. Furthermore, we plan to add an option for searching metabolite structures by entering partial structures, which will be helpful for metabolite research.

Keywords

Metabolic Network Graph Cluster Naphthenic Acid Taxonomic Hierarchy Common Metabolite 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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References

  1. Arabidopsis Genome Initiative (2000) Analysis of the genome sequence of the flowering plant Arabidopsis thaliana. Nature 408:796–815CrossRefGoogle Scholar
  2. Aharoni A, Ric de Vos CH, Verhoeven HA, Maliepaard CA, Kruppa G, Bino R, Goodenowe D (2002) Non-targeted metabolomic profiling using Fourier transform ion cyclotron mass spectrometry (FTMS). OMICS J Integr Biol 6:217–234CrossRefGoogle Scholar
  3. Amin MA, Kanaya S (2004) Detection of protein complexes in large interaction networks. Proc the 8th world multi-conference on systemics, cybernetics and informatics, vol VII, pp 119–123Google Scholar
  4. Arita M (2005) Scale-freeness and biological networks. J Biochem 138 (in press)Google Scholar
  5. Arlt K, Brandt S, Kehr J (2001) Amino acid analysis in five pooled single plant cell samples using capillary electrophoresis coupled to laser-induced fluorescence detection. J Chromatogr A 926:319–325PubMedCrossRefGoogle Scholar
  6. Bailey NJ, Stanley PD, Hadfield ST, Lindon JC, Nicholson JK (2000) Mass spectrometrically detected directly coupled high performance liquid chromatography/nuclear magnetic resonance spectroscopy/mass spectrometry for the identification of xenobiotic metabolites in maize plants. Rapid Commun Mass Spectrom 14:679–684PubMedCrossRefGoogle Scholar
  7. Bairoch A (2000) The ENZYME database in 2000. Nucleic Acids Res 28:304–305PubMedCrossRefGoogle Scholar
  8. Barabási AL, Albert R (1999) Emergence of scaling in random networks. Science 286:509–512PubMedCrossRefGoogle Scholar
  9. Barrow MP, Headley JV, Perub KM, Derrick PJ (2004) Fourier transformion cyclotron resonance mass spectrometry of principal components in oils ands naphthenic acids. J Chromatogr A 1058:51–59PubMedCrossRefGoogle Scholar
  10. Bligny R, Douce R (2001) NMR and plant metabolism. Curr Opin Plant Biol 4:191–196PubMedCrossRefGoogle Scholar
  11. Boyes DC, Zayed AM, Ascenzi R, McCaskill AJ, Hoffman NE, Davis KR, Görlach J (2001) Growth stage-based phenotypic analysis of Arabidopsis: A model for high throughput functional genomics in plants. Plant Cell 13:1499–1510PubMedCrossRefGoogle Scholar
  12. Chase MW, Morton CM, Kallunki JA (1999) Phylogenetic relationships of Rutaceae: A cladistic analysis of the subfamilies using evidence from rbcL and atpB sequence variation. Am J Bot 86:1191–1199PubMedCrossRefGoogle Scholar
  13. De Luca V, St Pierre B (2000) The cell and developmental biology of alkaloid biosynthesis. Trends Plant Sci 5:168–173PubMedCrossRefGoogle Scholar
  14. Feng Q, Zhang Y, Hao P, Wang S, Fu G, Huang Y, Li Y, Zhu J, Liu Y, Hu X et al. (2002) Sequence and analysis of rice chromosome 4. Nature 420:316–320PubMedCrossRefGoogle Scholar
  15. Fraser PD, Pinto ME, Holloway DE, Bramley PM (2000) Application of high-performance liquid chromatography with photodiode array detection to the metabolic profiling of plant isoprenoids. Plant J 24:551–558PubMedCrossRefGoogle Scholar
  16. Fukushima A, Ikemura A, Kinouchi M, Oshima T, Kudo Y, Mori H, Kanaya S (2002) Periodicity in prokaryotic and eukaryotic genomes identified by power spectrum analysis. Gene 300:203–211PubMedCrossRefGoogle Scholar
  17. Gabaix X (1999) Zipf’s law for cities: An explanation. Q J Econ 114:739–767CrossRefGoogle Scholar
  18. Gerstein M (1997) A structural census of genomes: comparing bacterial, eukaryotic, and archaeal genomes in terms of protein structure. J Mol Biol 274:562–576PubMedCrossRefGoogle Scholar
  19. Goff SA, Ricke D, Lan TH, Presting G, Wang R, Dunn M, Glazebrook J, Sessions A, Oeller P, Varma H et al. (2002) A draft sequence of the rice genome (Oryza sativa L. ssp. japonica). Science 296:92–100PubMedCrossRefGoogle Scholar
  20. Goto S, Okuno Y, Hattori M, Nishioka T, Kanehisa M (2002) LIGAND: Database of chemical compounds and reactions in biological pathways. Nucleic Acids Res 30:402–404PubMedCrossRefGoogle Scholar
  21. Huhman DV, Sumner LW (2002) Metabolic profiling of saponins in Medicago sativa and Medicago truncatula using HPLC coupled to an electrospray ion-trap mass spectometer. Phytochemistry 59:347–360PubMedCrossRefGoogle Scholar
  22. Huynen MA, van Nimwegen E (1998) The frequency distribution of gene family sizes in complete genomes. Mol Biol Evol 15:583–589PubMedGoogle Scholar
  23. Jeong H, Tombor B, Albert R, Oltvai ZN, Barabasi AL (2000) The large-scale organization of metabolic networks. Nature 407:651–654PubMedCrossRefGoogle Scholar
  24. Kanehisa M, Goto S, Kawashima S, Nakaya A (2002) The KEGG databases at GenomeNet. Nucleic Acids Res 30:42–46PubMedCrossRefGoogle Scholar
  25. Mantegna RN, Buldyrev SV, Goldberger AL, Havlin S, Peng C, Simons M, Stanley HE (1994) Linguistic features of noncoding DNA sequences. Phys Rev Lett 73:3169–3172PubMedCrossRefGoogle Scholar
  26. Oliver SG, Winson MK, Kell DB, Baganz F (1998) Systematic functional analysis of the yeast genome. Trends Biotech 16:373–378CrossRefGoogle Scholar
  27. Park J, Lappe M, Teichmann SA (2001) Mapping protein family interactions; intramolecular and inter molecular protein family interaction repertories in the PDB and yeast. J Mol Biol 307:929–938PubMedCrossRefGoogle Scholar
  28. Qian J, Luscombe NM, Gerstein M (2001) Protein family and fold occurance in genomes: Powerlaw behaviour and evolutionary model. J Mol Biol 313:673–681PubMedCrossRefGoogle Scholar
  29. Ratcliffe RG, Shachar-Hill Y (2001) Probing plant metabolism with NMR. Annu Rev Plant Physiol Plant Mol Biol 52:499–526PubMedCrossRefGoogle Scholar
  30. Ravasz E, Somera AL, Mongru DA, Oltvai ZN, Barabasi AL (2002) Hierarchical organization of modularity in metabolic networks. Science 297:1551–1555PubMedCrossRefGoogle Scholar
  31. Roberts JKM (2000) NMR adventures in the metabolic labyrinth within plants. Trends Plant Sci 5:30–34PubMedCrossRefGoogle Scholar
  32. Sasaki T, Matsumoto T, Yamamoto K, Sakata K, Baba T, Katayose Y, Wu J, Niimura Y, Cheng Z, Nagamura Y et al (2002) The genome sequence and structure of rice chromosome 1. Nature 420:312–316PubMedCrossRefGoogle Scholar
  33. Soga Y, Ueno Y, Naraoka H, Ohashi Y, Tomita M, Nishioka T (2002) Simultaneous determination of anionic intermediates for Bacillus subtilis metabolic pathways by capillary electrophoresis electrospray ionization mass spectrometry. Anal Chem 74:2233–2239PubMedCrossRefGoogle Scholar
  34. Tweeddale H, Notley-McRobb L, Ferenci T (1998) Effect of slow growth on metabolism of Escherichia coli, as revealed by global metabolite pool (“metabolome”) analysis. J Bacteriol 180:5109–5116PubMedGoogle Scholar
  35. Wallace RS, Cota JH (1996) An intron loss in the chloroplast gene rpoC1 supports a monophyletic origin for the subfamily Cactoideae of the Cactaceae. Curr Genet 29:275–281PubMedGoogle Scholar
  36. Wink M (1988) Plant breeding: importance of plant secondary metabolites for protection against pathogens and herbivores. Theor Appl Genet 75:225–233CrossRefGoogle Scholar
  37. Wink M (2003) Evolution of secondary metabolites from an ecological and molecular phylogenetic perspective. Phytochemistry 64:3–11PubMedCrossRefGoogle Scholar
  38. Yu J, Hu S, Wang J, Wong GK, Li S, Liu B, Deng Y, Dai L, Zhou Y, Zhang X et al (2002) A draft sequence of the rice genome (Oryza sativa L. ssp. indica). Science 296:79–92PubMedCrossRefGoogle Scholar
  39. Zipf GK (1949) Human behavior and the principle of least effort: an introduction to human ecology. Addison-Wesley, Cambridge, MAGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Y. Shinbo
    • 1
  • Y. Nakamura
    • 2
    • 3
  • M. Altaf-Ul-Amin
    • 2
  • H. Asahi
    • 2
  • K. Kurokawa
    • 2
  • M. Arita
    • 4
  • K. Saito
    • 5
  • D. Ohta
    • 6
  • D. Shibata
    • 7
  • S. Kanaya
    • 2
  1. 1.New Energy and Industrial Technology Development OrganizationToshima, TokyoJapan
  2. 2.Department of Bioinformatics and Genomics, Graduate School of Information ScienceNara Institute of Science and Technology (NAIST)NaraJapan
  3. 3.Ehime Women’s CollegeEhimeJapan
  4. 4.Department of Computational Biology, Graduate School of Frontier SciencesThe University of TokyoKashiwa ChibaJapan
  5. 5.Metabolomics Research GroupRIKEN Plant Science CenterYokohama, KanagawaJapan
  6. 6.Department of Plant Genes and Physiology, Graduate School of Agriculture and Biological SciencesOsaka Prefecture UniversitySakai, OsakaJapan
  7. 7.Kazusa DNA Research InstituteChibaJapan

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