What can metabolomics learn from genomics and proteomics?

Current Opinion in Biotechnology

Volumn 20, Issue 6, 2009, Pages 610-615

  Arita, Masanori ^a,b,c  

^a UNIVERSITY OF TOKYO   (Japan)

^b RIKEN Plant Science Center   (Japan)

^c KEIO UNIVERSITY   (Japan)

Author keywords

[No Author keywords available]

Indexed keywords

DATA-DRIVEN; EFFECTIVE MEDIUM; EXPERIMENTAL DATA; GENOMICS; METABOLOMICS; PROTEOMICS; RESEARCH COMMUNITIES; SCIENTIFIC COMMUNITY; WEB INFRASTRUCTURE;

MOLECULAR BIOLOGY;

PROTEINS;

ACCESS TO INFORMATION; ARABIDOPSIS; BACILLUS SUBTILIS; BACTERIAL METABOLISM; CAPILLARY ELECTROPHORESIS; DATA ANALYSIS; GAS CHROMATOGRAPHY; GENOMICS; INFORMATION PROCESSING; INTERNET; LIQUID CHROMATOGRAPHY; MASS SPECTROMETRY; MEDICAL RESEARCH; METABOLOMICS; NUCLEAR MAGNETIC RESONANCE SPECTROSCOPY; PLANT METABOLISM; PRIORITY JOURNAL; PROTEOMICS; REFERENCE DATABASE; REVIEW;

ACCESS TO INFORMATION; ANIMALS; BACTERIAL PROTEINS; COMPUTATIONAL BIOLOGY; DATABASES, FACTUAL; GENOMICS; HUMANS; INTERNET; METABOLOMICS; PLANT PROTEINS; PROGRAMMING LANGUAGES; PROTEOMICS;

EID: 70449732240     PISSN: 09581669     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.copbio.2009.09.011     Document Type: Review

References (53)

1. Villas-Boas S.G., Nielsen J., Smedsgaard J., Hansen M.A.E., and Roessner-Tunali U. Metabolome Analysis: An Introduction (2007), Wiley-Interscience Series on Mass Spectrometry
2. Saito K., Hirai M.Y., and Yonekura-Sakakibara K. Decoding genes with coexpression networks and metabolomics - 'majority report by precogs'. Trends Plant Sci 13 (2008) 36-43
3. Seki H., Ohyama K., Sawai S., Mizutani M., Ohnishi T., Sudo H., Akashi T., Aoki T., Saito K., and Muranaka T. Licorice beta-amyrin 11-oxidase, a cytochrome P450 with a key role in the biosynthesis of the triterpene sweetener glycyrrhizin. Proc Natl Acad Sci U S A 105 (2008) 14204-14209. To identify genes related with glycyrrhizin biosynthesis in the nonmodel plant Glycyrrhiza (fabaceae or beans family), the authors searched for candidate ESTs whose expression patterns coincided with the accumulation pattern of glycyrrhizin. The method is labor-intensive, and its acceleration is one of the major goals of metabolomics.
4. Hirai M.Y., Sugiyama K., Sawada Y., Tohge T., Obayashi T., Suzuki A., Araki R., Sakurai N., Suzuki H., Aoki K., et al. Omics-based identification of Arabidopsis Myb transcription factors regulating aliphatic glucosinolate biosynthesis. Proc Natl Acad Sci U S A 104 (2007) 6478-6483
5. Dettmer K., Aronov P.A., and Hammock B.D. Mass spectrometry-based metabolomics. Mass Spectrom Rev 26 (2007) 51-78
6. Coen M., Holmes E., Lindon J.C., and Nicholson J.K. NMR-based metabolic profiling and metabonomic approaches to problems in molecular toxicology. Chem Res Toxicol 21 (2008) 9-27
7. 13C-isotopomer-based metabolomics of microbial groups isolated from two forest soils. Metabolomics 5 (2009) 108-122
8. Wikoff W.R., Anfora A.T., Liu J., Schultz P.G., Lesley S.A., Peters E.C., and Siuzdak G. Metabolomics analysis reveals large effects of gut microflora on mammalian blood metabolites. Proc Natl Acad Sci U S A 106 (2009) 3698-3703
9. Ellis D.I., Dunn W.B., Griffin J.L., Allwood J.W., and Goodacre R. Metabolic fingerprinting as a diagnostic tool. Pharmacogenomics 8 (2007) 1243-1266
10. Walsh M.C., Nugent A., Brennan L., and Gibney M.J. Understanding the metabolome - challenges for metabolomics. Nutr Bull 33 (2009) 316-323
11. Scalbert A., Brennan L., Fiehn O., Hankemeier T., Kristal B.S., van Ommen B., Pujos-Guillot E., Verheij E., Wishart D., and Wopereis S. Mass-spectrometry-based metabolomics: limitations and recommendations for future progress with particular focus on nutrition research. Metabolomics (2009) (Epub ahead of print). Presented by eminent scientists in metabolomics research, this review spans topics from the technological foundation of metabolomics to its future challenges. It is highly recommended as an overview of the research trend.
12. Fernie A.R. The future of metabolic phytochemistry: larger numbers of metabolites, higher resolution, greater understanding. Phytochemistry 68 (2007) 2861-2880
13. Fiehn O., Kopka J., Dörmann P., Altmann T., Trethewey R.N., and Willmitzer L. Metabolite profiling for plant functional genomics. Nat Biotechnol 18 (2000) 1157-1161
14. Kusano M., Fukushima A., Arita M., Jonsson P., Moritz T., Kobayashi M., Hayashi N., Tohge T., and Saito K. Unbiased characterization of genotype-dependent metabolic regulations by metabolomic approach in Arabidopsis thaliana. BMC Syst Biol 1 (2007) 53
15. Lytovchenko A., Beleggia R., Schauer N., Isaacson T., Leuendorf J.E., Hellmann H., Rose J.K., and Fernie A.R. Application of GC-MS for the detection of lipophilic compounds in diverse plant tissues. Plant Methods 5 (2009) 4
16. Soga T., Ohashi Y., Ueno Y., Naraoka H., Tomita M., and Nishioka T. Quantitative metabolome analysis using capillary electrophoresis mass spectrometry. J Proteome Res 2 (2003) 488-494
17. van der Werf M.J., Overkamp K.M., Muilwijk B., Coulier L., and Hankemeier T. Microbial metabolomics: toward a platform with full metabolome coverage. Anal Biochem 370 (2007) 17-25
18. Gladwell M. The Tipping Point: How Little Things Can Make a Big Difference (2000), Little, Brown and Company
19. Liolios K., Mavromatis K., Tavernarakis N., and Kyrpides N.C. The Genomes On Line Database (GOLD) in 2007: status of genomic and metagenomic projects and their associated metadata. Nucleic Acids Res 36 (2008) D475-479
20. von Mering C., Krause R., Snel B., Cornell M., Oliver S.G., Fields S., and Bork P. Comparative assessment of large-scale data sets of protein-protein interactions. Nature 417 (2002) 399-403
21. Huang H., Jedynak B.M., and Bader J.S. Where have all the interactions gone? Estimating the coverage of two-hybrid protein interaction maps. PLoS Comput Biol 3 (2007) e214
22. Chen Y., Zhang J., Xing G., and Zhao Y. Mascot-derived false positive peptide identifications revealed by manual analysis of tandem mass spectra. J Proteome Res 8 (2009) 3141-3147. The authors explain five patterns of false-positives that have been overlooked in previous assessments in proteomics. The error patterns can be directly interpreted in terms of metabolomics and are useful for developing better identification algorithms.
23. Overbeek R., Bartels D., Vonstein V., and Meyer F. Annotation of bacterial and archaeal genomes: improving accuracy and consistency. Chem Rev 107 (2007) 3431-3447
24. Rabinowitz J.D. Cellular metabolomics of Escherichia coli. Expert Rev Proteomics 4 (2007) 187-198
25. Baba T., Ara T., Hasegawa M., Takai Y., Okumura Y., Baba M., Datsenko K.A., Tomita M., Wanner B.L., and Mori H. Construction of Escherichia coli K-12 in-frame, single-gene knockout mutants: the Keio collection. Mol Syst Biol 2 (2006) 2006-2008
26. Typas A., Nichols R.J., Siegele D.A., Shales M., Collins S.R., Lim B., Braberg H., Yamamoto N., Takeuchi R., Wanner B.L., et al. High-throughput, quantitative analyses of genetic interactions in E. coli. Nat Methods 5 (2008) 781-787. In addition to the single knock-out mutant library (KEIO collection in Ref. [25]), the E. coli community has established a library of double mutants. This library will be extremely useful for assessing other data acquired by high-throughput methods or in in silico strains.
27. Reed J.L., Vo T.D., Schilling C.H., and Palsson B.O. An expanded genome-scale model of Escherichia coli K-12 (iJR904 GSM/GPR). Genome Biol 4 (2003) R54
28. Koide T., Pang W.L., and Baliga N.S. The role of predictive modelling in rationally re-engineering biological systems. Nat Rev Microbiol 7 (2009) 297-305
29. •], the platform will become extremely useful to the entire metabolomics community.
30. •].
31. Cui Q., Lewis I.A., Hegeman A.D., Anderson M.E., Li J., Schulte C.F., Westler W.M., Eghbalnia H.R., Sussman M.R., and Markley J.L. Metabolite identification via the Madison Metabolomics Consortium Database. Nat Biotechnol 26 (2008) 162-164
32. •] contains useful and up-to-date lists of accessible web resources in metabolomics.
33. •].
34. Okuda S., Yamada T., Hamajima M., Itoh M., Katayama T., Bork P., Goto S., and Kanehisa M. KEGG Atlas mapping for global analysis of metabolic pathways. Nucleic Acids Res 36 (2008) W423-426
35. Arita M. The metabolic world of Escherichia coli is not small. Proc Natl Acad Sci U S A 101 (2004) 1543-1547
36. Prieto C., and De Las Rivas J. APID: Agile Protein Interaction DataAnalyzer. Nucleic Acids Res 34 (2006) W298-302
37. Brazma A., Hingamp P., Quackenbush J., Sherlock G., Spellman P., Stoeckert C., Aach J., Ansorge W., Ball C.A., Causton H.C., et al. Minimum information about a microarray experiment (MIAME) - toward standards for microarray data. Nat Genet 29 (2001) 365-371
38. Hucka M., Finney A., Sauro H.M., Bolouri H., Doyle J.C., Kitano H., Arkin A.P., Bornstein B.J., Bray D., Cornish-Bowden A., et al. The systems biology markup language (SBML): a medium for representation and exchange of biochemical network models. Bioinformatics 19 (2003) 524-531
39. Garny A., Noble D., Hunter P.J., and Kohl P. Cellular Open Resource (COR): current status and future directions. Philos Trans A Math Phys Eng Sci 367 (2009) 1885-1905
40. Luciano J.S. PAX of mind for pathway researchers. Drug Discov Today 10 (2005) 937-942
41. Fiehn O., Robertson D., Griffin J., van der Werf M., Nikolau B., Morrison N., Sumner L.W., Goodacre R., Hardy N.W., Taylor C., et al. The metabolomics standards initiative (MSI). Metabolomics 3 (2007) 175-178
42. Stonebraker M., Madden S., Abadi D.J., Harizopoulos S., Hachem N., and Helland P. The end of an architectural era: (It's time for a complete rewrite). Proceedings of the 33rd International Conference on Very Large Data Bases (VLDB). Vienna, Austria (2007) 1150-1160
43. Weikum G., Kasneci G., Ramanath M., and Suchanek F. Database and information-retrieval methods for knowledge discovery. Commun ACM 52 (2009) 56-64
44. Giles J. Key biology databases go wiki. Nature (News) 445 (2007) 691
45. Arita M., and Suwa K. Search extension transforms Wiki into a relational system: a case for flavonoid metabolite database. BioData Mining 1 (2008) 7
46. Mons B., Ashburner M., Chichester C., van Mulligen E., Weeber M., den Dunnen J., van Ommen G.J., Musen M., Cockerill M., Hermjakob H., et al. Calling on a million minds for community annotation in WikiProteins. Genome Biol 9 (2008) R89
47. Grethe J.S., Ross E., Little D., Sanders B., Gupta A., and Astakhov V. Mediator infrastructure for information integration and semantic data integration environment for biomedical research. Methods Mol Biol 569 (2009) 33-53
48. Stein L.D. Towards a cyberinfrastructure for the biological sciences: progress, visions and challenges. Nat Rev Genet 9 (2008) 678-688. Although the words cyberinfrastructure and e-Science have become familiar, their definition or purpose is usually unknown. This article explains their underlying concepts and their benefits by introducing the ongoing iPlant (URL: http://www.iplantcollaborative.org/) and CaBIG (URL: https://cabig.nci.nih.gov/) projects.
49. Hoffmann R. A wiki for the life sciences where authorship matters. Nat Genet 40 (2008) 1047-1051
50. Kopka J., Schauer N., Krueger S., Birkemeyer C., Usadel B., Bergmüller E., Dörmann P., Weckwerth W., Gibon Y., Stitt M., et al. GMD@CSB.DB: the Golm Metabolome Database. Bioinformatics 21 (2005) 1635-1638
51. Horai H., Arita M., and Nishioka T. Comparison of ESI-MS Spectra in MassBank Database. Proceedings of the First International Conference on BioMedical Engineering and Informatics (BMEI), vol 2. Sanya, China (2008) 853-857
52. Wishart D.S., Knox C., Guo A.C., Eisner R., Young N., Gautam B., Hau D.D., Psychogios N., Dong E., Bouatra S., et al. HMDB: a knowledgebase for the human metabolome. Nucleic Acids Res 37 (2009) D603-610
53. Kind T., Scholz M., and Fiehn O. How large is the metabolome? A critical analysis of data exchange practices in chemistry. PLoS One 4 (2009) e5440