Fungal metabolite analysis in genomics and phenomics

Current Opinion in Biotechnology

Volumn 17, Issue 2, 2006, Pages 191-197

  Jewett, Michael C ^a   Hofmann, Gerald ^a   Nielsen, Jens ^a  

^a TECHNICAL UNIVERSITY OF DENMARK   (Denmark)

Author keywords

[No Author keywords available]

Indexed keywords

BIOCHEMISTRY; CELLS; GENES; METABOLISM; METABOLITES;

CELLULAR FUNCTIONS; CELLULAR METABOLITES; METABOLIC LANDSCAPE; METABOLITE ANALYSIS;

FUNGI;

EUKARYOTE; FUSARIUM OXYSPORUM; GENOMICS; MATHEMATICAL MODEL; NONHUMAN; PHENOTYPE; PRIORITY JOURNAL; QUANTITATIVE ANALYSIS; REVIEW; SACCHAROMYCES CEREVISIAE; STANDARDIZATION;

FUNGI; GENOME, FUNGAL; GENOMICS; PHENOTYPE;

EUKARYOTA; FUNGI;

EID: 33645948768     PISSN: 09581669     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.copbio.2006.02.001     Document Type: Review

References (56)

1. Hohmann S. The Yeast Systems Biology Network: mating communities. Curr Opin Biotechnol 16 (2005) 356-360
2. Stephanopoulos G., Alper H., and Moxley J. Exploiting biological complexity for strain improvement through systems biology. Nat Biotechnol 22 (2004) 1261-1267
3. Forster J., Famili I., Fu P., Palsson B.O., and Nielsen J. Genome-scale reconstruction of the Saccharomyces cerevisiae metabolic network. Genome Res 13 (2003) 244-253
4. Nielsen J. It is all about metabolic fluxes. J Bacteriol 185 (2003) 7031-7035
5. Jeong H., Tombor B., Albert R., Oltvai Z.N., and Barabasi A.L. The large-scale organization of metabolic networks. Nature 407 (2000) 651-654
6. Price N.D., Papin J.A., Schilling C.H., and Palsson B.O. Genome-scale microbial in silico models: the constraints-based approach. Trends Biotechnol 21 (2003) 162-169
7. Borodina I., and Nielsen J. From genomes to in silico cells via metabolic networks. Curr Opin Biotechnol 16 (2005) 350-355
8. Patil K.R., and Nielsen J. Uncovering transcriptional regulation of metabolism by using metabolic network topology. Proc Natl Acad Sci USA 102 (2005) 2685-2689. This paper describes a novel algorithm for exploring metabolic regulation by inference from mRNA global expression and genome-scale metabolic models. By using topological network structure defined from stoichiometry, this approach is useful in uncovering coregulated structures in the metabolic network.
9. Goffeau A., Barrell B.G., Bussey H., Davis R.W., Dujon B., Feldmann H., Galibert F., Hoheisel J.D., Jacq C., Johnston M., et al. Life with 6000 genes. Science 274 546 (1996) 563-567
10. Oliver S.G., Winson M.K., Kell D.B., and Baganz F. Systematic functional analysis of the yeast genome. Trends Biotechnol 16 (1998) 373-378
11. Villas-Boas S.G., Mas S., Akesson M., Smedsgaard J., and Nielsen J. Mass spectrometry in metabolome analysis. Mass Spectrom Rev 24 (2005) 613-646. A seminal review on the use of MS in metabolomics, offering excellent comparisons of sample preparation methods (quenching and extraction) and suggested applications for specific types of MS analyses (e.g. GC-MS, LC-MS, CE-MS).
12. Kell D.B. Metabolomics and systems biology: making sense of the soup. Curr Opin Microbiol 7 (2004) 296-307
13. Goodacre R., Vaidyanathan S., Dunn W.B., Harrigan G.G., and Kell D.B. Metabolomics by numbers: acquiring and understanding global metabolite data. Trends Biotechnol 22 (2004) 245-252
14. Villas-Bôas S.G., Moxley J.F., Akesson M., Stephanopoulos G., and Nielsen J. High-throughput metabolic state analysis: the missing link in integrated functional genomics of yeasts. Biochem J 388 (2005) 669-677. This work demonstrates an experimental methodology and software platform for the rapid diagnosis of intracellular amino and non-amino organic acids using GC-MS. Of particular interest are discussions relative to normalization. The robustness and sensitivity of this approach offer significant promise for future studies that attempt to integrate metabolism to study or exploit biological complexity.
15. 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
16. Roessner U., Wagner C., Kopka J., Trethewey R.N., and Willmitzer L. Technical advance: simultaneous analysis of metabolites in potato tuber by gas chromatography-mass spectrometry. Plant J 23 (2000) 131-142
17. Villas-Boas S.G., Rasmussen S., and Lane G.A. Metabolomics or metabolite profiles?. Trends Biotechnol 23 (2005) 385-386
18. van Dam J.C., Eman M.R., Frank J., Lange H.C., van Dedem G.W.K., and Heijnen S.J. Analysis of glycolytic intermediates in Saccharomyces cerevisiae using anion exchange chromatography and electrospray ionization with tandem mass spectrometric detection. Anal Chim Acta 460 (2002) 209-218
19. Wittmann C., Hans M., van Winden W.A., Ras C., and Heijnen J.J. Dynamics of intracellular metabolites of glycolysis and TCA cycle during cell-cycle-related oscillation in Saccharomyces cerevisiae. Biotechnol Bioeng 89 (2005) 839-847
20. Mashego M.R., Jansen M.L., Vinke J.L., van Gulik W.M., and Heijnen J.J. Changes in the metabolome of Saccharomyces cerevisiae associated with evolution in aerobic glucose-limited chemostats. FEMS Yeast Res. 5 (2005) 419-430
21. Panagiotou G., Christakopoulos P., and Olsson L. The influence of different cultivation conditions on the metabolome of Fusarium oxysporum. J Biotechnol 118 (2005) 304-315
22. Sauer U. High-throughput phenomics: experimental methods for mapping fluxomes. Curr Opin Biotechnol 15 (2004) 58-63
23. van Winden W.A., van Dam J.C., Ras C., Kleijn R.J., Vinke J.L., van Gulik W.M., and Heijnen J.J. Metabolic-flux analysis of Saccharomyces cerevisiae CEN.PK113-7D based on mass isotopomer measurements of (13)C-labeled primary metabolites. FEMS Yeast Res 5 (2005) 559-568
24. Grotkjaer T., Akesson M., Christensen B., Gombert A.K., and Nielsen J. Impact of transamination reactions and protein turnover on labeling dynamics in (13)C-labeling experiments. Biotechnol Bioeng 86 (2004) 209-216
25. 13C-flux analysis reveals mechanistic principles of metabolic network robustness to null mutations in yeast. Genome Biol 6 (2005) R49
26. Fischer E., and Sauer U. Large-scale in vivo flux analysis shows rigidity and suboptimal performance of Bacillus subtilis metabolism. Nat Genet 37 (2005) 636-640. By comparing the flux distributions of 137 null mutants from Bacillus subtilis, the authors demonstrate fundamental principles of metabolic network rigidity and robustness.
27. Raamsdonk L.M., Teusink B., Broadhurst D., Zhang N., Hayes A., Walsh M.C., Berden J.A., Brindle K.M., Kell D.B., Rowland J.J., et al. A functional genomics strategy that uses metabolome data to reveal the phenotype of silent mutations. Nat Biotechnol 19 (2001) 45-50
28. Weckwerth W., Loureiro M.E., Wenzel K., and Fiehn O. Differential metabolic networks unravel the effects of silent plant phenotypes. Proc Natl Acad Sci USA 101 (2004) 7809-7814
29. Allen J., Davey H.M., Broadhurst D., Heald J.K., Rowland J.J., Oliver S.G., and Kell D.B. High-throughput classification of yeast mutants for functional genomics using metabolic footprinting. Nat Biotechnol 21 (2003) 692-696
30. Catchpole G.S., Beckmann M., Enot D.P., Mondhe M., Zywicki B., Taylor J., Hardy N., Smith A., King R.D., Kell D.B., et al. Hierarchical metabolomics demonstrates substantial compositional similarity between genetically modified and conventional potato crops. Proc Natl Acad Sci USA 102 (2005) 14458-14462. The authors detail a case study revealing the complementary nature of hierarchical metabolomics with physiological data, comparing GM and conventional potato crops. By utilizing metabolite profiling and targeted analysis, this work features one of the most complete analyses of metabolic content ever reported. It demonstrates the importance of coupling sensitive analysis techniques with data-mining strategies. Moreover, it illustrates the potential for metabolomics applications in food biotechnology.
31. Bennett J.W., and Klich M. Mycotoxins. Clin Microbiol Rev 16 (2003) 497-516
32. FAO. Worldwide regulations for mycotoxins in food and feed in 2003. FAO food and nutrition paper (2004) 81
33. Krska R., Welzig E., Berthiller F., Molinelli A., and Mizaikoff B. Advances in the analysis of mycotoxins and its quality assurance. Food Addit Contam 22 (2005) 345-353
34. Logrieco A., Arrigan D.W., Brengel-Pesce K., Siciliano P., and Tothill I. DNA arrays, electronic noses and tongues, biosensors and receptors for rapid detection of toxigenic fungi and mycotoxins: a review. Food Addit Contam 22 (2005) 335-344
35. Smedsgaard J., Hansen M.E., and Frisvad J.C. Classification of terverticillate Penicillia by electrospray mass spectrometric profiling. Stud Mycol 49 (2004) 243-251
36. Nielsen K.F., Smedsgaard J., Larsen T.O., Lund F., Thrane U., and Frisvad J.C. Chemical identification of fungi: metabolite profiling and metabolomics. In: Arora D.K. (Ed). Fungal Biotechnology in Agricultural, Food, and Environmental Application (2004), Marcel Dekker, New York 19-35. This chapter highlights the importance of standardizing analytical procedures and the utilization of databases for the identification and dereplication of fungal secondary metabolites.
37. Castrillo J.I., Hayes A., Mohammed S., Gaskell S.J., and Oliver S.G. An optimized protocol for metabolome analysis in yeast using direct infusion electrospray mass spectrometry. Phytochemistry 62 (2003) 929-937
38. Nielsen K.F., and Smedsgaard J. Fungal metabolite screening: database of 474 mycotoxins and fungal metabolites for dereplication by standardised liquid chromatography-UV-mass spectrometry methodology. J Chromatogr A 1002 (2003) 111-136
39. Hansen M.E., Smedsgaard J., and Larsen T.O. X-Hitting: an algorithm for novelty detection and dereplication by UV spectra of complex mixtures of natural products. Anal Chem 77 (2005) 6805-6817
40. Larsen T.O., Petersen B.O., Duus J.O., Sorensen D., Frisvad J.C., and Hansen M.E. Discovery of new natural products by application of X-hitting, a novel algorithm for automated comparison of full UV spectra, combined with structural determination by NMR spectroscopy. J Nat Prod 68 (2005) 871-874
41. Hansen M.E., and Smedsgaard J. A new matching algorithm for high resolution mass spectra. J Am Soc Mass Spectrom 15 (2004) 1173-1180
42. de Koning W., and van Dam K. A method for the determination of changes of glycolytic metabolites in yeast on a subsecond time scale using extraction at neutral pH. Anal Biochem 204 (1992) 118-123
43. Villas-Bôas S.G., Hojer-Pedersen J., Akesson M., Smedsgaard J., and Nielsen J. Global metabolite analysis of yeast: evaluation of sample preparation methods. Yeast 22 (2005) 1155-1169
44. Birkemeyer C., Luedemann A., Wagner C., Erban A., and Kopka J. Metabolome analysis: the potential of in vivo labeling with stable isotopes for metabolite profiling. Trends Biotechnol 23 (2005) 28-33. An excellent commentary on the applications of in vivo isotopomer labeling for surveying cellular metabolites and, perhaps more importantly, standardization of metabolite measurement data from diverse analytical platforms.
45. 13C-saturated microbial cultivations to generate a standard library of metabolites useful for standardization in quantitative metabolome analysis.
46. 13C-labeled cell extracts as internal standards. Anal Biochem 336 (2005) 164-171
47. 15N metabolic labeling for quantitative metabolic profiling. Anal Chem 77 (2005) 2026-2033
48. Jenkins H., Hardy N., Beckmann M., Draper J., Smith A.R., Taylor J., Fiehn O., Goodacre R., Bino R.J., Hall R., et al. A proposed framework for the description of plant metabolomics experiments and their results. Nat Biotechnol 22 (2004) 1601-1606
49. Lindon J.C., Nicholson J.K., Holmes E., Keun H.C., Craig A., Pearce J.T., Bruce S.J., Hardy N., Sansone S.A., Antti H., et al. Summary recommendations for standardization and reporting of metabolic analyses. Nat Biotechnol 23 (2005) 833-838. This paper draws attention to the importance of defining standards for analytical methods and reporting. Community efforts promise to facilitate more widespread incorporation in metabolomics.
50. Schauer N., Steinhauser D., Strelkov S., Schomburg D., Allison G., Moritz T., Lundgren K., Roessner-Tunali U., Forbes M.G., Willmitzer L., et al. GC-MS libraries for the rapid identification of metabolites in complex biological samples. FEBS Lett 579 (2005) 1332-1337
51. Halket J.M., Waterman D., Przyborowska A.M., Patel R.K., Fraser P.D., and Bramley P.M. Chemical derivatization and mass spectral libraries in metabolic profiling by GC/MS and LC/MS/MS. J Exp Bot 56 (2005) 219-243
52. Villas-Bôas SG, Noel S, Lane GA, Attwood G, Cookson A: Extracellular metabolomics: A metabolic footprinting approach to assess fiber degradation in complex media. Anal Biochem 2006: in press.
53. Larsen T.O., Smedsgaard J., Nielsen K.F., Hansen M.E., and Frisvad J.C. Phenotypic taxonomy and metabolite profiling in microbial drug discovery. Nat Prod Rep 22 (2005) 672-695
54. Machida M., Asai K., Sano M., Tanaka T., Kumagai T., Terai G., Kusumoto K., Arima T., Akita O., Kashiwagi Y., et al. Genome sequencing and analysis of Aspergillus oryzae. Nature 438 (2005) 1157-1161
55. Nierman W.C., Pain A., Anderson M.J., Wortman J.R., Kim H.S., Arroyo J., Berriman M., Abe K., Archer D.B., Bermejo C., et al. Genomic sequence of the pathogenic and allergenic filamentous fungus Aspergillus fumigatus. Nature 438 (2005) 1151-1156
56. Galagan J.E., Calvo S.E., Cuomo C., Ma L.J., Wortman J.R., Batzoglou S., Lee S.I., Basturkmen M., Spevak C.C., Clutterbuck J., et al. Sequencing of Aspergillus nidulans and comparative analysis with A. fumigatus and A. oryzae. Nature 438 (2005) 1105-1115