Different-batch metabolome analysis of Saccharomyces cerevisiae based on gas chromatography/mass spectrometry

Journal of Bioscience and Bioengineering

Volumn 117, Issue 2, 2014, Pages 248-255

  Kawase, Naoki ^a   Tsugawa, Hiroshi ^a,b   Bamba, Takeshi ^a   Fukusaki, Eiichiro ^a  

^a OSAKA UNIVERSITY   (Japan)

^b RIKEN Center for Sustainable Resource Science   (Japan)

Author keywords

Different batches; Gas chromatography mass spectrometry; Metabolomics; Saccharomyces cerevisiae; Time segmented selective ion monitoring

Indexed keywords

DIFFERENT BATCHES; GAS CHROMATOGRAPHY-MASS SPECTROMETRY; HIGH SIGNAL-TO-NOISE RATIO; METABOLOME ANALYSIS; METABOLOMICS; REPRODUCIBILITIES; SELECTIVE ION MONITORING; SENSITIVE ANALYSIS;

DATA PROCESSING; GAS CHROMATOGRAPHY; MASS SPECTROMETRY; OPTIMIZATION;

YEAST;

TRANSCRIPTION FACTOR;

ARTICLE; DATA PROCESSING; DELETION MUTANT; FALSE POSITIVE RESULT; ION MONITORING; MASS FRAGMENTOGRAPHY; METABOLOME; METABOLOMICS; NONHUMAN; QUANTITATIVE ANALYSIS; REPRODUCIBILITY; SACCHAROMYCES CEREVISIAE; SENSITIVITY ANALYSIS; SIGNAL NOISE RATIO; WORKFLOW;

SACCHAROMYCES CEREVISIAE;

DIFFERENT BATCHES; GAS CHROMATOGRAPHY MASS SPECTROMETRY; METABOLOMICS; SACCHAROMYCES CEREVISIAE; TIME SEGMENTED SELECTIVE ION MONITORING;

GAS CHROMATOGRAPHY-MASS SPECTROMETRY; METABOLOME; METABOLOMICS; REFERENCE STANDARDS; REPRODUCIBILITY OF RESULTS; RESEARCH DESIGN; SACCHAROMYCES CEREVISIAE;

EID: 84892366874     PISSN: 13891723     EISSN: 13474421     Source Type: Journal    
DOI: 10.1016/j.jbiosc.2013.07.008     Document Type: Article

References (29)

1. 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., other 3 authors Afunctional genomics strategy that uses metabolome data to reveal the phenotype of silent mutations. Nat. Biotechnol. 2001, 19:45-50.
2. Ding M.-Z., Zhou X., Yuan Y.-J. Metabolome profiling reveals adaptive evolution of Saccharomyces cerevisiae during repeated vacuum fermentations. Metabolomics 2009, 6:42-55.
3. Yoshida R., Tamura T., Takaoka C., Harada K., Kobayashi A., Mukai Y., Fukusaki E. Metabolomics-based systematic prediction of yeast lifespan and its application for semi-rational screening of ageing-related mutants. Aging Cell 2010, 9:616-625.
4. Cevallos-Cevallos J.M., Danyluk M.D., Reyes-De-Corcuera J.I. GC-MS based metabolomics for rapid simultaneous detection of Escherichia coli O157:H7, Salmonella typhimurium, Salmonella muenchen, and Salmonella hartford in ground beef and chicken. J.Food Sci. 2011, 76:M238-M246.
5. Jenkins H., Hardy N., Beckmann M., Draper J., Smith A.R., Taylor J., Fiehn O., Goodacre R., Bino R.J., Hall R., other 17 authors Aproposed framework for the description of plant metabolomics experiments and their results. Nat. Biotechnol. 2004, 22:1601-1606.
6. Jewison T., Knox C., Neveu V., Djoumbou Y., Guo A.C., Lee J., Liu P., Mandal R., Krishnamurthy R., Sinelnikov I., Wilson M., Wishart D.S. YMDB: the yeast metabolome database. Nucleic Acids Res. 2012, 40:D815-D820.
7. Canelas A.B., Ras C., Pierick A., Dam J.C., Heijnen J.J., Gulik W.M. Leakage-free rapid quenching technique for yeast metabolomics. Metabolomics 2008, 4:226-239.
8. Bolten C.J., Kiefer P., Letisse F., Portais J.-C., Wittmann C. Sampling for metabolome analysis of microorganisms. Anal. Chem. 2007, 79:3843-3849.
9. Tsugawa H., Bamba T., Shinohara M., Nishiumi S., Yoshida M., Fukusaki E. Practical non-targeted gas chromatography/mass spectrometry-based metabolomics platform for metabolic phenotype analysis. J.Biosci. Bioeng. 2011, 112:292-298.
10. Quackenbush J. Microarray data normalization and transformation. Nat. Genet. 2002, 32:496-501.
11. Koutsouba V., Heberer T., Fuhrmann B., Schmidt-Baumler K., Tsipi D., Hiskia a. Determination of polar pharmaceuticals in sewage water of Greece by gas chromatography-mass spectrometry. Chemosphere 2003, 51:69-75.
12. Stein S.E., Heller D.N. On the risk of false positive identification using multiple ion monitoring in qualitative mass spectrometry: large-scale intercomparisons with a comprehensive mass spectral library. J.Am. Soc. Mass Spectrom. 2006, 17:823-835.
13. Yoshikawa K., Tanaka T., Furusawa C., Nagahisa K., Hirasawa T., Shimizu H. Comprehensive phenotypic analysis for identification of genes affecting growth under ethanol stress in Saccharomyces cerevisiae. FEMS Yeast Res. 2009, 9:32-44.
14. Kato H., Izumi Y., Hasunuma T., Matsuda F., Kondo A. Widely targeted metabolic profiling analysis of yeast central metabolites. J.Biosci. Bioeng. 2012, 113:665-673.
15. Lommen A. MetAlign: interface-driven, versatile metabolomics tool for hyphenated full-scan mass spectrometry data preprocessing. Anal. Chem. 2009, 81:3079-3086.
16. Sakurai T., Yamada Y., Sawada Y., Matsuda F., Akiyama K., Shinozaki K., Hirai M.Y., Saito K. PRIMe update: innovative content for plant metabolomics and integration of gene expression and metabolite accumulation. Plant Cell Physiol. 2013, 54:e5.
17. Lange H.C., Eman M., Van Zuijlen G., Visser D., Van Dam J.C., Frank J., De Mattos M.J., Heijnen J.J. Improved rapid sampling for in vivo kinetics of intracellular metabolites in Saccharomyces cerevisiae. Biotechnol. Bioeng. 2001, 75:406-415.
18. Theobald U., Mailinger W., Baltes M., Rizzi M., Reuss M. In vivo analysis of metabolic dynamics in Saccharomyces cerevisiae: I. Experimental observations. Biotechnol. Bioeng 1997, 55:305-316.
19. Kim S., Lee D.Y., Wohlgemuth G., Park H.S., Fiehn O., Kim K.H. Evaluation and optimization of metabolome sample preparation methods for Saccharomyces cerevisiae. Anal. Chem. 2013, 85:2169-2176.
20. Tsugawa H., Tsujimoto Y., Arita M., Bamba T., Fukusaki E. GC/MS based metabolomics: development of a data mining system for metabolite identification by using soft independent modeling of class analogy (SIMCA). BMC Bioinformatics 2011, 12:131.
21. Sysi-Aho M., Katajamaa M., Yetukuri L., Oresic M. Normalization method for metabolomics data using optimal selection of multiple internal standards. BMC Bioinformatics 2007, 8:93.
22. Johnson W.E., Li C., Rabinovic A. Adjusting batch effects in microarray expression data using empirical Bayes methods. Biostatistics 2007, 8:118-127.
23. Zelena E., Dunn W.B., Broadhurst D., Francis-McIntyre S., Carroll K.M., Begley P., O'Hagan S., Knowles J.D., Halsall A., Wilson I.D., Kell D.B. Development of a robust and repeatable UPLC-MS method for the long-term metabolomic study of human serum. Anal. Chem. 2009, 81:1357-1364.
24. Trygg J., Wold S. Orthogonal projections to latent structures (O-PLS). J.Chemom. 2002, 16:119-128.
25. Taylor P., Efron B. Better bootstrap confidence intervals. J.Am. Stat. Assoc. 2012, 82:37-41.
26. Brown J.L., North S., Bussey H. SKN7, a yeast multicopy suppressor of a mutation affecting cell wall beta-glucan assembly, encodes a product with domains homologous to prokaryotic two-component regulators and to heat shock transcription factors. J.Bacteriol. 1993, 175:6908-6915.
27. Kim Y.J., Björklund S., Li Y., Sayre M.H., Kornberg R.D. Amultiprotein mediator of transcriptional activation and its interaction with the C-terminal repeat domain of RNA polymerase II. Cell 1994, 77:599-608.
28. De Boer M., Nielsen P.S., Bebelman J.P., Heerikhuizen H., Andersen H a, Planta R.J. Stp1p, Stp2p and Abf1p are involved in regulation of expression of the amino acid transporter gene BAP3 of Saccharomyces cerevisiae. Nucleic Acids Res. 2000, 28:974-981.
29. Liao X., Butow R.A. RTG1 and RTG2: two yeast genes Requbd for a novel path of communication from mitochondria to the nucleus. Cell 1993, 72:61-71.