Abstract
During saline stress, Saccharomyces cerevisiae changes its metabolic fluxes through the direct accumulation of metabolites such as glycerol and trehalose, which in turn provide tolerance to the cell against stress. Previous research shows that the various controls at both transcriptional and translational levels decide the phenomenon of stress, but details about such transition is still not very clear. This paper attempts to extract some hidden features through the information extraction approach from DNA microarray data during transition to osmotic tolerance, which are expected to be important in directing to the tolerance stage upon encountering osmotic stress in yeast. Time course of DNA microarray data during osmotic tolerance was analyzed by computational approach ‘self-organizing map (SOM) extended with hierarchical clustering’. Since eukaryotic gene expression is governed by short regulatory sequences found upstream in promoter regions, therefore clusters containing the similar profiles obtained by SOM were further analyzed for overrepresentation of known regulatory binding sites in promoter region. It was found that apart from known and expected ‘STRE’ during osmotic stress, the ‘GCN4’ binding site is also found to be significant. Hence, it was suggested that the process of osmotic tolerance proceeds through a stage of amino acid starvation. The intracellular amino acids pool also found to be depleted during transition and restoration is faster in brewing strain than laboratory strain. Experiments involving supplementation of amino acids helps in reducing the lag time for recovery, which was found to be similar to that of brewing strain.
Similar content being viewed by others
References
Attfield PV (1997) Stress tolerance: the key to effective strains of industrial baker’s yeast. Nat Biotechnol 15:1351–1357
Beer MA, Tavazoie S (2004) Predicting gene expression from sequence. Cell 117:185–198
Beudeker RF, van Dam HW, van der Plaat JB, Vellega K (1990) Developments in baker’s yeast production. In: Verachtert H, De Mot R (eds) Yeast biotechnology and biocatalysis, Marcel Dekker, New York, pp 103–146
Bidlingmeyer BA, Cohen SA, Tarvin TL (1984) Rapid analysis of amino acids using pre-column derivatization. J Chromatogr 336:93–104
Blomberg A (1997) The osmotic hypersensitivity of the yeast Saccharomyces cerevisiae is strain and growth media dependent: quantitative aspects of the phenomenon. Yeast 13:529–539
Causton HC, Ren B, Koh SS, Harbison CT, Kanin E, Jennings EG, Lee TI, True HL, Lander ES, Young RA (2001) Remodeling of yeast genome expression in response to environmental changes. Mol Biol Cell 12:323–337
Duggan DJ, Bittner M, Chen Y, Meltzer P, Trent JM (1999) Expression profiling using cDNA microarrays. Nat Genet 21:10–14
Evans IH (1990) Yeast strains for baking: recent developments. In: Spencer J, Spencer D (eds) Yeast technology. Springer, Berlin Heidelberg New York, pp 13–54
Gasch AP, Spellman PT, Kao CM, Carmel-Harel O, Eisen MB, Storz G, Botstein D, Brown PO (2000) Genomic expression programs in the response of yeast cells to environmental changes. Mol Biol Cell 11:4241–4257
Guthrie C, Fink GR (eds) (1991) Methods in enzymology, vol 194. Guide to Yeast Genetics and Molecular Biology. Academic, New York
Hans MA, Heinzle E, Wittmann C (2001) Quantification of intracellular amino acids in batch cultures of Saccharomyces cerevisiae. Appl Microbiol Biotechnol 56:776–779
Hinnebusch AG (1984) Evidence for translational regulation of the activator of general amino acid control in yeast. Proc Natl Acad Sci USA 81:6442–6446
Hinnebusch AG (2005) Translational regulation of GCN4 and the general amino acid control of yeast. Annu Rev Microbiol 59:407–450
Hirasawa T, Nakakura Y, Yoshikawa K, Ashitani K, Nagahisa K, Furusawa C, Katakura Y, Shimizu H, Shioya S (2006) Comparative analysis of transcriptional responses to saline stress in the laboratory and brewing strains of Saccharomyces cerevisiae with DNA microarray. Appl Microbiol Biotechnol 70:346–357
Huang J, Shimizu H, Shioya S (2003) Clustering gene expression pattern and extracting relationship in gene network based on artificial neural networks. J Biosci Bioeng 96:421–428
Lewis JG, Learmonth RP, Attfield PV, Watson K (1997) Stress co-tolerance and trehalose content in baking strains of Saccharomyces cerevisiae. J Ind Microbiol Biotechnol 18:30–36
Norbeck J, Blomberg A (1998) Amino acid uptake is strongly affected during exponential growth of Saccharomyces cerevisiae in 0.7 M NaCl medium. FEMS Microbiol Lett 158:121–126
Pascual-Ahuir A, Serrano R, Proft M (2001) The Sko1p repressor and Gcn4p activator antagonistically modulate stress-regulated transcription in Saccharomyces cerevisiae. Mol Cell Biol 21:16–25
Schmitt AP, McEntee K (1996) Msn2p, a zinc finger DNA-binding protein, is the transcriptional activator of the multistress response in Saccharomyces cerevisiae. Proc Natl Acad Sci USA 93:5777–5782
Schoondermark-Stolk SA, Jansen M, Veurink JH, Verkleij AJ, Verrips CT, Euverink GJ, Boonstra J, Dijkhuizen L (2005) Rapid identification of target genes for 3-methyl-1-butanol production in Saccharomyces cerevisiae. Appl Microbiol Biotechnol 70:237–246
Tamayo P, Slonim D, Mesirov J, Zhu Q, Kitareewan S, Dmitrovsky E, Lander ES, Golub TR (1999) Interpreting patterns of gene expression with self-organizing maps: methods and application to hematopoietic differentiation. Proc Natl Acad Sci USA 96:2907–2912
Winston F, Dollard C, Ricupero-Hovasse SL (1995) Construction of a set of convenient Saccharomyces cerevisiae strains that are isogenic to S288C. Yeast 11:53–55
Wolfsberg TG, Gabrielian AE, Campbell MJ, Cho RJ, Spouge JL, Landsman D (1999) Candidate regulatory sequence elements for cell cycle-dependent transcription in Saccharomyces cerevisiae. Genome Res 9:775–792
Yang YH, Dudoit S, Luu P, Lin DM, Peng V, Ngai J, Speed TP (2002) Normalization for cDNA microarray data: a robust composite method addressing single and multiple slide systematic variation. Nucleic Acids Res 30:e15
Yoshikawa K, Pandey G, Hirasawa T, Katakura Y, Nagahisa K, Furusawa C, Shioya S, Shimizu H (2004) Analysis of DNA microarray data using self-organizing map and hierarchical clustering. In: Proceeding of Asia Pacific Confederation of Chemical Engineering (APPCHE04), Kitakyushu, pp 10
Zhu J, Zhang MQ (1999) SCPD: a promoter database of the yeast Saccharomyces cerevisiae. Bioinformatics 15:607–611
Acknowledgement
This work was performed as a project of the Development of Technological Infrastructure for Industrial Bioprocesses of the New Energy and Industrial Technology Development Organization (NEDO). This work was also supported in part by a grant of 21st Century Center of Excellence (COE) program and “Special Coordination Funds for Promoting Science and Technology: Yuragi project” from the Ministry of Education, Culture, Sports, Science and Technology of Japan.
Author information
Authors and Affiliations
Corresponding author
Rights and permissions
About this article
Cite this article
Pandey, G., Yoshikawa, K., Hirasawa, T. et al. Extracting the hidden features in saline osmotic tolerance in Saccharomyces cerevisiae from DNA microarray data using the self-organizing map: biosynthesis of amino acids. Appl Microbiol Biotechnol 75, 415–426 (2007). https://doi.org/10.1007/s00253-007-0837-8
Received:
Revised:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s00253-007-0837-8