Extracting the hidden features in saline osmotic tolerance in Saccharomyces cerevisiae from DNA microarray data using the self-organizing map: biosynthesis of amino acids

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.

[1]  P. Attfield,et al.  Stress co-tolerance and trehalose content in baking strains of Saccharomyces cerevisiae , 1997, Journal of Industrial Microbiology and Biotechnology.

[2]  E. Heinzle,et al.  Quantification of intracellular amino acids in batch cultures of Saccharomyces cerevisiae , 2001, Applied Microbiology and Biotechnology.

[3]  H. Shimizu,et al.  Comparative analysis of transcriptional responses to saline stress in the laboratory and brewing strains of Saccharomyces cerevisiae with DNA microarray , 2006, Applied Microbiology and Biotechnology.

[4]  A. Blomberg,et al.  Amino acid uptake is strongly affected during exponential growth of Saccharomyces cerevisiae in 0.7 M NaCl medium. , 1998, FEMS microbiology letters.

[5]  A. Blomberg The Osmotic Hypersensitivity of the Yeast Saccharomyces cerevisiae is Strain and Growth Media Dependent: Quantitative Aspects of the Phenomenon , 1997, Yeast.

[6]  R. J. Cho,et al.  Candidate regulatory sequence elements for cell cycle-dependent transcription in Saccharomyces cerevisiae. , 1999, Genome research.

[7]  S. Colowick,et al.  Methods in Enzymology , Vol , 1966 .

[8]  E. Lander,et al.  Remodeling of yeast genome expression in response to environmental changes. , 2001, Molecular biology of the cell.

[9]  Fred Winston,et al.  Construction of a set of convenient saccharomyces cerevisiae strains that are isogenic to S288C , 1995, Yeast.

[10]  B. Bidlingmeyer,et al.  Rapid analysis of amino acids using pre-column derivatization. , 1984, Journal of chromatography.

[11]  Michael A. Beer,et al.  Predicting Gene Expression from Sequence , 2004, Cell.

[12]  Gerald R. Fink,et al.  Guide to yeast genetics and molecular biology , 1993 .

[13]  Hiroshi Shimizu,et al.  Analysis of DNA Microarray Data using Self-Organizing Map and Hierarchical Clustering , 2004 .

[14]  M. Bittner,et al.  Expression profiling using cDNA microarrays , 1999, Nature Genetics.

[15]  J. Mesirov,et al.  Interpreting patterns of gene expression with self-organizing maps: methods and application to hematopoietic differentiation. , 1999, Proceedings of the National Academy of Sciences of the United States of America.

[16]  G. Fink,et al.  Methods in enzymology vol 194 guide to yeast genetics and molecular biology , 1991 .

[17]  Paul V. Attfield,et al.  Stress tolerance: The key to effective strains of industrial baker's yeast , 1997, Nature Biotechnology.

[18]  A. Schmitt,et al.  Msn2p, a zinc finger DNA-binding protein, is the transcriptional activator of the multistress response in Saccharomyces cerevisiae. , 1996, Proceedings of the National Academy of Sciences of the United States of America.

[19]  A. Hinnebusch Translational regulation of GCN4 and the general amino acid control of yeast. , 2005, Annual review of microbiology.

[20]  R. Serrano,et al.  Proft Saccharomyces cerevisiae Transcription in Stress-RegulatedAntagonistically Modulate The Sko 1 p Repressor and Gcn 4 p Activator , 2000 .

[21]  D. Botstein,et al.  Genomic expression programs in the response of yeast cells to environmental changes. , 2000, Molecular biology of the cell.

[22]  Suteaki Shioya,et al.  Clustering gene expression pattern and extracting relationship in gene network based on artificial neural networks. , 2003, Journal of bioscience and bioengineering.

[23]  L. Dijkhuizen,et al.  Rapid identification of target genes for 3-methyl-1-butanol production in Saccharomyces cerevisiae , 2006, Applied Microbiology and Biotechnology.

[24]  Michael Q. Zhang,et al.  SCPD: a promoter database of the yeast Saccharomyces cerevisiae , 1999, Bioinform..

[25]  S. Dudoit,et al.  Normalization for cDNA microarray data: a robust composite method addressing single and multiple slide systematic variation. , 2002, Nucleic acids research.

[26]  A. Hinnebusch Evidence for translational regulation of the activator of general amino acid control in yeast. , 1984, Proceedings of the National Academy of Sciences of the United States of America.

[27]  Hubert Verachtert,et al.  Yeast: Biotechnology and Biocatalysis , 1989 .