Home LiteratureArticle Details
PMID: 17408508 Published · epublish English Journal Article Research Support, Non-U.S. Gov't

Effect of carbon source perturbations on transcriptional regulation of metabolic fluxes in Saccharomyces cerevisiae.

BMC systems biology ·Vol. 1 ·2007-03-27 ·Pages 18

Cakir T, Kirdar B, Onsan ZI, Ulgen KO, Nielsen J

Abstract

Control effective flux (CEF) of a reaction is the weighted sum of all fluxes through that reaction, derived from elementary flux modes (EFM) of a metabolic network. Change in CEFs under different environmental conditions has earlier been proven to be correlated with the corresponding changes in the transcriptome. Here we use this to investigate the degree of transcriptional regulation of fluxes in the metabolism of Saccharomyces cerevisiae. We do this by quantifying correlations between changes in CEFs and changes in transcript levels for shifts in carbon source, i.e. between the fermentative carbon source glucose and nonfermentative carbon sources like ethanol, acetate, and lactate. The CEF analysis is based on a simple stoichiometric model that includes reactions of the central carbon metabolism and the amino acid metabolism. The effect of the carbon shift on the metabolic fluxes was investigated for both batch and chemostat cultures. For growth on glucose in batch (respiro-fermentative) cultures, EFMs with no by-product formation were removed from the analysis of the CEFs, whereas those including any by-products (ethanol, glycerol, acetate, succinate) were omitted in the analysis of growth on glucose in chemostat (respiratory) cultures. This resulted in improved correlations between CEF changes and transcript levels. A regression correlation coefficient of 0.60 was obtained between CEF changes and gene expression changes in the central carbon metabolism for the analysis of 5 different perturbations. Out of 45 data points there were no more than 6 data points deviating from the correlation. Additionally, up- or down-regulation of at least 75% of the genes were in qualitative agreement with the CEF changes for all perturbations studied. The analysis indicates that changes in carbon source are associated with a high degree of hierarchical regulation of metabolic fluxes in the central carbon metabolism as the change in fluxes are correlating directly with the change in transcript levels of genes encoding their corresponding enzymes. For amino acid biosynthesis there was, however, not found to exist a similar correlation, and this may point to either post-transcriptional and/or metabolic regulation, or be due to the absence of a direct perturbation on the amino acid pathways in these experiments.

MeSH Terms
Acetic Acid/metabolism,pharmacology Carbon/metabolism,pharmacology Ethanol/metabolism,pharmacology Fermentation/genetics Gene Expression Regulation, Fungal Glucose/metabolism,pharmacology Lactic Acid/metabolism,pharmacology Metabolic Networks and Pathways/drug effects,genetics RNA, Messenger/metabolism Saccharomyces cerevisiae/drug effects,genetics,metabolism Transcription, Genetic/drug effects
Chemicals
RNA, Messenger Lactic Acid Ethanol Carbon Glucose Acetic Acid
Authors & Affiliations
5 authors, click to expand affiliations / ORCID
Cakir Tunahan
Boğaziçi University, Department of Chemical Engineering, 34342, Bebek, Istanbul, Turkey. [email protected]
Kirdar Betül
Onsan Z Ilsen
Ulgen Kutlu O
Nielsen Jens
References (35)
35 references, click to expand
  1. A general definition of metabolic pathways useful for systematic organization and analysis of complex metabolic networks.
    Nat Biotechnol. 2000 Mar;18(3):326-32 PMID: 10700151
  2. Integration of metabolome data with metabolic networks reveals reporter reactions.
    Mol Syst Biol. 2006;2:50 PMID: 17016516
  3. Genomic expression programs in the response of yeast cells to environmental changes.
    Mol Biol Cell. 2000 Dec;11(12):4241-57 PMID: 11102521
  4. Network identification and flux quantification in the central metabolism of Saccharomyces cerevisiae under different conditions of glucose repression.
    J Bacteriol. 2001 Feb;183(4):1441-51 PMID: 11157958
  5. A MILP-based flux alternative generation and NMR experimental design strategy for metabolic engineering.
    Metab Eng. 2001 Apr;3(2):124-37 PMID: 11289789
  6. Transcriptome meets metabolome: hierarchical and metabolic regulation of the glycolytic pathway.
    FEBS Lett. 2001 Jul 6;500(3):169-71 PMID: 11445079
  7. Visualizing plant metabolomic correlation networks using clique-metabolite matrices.
    Bioinformatics. 2001 Dec;17(12):1198-208 PMID: 11751228
  8. Genetic dissection of transcriptional regulation in budding yeast.
    Science. 2002 Apr 26;296(5568):752-5 PMID: 11923494
  9. Combinatorial complexity of pathway analysis in metabolic networks.
    Mol Biol Rep. 2002;29(1-2):233-6 PMID: 12241063
  10. Reproducibility of oligonucleotide microarray transcriptome analyses. An interlaboratory comparison using chemostat cultures of Saccharomyces cerevisiae.
    J Biol Chem. 2002 Oct 4;277(40):37001-8 PMID: 12121991
  11. The Ume6 regulon coordinates metabolic and meiotic gene expression in yeast.
    Proc Natl Acad Sci U S A. 2002 Oct 15;99(21):13431-6 PMID: 12370439
  12. Metabolic balance sheets.
    Nature. 2002 Nov 14;420(6912):129-30 PMID: 12432369
  13. Metabolic network structure determines key aspects of functionality and regulation.
    Nature. 2002 Nov 14;420(6912):190-3 PMID: 12432396
  14. FluxAnalyzer: exploring structure, pathways, and flux distributions in metabolic networks on interactive flux maps.
    Bioinformatics. 2003 Jan 22;19(2):261-9 PMID: 12538248
  15. Genome-scale reconstruction of the Saccharomyces cerevisiae metabolic network.
    Genome Res. 2003 Feb;13(2):244-53 PMID: 12566402
  16. Population genetic variation in genome-wide gene expression.
    Mol Biol Evol. 2003 Jun;20(6):955-63 PMID: 12716989
  17. Saccharomyces cerevisiae phenotypes can be predicted by using constraint-based analysis of a genome-scale reconstructed metabolic network.
    Proc Natl Acad Sci U S A. 2003 Nov 11;100(23):13134-9 PMID: 14578455
  18. It is all about metabolic fluxes.
    J Bacteriol. 2003 Dec;185(24):7031-5 PMID: 14645261
  19. The effects of alternate optimal solutions in constraint-based genome-scale metabolic models.
    Metab Eng. 2003 Oct;5(4):264-76 PMID: 14642354
  20. Ady2p is essential for the acetate permease activity in the yeast Saccharomyces cerevisiae.
    Yeast. 2004 Feb;21(3):201-10 PMID: 14968426
  21. Role of transcriptional regulation in controlling fluxes in central carbon metabolism of Saccharomyces cerevisiae. A chemostat culture study.
    J Biol Chem. 2004 Mar 5;279(10):9125-38 PMID: 14630934
  22. In-depth profiling of lysine-producing Corynebacterium glutamicum by combined analysis of the transcriptome, metabolome, and fluxome.
    J Bacteriol. 2004 Mar;186(6):1769-84 PMID: 14996808
  23. Metabolic pathway analysis of yeast strengthens the bridge between transcriptomics and metabolic networks.
    Biotechnol Bioeng. 2004 May 5;86(3):251-60 PMID: 15083505
  24. Differential metabolic networks unravel the effects of silent plant phenotypes.
    Proc Natl Acad Sci U S A. 2004 May 18;101(20):7809-14 PMID: 15136733
  25. Integrative analysis of the mitochondrial proteome in yeast.
    PLoS Biol. 2004 Jun;2(6):e160 PMID: 15208715
  26. The principle of flux minimization and its application to estimate stationary fluxes in metabolic networks.
    Eur J Biochem. 2004 Jul;271(14):2905-22 PMID: 15233787
  27. Exploring the metabolic and genetic control of gene expression on a genomic scale.
    Science. 1997 Oct 24;278(5338):680-6 PMID: 9381177
  28. Systematic changes in gene expression patterns following adaptive evolution in yeast.
    Proc Natl Acad Sci U S A. 1999 Aug 17;96(17):9721-6 PMID: 10449761
  29. Metabolic pathway analysis of enzyme-deficient human red blood cells.
    Biosystems. 2004 Dec;78(1-3):49-67 PMID: 15555758
  30. Comparative proteome analysis of Saccharomyces cerevisiae grown in chemostat cultures limited for glucose or ethanol.
    Mol Cell Proteomics. 2005 Jan;4(1):1-11 PMID: 15502163
  31. Quantitative analysis of wine yeast gene expression profiles under winemaking conditions.
    Yeast. 2005 Apr 15;22(5):369-83 PMID: 15806604
  32. Prolonged selection in aerobic, glucose-limited chemostat cultures of Saccharomyces cerevisiae causes a partial loss of glycolytic capacity.
    Microbiology. 2005 May;151(Pt 5):1657-69 PMID: 15870473
  33. YANA - a software tool for analyzing flux modes, gene-expression and enzyme activities.
    BMC Bioinformatics. 2005;6:135 PMID: 15929789
  34. Functional stoichiometric analysis of metabolic networks.
    Bioinformatics. 2005 Nov 15;21(22):4176-80 PMID: 16188931
  35. Gene expression profiling by DNA microarrays and metabolic fluxes in Escherichia coli.
    Biotechnol Prog. 2000 Mar-Apr;16(2):278-86 PMID: 10753455
Article Info
Journal
BMC systems biology
Abbr.
BMC Syst Biol
ISSN
1752-0509
Published
2007-03-27
Epub
2007-00-27
Pages
18
Language
English
Region
England
NLM ID
101301827
PMCID
PMC1855933
Subset
IM
Analysis Services
Analysis Services

Contact

No. 2 Wenbo Road, Zhangqiu District, Jinan, Shandong

Qilu Normal University · Genelibs Bioinformatics Lab

750 Shunhua Rd, Jinan

2F, Bldg F, University Science Park

Tel: 0531-88819269

WeChat Official Account

Follow our WeChat subscription account for real-time updates and the latest in medical and biological research.


Business Email

E-mail: [email protected]