Exploration of sulfur metabolism in the yeast Kluyveromyces lactis

  • Agnès Hébert
  • Marie-Pierre Forquin-Gomez
  • Aurélie Roux
  • Julie Aubert
  • Christophe Junot
  • Valentin Loux
  • Jean-François Heilier
  • Pascal Bonnarme
  • Jean-Marie Beckerich
  • Sophie Landaud
Genomics, transcriptomics, proteomics

Abstract

Hemiascomycetes are separated by considerable evolutionary distances and, as a consequence, the mechanisms involved in sulfur metabolism in the extensively studied yeast, Saccharomyces cerevisiae, could be different from those of other species of the phylum. This is the first time that a global view of sulfur metabolism is reported in the biotechnological yeast Kluyveromyces lactis. We used combined approaches based on transcriptome analysis, metabolome profiling, and analysis of volatile sulfur compounds (VSCs). A comparison between high and low sulfur source supplies, i.e., sulfate, methionine, or cystine, was carried out in order to identify key steps in the biosynthetic and catabolic pathways of the sulfur metabolism. We found that sulfur metabolism of K. lactis is mainly modulated by methionine. Furthermore, since sulfur assimilation is highly regulated, genes coding for numerous transporters, key enzymes involved in sulfate assimilation and the interconversion of cysteine to methionine pathways are repressed under conditions of high sulfur supply. Consequently, as highlighted by metabolomic results, intracellular pools of homocysteine and cysteine are maintained at very low concentrations, while the cystathionine pool is highly expandable. Moreover, our results suggest a new catabolic pathway for methionine to VSCs in this yeast: methionine is transaminated by the ARO8 gene product into 4-methylthio-oxobutyric acid (KMBA), which could be exported outside of the cell by the transporter encoded by PDR12 and demethiolated by a spontaneous reaction into methanethiol and its derivatives.

Keywords

Sulfur metabolism Volatile sulfur compounds Transcriptome Metabolome Kluyveromyces lactis 

Notes

Acknowledgments

This work was supported by the EcoMet program (ANR-06-PNRA-014) funded by the French National Research Agency (ANR). AH and MPF are grateful to the ANR (French National Research Agency: <http://www.agence-nationale-recherche.fr/>) for a PhD scholarship. We would also like to thank Armelle Delile, Roselyne Tâche, and Emmanuelle Rebours for their helpful technical assistance.

Supplementary material

253_2011_3481_MOESM1_ESM.pdf (30 kb)
ESM 1(PDF 30 kb)

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Copyright information

© Springer-Verlag 2011

Authors and Affiliations

  • Agnès Hébert
    • 1
    • 2
  • Marie-Pierre Forquin-Gomez
    • 2
  • Aurélie Roux
    • 3
  • Julie Aubert
    • 4
  • Christophe Junot
    • 3
  • Valentin Loux
    • 5
  • Jean-François Heilier
    • 6
  • Pascal Bonnarme
    • 2
  • Jean-Marie Beckerich
    • 1
  • Sophie Landaud
    • 2
    • 7
  1. 1.UMR MICALIS, INRAAgroParisTechThiverval GrignonFrance
  2. 2.INRAAgroParisTech, UMR 782 Génie et Microbiologie des Procédés Alimentaires, Centre de Biotechnologies Agro-IndustriellesThiverval-GrignonFrance
  3. 3.CEA, Service de Pharmacologie et d’ImmunoanalyseDSV/iBiTec-S, CEA/SaclayCedexFrance
  4. 4.UMR 518 Mathématiques et Informatiques AppliquéesAgroParisTech, INRAParis Cedex 05France
  5. 5.INRA, Unité Mathématique, Informatique et Génome UR1077Jouy-en-JosasFrance
  6. 6.Université Catholique de Louvain, Louvain Center for Toxicology and Applied Pharmacology (LTAP)BrusselsBelgium
  7. 7.INRA-AgroParisTechUMR 782 Génie et Microbiologie des Procédés Alimentaires, Centre de Biotechnologies Agro-IndustriellesThiverval-GrignonFrance

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