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Applied Microbiology and Biotechnology

, Volume 63, Issue 3, pp 315–321 | Cite as

Hydrogenases in sulfate-reducing bacteria function as chromium reductase

  • B. Chardin
  • M.-T. Giudici-Orticoni
  • G. De Luca
  • B. Guigliarelli
  • M. Bruschi
Original Paper

Abstract

The ability of sulfate-reducing bacteria (SRB) to reduce chromate VI has been studied for possible application to the decontamination of polluted environments. Metal reduction can be achieved both chemically, by H2S produced by the bacteria, and enzymatically, by polyhemic cytochromes c 3. We demonstrate that, in addition to low potential polyheme c-type cytochromes, the ability to reduce chromate is widespread among [Fe], [NiFe], and [NiFeSe] hydrogenases isolated from SRB of the genera Desulfovibrio and Desulfomicrobium. Among them, the [Fe] hydrogenase from Desulfovibrio vulgaris strain Hildenborough reduces Cr(VI) with the highest rate. Both [Fe] and [NiFeSe] enzymes exhibit the same K m towards Cr(VI), suggesting that Cr(VI) reduction rates are directly correlated with hydrogen consumption rates. Electron paramagnetic resonance spectroscopy enabled us to probe the oxidation by Cr(VI) of the various metal centers in both [NiFe] and [Fe] hydrogenases. These experiments showed that Cr(VI) is reduced to paramagnetic Cr(III), and revealed inhibition of the enzyme at high Cr(VI) concentrations. The significant decrease of both hydrogenase and Cr(VI)-reductase activities in a mutant lacking [Fe] hydrogenase demonstrated the involvement of this enzyme in Cr(VI) reduction in vivo. Experiments with [3Fe-4S] ferredoxin from Desulfovibrio gigas demonstrated that the low redox [Fe-S] (non-heme iron) clusters are involved in the mechanism of metal reduction by hydrogenases.

Keywords

Electron Paramagnetic Resonance Electron Paramagnetic Resonance Spectrum Reductase Activity Electron Paramagnetic Resonance Signal Methylviologen 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Notes

Acknowledgements

This work was supported by grants from the Fifth Program for RTD (EVK1-CT 1999-00033), from 99N33/0010 ECODEV-CNRS, from ADEME (France), and from BRGM (France). B. Chardin gratefully acknowledges receipt of a CIFRE research studentship from S.E.I Environnement et Procédés Industriels. We also thank Dr. A. Dolla for providing DvH strain hyd 100, Dr. C. Michel for her involvement in this work and Dr. A. Cornish-Bowden for his critical reading of the manuscript.

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Copyright information

© Springer-Verlag 2003

Authors and Affiliations

  • B. Chardin
    • 1
  • M.-T. Giudici-Orticoni
    • 1
  • G. De Luca
    • 2
  • B. Guigliarelli
    • 1
  • M. Bruschi
    • 1
  1. 1.Unité de Bioénergétique et Ingénierie des Protéines, UPR 9036CNRS-IBSMMarseille cedex 20France
  2. 2.Laboratoire d'écologie microbienne de la rhizosphère, LEMiRCNRS-CEA DEVM DSVSt Paul le Durance CedexFrance

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