Toxic Metal/Metalloid Tolerance in Fungi—A Biotechnology-Oriented Approach

  • István Pócsi


This review aims at the biotechnological evaluation of the wealth of data accumulated in the last decade on the molecular background of the toxic metal/metalloid tolerance of fungi. Yeast-based models are highly applicable when metal/metalloid transport and compartmentalization processes are mapped in other fungal species or higher eukaryotes but this approach has limitations, which necessitates further fungal models evolutionarily closer to heavy metal exposed fungal taxons. In terms of biotechnology, the most promising targets in the genetic engineering of metal/metalloid tolerant fungi include (i) increased secretion of extracellular metal chelators , (ii) elimination of metal transporters facilitating the uptake of toxic metals/metalloids, (iii) overexpression of transporters pumping metals and/or their complexes out of the cells or into cellular organelles, (iv) overproduction of intracellular metal chelators , (v) overproduction of elements of the antioxidative defense system, (vi) genetic modification of the regulatory network of metal/metalloid stress defense, and (vii) interfering with the metal/metalloid-dependent initialization of apoptotic cell death. Owing to the wide-spread application of robust ‘-omics’ technologies, the biotechnologically exploitable data including potential future targets for genetic manipulation are accumulating fast. In contrast, today’s genetic modifications often result in unforeseeable or even paradox phenotypes in this field, which clearly indicates that a deeper understanding of the underlying molecular mechanisms of fungal toxic metal/metalloid tolerance is needed to improve the biotechnological performance of the genetically modified strains.


Arbuscular Mycorrhizal Fungus Heavy Metal Tolerance Pullulan Production Chronological Life Span Pichia Guilliermondii 
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.



The author is indebted to Dr. M. J. Tamás, University of Gothenburg, for reading critically the manuscript.


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© Springer Science+Business Media B.V. 2011

Authors and Affiliations

  1. 1.Department of Microbial Biotechnology and Cell BiologyUniversity of DebrecenDebrecenHungary

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