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Research Article

Discrimination of Scedosporium prolificans against Pseudallescheria boydii and Scedosporium apiospermum by semiautomated repetitive sequence-based PCR

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Pages 475-483 | Received 24 Aug 2010, Accepted 09 Nov 2010, Published online: 26 Nov 2010
 

Abstract

The laboratory identification of Pseudallescheria and Scedosporium isolates at the species level is important for clinical and epidemiological purposes. This study used semiautomated repetitive sequence-based polymerase chain reaction (rep-PCR) to identify Pseudallescheria/Scedosporium. Reference strains of Pseudallescheria boydii (n = 12), Scedosporium prolificans (n = 8), Scedosporium apiospermum (n = 9), and clinical/environmental isolates (P. boydii, 7; S. prolificans, 7; S. apiospermum, 7) were analyzed by rep-PCR. All clinical isolates were identified by morphological and phenotypic characteristics and by sequence analysis. Species identification of reference strains was based on the results of available databases. Rep-PCR studies were also conducted with various molds to differentiate Pseudallescheria/Scedosporium spp. from other commonly encountered filamentous fungi. All tested Pseudallescheria/Scedosporium isolates were distinguishable from the other filamentous fungi. All Scedosporium prolificans strains clustered within the cutoff of 85%, and species identification by rep-PCR showed an agreement of 100% with sequence analysis. However, several isolates of P. boydii and S. apiospermum did not cluster within the 85% cutoff with the same species by rep-PCR. Although the identification of P. boydii and S. apiospermum was not correct, the semiautomated rep-PCR system is a promising tool for the identification of S. prolificans isolates.

Acknowledgment

We thank Regina Horre from the Federal Institute for Drugs and Medical Devices (BfArM), Bonn, Germany for providing reference strains.

Declaration of interest: The authors report no conflicts of interest. The authors alone are responsible for the content and writing of the paper.

This paper was first published online on Early Online on 30 November 2010.

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