4.7 Article

Diversity of black Aspergilli isolated from raisins in Argentina: Polyphasic approach to species identification and development of SCAR markers for Aspergillus ibericus

Journal

INTERNATIONAL JOURNAL OF FOOD MICROBIOLOGY
Volume 210, Issue -, Pages 92-101

Publisher

ELSEVIER SCIENCE BV
DOI: 10.1016/j.ijfoodmicro.2015.05.025

Keywords

Raisins; Aspergillus section Nigri; Polyphasic identification; Ochratoxin A; ISSR; SCAR-PCR marker; A. ibericus

Funding

  1. Consejo Nacional de Investigaciones Cientificas y Tecnicas (CONICET) [PIP 11220080101753]
  2. SECyT-UNRC(PPI)
  3. Guillermo Giaj Merlera studies at Universidade Federal Rural de Rio de Janeiro, Brazil

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Aspergillus section Nigri is a heterogeneous fungal group including some ochratoxin A producer species that usually contaminate raisins. The section contains the Series Carbonaria which includes the toxigenic species Aspergillus carbonarius and nontoxigenic Aspergillus ibericus that are phenotypically undistinguishable. The aim of this study was to examine the diversity of black aspergilli isolated from raisins and to develop a specific genetic marker to distinguish A. ibericus from A. carbonarius. The species most frequently found in raisins in this study were Aspergillus tubingensis (35.4%) and A. carbonarius (32.3%), followed by Aspergillus luchuensis (10.7%), Aspergillus japonicus (7.7%), Aspergillus niger (6.2%), Aspergillus welwitschiae (4.6%) and A. ibericus (3.1%). Based on inter-simple sequence repeat (ISSR) fingerprinting profiles of major Aspergillus section Nigri members, a sequence-characterized amplified region (SCAR) marker was identified. Primers were designed based on the conserved regions of the SCAR marker and were utilized in a PCR for simultaneous identification of A. carbonarius and A. ibericus. The detection level of the SCAR-PCR was found to be 0.01 ng of purified DNA The present SCAR-PCR is rapid and less cumbersome than conventional identification techniques and could be a supplementary strategy and a reliable tool for high-throughput sample analysis. (C) 2015 Elsevier B.V. All rights reserved.

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