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The objective of this work was to use the random amplification of the polymorphic DNA-polymerase chain reaction (RAPD-PCR) technique to select polymorphic patterns through qualitative and quantitative analyses to differentiate the species A. flavus, A. fumigatus, A. niger and A. tubingensis. Twenty-seven Aspergillus isolates from different species were typified using phenotypic (macro- and micromorphology) and genotypic (partial BenA gene sequencing) methods. Thirty-four primers were used to obtain polymorphic patterns, and with these a qualitative analysis was performed to select the primers that presented species-specific patterns to distinguish each species. For the quantitative selection, a database was built from the polymorphic patterns and used for the construction of logistic regression models; later, the model that presented the highest value of sensitivity against specificity was evaluated through ROC curves. The qualitative selection showed that the primers OPA-19, P54, 1253 and OPA-02 could differentiate the species. A quantitative analysis was carried out through logistic regression, whereby a species-specific correlation of sensitivity and specificity greater than 90% was obtained for the primers: OPC-06 with a 96.32% match to A. flavus; OPF-01 with a 100% match to A. fumigatus; OPG-13 with a 98.01% match to A. tubingensis; and OPF-07 with a 99.71% match to A. niger. The primer OPF-01 discriminated the four species as well as closely related species. The quantitative methods using the selected primers allowed discrimination between species and showed their usefulness for genotyping some of the species of medical relevance belonging to the genus Aspergillus.

Citation

Omar E Valencia-Ledezma, Carlos A Castro-Fuentes, Esperanza Duarte-Escalante, María Guadalupe Frías-De-León, María Del Rocío Reyes-Montes. Selection of Polymorphic Patterns Obtained by RAPD-PCR through Qualitative and Quantitative Analyses to Differentiate Aspergillus fumigatus. Journal of fungi (Basel, Switzerland). 2022 Mar 13;8(3)


PMID: 35330298

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