A Tool for Reliable Detection of Aflatoxin Biosynthetic Gene Clusters in Aflatoxigenic and Atoxigenic Aspergillus flavus Isolates
Mitema, A.; Feto, N. A.; Okoth, S.; Rafudeen, M. S.
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Molecular techniques and phenotypic characterisation have been used to differentiate aflatoxigenic and atoxigenic Aspergillus flavus strains. However, there is a lack of a consistent and reliable tool for discrimination between these strains of A. flavus. Here we report, an optimised real-time qPCR-based tool for reliable differentiation between aflatoxigenic and atoxigenic strains of A. flavus. Accordingly, expression profiles and deletion patterns of genes responsible for aflatoxin production in five representative aflatoxigenic and atoxigenic A. flavus strains (KSM012, KSM014, HB021, HB026 and HB027) were examined using the optimised real-time qPCR tool. We observed that under induced conditions, aflP, aflS, aflR and aflO transcripts were the most upregulated genes across the tested isolates while aflS and aflO were always expressed in both induced and uninduced isolates. However, aflR and aflP did not give clear distinctions between non-toxin and toxin producing isolates. The deletion patterns were prominent for aflD and aflR whereas alfO, aflS and aflP had no deletions among the isolates. Significant variation in transcript abundance for aflD, aflR and aflS were observed for aflatoxigenic isolate KSM014 under induced and uninduced states. False detection of aflD gene transcript in atoxigenic strain KSM012 was evident in both induced and uninduced conditions. With the exception of KSM012, aflP gene did not exhibit significant variation in expression in the isolates between induced and uninduced conditions. One-way ANOVA and Post-test analysis for linear trends revealed that aflatoxin biosynthetic cluster genes show significant (P < 0.05) differences between atoxigenic and aflatoxigenic isolates. Our optimized qPCR-based tool reliably discriminated between aflatoxigenic and atoxigenic A. flavus isolates and could complement existing detection methods.
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