Multiplex PCR assay to detect Aspergillus, Penicillium and Fusarium species simultaneously

ABSTRACT

A wide variety of mycotoxins is produced by mycotoxigenic fungi and naturally contaminates food and feed products worldwide. Synergistic effects of multi-toxins are potentially more harmful than exposure to a single compound and can induce acute and chronic toxicity to animals and humans. The aim of the present study is to timely and simultaneously identify the multiple mycotoxigenic fungi capable of causing synergistic toxicity to improve the safety level of food and feedstuff. Here, a multiplex polymerase chain reaction assay was developed for simultaneous detection of mycotoxigenic fungi belonging to the genera Aspergillus, Fusarium and Penicillium. Three pairs of genus-specific primers were designed based on internal transcribed spacer (ITS) sequences of Aspergillus and Penicillium, and Elongation factor 1 alpha (EF- 1α) of Fusarium. Amplicons of 170, 750 and 490 bp respectively for the corresponding primer pairs were detected; thus amplicon length is diagnostic for the individual fungal genus. The sensitivity of the developed method was tested with genomic DNA obtained from mould pure cultures and artificially contaminated maize grain powder. The sensitivity result showed that spore concentrations in the contaminated maize grain powder of 10² spores/mL were detected without prior incubation. This result suggests that the developed mPCR assay would allow a rapid, specific and simultaneous detection of various mycotoxigenic potential fungi based on the occurrence and size of the amplification products and thus to estimate the multi-mycotoxins contamination potential in food and feedstuff.

KEYWORDS:

• Multiplex PCR
• simultaneous detection
• internal transcribed spacer
• elongation factor 1 alpha

Acknowledgements

We are grateful to those researchers who helped us in the completion of this research work.

Author contributions

Conceptualization, Peiwu Li; Data curation, Hamid ur Rahman and Xianfeng Ren; Formal analysis, Wen Zhang; Funding acquisition, Qi Zhang; Investigation, Xiaofeng Yue and Wen Zhang; Methodology, Xiaofeng Yue; Project administration, Peiwu Li; Resources, Qi Zhang and Peiwu Li; Software, Hamid ur Rahman and Xiaofeng Yue; Supervision, Qi Zhang and Peiwu Li; Validation, Qi Zhang; Visualization, Xianfeng Ren and Peiwu Li; Writing – original draft, Hamid ur Rahman; Writing – review and editing, Hamid ur Rahman.

Conflicts of interest

The authors declare no competing and conflict of interest.

Additional information

Funding

This work was supported by the national key research and development plan (2017YFC1601202, 2017YFC1601205), Natural Science Foundation of China (31701726, 31860473), and The Major Project of Hubei Provincial Technical Innovation (2018ABA081).