4.1 Article

Fumonisins and their modified forms, a matter of concern in future scenario?

Journal

WORLD MYCOTOXIN JOURNAL
Volume 9, Issue 5, Pages 727-739

Publisher

WAGENINGEN ACADEMIC PUBLISHERS
DOI: 10.3920/WMJ2016.2058

Keywords

hidden fumonisins; Fusarium verticillioides; maize; climate change

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Masked mycotoxins are found in grains and derived foods as a result of plant phase II metabolism. Recently, masked mycotoxins senso strictu, together with other covalently or non-covalently conjugated forms, even formed upon processing, have been classified as modified mycotoxins. In this context, the issue of modified fumonisins is of great interest, on account of the wide range of factors affecting their formation and accumulation in maize pre- and postharvest. Fumonisins, indeed, may undergo modification in plants, along the growing season, but also during storage and drying of maize kernels, and upon processing. All these modifications strongly affect the analytical outcome, thus making more difficult the assessment of maize compliance. Since the ratio between free and modified fumonisins is affected by maize composition and environmental factors, a deeper knowledge on the phenomena driving the production and accumulation of free and modified forms in plants may support the selection of resistant hybrids. This review provides a critical picture of the state of the art on this topic, mainly focusing on those events occurring in field, identified as crucial in determining amount and partitioning of contamination. Nevertheless, knowledge on modified fumonisins is still in its dawn, on account of the wide range of factors involved. Anyway, reported results, taking altogether, clearly indicate that modified fumonisins should be included in the monitoring plans to have an overview of the possible contribution to human exposure. Furthermore, next efforts should focus on the events occurring in field and on the cross-talk between the plant and the fungus, to support the identification of resistant hybrids and to provide data for predictive models, the most suitable tool to forecast what is going to happens in the future changing climate.

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