Integrating gene expression, ecology and mycotoxin production by Fusarium and Aspergillus species in relation to interacting environmental factors

Environmental factors, such as water availability (water activity, a(w)), temperature and their interactions, have a significant impact on the life cycle of mycotoxigenic fungi. Growth and mycotoxin production are influenced by these interacting factors resulting in a broader range of a(w) x temperature conditions for germination, than growth or mycotoxin production. The biosynthetic genes are mostly clustered together and by using microarrays with subarrays for specific mycotoxins, such as trichothecenes, fumonisins and aflatoxins it has been possible to examine the relationship between interacting aw x temperature conditions on growth, toxin gene cluster expression and relate these to phenotypic toxin production. The data for groups of biosynthetic genes (Fusarium culmorum/Fusarium graminearum; Fusarium verticillioides; Aspergillus flavus) were integrated with data on growth and mycotoxin production under different a(w) x temperature conditions using a mixed growth model. This was used to correlate these factors and predict toxin levels which may be produced under different abiotic stress conditions. Indeed, the relative importance of the different genes could be examined using ternary diagrams of the relative expression of 3 genes at a time in relation to a(w), temperature and mycotoxin production to identify the most important relationships. The effect of three-way interacting environmental factors representative of climate change (CC) scenarios (water stress x temperature (+ 2-4 degrees C) x elevated CO2 (350-400 vs 650 and 1000 ppm) on growth and mycotoxin production by A. flavus and by species of the Aspergillus section Circumdati and section Nigri have been determined. These studies on maize grain and coffee, respectively, suggest that while growth may not be significantly affected, mycotoxin production may be stimulated by CC factors. This approach to integrate such data sets and model the relationships could be a powerful tool for predicting the relative toxin production under extreme stress conditions, including CC scenarios.