A systems approach to model the relationship between aflatoxin gene cluster expression, environmental factors, growth and toxin production by Aspergillus flavus.

A microarray analysis was used to examine the effect of combinations of water activity (a(w), 0.995-0.90) and temperature (20-42°C) on the activation of aflatoxin biosynthetic genes (30 genes) in Aspergillus flavus grown on a conducive YES (20 g yeast extract, 150 g sucrose, 1 g MgSO(4)·7H(2)O) medium. The relative expression of 10 key genes (aflF, aflD, aflE, aflM, aflO, aflP, aflQ, aflX, aflR and aflS) in the biosynthetic pathway was examined in relation to different environmental factors and phenotypic aflatoxin B(1) (AFB(1)) production. These data, plus data on relative growth rates and AFB(1) production under different a(w) × temperature conditions were used to develop a mixed-growth-associated product formation model. The gene expression data were normalized and then used as a linear combination of the data for all 10 genes and combined with the physical model. This was used to relate gene expression to a(w) and temperature conditions to predict AFB(1) production. The relationship between the observed AFB(1) production provided a good linear regression fit to the predicted production based in the model. The model was then validated by examining datasets outside the model fitting conditions used (37°C, 40°C and different a(w) levels). The relationship between structural genes (aflD, aflM) in the biosynthetic pathway and the regulatory genes (aflS, aflJ) was examined in relation to a(w) and temperature by developing ternary diagrams of relative expression. These findings are important in developing a more integrated systems approach by combining gene expression, ecophysiological influences and growth data to predict mycotoxin production. This could help in developing a more targeted approach to develop prevention strategies to control such carcinogenic natural metabolites that are prevalent in many staple food products. The model could also be used to predict the impact of climate change on toxin production.