Analysis of aspergillus flavus transcriptome expressed during stressing growth conditions (oxidative stress and hypoxia) and consequential aflatoxin b1 synthesis

Aspergillus flavus is a cosmopolitan, saprotrophic and pathogenic fungus with a diverse host range, although it is mainly associated to cereal grains, legumes, and tree nuts. A. flavus is able to grow on several substrates exploiting its distinctive, enormous production of conidia. Infections may occur in the field, but often show no symptoms until postharvest storage and/or transport. In fact, it is during postharvest phase that ideal conditions of growth usually take place. Furthermore, many A. flavus strains produce significant quantities of aflatoxins, in particular Aflatoxin B1, which is regarded as the most carcinogenic metabolite synthesized in nature. Several environmental conditions are responsible of inducing aflatoxin synthesis, but it is oxidative stress in particular that has been identified as the main event capable of causing a major boost in toxin production. To better understand how the different conditions may influence aflatoxin biosynthesis, and the metabolic patterns responsible, we used RNA-Seq technology to profile the A. flavus transcriptome under different stressing events. Oxidative stress has been induced by addition of menadione 0,1mM to the colture medium. We also investigated transcriptome expression during hypoxia, as hypoxic stress is a condition that a fungus thriving on a substrate usually undergoes. Twelve cDNA libraries were prepared, according to the Illumina protocol, representing the transcriptome of A. flavus during hypoxic and normoxic environment, and separately during presence or absence of oxidative stress. All the experiments were conducted three different time points (24, 48 and 96 h). Sequencing produced 9-19 million reads (50 bp each) per library, 94% of which was above the quality score 30. The raw sequence data were processed, filtered and normalized using the Illumina pipeline to generate fast-q files. All reads were mapped to A. flavus coding sequences using commercially available software, to calculate the RPKM (Reads Per Kilobase of transcript per Million mapped reads) for each clustered gene. Multiple gene lists were generated corresponding to different RPKM-based comparisons between stress conditions and sampling times, ultimately enabling the identification of new and known differentially expressed genes. Protein interaction networks were then built. Fifteen genes were selected for differential expression validation by Real-time PCR. In conclusion, with the high resolution and sensitivity afforded by RNA-Seq, we were able to get remarkable insight into the regulatory processes of aflatoxin biosynthesis in A. flavus.