Comparison of Fusarium graminearum Transcriptomes on Living or Dead Wheat Differentiates Substrate-Responsive and Defense-Responsive Genes - Boedi, Stefan; Berger, Harald; Sieber, Christian; Münsterkötter, Martin; Maloku, Imer; Warth, Benedikt; Sulyok, Michael; Lemmens, Marc; Schuhmacher, Rainer; Güldener, Ulrich; Strauss, Joseph (Frontiers in Microbiology, 2016)
Fusarium graminearum is an opportunistic pathogen of cereals where it causes severe yield losses and concomitant mycotoxin contamination of the grains. The pathogen has mixed biotrophic and necrotrophic (saprophytic) growth phases during infection and the regulatory networks associated with these phases have so far always been analyzed together. In this study we compared the transcriptomes of fungal cells infecting a living, actively defending plant representing the mixed live style (pathogenic growth on living flowering wheat heads) to the response of the fungus infecting identical, but dead plant tissues (cold-killed flowering wheat heads) representing strictly saprophytic conditions. We found that the living plant actively suppressed fungal growth and promoted much higher toxin production in comparison to the identical plant tissue without metabolism suggesting that molecules signaling secondary metabolite induction are not pre-existing or not stable in the plant in sufficient amounts before infection. Differential gene expression analysis was used to define gene sets responding to the active or the passive plant as main impact factor and driver for gene expression. We correlated our results to the published F. graminearum transcriptomes, proteomes and secretomes and found that only a limited number of in planta- expressed genes require the living plant for induction but the majority uses simply the plant tissue as signal. Many secondary metabolite (SM) gene clusters show a heterogeneous expression pattern within the cluster indicating that different genetic or epigenetic signals govern the expression of individual genes within a physically linked cluster. Our bioinformatic approach also identified fungal genes which were actively repressed by signals derived from the active plant and may thus represent direct targets of the plant defense against the invading pathogen.