The emerging mycotoxin fusaproliferin is produced by Fusarium proliferatum and other related Fusarium species. Several fungi from other taxonomic groups were also reported to produce fusaproliferin or the deacetylated derivative, known as siccanol or terpestacin. Here, we describe the identification and functional characterization of the Fusarium proliferatum genes encoding the fusaproliferin biosynthetic enzymes: a terpenoid synthase, two cytochrome P450s, a FAD-oxidase and an acetyltransferase. With the exception of one gene encoding a CYP450 (FUP2, FPRN_05484), knock-out mutants of the candidate genes could be generated, and the production of fusaproliferin and intermediates was tested by LC-MS/MS. Inactivation of the FUP1 (FPRN_05485) terpenoid synthase gene led to complete loss of fusaproliferin production. Disruption of a putative FAD-oxidase (FUP4, FPRN_05486) did not only affect oxidation of preterpestacin III to terpestacin, but also of new side products (11-oxo-preterpstacin and terpestacin aldehyde). In the knock-out strains lacking the predicted acetyltransferase (FUP5, FPRN_05487) fusaproliferin was no longer formed, but terpestacin was found at elevated levels. A model for the biosynthesis of fusaproliferin and of novel derivatives found in mutants is presented.

Stable isotope-assisted approaches can improve untargeted liquid chromatography-high resolution mass spectrometry (LC-HRMS) metabolomics studies. Here, we demonstrate at the example of chemically stressed wheat that metabolome-wide internal standardization by globally 13C-labeled metabolite extract (GLMe-IS) of experimental-condition-matched biological samples can help to improve the detection of treatment-relevant metabolites and can aid in the post-acquisition assessment of putative matrix effects in samples obtained upon different treatments. For this, native extracts of toxin- and mock-treated (control) wheat ears were standardized by the addition of uniformly 13C-labeled wheat ear extracts that were cultivated under similar experimental conditions (toxin-treatment and control) and measured with LC-HRMS. The results show that 996 wheat-derived metabolites were detected with the non-condition-matched 13C-labeled metabolite extract, while another 68 were only covered by the experimental-condition-matched GLMe-IS. Additional testing is performed with the assumption that GLMe-IS enables compensation for matrix effects. Although on average no severe matrix differences between both experimental conditions were found, individual metabolites may be affected as is demonstrated by wrong decisions with respect to the classification of significantly altered metabolites. When GLMe-IS was applied to compensate for matrix effects, 272 metabolites showed significantly altered levels between treated and control samples, 42 of which would not have been classified as such without GLMe-IS.