SummaryEukaryotic filamentous plant pathogens secrete effector proteins that modulate the host cell to facilitate infection. Computational effector candidate identification and subsequent functional characterization delivers valuable insights into plant-pathogen interactions. However, effector prediction in fungi has been challenging due to a lack of unifying sequence features such as conserved N-terminal sequence motifs. Fungal effectors are commonly predicted from secretomes based on criteria such as small size and cysteine-rich, which suffers from poor accuracy.We present EFFECTORP which pioneers the application of machine learning to fungal effector prediction.EFFECTORP improves fungal effector prediction from secretomes based on a robust signal of sequence-derived properties, achieving sensitivity and specificity of over 80%. Features that discriminate fungal effectors from secreted noneffectors are predominantly sequence length, molecular weight and protein net charge, as well as cysteine, serine and tryptophan content. We demonstrate that EFFECTORP is powerful when combined with in planta expression data for predicting high-priority effector candidates.EFFECTORP is the first prediction program for fungal effectors based on machine learning. Our findings will facilitate functional fungal effector studies and improve our understanding of effectors in plant-pathogen interactions. EFFECTORP is available at http://effectorp. csiro.au.

In this study, durum wheat kernels harvested in three climatically different Italian cultivation areas (Emilia Romagna, Umbria and Sardinia) in 2015, were analyzed with a combination of different isolation methods to determine their fungal communities, with a focus on Fusarium head blight (FHB) complex composition, and to detect fungal secondary metabolites in the grains. The genus Alternaria was the main component of durum wheat mycobiota in all investigated regions, with the Central Italian cultivation area showing the highest incidence of this fungal genus and of its secondary metabolites. Fusarium was the second most prevalent genus of the fungal community in all cultivation environments, even if regional differences in species composition were detected. In particular, Northern areas showed the highest Fusarium incidence, followed by Central and then Southern cultivation areas. Focusing on the FHB complex, a predominance of Fusarium poae, in particular in Northern and Central cultivation areas, was found. Fusarium graminearum, in the analyzed year, was mainly detected in Emilia Romagna. Because of the highest Fusarium incidence, durum wheat harvested in the Northern cultivation area showed the highest presence of Fusarium secondary metabolites. These results show that durum wheat cultivated in Northern Italy may be subject to a higher FHB infection risk and to Fusarium mycotoxins accumulation.

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A study was carried out on 43 malting barley samples collected in 2013 across the Umbria region (central Italy) to determine the incidence of the principal mycotoxigenic fungal genera, to identify the Fusarium species isolated from the grains, and to detect the presence of 34 fungal secondary metabolites by liquid chromatography-high-resolution mass spectrometry. The multimycotoxin-method development involved the evaluation of both a two-step solvent and QuEChERS protocol for metabolite extraction. The former protocol was selected because of better accuracy, which was evaluated on the basis of spike-recovery experiments. The most frequently isolated fungal species belonged to the genera Alternaria and Fusarium. The predominant Fusarium species was F. avenaceum, followed by F. graminearum. HT-2 toxin was the most frequently detected mycotoxin, followed by enniatin B, enniatin B1, T-2 toxin, and nivalenol. As a consequence of the observed mixed fungal infections, mycotoxin co-occurrence was also detected. A combination of mycological and mycotoxin analyses allowed the ability to obtain comprehensive information about the presence of mycotoxigenic fungi and their contaminants in malting barley cultivated in a specific geographic area.