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.

Comparative analyses of pathogen genomes provide new insights into how pathogens have evolved common and divergent virulence strategies to invade related plant species. Fusarium crown and root rots are important diseases of wheat and barley world-wide. In Australia, these diseases are primarily caused by the fungal pathogen Fusarium pseudograminearum. Comparative genomic analyses showed that the F. pseudograminearum genome encodes proteins that are present in other fungal pathogens of cereals but absent in non-cereal pathogens. In some cases, these cereal pathogen specific genes were also found in bacteria associated with plants. Phylogenetic analysis of selected F. pseudograminearum genes supported the hypothesis of horizontal gene transfer into diverse cereal pathogens. Two horizontally acquired genes with no previously known role in fungal pathogenesis were studied functionally via gene knockout methods and shown to significantly affect virulence of F. pseudograminearum on the cereal hosts wheat and barley. Our results indicate using comparative genomics to identify genes specific to pathogens of related hosts reveals novel virulence genes and illustrates the importance of horizontal gene transfer in the evolution of plant infecting fungal pathogens.

Climatic conditions were confirmed to be predominant factors influencing mycotoxigenic species composition and mycotoxin contaminations. However, NIV contamination was found to occur irrespective of climatic conditions, suggesting that it may often represent an under-estimated risk to be further investigated.