CATAStrophy! Is your plant-pathogenic fungus what you think it is?

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The biotroph-necrotroph-hemibiotroph division for classification of the trophic phenotypes of fungal plant-pathogens has been extremely useful, but also has a few issues. We have just published a predictor of trophic phenotype called CATAStrophy, which is available for download here and is also part of a recent special issue on “bioinformatics applications in plant pathology” in Frontiers in Microbiology.

CATAStrophy uses CAZyme gene content as the basis for its predictions. We have created novel trophic classes more in line with the CAZyme-based dataset that we use for prediction, but conceptially these are roughly equivalent to the traditional trophic terms as shown below:
trophicclasses

Rare Ortholog effector candidates added

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Fungal effectors do not often exhibit conserved sequence similarity, however some effectors are only present a small number of species, which may be distantly related.  A typical example of this is the ToxA effector which is found by orthology in only a handful of species (Pyrenophora tritici-repentis, Parastagonospora nodorum, and Bipolaris maydis). By predicting rare orthologous groups (ROGs) (with reciprocal best blastp hits), potential LGT events, and effector-like ROGs can be predicted (see menu above “ROG Predictions”).

The image above shows the taxonomic locations and frequency of effector-like ROGs produced by this study.

If you use this data cite this URL and check back here soon for upcoming publication details.

 

Orthology was predicted with BLASTP and ProteinOrtho.  Additional filters (signalP, EffectorP) were applied to predicted LGTs.

BLASTP – Altschul, S.F., Gish, W., Miller, W., Myers, E.W. and Lipman, D.J., 1990. Basic local alignment search tool. Journal of molecular biology, 215(3), pp.403-410.
ProteinOrtho – Lechner, M., Findeiß, S., Steiner, L., Marz, M., Stadler, P.F. and Prohaska, S.J., 2011. Proteinortho: detection of (co-) orthologs in large-scale analysis. BMC bioinformatics, 12(1), p.1.
signalP – Petersen, T.N., Brunak, S., von Heijne, G. and Nielsen, H., 2011. SignalP 4.0: discriminating signal peptides from transmembrane regions. Nature methods, 8(10), pp.785-786.
EffectorP – Sperschneider, J., Gardiner, D.M., Dodds, P.N., Tini, F., Covarelli, L., Singh, K.B., Manners, J.M. and Taylor, J.M., 2015. EffectorP: predicting fungal effector proteins from secretomes using machine learning. New Phytologist.