Molecular Plant PathologyBiochemistryPeer-reviewed journal article
Zymoseptoria tritici effectors structurally related to killer proteins UmV-KP4 and UmV-KP6 are toxic to fungi, and define extended protein families in fungi
Karine Montet de GuilleniD, Léa MammriiD, Jérôme GracyiD, André PadillaiD, Philippe BartheiD, François HohiD, Mounia LahfaiD, Justine Rouffet, Yohann PetitiD, Thomas KrojiD, Marc‐Henri LebruniD
Abstract
Fungal effectors play crucial roles in plant infection. Despite low sequence identity, effectors were recently classified into structural families. In this study, we have elucidated the structures of Zt-NIP1 and Mycgr3-91409 effectors of the wheat fungal pathogen Zymoseptoria tritici, using X-ray crystallography and NMR. These effectors displayed a structural homology with, respectively, KP4 and KP6 killer toxins, from UmV dsRNA viruses infecting the corn fungal pathogen Ustilago maydis. Consequently, Zt-NIP1 and Mycgr3-91409 were renamed Zt-KP4-1 and Zt-KP6-1. Orthologs and paralogs of Zt-KP4-1 and Zt-KP6-1 were identified in Zymoseptoria, but not in other fungi, except Ecp2 effectors related to Zt-KP4-1. Assessment of the biological activities of Zt-KP6-1 and Zt-KP4-1 revealed their toxicity to fungi such as Botrytis cinerea and Z. tritici, but not to wheat. A novel pipeline relying on Foldseek and cysteine-pattern constrained HMM searches of AlphaFold2 predicted structures from Uniprot generated a comprehensive inventory of KP4 and KP6 proteins in fungi and plants. A structure-based classification of these proteins revealed four KP4 and three KP6 structural super families. This classification provided far-reaching hypotheses on their biological function and evolution. This unifying structural framework highlights the power of structure determination for the classification of effectors, and their functional investigation.
- organism: fungi (other)
- assay: protein structure
- research focus: cell type annotation
- research focus: clustering
- research focus: gene regulatory network inference
- approach: experimental assay
- approach: deep learning
- approach: statistical model
- Citations
- 11
- FWCI
- 5.78
- Field percentile
- top 3%
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