Structural phylogenetics identifies conserved effector families and cell death-inducing proteins from Cercospora zeina
Research highlight
Gray leaf spot, caused by the fungus Cercospora zeina, is a major threat to maize production across Africa. To successfully infect maize, C. zeina releases virulence proteins called "effectors" that disable plant defences and allow the disease to spread.
Historically, identifying these effector proteins has been exceptionally difficult. Fungi evolve rapidly, meaning two effectors might perform similar functions even if their genetic codes look completely different - making standard amino acid sequence comparisons ineffective.
A new study published in Molecular Plant-Microbe Interactions, solved this problem by looking at shape instead of sequence. Using advanced structural modelling tools like AlphaFold, the team predicted the 3D physical shapes of C. zeina effectors. They discovered that while the amino acid sequences had changed, the overall 3D structural "folds" remained remarkably conserved.
By matching these structural shapes to known proteins in other fungal species, the researchers identified key C. zeina effectors that trigger plant cell death. Testing these proteins confirmed their predicted functions in model plants.
This shape-based approach gives scientists a powerful framework to pinpoint the fungus's most critical virulence weapons, opening new avenues for breeding disease-resistant maize crops.
Abstract
Cercospora zeina causes gray leaf spot disease on maize in Africa. As a foliar fungal pathogen, C. zeina relies on effectors to facilitate successful infection and colonization of its host. Predicting effector function is therefore essential for understanding the molecular mechanisms underlying this plant–pathogen interaction. Although effector functions are typically predicted using sequence-based analyses, many effectors are subject to strong selection pressure and exhibit high sequence diversity, reducing the accuracy of such predictions. In this study, a structural phylogenetics approach was employed to identify effector structural families in C. zeina and to assign putative functions to C. zeina candidate secreted effector proteins based on their structural clustering with functionally characterized effectors from related phytopathogens. Twenty-two effector structural families were identified, several of which contained effectors shown to induce cell death in Nicotiana benthamiana. It was therefore hypothesized that C. zeina effectors belonging to these structural families would exhibit similar functions. Three families containing cell death–inducing effectors ZtCDI1, MgNLP, and VdPEVD1, with high structural similarity and low sequence identity to C. zeina effectors, were selected for biological validation. Transient expression of C. zeina effectors from these families induced cell death in N. benthamiana. These C. zeina structural analogs were expressed in either the latent (CDI1-like) or necrotrophic (NLP-like, PevD1-like) phase of gray leaf spot disease in maize. This work shows that structural phylogenetics can be employed to predict effector function and identify structurally related, cell death–inducing effectors with divergent amino acid sequences in C. zeina.
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