EffectorO: Motif-Independent Prediction of Effectors in Oomycete Genomes Using Machine Learning and Lineage Specificity

Definition of the effector landscape across 13 phytoplasma proteomes with LEAPH and EffectorComb

'Candidatus Phytoplasma' genus, a group of fastidious phloem-restricted bacteria, can infect a wide variety of both ornamental and agro-economically important plants. Phytoplasmas secrete effector proteins responsible for the symptoms associated with the disease. Identifying and characterizing these proteins is of prime importance for expanding our knowledge of the molecular bases of the disease. We faced the challenge of identifying phytoplasma's effectors by developing LEAPH, a machine learning ensemble predictor composed of four models. LEAPH was trained on 479 proteins from 53 phytoplasma species, described by 30 features. LEAPH achieved 97.49% accuracy, 95.26% precision and 98.37% recall, ensuring a low false-positive rate and outperforming available state-of-the-art methods. The application of LEAPH to 13 phytoplasma proteomes yields a comprehensive landscape of 2089 putative pathogenicity proteins. We identified three classes according to different secretion models: 'classical', 'classical-like' and 'non-classical'. Importantly, LEAPH identified 15 out of 17 known experimentally validated effectors belonging to the three classes. Furthermore, to help the selection of novel candidates for biological validation, we applied the Self-Organizing Maps algorithm and developed a Shiny app called EffectorComb. LEAPH and the EffectorComb app can be used to boost the characterization of putative effectors at both computational and experimental levels, and can be employed in other phytopathological models.

ATR2C ala2 from Arabidopsis-infecting downy mildew requires 4 TIR-NLR immune receptors for full recognition

Arabidopsis Col-0 RPP2A and RPP2B confer recognition of Arabidopsis downy mildew (Hyaloperonospora arabidopsidis [Hpa]) isolate Cala2, but the identity of the recognized ATR2Cala2 effector was unknown. To reveal ATR2Cala2, an F2 population was generated from a cross between Hpa-Cala2 and Hpa-Noks1. We identified ATR2Cala2 as a non-canonical RxLR-type effector that carries a signal peptide, a dEER motif, and WY domains but no RxLR motif. Recognition of ATR2Cala2 and its effector function were verified by biolistic bombardment, ectopic expression and Hpa infection. ATR2Cala2 is recognized in accession Col-0 but not in Ler-0 in which RPP2A and RPP2B are absent. In ATR2Emoy2 and ATR2Noks1 alleles, a frameshift results in an early stop codon. RPP2A and RPP2B are essential for the recognition of ATR2Cala2. Stable and transient expression of ATR2Cala2 under 35S promoter in Arabidopsis and Nicotiana benthamiana enhances disease susceptibility. Two additional Col-0 TIR-NLR (TNL) genes (RPP2C and RPP2D) adjacent to RPP2A and RPP2B are quantitatively required for full resistance to Hpa-Cala2. We compared RPP2 haplotypes in multiple Arabidopsis accessions and showed that all four genes are present in all ATR2Cala2-recognizing accessions.

POOE: predicting oomycete effectors based on a pre-trained large protein language model

Oomycetes are fungus-like eukaryotic microorganisms which can cause catastrophic diseases in many plants. Successful infection of oomycetes depends highly on their effector proteins that are secreted into plant cells to subvert plant immunity. Thus, systematic identification of effectors from the oomycete proteomes remains an initial but crucial step in understanding plant-pathogen relationships. However, the number of experimentally identified oomycete effectors is still limited. Currently, only a few bioinformatics predictors exist to detect potential effectors, and their prediction performance needs to be improved. Here, we used the sequence embeddings from a pre-trained large protein language model (ProtTrans) as input and developed a support vector machine-based method called POOE for predicting oomycete effectors. POOE could achieve a highly accurate performance with an area under the precision-recall curve of 0.804 (area under the receiver operating characteristic curve = 0.893, accuracy = 0.874, precision = 0.777, recall = 0.684, and specificity = 0.936) in the fivefold cross-validation, considerably outperforming various combinations of popular machine learning algorithms and other commonly used sequence encoding schemes. A similar prediction performance was also observed in the independent test. Compared with the existing oomycete effector prediction methods, POOE provided very competitive and promising performance, suggesting that ProtTrans effectively captures rich protein semantic information and dramatically improves the prediction task. We anticipate that POOE can accelerate the identification of oomycete effectors and provide new hints to systematically understand the functional roles of effectors in plant-pathogen interactions. The web server of POOE is freely accessible at http://zzdlab.com/pooe/index.php. The corresponding source codes and data sets are also available at https://github.com/zzdlabzm/POOE.IMPORTANCEIn this work, we use the sequence representations from a pre-trained large protein language model (ProtTrans) as input and develop a Support Vector Machine-based method called POOE for predicting oomycete effectors. POOE could achieve a highly accurate performance in the independent test set, considerably outperforming existing oomycete effector prediction methods. We expect that this new bioinformatics tool will accelerate the identification of oomycete effectors and further guide the experimental efforts to interrogate the functional roles of effectors in plant-pathogen interaction.