Infection assays in <i>Arabidopsis</i> reveal candidate effectors from the poplar rust fungus that promote susceptibility to bacteria and oomycete pathogens

Germain, Hugo; Joly, David L.; Mireault, Caroline; Plourde, Mélodie B.; Letanneur, Claire; Stewart, Donald J.; Morency, Marie-Josée; Pêtre, Benjamin; Duplessis, Sébastien; Séguin, Armand

doi:10.1111/mpp.12514

Cited by 56 publications

(76 citation statements)

References 42 publications

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“…Necrotrophic pathogens utilize many secreted PCWDEs to overcome the barrier of the plant cell wall. EffectorP predicts some secreted proteins with enzymatic domains as effectors, such as the Fusarium graminearum xylanase XylA, which has the ability to induce necrosis in wheat independent of its enzymatic activity (Table ) (Belien et al ., ; Sella et al ., ; Sperschneider et al ., ). However, EffectorP has been trained on effectors that predominantly lack recognizable functional domains and interfere with host processes in different ways from PCWDEs which act on the plant cell wall.…”

Section: Resultsmentioning

confidence: 99%

Improved prediction of fungal effector proteins from secretomes with EffectorP 2.0

Sperschneider

Dodds

Gardiner

et al. 2018

Molecular Plant Pathology

363

335

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Plant-pathogenic fungi secrete effector proteins to facilitate infection. We describe extensive improvements to EffectorP, the first machine learning classifier for fungal effector prediction. EffectorP 2.0 is now trained on a larger set of effectors and utilizes a different approach based on an ensemble of classifiers trained on different subsets of negative data, offering different views on classification. EffectorP 2.0 achieves an accuracy of 89%, compared with 82% for EffectorP 1.0 and 59.8% for a small size classifier. Important features for effector prediction appear to be protein size, protein net charge as well as the amino acids serine and cysteine. EffectorP 2.0 decreases the number of predicted effectors in secretomes of fungal plant symbionts and saprophytes by 40% when compared with EffectorP 1.0. However, EffectorP 1.0 retains value, and combining EffectorP 1.0 and 2.0 results in a stringent classifier with a low false positive rate of 9%. EffectorP 2.0 predicts significant enrichments of effectors in 12 of 13 sets of infection-induced proteins from diverse fungal pathogens, whereas a small cysteine-rich classifier detects enrichment in only seven of 13. EffectorP 2.0 will fast track the prioritization of high-confidence effector candidates for functional validation and aid in improving our understanding of effector biology. EffectorP 2.0 is available at http://effectorp.csiro.au.

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Section: Resultsmentioning

confidence: 99%

Improved prediction of fungal effector proteins from secretomes with EffectorP 2.0

Sperschneider

Dodds

Gardiner

et al. 2018

Molecular Plant Pathology

363

335

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“…We collected 13 gene sets from the literature that were labelled as containing effector candidates based on their induction during infection as well as other criteria (Table 4). For example, a study by Germain et al (2016) identified 16 candidate effectors from 1,184 small, secreted Melampsora larici-populina proteins. These 16 candidates were selected based on their expression in a haustoria-specific cDNA library and the transcriptome of laser microdissected, rust-infected poplar leaves as well as their small size of less than 300 aas.…”

Section: Resultsmentioning

confidence: 99%

Improved prediction of fungal effector proteins from secretomes with EffectorP 2.0

Sperschneider

Dodds

Gardiner

et al. 2018

Preprint

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4Plant-pathogenic fungi secrete effector proteins to facilitate infection. We describe extensive improvements to 1 5EffectorP, the first machine learning classifier for fungal effector prediction. EffectorP 2.0 is now trained on a larger 1 6 set of effectors and utilizes a different approach based on an ensemble of classifiers trained on different subsets of 1 7 negative data, offering different views on classification. EffectorP 2.0 achieves accuracy of 89%, compared to 82% for 1 8 EffectorP 1.0 and 59.8% for a small size classifier. Important features for effector prediction appear to be protein size, 1 9protein net charge as well as the amino acids serine and cysteine. EffectorP 2.0 decreases the number of predicted 2 0 effectors in secretomes of fungal plant symbionts and saprophytes by 40% when compared to EffectorP 1.0. However, 2 1 EffectorP 1.0 retains value and combining EffectorP 1.0 and 2.0 results in a stringent classifier with low false positive 2 2 rate of 9%. EffectorP 2.0 predicts significant enrichments of effectors in 12 out of 13 sets of infection-induced 2 3proteins from diverse fungal pathogens, whereas a small cysteine-rich classifier detects enrichment only in 7 out of 13. 2 4 EffectorP 2.0 will fast-track prioritization of high-confidence effector candidates for functional validation and aid in 2 5 improving our understanding of effector biology. EffectorP 2.0 is available at http://effectorp.csiro.au. 2 6

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“…This gene is part of a Melampsoraceae family and other members were shown to possess a transit peptide to target chloroplasts in plant cells. Sixteen other M. larici-populina candidates were assayed in A. thaliana through stable expression of GFP fusion to determine their subcellular localization (Germain et al, 2017). Three candidates showed an accumulation in plasmodesmata, chloroplasts and cytoplasmic bodies, respectively; 11 promoted Hyaloperonospora arabidopsidis growth in planta, and five promoted P. syringae growth in planta.…”

Section: Effectoromics Of the Poplar Rust Fungus M Larici-populinamentioning

confidence: 99%

“…Three candidates showed an accumulation in plasmodesmata, chloroplasts and cytoplasmic bodies, respectively; 11 promoted Hyaloperonospora arabidopsidis growth in planta, and five promoted P. syringae growth in planta. Three candidates promoted both oomycete and bacterial growth and may be considered as virulence factors (Germain et al, 2017). Investigations conducted in two different heterologous systems highlighted 11 poplar-rust candidate effectors with specific localization and/or plant protein interactors (Table 1).…”

Section: Effectoromics Of the Poplar Rust Fungus M Larici-populinamentioning

confidence: 99%

“…Maia et al (2017) Plant cell death suppression in coffee leaves 30 1 Melampsora larici-populinaPetre et al (2015Petre et al ( , 2016a Localization and CoIP/MS a of CSEPs in Nicotiana benthamiana 20 8Germain et al (2017) Infection assays and localization of CSEPs in Arabidopsis 16 3 Melampsora liniZhang et al (2016) Recombinant protein production and structural analysis 4 4 Phakopsora pachyrihziQi et al (2018) Localization and plant cell death suppression in N. benthamiana, Arabidopsis and yeast, immune response in tomato and pepper 82 17 de Carvalho et al (2017) Localization and plant cell death suppression in N. benthamiana 6 6 Kunjeti et al (2016) Plant cell death suppression in N. benthamiana 6 2 Puccinia graminis f. sp. tritici Yin et al (2015) HIGS a to select proteins related to pathogenesis 76 10 Upadhyaya et al (2014) HIGS to select proteins related to pathogenesis 25 1 Ramachandran et al (2017) Plant cell death suppression in N. benthamiana 11 2 Chen et al (2017) Coexpression with R gene in N. benthamiana 41 1 Puccinia striiformis f. sp.…”

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Advances in understanding obligate biotrophy in rust fungi

et al. 2019

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Summary Rust fungi (Pucciniales) are the largest group of plant pathogens and represent one of the most devastating threats to agricultural crops worldwide. Despite the economic importance of these highly specialized pathogens, many aspects of their biology remain obscure, largely because rust fungi are obligate biotrophs. The rise of genomics and advances in high‐throughput sequencing technology have presented new options for identifying candidate effector genes involved in pathogenicity mechanisms of rust fungi. Transcriptome analysis and integrated bioinformatics tools have led to the identification of key genetic determinants of host susceptibility to infection by rusts. Thousands of genes encoding secreted proteins highly expressed during host infection have been reported for different rust species, which represents significant potential towards understanding rust effector function. Recent high‐throughput in planta expression screen approaches (effectoromics) have pushed the field ahead even further towards predicting high‐priority effectors and identifying avirulence genes. These new insights into rust effector biology promise to inform future research and spur the development of effective and sustainable strategies for managing rust diseases.

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Infection assays in Arabidopsis reveal candidate effectors from the poplar rust fungus that promote susceptibility to bacteria and oomycete pathogens

Cited by 56 publications

References 42 publications

Improved prediction of fungal effector proteins from secretomes with EffectorP 2.0

Improved prediction of fungal effector proteins from secretomes with EffectorP 2.0

Improved prediction of fungal effector proteins from secretomes with EffectorP 2.0

Advances in understanding obligate biotrophy in rust fungi

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