Mutational Analysis of Endoxylanases XylA and XylB from the Phytopathogen
            <i>Fusarium graminearum</i>
            Reveals Comprehensive Insights into Their Inhibitor Insensitivity

Beliën, Tim; Campenhout, Steven Van; Acker, Maarten Van; Robben, Johan; Courtin, Christophe M.; Delcour, Jan A.; Volckaert, Guido

doi:10.1128/aem.00442-07

Cited by 29 publications

(14 citation statements)

References 41 publications

(38 reference statements)

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“…Diversifying selection at interaction surfaces between enzyme and their inhibitors are common in plant-pathogen interactions (reviewed in Misas-Villamil and . Similar diversifying residues at the interaction surfaces have been reported for xylanase, polygalacturonase, and glucanase inhibiting proteins and their target enzymes (Stotz et al, 2000;Bishop et al, 2005;Belië n et al, 2007;Raiola et al, 2008).…”

Section: Discussionsupporting

confidence: 72%

Fungal Effector Protein AVR2 Targets Diversifying Defense-Related Cys Proteases of Tomato

et al. 2008

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The interaction between the fungal pathogen Cladosporium fulvum and its host tomato (Solanum lycopersicum) is an ideal model to study suppression of extracellular host defenses by pathogens. Secretion of protease inhibitor AVR2 by C. fulvum during infection suggests that tomato papain-like cysteine proteases (PLCPs) are part of the tomato defense response. We show that the tomato apoplast contains a remarkable diversity of PLCP activities with seven PLCPs that fall into four different subfamilies. Of these PLCPs, transcription of only PIP1 and RCR3 is induced by treatment with benzothiadiazole, which triggers the salicylic acid-regulated defense pathway. Sequencing of PLCP alleles of tomato relatives revealed that only PIP1 and RCR3 are under strong diversifying selection, resulting in variant residues around the substrate binding groove. The doubled number of variant residues in RCR3 suggests that RCR3 is under additional adaptive selection, probably to prevent autoimmune responses. AVR2 selectively inhibits only PIP1 and RCR3, and one of the naturally occurring variant residues in RCR3 affects AVR2 inhibition. The higher accumulation of PIP1 protein levels compared with RCR3 indicates that PIP1 might be the real virulence target of AVR2 and that RCR3 acts as a decoy for AVR2 perception in plants carrying the Cf-2 resistance gene.

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Section: Discussionsupporting

confidence: 72%

Fungal Effector Protein AVR2 Targets Diversifying Defense-Related Cys Proteases of Tomato

et al. 2008

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“…Thus, the negative training set includes both undiscovered effectors and non‐effectors, and therefore poses an unlabelled data classification problem. Although Naïve Bayes classifiers are fairly robust to unlabelled data classification and can tolerate noisy data (Bing et al ., ), other machine learning classifiers might not be able to learn effectively from such sets. To improve predictions, we collected three different subsets of negative training data that are less likely to contain positive instances, i.e.…”

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

364

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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“…XIP-I is an efficient inhibitor of Botrytis XynBc1 GH11 xylanase [12 ] but it cannot inhibit XylA and XylB GH11 xylanases of F. graminearum [11 ]. Mutagenesis revealed that the absence of inhibition was because of amino acid adaptations in the 'thumb' structural region [21 ]. For example, a V151T mutation in XylA restores inhibition by XIP-I by the formation of one additional hydrogen bond with XIP-I [21 ].…”

Section: Wheat Inhibitor Xip-i Targets Fungal Gh11 and Gh10 Xylanasesmentioning

confidence: 99%

“…For example, a V151T mutation in XylA restores inhibition by XIP-I by the formation of one additional hydrogen bond with XIP-I [21 ]. Notably, amino acid variations in this 'thumb' region are common to GH11 xylanases of plant pathogens, suggesting that adaptations in this region are a frequent strategy to prevent XIP-I inhibition [21 ].…”

Section: Wheat Inhibitor Xip-i Targets Fungal Gh11 and Gh10 Xylanasesmentioning

confidence: 99%

Enzyme–inhibitor interactions at the plant–pathogen interface

Misas-Villamil

Hoorn

2008

Current Opinion in Plant Biology

120

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The plant apoplast during plant-pathogen interactions is an ancient battleground that holds an intriguing range of attacking enzymes and counteracting inhibitors. Examples are pathogen xylanases and polygalacturonases that are inhibited by plant proteins like TAXI, XIP, and PGIP; and plant glucanases and proteases, which are targeted by pathogen proteins such as GIP1, EPI1, EPIC2B, and AVR2. These seven well-characterized inhibitors have different modes of action and many probably evolved from inactive enzymes themselves. Detailed studies of the structures, sequence variation, and mutated proteins uncovered molecular struggles between these enzymes and their inhibitors, resulting in positive selection for variant residues at the contact surface, where single residues determine the outcome of the interaction. Introduction Extracellular plant-pathogen interactions probably existed long before the evolution of pathogen effector translocation systems and plant resistance (R) genes. The molecular basis of these interactions is mostly undiscovered but some have been investigated in detail and reveal intriguing mechanisms. Here we will highlight major recent findings of extracellular enzyme-inhibitor interactions at the plant-pathogen interface.Although extracellular plant-pathogen interactions are complex, they can be simplified by assuming that they evolved in several stages (Figure 1). First, micro-organisms became pathogens by attacking plants using cell-wall-degrading enzymes and other hydrolases ( Figure 1A). In response to this attack, plants secrete inhibitors that suppress these hydrolases ( Figure 1B). Initially, these inhibitors were probably constitutively produced, but upon evolution of pathogen recognition systems the production and secretion of these proteins became inducible, becoming part of the arsenal of pathogenesis-related (PR) proteins. Besides suppression of pathogen attack, counter attack mechanisms also evolved in plants through the induced secretion of hydrolytic enzymes ( Figure 1C). Examples are the well-studied PR proteins including endo-b-1,3-glucanases (PR-2), chitinases (PR-3), and proteases (PR-7) [1 ]. Pathogens, in turn, responded to this counter attack by producing inhibitors that suppress these enzymes ( Figure 1D). The fifth and latest step was a sophisticated refinement of the pathogen recognition system by the evolution of R genes that recognize the manipulation of plant targets by pathogens, inducing a severe defense response that includes cell death ( Figure 1E). Aspects of this simplified model are consistent with the 'zigzag' model for the plant immune system, which explains the suppression of basal defense responses by pathogen effector proteins, followed by the evolution of efficient effector recognition by R proteins [2].Antagonistic interactions between organisms at the molecular level result in enzymes that evade inhibition, and inhibitors that adapt to these new enzymes. These 'molecular struggles' result in positive selection for variation of residues at the interactio...

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Mutational Analysis of Endoxylanases XylA and XylB from the Phytopathogen Fusarium graminearum Reveals Comprehensive Insights into Their Inhibitor Insensitivity

Cited by 29 publications

References 41 publications

Fungal Effector Protein AVR2 Targets Diversifying Defense-Related Cys Proteases of Tomato

Fungal Effector Protein AVR2 Targets Diversifying Defense-Related Cys Proteases of Tomato

Improved prediction of fungal effector proteins from secretomes with EffectorP 2.0

Enzyme–inhibitor interactions at the plant–pathogen interface

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