Fungal Effector Proteins Research Articles

The Molecular Docking of MAX Fungal Effectors with Plant HMA Domain-Binding Proteins.

Fungal effector proteins are important in mediating disease infections in agriculturally important crops. These secreted small proteins are known to interact with their respective host receptor binding partners in the host, either inside the cells or in the apoplastic space, depending on the localisation of the effector proteins. Consequently, it is important to understand the interactions between fungal effector proteins and their target host receptor binding partners, particularly since this can be used for the selection of potential plant resistance or susceptibility-related proteins that can be applied to the breeding of new cultivars with disease resistance. In this study, molecular docking simulations were used to characterise protein-protein interactions between effector and plant receptors. Benchmarking was undertaken using available experimental structures of effector-host receptor complexes to optimise simulation parameters, which were then used to predict the structures and mediating interactions of effector proteins with host receptor binding partners that have not yet been characterised experimentally. Rigid docking was applied for both the so-called bound and unbound docking of MAX effectors with plant HMA domain protein partners. All bound complexes used for benchmarking were correctly predicted, with 84% being ranked as the top docking pose using the ZDOCK scoring function. In the case of unbound complexes, a minimum of 95% of known residues were predicted to be part of the interacting interface on the host receptor binding partner, and at least 87% of known residues were predicted to be part of the interacting interface on the effector protein. Hydrophobic interactions were found to dominate the formation of effector-plant protein complexes. An optimised set of docking parameters based on the use of ZDOCK and ZRANK scoring functions were established to enable the prediction of near-native docking poses involving different binding interfaces on plant HMA domain proteins. Whilst this study was limited by the availability of the experimentally determined complexed structures of effectors and host receptor binding partners, we demonstrated the potential of molecular docking simulations to predict the likely interactions between effectors and their respective host receptor binding partners. This computational approach may accelerate the process of the discovery of putative interacting plant partners of effector proteins and contribute to effector-assisted marker discovery, thereby supporting the breeding of disease-resistant crops.

Suppression of Chitin-Triggered Immunity by Plant Fungal Pathogens: A Case Study of the Cucurbit Powdery Mildew Fungus Podosphaera xanthii.

Fungal pathogens are significant plant-destroying microorganisms that present an increasing threat to the world's crop production. Chitin is a crucial component of fungal cell walls and a conserved MAMP (microbe-associated molecular pattern) that can be recognized by specific plant receptors, activating chitin-triggered immunity. The molecular mechanisms underlying the perception of chitin by specific receptors are well known in plants such as rice and Arabidopsis thaliana and are believed to function similarly in many other plants. To become a plant pathogen, fungi have to suppress the activation of chitin-triggered immunity. Therefore, fungal pathogens have evolved various strategies, such as prevention of chitin digestion or interference with plant chitin receptors or chitin signaling, which involve the secretion of fungal proteins in most cases. Since chitin immunity is a very effective defensive response, these fungal mechanisms are believed to work in close coordination. In this review, we first provide an overview of the current understanding of chitin-triggered immune signaling and the fungal proteins developed for its suppression. Second, as an example, we discuss the mechanisms operating in fungal biotrophs such as powdery mildew fungi, particularly in the model species Podosphaera xanthii, the main causal agent of powdery mildew in cucurbits. The key role of fungal effector proteins involved in the modification, degradation, or sequestration of immunogenic chitin oligomers is discussed in the context of fungal pathogenesis and the promotion of powdery mildew disease. Finally, the use of this fundamental knowledge for the development of intervention strategies against powdery mildew fungi is also discussed.

Ab Initio Modelling of the Structure of ToxA-like and MAX Fungal Effector Proteins

Pathogenic fungal diseases in crops are mediated by the release of effector proteins that facilitate infection. Characterising the structure of these fungal effectors is vital to understanding their virulence mechanisms and interactions with their hosts, which is crucial in the breeding of plant cultivars for disease resistance. Several effectors have been identified and validated experimentally; however, their lack of sequence conservation often impedes the identification and prediction of their structure using sequence similarity approaches. Structural similarity has, nonetheless, been observed within fungal effector protein families, creating interest in validating the use of computational methods to predict their tertiary structure from their sequence. We used Rosetta ab initio modelling to predict the structures of members of the ToxA-like and MAX effector families for which experimental structures are known to validate this method. An optimised approach was then used to predict the structures of phenotypically validated effectors lacking known structures. Rosetta was found to successfully predict the structure of fungal effectors in the ToxA-like and MAX families, as well as phenotypically validated but structurally unconfirmed effector sequences. Interestingly, potential new effector structural families were identified on the basis of comparisons with structural homologues and the identification of associated protein domains.

Template-Based Modelling of the Structure of Fungal Effector Proteins

The discovery of new fungal effector proteins is necessary to enable the screening of cultivars for disease resistance. Sequence-based bioinformatics methods have been used for this purpose, but only a limited number of functional effector proteins have been successfully predicted and subsequently validated experimentally. A significant obstacle is that many fungal effector proteins discovered so far lack sequence similarity or conserved sequence motifs. The availability of experimentally determined three-dimensional (3D) structures of a number of effector proteins has recently highlighted structural similarities amongst groups of sequence-dissimilar fungal effectors, enabling the search for similar structural folds amongst effector sequence candidates. We have applied template-based modelling to predict the 3D structures of candidate effector sequences obtained from bioinformatics predictions and the PHI-BASE database. Structural matches were found not only with ToxA- and MAX-like effector candidates but also with non-fungal effector-like proteins—including plant defensins and animal venoms—suggesting the broad conservation of ancestral structural folds amongst cytotoxic peptides from a diverse range of distant species. Accurate modelling of fungal effectors were achieved using RaptorX. The utility of predicted structures of effector proteins lies in the prediction of their interactions with plant receptors through molecular docking, which will improve the understanding of effector–plant interactions.

Genome and Transcriptome Sequence Resources and Effector Repertoire of Pythium myriotylum Drechsler.

Genome and Transcriptome Sequence Resources and Effector Repertoire of Pythium myriotylum Drechsler.

Transgenic Soybeans Expressing Phosphatidylinositol-3-Phosphate-Binding Proteins Show Enhanced Resistance Against the Oomycete Pathogen Phytophthora sojae

Oomycete and fungal pathogens cause billions of dollars of damage to crops worldwide annually. Therefore, there remains a need for broad-spectrum resistance genes, especially ones that target pathogens but do not interfere with colonization by beneficial microbes. Motivated by evidence suggesting that phosphatidylinositol-3-phosphate (PI3P) may be involved in the delivery of some oomycete and fungal virulence effector proteins, we created stable transgenic soybean plants that express and secrete two different PI3P-binding proteins, GmPH1 and VAM7, in an effort to interfere with effector delivery and confer resistance. Soybean plants expressing the two PI3P-binding proteins exhibited reduced infection by the oomycete pathogen Phytophthora sojae compared to control lines. Measurements of nodulation by nitrogen-fixing mutualistic bacterium Bradyrhizobium japonicum, which does not produce PI3P, revealed that the two lines with the highest levels of GmPH1 transcripts exhibited reductions in nodulation and in benefits from nodulation. Transcriptome and plant hormone measurements were made of soybean lines with the highest transcript levels of GmPH1 and VAM7, as well as controls, following P. sojae- or mock-inoculation. The results revealed increased levels of infection-associated transcripts in the transgenic lines, compared to controls, even prior to P. sojae infection, suggesting that the plants were primed for increased defense. The lines with reduced nodulation exhibited elevated levels of jasmonate-isoleucine and of transcripts of a JAR1 ortholog encoding jasmonate-isoleucine synthetase. However, lines expressing VAM7 transgenes exhibited normal nodulation and no increases in jasmonate-isoleucine. Overall, together with previously published data from cacao and from P. sojae transformants, the data suggest that secretion of PI3P-binding proteins may confer disease resistance through a variety of mechanisms.

Effector-GAN: prediction of fungal effector proteins based on pretrained deep representation learning methods and generative adversarial networks.

Phytopathogenic fungi secrete effector proteins to subvert host defenses and facilitate infection. Systematic analysis and prediction of candidate fungal effector proteins are crucial for experimental validation and biological control of plant disease. However, two problems are still considered intractable to be solved in fungal effector prediction: one is the high-level diversity in effector sequences that increases the difficulty of protein feature learning, and the other is the class imbalance between effector and non-effector samples in the training dataset. In our study, pretrained deep representation learning methods are presented to represent multiple characteristics of sequences for predicting fungal effectors and generative adversarial networks are adapted to create synthetic feature samples to address the data imbalance problem. Compared with the state-of-the-art fungal effector prediction methods, Effector-GAN shows an overall improvement in accuracy in the independent test set. Effector-GAN offers a user-friendly interface to inspect potential fungal effector proteins (http://lab.malab.cn/~wys/webserver/Effector-GAN). The Python script can be downloaded from http://lab.malab.cn/~wys/gitlab/effector-gan. Supplementary data are available at Bioinformatics online.

Pro-domain processing of fungal effector proteins from plant pathogens.

Pro-domain processing of fungal effector proteins from plant pathogens.

An automated and combinative method for the predictive ranking of candidate effector proteins of fungal plant pathogens

Fungal plant-pathogens promote infection of their hosts through the release of ‘effectors’—a broad class of cytotoxic or virulence-promoting molecules. Effectors may be recognised by resistance or sensitivity receptors in the host, which can determine disease outcomes. Accurate prediction of effectors remains a major challenge in plant pathology, but if achieved will facilitate rapid improvements to host disease resistance. This study presents a novel tool and pipeline for the ranking of predicted effector candidates—Predector—which interfaces with multiple software tools and methods, aggregates disparate features that are relevant to fungal effector proteins, and applies a pairwise learning to rank approach. Predector outperformed a typical combination of secretion and effector prediction methods in terms of ranking performance when applied to a curated set of confirmed effectors derived from multiple species. We present Predector (https://github.com/ccdmb/predector) as a useful tool for the ranking of predicted effector candidates, which also aggregates and reports additional supporting information relevant to effector and secretome prediction in a simple, efficient, and reproducible manner.

Structure-function analysis reveals Trichoderma virens Tsp1 to be a novel fungal effector protein modulating plant defence

Trichoderma virens colonizes roots and develops a symbiotic relationship with plants where the fungal partner derives nutrients from plants and offers defence, in return. Tsp1, a small secreted cysteine-rich protein, was earlier found to be upregulated in co-cultivation of T. virens with maize roots. Tsp1 is well conserved in Ascomycota division of fungi, but none of its homologs have been studied yet. We have expressed and purified recombinant Tsp1, and resolved its structure to 1.25 Å resolutions, from two crystal forms, using Se-SAD methods. The Tsp1 adopts a β barrel fold and forms dimer in structure as well as in solution form. DALI based structure analysis revealed the structure similarity with two known fungal effector proteins: Alt a1 and PevD1. Structure and evolutionary analysis suggested that Tsp1 belongs to a novel effector protein family. Tsp1 acted as an inducer of salicylic acid mediated susceptibility in plants, rendering maize plants more susceptible to a necrotrophic pathogen Cochliobolus heterostrophus, as observed using plant defence assay and RT-qPCR analysis.

Remote homology clustering identifies lowly conserved families of effector proteins in plant-pathogenic fungi.

Plant diseases caused by fungal pathogens are typically initiated by molecular interactions between ‘effector’ molecules released by a pathogen and receptor molecules on or within the plant host cell. In many cases these effector-receptor interactions directly determine host resistance or susceptibility. The search for fungal effector proteins is a developing area in fungal-plant pathology, with more than 165 distinct confirmed fungal effector proteins in the public domain. For a small number of these, novel effectors can be rapidly discovered across multiple fungal species through the identification of known effector homologues. However, many have no detectable homology by standard sequence-based search methods. This study employs a novel comparison method (RemEff) that is capable of identifying protein families with greater sensitivity than traditional homology-inference methods, leveraging a growing pool of confirmed fungal effector data to enable the prediction of novel fungal effector candidates by protein family association. Resources relating to the RemEff method and data used in this study are available from https://figshare.com/projects/Effector_protein_remote_homology/87965.

Genetic response to nitrogen starvation in the aggressive Eucalyptus foliar pathogen Teratosphaeria destructans.

Teratosphaeria destructans is one of the most aggressive foliar pathogens of Eucalyptus. The biological factors underpinning T. destructans infections, which include shoot and leaf blight on young trees, have never been interrogated. Thus, the means by which the pathogen modifies its host environment to overcome host defences remain unknown. By applying transcriptome sequencing, the aim of this study was to compare gene expression in a South African isolate of T. destructans grown on nitrogen-deficient and complete media. This made it possible to identify upregulated genes in a nitrogen-starved environment, often linked to the pathogenicity of the fungus. The results support the hypothesis that nitrogen starvation in T. destructans likely mirrors an in planta genetic response. This is because 45% of genes that were highly upregulated under nitrogen starvation have previously been reported to be associated with infection in other pathogen systems. These included several CAZymes, fungal effector proteins, peptidases, kinases, toxins, lipases and proteins associated with detoxification of toxic compounds. Twenty-five secondary metabolites were identified and expressed in both nitrogen-deficient and complete conditions. Additionally, the most highly expressed genes in both growth conditions had pathogenicity-related functions. This study highlights the large number of expressed genes associated with pathogenicity and overcoming plant defences. As such, the generated baseline knowledge regarding pathogenicity and aggressiveness in T. destructans is a valuable reference for future in planta work.

English

Secreted fungal effector proteins and their host targets are good examples to understand the mechanism of host-pathogen co-evolution with genes involved in the interaction undergoing positive selection. SIX genes (secreted in xylem) are obtained via horizontal transfer and can be found within the formae speciales of Fusarium oxysporum. SIX6 and SIX9 of F. oxysporum f. spp. cubense (Foc) are predicted to play a role as effectors. However, their involvement in the pathogenicity of Foc in banana plants has not been determined yet. In the susceptible banana cultivar, we found that the SIX6 and SIX9 genes of Foc TR4 were highly expressed in roots, but not in corms or leaves. The host, however, expressed the pathogenesis-related (PR) genes, PR-1 and PR-3, in corms earlier than in the roots. Phylogenetic analysis on SIX6 and SIX9 genes of F. oxysporum has revealed the separation of SIX6 and SIX9 of Foc from other formae speciales. This leads to detecting genes under positive selection using the ratio nonsynonymous to synonymous substitution rates (Ka/Ks). SIX6 of Foc showed an increase in diversity, but insufficient to drive positive selection. Conversely, SIX9 of Foc showed no divergence in the dN/dS ratio distribution, indicating purifying selection. © 2021 Friends Science Publishers

Identification of specificity-defining amino acids of the wheat immune receptor Pm2 and powdery mildew effector AvrPm2.

Plant nucleotide-binding leucine-rich repeat receptors (NLRs) act as intracellular sensors for pathogen-derived effector proteins and trigger an immune response, frequently resulting in the hypersensitive cell death response (HR) of the infected host cell. The wheat (Triticum aestivum) NLR Pm2 confers resistance against the fungal pathogen Blumeria graminis f. sp. tritici (Bgt) if the isolate contains the specific RNase-like effector AvrPm2. We identified and isolated seven new Pm2 alleles (Pm2e-i) in the wheat D-genome ancestor Aegilops tauschii and two new natural AvrPm2 haplotypes from Bgt. Upon transient co-expression in Nicotiana benthamiana, we observed a variant-specific HR of the Pm2 variants Pm2a and Pm2i towards AvrPm2 or its homolog from the AvrPm2 effector family, BgtE-5843, respectively. Through the introduction of naturally occurring non-synonymous single nucleotide polymorphisms and structure-guided mutations, we identified single amino acids in both the wheat NLR Pm2 and the fungal effector proteins AvrPm2 and BgtE-5843 responsible for the variant-specific HR of the Pm2 variants. Exchanging these amino acids led to a modified HR of the Pm2-AvrPm2 interaction and allowed the identification of the effector head epitope, a 20-amino-acid long unit of AvrPm2 involved in the HR. Swapping of the AvrPm2 head epitope to the non-HR-triggering AvrPm2 family member BgtE-5846 led to gain of a HR by Pm2a. Our study presents a molecular approach to identify crucial effector surface structures involved in the HR and demonstrates that natural and induced diversity in an immune receptor and its corresponding effectors can provide the basis for understanding and modifying NLR-effector specificity.

Putative LysM Effectors Contribute to Fungal Lifestyle.

Fungal LysM effector proteins can dampen plant host–defence responses, protecting hyphae from plant chitinases, but little is known on these effectors from nonpathogenic fungal endophytes. We found four putative LysM effectors in the genome of the endophytic nematophagous fungus Pochonia chlamydosporia (Pc123). All four genes encoding putative LysM effectors are expressed constitutively by the fungus. Additionally, the gene encoding Lys1—the smallest one—is the most expressed in banana roots colonised by the fungus. Pc123 Lys1, 2 and 4 display high homology with those of other strains of the fungus and phylogenetically close entomopathogenic fungi. However, Pc123 Lys3 displays low homology with other fungi, but some similarities are found in saprophytes. This suggests evolutionary divergence in Pc123 LysM effectors. Additionally, molecular docking shows that the NAcGl binding sites of Pc123 Lys 2, 3 and 4 are adjacent to an alpha helix. Putative LysM effectors from fungal endophytes, such as Pc123, differ from those of plant pathogenic fungi. LysM motifs from endophytic fungi show clear conservation of cysteines in Positions 13, 51 and 63, unlike those of plant pathogens. LysM effectors could therefore be associated with the lifestyle of a fungus and give us a clue of how organisms could behave in different environments.

Conservation and expansion of a necrosis-inducing small secreted protein family from host-variable phytopathogens of the Sclerotiniaceae.

Fungal effector proteins facilitate host‐plant colonization and have generally been characterized as small secreted proteins (SSPs). We classified and functionally tested SSPs from the secretomes of three closely related necrotrophic phytopathogens: Ciborinia camelliae, Botrytis cinerea, and Sclerotinia sclerotiorum. Alignment of predicted SSPs identified a large protein family that share greater than 41% amino acid identity and that have key characteristics of previously described microbe‐associated molecular patterns (MAMPs). Strikingly, 73 of the 75 SSP family members were predicted within the secretome of the host‐specialist C. camelliae with single‐copy homologs identified in the secretomes of the host generalists S. sclerotiorum and B. cinerea. To explore the potential function of this family of SSPs, 10 of the 73 C. camelliae proteins, together with the single‐copy homologs from S. sclerotiorum (SsSSP3) and B. cinerea (BcSSP2), were cloned and expressed as recombinant proteins. Infiltration of SsSSP3 and BcSSP2 into host tissue induced rapid necrosis. In contrast, only one of the 10 tested C. camelliae SSPs was able to induce a limited amount of necrosis. Analysis of chimeric proteins consisting of domains from both a necrosis‐inducing and a non‐necrosis‐inducing SSP demonstrated that the C‐terminus of the S. sclerotiorum SSP is essential for necrosis‐inducing function. Deletion of the BcSSP2 homolog from B. cinerea did not affect growth or pathogenesis. Thus, this research uncovered a family of highly conserved SSPs present in diverse ascomycetes that exhibit contrasting necrosis‐inducing functions.

Custom selected reference genes outperform pre-defined reference genes in transcriptomic analysis

BackgroundRNA sequencing allows the measuring of gene expression at a resolution unmet by expression arrays or RT-qPCR. It is however necessary to normalize sequencing data by library size, transcript size and composition, among other factors, before comparing expression levels. The use of internal control genes or spike-ins is advocated in the literature for scaling read counts, but the methods for choosing reference genes are mostly targeted at RT-qPCR studies and require a set of pre-selected candidate controls or pre-selected target genes.ResultsHere, we report an R-based pipeline to select internal control genes based solely on read counts and gene sizes. This novel method first normalizes the read counts to Transcripts per Million (TPM) and then excludes weakly expressed genes using the DAFS script to calculate the cut-off. It then selects as references the genes with lowest TPM coefficient of variation. We used this method to pick custom reference genes for the differential expression analysis of three transcriptome sets from transgenic Arabidopsis plants expressing heterologous fungal effector proteins tagged with GFP (using GFP alone as the control). The custom reference genes showed lower coefficient of variation and fold change as well as a broader range of expression levels than commonly used reference genes. When analyzed with NormFinder, both typical and custom reference genes were considered suitable internal controls, but the custom selected genes were more stably expressed. geNorm produced a similar result in which most custom selected genes ranked higher (i.e. were more stably expressed) than commonly used reference genes.ConclusionsThe proposed method is innovative, rapid and simple. Since it does not depend on genome annotation, it can be used with any organism, and does not require pre-selected reference candidates or target genes that are not always available.

Improved prediction of fungal effector proteins from secretomes with EffectorP 2.0.

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.

A single fungal MAP kinase controls plant cell-to-cell invasion by the rice blast fungus.

Blast disease destroys up to 30% of the rice crop annually and threatens global food security. The blast fungus Magnaporthe oryzae invades plant tissue with hyphae that proliferate and grow from cell to cell, often through pit fields, where plasmodesmata cluster. We showed that chemical genetic inhibition of a single fungal mitogen-activated protein (MAP) kinase, Pmk1, prevents M. oryzae from infecting adjacent plant cells, leaving the fungus trapped within a single plant cell. Pmk1 regulates expression of secreted fungal effector proteins implicated in suppression of host immune defenses, preventing reactive oxygen species generation and excessive callose deposition at plasmodesmata. Furthermore, Pmk1 controls the hyphal constriction required for fungal growth from one rice cell to the neighboring cell, enabling host tissue colonization and blast disease.

Positively Selected Effector Genes and Their Contribution to Virulence in the Smut Fungus Sporisorium reilianum.

Plants and fungi display a broad range of interactions in natural and agricultural ecosystems ranging from symbiosis to parasitism. These ecological interactions result in coevolution between genes belonging to different partners. A well-understood example is secreted fungal effector proteins and their host targets, which play an important role in pathogenic interactions. Biotrophic smut fungi (Basidiomycota) are well-suited to investigate the evolution of plant pathogens, because several reference genomes and genetic tools are available for these species. Here, we used the genomes of Sporisorium reilianum f. sp. zeae and S. reilianum f. sp. reilianum, two closely related formae speciales infecting maize and sorghum, respectively, together with the genomes of Ustilago hordei, Ustilago maydis, and Sporisorium scitamineum to identify and characterize genes displaying signatures of positive selection. We identified 154 gene families having undergone positive selection during species divergence in at least one lineage, among which 77% were identified in the two investigated formae speciales of S. reilianum. Remarkably, only 29% of positively selected genes encode predicted secreted proteins. We assessed the contribution to virulence of nine of these candidate effector genes in S. reilianum f. sp. zeae by deleting individual genes, including a homologue of the effector gene pit2 previously characterized in U. maydis. Only the pit2 deletion mutant was found to be strongly reduced in virulence. Additional experiments are required to understand the molecular mechanisms underlying the selection forces acting on the other candidate effector genes, as well as the large fraction of positively selected genes encoding predicted cytoplasmic proteins.