scholarly journals EffectorO: motif-independent prediction of effectors in oomycete genomes using machine learning and lineage specificity

2021 ◽  
Author(s):  
Munir J Nur ◽  
Kelsey Jordan Wood ◽  
Richard W Michelmore
Keyword(s):  
Machine Learning ◽  
Late Blight ◽  
Downy Mildew ◽  
Plant Pathogens ◽  
Sequence Similarity ◽  
Biochemical Properties ◽  
Effector Proteins ◽  
Evolutionary Divergence ◽  
Lineage Specificity ◽  
Late Blight Of Potato

Oomycete plant pathogens cause a wide variety of diseases, including late blight of potato, sudden oak death, and downy mildew of many plants. These pathogens are major contributors to losses in many food crops. Oomycetes secrete "effector" proteins to manipulate their hosts to the advantage of the pathogen. Plants have evolved to recognize effectors, resulting in an evolutionary cycle of defense and counter-defense in plant-microbe interactions. This selective pressure results in highly diverse effector sequences that can be difficult to computationally identify using sequence similarity. We developed a pipeline, EffectorO, that uses two complementary approaches to predict effectors in oomycete pathogen genomes: (1) a machine learning-based pipeline that predicts effector probability based on the biochemical properties of the N-terminal amino acid sequence of a protein and is trained on experimentally verified oomycete effectors and (2) a pipeline based on lineage-specificity to find proteins that are unique to one species or genus, a sign of evolutionary divergence due to adaptation to the host. We tested EffectorO on Bremia lactucae, which causes lettuce downy mildew, and Phytophthora infestans, which causes late blight of potato and tomato, and predicted many novel effector candidates, while still recovering the majority of known effector candidates. EffectorO will be useful for discovering novel families of oomycete effectors without relying on sequence similarity to known effectors.

scholarly journals Structural genomic applied on the rust fungus Melampsora larici-populina reveals two candidate effector proteins adopting cystine-knot and nuclear transport factor 2-like protein folds

2019 ◽  
Author(s):  
Karine de Guillen ◽  
Cécile Lorrain ◽  
Pascale Tsan ◽  
Philippe Barthe ◽  
Benjamin Petre ◽  
...  
Keyword(s):  
Plant Pathogens ◽  
Nuclear Transport ◽  
Sequence Similarity ◽  
Rust Fungus ◽  
Parasitic Infection ◽  
Effector Proteins ◽  
Host Cells ◽  
Dimensional Structure ◽  
Structural Genomic ◽  
Transport Factor

ABSTRACTRust fungi are plant pathogens that secrete an arsenal of effector proteins interfering with plant functions and promoting parasitic infection. Effectors are often species-specific, evolve rapidly, and display low sequence similarities with known proteins or domains. How rust fungal effectors function in host cells remains elusive, and biochemical and structural approaches have been scarcely used to tackle this question. In this study, we used a strategy based on recombinant protein production in Escherichia coli to study eleven candidate effectors of the leaf rust fungus Melampsora larici-populina. We successfully purified and solved the three-dimensional structure of two proteins, MLP124266 and MLP124017, using NMR spectroscopy. Although both proteins show no sequence similarity with known proteins, they exhibit structural similarities to knottin and nuclear transport factor 2-like proteins, respectively. Altogether, our findings show that sequence-unrelated effectors can adopt folds similar to known proteins, and encourage the use of biochemical and structural approaches to functionally characterize rust effector candidates.

scholarly journals Host targets of effectors of the lettuce downy mildew Bremia lactucae from cDNA-based yeast two-hybrid screening

2019 ◽  
Author(s):  
Alexandra J.E. Pelgrom ◽  
Claudia-Nicole Meisrimler ◽  
Joyce Elberse ◽  
Thijs Koorman ◽  
Mike Boxem ◽  
...  
Keyword(s):  
Downy Mildew ◽  
Pathogenic Bacteria ◽  
Plant Pathogens ◽  
Molecular Mechanisms ◽  
Effector Proteins ◽  
Bremia Lactucae ◽  
Yeast Two Hybrid ◽  
Target Proteins ◽  
Successful Infection ◽  
Two Hybrid

AbstractPlant pathogenic bacteria, fungi and oomycetes secrete effector proteins to manipulate host cell processes to establish a successful infection. Over the last decade the genomes and transcriptomes of many agriculturally important plant pathogens have been sequenced and vast candidate effector repertoires were identified using bioinformatic analyses. Elucidating the contribution of individual effectors to pathogenicity is the next major hurdle. To advance our understanding of the molecular mechanisms underlying lettuce susceptibility to the downy mildew Bremia lactucae, we mapped a network of physical interactions between B. lactucae effectors and lettuce target proteins. Using a lettuce cDNA library-based yeast-two-hybrid system, 61 protein-protein interactions were identified, involving 21 B. lactucae effectors and 46 unique lettuce proteins. The top ten targets based on the number of independent colonies identified in the Y2H and two targets that belong to gene families involved in plant immunity, were further characterized. We determined the subcellular localization of the fluorescently tagged target proteins and their interacting effectors. Importantly, relocalization of effectors or targets to the nucleus was observed for four effector-target pairs upon their co-expression, supporting their interaction in planta.

scholarly journals Putative LysM Effectors Contribute to Fungal Lifestyle

2021 ◽  
Vol 22 (6) ◽  
pp. 3147
Author(s):  
Marta Suarez-Fernandez ◽  
Ana Aragon-Perez ◽  
Luis Vicente Lopez-Llorca ◽  
Federico Lopez-Moya
Keyword(s):  
Plant Pathogens ◽  
Fungal Endophytes ◽  
Alpha Helix ◽  
Pathogenic Fungi ◽  
Nematophagous Fungus ◽  
Effector Proteins ◽  
Evolutionary Divergence ◽  
Plant Host ◽  
Gene Encoding ◽  
Genes Encoding

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.

scholarly journals Structural genomics applied to the rust fungus Melampsora larici-populina reveals two candidate effector proteins adopting cystine knot and NTF2-like protein folds

2019 ◽  
Vol 9 (1) ◽  
Author(s):  
Karine de Guillen ◽  
Cécile Lorrain ◽  
Pascale Tsan ◽  
Philippe Barthe ◽  
Benjamin Petre ◽  
...  
Keyword(s):  
Plant Pathogens ◽  
Sequence Similarity ◽  
Rust Fungus ◽  
Parasitic Infection ◽  
Three Dimensional ◽  
Effector Proteins ◽  
Host Cells ◽  
Dimensional Structure ◽  
Small Proteins ◽  
Wide Range

AbstractRust fungi are plant pathogens that secrete an arsenal of effector proteins interfering with plant functions and promoting parasitic infection. Effectors are often species-specific, evolve rapidly, and display low sequence similarities with known proteins. How rust fungal effectors function in host cells remains elusive, and biochemical and structural approaches have been scarcely used to tackle this question. In this study, we produced recombinant proteins of eleven candidate effectors of the leaf rust fungus Melampsora larici-populina in Escherichia coli. We successfully purified and solved the three-dimensional structure of two proteins, MLP124266 and MLP124017, using NMR spectroscopy. Although both MLP124266 and MLP124017 show no sequence similarity with known proteins, they exhibit structural similarities to knottins, which are disulfide-rich small proteins characterized by intricate disulfide bridges, and to nuclear transport factor 2-like proteins, which are molecular containers involved in a wide range of functions, respectively. Interestingly, such structural folds have not been reported so far in pathogen effectors, indicating that MLP124266 and MLP124017 may bear novel functions related to pathogenicity. Our findings show that sequence-unrelated effectors can adopt folds similar to known proteins, and encourage the use of biochemical and structural approaches to functionally characterize effector candidates.

scholarly journals Complete Genome Sequence ofPseudomonas fluorescensLBUM636, a Strain with Biocontrol Capabilities against Late Blight of Potato

2016 ◽  
Vol 4 (3) ◽  
Author(s):  
Christopher K. Morrison ◽  
Amy Novinscak ◽  
Vijay J. Gadkar ◽  
David L. Joly ◽  
Martin Filion
Keyword(s):  
Carboxylic Acid ◽  
Plant Growth ◽  
Late Blight ◽  
Genome Sequence ◽  
Plant Pathogens ◽  
Full Genome Sequence ◽  
Full Genome ◽  
Plant Growth Promoting ◽  
Growth Promoting ◽  
Late Blight Of Potato

Herein provided is the full-genome sequence ofPseudomonas fluorescensLBUM636. This strain is a plant growth-promoting rhizobacterium (PGPR) which produces phenazine-1-carboxylic acid, an antibiotic involved in the biocontrol of numerous plant pathogens, including late blight of potato caused by the plant pathogenPhytophthora infestans.

scholarly journals Effector prediction and characterization in the oomycete pathogen Bremia lactucae reveal host-recognized WY domain proteins that lack the canonical RXLR motif

2019 ◽  
Author(s):  
Kelsey Wood ◽  
Munir Nur ◽  
Juliana Gil ◽  
Kyle Fletcher ◽  
Kim Lakeman ◽  
...  
Keyword(s):  
Immune System ◽  
Downy Mildew ◽  
Plant Pathogens ◽  
Domain Architecture ◽  
Host Cells ◽  
Evolutionary Divergence ◽  
Host Immune System ◽  
Bremia Lactucae ◽  
Structural Element ◽  
Lettuce Downy Mildew

AbstractPathogens infecting plants and animals use a diverse arsenal of effector proteins to suppress the host immune system and promote infection. Identification of effectors in pathogen genomes is foundational to understanding mechanisms of pathogenesis, for monitoring field pathogen populations, and for breeding disease resistance. We identified candidate effectors from the lettuce downy mildew pathogen, Bremia lactucae, using comparative genomics and bioinformatics to search for the WY domain. This conserved structural element is found in Phytophthora effectors and some other oomycete pathogens; it has been implicated in the immune-suppressing function of these effectors as well as their recognition by host resistance proteins. We predicted 54 WY domain containing proteins in isolate SF5 of B. lactucae that have substantial variation in both sequence and domain architecture. These candidate effectors exhibit several characteristics of pathogen effectors, including an N-terminal signal peptide, lineage specificity, and expression during infection. Unexpectedly, only a minority of B. lactucae WY effectors contain the canonical N-terminal RXLR motif, which is a conserved feature in the majority of cytoplasmic effectors reported in Phytophthora spp. Functional analysis effectors containing WY domains revealed eleven out of 21 that triggered necrosis, which is characteristic of the immune response on wild accessions and domesticated lettuce lines containing resistance genes. Only two of the eleven recognized effectors contained a canonical RXLR motif, suggesting that there has been an evolutionary divergence in sequence motifs between genera; this has major consequences for robust effector prediction in oomycete pathogens.Author SummaryThere is a microscopic battle that takes place at the molecular level during infection of plants and animals by pathogens. Some of the weapons that pathogens battle with are known as “effectors,” which are secreted proteins that enter host cells to alter physiology and suppress the immune system. Effectors can also be a liability for plant pathogens because plants have evolved ways to recognize these effectors, triggering a defense response leading to localized cell death, which prevents the spread of the pathogen. Here we used computer models to predict effectors from the genome of Bremia lactucae, the causal agent of lettuce downy mildew. Three effectors were demonstrated to suppress the basal immune system of lettuce. Eleven effectors were recognized by one or more resistant lines of lettuce. In addition to contributing to our understanding of the mechanisms of pathogenesis, this study of effectors is useful for breeding disease resistant lettuce, decreasing agricultural reliance on fungicides.

scholarly journals Effector prediction and characterization in the oomycete pathogen Bremia lactucae reveal host-recognized WY domain proteins that lack the canonical RXLR motif

2020 ◽  
Vol 16 (10) ◽  
pp. e1009012
Author(s):  
Kelsey J. Wood ◽  
Munir Nur ◽  
Juliana Gil ◽  
Kyle Fletcher ◽  
Kim Lakeman ◽  
...  
Keyword(s):  
Immune System ◽  
Domain Architecture ◽  
Effector Proteins ◽  
Evolutionary Divergence ◽  
Host Immune System ◽  
Sequence Motifs ◽  
Bremia Lactucae ◽  
Pathogen Effectors ◽  
Lineage Specificity ◽  
Pathogen Populations

Pathogens that infect plants and animals use a diverse arsenal of effector proteins to suppress the host immune system and promote infection. Identification of effectors in pathogen genomes is foundational to understanding mechanisms of pathogenesis, for monitoring field pathogen populations, and for breeding disease resistance. We identified candidate effectors from the lettuce downy mildew pathogen Bremia lactucae by searching the predicted proteome for the WY domain, a structural fold found in effectors that has been implicated in immune suppression as well as effector recognition by host resistance proteins. We predicted 55 WY domain containing proteins in the genome of B. lactucae and found substantial variation in both sequence and domain architecture. These candidate effectors exhibit several characteristics of pathogen effectors, including an N-terminal signal peptide, lineage specificity, and expression during infection. Unexpectedly, only a minority of B. lactucae WY effectors contain the canonical N-terminal RXLR motif, which is a conserved feature in the majority of cytoplasmic effectors reported in Phytophthora spp. Functional analysis of 21 effectors containing WY domains revealed 11 that elicited cell death on wild accessions and domesticated lettuce lines containing resistance genes, indicative of recognition of these effectors by the host immune system. Only two of the 11 recognized effectors contained the canonical RXLR motif, suggesting that there has been an evolutionary divergence in sequence motifs between genera; this has major consequences for robust effector prediction in oomycete pathogens.

scholarly journals ApoplastP: prediction of effectors and plant proteins in the apoplast using machine learning

2017 ◽  
Author(s):  
Jana Sperschneider ◽  
Peter N. Dodds ◽  
Karam B. Singh ◽  
Jennifer M. Taylor
Keyword(s):  
Machine Learning ◽  
Plant Pathogens ◽  
False Positive Rate ◽  
Effector Proteins ◽  
Computational Method ◽  
Host Cells ◽  
Plant Proteins ◽  
Functional Studies ◽  
Positive Rate ◽  
Improving Accuracy

AbstractThe plant apoplast is integral to intercellular signalling, transport and plant-pathogen interactions. Plant pathogens deliver effectors both into the apoplast and inside host cells, but no computational method currently exists to discriminate between these localizations. We present ApoplastP, the first method for predicting if an effector or plant protein localizes to the apoplast. ApoplastP uncovers features for apoplastic localization common to both effectors and plant proteins, namely an enrichment in small amino acids and cysteines as well as depletion in glutamic acid. ApoplastP predicts apoplastic localization in effectors with sensitivity of 75% and false positive rate of 5%, improving accuracy of cysteine-rich classifiers by over 13%. ApoplastP does not depend on the presence of a signal peptide and correctly predicts the localization of unconventionally secreted plant and effector proteins. The secretomes of fungal saprophytes, necrotrophic pathogens and extracellular pathogens are enriched for predicted apoplastic proteins. Rust pathogen secretomes have the lowest percentage of apoplastic proteins, but these are highly enriched for predicted effectors. ApoplastP pioneers apoplastic localization prediction using machine learning. It will facilitate functional studies and will be valuable for predicting if an effector localizes to the apoplast or if it enters plant cells. ApoplastP is available at http://apoplastp.csiro.au.

Predector: an automated and combinative method for the predictive ranking of candidate effector proteins of fungal plant-pathogens

2021 ◽  
Author(s):  
Darcy A. B. Jones ◽  
Lina Rozano ◽  
Johannes Debler ◽  
Ricardo L. Mancera ◽  
Paula Moolhuijzen ◽  
...  
Keyword(s):  
Plant Pathogens ◽  
Broad Class ◽  
Sequence Similarity ◽  
Learning To Rank ◽  
Research Area ◽  
Effector Proteins ◽  
Pairwise Learning ◽  
Fungal Plant Pathogens ◽  
Multiple Species ◽  
Distinguishing Features

Abstract ‘Effectors’ are a broad class of cytotoxic or virulence-promoting molecules that are released from plant-pathogen cells to cause disease in their host. Fungal effectors are a core research area for improving host disease resistance; however, because they generally lack common distinguishing features or obvious sequence similarity, discovery of effectors remains a major challenge. This study presents a novel tool and pipeline for effector prediction - Predector - which interfaces with multiple software tools and methods, aggregates disparate features that are relevant to fungal effector proteins, and ranks effector candidate proteins using a pairwise learning to rank approach. Predector outperformed alternative effector prediction methods that were applied to a curated set of confirmed effectors derived from multiple species. We present Predector as a useful tool for the prediction and ranking of effector candidates, which aggregates and reports additional supporting information relevant to effector and secretome prediction in a simple, efficient, and reproducible manner. Predector is available from https://github.com/ccdmb/predector and associated data from https://github.com/ccdmb/predector-data.

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