Effector Prediction Research Articles

Algorithms for Effector Prediction in Plant Pathogens and Pests: Achievements and Current Challenges

Algorithms for Effector Prediction in Plant Pathogens and Pests: Achievements and Current Challenges

Pan-genome survey of Septoria pistaciarum, causal agent of Septoria leaf spot of pistachios, across three Aegean sub-regions of Greece.

Septoria pistaciarum, a causal agent of Septoria leaf spot disease of pistachio, is a fungal pathogen that causes substantial losses in the cultivation, worldwide. This study describes the first pan-genome-based survey of this phytopathogen-comprising a total of 27 isolates, with 9 isolates each from 3 regional units of Greece (Pieria, Larissa and Fthiotida). The reference isolate (SPF8) assembled into a total of 43.1 Mb, with 38.6% contained within AT-rich regions of approximately 37.5% G:C. The genomes of the 27 isolates exhibited on average 42% gene-coding and 20% repetitive regions. The genomes of isolates from the southern Fthiotida region appeared to more diverged from each other than the other regions based on SNP-derived trees, and also contained isolates similar to both the Pieria and Larissa regions. In contrast, isolates of the Pieria and Larissa were less diverse and distinct from one another. Asexual reproduction appeared to be typical, with no MAT1-2 locus detected in any isolate. Genome-based prediction of infection mode indicated hemibiotrophic and saprotrophic adaptations, consistent with its long latent phase. Gene prediction and orthology clustering generated a pan-genome-wide gene set of 21,174 loci. A total of 59 ortholog groups were predicted to contain candidate effector proteins, with 36 (61%) of these either having homologs to known effectors from other species or could be assigned predicted functions from matches to conserved domains. Overall, effector prediction suggests that S. pistaciarum employs a combination of defensive effectors with roles in suppression of host defenses, and offensive effectors with a range of cytotoxic activities. Some effector-like ortholog groups presented as divergent versions of the same protein, suggesting region-specific adaptations may have occurred. These findings provide insights and future research directions in uncovering the pathogenesis and population dynamics of S. pistaciarum toward the efficient management of Septoria leaf spot of pistachio.

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.

Distribution and diversity of type VI secretion system clusters in Enterobacter bugandensis and Enterobacter cloacae.

Gram-negative bacteria use type VI secretion systems (T6SSs) to antagonize neighbouring cells. Although primarily involved in bacterial competition, the T6SS is also implicated in pathogenesis, biofilm formation and ion scavenging. Enterobacter species belong to the ESKAPE pathogens, and while their antibiotic resistance has been well studied, less is known about their pathogenesis. Here, we investigated the distribution and diversity of T6SS components in isolates of two clinically relevant Enterobacter species, E. cloacae and E. bugandensis. T6SS clusters are grouped into four types (T6SSi-T6SSiv), of which type i can be further divided into six subtypes (i1, i2, i3, i4a, i4b, i5). Analysis of a curated dataset of 31 strains demonstrated that most of them encode T6SS clusters belonging to the T6SSi type. All T6SS-positive strains possessed a conserved i3 cluster, and many harboured one or two additional i2 clusters. These clusters were less conserved, and some strains displayed evidence of deletion. We focused on a pathogenic E. bugandensis clinical isolate for comprehensive in silico effector prediction, with comparative analyses across the 31 isolates. Several new effector candidates were identified, including an evolved VgrG with a metallopeptidase domain and a Tse6-like protein. Additional effectors included an anti-eukaryotic catalase (KatN), M23 peptidase, PAAR and VgrG proteins. Our findings highlight the diversity of Enterobacter T6SSs and reveal new putative effectors that may be important for the interaction of these species with neighbouring cells and their environment.

PhyEffector, the First Algorithm That Identifies Classical and Non-Classical Effectors in Phytoplasmas.

Phytoplasmas are the causal agents of more than 100 plant diseases in economically important crops. Eleven genomes have been fully sequenced and have allowed us to gain a better understanding of the biology and evolution of phytoplasmas. Effectors are key players in pathogenicity and virulence, and their identification and description are becoming an essential practice in the description of phytoplasma genomes. This is of particular importance because effectors are possible candidates for the development of new strategies for the control of plant diseases. To date, the prediction of effectors in phytoplasmas has been a great challenge; the reliable comparison of effectoromes has been hindered because research teams have used the combination of different programs in their predictions. This is not trivial since significant differences in the results can arise, depending on the predictive pipeline used. Here, we tested different predictive pipelines to create the PhyEffector algorithm; the average value of the F1 score for PhyEffector was 0.9761 when applied to different databases or genomes, demonstrating its robustness as a predictive tool. PhyEffector can recover both classical and non-classical phytoplasma effectors, making it an invaluable tool to accelerate effectoromics in phytoplasmas.

Features and algorithms: facilitating investigation of secreted effectors in Gram-negative bacteria.

Gram-negative bacteria deliver effector proteins through type III, IV, or VI secretion systems(T3SSs, T4SSs,and T6SSs) into host cells, causing infections and diseases. In general, effector proteins for each of these distinct secretion systems lack homology and are difficult to identify. Sequence analysis has disclosed many common features, helping us to understand the evolution, function, and secretion mechanisms of the effectors. In combination with various algorithms, the known common features have facilitated accurate prediction of new effectors. Ensemblers or integrated pipelines achieve a better prediction of performance, which combines multiple computational models or modules with multidimensional features. Natural language processing (NLP) models also show the merits, which could enable discovery of novel features and, in turn, facilitate more precise effector prediction, extending our knowledge about each secretion mechanism.

Systemic screening of Fusarium oxysporum candidate effectors reveals FoSSP17 that suppresses plant immunity and contributes to virulence

Fusarium oxysporum f. sp. cubense (Foc) causes Fusarium wilt of banana (Musa spp.), a notorious soil-borne vascular fungal disease threatening the global banana industry. Phytopathogens secrete effectors to suppress plant immunity. However, little is known about the effectors of Foc race 4 (Foc4). In this study, we built a streamlined screening system (candidate effector prediction, RNA-seq-based expression level analysis, and cell death manipulative activity assessment based on transient expression in Nicotiana benthamiana) to identify candidate virulence-related effectors. In total, 80 candidate effector genes (CEGs) differentially expressed during plant colonization were predicted; 12 out of 15 characterized CEGs, including FoSSP17, could suppress BAX-triggered programmed cell death (PCD) in N. benthamiana and were induced during the infection of plants. FoSSP17 encodes a novel protein conserved in the Fusarium genus. FoSSP17 gene deletion mutants were not affected in vegetative growth and conidiation but showed reduced virulence. Furthermore, the deletion mutants triggered higher expression levels of host defense-related genes including PR1, PR3, PR5, and PR10. Signal peptide activity assay and subcellular localization assay suggested that FoSSP17 is a conventional secretory protein that exerts cell-death-suppressive activity inside plant cells. In addition, FoSSP17 suppressed pattern-triggered immunity in plants by inhibiting reactive oxygen species (ROS) accumulation, reducing callose deposition, and suppressing the expression of NbLOX and NbERF1 genes related to jasmonic acid (JA)-pathway and ethylene (ET)-pathway, respectively. Overall, a systemic screening of Foc4 candidate effectors reveals that FoSSP17 contributes to the virulence of Foc4 and suppresses pattern-triggered immunity in plants.

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

Oomycete plant pathogens cause a wide variety of diseases, including late blight of potato, sudden oak death, and downy mildews of plants. These pathogens are major contributors to loss in numerous 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 only sequence similarity. We developed a novel effector prediction tool, EffectorO, that uses two complementary approaches to predict effectors in oomycete pathogen genomes: i) 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 ii) 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 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. [Formula: see text] Copyright © 2023 The Author(s). This is an open access article distributed under the CC BY-NC-ND 4.0 International license.

Genome-Wide Characterization of Effector Protein-Encoding Genes in Sclerospora graminicola and Its Validation in Response to Pearl Millet Downy Mildew Disease Stress.

Pearl millet [Pennisetum glaucum (L.) R. Br.] is the essential food crop for over ninety million people living in drier parts of India and South Africa. Pearl millet crop production is harshly hindered by numerous biotic stresses. Sclerospora graminicola causes downy mildew disease in pearl millet. Effectors are the proteins secreted by several fungi and bacteria that manipulate the host cell structure and function. This current study aims to identify genes encoding effector proteins from the S. graminicola genome and validate them through molecular techniques. In silico analyses were employed for candidate effector prediction. A total of 845 secretory transmembrane proteins were predicted, out of which 35 proteins carrying LxLFLAK (Leucine-any amino acid-Phenylalanine-Leucine-Alanine-Lysine) motif were crinkler, 52 RxLR (Arginine, any amino acid, Leucine, Arginine), and 17 RxLR-dEER putative effector proteins. Gene validation analysis of 17 RxLR-dEER effector protein-producing genes was carried out, of which 5genes were amplified on the gel. These novel gene sequences were submitted to NCBI. This study is the first report on the identification and characterization of effector genes in Sclerospora graminicola. This dataset will aid in the integration of effector classes that act independently, paving the way to investigate how pearl millet responds to effector protein interactions. These results will assist in identifying functional effector proteins involving the omic approach using newer bioinformatics tools to protect pearl millet plants against downy mildew stress. Considered together, the identified effector protein-encoding functional genes can be utilized in screening oomycetes downy mildew diseases in other crops across the globe.

The Missing Pieces: The Role of Secretion Systems in Campylobacter jejuni Virulence.

Campylobacter jejuni is likely the most common bacterial cause of gastroenteritis worldwide, responsible for millions of cases of inflammatory diarrhea characterized by severe abdominal cramps and blood in the stool. Further, C. jejuni infections are associated with post-infection sequelae in developed countries and malnutrition and growth-stunting in low- and middle-income countries. Despite the increasing prevalence of the disease, campylobacteriosis, and the recognition that this pathogen is a serious health threat, our understanding of C. jejuni pathogenesis remains incomplete. In this review, we focus on the Campylobacter secretion systems proposed to contribute to host-cell interactions and survival in the host. Moreover, we have applied a genomics approach to defining the structural and mechanistic features of C. jejuni type III, IV, and VI secretion systems. Special attention is focused on the flagellar type III secretion system and the prediction of putative effectors, given that the proteins exported via this system are essential for host cell invasion and the inflammatory response. We conclude that C. jejuni does not possess a type IV secretion system and relies on the type III and type VI secretion systems to establish a niche and potentiate disease.

WideEffHunter: An Algorithm to Predict Canonical and Non-Canonical Effectors in Fungi and Oomycetes.

Newer effectorome prediction algorithms are considering effectors that may not comply with the canonical characteristics of small, secreted, cysteine-rich proteins. The use of effector-related motifs and domains is an emerging strategy for effector identification, but its use has been limited to individual species, whether oomycete or fungal, and certain domains and motifs have only been associated with one or the other. The use of these strategies is important for the identification of novel, non-canonical effectors (NCEs) which we have found to constitute approximately 90% of the effectoromes. We produced an algorithm in Bash called WideEffHunter that is founded on integrating three key characteristics: the presence of effector motifs, effector domains and homology to validated existing effectors. Interestingly, we found similar numbers of effectors with motifs and domains within two different taxonomic kingdoms: fungi and oomycetes, indicating that with respect to their effector content, the two organisms may be more similar than previously believed. WideEffHunter can identify the entire effectorome (non-canonical and canonical effectors) of oomycetes and fungi whether pathogenic or non-pathogenic, unifying effector prediction in these two kingdoms as well as the two different lifestyles. The elucidation of complete effectoromes is a crucial step towards advancing effectoromics and disease management in agriculture.

Comprehensive prediction of plant cytoplasmic and apoplastic effectors underlying Erwinia psidii pathogenicity

AbstractErwinia psidii (Eps) is the causal agent of emerging diseases of eucalypt and guava; however, the mechanisms underlying its pathogenicity are not fully understood. Here, we predicted factors involved in the ability of Eps to cause disease on its host plants. For that, the genomes of four Eps strains exhibiting different virulence on eucalypt were sequenced, and hrp/hrc genes coding for the type III secretion system (T3SS), effectors injected into the plant cell cytoplasm through the T3SS (T3SEs) and their plant subcellular localizations, as well as proteins deployed to the host apoplast, were predicted. It was found that Eps possesses a complete hrp/hrc gene cluster based on comparison with Erwinia amylovora. A total of 18 T3SEs were predicted, 11 of which were shared among all strains, none were exclusive to any strain and seven were absent in at least one strain. No sequence variation among strains was found for five T3SE candidates whereas extensive variation was found for six, suggesting the latter may be determinants of virulence differences. The T3SE candidates are predicted to target the plant cell nucleus, cytoplasm, mitochondrion, chloroplast and peroxisome. The predicted apoplastic effector repertoire common to all four strains was over‐represented in proteins of unknown functions or predicted to possess enzymatic activities, among which the most abundant were oxidoreductases and peptidases. Proteins with lytic transglycosylase activity were predicted in strain‐specific apoplastic effector repertoires. These results provide an important framework for future research aimed at uncovering the factors underlying Eps pathogenicity.

Functional Identification of Arthrinium phaeospermum Effectors Related to Bambusa pervariabilis × Dendrocalamopsis grandis Shoot Blight.

The shoot blight of Bambusa pervariabilis × Dendrocalamopsis grandis caused by Arthrinium phaeospermum made bamboo die in a large area, resulting in serious ecological and economic losses. Dual RNA-seq was used to sequence and analyze the transcriptome data of A. phaeospermum and B. pervariabilis × D. grandis in the four periods after the pathogen infected the host and to screen the candidate effectors of the pathogen related to the infection. After the identification of the effectors by the tobacco transient expression system, the functions of these effectors were verified by gene knockout. Fifty-three differentially expressed candidate effectors were obtained by differential gene expression analysis and effector prediction. Among them, the effectors ApCE12 and ApCE22 can cause programmed cell death in tobacco. The disease index of B. pervariabilis × D. grandis inoculated with mutant ΔApCE12 and mutant ΔApCE22 strains were 52.5% and 47.5%, respectively, which was significantly lower than that of the wild-type strains (80%), the ApCE12 complementary strain (77.5%), and the ApCE22 complementary strain (75%). The tolerance of the mutant ΔApCE12 and mutant ΔApCE22 strains to H2O2 and NaCl stress was significantly lower than that of the wild-type strain and the ApCE12 complementary and ApCE22 complementary strains, but there was no difference in their tolerance to Congo red. Therefore, this study shows that the effectors ApCE12 and ApCE22 play an important role in A. phaeospermum virulence and response to H2O2 and NaCl stress.

Genome Sequence Resource of Albifimbria verrucaria Causing the Leaf Spot Disease of the Spinach Plant Spinacia oleracea.

Genome Sequence Resource of Albifimbria verrucaria Causing the Leaf Spot Disease of the Spinach Plant Spinacia oleracea.

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

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

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.

Genome Resource of Colletotrichum spaethianum, the Causal Agent of Leaf Anthracnose in Polygonatum falcatum

Genome Resource of <i>Colletotrichum spaethianum</i>, the Causal Agent of Leaf Anthracnose in <i>Polygonatum falcatum</i>

Effectidor: an automated machine-learning-based web server for the prediction of type-III secretion system effectors.

Type-III secretion systems are utilized by many Gram-negative bacteria to inject type-3 effectors (T3Es) to eukaryotic cells. These effectors manipulate host processes for the benefit of the bacteria and thus promote disease. They can also function as host-specificity determinants through their recognition as avirulence proteins that elicit immune response. Identifying the full effector repertoire within a set of bacterial genomes is of great importance to develop appropriate treatments against the associated pathogens. We present Effectidor, a user-friendly web server that harnesses several machine-learning techniques to predict T3Es within bacterial genomes. We compared the performance of Effectidor to other available tools for the same task on three pathogenic bacteria. Effectidor outperformed these tools in terms of classification accuracy (area under the precision-recall curve above 0.98 in all cases). Effectidor is available at: https://effectidor.tau.ac.il, and the source code is available at: https://github.com/naamawagner/Effectidor. Supplementary data are available at Bioinformatics online.

EffectorP 3.0: Prediction of Apoplastic and CytoplasmicEffectors in Fungi and Oomycetes.

Many fungi and oomycete species are devasting plant pathogens. These eukaryotic filamentous pathogens secrete effector proteins to facilitate plant infection. Fungi and oomycete pathogens have diverse infection strategies and their effectors generally do not share sequence homology. However, they occupy similar host environments, either the plant apoplast or plant cytoplasm, and, therefore, may share some unifying properties based on the requirements of these host compartments. Here, we exploit these biological signals and present the first classifier (EffectorP 3.0) that uses two machine-learning models: one trained on apoplastic effectors and one trained on cytoplasmic effectors. EffectorP 3.0 accurately predicts known apoplastic and cytoplasmic effectors in fungal and oomycete secretomes with low estimated false-positive rates of 3 and 8%, respectively. Cytoplasmic effectors have a higher proportion of positively charged amino acids, whereas apoplastic effectors are enriched for cysteine residues. The combination of fungal and oomycete effectors in training leads to a higher number of predicted cytoplasmic effectors in biotrophic fungi. EffectorP 3.0 expands predicted effector repertoires beyond small, cysteine-rich secreted proteins in fungi and RxLR-motif containing secreted proteins in oomycetes. We show that signal peptide prediction is essential for accurate effector prediction, because EffectorP 3.0 recognizes a cytoplasmic signal also in intracellular, nonsecreted proteins.[Formula: see text] Copyright © 2022 The Author(s). This is an open access article distributed under the CC BY-NC-ND 4.0 International license.

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.