effectR: An Expandable R Package to Predict Candidate RxLR and CRN Effectors in Oomycetes Using Motif Searches
- Javier F. Tabima1
- Niklaus J. Grünwald2 †
- 1Department of Botany and Plant Pathology, Oregon State University, Corvallis, OR 97331, U.S.A.
- 2Horticultural Crops Research Laboratory, USDA-ARS, Corvallis, OR 97330, U.S.A.
Effectors are small, secreted proteins that facilitate infection of host plants by all major groups of plant pathogens. Effector protein identification in oomycetes relies on identification of open reading frames with certain amino acid motifs among additional minor criteria. To date, identification of effectors relies on custom scripts to identify motifs in candidate open reading frames. Here, we developed the R package effectR, which provides a convenient tool for rapid prediction of effectors in oomycete genomes, or with custom scripts for any genome, in a reproducible way. The effectR package relies on a combination of regular expressions statements and hidden Markov model approaches to predict candidate RxLR and crinkler effectors. Other custom motifs for novel effectors can easily be implemented and added to package updates. The effectR package has been validated with published oomycete genomes. This package provides a convenient tool for wet lab researchers interested in reproducible identification of candidate effectors in oomycete genomes.
Secreted effector proteins have been reported for all major groups of plant pathogens, including bacteria, fungi, oomycetes, nematodes, and viruses (Jones and Dangl 2006; Toruño et al. 2016). Effector proteins are defined as secreted proteins that manipulate plant processes to the advantage of the parasite, in order to promote infection and generate disease (Petre and Kamoun 2014). Effector proteins modulate and interfere with the normal physiology of the plant host in order to facilitate disease and infection (Jiang and Tyler 2012; Jones and Dangl 2006; Kamoun 2007). Effector proteins are common in bacterial plant pathogens. Up to 29 effector proteins are injected by the bacterial pathogen Pseudomonas syringae into the host plant cells via the type III secretion system (Chang et al. 2005). Effector discovery has predicted over 50 candidate effector proteins for the species Heterodera glycines, a plant-parasitic nematode that affects soybean crops across the world (Wang et al. 2001). Multiple effector proteins have been reported for a plethora of plant-pathogenic fungal and oomycete species (Selin et al. 2016).
The discovery of secreted effector proteins in these organisms was a major breakthrough, as these proteins are directly involved in pathogenicity. These effector proteins that disrupt the normal physiology of the plant can be recognized by specific resistance (R) genes coding for proteins (Jones and Dangl 2006). The recognition of an effector protein by an R gene leads to effector triggered immunity, which generates a signaling cascade that results in programmed cell death via a hypersensitive response. This programed cell death slows the growth of the pathogen and avoids the proliferation of disease into neighboring plant cells (Jones and Dangl 2006).
Plant-pathogenic oomycetes or water molds are a group of highly devastating plant pathogens. The diseases caused by these organisms can affect hundreds of plant species and have led to high mortality of trees in forest ecosystems, losses of millions of dollars to agriculture, and have been implicated in the Irish potato famine (Fry 2008; Grünwald et al. 2008). Widely recognized plant-pathogen genera found in the water molds include Phytophthora, Pythium, Albugo, and Peronospora (Erwin and Ribeiro 1996).
Oomycetes contain a high number of predicted effector proteins (Birch et al. 2006; Kamoun 2006, 2007; Pais et al. 2013). These proteins are secreted by the pathogen haustorium and are translocated into the host plant cell, in which they are transported to different organelles to disrupt physiological functions and facilitate disease (Anderson et al. 2015; Birch et al. 2006; Kamoun 2006). Recent advances in molecular and computational biology have provided new information about the amino acid sequence of these oomycete effectors. Conserved motifs were identified in two key oomycete effector protein families, namely, the RxLR-dEER motif for RxLR effectors and the LFLAK-HVLV motif for crinkler (CRN) effectors. These canonical motifs are located near the N terminus of an amino acid sequence following the signal peptide sequence. To date, hundreds of these RxLR and CRN effectors per species have been predicted for important plant-pathogenic oomycetes from the genus Phytophthora, such as Phytophthora infestans (approximately 580 RxLR and 196 CRN effector genes [Haas et al. 2009]), P. sojae (approximately 470 RxLR and 100 CRN effector genes [Tyler et al. 2006]), and P. ramorum (approximately 260 RxLR and 19 CRN effector genes [Tyler et al. 2006]). In addition, the downy mildew pathogen Hyaloperonospora arabidopsidis contains up to 134 predicted RxLR-coding genes in its genome (Baxter et al. 2010). However, not all oomycete species contain evidence of RxLR-coding genes in their genomes. No RxLR effector proteins have, to date, been found in the genera Pythium, Saprolegnia, or Albugo (Lévesque et al. 2010; Links et al. 2011; Win et al. 2012).
The process of identifying candidate effector-coding genes in sequenced genomes has, to date, been ad hoc and not reproducible, due to the fact that each user can choose slightly varying thresholds. The most common bioinformatic process used for predicting effectors was described by Hass et al. (2009), in which the authors used a combination of pattern matching via regular expressions (REGEX) based on the canonical RxLR and LFLAK motifs, followed by homology searches using Markov models. This method was used for the prediction of effector proteins in Phytophthora infestans, resulting in a total of 562 predicted RxLR effectors and 196 CRN effectors (Hass et al. 2009). The approach used by Hass et al. (2009) has been modified to include predicted effector sequences from other species, in order to improve the homology search (Govers and Bouwmeester 2008). Several independent tools are available to perform different steps to recognize effectors (Dalio et al. 2018; Sonah et al. 2016), but, to date, no bioinformatic tool allows the prediction of oomycete effectors in a simple and fast manner.
We developed the expandable effectR R package designed to predict effector proteins in a fast and reproducible way. This package uses both REGEX and homology search mechanisms based on hidden Markov models (HMM) to predict candidate effector proteins, using gene models obtained from whole-genome sequences. effectR provides functions in the statistical and computer language R (R Core Team 2018), which allows rapid identification and evaluation of candidate effector proteins for any oomycete species with a sequenced genome. The effectR package has been developed to facilitate the prediction of effector proteins for researchers with limited expertise in computational biology. We tested the effectR package using available oomycete genomes and successfully validated effector prediction for the species Phytophthora infestans. The effectR package is modular and can be expanded to predict effector proteins with different canonical motifs specified by the user. In fact, we encourage contributions to our GitHub repository of new functionality for effector prediction for other organismal groups. We included an example of a custom implementation for bacterial proteins that contain PAAR repeats. The effectR package is released on CRAN and a brief user tutorial is provided on GitHub.
RESULTS AND DISCUSSION
We developed the R package effectR that provides a convenient tool for rapid prediction of effectors in oomycete genomes or with custom scripts for any genome in a reproducible way. The effectR package relies on a combination of REGEX statements and HMM approaches to predict candidate RxLR and CRN effectors using several function calls (Table 1). Three steps are required to call effectors: i) A REGEX search to select amino acid sequences translated from all the open reading frames (ORFs) in a genome that match the motifs of interest, ii) a second, broad search of the amino acid sequences that match a Markov chain profile created with the amino acids that matched the motifs of interest in the REGEX step, and iii) a posthoc set of tools combining the results from the two previous steps after filtering for redundancies (Fig. 1). We are using the oomycete P. infestans as an example application.
Step 1: Obtaining candidate RxLR and CRN effectors using REGEX.
The following example uses the function
This code snippet illustrates that
Step 2: Using HMMs to predict additional candidate effectors.
The following code example uses the function
The previous code snippet shows that
Step 3: Post hoc tools for curation of candidate effector genes.
In the following steps, effectR provides functions to summarize and visualize candidate effector genes.
Step 3a: Summarizing the predicted effector proteins and determining the number and position of each motif in the effector sequences.
The effectR package includes the
Step 3b: Plotting the HMM profile.
To visualize the position of the motifs of interest, we include the function
To test the developed package, we used a set of four sequenced genomes of three oomycete species and one fungal species to predict RxLR effectors. We used the genomes of the oomycete species Phytophthora infestans T30-4 version 1 (Haas et al. 2009), Pythium ultimum DAOM BR144 version 1 (Lévesque et al. 2010), and Albugo candida Ac2VRR version 1 (Links et al. 2011). P. infestans has more than 580 predicted RxLR effectors (Haas et al. 2009), while neither Pythium ultimum nor A. candida have any reported RxLR motifs. Instead, A. candida has 26 predicted gene models with a variant RxLR motif called Ac-RXL (Links et al. 2011). The genome of the ascomycete Fusarium oxysporum f. sp. lycopersici (Ma et al. 2010) was used as a negative control or outgroup with no expectation of finding RxLR motives.
The assembled reference genomes of each of the four species were downloaded from Fungi-DB (Stajich et al. 2012). For each genome assembly, a six-frame translation of all ORFs was predicted using getorf from EMBOSS (Rice et al. 2000). Translated ORFs with a length of less than 100 nucleotides were discarded, as none of the reported functional effector proteins in oomycete species are shorter than this size. We predicted all RxLR effector proteins for each translated sequence from the genome assembly in the effectR package. Additionally, prediction of the signal peptide using all predicted candidate effectors was performed in SignalP 3.0 (Bendtsen et al. 2004), as recommended by Sperschneider et al. (2015). A threshold D score > 0.8 was used. Any candidate effector with a predicted signal peptide was considered a high-confidence candidate effector. All high-confidence candidate effector proteins from P. infestans were compared with the published list of effectors from Haas et al. (2009). We created a custom blast database using the RxLR effector proteins and identified matches between the amino acid sequences of the effectR predictions against the RxLR candidate database in Blastp (Altschul et al. 1990).
As a first step, we identified a total of 174 proteins for Pythium ultimum, 47 for A. candida, and three for F. oxysporum f. sp. lycopersici that showed evidence for one or both the RxLR and EER motifs. However, in the second step, none of these proteins were considered high-confidence candidate effectors, as they lacked a signal peptide cleavage site (Table 2). These results are consistent with previous reports of finding no RxLR effector proteins in genomes of these oomycete and fungal species. The Ac-RXL effector proteins of A. candida have an independent origin from the RxLR effector proteins found in Phytophthora species (Links et al. 2011), and no functional RxLR effector proteins have been reported for F. oxysporum f. sp. lycopersici (Ellis et al. 2009; Ma et al. 2010).
For P. infestans, we predicted 395 candidate effector proteins in the REGEX step and 827 candidate effector proteins in the HMM step, for a total of 900 nonredundant RxLR effector proteins (a number similar to the 831 RxLR effectors predicted by Haas et al. , using only a combination of REGEX and HMM methods). In contrast to the predictions in the previously screened fungal and oomycete species, a high number of high-confidence candidate effectors (631 high-confidence candidate effectors with evidence of a signal peptide cleavage site) were predicted for the translated sequences of P. infestans (Table 2). The logo plot shows the prevalence of the RxLR-EER motifs obtained by the REGEX step between residues 49 and 67 (Fig. 2). The high number of effector proteins predicted in the HMM step is a result of the low thresholds used by our package in order to obtain as many candidate effectors as possible. Of the 631 high-confidence candidate effector proteins, we predicted 453 RxLR effectors with a Blastp match of more than 90% identity with the Haas et al. (2009) RxLR predicted effector proteins. The genomic position of each of these 453 predicted proteins also corresponded with the position of the homologous effector protein reported by Haas et a. (2009), indicating that effectR successfully predicted previously known RxLR effector proteins from P. infestans. The functionally validated effector proteins PexRD36, PexRD1, ipi01/Avrblb1, Avr4, and Avr3a were correctly predicted by the effectR package, providing a successful positive control. The prediction of these 631 effector proteins was performed in under 20 min on a laptop computer using two cores and less than 1 Gb of RAM. This is a modest processing time for a FASTA file with more than 200,000 translated ORFs.
A total of 99 of the RXLR effectors listed by Hass et al. (2009) were not predicted by effectR. Of these 99 RxLR effector proteins, we find 18 proteins with no presence of either a RxLR or EER domain, 24 proteins with no evidence of an EER domain, and the remainder 57 proteins with the RxLR or EER domain with higher upstream or downstream distances from the REGEX expected residue position. These results are expected, as Haas et al. (2009) used a combination of Blastp searches, TribeMCL clustering, and homology searches from reference effectors in addition to the REGEX + HMM searches, leading to a higher number of candidate effector proteins than expected just from the searches implemented in this package. effectR also predicted 79 candidate effector proteins not present in Hass et al. (2009) that include evidence of a signal peptide cleavage site (Table 3). Of these 79 newly predicted effector proteins, 40 proteins contained both RxLR and EER domains, 14 only contained the RxLR motif, 18 only had the EER motif, and seven proteins did not include any of the motifs of interest (Table 3).
In addition to the prediction of RxLR effector proteins, we predicted the CRN effector proteins for the ORF translations of the species used in the proof of concept (Supplementary Table 1). The results of the CRN prediction are consistent with the expectation of a high number of CRN effector proteins present in the genomes of oomycete species, with 21 candidate CRN effectors predicted for Pythium ultimum and 214 CRN effectors predicted for P. infestans. Of the 214 CRN effectors of P. infestans predicted by effectR, we found 159 CRN effector proteins with a Blastp match of more than 90% identity with the Haas et al. (2009) CRN predicted effector proteins. The number of predicted effectors for these two species is similar to the reported number of CRN effectors (196 CRN effectors for P. infestans and 26 CRN effectors for Pythium ultimum) (Lévesque et al. 2010). However, only four CRN effectors in A. candida were predicted. These four predicted CRN proteins for A. candida only have the LxLAK motif, as reported by Links et al. (2011), and are not considered canonical CRN effectors (Links et al. 2011). Finally, only three CRN effectors were predicted for the fungal species Fusarium oxysporum f. sp. lycopersici. No CRN effectors have been reported for this fungal pathogen, and the CRN effectors predicted by effectR only contain the LxLAK motif, indicating that these proteins are not canonical CRN effectors, as in A. candida.
Custom scripts for other effectors.
effectR can easily be modified for identifying candidate effectors. For example, regex.search() can be modified to call bacterial proteins that contain PAAR repeats. These proteins are associated with the VgrG-like spikes found in the type VI secretion system of bacteria and have been shown to be essential in target cell killing by the bacterial species Vibrio cholerae and Acitenobacter baylyi (Shneider et al. 2013). The PAAR proteins have a homonymous amino acid sequence motif (PAAR) with one or more repeats. We created an example showing how the effectR can predict potential candidate proteins in the proteome of the reference strain ATCC 39315 of V. cholerae (Heidelberg et al. 2000) by using the PAAR motif as part of the REGEX search. Our results indicate that effectR successfully predicted 19 candidate PAAR proteins, two of which are homologous to the previously reported PAAR proteins. These two predicted proteins only differ in one amino acid when compared with PAAR homolog proteins previously described by Shneider et al. (2013) from other strains of V. cholerae (Supplementary Fig. 1). These results show that effectR can correctly predict different proteins with other motifs than the canonical oomycete effectors and, also, illustrate the importance of using manual curation based on homologous proteins to avoid the detection of false positives.
Our effectR package provides a novel tool for reproducible prediction of candidate effectors in oomycete genomes. The package is modular, in which every step can be modified to predict candidate effector proteins. Custom motifs for any new effector family can easily be added via custom scripts or by contributing to the GitHub repository. The package has been successfully tested for translated ORFs predicted from genomes of oomycete plant pathogens and can be used for a quick survey of the effector arsenal involved in any plant-pathogen interactions for any species of interest in which effector motifs are known. However, effectR only identifies candidate effectors, and further functional validation in the wet lab is needed.
MATERIALS AND METHODS
The effectR package.
The effectR package is written in the R computer language (R Core Team 2018). effectR allows prediction of oomycete effector proteins. The package requires as input a FASTA file, ideally containing all six-frame amino acid translations for each ORF of the sequenced genome of interest or, at a minimum, all translated gene models. The package then returns the total number of predicted effectors, the amino acid sequence for each of the predicted effectors, the number and position of the motifs of interest for each predicted effector, and the Markov chain profile table, which can be conveniently visualized.
External programs required to execute effectR.
The effectR package requires installation of additional programs and the recommended use of external software to assure its correct functionality (reference resources provided on GitHub for instructions). effectR can use amino acid translations of the gene models predicted in a genome of interest, but we recommend the use of six-frame translations of all ORFs from a genome assembly. Including all six translations for each ORF in a genome will allow prediction of more candidate effectors present in an organism of interest. To generate six-frame ORF translations, we use getorf from EMBOSS (Rice et al. 2000). getorf can be run locally on the command line or online via the EMBOSS explorer (Rice et al. 2000). Other additional programs required by effectR are the MAFFT v7 MSA tool (Katoh and Standley 2013) and HMMER 3.1b2 (Eddy 2011). MAFFT performs a sequence alignment of the candidate effectors in order to build a reference profile to be scanned by HMMER. HMMER executes the searches based on HMMs (Fig. 1, step 2). These two programs are external to R and must be installed on the same machine as effectR. The effectR package includes functions to detect if both HMMER and MAFFT are available in the default user path or the user can specify the location of each of the binaries when executing effectR.
While these additional programs are indispensable to complete step 2 of the effectR package, step 1 and step 3 can be executed independently of them. The user can execute step 1 within effectR, use the output from step 1 and the original ORF file to perform a MSA using other tools of interest, and execute HMM searches externally. The effectR package can import external MSA in FASTA format and HMM results in table format to be used in step 3. The inclusion of step 2 as part of the package was created for a more streamlined process in which the user can identify candidates using REGEX searches, broaden the number of candidate proteins via HMM searches, and summarize and quality control the results obtained from the effectR package in a fast and seamless manner.
Obtaining candidate RxLR and CRN effectors using REGEX.
To predict the first set of candidate effector proteins, effectR searches the ORF translation file to find sequences that match the motifs of interest (Fig. 1). These searches are based on REGEX matching. For the RxLR motif, the REGEX reported by Haas et al. (2009) is used:
This REGEX example shows the three parts used in step 1 to identify an RxLR effector candidate (Fig. 3). Part 1A (
A limitation of effectR is that it does not directly predict the presence of a signal peptide. Other programs (i.e., SignalP version 3.0 [Bendtsen et al. 2004]), rather than higher versions, as recommended by Sperschneider et al. (2015) for oomycetes, can be used to predict the presence of the signal peptide.
Step 1 is summarized in the
Using HMMs to predict additional candidate effectors.
A second independent method of identifying candidate effectors relies on HMM (Fig. 1, step 2). This search allows the identification of additional sequences that match a HMM profile. In effectR, the HMM profile is built using the candidate effectors predicted in the REGEX step. The HMM profile includes the probabilities of any amino acid occurring in a given position of the consensus sequence. The consensus sequence is the product of a MSA of the sequences used to build the HMM profile (Eddy 1998).
To create the HMM profile, effectR aligns the candidate effectors to identify common motifs and builds the HMM profile based on these common motifs. effectR uses MAFFT (Katoh and Standley 2013) to calculate the MSA of the candidate effectors predicted from the REGEX step. The package effectR uses the E-INS-i iterative refinement algorithm for creating the alignment. The E-INS-i algorithm is suitable for sequences containing common domains of interest flanked by large unalignable regions (Katoh and Standley 2013). These large, unalignable regions are typically observed in RxLR and CRN motifs. After generating the MSA from REGEX candidate effectors, effectR creates a HMM profile using HMMER hmmbuild and hmmpress modules. After the HMM profile is built, effectR searches the original ORF file for sequences with hits against the HMM profile, using hmmscan. To encourage the user to perform manual curation, effectR does not apply any significance thresholds in the hmmsearch step and returns all the sequences that match the HMM profile to the user by default. However, we have included an option within hmm.search, called hmm.thres, that establishes a threshold of the bit score cutoff to return candidate effector proteins. The hmm.thres option is equivalent to the -T option used as bit score cutoff for the per-sequence ranked hit hmmsearch program. The result from the HMM search is a list of translated ORFs that match the HMM profile. This list can be used for manual curation steps (Fig. 1, step 3) or can be exported using the
Post hoc tools for combining candidate effector genes from REGEX and HMM searches.
The effectR package allows for manual curation of the predicted effector proteins to screen for highly heterogeneous sequences or for sequences that match the HMM profile but do not include any of the motifs of interest in their amino acid sequence. The manual curation functions created for the effectR package include the
The effectR package can be downloaded from CRAN or GitHub. effectR can be used on the command line or can be deployed in a user-friendly, point-and-click graphical interface, via the shiny R framework (Chang et al. 2017), using the
We thank B. Knaus, Z. Kamvar, and Z. Foster for their technical advice, extensive code review, and vast expertise in R programing.
AUTHOR-RECOMMENDED INTERNET RESOURCE
GitHub effectR page: https://github.com/grunwaldlab/effectR
The author(s) declare no conflict of interest.
- 1990. Basic local alignment search tool. J. Mol. Biol. 215:403-410. https://doi.org/10.1016/S0022-2836(05)80360-2 Crossref, Medline, ISI, Google Scholar
- 2015. Recent progress in RXLR effector research. Mol. Plant-Microbe Interact 28:1063-1072. https://doi.org/10.1094/MPMI-01-15-0022-CR Link, ISI, Google Scholar
- 2018. shinyjs: Easily improve the user experience of your shiny apps in seconds. R package version 1.0. CRAN Project. Published online. https://cran.r-project.org/web/packages/shinyjs/index.html Google Scholar
- 2010. Signatures of adaptation to obligate biotrophy in the Hyaloperonospora arabidopsidis genome. Science 330:1549-1551. https://doi.org/10.1126/science.1195203 Crossref, Medline, ISI, Google Scholar
- 2004. Improved prediction of signal peptides: SignalP 3.0. J. Mol. Biol. 340:783-795. https://doi.org/10.1016/j.jmb.2004.05.028 Crossref, Medline, ISI, Google Scholar
- 2006. Trafficking arms: Oomycete effectors enter host plant cells. Trends Microbiol. 14:8-11. https://doi.org/10.1016/j.tim.2005.11.007 Crossref, Medline, ISI, Google Scholar
- 2005. A high-throughput, near-saturating screen for type III effector genes from Pseudomonas syringae. Proc. Natl. Acad. Sci. U.S.A. 102:2549-2554. https://doi.org/10.1073/pnas.0409660102 Crossref, Medline, ISI, Google Scholar
- 2017. Shiny: web application framework for R. R package version 1.0.5. CRAN Project. Published online. https://cran.r-project.org/web/packages/shiny/index.html Google Scholar
- 2007. SeqinR 1.0-2: A contributed package to the R project for statistical computing devoted to biological sequences retrieval and analysis. Pages 207-232 in: Structural Approaches to Sequence Evolution. U. Bastiola and M. Porto, E. Roman, and M. Vendruscolo. Springer-Verlag, Berlin. Crossref, Google Scholar
- 2018. Effector biology in focus: A primer for computational prediction and functional characterization. Mol. Plant-Microbe Interact 31:22-33. https://doi.org/10.1094/MPMI-07-17-0174-FI Link, ISI, Google Scholar
- 1998. Profile hidden Markov models. Bioinformatics 14:755-763. https://doi.org/10.1093/bioinformatics/14.9.755 Crossref, Medline, ISI, Google Scholar
- 2011. Accelerated profile HMM searches. PLOS Comput. Biol. 7:e1002195. Crossref, Medline, ISI, Google Scholar
- 2009. Recent progress in discovery and functional analysis of effector proteins of fungal and oomycete plant pathogens. Curr. Opin. Plant Biol. 12:399-405. https://doi.org/10.1016/j.pbi.2009.05.004 Crossref, Medline, ISI, Google Scholar
- 1996. Phytophthora diseases worldwide. APS Press, St. Paul, MN, U.S.A. Google Scholar
- 2008. Phytophthora infestans: The plant (and R gene) destroyer. Mol. Plant Pathol. 9:385-402. https://doi.org/10.1111/j.1364-3703.2007.00465.x Crossref, Medline, ISI, Google Scholar
- 2008. Effector trafficking: RXLR-dEER as extra gear for delivery into plant cells. Plant Cell 20:1728-1730. https://doi.org/10.1105/tpc.108.062075 Crossref, Medline, ISI, Google Scholar
- 2008. Phytophthora ramorum: A pathogen with a remarkably wide host range causing sudden oak death on oaks and ramorum blight on woody ornamentals. Mol. Plant Pathol. 9:729-740. https://doi.org/10.1111/j.1364-3703.2008.00500.x Crossref, Medline, ISI, Google Scholar
- 2009. Genome sequence and analysis of the Irish potato famine pathogen Phytophthora infestans. Nature 461:393-398. https://doi.org/10.1038/nature08358 Crossref, Medline, ISI, Google Scholar
- 2000. DNA sequence of both chromosomes of the cholera pathogen Vibrio cholerae. Nature 406:477-483. https://doi.org/10.1038/35020000 Crossref, Medline, ISI, Google Scholar
- 2012. Mechanisms and evolution of virulence in oomycetes. Annu. Rev. Phytopathol. 50:295-318. https://doi.org/10.1146/annurev-phyto-081211-172912 Crossref, Medline, ISI, Google Scholar
- 2006. The plant immune system. Nature 444:323-329. https://doi.org/10.1038/nature05286 Crossref, Medline, ISI, Google Scholar
- 2006. A catalogue of the effector secretome of plant pathogenic oomycetes. Annu. Rev. Phytopathol. 44:41-60. https://doi.org/10.1146/annurev.phyto.44.070505.143436 Crossref, Medline, ISI, Google Scholar
- 2007. Groovy times: Filamentous pathogen effectors revealed. Curr. Opin. Plant Biol. 10:358-365. https://doi.org/10.1016/j.pbi.2007.04.017 Crossref, Medline, ISI, Google Scholar
- 2013. MAFFT multiple sequence alignment software version 7: Improvements in performance and usability. Mol. Biol. Evol. 30:772-780. https://doi.org/10.1093/molbev/mst010 Crossref, Medline, ISI, Google Scholar
- 2010. Genome sequence of the necrotrophic plant pathogen Pythium ultimum reveals original pathogenicity mechanisms and effector repertoire. Genome Biol. 11:R73. https://doi.org/10.1186/gb-2010-11-7-r73 Crossref, Medline, ISI, Google Scholar
- 2011. De novo sequence assembly of Albugo candida reveals a small genome relative to other biotrophic oomycetes. BMC Genomics 12:503. https://doi.org/10.1186/1471-2164-12-503 Crossref, Medline, ISI, Google Scholar
- 2010. Comparative genomics reveals mobile pathogenicity chromosomes in Fusarium. Nature 464:367-373. https://doi.org/10.1038/nature08850 Crossref, Medline, ISI, Google Scholar
- 2013. From pathogen genomes to host plant processes: The power of plant parasitic oomycetes. Genome Biol. 14:211. https://doi.org/10.1186/gb-2013-14-6-211 Crossref, Medline, ISI, Google Scholar
- 2014. How do filamentous pathogens deliver effector proteins into plant cells? PLoS Biol. 12:e1001801. https://doi.org/10.1371/journal.pbio.1001801 Crossref, Medline, ISI, Google Scholar
R Core Team. 2018. R: A Language and Environment for Statistical Computing. R Foundation for Statistical Computing, Vienna, Austria. Google Scholar
- 2000. EMBOSS: The European molecular biology open software suite. Trends Genet. 16:276-277. https://doi.org/10.1016/S0168-9525(00)02024-2 Crossref, Medline, ISI, Google Scholar
- 1990. Sequence logos: A new way to display consensus sequences. Nucleic Acids Res. 18:6097-6100. https://doi.org/10.1093/nar/18.20.6097 Crossref, Medline, ISI, Google Scholar
- 2016. Elucidating the role of effectors in plant-fungal interactions: Progress and challenges. Front. Microbiol. 7:600. https://doi.org/10.3389/fmicb.2016.00600 Crossref, Medline, ISI, Google Scholar
- 2013. PAAR-repeat proteins sharpen and diversify the type VI secretion system spike. Nature 500:350-353. https://doi.org/10.1038/nature12453 Crossref, Medline, ISI, Google Scholar
- 2016. Computational prediction of effector proteins in fungi: Opportunities and challenges. Front. Plant Sci. 7:126. https://doi.org/10.3389/fpls.2016.00126 Crossref, Medline, ISI, Google Scholar
- 2015. Evaluation of secretion prediction highlights differing approaches needed for oom ycete and fungal effectors. Front. Plant Sci. 6:1168. https://doi.org/10.3389/fpls.2015.01168 Crossref, Medline, Google Scholar
- 2012. FungiDB: An integrated functional genomics database for fungi. Nucleic Acids Res. 40:D675-D681. https://doi.org/10.1093/nar/gkr918 Crossref, Medline, ISI, Google Scholar
- 2016. Plant-pathogen effectors: Cellular probes interfering with plant defenses in spatial and temporal manners. Annu. Rev. Phytopathol. 54:419-441. https://doi.org/10.1146/annurev-phyto-080615-100204 Crossref, Medline, ISI, Google Scholar
- 2006. Phytophthora genome sequences uncover evolutionary origins and mechanisms of pathogenesis. Science 313:1261-1266. https://doi.org/10.1126/science.1128796 Crossref, Medline, ISI, Google Scholar
- 2001. Signal peptide-selection of cDNA cloned directly from the esophageal gland cells of the soybean cyst nematode Heterodera glycines. Mol. Plant-Microbe Interact 14:536-544. https://doi.org/10.1094/MPMI.2001.14.4.536 Link, ISI, Google Scholar
- 2016. ggplot2: Elegant graphics for data analysis. Springer, New York. Crossref, Google Scholar
- 2012. Sequence divergent RXLR effectors share a structural fold conserved across plant pathogenic oomycete species. PLoS Pathog. 8:e1002400. https://doi.org/10.1371/journal.ppat.1002400 Crossref, Medline, ISI, Google Scholar
The author(s) declare no conflict of interest.
Funding: This work was supported by funds from the United States Department of Agriculture Agricultural Research Service project 2072-22000-041-00-D and National Institute of Food and Agriculture project 2010-511001-21649. Mention of trade names or commercial products in this manuscript are solely for the purpose of providing specific information and do not imply recommendation or endorsement.