Molecular Evolution of Pseudomonas syringae Type III Secreted Effector Proteins

Diverse Gram-negative pathogens like Pseudomonas syringae employ type III secreted effector (T3SE) proteins as primary virulence factors that combat host immunity and promote disease. T3SEs can also be recognized by plant hosts and activate an effector triggered immune (ETI) response that shifts the interaction back toward plant immunity. Consequently, T3SEs are pivotal in determining the virulence potential of individual P. syringae strains, and ultimately help to restrict P. syringae pathogens to a subset of potential hosts that are unable to recognize their repertoires of T3SEs. While a number of effector families are known to be present in the P. syringae species complex, one of the most persistent challenges has been documenting the complex variation in T3SE contents across a diverse collection of strains. Using the entire pan-genome of 494 P. syringae strains isolated from more than 100 hosts, we conducted a global analysis of all known and putative T3SEs. We identified a total of 14,613 putative T3SEs, 4,636 of which were unique at the amino acid level, and show that T3SE repertoires of different P. syringae strains vary dramatically, even among strains isolated from the same hosts. We also find substantial diversification within many T3SE families, and in many cases find strong signatures of positive selection. Furthermore, we identify multiple gene gain and loss events for several families, demonstrating an important role of horizontal gene transfer (HGT) in the evolution of P. syringae T3SEs. These analyses provide insight into the evolutionary history of P. syringae T3SEs as they co-evolve with the host immune system, and dramatically expand the database of P. syringae T3SEs alleles.

Introduction

Over the past three decades, type III secreted effectors (T3SEs) have been recognized as primary mediators of many host–microbe interactions (Michiels and Cornelis, 1991; Salmond and Reeves, 1993; Hueck, 1998; Coburn et al., 2007; Deng et al., 2017; Hu et al., 2017; Rapisarda and Fronzes, 2018). These proteins are translocated directly from the pathogen cell into the host cytoplasm by the type III secretion system (T3SS), where they perform a variety of functions that generally promote virulence and suppress host immunity (Coburn et al., 2007; Zhou and Chai, 2008; Cunnac et al., 2009; Oh et al., 2010; Buttner, 2016; Khan et al., 2018). However, T3SEs can also be recognized by the host immune system, which allows the host to challenge the invading microbe. In plants, this immune response is called effector triggered immunity (ETI) (Jones and Dangl, 2006; Dodds and Rathjen, 2010; Khan et al., 2016). The interaction between pathogen T3SEs and the host immune system results in an evolutionary arms race, where pathogen T3SEs evolve to avoid detection while still maintaining their role in the virulence process, and the host immune system evolves to recognize the diversity of T3SEs and their actions, while maintaining a clear distinction between self and non-self to avoid autoimmune activation.

One of the best studied arsenals of T3SEs is carried by the plant pathogenic bacterium Pseudomonas syringae (Lindeberg et al., 2009, 2012; Mansfield et al., 2012). P. syringae is a highly diverse plant pathogenic species complex responsible for a wide-range of diseases on many agronomically important crop species (Mansfield et al., 2012). While the species as a whole has a very broad host range, individual strains can only cause disease on a small range of plant hosts (Sarkar et al., 2006; Lindeberg et al., 2009; Baltrus et al., 2017; Xin et al., 2018). A growing number of P. syringae strains have also recently been recovered from non-agricultural habitats, including wild plants, soil, lakes, rainwater, snow, and clouds (Morris et al., 2007, 2008, 2013; Clarke et al., 2010). This expanding collection of strains and the increased availability of comparative genomics data presents unique opportunities for obtaining insight into the determinants of host specificity in P. syringae (Baltrus et al., 2011, 2012; O’Brien et al., 2011, 2012; Dillon et al., 2019).

Pseudomonas syringae T3SEs have been the focus of both fundamental and applied plant pathology research for decades, going back to some of the early work on gene-for-gene resistance and avirulence proteins (Mukherjee et al., 1966; Staskawicz et al., 1984, 1987; Keen and Staskawicz, 1988; Kobayashi et al., 1989; Keen, 1990; Jenner et al., 1991; Fillingham et al., 1992). Since then, over 1000 publications have focused on P. syringae T3SEs (Web of Science {“P. syringae” AND [avirulence OR (“type III” AND effector)]}, October 2018), making it one of the most comprehensively studied T3SE systems. To date a total of 66 T3SE families and 764 T3SE alleles have been cataloged in the P. syringae Genome Resources Homepage¹. Many of these T3SE families are small, relatively conserved, and only distributed in a subset of P. syringae strains, while others are more diverse and distributed across the majority of sequenced P. syringae strains (Baltrus et al., 2011; O’Brien et al., 2011; Dillon et al., 2019). Given the irregular distribution of T3SEs among strains and their frequent association with mobile genetic elements, it has long been recognized that horizontal transfer plays an important role in the dissemination of T3SEs among strains (Kim and Alfano, 2002; Rohmer et al., 2004; Stavrinides and Guttman, 2004; Lovell et al., 2009, 2011; Godfrey et al., 2011; Neale et al., 2016). Nucleotide composition and phylogenetic analyses of a subset of T3SEs identified eleven P. syringae T3SE families that were acquired by recent horizontal transfer events. However, the remaining thirteen families appeared to be ancestral and vertically inherited, suggesting that pathoadaptation may also play a major role in T3SE evolution through mutations that modify the function of T3SEs (Rohmer et al., 2004; Stavrinides et al., 2006; O’Brien et al., 2011). While T3SE repertoires are thought to be key determinants of host specificity, strains with divergent repertoires are at times capable of causing disease on the same host (Almeida et al., 2009; O’Brien et al., 2011, 2012; Lindeberg et al., 2012), signifying that we have much to learn about the ways in which T3SEs contribute to P. syringae virulence.

Two major issues impact our understanding of T3SE diversity in P. syringae; namely, sampling, and nomenclature. From a sampling perspective, the current catalog of T3SEs listed on the P. syringae Genome Resources Homepage were identified from approximately 120 strains that represent only a subset of the phylogroups and overall diversity in the P. syringae species complex. Expanding this strain collection to include more diversity, including less biased agricultural collections and more natural isolates, will undoubtedly expand our understanding of diversity within T3SE families and reveal as-yet identified families.

Nomenclature problems are certainly less interesting from a biological perspective, but are very important operationally since poor classification and naming practices can lead to substantial confusion and even spurious conclusions. Efforts to address this problem have been made in the past. Most notably, a standardized set of criteria for the identification and naming of P. syringae T3SEs was agreed upon by the majority of labs heavily invested in T3SE research in 2005 (Lindeberg et al., 2005). Specifically, we proposed that new T3SEs be classified into existing families using a BLASTP analysis against previously characterized T3SEs to designated families (E < 10^-5, alignment length > 60%), at which point each family would be subject to rigorous phylogenetic analyses to assign subgroups and truncations. T3SEs that did not fit into any existing families were assigned a new family designation. While the guidelines proposed by this work were widely accepted and implemented, they were not universally applied to all new candidate T3SEs for a number of reasons. Some of the problems stemmed from the inherent biological complexity found in many T3SE, which are often multidomain proteins that share similarity with multiple divergent T3SE families (Stavrinides et al., 2006; McCann and Guttman, 2008). Further, at the time of their discovery, many families also had fewer than three T3SE alleles, making robust phylogenetic analyses impossible. Regardless of the root cause, we are currently in a situation where many T3SEs are annotated without family assignment, some very similar T3SEs have been assigned to different T3SE families, and some highly divergent T3SEs are assigned to the same family based on short tracts of local similarity. This situation should be rectified in order to facilitate more comprehensive analyses of the role of T3SEs in the outcomes of host–pathogen interactions, particularly in light of the growing database of P. syringae genomics resources.

Here, we present an expanded catalog of T3SEs in P. syringae and an updated phylogenetic analysis of the diversity within each T3SE family. We identified a total of 14,613 T3SEs from 494 P. syringae whole-genomes that include strains from 11 of the 13 P. syringae species complex phylogroups. These strains allowed us to redefine evolutionarily distinct family barriers for T3SEs, examine the distribution of each family across the P. syringae species complex, quantify the diversity within each T3SE family, and explore how T3SEs are inherited. By expanding and diversifying the database of confirmed and predicted P. syringae T3SEs and placing all alleles in an appropriate phylogenetic context, these analyses will ultimately enable more comprehensive studies of the roles of individual T3SEs in pathogenicity and allow us to more effectively explore the contribution of T3SEs to host specificity.

Materials and Methods

Genome Sequencing, Assembly, and Gene Identification

Four hundred and ninety-four P. syringae species complex strains were analyzed (Supplementary Dataset S1), of which 102 assemblies were obtained from public sequence databases, including NCBI/GenBank, JGI/IMG-ER, and PATRIC (Markowitz et al., 2012; Wattam et al., 2014; NCBI Resource Coordinators, 2018), and 392 strains were sequenced in house by the University of Toronto Center for the Analysis of Genome Evolution and Function (CAGEF). Two hundred and sixty-eight of these sequenced strains were provided by the International Collection of Microorganisms from Plants (ICMP). For the strains sequenced by CAGEF, DNA was isolated using the Gentra Puregene Yeast and Bacteria Kit (Qiagen, MD, United States), and purified DNA was then suspended in TE buffer and quantified with the Qubit dsDNA BR Assay Kit (ThermoFisher Scientific, NY, United States). Paired-end libraries were generated using the Illumina Nextera XT DNA Library Prep Kit following the manufacturer’s instructions (Illumina, CA, United States), with 96-way multiplexed indices and an average insert size of ∼400 bps. All sequencing was performed on either the Illumina MISeq or GAIIx platform using V2 chemistry (300 cycles). Following sequencing, read quality was assessed with FastQC v.0.11.5 (Andrews, 2010) and low-quality bases and adapters were trimmed using Trimmomatic v0.36 (Bolger et al., 2014) (ILLUMINACLIP: NexteraPE-PE.fa, Maximum Mismatch = 2, PE Palindrome Match = 30, Adapter Read Match = 10, Maximum Adapter Length = 8; SLIDINGWINDOW: Window Size = 4, Average Quality = 5; MENLEN = 20). All genomes were then de novo assembled into contigs with CLC v4.2 (Mode = fb, Distance mode = ss, Minimum Read Distance = 180, Maximum Read Distance = 250, Minimum Contig Length = 1000). Raw reads were then re-mapped to the remaining contigs using samtools v1.5 with default settings to calculate the read coverage for each contig (Li and Durbin, 2009). Any contigs with a coverage depth of less than the average contig coverage by more than two standard deviations were filtered out of the assembly. Finally, gene prediction was performed on each genome using Prodigal v2.6.3 with default settings (Hyatt et al., 2010).

Identifying, Annotating, and Delimitation of Type III Secreted Effector Families

To characterize the effector repertoire of each of the 494 P. syringae strains used in this study, we first downloaded all available P. syringae effector, helper, and chaperone sequences from three public databases: NCBI² (18,120), Bean 2.0 (225) (Dong et al., 2015), and the P. syringae Genome Resources Homepage³ (843). Additional type III associated proteins were recovered from NCBI by querying the NCBI protein database with “type III effector,” “type III helper,” and “type III chaperone” along with “P. Syringae.” These were combined with all available type III secretion associated genes from the Bean 2.0 database and the P. syringae Genome Resources database, which were downloaded directly. Using this database of 19,188 T3SE associated sequences in P. syringae, we then performed an all-vs-all BLASTP analysis to ensure that all sequences that we downloaded were assigned to appropriate families, which was essential given that many of the sequences downloaded from NCBI are ambiguously labeled. Any unassigned T3SE associated gene that had significant reciprocal blast hits (E < 1e-24) with an assigned T3SE associated gene was assigned to the corresponding family. This strict E-value cutoff was chosen to avoid incorrectly assigning families to sequences based on short-tracts of similarity that are common in the N-terminal region of T3SEs from different families (Stavrinides et al., 2006). Sequences that had reciprocal significant hits from multiple families were assigned to the family where they had more significant hits, which means that smaller families could be dissolved into a larger family if all sequences from the two families were sufficiently similar. However, this only occurred in one case, which resulted in all HopF and HopBB sequences being assigned to the HopF/HopBB family. In sum, our final seed database of P. syringae T3SEs contained a total of 7,974 effector alleles from 66 independent families, 1,585 discontinued effector alleles from 6 independent families, 2,230 helper alleles from 23 independent families, and 1,569 chaperones alleles from 10 independent families. Any sequences that were not able to be assigned to an appropriate T3SE family were discarded because of the possibility that these are not true T3SE associated genes.

Identifying and Classifying Individual Type III Secreted Effectors

We identified all putative T3SE protein sequences from the 494 P. syringae genomes by querying each predicted protein against the T3SE seed database using BLASTP if it passed a significance threshold of E < 1e-24. This resulted in the annotation of 14,613 T3SEs across the 494 P. syringae strains. Family names were initially assigned to these T3SEs based on the name that had been assigned to the hit in the seed database, and then later refined based on more rigorous criterion. First, we blasted each T3SE amino acid sequence against a database of all 14,613 T3SEs and retained only hits with an E-value of less than 1e-24 and a length that covered at least 60% of the shorter sequence. Sequences that had multiple non-contiguous hits (i.e., high-scoring segment pairs) with an E-value less than 1e-24 whose cumulative lengths covered at least 60% of the shorter sequence were also retained. As was the case above, the strict e-value cutoff prevents us from assigning significant hits between T3SE sequences that only share strong local identity, which is most commonly seen in the N-terminal secretion signal. The 60% length cutoff prevents chimeric T3SEs from linking the two unrelated T3SE families that combined to form the chimera.

Second, a final list of all T3SE pairs that shared significant hits was gathered and T3SE sequences were collectively binned based on their similarity relationships. With this method, T3SE families were built based on all-by-all pairwise similarity between T3SEs rather than the similarity between individual T3SEs and an arbitrary seed T3SE or collection of centroid T3SEs, as is the case with some clustering methods. Significantly, our approach binned all significantly similar T3SEs regardless of whether any two T3SEs were connected through direct or transitive similarity. For example, if T3SE sequence A was significantly similar to T3SE sequence B, and sequence B was significantly similar to sequence C, all three sequences would be binned together, regardless of whether there was significant similarity between sequence A and sequence C. This is important for appropriately clustering particularly diverse T3SE families, which may contain highly divergent alleles that have intermediate variants.

Finally, we assigned the same T3SE family designation to all T3SEs within each cluster based on the most commonly assigned T3SE family name that had initially been assigned to sequences within that cluster. In the majority of cases, all sequences in a single cluster had the same initially assigned T3SE family. However, for cases where there were multiple family names assigned to sequences within a single cluster, both Hop designations (i.e., HopD/HopAO) were assigned to all sequences in the cluster, with the lower designation being considered the short-form for the family. Conversely, for cases where T3SEs that had initially been assigned the same family designation formed two separate clusters, T3SEs from the larger cluster were assigned the initial family name, and T3SEs from the smaller cluster(s) were assigned a novel family name, starting with HopBO, which is the first available Hop designation. In these cases, the initial family designation of the T3SEs in the family were also kept following a forward-slash (“/”) so that the source of the family was known (i.e., HopBO/HopX). Ultimately, this method allowed us to effectively delimit all T3SEs in this dataset into separate families with consistent definitions and performed considerably better at partitioning established T3SE families than standard orthology delimitation software like PorthoMCL (Tabari and Su, 2017) (Supplementary Dataset S2), likely because of the widespread presence of chimeric T3SEs in the P. syringae species complex.

We then validated our approach and assessed whether the reliance on protein queries (e.g., BLASTP) substantially increased the likelihood of missing T3SEs without predicted protein sequences due to the lack of a properly called coding sequence. To do this we examined whether we recovered all T3SEs from the well characterized repertoires of P. syringae strains PtoDC3000, PsyB728a, Pph1448a, and PtoT1 (Cunnac et al., 2009; Xin et al., 2018). Of the 123 non-pseudogene T3SEs carried by these four strains, we only failed to identify a single T3SE; namely, AvrPto1 from PsyB728a. This T3SE was not identified because in this region of the chromosome an alternative coding sequence was identified on the opposite strand and the AvrPto1 region was not called as a coding region. While we can identify these additional T3SEs by directly querying the genome assemblies with TBLASTN, this approach increases our false positive rate by pulling non-effectors via their chimeric relationships with known T3SEs and often results in the inaccurate identification of start and stop codons, which muddles downstream evolutionary analyses, Therefore, only T3SEs that were identified as coding sequences in this study were analyzed.

In order to classify short chimeric relationships between families, as illustrated in Figure 2, we used a similar approach to the one outlined above. Specifically, we parsed our reciprocal BLASTP results to capture hits that occurred between alleles that had been assigned to different families. Here, we used a significance cutoff of E-value < 1e-10, with no length limitation. All chimeric relationship are shown in Figure 2, with the length of each allele and their overlapping regions shown proportionally. These local relationships between alleles in distinct families were not considered in the following evolutionary analyses.

Phylogenetic Analyses

We generated three separate phylogenetic trees in this study to ask whether core-genome diversity, pan-genome content, or effector content could effectively sort P. syringae strains based on their host of isolation. For the core genome tree, we clustered all protein sequences from the 494 P. syringae genomes used in this study into ortholog families using PorthoMCL v3 with default settings (Tabari and Su, 2017). All ortholog families that were identified in at least 95% of the P. syringae strains in our dataset were considered part of the soft-core genome and each of these families was independently aligned using MUSCLE v3.8.31 with default settings (Edgar, 2004). These alignments were then concatenated end-to-end using a custom python script and a maximum likelihood phylogenetic tree was constructed based on the concatenated alignment using FastTree v2.1.10 with default parameters (Price et al., 2010). For the pan-genome tree, we generated a binary presence-absence matrix for all ortholog families that were identified in more than one P. syringae strain. This presence-absence matrix was used to compute a distance matrix in R v3.3.1 using the “dist” function with the Euclidean distance method. The phylogenetic tree was then constructed using the “hclust” function with the complete linkage hierarchical clustering method. We used the same approach to generate the effector content tree, except the input binary presence-absence matrix contained information on the 70 effector families rather than all ortholog families that made up the P. syringae pan-genome.

Estimating Pairwise Ka, Ks, and Ka/Ks

Evolutionary rate parameters were calculated independently for each T3SE family. First, amino acid sequences were multiple aligned with MUSCLE v.3.8.31 using default settings (Edgar, 2004). Each multiple alignment was then reverse translated based on the corresponding nucleotide sequences using RevTrans v1.4 (Wernersson and Pedersen, 2003) and all pairwise Ka and Ks values were calculated for each family using the Nei-Gojobori Method, implemented by MEGA7-CC (Kumar et al., 2016). Output files were parsed using custom python scripts to convert the Ka and Ks matrices to stacked data frames with four columns: Sequence 1 Header, Sequence 2 Header, Ka, and Ks. The alignment-wide ratio of non-synonymous to synonymous substitutions (Ka/Ks) was then calculated for all T3SE pairs that had both a Ka and a Ks value greater than 0 in each family. For codon-level analysis of positive selection in each family, we used Fast Unconstrained Bayesian Approximation (FUBAR) to detect signatures of positive selection in all families that were present in at least five strains with default settings (Murrell et al., 2013).

For comparisons between T3SE family evolutionary rates and core genome evolutionary rates, we converted each individual core genome family alignment that was generated with MUSCLE to a nucleotide alignment with RevTrans, then concatenated these alignments end-to-end as described above. As was the case with each T3SE family, we then calculated Ka and Ks for all possible pairs of core genomes using the Nei-Gojobori Method and parsed the output files into stacked data frames using our custom python script. The core genome data frame was then merged with each T3SE family data frame independently based on the genomes that the two T3SE sequences were from so that the evolutionary rates between these two T3SEs could be directly compared to the evolutionary rates of the corresponding core genomes.

Gain-Loss Analysis

We used Gain Loss Mapping Engine (GLOOME) to estimate the number of gain and loss events that have occurred for each T3SE family over the course of the evolution of the P. syringae species complex (Cohen et al., 2010). The gain-loss analysis implemented by GLOOME integrates the presence-absence data for each gene family of interest across the phylogenetic profile to estimate the posterior expectation of gain and loss across all branches. These events are then summed to calculate the total number of gene gain and loss events that have occurred for each family across the phylogenetic tree. We performed this analysis on each T3SE family using the mixture model with variable gain/loss ratio and a gamma rate distribution. The phylogenetic tree that we used for this analysis was the concatenated core genome tree, which gives us the best estimation of the evolutionary relationships between strains, given the ample recombination known to occur within the P. syringae species complex (Dillon et al., 2019).

Results

In this study, we analyzed the collective type III effector repertoire of the P. syringae species complex using whole-genome assemblies from 494 strains representing 11 of the 13 established phylogroups and 72 distinct pathovars (Supplementary Dataset S1). These strains were isolated from 28 countries between 1935 and 2016, and include 62 P. syringae type and pathotype strains (Thakur et al., 2016). Although the majority of the strains were isolated from a diverse collection of more than 100 infected host species, we also included strains isolated from environmental reservoirs, which have been dramatically under-sampled in P. syringae studies (Morris et al., 2007, 2013; Mohr et al., 2008; Clarke et al., 2010; Demba Diallo et al., 2012;Monteil et al., 2013, 2016; Karasov et al., 2018). As per Dillon et al. (Dillon et al., 2019), we designate phylogroups 1, 2, 3, 4, 5, 6, and 10 as primary phylogroups and 7, 9, 11, and 13 as secondary phylogroups (we have no representatives from phylogroups 8 or 12, although presumably they would also be secondary phylogroups) (Berge et al., 2014). The primary phylogroups are phylogenetically quite distinct from the secondary phylogroups and include all of the well-studied P. syringae strains. Nearly all of the primary phylogroup strains carry a canonical P. syringae type III secretion system and were isolated from plant hosts. In contrast, many of the strains in the secondary phylogroups do not carry a canonical P. syringae type III secretion system and were isolated from environmental reservoirs (e.g., soil or water).

All of the P. syringae genome assemblies used in this study were downloaded directly from NCBI or generated in-house by the University of Toronto Centre for the Analysis of Genome Evolution & Function using paired-end data from the Illumina GAIIx or the Illumina MiSeq platform. There was some variation in the genome sizes, contig numbers, and N50s among strains due to the fact that the majority of the genomes are de novo assemblies in draft format (Supplementary Figure S1); however, the number of coding sequences identified in each strain were largely consistent with the six finished (closed and complete) genome assemblies in our dataset. Given the large size of the P. syringae pan-genome, the fact that some strains have acquired large plasmids, and the relatively high frequency of horizontal gene transfer in the P. syringae species complex (Baltrus et al., 2011; Dillon et al., 2019), we expect there to be some variation in genome size and coding content of different strains.

Distribution of Type III Secreted Effectors in the P. syringae Species Complex

To explore the distribution of T3SEs across the P. syringae species complex, we first identified all putative T3SEs present in each of our 494 genome assemblies using a BLASTP analysis (Altschul et al., 1997), where all protein sequences from each P. syringae genome were queried against a database of known P. syringae T3SEs obtained from the P. syringae Genome Resource Database⁴, the Bean 2.0 T3SE Database⁵, and the NCBI Protein Database⁶. In sum, we identified a total of 14,613 confirmed and putative T3SEs (Supplementary Datasets S2–S4), 4,636 of which were unique at the amino acid level, and 5,127 of which were unique at the nucleotide level. We consider these T3SEs to be putative because their presence within a genome does not necessarily mean that they are expressed and translocated into the host cytoplasm. Individual P. syringae strains in the dataset harbored between one and 53 putative T3SEs, with a mean of 29.58 ± 10.13 (SEM), highlighting considerable variation in both the composition and size of each strain’s suite of T3SEs (Figure 1). As expected, primary phylogroup strains tended to harbor substantially more T3SEs than secondary phylogroups strains (30.55 ± 8.97 vs. 3.89 ± 1.64, respectively), which frequently do not contain a canonical T3SS (Dillon et al., 2019). However, a subset of strains from phylogroups 2 and 3, and all strains from phylogroup 10 harbored fewer than 10 T3SEs, more closely mirroring secondary phylogroup strains in their T3SE content. The extensive T3SE repertoires found in most primary phylogroup strains supports the idea that these effectors play an important role in the ecological interactions of the majority of strains in this species complex.

FIGURE 1

Figure 1. Total number of coding T3SEs in each P. syringae strain, sorted by phylogroup. Closed circles represent the number of effectors in each strain, boxes show the first quartile effector count, median effector count, and third quartile effector count for the whole phylogroup, and whiskers extend to the highest and lowest effector counts in the phylogroup that are not identified as outliers (> 1.5 times the interquartile range).

Objective criteria are required for partitioning and classifying T3SEs prior to any study of their distribution and evolution. In 2005, an effort was made to unify the disparate classification and naming conventions applied to P. syringae T3SEs (Lindeberg et al., 2005). While this effort was very successful overall, the criteria have not been universally or consistently applied, resulting in some problematic families. For example, the HopK and AvrRps4 families have high similarity over the majority of their protein sequences, but are assigned to distinct families, while the HopX family contains highly divergent subfamilies that only share short tracts of local similarity.

We reassessed the relationship between all 14,613 T3SEs using a formalized protocol in order to objectively delimit families and suggest new family designations for some similar families. While the selection of the specific delimiting criteria is arbitrary and open to debate, we have elected to use a well-established protocol with fairly conservative thresholds. We identified shared similarity using a BLASTP-based pairwise reciprocal best hit approach (Altschul et al., 1997; Eisen, 2000;Daubin et al., 2002), with a stringent Expect-value acceptance threshold of E < 1e-24 and a length coverage cutoff of ≥60% of the shorter sequence (regardless of whether it is query or subject). It should be noted that since this approach uses BLAST it requires only local similarity between family members. Nevertheless, our stringent E-value and coverage thresholds select for matches that share more extensive similarity than would typically be observed when proteins only share a single domain. We feel that these criteria provide a reasonable compromise between very relaxed local similarity criteria (using default BLAST parameters) and very conservative global similarity criteria. All T3SEs that exceeded our acceptance thresholds were sorted into family bins. T3SEs in each bin can therefore be either connected through direct similarity or transitive similarity. Finally, we assigned a primary name to all T3SEs in each bin based on the most common effector family name in that bin and included all secondary family names following a “/”.

Our analysis identified 70 T3SE families and sorted T3SEs into their historical families in the majority of cases. We found that our particular delimitation criteria created T3SE family partitions with the best match to those commonly used in the literature. Unfortunately, it was impossible to find any one objective set of standards that did not require some family renaming and shuffling of alleles among families. These exceptions include merging existing effector families that shared significant local similarity (Table 1) and assigning previously named T3SEs into new families (Table 2). A number of these new families only contain a single allele, so it is likely that they are recent pseudogenes still annotated as coding sequences by Prodigal. Finally, in two cases, a subset of alleles from one T3SE family were assigned to a different family due to the extent of shared local similarity. This included the assignment of all originally designated HopS1 subfamily alleles to HopO, and the assignment of all originally designated HopX3 alleles to HopF/HopBB.

TABLE 1

Table 1. T3SE Families Merged into a New Family.

TABLE 2

Table 2. New T3SE Families.

It is important to emphasize that the new criteria do not bin T3SEs that share less than 60% similarity across the shortest sequence. This was done to prevent families from being combined due to short chimeric relationships between a subset of the alleles in distinct families (Stavrinides et al., 2006). These relationships could be considered super-families, although the reticulated nature of these relationships makes this unwieldly. We list families that share these short regions of similarity in Figure 2, although it is important to recognize that some of these chimeric relationships are only found in a subset of alleles in each family.

FIGURE 2

Figure 2. Interfamily blast hits (E < 1e–10) that did not pass our e-value and/or length cut-offs for combining T3SEs into families. Each superfamily represents a cluster of families that have some overlapping sequence. Colored blocks represent the regions of the representative sequence pairs that are homologous, where the length of the blocks is proportional to the length of the homologous sequence. Black lines represent the remainder of each representative sequence that is not homologous, where the length of the lines is proportional to the length of the 5′ and 3′ non-homologous regions. Not all families within a superfamily need to contain a significant blast hit with all other families in the superfamily because they can be homologous to the same intermediate sequence in different regions. Short form family names are used for merged or separated families.

The distribution of each of these 70 T3SE families across the P. syringae species complex reveals that the majority of families are present in only a small subset of P. syringae strains, typically from a few primary phylogroups (Figure 3 and Supplementary Figure S2). Among T3SE effector families, only AvrE, HopB/HopAC, HopM, and HopAA are considered part of the soft-core genome of P. syringae (present in >95% of strains). This designation of core T3SE is not impacted by the inclusion or exclusion of the secondary phylogroup strains. Interestingly, three of these core families, AvrE, HopM, and HopAA are part of the conserved effector locus (CEL), a well characterized and evolutionarily conserved sequence region that is present in most P. syringae strains (Alfano et al., 2000; Dillon et al., 2019). The fourth CEL effector, HopN, is only present in 14.98% of strains, all of which are from phylogroup 1. HopB/HopAC emerged as a core effector family in our analysis because of the merging of HopB with the broadly distributed HopAC family, which is present in nearly all P. syringae strains (491/494). The significant similarity between HopB and HopAC (E < 1e-24) occurs across the full length of HopB (the shorter of the two). Despite the high prevalence of this family throughout the P. syringae species complex, very little is known about its function. It would be particularly interesting to see if there has been neofunctionalization between the shorter HopB alleles, which are generally localized between the HrpK locus protein and a 315 amino acid hypothetical protein, and the longer alleles formally classified as HopAC, localized between a nebramycin 5′ synthase and a 481 amino acid hypothetical protein. While the remainder of T3SE families are also mostly present in a small subset of strains, there is a wide distribution in the number of strains harboring individual T3SE families, further highlighting the dramatic variation in T3SE content across P. syringae strains (Supplementary Figure S3).

FIGURE 3

Figure 3. Heat map demonstrating the proportion of strains in each phylogroup that harbor each of the T3SE families. Only four T3SE families, AvrE, HopB/HopAC, HopM, and HopAA are considered part of the soft-core P. syringae complex genome (present in >95% of strains). Other T3SE families are mostly sparsely distributed across the P. syringae species complex, with several families only being present in a few phylogroups. Short form family names are used for merged or separated families.

Following family and strain T3SE classification, we also performed hierarchical clustering using the T3SE content of each strain to determine if T3SE profiles are a good predictor of host specificity. We previously reported that in P. syringae, neither the core genome or gene content phylogenetic trees correlate well with the hosts from which the strains were isolated (Dillon et al., 2019). This remains true in this study, where we’ve updated the core and pan-genome analyses with an expanded set of strains (Supplementary Figures S4, S5). The T3SE content tree is not as well resolved due to the smaller number of phylogenetically informative signals in the T3SE dataset. However, we were able to largely recapitulate the established P. syringae phylogroups with this analysis, suggesting that more closely related strains do tend to have more similar T3SE repertoires (Supplementary Figure S6). We also see that the phylogroup 2, phylogroup 3, and phylogroup 10 strains that have smaller T3SE repertoires than other primary phylogroups, cluster more closely with secondary phylogroup strains in the effector content tree. However, as was the case in the core genome and gene content trees, hierarchical clustering based on effector content did not effectively separate strains based on their host of isolation. We therefore conclude that overall T3SE content is not a good predictor of host specificity.

Diversification of Type III Secreted Effectors in the P. syringae Species Complex

Substantial genetic and functional diversity has been shown to exist within individual T3SE families (Lewis et al., 2014; Dillon et al., 2019). While some T3SE families are relatively small, restricted to only a subset of P. syringae strains, and present in only a single copy in each strain, others are found in nearly all strains, and often appear in multiple copies within a single genome (Figure 4). Many of the largest families, including those that are part of the core genome (AvrE, HopB/HopAC, HopM, and HopAA), are among those that are often present in multiple copies. However, we also found that some families that are present in less than half of P. syringae strains (e.g., HopF/HopBB, HopO, HopZ, and HopBL) frequently appear in multiple copies. While the average copy number of individual T3SEs per strain across all families is 1.30 and some families are present in copy numbers as high as six, it is important to recognize that in many cases these multi-copy effectors are not full-length T3SEs. Rather, they are partial copies derived from the same full-length allele that have been split due to the introduction of a premature stop codon (Supplementary Dataset S2). Therefore, at least in these cases, we think it is unlikely that both copies retain function. Nevertheless, the fact that both of these two coding sequences retain enough protein similarity to be linked to other T3SEs in the same family suggests that these disruption events either occur very commonly, or that these regions are still subject to purifying selection due to the retention of some functional role.

FIGURE 4

Figure 4. Total number of P. syringae strains harboring an allele from each T3SE family. Color categories denote the copy number of each effector family in the corresponding strains. While the majority of families are mostly present in a single copy, some of the more broadly distributed families have higher copy numbers in a subset of P. syringae genomes. Short form family names are used for merged or separated families.

To quantify the extent of genetic diversification within each T3SE family, we aligned the amino acid sequences of all members from each family with MUSCLE, then reverse translated these amino acid alignments and calculated all pairwise non-synonymous (Ka) and synonymous (Ks) substitution rates for all pairs of alleles within each family. There was a broad range of pairwise substitution rates in the majority of T3SE families, which is expected given the range of divergence times in the core-genomes of strains from different P. syringae phylogroups (Dillon et al., 2019). The three families with the highest non-synonymous substitution rates were HopF/HopBB, HopAB/HopAY, and HopAT/HopAV (Figure 5A), which all have an average Ka greater than 0.5. These families also tended to have relatively high synonymous substitution rates, but several other families also have Ks values that are greater than 1.0 (Figure 5B).

FIGURE 5

Figure 5. Non-synonymous substitution rates (Ka) (A), synonymous substitution rates (Ks) (B), and Ka/Ks ratio (C) for each T3SE family. All alleles in each family were aligned using MUSCLE v. 3.8 and all pairwise Ka and Ks values within each family were calculated using MEGA7 with the Nei-Gojobori Method. Boxes show the first quartile substitution rates, median substitution rates, and third quartile substitution rates for each family, and whiskers extend to the highest and lowest substitution rates in the family that are not identified as outliers (> 1.5 times the interquartile range). Average pairwise Ka, Ks, and Ka/Ks values for each family are denoted by red diamonds. Short form family names are used for merged or separated families.

While some pairwise comparisons of effector alleles did yield a Ka/Ks ratio greater than 1, the predominance of purifying selection operating in the conserved domains of these families likely overwhelms signals of positive selection at individual sites. Indeed, the average global pairwise Ka/Ks values were less than 1 for all T3SE families (Figure 5C). Therefore, we also analyzed the Ka and Ks on a per codon basis using FUBAR to search for site-specific signals of positive selection in each family (Bayes Empirical Bayes P-Value ≥ 0.9; Ka/Ks > 1) (Murrell et al., 2013). We find that 37 out of the 64 (57.81%) T3SE families with at least five alleles have at least one positively selected site. The number of positively selected sites in these families was relatively low, ranging from 1 to 17, with the percentage of positively selected sites in a single family never rising above 2.29% (Table 3). By comparison, we found that only 3,888/17,807 (21.83%) ortholog families from the pangenome of P. syringae that were present in at least five strains demonstrated signatures of positive selection at one or more sites (Dillon et al., 2019), suggesting that T3SE families experience elevated rates of positive selection. Nevertheless, these results should be interpreted cautiously given the variable rates of recombination and horizontal transfer among strains and the confounding impact this can have on detecting selection (O’Reilly et al., 2008; Betancourt et al., 2009).

TABLE 3

Table 3. Positive Selection among T3SE Families.

Finally, to explore whether T3SE families display different levels of diversity than core gene families carried by the same P. syringae strains, we compared all pairwise Ka and Ks values within each effector family to the pairwise Ka and Ks values for the core genes carried in the corresponding genomes. We would expect T3SEs and core genes to share the same Ka and Ks values if they were evolving under the same evolutionary pressures. Deviation from this null expectation could be due to either differences in selective pressures, or the movement of the T3SE via horizontal gene transfer (HGT). We find that the pairwise Ka values for T3SEs are substantially higher than those of the corresponding core genes for the majority of T3SEs (Figure 6A and Supplementary Figure S7). This was also true for pairwise Ks values, although the differences between T3SE pairs and core genes were not as high and there were many more examples of T3SE pairs that had lower Ks values than the corresponding core genes (Figure 6B and Supplementary Figure S8).

FIGURE 6

Figure 6. Relationship between the average pairwise non-synonymous substitution rate (Ka) (A) and the average pairwise synonymous substitution rate (Ks) (B) for each effector family with the average core genome non-synonymous and synonymous substitution rates of the corresponding P. syringae strains. Pairwise substitution rates for all sequences within a family were estimated by reverse translating the effector family and concatenated core genome amino acid alignments, then calculating pairwise substitution rates in MEGA7 with the Nei-Gojobori Method. Each point on the scatter plot represents the average of these pairwise rates for a single family and the red dotted lines represent the null-hypothesis that the substitution rates in the effector family will be the same as the substitution rates of the core genes in the same collection of genomes.

Gene Gain and Loss of Type III Secreted Effectors in the P. syringae Species Complex

Both the patchy distribution of T3SE families across the P. syringae species complex and the inconsistent relationships between T3SE and core gene substitution rates suggest that HGT may be an important evolutionary force contributing to the evolution of T3SEs in the P. syringae species complex. Therefore, we also sought to analyze the expected number of gene gain events across the P. syringae phylogenetic tree in order to more accurately quantify the extent to which HGT has actively transferred T3SEs between P. syringae strains over the evolutionary history of the species complex. We used the Gain Loss Mapping Engine (GLOOME) to estimate the number of gain and loss events (Cohen et al., 2010; Cohen and Pupko, 2010), and found extensive evidence for HGT in several T3SE families, with some families experiencing as many as 40 HGT events over the course of the history of the P. syringae species complex (Figure 7). Outlier T3SE families that did not appear to have undergone much HGT in P. syringae include the smallest families, like HopU, HopBE, and HopBR/HopBN, and the largest families, like AvrE, HopB/HopAC, HopM, and HopAA. Smaller families were less likely to have undergone HGT because they were only identified in a subset of closely related strains, so are not expected to have been part of the P. syringae species complex through the majority of its evolutionary history. Larger families may experience less HGT because they are more likely to already be present in the recipient strain and therefore will quickly be lost following an HGT event. However, because GLOOME only identifies HGT events that result in the gain of a new family, we cannot be certain whether P. syringae genomes with multiple copies were generated by HGT or gene duplication.

FIGURE 7

Figure 7. Expected number of gene gain and gene loss events for each T3SE family. The posterior expectation for gain and loss events was estimated for each family on each branch of the P. syringae core-genome tree using GLOOME with the stochastic mapping approach. The sum of these posterior expectations across all branches yields the total expected number of events for each family. Short form family names are used for merged or separated families.

An opposing evolutionary force that is also expected to have a disproportional effect on the evolution of T3SE families is gene loss. Specifically, loss of a given T3SE may allow a P. syringae strain to infect a new host by shedding an effector that elicits the hosts’ ETI response. Indeed, we found that gene loss events were also common in many T3SE families, with more than 50 events estimated to have occurred in the HopAT/HopAV and HopAZ families (Figure 7). T3SE families that experienced more gene loss events also tended to experience more gene gain events, as demonstrated by a strong positive correlation between gene loss and gene gain in T3SE families (Supplementary Figure S9) (linear regression; F = 140.50, df = 1, 68, p < 0.0001, r² = 0.67). However, as was the case with gene gain events, we observed few gene loses in the smallest and the largest T3SE families. For small families, this is again likely to be the result of the fact that they have spent less evolutionary time in the P. syringae species complex. For large families, we are again blind to gene loss events that occur in a genome that has multiple copies of the effector prior to the loss event. Therefore, there are likely many more T3SE losses occurring in larger families than we observe here because these T3SE families tend to be present in multiple copies within the same genome.

Finally, we also observed that there is a significant positive correlation between both evolutionary rate parameters and the rates of gene gain and loss for T3SE families (Ka-Gene Gain: F = 8.48, df = 1, 63, p = 0.0050, r² = 0.1186; Ka-Gene Loss: F = 16.15, df = 1, 63, p = 0.0002, r² = 0.2041; Ks-Gene Gain: F = 6.46, df = 1, 63, p = 0.0135, r² = 0.0930; Ks-Gene Loss: F = 7.70, df = 1, 63, p = 0.0072, r² = 0.1089) (Supplementary Figure S10). This implies that the same evolutionary forces resulting in diversification of T3SEs are also causing them to undergo elevated rates of gain or loss. However, there was substantial unexplained variance in these correlations, resulting in some T3SE families that have high evolutionary rates and low levels of gain and loss, and other T3SE families that have low evolutionary rates and high levels of gain and loss. These families tended to be the same for all correlations.

Discussion

Bacterial T3SEs are primary virulence factors in a wide-range of plant and animal pathogens (Hueck, 1998; Desveaux et al., 2006; Zhou and Chai, 2008; Block and Alfano, 2011; Buttner, 2016; Khan et al., 2016; Hu et al., 2017; Khan et al., 2018; Xin et al., 2018). T3SEs are particularly interesting from an evolutionary perspective due to their dual and diametrically opposed roles in host–pathogen interactions. While T3SEs have evolved in order to promote bacterial fitness, usually via the suppression of host immunity or disruption of host cellular homeostasis, hosts have evolved mechanisms to recognize the presence or activity of T3SEs, and this recognition often elicits an immune response that shifts the interaction back into the host’s favor. To explore the distribution and evolutionary history of P. syringae T3SEs and gain insight into their role in host specificity, we cataloged the T3SE repertoires of a large and diverse collection of 494 P. syringae isolates. These phylogenetically diverse strains allowed us to generate an expanded database of more than 14,000 putative T3SE alleles and investigate the evolutionary mechanisms through which these important molecules have enabled P. syringae to become one of the most globally important bacterial plant pathogens (Mansfield et al., 2012).

Expanded Database of Type III Secreted Effectors in P. syringae

This study increases the number of confirmed and putative T3SE alleles available in the P. syringae Genome Resources Database by 20-fold, resulting in a final database of 14,613 T3SE alleles from the P. syringae species complex, 5,127 of which are unique at the nucleotide level. Although these new, putative T3SEs all share an ancestral sequence with known T3SE families, we did not confirm expression or translocation of any of these T3SEs, so some of these coding regions may represent recently pseudogenized effectors. However, the extensive diversification that has occurred within many of these families clearly indicates that some level of functional diversification has occurred.

Consistent with our earlier analysis, we find that primary phylogroup strains harbor considerably larger repertoires of T3SEs than secondary phylogroup strains (Baltrus et al., 2011; O’Brien et al., 2011; Dudnik and Dudler, 2014; Dillon et al., 2019). We also find that a small number of primary phylogroup strains have significantly smaller effector repertoires; including phylogroup 10 strains, which were primarily isolated from non-agricultural sources similar to most secondary phylogroup strains, and the phylogroup 2 strain Psy642, which has previously been highlighted as an outlier in its T3SE content and has been characterized as non-pathogenic (Clarke et al., 2010; O’Brien et al., 2011). In general, phylogroup 2 strains have somewhat smaller T3SE repertoires and employ a greater number of phytotoxins relative to other primary phylogroup strains (Baltrus et al., 2011; O’Brien et al., 2011; Dillon et al., 2019). This may indicate that phylogroup 2 strains have evolved a different host-microbe lifestyle than other P. syringae primary phylogroup strains, e.g., one tending toward low virulence, epiphytic interactions, rather than high virulence, invasive pathogenesis (Hirano and Upper, 2000).

Among the 70 T3SE families that were delimited in this study, seven of the newly assigned families had fewer than five total members (HopBR/HopBN, HopBS/HopAV, HopBT/HopAB, HopBU/HopAB, HopBV/HopAJ, HopBW/HopBH, HopBX/HopL). These families all consist of alleles that were separated from a larger T3SE family during the delimitation stage of our analysis because they shared only very limited regions of local similarity with the larger family. The small size of these families suggests that they may be pseudogenes degenerating due to a lack of selective constraints. The 63 remaining families are similar to the ∼60 families that have been discussed in earlier studies (Baltrus et al., 2011; Lindeberg et al., 2012). While we do merge seven families based on our delimitation analysis, seven new families have been discovered in the past 5 years (McCann et al., 2013; Hockett et al., 2014; Lam et al., 2014; Matas et al., 2014; Mucyn et al., 2014). Furthermore, our objective delimitation analysis separated HopX2 from HopX, HopZ3 from HopZ, and HopH3 from HopH, forming the HopBO/HopX, HopBP/HopZ, and HopBQ/HopH families, respectively. Despite these differences, we arrive at several similar conclusions to prior work on the distribution of individual T3SEs across P. syringae strains. Specifically, we find that few T3SE families are considered part of the core genome (Baltrus et al., 2011; O’Brien et al., 2011; Lindeberg et al., 2012), with only AvrE, HopB/HopAC, HopM, and HopAA being present in more than 95% of strains. Three of these families (AvrE, HopM, and HopAA) are part of the CEL, while the other CEL effector, HopN, is only present in 14.98% of P. syringae strains, all from phylogroup 2. This suggests that HopN arose in the CEL after the divergence of this phylogroup. Other families that have previously been characterized as core T3SEs in P. syringae include HopI and HopAH (Baltrus et al., 2011), which are only present in 79.76 and 89.07% of strains from our study, respectively. Neither HopB or HopAC has been highlighted as a core T3SE in prior studies, but the HopB/HopAC family in this study was one of the largest and most broadly distributed T3SE families. Although HopB and HopAC do vary substantially in length and occur in different genomic contexts, they typically share reciprocal BLASTP hits across more than 80% of the HopB sequence with E-values less than 1e-24, indicating shared ancestry. The remainder of T3SE families have a considerably sparser distribution across the P. syringae species complex, ranging in frequency from 1.62% to 80.97%. This demonstrates that different T3SE families were likely acquired episodically throughout the evolutionary history of the P. syringae species complex and are subject to strong evolutionary pressures for gain and loss due to the widespread and diverse ETI surveillance system of plants (Cunnac et al., 2009; Xin et al., 2018).

Finally, we find that highly divergent combinations of T3SEs can enable P. syringae to infect the same host (Supplementary Figure S6). While this observation is consistent with prior studies in P. syringae (Baltrus et al., 2011; Lindeberg et al., 2012; O’Brien et al., 2012), it is in contrast to the convergence in T3SE repertoires that has been observed in Xanthomonas, another phytopathogen that employs a T3SS (Hajri et al., 2009). Importantly, this limits our ability to detect and differentiate P. syringae pathogens of different hosts using this fairly crude application of comparative genomics. The lack of correlation between T3SE repertoires and host specificity may be a direct result of the fact that there is substantial functional redundancy among P. syringae T3SEs from different families, or that certain T3SEs in combination can mask the detection of other T3SEs in a given P. syringae background (Cunnac et al., 2009; Cunnac et al., 2011; Lindeberg et al., 2012; Wei et al., 2018). However, it will be important moving forward to assess the true host range of a broader collection of P. syringae strains in order to determine whether specific T3SEs promote or suppress growth on particular hosts.

Genetic and Functional Evolution of P. syringae Type III Secreted Effectors

Given the broad array of unique T3SEs that exist within the P. syringae species complex, mining this untapped diversity is likely to reveal a number of new functions and interactions for T3SEs in P. syringae. By quantifying Ka, Ks, and Ka/Ks for each pair of T3SE alleles in each family, we identified substantial genetic diversity in most T3SE families (Figure 5). Our codon-level analysis of positive selection also revealed that T3SE families were substantially more likely than non-T3SE families to contain positively selected sites (Table 3). Finally, we confirmed that this divergence is not simply a reflection of the immense diversity exhibited by the strains used in this study, since the divergence observed for T3SE families is consistently higher than the divergence observed across core genes (Supplementary Figures S7, S8). Elevated non-synonymous substitution rates in T3SE families implies that there may be elevated positive selection operating on these families, while elevated synonymous substitution rates show that this elevated positive selection may extend to synonymous sites, that many T3SEs arose prior to the last common ancestor (LCA) of the P. syringae species complex, and/or that T3SEs undergo considerably higher rates of HGT than core genes. However, it is difficult to pinpoint the timing and strength of positive selection on T3SEs because of the confounding effects of variable rates of recombination and horizontal transfer throughout their evolutionary history.

Fast-evolving T3SEs will also provide numerous opportunities for studying Red Queen dynamics (Van Valen, 1973). Under Fluctuating Red Queen (FRQ) dynamics, fluctuating selection drives oscillations in allele frequencies at the focal genetic loci in both the pathogen and the host, resulting in rapid evolutionary change on both sides (Brockhurst et al., 2014). In the case of P. syringae and their plant hosts, bacterial T3SEs are the key players on the pathogen side, and plant resistance genes are the key players on the host side. These FRQ dynamics are expected to maintain high levels of within-population genetic diversity at focal loci, as we’ve observed in many T3SE families. The majority of T3SE families in P. syringae are highly divergent and display strong signatures of positive selection, likely in response to intense host-imposed selection to evade recognition (Rohmer et al., 2004; Baltrus et al., 2011; Lindeberg et al., 2012). This implies that few T3SEs are broadly unrecognized, making interactions between individual T3SEs and the corresponding plant resistance genes an excellent resource for exploring FRQ dynamics.

The highly dynamic nature of T3SE evolution is also seen in our analysis of T3SE gain and loss across the P. syringae phylogenetic tree. More than five gene gain events are estimated to have occurred in 52 out of the 70 T3SE families analyzed in this study, with a maximum of 41 HGT events estimated in the HopZ family. Gene loss events were even more common, with 57 out of 70 T3SE families experiencing more than five loss events and a maximum of 53 events in the HopAZ family. Earlier studies have also suggested that both gene gain and loss were quite common among T3SE families. One specific study using nucleotide composition and phylogenetics found that members from 11 out of 24 tested P. syringae T3SE families were recently acquired by HGT (Rohmer et al., 2004). These families included AvrA, AvrB, AvrD, AvrRpm, HopG, HopQ, HopX, HopZ, HopAB, HopAF, and HopAM (although AvrD is not a T3SE Leach and White, 1996; Mucyn et al., 2014). The T3SEs from this dataset were also highlighted by this study as undergoing considerably higher rates of gene gain and loss within the P. syringae species complex. Specifically, all of these T3SEs were demonstrated to have undergone at least ten gene gain events and many were among the most dynamic T3SEs in our dataset. Other studies have shown that many T3SEs are present on mobile genetic elements and that T3SEs from the same family are often found at different genomic locations (Kim and Alfano, 2002; Charity et al., 2003; Lovell et al., 2009, 2011; Godfrey et al., 2011; Neale et al., 2016), which may both promote and be a consequence of the high rates of gene gain and loss for particular T3SE families. From a selective perspective, it is also likely that host immune recognition can drive selection for gene gain or loss (Vinatzer et al., 2006), while the functional redundancy of different T3SE families carried in the same genetic background may limit the negative impacts of the loss of such T3SEs (Kvitko et al., 2009; Cunnac et al., 2011; Wei et al., 2018). Finally, as has been previously reported (Baltrus et al., 2011), we find that there is a significant positive correlation between rates of evolution and rates of gene gain and loss (Supplementary Figure S10), suggesting that similar evolutionary forces that cause the diversification of T3SEs are contributing to the loss and gain of T3SEs. However, not all T3SEs fit this model which could reflect that T3SEs vary in their mutational robustness and/or that the genomic context of different T3SEs makes them more or less prone to HGT. In any event, the extensive gene gain and loss that occurs in the majority of T3SE families lends further support to the hypothesis that few T3SE alleles are broadly unrecognized (Baltrus et al., 2011).

Given the highly dynamic nature of T3SE evolution, we predict that there are still numerous T3SEs that will be found to elicit ETI. Most research on ETI elicitation to date has focused on a small number of T3SE families, and an even smaller number of alleles from each family (Mansfield, 2009). The immense diversification that we observe in many T3SE families points to strong selective pressures that may be explained by as-yet discovered ETI responses. If this prediction holds true, it will be particularly interesting to study T3SE families with alleles that induce different ETI responses in the same host. These patterns will help reveal how strains shift onto new hosts or break immunity in an existing host, perhaps explaining the evolutionary driving force behind new disease outbreaks.

Author Contributions

MD, DD, and DG designed the research. MD, BL, AM, and BW performed the experiments. MD and RA analyzed the data. MD and DG wrote the manuscript.

Funding

This work was supported by Natural Sciences and Engineering Research Council of Canada Discovery grants (DG and DD), Canada Research Chairs in Comparative Genomics (DG) and Plant-Microbe Systems Biology (DD), and the Center for the Analysis of Genome Evolution and Function (DG and DD).

Conflict of Interest Statement

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Acknowledgments

We thank all members of the Guttman and Desveaux labs for helpful discussion and valuable input on this project.

Supplementary Material

The Supplementary Material for this article can be found online at: https://www.frontiersin.org/articles/10.3389/fpls.2019.00418/full#supplementary-material

Footnotes

1. ^ www.pseudomonas-syringae.org/
2. ^ https://www.ncbi.nlm.nih.gov
3. ^ https://pseudomonas-syringae.org
4. ^ https://pseudomonas-syringae.org
5. ^ http://systbio.cau.edu.cn/bean
6. ^ https://www.ncbi.nlm.nih.gov

References

Alfano, J. R., Charkowski, A. O., Deng, W. L., Badel, J. L., Petnicki-Ocwieja, T., Van Dijk, K., et al. (2000). The Pseudomonas syringae Hrp pathogenicity island has a tripartite mosaic structure composed of a cluster of type III secretion genes bounded by exchangeable effector and conserved effector loci that contribute to parasitic fitness and pathogenicity in plants. Proc. Natl. Acad. Sci. U.S.A. 97, 4856–4861. doi: 10.1073/pnas.97.9.4856

PubMed Abstract | CrossRef Full Text | Google Scholar

Almeida, N. F., Yan, S., Lindeberg, M., Studholme, D. J., Schneider, D. J., Condon, B., et al. (2009). A draft genome sequence of Pseudomonas syringae pv. tomato T1 reveals a type III effector repertoire significantly divergent from that of Pseudomonas syringae pv. tomato DC3000. Mol. Plant Microbe Interact. 22, 52–62. doi: 10.1094/MPMI-22-1-0052

PubMed Abstract | CrossRef Full Text | Google Scholar

Altschul, S. F., Madden, T. L., Schaffer, A. A., Zhang, J., Zhang, Z., Miller, W., et al. (1997). Gapped BLAST and PSI-BLAST: a new generation of protein database search programs. Nucleic Acids Res. 25, 3389–3402. doi: 10.1093/nar/25.17.3389

PubMed Abstract | CrossRef Full Text | Google Scholar

Andrews, S. C. (2010). FastQC: A Quality Control Tool for High Throughput Sequence Data. Available at: http://www.bioinformatics.babraham.ac.uk/projects/fastqc (accessed November 11, 2016).

Google Scholar

Baltrus, D. A., Mccann, H. C., and Guttman, D. S. (2017). Evolution, genomics and epidemiology of Pseudomonas syringae: challenges in bacterial molecular plant pathology. Mol. Plant Pathol. 18, 152–168. doi: 10.1111/mpp.12506

PubMed Abstract | CrossRef Full Text | Google Scholar

Baltrus, D. A., Nishimura, M. T., Dougherty, K. M., Biswas, S., Mukhtar, M. S., Vicente, J., et al. (2012). The molecular basis of host specialization in bean pathovars of Pseudomonas syringae. Mol. Plant Microbe Interact. 25, 877–888. doi: 10.1094/MPMI-08-11-0218

PubMed Abstract | CrossRef Full Text | Google Scholar

Baltrus, D. A., Nishimura, M. T., Romanchuk, A., Chang, J. H., Mukhtar, M. S., Cherkis, K., et al. (2011). Dynamic evolution of pathogenicity revealed by sequencing and comparative genomics of 19 Pseudomonas syringae isolates. PLoS Pathog. 7:e1002132. doi: 10.1371/journal.ppat.1002132

PubMed Abstract | CrossRef Full Text | Google Scholar

Berge, O., Monteil, C. L., Bartoli, C., Chandeysson, C., Guilbaud, C., Sands, D. C., et al. (2014). A user’s guide to a data base of the diversity of Pseudomonas syringae and its application to classifying strains in this phylogenetic complex. PLoS One 9:e105547. doi: 10.1371/journal.pone.0105547

PubMed Abstract | CrossRef Full Text | Google Scholar

Betancourt, A. J., Welch, J. J., and Charlesworth, B. (2009). Reduced effectiveness of selection caused by a lack of recombination. Curr. Biol. 19, 655–660. doi: 10.1016/j.cub.2009.02.039

PubMed Abstract | CrossRef Full Text | Google Scholar

Block, A., and Alfano, J. R. (2011). Plant targets for Pseudomonas syringae type III effectors: virulence targets or guarded decoys? Curr. Opin. Microbiol. 14, 39–46. doi: 10.1016/j.mib.2010.12.011

PubMed Abstract | CrossRef Full Text | Google Scholar

Bolger, A. M., Lohse, M., and Usadel, B. (2014). Trimmomatic: a flexible trimmer for Illumina sequence data. Bioinformatics 30, 2114–2120. doi: 10.1093/bioinformatics/btu170

PubMed Abstract | CrossRef Full Text | Google Scholar

Brockhurst, M. A., Chapman, T., King, K. C., Mank, J. E., Paterson, S., and Hurst, G. D. (2014). Running with the Red Queen: the role of biotic conflicts in evolution. Proc. R. Soc. B Biol. Sci. 281:20141382. doi: 10.1098/rspb.2014.1382

PubMed Abstract | CrossRef Full Text | Google Scholar

Buttner, D. (2016). Behind the lines-actions of bacterial type III effector proteins in plant cells. FEMS Microbiol. Rev. 40, 894–937. doi: 10.1093/femsre/fuw026

PubMed Abstract | CrossRef Full Text | Google Scholar

Charity, J. C., Pak, K., Delwiche, C. F., and Hutcheson, S. W. (2003). Novel exchangeable effector loci associated with the Pseudomonas syringae hrp pathogenicity island: evidence for integron-like assembly from transposed gene cassettes. Mol. Plant Microbe Interact. 16, 495–507. doi: 10.1094/MPMI.2003.16.6.495

PubMed Abstract | CrossRef Full Text | Google Scholar

Clarke, C. R., Cai, R., Studholme, D. J., Guttman, D. S., and Vinatzer, B. A. (2010). Pseudomonas syringae strains naturally lacking the classical P. syringae hrp/hrc locus are common leaf colonizers equipped with an atypical type III secretion system. Mol. Plant Microbe Interact. 23, 198–210. doi: 10.1094/MPMI-23-2-0198

PubMed Abstract | CrossRef Full Text | Google Scholar

Coburn, B., Sekirov, I., and Finlay, B. B. (2007). Type III secretion systems and disease. Clin. Microbiol. Rev. 20, 535–549. doi: 10.1128/CMR.00013-07

PubMed Abstract | CrossRef Full Text | Google Scholar

Cohen, O., Ashkenazy, H., Belinky, F., Huchon, D., and Pupko, T. (2010). GLOOME: gain loss mapping engine. Bioinformatics 26, 2914–2915. doi: 10.1093/bioinformatics/btq549

PubMed Abstract | CrossRef Full Text | Google Scholar

Cohen, O., and Pupko, T. (2010). Inference and characterization of horizontally transferred gene families using stochastic mapping. Mol. Biol. Evol. 27, 703–713. doi: 10.1093/molbev/msp240

PubMed Abstract | CrossRef Full Text | Google Scholar

Cunnac, S., Chakravarthy, S., Kvitko, B. H., Russell, A. B., Martin, G. B., and Collmer, A. (2011). Genetic disassembly and combinatorial reassembly identify a minimal functional repertoire of type III effectors in Pseudomonas syringae. Proc. Natl. Acad. Sci. U.S.A. 108, 2975–2980. doi: 10.1073/pnas.1013031108

PubMed Abstract | CrossRef Full Text | Google Scholar

Cunnac, S., Lindeberg, M., and Collmer, A. (2009). Pseudomonas syringae type III secretion system effectors: repertoires in search of functions. Curr. Opin. Microbiol. 12, 53–60. doi: 10.1016/j.mib.2008.12.003

PubMed Abstract | CrossRef Full Text | Google Scholar

Daubin, V., Gouy, M., and Perriere, G. (2002). A phylogenomic approach to bacterial phylogeny: evidence of a core of genes sharing a common history. Genome Res. 12, 1080–1090. doi: 10.1101/gr.187002

PubMed Abstract | CrossRef Full Text | Google Scholar

Demba Diallo, M., Monteil, C. L., Vinatzer, B. A., Clarke, C. R., Glaux, C., Guilbaud, C., et al. (2012). Pseudomonas syringae naturally lacking the canonical type III secretion system are ubiquitous in nonagricultural habitats, are phylogenetically diverse and can be pathogenic. ISME J. 6, 1325–1335. doi: 10.1038/ismej.2011.202

PubMed Abstract | CrossRef Full Text | Google Scholar

Deng, W., Marshall, N. C., Rowland, J. L., Mccoy, J. M., Worrall, L. J., Santos, A. S., et al. (2017). Assembly, structure, function and regulation of type III secretion systems. Nat. Rev. Microbiol. 15, 323–337. doi: 10.1038/nrmicro.2017.20

PubMed Abstract | CrossRef Full Text | Google Scholar

Desveaux, D., Singer, A. U., and Dangl, J. L. (2006). Type III effector proteins: doppelgangers of bacterial virulence. Curr. Opin. Plant Biol. 9, 376–382. doi: 10.1016/j.pbi.2006.05.005

PubMed Abstract | CrossRef Full Text | Google Scholar

Dillon, M. M., Thakur, S., Almeida, R. N. D., Wang, P. W., Weir, B. S., and Guttman, D. S. (2019). Recombination of ecologically and evolutionarily significant loci maintains genetic cohesion in the Pseudomonas syringae species complex. Genome Biol. 20:3. doi: 10.1186/s13059-018-1606-y

PubMed Abstract | CrossRef Full Text | Google Scholar

Dodds, P. N., and Rathjen, J. P. (2010). Plant immunity: towards an integrated view of plant-pathogen interactions. Nat. Rev. Genet. 11, 539–548. doi: 10.1038/nrg2812

PubMed Abstract | CrossRef Full Text | Google Scholar

Dong, X., Lu, X., and Zhang, Z. (2015). BEAN 2.0: an integrated web resource for the identification and functional analysis of type III secreted effectors. Database 2015:bav064. doi: 10.1093/database/bav064

PubMed Abstract | CrossRef Full Text | Google Scholar

Dudnik, A., and Dudler, R. (2014). Virulence determinants of Pseudomonas syringae strains isolated from grasses in the context of a small type III effector repertoire. BMC Microbiol. 14:304. doi: 10.1186/s12866-014-0304-5

PubMed Abstract | CrossRef Full Text | Google Scholar

Edgar, R. C. (2004). MUSCLE: a multiple sequence alignment method with reduced time and space complexity. BMC Bioinformatics 5:113. doi: 10.1186/1471-2105-5-113

PubMed Abstract | CrossRef Full Text | Google Scholar

Eisen, J. A. (2000). Assessing evolutionary relationships among microbes from whole-genome analysis. Curr. Opin. Microbiol. 3, 475–480. doi: 10.1016/S1369-5274(00)00125-9

PubMed Abstract | CrossRef Full Text | Google Scholar

Fillingham, A. J., Wood, J., Bevan, J. R., Crute, I. R., Mansfield, J. W., Taylor, J. D., et al. (1992). Avirulence genes from Pseudomonas syringae pathovars phaseolicola and pisi confer specificity towards both host and non-host species. Physiol. Mol. Plant Pathol. 40, 1–15. doi: 10.1016/0885-5765(92)90066-5

CrossRef Full Text | Google Scholar

Godfrey, S. A., Lovell, H. C., Mansfield, J. W., Corry, D. S., Jackson, R. W., and Arnold, D. L. (2011). The stealth episome: suppression of gene expression on the excised genomic island PPHGI-1 from Pseudomonas syringae pv. phaseolicola. PLoS Pathog. 7:e1002010. doi: 10.1371/journal.ppat.1002010

PubMed Abstract | CrossRef Full Text | Google Scholar

Hajri, A., Brin, C., Hunault, G., Lardeux, F., Lemaire, C., Manceau, C., et al. (2009). A “repertoire for repertoire” hypothesis: repertoires of type three effectors are candidate determinants of host specificity in Xanthomonas. PLoS One 4:e6632. doi: 10.1371/journal.pone.0006632

PubMed Abstract | CrossRef Full Text | Google Scholar

Hirano, S. S., and Upper, C. D. (2000). Bacteria in the leaf ecosystem with emphasis on Pseudomonas syringae-a pathogen, ice nucleus, and epiphyte. Microbiol. Mol. Biol. Rev. 64, 624–653. doi: 10.1128/MMBR.64.3.624-653.2000

PubMed Abstract | CrossRef Full Text | Google Scholar

Hockett, K. L., Nishimura, M. T., Karlsrud, E., Dougherty, K., and Baltrus, D. A. (2014). Pseudomonas syringae CC1557: a highly virulent strain with an unusually small type III effector repertoire that includes a novel effector. Mol. Plant Microbe Interact. 27, 923–932. doi: 10.1094/MPMI-11-13-0354-R

PubMed Abstract | CrossRef Full Text | Google Scholar

Hu, Y., Huang, H., Cheng, X., Shu, X., White, A. P., Stavrinides, J., et al. (2017). A global survey of bacterial type III secretion systems and their effectors. Environ. Microbiol. 19, 3879–3895. doi: 10.1111/1462-2920.13755

PubMed Abstract | CrossRef Full Text | Google Scholar

Hueck, C. J. (1998). Type III protein secretion systems in bacterial pathogens of animals and plants. Microbiol. Mol. Biol. Rev. 62, 379–433.

Google Scholar

Hyatt, D., Chen, G. L., Locascio, P. F., Land, M. L., Larimer, F. W., and Hauser, L. J. (2010). Prodigal: prokaryotic gene recognition and translation initiation site identification. BMC Bioinformatics 11:119. doi: 10.1186/1471-2105-11-119

PubMed Abstract | CrossRef Full Text | Google Scholar

Jenner, C., Hitchin, E., Mansfield, J., Walters, K., Betteridge, P., Teverson, D., et al. (1991). Gene-for-gene interactions between Pseudomonas syringae pv. phaseolicola and Phaseolus. Mol. Plant Microbe Interact. 4, 553–562. doi: 10.1094/MPMI-4-553

PubMed Abstract | CrossRef Full Text | Google Scholar

Jones, J. D., and Dangl, J. L. (2006). The plant immune system. Nature 444, 323–329. doi: 10.1038/nature05286

PubMed Abstract | CrossRef Full Text | Google Scholar

Karasov, T. L., Almario, J., Friedemann, C., Ding, W., Giolai, M., Heavens, D., et al. (2018). Arabidopsis thaliana and Pseudomonas pathogens exhibit stable associations over evolutionary timescales. Cell Host Microbe 24, 168–179.e4. doi: 10.1016/j.chom.2018.06.011

PubMed Abstract | CrossRef Full Text | Google Scholar

Keen, N. T. (1990). Gene-for-gene complementarity in plant-pathogen interactions. Annu. Rev. Genet. 24, 447–463. doi: 10.1146/annurev.ge.24.120190.002311

PubMed Abstract | CrossRef Full Text | Google Scholar

Keen, N. T., and Staskawicz, B. (1988). Host range determinants in plant-pathogens and symbionts. Annu. Rev. Microbiol. 42, 421–440. doi: 10.1146/annurev.mi.42.100188.002225

CrossRef Full Text | Google Scholar

Khan, M., Seto, D., Subramaniam, R., and Desveaux, D. (2018). Oh, the places they’ll go! A survey of phytopathogen effectors and their host targets. Plant J. 93, 651–663. doi: 10.1111/tpj.13780

PubMed Abstract | CrossRef Full Text | Google Scholar

Khan, M., Subramaniam, R., and Desveaux, D. (2016). Of guards, decoys, baits and traps: pathogen perception in plants by type III effector sensors. Curr. Opin. Microbiol. 29, 49–55. doi: 10.1016/j.mib.2015.10.006

PubMed Abstract | CrossRef Full Text | Google Scholar

Kim, J. F., and Alfano, J. R. (2002). Pathogenicity islands and virulence plasmids of bacterial plant pathogens. Curr. Top. Microbiol. Immunol. 264, 127–147.

Google Scholar

Kobayashi, D. Y., Tamaki, S. J., and Keen, N. T. (1989). Cloned avirulence genes from the tomato pathogen Pseudomonas syringae pv. tomato confer cultivar specificity on soybean. Proc. Natl. Acad. Sci. U.S.A. 86, 157–161. doi: 10.1073/pnas.86.1.157

PubMed Abstract | CrossRef Full Text | Google Scholar

Kumar, S., Stecher, G., and Tamura, K. (2016). MEGA7: molecular evolutionary genetics analysis version 7.0 for bigger datasets. Mol. Biol. Evol. 33, 1870–1874. doi: 10.1093/molbev/msw054

PubMed Abstract | CrossRef Full Text | Google Scholar

Kvitko, B. H., Park, D. H., Velasquez, A. C., Wei, C. F., Russell, A. B., Martin, G. B., et al. (2009). Deletions in the repertoire of Pseudomonas syringae pv. tomato DC3000 type III secretion effector genes reveal functional overlap among effectors. PLoS Pathog. 5:e1000388. doi: 10.1371/journal.ppat.1000388

PubMed Abstract | CrossRef Full Text | Google Scholar

Lam, H. N., Chakravarthy, S., Wei, H. L., Buinguyen, H., Stodghill, P. V., Collmer, A., et al. (2014). Global analysis of the HrpL regulon in the plant pathogen Pseudomonas syringae pv. tomato DC3000 reveals new regulon members with diverse functions. PLoS One 9:e106115. doi: 10.1371/journal.pone.0106115

PubMed Abstract | CrossRef Full Text | Google Scholar

Leach, J. E., and White, F. F. (1996). Bacterial avirulence genes. Annu. Rev. Phytopathol. 34, 153–179. doi: 10.1146/annurev.phyto.34.1.153

CrossRef Full Text | Google Scholar

Lewis, J. D., Wilton, M., Mott, G. A., Lu, W., Hassan, J. A., Guttman, D. S., et al. (2014). Immunomodulation by the Pseudomonas syringae HopZ type III effector family in Arabidopsis. PLoS One 9:e116152. doi: 10.1371/journal.pone.0116152

PubMed Abstract | CrossRef Full Text | Google Scholar

Li, H., and Durbin, R. (2009). Fast and accurate short read alignment with Burrows-Wheeler transform. Bioinformatics 25, 1754–1760. doi: 10.1093/bioinformatics/btp324

PubMed Abstract | CrossRef Full Text | Google Scholar

Lindeberg, M., Cunnac, S., and Collmer, A. (2009). The evolution of Pseudomonas syringae host specificity and type III effector repertoires. Mol. Plant Pathol. 10, 767–775. doi: 10.1111/j.1364-3703.2009.00587.x

PubMed Abstract | CrossRef Full Text | Google Scholar

Lindeberg, M., Cunnac, S., and Collmer, A. (2012). Pseudomonas syringae type III effector repertoires: last words in endless arguments. Trends Microbiol. 20, 199–208. doi: 10.1016/j.tim.2012.01.003

PubMed Abstract | CrossRef Full Text | Google Scholar

Lindeberg, M., Stavrinides, J., Chang, J. H., Alfano, J. R., Collmer, A., Dangl, J. L., et al. (2005). Proposed guidelines for a unified nomenclature and phylogenetic analysis of type III Hop effector proteins in the plant pathogen Pseudomonas syringae. Mol. Plant Microbe Interact. 18, 275–282. doi: 10.1094/MPMI-18-0275

PubMed Abstract | CrossRef Full Text | Google Scholar

Lovell, H. C., Jackson, R. W., Mansfield, J. W., Godfrey, S. A., Hancock, J. T., Desikan, R., et al. (2011). In planta conditions induce genomic changes in Pseudomonas syringae pv. phaseolicola. Mol. Plant Pathol. 12, 167–176. doi: 10.1111/j.1364-3703.2010.00658.x

PubMed Abstract | CrossRef Full Text | Google Scholar

Lovell, H. C., Mansfield, J. W., Godfrey, S. A., Jackson, R. W., Hancock, J. T., and Arnold, D. L. (2009). Bacterial evolution by genomic island transfer occurs via DNA transformation in planta. Curr. Biol. 19, 1586–1590. doi: 10.1016/j.cub.2009.08.018

PubMed Abstract | CrossRef Full Text | Google Scholar

Mansfield, J., Genin, S., Magori, S., Citovsky, V., Sriariyanum, M., Ronald, P., et al. (2012). Top 10 plant pathogenic bacteria in molecular plant pathology. Mol. Plant Pathol. 13, 614–629. doi: 10.1111/j.1364-3703.2012.00804.x

PubMed Abstract | CrossRef Full Text | Google Scholar

Mansfield, J. W. (2009). From bacterial avirulence genes to effector functions via the hrp delivery system: an overview of 25 years of progress in our understanding of plant innate immunity. Mol. Plant Pathol. 10, 721–734. doi: 10.1111/j.1364-3703.2009.00576.x

PubMed Abstract | CrossRef Full Text | Google Scholar

Markowitz, V. M., Chen, I. M., Palaniappan, K., Chu, K., Szeto, E., Grechkin, Y., et al. (2012). IMG: the Integrated Microbial Genomes database and comparative analysis system. Nucleic Acids Res. 40, D115–D122. doi: 10.1093/nar/gkr1044

PubMed Abstract | CrossRef Full Text | Google Scholar

Matas, I. M., Castaneda-Ojeda, M. P., Aragon, I. M., Antunez-Lamas, M., Murillo, J., Rodriguez-Palenzuela, P., et al. (2014). Translocation and functional analysis of Pseudomonas savastanoi pv. savastanoi NCPPB 3335 type III secretion system effectors reveals two novel effector families of the Pseudomonas syringae complex. Mol. Plant Microbe Interact. 27, 424–436. doi: 10.1094/MPMI-07-13-0206-R

PubMed Abstract | CrossRef Full Text | Google Scholar

McCann, H. C., and Guttman, D. S. (2008). Evolution of the type III secretion system and its effectors in plant-microbe interactions. New Phytol. 177, 33–47. doi: 10.1111/j.1469-8137.2007.02293.x

PubMed Abstract | CrossRef Full Text | Google Scholar

McCann, H. C., Rikkerink, E. H., Bertels, F., Fiers, M., Lu, A., Rees-George, J., et al. (2013). Genomic analysis of the Kiwifruit pathogen Pseudomonas syringae pv. actinidiae provides insight into the origins of an emergent plant disease. PLoS Pathog. 9:e1003503. doi: 10.1371/journal.ppat.1003503

PubMed Abstract | CrossRef Full Text | Google Scholar

Michiels, T., and Cornelis, G. R. (1991). Secretion of hybrid proteins by the Yersinia Yop export system. J. Bacteriol. 173, 1677–1685. doi: 10.1128/jb.173.5.1677-1685.1991

CrossRef Full Text | Google Scholar

Mohr, T. J., Liu, H., Yan, S., Morris, C. E., Castillo, J. A., Jelenska, J., et al. (2008). Naturally occurring nonpathogenic isolates of the plant pathogen Pseudomonas syringae lack a type III secretion system and effector gene orthologues. J. Bacteriol. 190, 2858–2870. doi: 10.1128/JB.01757-07

PubMed Abstract | CrossRef Full Text | Google Scholar

Monteil, C. L., Cai, R., Liu, H., Llontop, M. E., Leman, S., Studholme, D. J., et al. (2013). Nonagricultural reservoirs contribute to emergence and evolution of Pseudomonas syringae crop pathogens. New Phytol. 199, 800–811. doi: 10.1111/nph.12316

PubMed Abstract | CrossRef Full Text | Google Scholar

Monteil, C. L., Yahara, K., Studholme, D. J., Mageiros, L., Meric, G., Swingle, B., et al. (2016). Population-genomic insights into emergence, crop adaptation and dissemination of Pseudomonas syringae pathogens. Microb. Genom. 2:e000089. doi: 10.1099/mgen.0.000089

PubMed Abstract | CrossRef Full Text | Google Scholar

Morris, C. E., Kinkel, L. L., Xiao, K., Prior, P., and Sands, D. C. (2007). Surprising niche for the plant pathogen Pseudomonas syringae. Infect. Genet. Evol. 7, 84–92. doi: 10.1016/j.meegid.2006.05.002

PubMed Abstract | CrossRef Full Text | Google Scholar

Morris, C. E., Monteil, C. L., and Berge, O. (2013). The life history of Pseudomonas syringae: linking agriculture to earth system processes. Annu. Rev. Phytopathol. 51, 85–104. doi: 10.1146/annurev-phyto-082712-102402

PubMed Abstract | CrossRef Full Text | Google Scholar

Morris, C. E., Sands, D. C., Vinatzer, B. A., Glaux, C., Guilbaud, C., Buffiere, A., et al. (2008). The life history of the plant pathogen Pseudomonas syringae is linked to the water cycle. ISME J. 2, 321–334. doi: 10.1038/ismej.2007.113

PubMed Abstract | CrossRef Full Text | Google Scholar

Mucyn, T. S., Yourstone, S., Lind, A. L., Biswas, S., Nishimura, M. T., Baltrus, D. A., et al. (2014). Variable suites of non-effector genes are co-regulated in the type III secretion virulence regulon across the Pseudomonas syringae phylogeny. PLoS Pathog. 10:e1003807. doi: 10.1371/journal.ppat.1003807

PubMed Abstract | CrossRef Full Text | Google Scholar

Mukherjee, D., Lambert, J. W., Cooper, R. L., and Kennedy, B. W. (1966). Inheritance of resistance to bacterial blight (Pseudomonas glycinea Coerper) in soybeans (Glycine max L.). Crop Sci. 6, 324–326. doi: 10.2135/cropsci1966.0011183X000600040006x

CrossRef Full Text | Google Scholar

Murrell, B., Moola, S., Mabona, A., Weighill, T., Sheward, D., Kosakovsky Pond, S. L., et al. (2013). FUBAR: a fast, unconstrained bayesian approximation for inferring selection. Mol. Biol. Evol. 30, 1196–1205. doi: 10.1093/molbev/mst030

PubMed Abstract | CrossRef Full Text | Google Scholar

NCBI Resource Coordinators (2018). Database resources of the National Center for Biotechnology Information. Nucleic Acids Res. 46, D8–D13. doi: 10.1093/nar/gkx1095

PubMed Abstract | CrossRef Full Text | Google Scholar

Neale, H. C., Laister, R., Payne, J., Preston, G., Jackson, R. W., and Arnold, D. L. (2016). A low frequency persistent reservoir of a genomic island in a pathogen population ensures island survival and improves pathogen fitness in a susceptible host. Environ. Microbiol. 18, 4144–4152. doi: 10.1111/1462-2920.13482

PubMed Abstract | CrossRef Full Text | Google Scholar

O’Brien, H. E., Thakur, S., Gong, Y., Fung, P., Zhang, J., Yuan, L., et al. (2012). Extensive remodeling of the Pseudomonas syringae pv. avellanae type III secretome associated with two independent host shifts onto hazelnut. BMC Microbiol. 12:141. doi: 10.1186/1471-2180-12-141

PubMed Abstract | CrossRef Full Text | Google Scholar

O’Brien, H. E., Thakur, S., and Guttman, D. S. (2011). Evolution of plant pathogenesis in Pseudomonas syringae: a genomics perspective. Annu. Rev. Phytopathol. 49, 269–289. doi: 10.1146/annurev-phyto-072910-095242

PubMed Abstract | CrossRef Full Text | Google Scholar

Oh, H. S., Park, D. H., and Collmer, A. (2010). Components of the Pseudomonas syringae type III secretion system can suppress and may elicit plant innate immunity. Mol. Plant Microbe Interact. 23, 727–739. doi: 10.1094/MPMI-23-6-0727

PubMed Abstract | CrossRef Full Text | Google Scholar

O’Reilly, P. F., Birney, E., and Balding, D. J. (2008). Confounding between recombination and selection, and the Ped/Pop method for detecting selection. Genome Res. 18, 1304–1313. doi: 10.1101/gr.067181.107

PubMed Abstract | CrossRef Full Text | Google Scholar

Price, M. N., Dehal, P. S., and Arkin, A. P. (2010). FastTree 2–approximately maximum-likelihood trees for large alignments. PLoS One 5:e9490. doi: 10.1371/journal.pone.0009490

PubMed Abstract | CrossRef Full Text | Google Scholar

Rapisarda, C., and Fronzes, R. (2018). Secretion systems used by bacteria to subvert host functions. Curr. Issues Mol. Biol. 25, 1–42. doi: 10.21775/cimb.025.001

PubMed Abstract | CrossRef Full Text | Google Scholar

Rohmer, L., Guttman, D. S., and Dangl, J. L. (2004). Diverse evolutionary mechanisms shape the type III effector virulence factor repertoire in the plant pathogen Pseudomonas syringae. Genetics 167, 1341–1360. doi: 10.1534/genetics.103.019638

PubMed Abstract | CrossRef Full Text | Google Scholar

Salmond, G. P., and Reeves, P. J. (1993). Membrane traffic wardens and protein secretion in gram-negative bacteria. Trends Biochem. Sci. 18, 7–12. doi: 10.1016/0968-0004(93)90080-7

PubMed Abstract | CrossRef Full Text | Google Scholar

Sarkar, S. F., Gordon, J. S., Martin, G. B., and Guttman, D. S. (2006). Comparative genomics of host-specific virulence in Pseudomonas syringae. Genetics 174, 1041–1056. doi: 10.1534/genetics.106.060996

PubMed Abstract | CrossRef Full Text | Google Scholar

Staskawicz, B., Dahlbeck, D., Keen, N., and Napoli, C. (1987). Molecular characterization of cloned avirulence genes from race 0 and race 1 of Pseudomonas syringae pv. glycinea. J. Bacteriol. 169, 5789–5794. doi: 10.1128/jb.169.12.5789-5794.1987

PubMed Abstract | CrossRef Full Text | Google Scholar

Staskawicz, B. J., Dahlbeck, D., and Keen, N. T. (1984). Cloned avirulence gene of Pseudomonas syringae pv. glycinea determines race-specific incompatibility on Glycine max (L.) Merr. Proc. Natl. Acad. Sci. U.S.A. 81, 6024–6028. doi: 10.1073/pnas.81.19.6024

PubMed Abstract | CrossRef Full Text | Google Scholar

Stavrinides, J., and Guttman, D. S. (2004). Nucleotide sequence and evolution of the five-plasmid complement of the phytopathogen Pseudomonas syringae pv. maculicola ES4326. J. Bacteriol. 186, 5101–5115. doi: 10.1128/JB.186.15.5101-5115.2004

PubMed Abstract | CrossRef Full Text | Google Scholar

Stavrinides, J., Ma, W., and Guttman, D. S. (2006). Terminal reassortment drives the quantum evolution of type III effectors in bacterial pathogens. PLoS Pathog. 2:e104. doi: 10.1371/journal.ppat.0020104

PubMed Abstract | CrossRef Full Text | Google Scholar

Tabari, E., and Su, Z. (2017). PorthoMCL: parallel orthology prediction using MCL for the realm of massive genome availability. Big Data Anal. 2:4. doi: 10.1186/s41044-016-0019-8

CrossRef Full Text | Google Scholar

Thakur, S., Weir, B. S., and Guttman, D. S. (2016). Phytopathogen Genome Announcement: draft genome sequences of 62 Pseudomonas syringae type and pathotype strains. Mol. Plant Microbe Interact. 29, 243–246. doi: 10.1094/MPMI-01-16-0013-TA

PubMed Abstract | CrossRef Full Text | Google Scholar

Van Valen, L. (1973). A new evolutionary law. Evol. Theory 1, 1–30.

Google Scholar

Vinatzer, B. A., Teitzel, G. M., Lee, M. W., Jelenska, J., Hotton, S., Fairfax, K., et al. (2006). The type III effector repertoire of Pseudomonas syringae pv. syringae B728a and its role in survival and disease on host and non-host plants. Mol. Microbiol. 62, 26–44. doi: 10.1111/j.1365-2958.2006.05350.x

PubMed Abstract | CrossRef Full Text | Google Scholar

Wattam, A. R., Abraham, D., Dalay, O., Disz, T. L., Driscoll, T., Gabbard, J. L., et al. (2014). PATRIC, the bacterial bioinformatics database and analysis resource. Nucleic Acids Res. 42, D581–D591. doi: 10.1093/nar/gkt1099

PubMed Abstract | CrossRef Full Text | Google Scholar

Wei, H. L., Zhang, W., and Collmer, A. (2018). Modular study of the type III effector repertoire in Pseudomonas syringae pv. tomato DC3000 reveals a matrix of effector interplay in pathogenesis. Cell Rep. 23, 1630–1638. doi: 10.1016/j.celrep.2018.04.037

PubMed Abstract | CrossRef Full Text | Google Scholar

Wernersson, R., and Pedersen, A. G. (2003). RevTrans: multiple alignment of coding DNA from aligned amino acid sequences. Nucleic Acids Res. 31, 3537–3539. doi: 10.1093/nar/gkg609

PubMed Abstract | CrossRef Full Text | Google Scholar

Xin, X. F., Kvitko, B., and He, S. Y. (2018). Pseudomonas syringae: what it takes to be a pathogen. Nat. Rev. Microbiol. 16, 316–328. doi: 10.1038/nrmicro.2018.17

PubMed Abstract | CrossRef Full Text | Google Scholar

Zhou, J. M., and Chai, J. (2008). Plant pathogenic bacterial type III effectors subdue host responses. Curr. Opin. Microbiol. 11, 179–185. doi: 10.1016/j.mib.2008.02.004

PubMed Abstract | CrossRef Full Text | Google Scholar