Soft rot disease is economically one of the most devastating bacterial diseases affecting plants worldwide. In this study, we present novel insights into the phylogeny and virulence of the soft rot model Pectobacterium sp. SCC3193, which was isolated from a diseased potato stem in Finland in the early 1980s. Genomic approaches, including proteome and genome comparisons of all sequenced soft rot bacteria, revealed that SCC3193, previously included in the species Pectobacterium carotovorum, can now be more accurately classified as Pectobacterium wasabiae. Together with the recently revised phylogeny of a few P. carotovorum strains and an increasing number of studies on P. wasabiae, our work indicates that P. wasabiae has been unnoticed but present in potato fields worldwide. A combination of genomic approaches and in planta experiments identified features that separate SCC3193 and other P. wasabiae strains from the rest of soft rot bacteria, such as the absence of a type III secretion system that contributes to virulence of other soft rot species. Experimentally established virulence determinants include the putative transcriptional regulator SirB, two partially redundant type VI secretion systems and two horizontally acquired clusters (Vic1 and Vic2), which contain predicted virulence genes. Genome comparison also revealed other interesting traits that may be related to life in planta or other specific environmental conditions. These traits include a predicted benzoic acid/salicylic acid carboxyl methyltransferase of eukaryotic origin. The novelties found in this work indicate that soft rot bacteria have a reservoir of unknown traits that may be utilized in the poorly understood latent stage in planta. The genomic approaches and the comparison of the model strain SCC3193 to other sequenced Pectobacterium strains, including the type strain of P. wasabiae, provides a solid basis for further investigation of the virulence, distribution and phylogeny of soft rot bacteria and, potentially, other bacteria as well.
The soft rot bacteria Pectobacterium and Dickeya of the family Enterobacteriaceae are responsible for significant, global economic losses of crops and ornamental plants, both in the field and in storage. Although soft rot enterobacteria can infect a wide variety of plants, the main crop affected is potato (Solanum tuberosum L.). Potato is number five among food crops in the world and is a staple food in a number of countries (FAOSTAT 2009, http: //faostat. fao. org/). Previously, the Pectobacterium and Dickeya species were classified into the Erwinia genus, and relatively recent phylogenetic analyses have elevated them to novel genera [1], [2]. In addition, some of the subspecies have been raised to the species level [3]. For historical reasons, three distinct potato diseases caused by soft rot enterobacteria are described: common soft rot (tuber symptoms), blackleg (tuber-born stem disease) and aerial stem rot (spread mechanically or via insects) [4]. Pectobacterium and Dickeya are characterized as opportunistic pathogens that switch from an asymptomatic latent phase into a virulent phase in suitable environmental conditions [5]. The virulent phase is thought to begin under anoxic conditions when oxygen radical-dependent plant defense mechanisms decline, allowing bacterial multiplication and the induction of plant cell wall-degrading enzymes (PCWDEs). The induction of PCWDEs occurs when the bacterial cell density reaches a quorum of 107 cfu/g of plant tissue [5]. The soft rot enterobacteria are necrotrophs and are generally considered to be brute-force pathogens relying on PCWDEs for pathogenicity. In fact, several regulatory mutants affecting enzyme production are essentially avirulent [6]–[9]. Studies of more fine-tuned virulence mechanisms may have been hampered by this massive production of PCWDEs [6]. Therefore, the latent stage preceding necrotrophy remains poorly understood. However, during the last 25 years, a number of other virulent lifestyle promoting determinants have been identified from Pectobacterium. These determinants include the following: flagella-based motility, cell membrane structures, such as enterobacterial common antigen (ECA) and lipopolysaccharide (LPS), type III secretion systems (T3SS), type IV secretion systems (T4SS), type VI secretion systems (T6SS), necrosis-inducing protein (Nip), a protein similar to an avirulence protein in Xanthomonas (Svx), coronafacic acid synthesis pathways (cfa genes), plant ferredoxin-like protein (FerE) and citrate uptake and 3-hydroxy-2-butanone pathways (bud) [10]–[19]. The virulence strategies of necrotrophic bacteria differ from hemibiotrophic and biotrophic phytopathogens, such as Erwinia amylovora, Pantoea sp. and Agrobacterium tumefaciens, which rely mainly on T3SS or T4SS for pathogenicity [20], [21]. The genomic era has provided novel tools for the identification of previously unknown virulence determinants without large-scale biological experiments. Several genomic studies of plant and animal pathogenic enterobacteria have been conducted, but only two have been published on soft rot bacteria. The first study compared P. atrosepticum and Salmonella, and the second study characterized P. atrosepticum, P. carotovorum and P. carotovorum subsp. brasiliensis [11], [22]. The soft rot pathogen Pectobacterium sp. SCC3193 was originally isolated from a diseased potato stem from a Finnish field in the early 1980s and was characterized as P. carotovorum (previously called Erwinia carotovora subsp. carotovora) [23]. Since its discovery, SCC3193 has been a model strain in soft rot research, and much is known about its virulence and molecular biology [7], [15], [17], [23]–[33]. In this work, we present a revised phylogeny, an analysis of virulence determinants and a characterization of genomic islands; we also discuss novel virulence strategies utilized throughout the lifestyle of Pectobacterium sp. SCC3193. We show that SCC3193 can be taxonomically classified as Pectobacterium wasabiae and that P. wasabiae has unique features when compared with other Pectobacterium strains; these features were most likely acquired via horizontal gene transfer. This work indicates that P. wasabiae has been present, though unnoticed, in European and maybe in American potato fields for a long time. Genome analysis is supplemented with experimental results to suggest novel virulence determinants of Pectobacterium; many of these determinants could be important during the poorly characterized latent stage of infection.
The species status of Pectobacterium sp. SCC3193 was questioned after an initial review of the recently sequenced genome of SCC3193 by our Pectobacterium sequencing consortium in Helsinki, Finland (CP003415, Koskinen et al. , in press). We discovered distinctive sequence similarity of SCC3193 to the strain WPP163 (NC_013421. 1), which was sequenced in 2009 by another group (Nicole Perna and coworkers, and US DOE Joint Genome Institute; unpublished). WPP163 was isolated from potato stem and classified as P. wasabiae prior to the genome sequencing [34]. Originally, Pectobacterium sp. SCC3193 was identified as Pectobacterium carotovorum based on disease symptoms in potato, the ability to produce PCWDEs, fatty acid composition and other biochemical properties. Subsequent studies suggested that SCC3193 may not be a typical P. carotovorum strain due to its LPS composition, sensitivity to T4 phage, decreased ability to macerate plant tissues and inability to grow at +37°C [23]. However, P. carotovorum has been recognized as a highly variable species that is composed of several subspecies. Thus, differences in phenotype have been accepted [3]. At the time of the isolation and characterization of SCC3193, the species Pectobacterium wasabiae (previously called Erwinia carotovora subsp. wasabiae) had not yet been described. The type strain (CFBP 3304T) of P. wasabiae was isolated from wasabi (Japanese horseradish) in 1987 [3], [35]. Reports of P. wasabiae isolates are limited when compared to the number of P. carotovorum and P. atrosepticum reports [35]–[40]. To evaluate the species status of SCC3193, we conducted a thorough phylogenetic analysis of SCC3193, which included biochemical and genomic methods. The genome of P. wasabiae CFPB 3304T was sequenced by our consortium to be used as a reference in this phylogenetic analysis. To obtain an overall view of SCC3193 virulence factors, we mined the genome for known virulence determinants of Pectobacterium. Because we are working with an established model strain, many virulence-associated determinants of SCC3193 have already been identified in genetic studies, and some of these were even originally described in SCC3193. This may create a bias that is reflected in the number of published virulence-associated genes found in the genome of SCC3193. Only a few previously described virulence-related genes in Pectobacterium were not found in SCC3193: namely, the genes for coronafacic acid synthesis (specific to P. atrosepticum) and the type III secretion system (T3SS) present in many P. atrosepticum and P. carotovorum strains [11], [13]. The T3SS is composed of the injection machinery encoded by the conserved hrp/hrc gene cluster and of a species/strain-specific collection of effectors required to suppress the basal defenses of the host [55]. Analysis of the SCC3193, P. wasabiae WPP163 and the type strain (CFBP 3304T) genomes failed to identify any traces of T3SS. It appears typical of P. wasabiae that it lacks this widespread and important virulence determinant. These results are in agreement with previous failed efforts to detect T3SS in SCC3193 or other P. wasabiae strains; for example, no signs of this injection machinery or the associated effectors (encoded by hrpN, dspE and hecB genes) have been found [34], [36], [56]. Although absent from P. wasabiae, T3SS contributes to the virulence of other soft rot species [57]–[59]. Contrary to hemibiotrophic pathogens such as Pseudomonas syringae, where T3SS is essential for pathogenicity [21], the role of T3SS in necrotrophic soft rot bacteria appears quite complex. T3SS contributes to the virulence of P. carotovorum, P. atrosepticum and Dickeya strains, but even strains naturally lacking T3SS, such as P. wasabiae, are still able to infect potatoes. A recent study showed no clear correlation between virulence and the presence of T3SS in P. carotovorum [34]. The relatively modest significance of T3SS to pathogenicity in pectobacteria is also reflected in the small number of T3 effectors found in these species compared, for example, to the dozens of known effectors of Pseudomonas syringae [60]. Thus, Pectobacterium may have alternative ways of modifying the host plant at the initiation of infection. The lack of T3SS and numerous effectors may also benefit Pectobacterium by widening its host range. Soft rot bacteria, excluding the potato-specific P. atrosepticum, are often found to colonize a wide range of food crops and ornamental plants, while many T3SS-dependent plant pathogens, such as Erwinia, Pseudomonas, Xanthomonas and Xylella, have a very narrow host range. It is well documented that some T3 effectors may act as colonization-inhibiting avirulence proteins that are recognized by the plant [55]. Virulence assays to investigate the role of T3SS in soft rot bacteria in planta thus far indicate that in a complex natural niche, T3SS may have an important role under certain conditions but not in others. Horizontal gene transfer plays an important role in the evolution of bacteria. It enables the rapid acquisition of beneficial traits in a single event. These gene clusters of probable horizontal origin are termed genomic islands (GIs/GEIs) or horizontally acquired islands (HAIs) [11], [61], [62]. To characterize the genome composition of SCC3193 and identify horizontally acquired virulence determinants or other adaptive traits, we determined putative GIs in the genome of SCC3193. We used two sequence composition-based GI prediction methods, SIGI-HMM and IslandPath-DIMOB [63], [64], which were found to have the highest overall accuracy of the six methods tested in a recent bioinformatic study [65]. In addition, we used one comparative genomic-based GI prediction method, IslandPick [65]. The three methods predicted a different number of islands and smaller islets: six for IslandPath-DIMOB, ten for IslandPick and over hundred for SIGI-HMM (63 consisted of five or more successive ORFs) (Figure 4). Automated predictions were subsequently manually curated, resulting in a total of 56 genomic islands (Table S2). The GIs comprise ∼0. 86 Mb, which is 16. 7% of the size of the genome and encompasses 21. 6% of all ORFs (1040 of the 4804 ORFs). In comparison, 17. 6% of the ORFs of Escherichia coli MG1655 were estimated to have been acquired horizontally [66]. In general, GIs are estimated to comprise between 1. 6% and 32. 6% of the ORFs in prokaryotic genomes [67]. A majority of the SCC3193 islands can be found identically or with slight permutations in P. wasabiae WPP163, whereas less than half of the islands are present in the genome of P. wasabiae CFBP 3304T (Table S2). Approximately 15 islands were specific to P. wasabiae among the soft rot group. Some Pectobacterium-specific islands (for example, GI_44) or islands partially present in other Pectobacterium strains but completely lacking from Dickeya (for example GI_13 and GI_36) were also discovered. A number of islands were specific to SCC3193 and could not be found in any other strains of soft rot bacteria (for example, GI_20, GI_40, GI_43 and GI_54). Furthermore, a few islands showed a significant similarity to the genomes of bacteria outside the soft rot species (GI_9, GI_10, GI_30, GI_40, GI_44 and GI_50). Functional predictions for the genes on the islands in SCC3193 suggested the presence of a high number of mobile element genes and genes with unknown functions. The islands were also found to contain several genes for known virulence determinants, such as Nip (GI_17), which is necessary for the full virulence of SCC3193 [15], and DsbA (GI_56), which is required for the correct conformation of many secreted virulence proteins in P. atrosepticum and P. carotovorum [68], [69]. Most of the islands were shown to carry genes with predicted functions that could often be potentially associated with plant colonization or virulence (Table 1, Table S2, Figure 4). The characterization of the SCC3193 genome revealed novel genes that could contribute to the virulent lifestyle. We selected a group of interesting potential virulence determinants for experimental verification. The corresponding genes were inactivated by targeted mutagenesis, and their contribution to virulence was tested on axenic tobacco seedlings and potato tuber slices. A previously characterized phytase gene in P. wasabiae CFBP 3304T (Y17_1078) [106] is also present in P. wasabiae SCC3193 (W5S_4347) and P. wasabiae WPP163 (Pecwa_4189). In our virulence assays the phenotype of an SCC3193 phytase gene knock-out mutant was inconclusive and its potential role in virulence would require additional studies. However, the phytase gene is not unique to P. wasabiae as it can be found from all sequenced Pectobacterium strains and many other plant associated bacteria. The mutants that had phenotypes in planta were tested for their ability to grow in vitro and produce polygalacturonases and cellulases. No differences were observed between the mutants and the wild- type strain (data not shown), indicating that the phenotypes in planta are due to plant-microbe interactions and not general growth defects or a major reduction in PCWDE production. The model strain Pectobacterium sp. SCC3193 has been one of the most intensively studied soft rot strains for over two decades. Therefore, the molecular-level information on its virulence has had a significant impact on the theory of virulence in Pectobacterium. However, SCC3193 was originally incorrectly identified as P. carotovorum at the time of isolation, and our extensive phylogenetic analysis in this report reveals that it belongs to the species P. wasabiae, which is a less characterized species in the soft rot group. It is likely that other Pectobacterium isolates could also be incorrectly classified, and P. wasabiae may be more common than previously thought. Our report will facilitate further studies of the distribution and molecular biology of P. wasabiae, as a well-studied model strain has been added to the species, and three genome sequences are now publicly available, including the Japanese type strain sequenced in this study. The absence of the T3SS and T3 effectors is the most distinctive feature separating Pectobacterium wasabiae SCC3193 and other P. wasabiae strains from other soft rot bacteria, other plant pathogens and other animal pathogenic enterobacteria. However, the number of confirmed T3 effectors is limited in soft rot bacteria overall, compared with the dozens in hemibiotrophs, and the effect of T3SS on virulence is not central in Pectobacterium. Our genomic approach, which was supplemented with in planta experiments, revealed putative ways for Pectobacterium wasabiae and other pectobacteria to establish infection. Promising candidates for novel virulence determinants having an effect at the early stage of infection are Virulence cluster 2, which carries genes for a putative novel lipoprotein transport system; HopL1, which is an adjacent T3SS effector-like protein; and T6SS. The latter has similar features to T3SS, which injects effectors into the host cell. T6SS is found in a variety of bacterial species and is not confined to pathogens. On the nitrogen-fixing plant symbiont Rhizobium leguminosarum, T6SS is related to host specificity [121]. The P. wasabiae benzoic acid/salicylic acid methyltransferase may also represent a novel way to manipulate the host in the latent stage, but we have not yet found experimental evidence to support this hypothesis. In the future, to learn more about the latent stage of Pectobacterium infection, we should also consider bacteria outside the pathogens relying on T3SS. Many endophytic bacteria lack T3SS, T4SS and/or the production of pectinolytic enzymes common in phytopathogens [122]–[125]. They are known to use a diverse set of attachment structures and flagella-based motility to colonize plants and to produce plant hormones and other compounds for the modification of plant metabolism. Altogether, endophytic bacteria and symbionts, which efficiently establish interactions with plants, could be viewed as an opportunity to learn more about colonization mechanisms, which may also be important for the pathogenic lifestyle of soft rot bacteria during the latent stage of infection.
In this work, we used Pectobacterium wasabiae SCC3193, its derivatives and Pectobacterium wasabiae CFBP 3304T for the sequencing and/or biological experiments (Table S3). Standard growth conditions included culturing bacteria on Luria Broth (L3522, Sigma-Aldrich, US) for 1 d at +28°C. The antibiotics chloramphenicol (20 µg/mL) or ampicillin (100 or 150 µg/mL) were added when appropriate. Genomic DNA of Pectobacterium wasabiae CFBP 3304T was extracted from an overnight culture using phenol-ether purification and ethanol precipitation. The quality and quantity of the DNA was assessed using spectrophotometry and agarose gel electrophoresis. The DNA concentration used for the hybridization step was 8 pM. Template amplification/cluster generation was performed using the Cluster station and the Single-Read Cluster generation kit v. 4 (Catalog # GD-103-4001). The sequencing was performed with an Illumina Genome Analyzer IIx using a v. 5 sequencing kit (FC-104-5001). All operations were performed following the manufacturer' s protocols (Cluster Station User Guide, Part # 15005236 Rev. B, November 2009, Sequencing Kit v5 Reagent Preparation Guide, Part # 15013595 Rev. A, May 2010). Data generated by Illumina Solexa GAIIx were analyzed with SCS-RTA v. 2. 9. Matrix and phasing parameters that were estimated from the PhiX control were used for base-calling. The demultiplexing and conversion of bcl-files to FASTQ-files were performed using OLB v. 1. 9. 0 and CASAVA GERALD v. 1. 7. 0. The instrument vendor provided all the software. Adapter sequences were clipped from the reads using cutadapt-tool. Additionally, if an adapter was removed from one read, the other read was shortened to reflect the change when necessary. The read data were assembled with ABySS using the following parameters: −j = 2 k = 60 n = 10 q = 15 ABYSS_OPTIONS = ‘–illumina-quality -c 25 -e 25’. This Whole Genome Shotgun project has been deposited at DDBJ/EMBL/GenBank under the accession AKVS00000000. The version described in this paper is the first version, AKVS01000000. The ORF prediction was completed for CFBP 3304T using the Prodigal gene prediction program [126]. Systematic errors made by Prodigal were corrected, and the intergenic areas were double checked for missed gene predictions with the GenePRIMP program [127] and manual correction. Predicted and corrected protein sequences were then functionally annotated with descriptions (DE), Gene Ontologies (GO) and Enzyme Commission Numbers (EC) using the PANNZER tool (Koskinen et al. , in preparation; method is unpublished). The COGnitor tool [128] was used to link sequences into COG database clusters. Finally, functional elements (for example, domains) were searched using the InterProScan tool [129]. The annotated ORFs of SCC3193 (Koskinen et al. , submitted) were grouped into hypothetical operons using the ofs1. 2 software [130]. The grouping was based on link probabilities, which represent the probability of a given ORF to be co-expressed with a downstream gene (see Dataset S2). Based on an E. coli benchmark (RegulonDB 6. 7 release [131]), our estimates suggest that thresholding these link probabilities at 0. 54 will recall approximately 83% of true operon links with 81% specificity. The OFS operon prediction is based on intergenic distances, similarities between annotations and the conservative clustering of similar genes in other species. The set of “other species” (referred to as informant species) used in this analysis was compiled using a single representative of each bacterial order enlisted in the NCBI bacteria taxonomy. At the time of compilation, there were 119 orders with at least one full genome sequenced; therefore, the list contains 119 entries (Dataset S3). In addition to the predicted protein sequences of Pectobacterium wasabiae SCC3193 and Pectobacterium wasabiae CFBP 3304T, full RefSeq proteomes were fetched from NCBI for 52 additional species. One-to-one orthologs of SCC3193 proteins were determined using the RBH (reciprocal best hit) criterion. Protein X. G1 from proteome G1 and protein X. G2 from proteome G2 are reciprocal best hits, if there X. G2 is the best match of X. G1 in proteome G2 and X. G1 is the best match of X. G2 in proteome G1. The full proteomes of SCC3193 and 53 target species were compared using SANS [41] with window size of 100. For phylogenetic analysis, we selected 51 groups of orthologs of SCC3193 proteins present in each of the 53 other species. The ortholog groups were then aligned using Muscle v. 3. 8. 31 [132]. For all multiple alignments, 1000 bootstrap trees were created using RAxML v. 7. 0. 4 [133]. The settings used for RAxML were “raxmlHPC -m PROTGAMMAJTT -c 4 -f d -n %s -s %s -x 137 -N 1000”, where %s was replaced with the correct input and corresponding output file names. All the bootstrapped trees were merged using the Consense program v. 3. 68 from the Phylip package [134]. The merged trees were visualized using iTOL webtools [135]. Biochemical tests for the ability to reduce sugars, phosphatase activity, indole production, growth on sorbitol, growth on melibiose, growth on raffinose, growth on lactose, utilization of keto-methyl glucoside, growth in 5% NaCl and growth at +37°C were conducted for P. wasabiae SCC3193 and the type strains P. wasabiae CFBP 3304T, P. carotovorum CFBP 2046T and P. atrosepticum HAMBI 1429T according to the previously described protocols [136], [137]. In the proteome comparison, we re-identified all the ORFs for the sequenced Pectobacterium and Dickeya species and for the outgroup Yersinia pestis CO92. This was performed using Prodigal for gene prediction. The proteomes were then aligned using the blastp program and clustered into orthologous groups with OrthoMCL program. OrthoMCL clusters were converted into an orthologs vs. species (OvsS) matrix. The obtained OvsS matrix can be altered for ease of interpretation by ordering similar columns and rows next to each other, which was accomplished by creating a hierarchical cluster tree from the rows and columns. The internal nodes of the tree were flipped to place more similar neighboring clusters next to each other [138]. The similarity of the columns was based on the Pearson correlation, while the similarity of the rows was based on the cosine similarity. These measures were selected by reviewing the obtained visualization from the different similarity measures. The ordered matrix was then visualized on a heat map. A distance matrix was created by calculating the Pearson correlation similarity scores between species in the OvsS matrix and then visualized as a heat map. Pectobacterium wasabiae CFBP 3304T contigs were ordered by aligning them against a reference genome, Pectobacterium wasabiae SCC3193. This alignment was created with Mauve v. 2. 3. 1 [139]. The reordered Pectobacterium wasabiae CFBP 3304T contigs were then aligned against the genomes of Pectobacterium wasabiae WPP163 and Pectobacterium atrosepticum SCRI1043. Mauve produced a genome content distance matrix as an output from the pairwise alignments, which was used to quantify the differences in the aligned sequences. Genomic island predictions were performed using three computational tools: two that utilize sequence composition-based GI prediction methods, SIGI-HMM from the Colombo package [63] and IslandPath-DIMOB [64], and one that is based on a comparative genomic-based GI prediction, IslandPick [65]. Automated predictions were manually curated. The borders were adjusted such that genes known to be frequently associated with GIs or mobile genetic elements, such as integrase and phage genes, were added when these genes were located adjacent to automatically predicted islands. In addition, when necessary, the proximity of tRNA genes, similar functions encoded adjacent to the predicted island and the absence of the region in closely related strains based on blastn were used as criteria to modify the islands. Finally, we filtered out all islands consisting of less than five ORFs. To determine whether the SCC3193 genomic islands are also present in the genomes of other soft rot bacteria, blastn searches were performed with the nucleotide sequences of the islands against all available Pectobacterium and Dickeya genomes in GenBank. For P. carotovorum WPP14 and P. carotovorum subsp. brasiliensis PBR1692, whose genomes have not been completed, the combined query coverage of all contigs for each island was estimated based on a WGS-blastn search against these genomes. For P. wasabiae CFBP 3304T, the estimation was based on blastn pairwise alignment. The comparison of orthologous proteins of Pectobacterium wasabiae SCC3193 was conducted against 41 selected soft rot bacteria, plant pathogens, animal pathogens and insect pathogens (Dataset S4) to identify novel virulence genes in SCC3193, in P. wasabiae or in soft rot bacteria. The comparison was performed manually, based on clusters created with OrthoMCL [140]. The genome and specific proteins identified were analyzed using the genome viewer Argo v. 1. 0. 31 [141], operon predictions, tblastn via Embster 2. 0 beta launched from the CSC Chipster platform (http: //chipster. csc. fi/embster/), annotation, blastn and blastp in GenBank NCBI [142], [143]. These analyses were conducted to identify known virulence determinants, detect missing virulence determinants and identify novel putative virulence determinants. The analysis of PCWDEs was performed by utilizing publications, sequence similarity and selected GO terms (Table S4) known to be associated with the enzymes of interest. The enzymes with the selected GO terms were mined using PANNZER (Koskinen et al. , in preparation; method is unpublished), InterProScan [129] or BioMart [144]. The proteinases present in E. coli were discarded according to Glasner et al. [22], and they were not considered as putative enzymes targeting plants. The inactivation of individual genes and gene clusters was performed by deleting target sequences and replacing them with an antibiotic cassette, according to Datsenko and Wanner [145]. The antibiotic cassette was amplified from a pKD3 template plasmid with primers carrying 50 bp of similar sequence to the genomic DNA of Pectobacterium wasabiae SCC3193 (Table S5). The cloning was conducted using the proofreading PCR enzyme Phusion according to the manufacturer' s 3-step protocol (Finnzymes). The insert was gel purified. For the electrocompetent cells, bacteria were grown overnight, diluted 1∶50 and grown to OD600 0. 4. The cells were cooled down and washed twice with sterile ice-cold ddH2O and once with sterile ice-cold 10% glycerol. The cells were resuspended in 1. 5–2x volume of sterile ice-cold 10% glycerol. Electroporation was performed with the following settings: 2. 5 kV, 25 µF and 200 Ω in 0. 2 cm cuvettes (Bio-Rad Laboratories). The recovery times for pKD46 and the antibiotic cassette insertion were 15 min and 3. 5 h, respectively. The mutations and the position of the insert were confirmed with two PCR reactions according to Datsenko and Wanner [145] in addition to sequencing (Table S5). For the double mutant of T6SS, the first antibiotic cassette was digested using flipase produced in trans from the pFLP2 plasmid [146]. For the complementation experiments, the genes of the sirB locus were amplified by PCR from wild-type SCC3193 genomic DNA using the proofreading PCR enzyme Phusion (Finnzymes). The following primers were utilized: SirB1_compl_F and SirB1_compl_R for sirB1; SirB2_compl_F and SirB2_compl_R for sirB2; and SirB2_compl_F and SirB1_compl_R for the complete sirB locus (Table S5). The PCR products were gel purified, digested with HindIII and SacI and ligated into pMW119 (Nippon Gene), which was digested with the corresponding enzymes. The constructs were confirmed via PCR and sequencing. The bacterial virulence was tested on axenic tobacco seedlings (Nicotiana tabacum cv. ‘Samsun’) according to Pirhonen et al. [24]. The seedlings were propagated in 24-well tissue culture plates on ½ MS medium supplemented with vitamins (Duchefa) and 2% sucrose and solidified with 0. 8% agar. The seedlings were grown for 16 days with a 16/8 h day/night cycle at 26/22°C. The bacterial strains were grown overnight, washed and diluted in 10 mM MgSO4. A single leaf from each of 48 plants was wounded with a needle and inoculated with 1. 5 µl of bacterial solution (OD600 0. 05). The inoculated plants were kept in the dark at room temperature, and the development of disease symptoms (leaf maceration) was scored visually at 24 and 48 h after inoculation on a scale of 0 to 6 according to the severity of symptoms. To determine bacterial growth in planta (cfu/plant), the inoculated plants were kept in the dark at 26°C and homogenized into 10 mM MgSO4 after 24 and 44 h; serial dilutions of bacteria were plated. All experiments were repeated three times, and the data of each replicate were analyzed statistically utilizing a Mann-Whitney significance test for the pairwise comparison of two independent samples using PASW Statistics 18. For the tuber slice assay [17], bacterial strains were grown overnight, washed and resuspended into 10 mM MgSO4. Tubers (cv. Van Gogh, H&H Tuominen, Finland) were washed with tap water and surface sterilized with 10% Na-hypochlorite for 5 min. The tubers were sliced 0. 6 cm thick, surface sterilized again with flaming and placed on wet paper tissue in a Petri dish. Ten to 12 tuber slices were inoculated with 10 µl of bacterial solution containing 106 cfu/ml and incubated at room temperature in a shady place for three days. The macerated area was assessed, and the results were classified into seven groups: 0%, ∼5%, ∼20%, ∼50%, ∼75%, ∼90% and 100% rotten tissue per tuber slice surface area. The experiments were repeated five times, and data from all the replicates were combined for statistical analyses utilizing the Mann-Whitney significance test for the pairwise comparison of two independent samples using PASW Statistics 18. Cellulase (Cel) and polygalacturonase (PehA) activities were assayed from 10 µl of supernatant of liquid cultures grown overnight and from appropriate dilutions of the supernatant on corresponding enzyme indicator plates [7]. The growth of the sirB, Vic1, Vic2 and T6SS-double mutants were compared with the SCC3193 wild-type strain in an hrp-inducing minimal medium [147] using 0. 4% polygalacturonic acid (Sigma P3850) as a sole carbon source. The bacteria were washed once with 1x minimal medium, diluted to OD600 0. 1 and then grown for 20 h at +28°C with shaking (200 rpm).
The soft rot bacteria of the genus Pectobacterium are an economically important group of plant pathogens in the Enterobacteriaceae family. Pectobacteria are characterized by their massive production of plant cell wall-degrading enzymes, which are aggressively used to decay the living plant tissue for nutrient acquisition. Genome analysis of Pectobacterium sp. SCC3193, a genetically well characterized model in soft rot research, provided several surprises. Phylogenetic analysis demonstrated that SCC3193 belongs to the species Pectobacterium wasabiae, which has recently been reported from potato in increasing numbers worldwide. It is not clear whether P. wasabiae represents a true emerging pathogen in potato fields or whether traditional methods have been unable to differentiate between P. wasabiae and P. carotovorum. The genome of SCC3193 was found to harbor novel virulence determinants, some of which may have been acquired from distantly related bacterial species or even from other kingdoms, such as plants. The 25 years of SCC3193 research and the novelties found in this study will provide the basis for understanding the host pathogen interactions of P. wasabiae and improving strategies to combat bacterial soft rot.
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genetics and genomics
Revised Phylogeny and Novel Horizontally Acquired Virulence Determinants of the Model Soft Rot Phytopathogen Pectobacterium wasabiae SCC3193