Genome-Wide Comparative Functional Analyses Reveal Adaptations of Salmonella sv. Newport to a Plant Colonization Lifestyle

Abstract

Outbreaks of salmonellosis linked to the consumption of vegetables have been disproportionately associated with strains of serovar Newport. We tested the hypothesis that strains of sv. Newport have evolved unique adaptations to persistence in plants that are not shared by strains of other Salmonella serovars. We used a genome-wide mutant screen to compare growth in tomato fruit of a sv. Newport strain from an outbreak traced to tomatoes, and a sv. Typhimurium strain from animals. Most genes in the sv. Newport strain that were selected during persistence in tomatoes were shared with, and similarly selected in, the sv. Typhimurium strain. Many of their functions are linked to central metabolism, including amino acid biosynthetic pathways, iron acquisition, and maintenance of cell structure. One exception was a greater need for the core genes involved in purine metabolism in sv. Typhimurium than in sv. Newport. We discovered a gene, papA, that was unique to sv. Newport and contributed to the strain’s fitness in tomatoes. The papA gene was present in about 25% of sv. Newport Group III genomes and generally absent from other Salmonella genomes. Homologs of papA were detected in the genomes of Pantoea, Dickeya, and Pectobacterium, members of the Enterobacteriacea family that can colonize both plants and animals.

Introduction

Salmonellosis outbreaks linked to the consumption of fruits (tomatoes, cucumbers, cantaloupes), leafy green vegetables and sprouts became an important public health issue over the last decade, defying the traditional notion that this pathogen is only associated with products of animal origin (Teplitski et al., 2009; Hernandez-Reyes and Schikora, 2013; Jackson et al., 2013; Wiedemann et al., 2014; Bennett et al., 2015). A CDC study identified Salmonella sv. Newport as the predominant serovar involved in outbreaks traced to vegetables. It was responsible for 57% of outbreaks associated with all fresh vegetables, and for 29% of outbreaks associated with vine-stalk vegetables (such as tomatoes and cucumbers). Tomatoes were a major source of salmonellosis outbreaks, and were implicated in up to 90% of outbreaks linked to vine-stalk vegetables, with sv. Newport responsible for 32% of them (Jackson et al., 2013). An analysis of outbreak occurrence from 1990 to 2010 identified 15 outbreaks that were associated with fresh tomatoes, and sv. Newport was responsible for six of them (Bennett et al., 2015). The association of salmonellosis outbreaks with sv. Newport and fresh vegetables reported in independent geographical locations suggests that this serovar may have a close relationship with plants and/or has evolved to persist in the vegetable production environment. At least three hypotheses can explain this phenomenon.

One hypothesis for the overrepresentation of sv. Newport in produce-associated outbreaks is that it evolved functions that make it more fit in plants. This hypothesis is supported by the evidence that sv. Newport outcompetes other serovars during plant colonization. When tested for proliferation in tomatoes, Salmonella sv. Newport reached higher cell numbers in green and pink tomatoes than sv. Typhimurium, Braenderup, and Montevideo (Marvasi et al., 2013b). Cells of serovar Newport were also recovered at higher rates from tomato rhizosphere than those of the serovars Saintpaul, Typhimurium, and Montevideo (Zheng et al., 2013). At least in part, the ability of some strains of sv. Newport to be more competitive within tomatoes could be due to spontaneous non-rdar mutations, which increased Salmonella fitness inside tomatoes (Zaragoza et al., 2012).

Secondly, strains of serovar Newport may be more fit in plants than those of other serovars because they are better able to adapt their physiology to enter and proliferate in tomato tissues (Teplitski and de Moraes, 2018). Studies with regulatory mutants and mutants in metabolic pathways suggest that during tomato colonization, Salmonella utilizes carbohydrates and inorganic sources of nitrogen and then uses the acquired nutrients to synthesize amino acids, LPS, and capsule. For example, genes involved in amino acid biosynthesis and iron acquisition were important for Salmonella growth in tomato pericarps. Inside tomato fruits, at least 51 Salmonella genes were differently regulated, including fadH, involved in fatty acid degradation, and cysB, the regulator for cysteine biosynthesis and acquisition. Changes in surface structures are also part of the Salmonella strategy for proliferation within plant tissues. In unripe tomatoes, the yihT gene, involved in the synthesis of O-antigen, is required for successful colonization of pericarps (Noel et al., 2010; Marvasi et al., 2013a; Nugent et al., 2015; de Moraes et al., 2017). These observations suggest that the environment in plant hosts is nutritionally unbalanced, and Salmonella has to employ and coordinate a diverse set of functions to thrive and establish itself within a niche already occupied by the native microbiota. In many respects, these observations are in line with the requirements of other phytobacteria during their interactions with plants (Lindow and Leveau, 2002; Lindow and Brandl, 2003). However, an earlier analysis of the totality of functions used by S. Typhimurium to colonize tomatoes revealed that, despite some similarities with phytobacteria in its plant colonization strategies, it relies on a distinct set of functions to establish itself within tomatoes (de Moraes et al., 2017).

Lastly, the advantage of sv. Newport over other serovars in colonization of plants could be enhanced by the presence of intrinsic colonization factors in this Salmonella clade. Studies of Salmonella virulence in animals show how the serovars’ genetic diversity can affect interactions with the hosts. For example, distribution of virulence plasmids and pathogenicity islands is serovar-specific, and the presence of these genetic determinants appears to impact host range (Barrow et al., 1987; Barrow and Lovell, 1988; Libby et al., 1997; Desai et al., 2013). Is it possible that sv. Newport has functions, not commonly present in other serovars of Salmonella, that allow it to colonize plants more efficiently?

In this study, we applied comparative genomics combined with transposon insertion screening analysis to identify unique features that distinguish sv. Newport from sv. Typhimurium in the Salmonella-tomato interaction model.

Materials and Methods

Bacterial Strains and Culture Conditions

Bacterial strains and plasmids used in this study are listed in Table 1. Salmonella enterica and Escherichia coli strains were propagated in LB (Luria Bertani) broth (Fisher Scientific) at 37 or 30°C, as specified in the text. When necessary, bacteria were plated onto LB agar (Fisher Scientific) or XLD (Oxoid) agar plates. As appropriate, growth media were supplemented with 100 μg/ml ampicillin, 60 μg/ml kanamycin or 20 μg/ml chloramphenicol. Construction of mutants was done using λ Red mutagenesis (Datsenko and Wanner, 2000). Typically, entire ORFs (from the start to the stop codon) were excised and replaced with a frt-kan-frt or a frt-cat-frt cassette, and the kanamycin (or chloramphenicol) resistance gene was flipped out as in Datsenko and Wanner (2000).

Transposon Insertion Library Screening in Tomato Pericarps

Tomato inoculation with a S. enterica Newport C4.2 transposon library was performed as previously done with S. enterica Typhimurium ATCC14028 (de Moraes et al., 2017). Tomatoes (cultivar Campari) were obtained from a local grocery store, where they are sold on the vine in a clam shell plastic container. The S. enterica sv. Newport transposon library was grown overnight in LB with the addition of kanamycin at 37°C, and the resulting cultures were resuspended in PBS. Approximately 10⁸ CFU/ml in 3 μL of PBS were inoculated into three shallow wounds in tomato pericarps, and six fruits were used. Tomato fruits were incubated at 22°C and a relative humidity of ∼60% for 7 days. Under these conditions, no gross changes in the appearance of the fruit were observed, although ripening clearly progressed. Salmonella was recovered by collecting 1 g samples of tomato tissue around ∼1 cm of the inoculation site; samples from the same fruit were combined and homogenized in a stomacher (Sevard). Salmonella cells were recovered by centrifugation and were then resuspended and cultured in 50 ml of LB broth for 6 h at 37°C and 250 rpm.

Transposon Insertion Library Construction and Analysis

The transposon insertion library construction and analysis in sv. Newport were performed as described before (de Moraes et al., 2017). Briefly, a library of S. enterica serovar Newport C4.2 Tn5 insertion mutants was constructed with a mini-Tn5 derivative into which we inserted an N₁₈ random barcode using PCR. This derivative was integrated into the genome using the EZ-Tn5 < T7/KAN-2 > promoter insertion kit (Epicentre Biotechnologies, Madison, WI, United States). The transposome complex was dialyzed against water before electroporation into fresh electrocompetent cells of S. enterica sv. Newport C4.2. Transformed cells were recovered on LB agar with kanamycin after overnight growth at 37°C.

Mapping of barcoded transposons to specific locations in the genome was performed as described before (de Moraes et al., 2017). Briefly, genomic DNA from the library was extracted using the GenElute bacterial genomic DNA kit (Sigma-Aldrich). DNA was fragmented by sonication and ligated to Illumina primers. This product was used to amplify the regions, including the N₁₈ barcode and the genomic DNA adjacent to the transposon insertion, using a stepwise nested PCR (de Moraes et al., 2017). Resulting amplicons were purified with the QIAquick PCR Product Purification kit (Qiagen), and 150-base reads were obtained at both ends. The resulting reads were mapped against the de novo assembled sv. Newport C4.2 genome (see below) using Bowtie2. The N₁₈ barcode tag for each mapped read was identified using custom Perl scripts. The same reads, trimmed to remove Tn5 sequences, were also employed to assemble the sv. Newport C4.2 genome, which was then annotated using the RAST package¹.

For experiments in tomatoes, transposons were quantified as before (de Moraes et al., 2017). In brief, bacteria were recovered from tomatoes and grown in LB+60 μg/ml kanamycin. Bacteria were pelleted, lysed and subjected to PCR using primers directly flanking the N₁₈ barcode. The frequency of each barcode was enumerated by Illumina sequencing of 20 bases. The aggregated abundances for the input and output libraries were statistically analyzed using edgeR, and the log₂-fold changes and FDRs were reported.

Genome Sequence Retrieval, Quality Control and Assembly

Raw reads from Illumina sequencing of S. enterica sv. Newport and sv. Typhimurium strains were recovered from the NCBI Sequence Read Archive (SRA). The SRA identifiers of the strains used are listed in Supplementary Table S1. We opted to assemble genomes de novo to remove biases associated with different assembly methods and to employ the same quality standards. The genomes of the type strains (GCA_000022165.1, GCA_000016045.1, and GCA_000171415.1) were recovered from the NCBI Genome databases. Read quality control and visualization were done using the package Trim Galore (Andrews et al., 2015). Genome assembly was done using SPAdes, using default parameters (Bankevich et al., 2012). Assessment of the genome assembly quality was done with CheckM (Parks et al., 2015). Genomes with more than 1% of contamination and less than 99% of completeness were excluded. Prokka was used to annotate genomes (Seemann, 2014). The resulting.gff files were fed into Roary to build the pan-genome matrix (Page et al., 2015). The analysis of the pan-genome matrix was performed using ad hoc R scripts. We used power-law regression to model the total size of the Salmonella pan-genome. To that end, analysis of random permutations of the addition of new genomes was performed and the number of new genes found per addition was recorded, and used in the regression to estimate the expansion of the pan-genome.

Competition Assays

To confirm the results from the transposon insertion sequencing analysis, the fitness of individual isogenic mutants in relation to the wild-type was estimated. The bacterial population densities of overnight cultures of the wild type strain sv. Newport C4.2 and an isogenic mutant built in this background were set to similar numbers by adjusting their OD₆₀₀ to the same level. Cells were then spun and resuspended in PBS to the original volume and mixed in a 1:1 ratio, followed by a 10,000-fold dilution in PBS. 3 μl of this mix were inoculated into the tomato pericarp in three separate wounds resulting in ∼10³ CFU per tomato fruit. To get an accurate count of the wild type and mutant cells in the inoculum, an aliquot was serially diluted and plated onto XLD agar and incubated overnight at 37°C; fifty randomly picked colonies were then patched onto LB agar with chloramphenicol and the initial mutant:wild-type ratio was determined.

The inoculated tomatoes were incubated at 22°C for 7 days. Wound sites were then sampled using a sterile loop and Salmonella cells were recovered from a streak on XLD plates. Fifty colonies were patched onto LB agar with chloramphenicol to determine the mutant:wild-type ratio in the recovered sample. The competition index (CI) was calculated using the formula (MUT_out:WT_out)/(MUT_in:WT_in) (Noel et al., 2010). Statistical significance was determined using ANOVA against the CI of the neutral mutant ISG7. This neutral mutant was constructed with a FRT-cm-FRT insertion downstream of phoN known to not affect Salmonella fitness in tomatoes (Cox et al., 2013). The neutral phenotype of ISG7 was confirmed by competitions against the wild type. The software JMP version 12 was used for all CI analyses.

Growth Curve and rdar Phenotype Characterization

Responses of the strains to oxidative stress were compared by diluting overnight cultures to OD₆₀₀ = 0.01 in 3 ml of LB with or without 0.5 mM paraquat. The cultures were incubated at 37°C with shaking at 200 rpm. 100 μl aliquots were collected hourly to estimate culture concentrations by serial dilution and plating onto LB agar. All experiments were replicated three times. The rdar phenotype was evaluated by spotting 5 μl of an overnight culture onto salt-less LB with Congo Red as described by Zaragoza et al. (2012).

Results and Discussion

Clade Separation Using Phylogenetic Analysis

In this study, we compared the genes required for growth of an S. enterica sv. Newport strain in tomatoes to previous results (de Moraes et al., 2017) obtained for a Typhimurium strain. We first used comparative genomics analysis to determine whether the strain of sv. Newport recovered from a tomato-linked outbreak and used in this study is an outlier within serovar Newport or whether it is a typical representative. A phylogenetic analysis, constructed with minimum-evolution using SNP distances obtained from 1526 Salmonella genomes (Supplementary Table S1), formed distinct clades for sv. Typhimurium and sv. Newport and displayed a small number of fast evolving genomes with no clear grouping (Figure 1A and Supplementary Figure S1). This result is consistent with previous Salmonella phylogenetic analyses, supporting the notion that sv. Typhimurium strains group as one clade and serovar Newport strains are divided into three different clades (Group I, Group II, Group III) (Sangal et al., 2010).

FIGURE 1

We identified the sv. Newport clades obtained in our analysis by placing the sv. Newport type strains SL257, known to be in Group II, and SL317, known to be in Group III, in the phylogeny tree. The tomato outbreak strain C4.2 used in this study was placed in sv. Newport Group III, consistent with a previous phylogenetic analysis (Cao et al., 2013). Based on its position in the phylogenetic tree, it appears to be a typical representative of the sv. Newport Group III.

Comparison of the Salmonella sv. Typhimurium and sv. Newport Pan-Genomes

The association of multiple strains of sv. Newport with recurrent produce-related outbreaks and the lack of such a strong association for sv. Typhimurium, coupled with the availability of parallel tools for the functional genomics characterization of the interactions of these organisms with diverse hosts, offers an opportunity to address the hypothesis that sv. Newport strains might have additional genes associated with success in tomatoes. Within 1526 Salmonella genomes that passed phylogeny quality controls, gene prediction using Prokka identified 31,675 gene orthologs. These data were used to build a pan-genome matrix (Supplementary Table S1) containing gene orthologs present in each genome. While sv. Typhimurium exhibited a small standard deviation in the number of genes per genome, the sv. Newport strains contained genomes that had up to 2,000 additional genes, primarily due to prophage and plasmids.

Salmonella sv. Typhimurium had an average of 4,628 genes per genome, while sv. Newport Group II had 4,554 genes per genome and sv. Newport Group III had 4,413 genes per genome (Supplementary Figure S1). The presence and absence gene profiles of each group were visualized by Principal Coordinate Analysis (Figure 1B). Principal Coordinate Analysis clustered the sv. Newport Groups II/III and sv. Typhimurium in two distinct groups separated by the first principal coordinate, showing that the difference among serovars corresponded to the presence and absence of genes, not only to serological or SNPs differences. The strain Newport C4.2, used in our model for tomato colonization, was within the cluster of sv. Newport Group III, confirming that its genetic profile is similar to most sv. Newport isolates.

Core and accessory genome analyses revealed similar genome structures for sv. Newport and Typhimurium. To further characterize the differences between Salmonella genomes, we assessed their core and accessory genome content. We used the definition of “core genome” as the genes shared by more than 95% of all the genomes per group, and “accessory genome” as the genes shared by less than 5% of the genomes per group. All the genes between these two classifications are defined as “shell genome.” In sv. Newport Group II and sv. Newport Group III, 61–68% of all identified gene orthologs were part of the accessory genome and 11–12% of all identified gene orthologs were in the core genome (Figure 2A). The relative abundance of genes in the accessory genome in relation to the core genome was similar in the three Salmonella groups tested in this study. Moreover, these data showed that most of the Salmonella genes identified in the groups investigated here were in the accessory genome. The number of genes in the core genome was similar within all groups, ranging from 3,489 to 3,820 genes. All groups shared 3,155 genes (90%) of their core genomes. The sv. Newport groups shared consistently more of their core genomes than they shared with sv. Typhimurium (Figure 2B).

FIGURE 2

We also investigated whether the serovars’ pan-genomes are open or closed according to the definition of (Medini et al., 2005; Tettelin et al., 2005). Using power-law regression, we found that all pan-genomes of Salmonella serovars analyzed in this study are closed, with an α parameter of 2.92, 2.94, and 2.39 (Supplementary Figure S2) for sv. Newport Group II, sv. Newport Group III and sv. Typhimurium, respectively. All regression curves exhibited similar slopes, although they reached saturation of new genes per genome at different points (around 20 for sv. Newport Group II and Group III, and 10 for sv. Typhimurium, Supplementary Figure S2). The genus Salmonella was previously identified as a closed genome species (Jacobsen et al., 2011).

The characterization of the presence/absence profile of genes in these genomes allowed the identification of 724 group-specific genes in the sv. Newport Group III shell genome and 84 group-specific genes unique to its core genome, compared with sv. Typhimurium. Further analysis was performed to determine if any of these genes were part of the genetic basis of the adaptation of the Group III Newport C4.2 strain to plant colonization.

Transposon Insertion Sequencing Identifies Functions Required for Persistence in Tomatoes

To identify genome regions that encode functions important in colonization of tomato pericarps, we employed a library of transposon insertion mutants in sv. Newport C4.2, using the same screening methods applied before for sv. Typhimurium ATCC 14028 (de Moraes et al., 2017). The reads obtained from the transposon library screening were used to reconstruct the sv. Newport C4.2 genome (NCBI) using the RAST package. The fully annotated genome was deposited (see foot note text 1, username:guest, password:guest), and a comprehensive CSV file with locus tags, transposon insertion counts and orientation, and read counts is presented in Supplementary Table S2.

We mapped 4,811 coding sequences disrupted by the transposon, and mutants in 796 of these had a significant change in their abundance after growth in tomato wounds (FDR < 0.1). 781 of these mutants displayed reduced fitness and 15 had increased fitness (Figure 3). Most of the genes (689) whose disruption led to a reduction of fitness were shared by sv. Newport C4.2 and sv. Typhimurium ATCC 14028.

FIGURE 3

To understand the cellular functions employed by sv. Newport C4.2 to colonize tomatoes, we identified the metabolic functions involved in this interaction. We mapped the genes identified as required for full fitness in pericarps and retrieved their COGs using the BlastKOALA web interface. Results were plotted against the sv. Newport C4.2 metabolic map. These results were compared with the outcomes of the sv. Typhimurium ATCC 14028 transposon insertion screening in a meta-analysis, with the goal of identifying potential serovar-specific factors. The comparison was done by overlapping the sv. Newport C4.2 and sv. Typhimurium ATCC 14028 metabolic maps. The main functions required for sv. Newport C4.2 colonization of tomato pericarps involved biosynthetic pathways such as amino acid and LPS biosynthesis, fatty acid catabolism and glycolysis. These same pathways were also required by sv. Typhimurium ATCC 14028 (Figure 4). The shared metabolic requirements by sv. Newport C4.2 and sv. Typhimurium ATCC 14028 corroborate the earlier conclusion that Salmonella relies on its robust and diverse metabolism to fully colonize tomatoes (de Moraes et al., 2017).

FIGURE 4

Biosynthesis of amino acids by Salmonella during its interaction with tomatoes is of special interest for food safety. Different tomato cultivars are known to differ in the amounts of amino acids that accumulate within fruit during ripening (DiLeo et al., 2011; Osorio et al., 2011), and tomato genotypes with different amino acid profiles were shown to support different levels of Salmonella growth (Marvasi et al., 2014). To confirm the results obtained from the transposon insertion screening, we constructed isogenic mutants in the genes associated with amino acid biosynthesis and used these mutants to evaluate their fitness during tomato colonization in competition experiments against their parental strain. The genes selected were thrC, metA, ilvD, trpC, and glnA, involved in the biosynthesis of threonine, methionine, branched amino acids, tryptophan, and glutamine (respectively), and glnG, which codes for the master nitrogen regulator. Competition assays confirmed the results obtained from the transposon library screening. All isogenic mutants tested had a severe defect in fitness as estimated by their competitive indices (CI). The glnA isogenic mutant had the most severe defect in fitness [log₂(CI) = -4.4], while the strain lacking the global nitrogen regulator had the smallest reduction of fitness [log₂(CI) = -2.4] (Figure 5). The competition assays established that amino acid biosynthesis is a fundamental feature that confers an advantage for Salmonella to colonize tomato pericarps.

FIGURE 5

The main point of divergence between the sv. Newport C4.2 and sv. Typhimurium ATCC 14028 metabolic requirements for tomato colonization was the biosynthesis of nucleotides (Figure 4). Both purine and pyrimidine synthesis pathways identified as needed for efficient colonization by sv. Typhimurium ATCC 14028 at high titer were not under selection in the sv. Newport C4.2 transposon insertion screening at similar titers, indicating that S. Newport obtains sufficient purines and pyrimidines by scavenging in this environment. Notably, purine and pyrimidine biosynthesis was required by sv. Typhimurium ATCC 14028 even when inoculation was performed with a 10,000-fold lower titer (de Moraes et al., 2017). This result suggests that sv. Newport C4.2 could have a more efficient scavenging system for purines and pyrimidines, which could be advantageous for this strain during plant colonization.

Phenotypic Analysis of the Genes Unique to sv. Newport Reveals Potential Adaptions to Persistence in Plants

The main objective of this work was to explore the hypothesis that the sv. Newport C4.2 isolate from a tomato outbreak has additional genetic features not present in Typhimurium that enable it to colonize plants. Many sv. Newport-specific genes under selection had functions that could not be identified by BlastKOALA or Pfam. A few of those genes had putative metabolic functions. The locus peg.4637-peg.4641 is composed of genes that are absent from other Salmonella genomes and code for predicted proteins with unknown functions and a D-glucuronate permease (peg.4640). Since D-glucuronate is present in tomato fruit, we hypothesized that the reduction of fitness resulting from transposon insertions in this region indicates that Salmonella may be using D-glucuronate permease to scavenge D-glucuronate to proliferate in tomato pericarps. We therefore explored the role of this region during tomato colonization using a competition assay with isogenic mutants for the genes peg.4638, peg.4639, and peg.4640. The competitive indices for these isogenic mutants were not reduced when compared to ISG7, a strain that carries a neutral mutation (Figure 6A). However, this competition assay was performed with an inoculation titer thousands of fold lower than when the phenotype was observed in the transposon screen. It remains possible that this nutrient is used by Salmonella when the bacterial population is high.

FIGURE 6

Another gene (peg.4132), coding for a small putative protein (44 amino acids), was present in the shell genome of S. enterica sv. Newport C4.2, and a corresponding mutant had a strong reduction of fitness [log₂(FC) = -4.66] (Figure 6B). This gene was probably horizontally acquired: it is associated with a region containing mobile elements that includes remnants of phage genes, and it is located near a tRNA gene. The gene has a GC content of 40%, in contrast to the genome average of 52%. We further investigated the role of this gene during tomato colonization using competition assays with an isogenic mutant. The peg4132 gene was required for fitness of the strain S. enterica sv. Newport C4.2 in tomatoes [(log₂(CI) = -2.44] (Figure 6). The mutant did not have a growth defect, displayed normal resistance to oxidative stress and was indistinguishable from the wild type when tested for the rdar phenotype (Figure 7). Due to its potential requirement for the colonization of plants, we named peg4132 “papA” (Plant Associated Protein A).

FIGURE 7

To exclude the possibility that the reduction of fitness in this mutant in tomatoes was a result of a polar mutation, papA was cloned with its native promoter region in the vector pKD3 and then reinserted in the phoN locus in the ΔpapA strain, creating a complemented papA strain with a single copy of the gene, stably maintained on the chromosome. The phoN locus was previously shown to be neutral during tomato colonization (Cox et al., 2013). The colonization fitness measured by the competitive index (CI) was not significantly different between the wild type and complemented papA strains (Figure 6B), corroborating that papA is a factor required for full fitness in sv. Newport C4.2 colonization of tomato pericarps.

We analyzed the distribution of papA in the Salmonella genomes, and determined that this gene was exclusive to Newport Group III, where it was found in 71 genomes out of 306, most of them closely associated in one clade. Using BlastP over the NCBI microbial database, we found potential orthologs of papA. Interestingly, many of these orthologs were in other members of the Enterobacteriaceae family that have a lifestyle associated with soil and plants, including Pectobacterium ssp. and Dickeya ssp, suggesting that this gene may be involved in persistence in or on plants.

References

• 1

  AndrewsS.KruegerF.Segonds-PichonA.BigginsL.VirkB.Dalle-PezzeP.et al (2015). Trim Galore!Babraham: Babraham Institute.

  • Google Scholar

• 2

  BankevichA.NurkS.AntipovD.GurevichA. A.DvorkinM.KulikovA. S.et al (2012). SPAdes: a new genome assembly algorithm and its applications to single-cell sequencing.J. Comput. Biol.19455–477. 10.1089/cmb.2012.0021

  • CrossRef
  • Google Scholar

• 3

  BarrowP. A.LovellM. A. (1988). The association between a large molecular mass plasmid and virulence in a strain of Salmonella-Pullorum.J. Gen. Microbiol.1342307–2316. 10.1099/00221287-134-8-2307

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 4

  BarrowP. A.SimpsonJ. M.LovellM. A.BinnsM. M. (1987). Contribution of Salmonella Gallinarum large plasmid toward virulence in fowl typhoid.Infect. Immun.55388–392.

  • Pubmed Abstract
  • Google Scholar

• 5

  BennettS. D.LittrellK. W.HillT. A.MahovicM.BehraveshC. B. (2015). Multistate foodborne disease outbreaks associated with raw tomatoes, United States, 1990-2010: a recurring public health problem.Epidemiol. Infect.1431352–1359. 10.1017/S0950268814002167

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 6

  CaoG.MengJ.StrainE.StonesR.PettengillJ.ZhaoS.et al (2013). Phylogenetics and differentiation of Salmonella Newport lineages by whole genome sequencing.PLoS One8:e55687. 10.1371/journal.pone.0055687

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 7

  CoxC. E.McclellandM.TeplitskiM. (2013). Consequences of disrupting Salmonella AI-2 signaling on interactions within soft rots.Phytopathology103352–361. 10.1094/PHYTO-09-12-0237-FI

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 8

  DatsenkoK. A.WannerB. L. (2000). One-step inactivation of chromosomal genes in Escherichia coli K-12 using PCR products.Proc. Natl. Acad. Sci. U.S.A.976640–6645. 10.1073/pnas.120163297

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 9

  de MoraesM. H.DesaiP.PorwollikS.CanalsR.PerezD. R.ChuW.et al (2017). Salmonella persistence in tomatoes requires a distinct set of metabolic functions identified by transposon insertion sequencing.Appl. Environ. Microbiol.83e3028-16. 10.1128/AEM.03028-16

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 10

  DesaiP. T.PorwollikS.LongF.ChengP.WollamA.Bhonagiri-PalsikarV.et al (2013). Evolutionary genomics of Salmonella enterica subspecies.mBio4:e198-13. 10.1128/mBio.00198-13

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 11

  DiLeoM. V.StrahanG. D.Den BakkerM.HoekengaO. A. (2011). Weighted correlation network analysis (WGCNA) applied to the tomato fruit metabolome.PLoS One6:e26683. 10.1371/journal.pone.0026683

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 12

  Hernandez-ReyesC.SchikoraA. (2013). Salmonella, a cross-kingdom pathogen infecting humans and plants.FEMS Microbiol. Lett.3431–7. 10.1111/1574-6968.12127

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 13

  JacksonB. R.GriffinP. M.ColeD.WalshK. A.ChaiS. J. (2013). Outbreak-associated Salmonella enterica serotypes and food commodities, United States, 1998-2008.Emerg. Infect. Dis.191239–1244. 10.3201/eid1908.121511

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 14

  JacobsenA.HendriksenR. S.AaresturpF. M.UsseryD. W.FriisC. (2011). The Salmonella enterica pan-genome.Microb. Ecol.62487–504. 10.1007/s00248-011-9880-1

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 15

  LetunicI.BorkP. (2016). Interactive tree of life (iTOL) v3: an online tool for the display and annotation of phylogenetic and other trees.Nucleic Acids Res.44W242–W245. 10.1093/nar/gkw290

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 16

  LibbyS. J.AdamsL. G.FichtT. A.AllenC.WhitfordH. A.BuchmeierN. A.et al (1997). The spv genes on the Salmonella Dublin virulence plasmid are required for severe enteritis and systemic infection in the natural host.Infect. Immun.651786–1792.

  • Pubmed Abstract
  • Google Scholar

• 17

  LindowS. E.BrandlM. T. (2003). Microbiology of the phyllosphere.Appl. Environ. Microbiol.691875–1883. 10.1128/AEM.69.4.1875-1883.2003

  • CrossRef
  • Google Scholar

• 18

  LindowS. E.LeveauJ. H. (2002). Phyllosphere microbiology.Curr. Opin. Biotechnol.13238–243. 10.1016/S0958-1669(02)00313-0

  • CrossRef
  • Google Scholar

• 19

  MarvasiM.CoxC. E.XuY.NoelJ. T.GiovannoniJ. J.TeplitskiM. (2013a). Differential regulation of Salmonella Typhimurium genes involved in O-antigen capsule production and their role in persistence within tomato fruit.Mol. Plant. Microbe. Interact.26793–800. 10.1094/MPMI-09-12-0208-R

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 20

  MarvasiM.HochmuthG. J.GiurcanuM. C.GeorgeA. S.NoelJ. T.BartzJ.et al (2013b). Factors that affect proliferation of Salmonella in tomatoes post-harvest: the roles of seasonal effects, irrigation regime, crop and pathogen genotype.PLoS One8:e80871. 10.1371/journal.pone.0080871

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 21

  MarvasiM.NoelJ. T.GeorgeA. S.FariasM. A.JenkinsK. T.HochmuthG.et al (2014). Ethylene signalling affects susceptibility of tomatoes to Salmonella.Microb. Biotechnol.7545–555. 10.1111/1751-7915.12130

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 22

  MediniD.DonatiC.TettelinH.MasignaniV.RappuoliR. (2005). The microbial pan-genome.Curr. Opin. Genet. Dev.15589–594. 10.1016/j.gde.2005.09.006

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 23

  NoelJ. T.ArrachN.AlagelyA.McclellandM.TeplitskiM. (2010). Specific responses of Salmonella enterica to tomato varieties and fruit ripeness identified by in vivo expression technology.PLoS One5:e12406. 10.1371/journal.pone.0012406

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 24

  NugentS. L.MengF.MartinG. B.AltierC. (2015). Acquisition of iron is required for growth of Salmonella spp. in tomato fruit.Appl. Environ. Microbiol.813663–3670. 10.1128/AEM.04257-14

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 25

  OsorioS.AlbaR.DamascenoC. M.Lopez-CasadoG.LohseM.ZanorM. I.et al (2011). Systems biology of tomato fruit development: combined transcript, protein, and metabolite analysis of tomato transcription factor (nor, rin) and ethylene receptor (Nr) mutants reveals novel regulatory interactions.Plant. Physiol.157405–425. 10.1104/pp.111.175463

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 26

  PageA. J.CumminsC. A.HuntM.WongV. K.ReuterS.HoldenM. T.et al (2015). Roary: rapid large-scale prokaryote pan genome analysis.Bioinformatics313691–3693. 10.1093/bioinformatics/btv421

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 27

  ParksD. H.ImelfortM.SkennertonC. T.HugenholtzP.TysonG. W. (2015). CheckM: assessing the quality of microbial genomes recovered from isolates, single cells, and metagenomes.Genome Res.251043–1055. 10.1101/gr.186072.114

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 28

  SangalV.HarbottleH.MazzoniC. J.HelmuthR.GuerraB.DidelotX.et al (2010). Evolution and population structure of Salmonella enterica serovar Newport.J. Bacteriol.1926465–6476. 10.1128/JB.00969-10

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 29

  SeemannT. (2014). Prokka: rapid prokaryotic genome annotation.Bioinformatics302068–2069. 10.1093/bioinformatics/btu153

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 30

  TeplitskiM.BarakJ. D.SchneiderK. R. (2009). Human enteric pathogens in produce: un-answered ecological questions with direct implications for food safety.Curr. Opin. Biotechnol.20166–171. 10.1016/j.copbio.2009.03.002

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 31

  TeplitskiM.de MoraesM. (2018). Of mice and men....and plants: comparative genomics of the dual lifestyles of enteric pathogens.Trends Microbiol.10.1016/j.tim.2018.02.008[Epub ahead of print].

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 32

  TettelinH.MasignaniV.CieslewiczM. J.DonatiC.MediniD.WardN. L.et al (2005). Genome analysis of multiple pathogenic isolates of Streptococcus agalactiae: implications for the microbial “pan-genome”.Proc. Natl. Acad. Sci. U.S.A.10213950–13955. 10.1073/pnas.0506758102

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 33

  WiedemannA.Virlogeux-PayantI.ChausseA. M.SchikoraA.VelgeP. (2014). Interactions of Salmonella with animals and plants.Front. Microbiol.5:791. 10.3389/fmicb.2014.00791

  • CrossRef
  • Google Scholar

• 34

  ZaragozaW. J.NoelJ. T.TeplitskiM. (2012). Spontaneous non-rdar mutations increase fitness of Salmonella in plants.Environ. Microbiol. Rep.4453–458. 10.1111/j.1758-2229.2012.00364.x

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 35

  ZhengJ.AllardS.ReynoldsS.MillnerP.ArceG.BlodgettR. J.et al (2013). Colonization and internalization of Salmonella enterica in tomato plants.Appl. Environ. Microbiol.792494–2502. 10.1128/AEM.03704-12

  • Pubmed Abstract
  • CrossRef
  • Google Scholar