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J Bacteriol. Sep 2007; 189(17): 6312–6323.
Published online Jun 29, 2007. doi:  10.1128/JB.00725-07
PMCID: PMC1951903

Identification of a Biosynthetic Gene Cluster and the Six Associated Lipopeptides Involved in Swarming Motility of Pseudomonas syringae pv. tomato DC3000[down-pointing small open triangle]

Abstract

Pseudomonas species are known to be prolific producers of secondary metabolites that are synthesized wholly or in part by nonribosomal peptide synthetases. In an effort to identify additional nonribosomal peptides produced by these bacteria, a bioinformatics approach was used to “mine” the genome of Pseudomonas syringae pv. tomato DC3000 for the metabolic potential to biosynthesize previously unknown nonribosomal peptides. Herein we describe the identification of a nonribosomal peptide biosynthetic gene cluster that codes for proteins involved in the production of six structurally related linear lipopeptides. Structures for each of these lipopeptides were proposed based on amino acid analysis and mass spectrometry analyses. Mutations in this cluster resulted in the loss of swarming motility of P. syringae pv. tomato DC3000 on medium containing a low percentage of agar. This phenotype is consistent with the loss of the ability to produce a lipopeptide that functions as a biosurfactant. This work gives additional evidence that mining the genomes of microorganisms followed by metabolite and phenotypic analyses leads to the identification of previously unknown secondary metabolites.

Nonribosomal peptide synthetase (NRPS) enzymology is involved in the biosynthesis of many natural products with diverse biological activities, such as antifungal, antibacterial, anticancer, immunosuppressant, and metal chelation (reviewed in references 18 and 55). While the chemical structures of the natural products biosynthesized by NRPSs are diverse and explain their wide-ranging biological activities, the core enzymology used to synthesize these molecules is conserved. This conservation comes from NRPSs consisting of a set of repeating core protein domains grouped into enzymatic modules, with each module typically controlling the incorporation of one amino acid precursor into the nonribosomal peptide. The structural diversity of the NRPS-synthesized natural products comes from variations in the number and order of the modules, alterations in the substrate incorporated by the modules, and the potential addition of catalytic domains into modules that result in modifications to the growing peptide chain.

The core domains that are repeated for each NRPS module are the adenylation (A) domain, peptidyl carrier protein (PCP) domain, and condensation (C) domain. The A domains are commonly referred to as the “gatekeepers” of a module, because they recognize the substrate that is incorporated into the growing peptide chain. In a significant breakthrough in understanding NRPS enzymology, an amino acid substrate specificity code was identified that enables one to deduce the amino acid that is likely recognized by an A domain, given the protein sequence of the A domain (8, 9, 35, 60). This offers the ability to predict which module incorporates each amino acid of a nonribosomal peptide of known structure, but it also provides a means for proposing what amino acid is recognized by an NRPS module of unknown function. Once it recognizes the amino acid substrate, an A domain generates the corresponding aminoacyladenylate and subsequently works in conjunction with its partner PCP domain to tether the amino acid onto the 4′-phosphopantetheinyl prosthetic group of the PCP domain via a thioester linkage. Once the aminoacylthioester is formed, the C domain of the module catalyzes directional peptide bond formation with the amino acid tethered to the PCP domain of the preceding module. Thus, in a stepwise process, the nonribosomal peptide is biosynthesized.

The conservation of these repeating catalytic domains provides the ability to identify new nonribosomal peptides using an in silico approach, also referred to as “genome mining” (4, 12, 31, 51). This approach involves the scanning of the proposed proteome of a sequenced genome for enzymes showing sequence similarity to the three core domains of NRPSs, followed by genetic, biochemical, and metabolite analysis of wild-type and mutant strains. We have previously used such an approach to identify a biosynthetic gene cluster and purify its associated siderophore from the plant pathogen Agrobacterium tumefaciens C58 (51), and others have taken a similar approach to identify the nonribosomal peptide siderophore coelichelin from Streptomyces coelicolor (7, 31).

Pseudomonas species are prolific producers of secondary metabolites, in particular, metabolites synthesized wholly or in part by NRPSs. These nonribosomal peptide-containing metabolites are used by pseudomonads in many biological contexts, such as siderophores (e.g., pyoverdine [37]), biosurfactants (e.g., arthrofactin [41]), and virulence factors (e.g., syringomycin [5] and coronatine [39]). These various biological activities, the well-established production of nonribosomal peptides, and the public availability of many Pseudomonas genomes (6, 13, 23, 43, 46, 61) suggest that in silico mining of the genomes of Pseudomonas species will lead to the discovery of new nonribosomal peptides. In fact, this approach was recently used to identify a cyclic lipopeptide from Pseudomonas fluorescens Pf0-1 (12).

Here we present the in silico identification of a biosynthetic gene cluster in Pseudomonas syringae pv. tomato DC3000 that we hypothesized to be involved in the production of a lipopeptide. Metabolite profiles of wild-type strains compared to mutant strains containing gene disruptions in the targeted biosynthetic gene cluster identified a set of metabolites associated with this gene cluster. Purification of these metabolites followed by amino acid and mass spectrometry analyses identified these metabolites as six related linear lipopeptides. Phenotypic characterization of the mutant strains determined these lipopeptides were essential for the swarming motility of P. syringae pv. tomato DC3000. This work demonstrated that in silico genome mining of organisms was a valuable approach to identify previously unknown nonribosomal peptides.

MATERIALS AND METHODS

Bacterial strains, plasmids, and media.

All bacterial strains and plasmids are listed in Table Table1.1. P. syringae pv. tomato DC3000 strains were grown at 28°C in King's medium B (KB) (26). Escherichia coli strains were grown in Luria-Bertani (LB) medium. As needed, strains were grown on KB or LB medium solidified with 1.5% (wt/vol) agar. When required, antibiotics were added at the following concentrations: spectinomycin (100 μg/ml), nalidixic acid (5 μg/ml), gentamicin (5 μg/ml for E. coli, 2 μg/ml for P. syringae pv. tomato DC3000), and kanamycin (50 μg/ml).

TABLE 1.
Strains used in this study

Construction of a pspto_2829::lacZ-nptII strain (ADB1001).

A 650-bp internal fragment of pspto_2829 was introduced into pVIK112 using PCR-based cloning. Briefly, the primers used for PCR amplification were 2829-EcoRI (5′-AACGAATTCTGGCTTGCCTCTTCAGACTT-3′) and 2829-SpeI (5′-CCGACTAGTTTACCTGGGCAAAGTGGC-3′). The amplicon generated by these primers was digested with EcoRI and SpeI and ligated into the corresponding sites of pVIK112. The resulting construct, pADB1, contained the internal pspto_2829 fragment inserted in the same orientation as the promoterless lacZ. The plasmid was subsequently introduced into E. coli S17-1 for conjugation into P. syringae pv. tomato DC3000. For conjugation, E. coli S17-1/pADB1 and P. syringae pv. tomato DC3000 were mixed in a ratio of 1:2, plated on KB agar, and grown for 18 to 24 h. The mixed colony was scraped from the plate, resuspended in 1 ml of 10 mM MgSO4, and plated on KB medium containing nalidixic acid (5 μg/ml) and kanamycin (50 μg/ml). Exconjugants were streaked for isolation on the same medium and subsequently characterized for proper pspto_2829::lacZ-nptII insertion by PCR amplification.

Construction of a Δpspto_2829 strain (ADB1002).

To construct the plasmid used to delete pspto_2829, two PCR amplicons were cloned into the vector pEX19-Gm, resulting in the fusion of the DNA upstream of pspto_2829 to the start of pspto_2830, thereby generating a deletion of pspto_2829. This generated a vector, pADB2, that placed the start codon of pspto_2830 at the start site of the deleted pspto_2829. The amplicons were generated using two primer sets: 2829-5′EcoRI (5′-ATGAATTCGACCCCCAGTTTGGCGGTGGCG-3′) and 2829-3′NdeI (5′-ATCATATGTCAAGGTCCTTCTTGGCAG-3′), 2930-5′NdeI (5′-ATCATATGAGCATCAACGAACTTCTGGC-3′), and 2930-3′KpnI (5′-ATGGTACCTCAGGCGCTGGTGGGTGGTGCG-3′). The amplicon from the first primer set was cloned into the corresponding EcoRI/NdeI sites of pEX19-Gm. The amplicon from the second primer set was cloned into the NdeI/KpnI sites of the pEX19-Gm vector containing the first amplicon. The resulting plasmid, pADB2, was transformed into E. coli S17-1, and the resulting strain was used for conjugations with P. syringae pv. tomato DC3000, selecting for gentamicin (2 μg/ml) resistance. The resulting exconjugants were grown in KB agar lacking gentamicin and subsequently plated on KB agar supplemented with 5% (wt/vol) sucrose. Strains that grew were screened for gentamicin sensitivity, and the deletion of pspto_2829 was confirmed by PCR amplification analysis. One of these resulting strains was used for further analysis (ADB1002).

Construction of pspto_2828::spc (ADB1003) and pspto_2833::spc (ADB1004) strains.

A 2.1-kb region of the P. syringae pv. tomato DC3000 genome containing pspto_2828 was PCR amplified using the following primers: 2828-5′SpeI (5′-TTAACTAGTGAGCTTTGATAGCCGGGTATT-3′) and 2828-3′SpeI (5′-GAACTAGTAAGTCTGAAGAGGCAAGCCA-3′). The amplicon was digested with SpeI and cloned into the corresponding site of pEX19-Gm. The resulting plasmid, pADB3, was digested with EcoNI, and the SmaI fragment of pHP45-Ω containing the spectinomycin resistance gene cassette was ligated into this site. This resulted in a vector construct, pADB4, which contained a spectinomycin resistance cassette inserted into the cloned pspto_2828.

For construction of a pspto_2833::spc strain, a 2.1-kb region of the P. syringae pv. tomato DC3000 genome containing pspto_2833 was PCR amplified using the following primers: 2833-5′SacI (5′-AAGGAGCTCGACCACAACACCGCTCAA-3′) and 2833-3′SacI (5′-CTTGAGCTCGATATTCCGGCAGCTCTGAA-3′). The amplicon was digested with SacI and cloned into the corresponding site of pEX19-Gm. The resulting plasmid, pADB5, was digested with FspI, and the SmaI fragment of pHP45-Ω containing the spectinomycin resistance gene cassette was ligated into this site. This resulted in a vector construct, pADB6, which contained a spectinomycin resistance cassette inserted into the cloned pspto_2833.

To introduce the inactive gene into P. syringae pv. tomato DC3000, each of these plasmids was introduced into E. coli S17-1, and the resulting strains were used in conjugation reactions with P. syringae pv. tomato DC3000. Exconjugants were selected using gentamicin (2 μg/ml) resistance and screened for spectinomycin resistance (100 μg/ml). Strains showing resistance to both antibiotics were grown in KB medium containing spectinomycin (100 μg/ml) and then plated on KB agar containing sucrose (5% [wt/vol]) and spectinomycin (50 μg/ml). Those showing sucrose and spectinomycin resistance were screened for gentamicin sensitivity. Insertion of the spectinomycin cassette into the genome of these strains was confirmed by PCR.

Swarming motility assay.

Cultures of the analyzed strains were grown in KB medium and adjusted to an optical density at 600 nm of 1.0. Five μl of these cultures was spotted on KB agar containing 0.3, 0.5, 0.7, 0.9, and 1.5% (wt/vol) agar. The plates were incubated at 28°C for 48 to 72 h. It was determined that an agar concentration of 0.5% enabled wild-type P. syringae pv. tomato to swarm across the KB agar surface. When analyzing expression of lacZ, the KB agar was supplemented with 40 μg/ml of 5-bromo-4-chloro-3-indolyl-β-d-galactopyranoside.

Droplet collapse assay.

Standard droplet collapse assays were used (22). Briefly, for strains grown in KB medium, 1 ml of cells was removed from a saturated culture and the cells were removed by centrifugation. Twenty μl of cell-free supernatant was mixed with 1 μl of methylene blue (0.1% [wt/vol] in water). The resulting mixture was spotted onto Parafilm and allowed to settle for 5 min before analyzing for droplet collapse. The methylene blue was added solely for visualization purposes and does not influence droplet collapse activity. For strains grown on KB agar, cells were removed from the plate by scraping, resuspended in liquid KB medium, and then removed by centrifugation. Twenty μl of the cell-free supernatant was spotted on Parafilm and allowed to settle for 5 min before analyzing droplet collapse.

Thin-layer chromatography (TLC) and lipopeptide detection.

Two μl of a sample from the different steps of metabolite purification was spotted on a silica gel chromatography plate (silica gel F-254; Fisher). Metabolites were separated using a mobile phase of chloroform-methanol-NH4OH (5 N) in a ratio of 80:25:4. Lipopeptides were visualized by staining with bromothymol blue (0.1% [wt/vol] in 10% [vol/vol] ethanol), followed by brief heating.

Metabolite purification.

The P. syringae pv. tomato DC3000 strains were grown in 3-ml cultures of KB broth with vigorous shaking for 80 h at 28°C. Six hundred ml of total volume was used for the purification of metabolites from each strain. The cells were removed by centrifugation (7,000 rpm, 10 min), and cell-free supernatant from pooled cultures was extracted with ethyl acetate containing 1% (vol/vol) formic acid. The culture supernatant and acidified ethyl acetate were mixed in a ratio of 3:5 (vol/vol). The ethyl acetate fraction was collected and dried to completion in a rotary evaporator. The metabolites were suspended in deionized water and neutralized to pH 8 with dilute NaOH. The aqueous mixture was applied to a silica chromatography column (SelectoScientific), and the metabolites were eluted with deionized water. The fractions active for droplet collapse activity were dried to completion and resuspended in methanol. Methanol-soluble compounds were filtered through a 0.8-μm nylon filter (CoStar; Corning), dried to completion, resuspended in deionized water, and incubated at −20°C for several hours. Fortuitously, the metabolites of interest precipitated from solution after freezing. The precipitated metabolites were recovered by centrifugation at 13,000 rpm. The pellet was resuspended in acetonitrile-water (3:7).

The metabolites were separated by reverse-phase high-performance liquid chromatography (HPLC) using an analytical C18 small-pore column (Vydac) at a flow rate of 1 ml min−1 and monitored at 210 nm. The solvents used were as follows: A, deionized water with 0.1% trifluoroacetic acid; B, acetonitrile with 0.1% trifluoroacetic acid. The HPLC separation profile consisted of a 5-min isocratic development at 100% A-0% B, a 7-min linear gradient from 100% A-0% B to 50% A-50% B, a 15-min linear gradient from 50% A-50% B to 0% A-100% B, and a 10-min isocratic development at 0% A-100% B. Metabolites of interest were collected as they eluted from the HPLC column and dried to completion under vacuum.

When investigating surfactin for hydrolysis during ethyl acetate extraction, 1 mg of surfactin (Sigma-Aldrich) was added to cell-free supernatant of a 15-ml culture of P. syringae pv. tomato DC3000 that was grown under conditions optimized for lipopeptide production. The sample was then extracted with acidified ethyl acetate as described above, resuspended in methanol, and submitted to the University of Wisconsin Biotechnology Center for matrix-assisted laser desorption ionization-time of flight mass spectrometry (MALDI-TOF MS) analysis as described below.

Amino acid analysis.

A portion of each sample that eluted from the HPLC column was submitted to the University of California at Davis Molecular Structure Facility for amino acid analysis.

Mass spectrometry analysis.

A portion of each individual fraction recovered from the C18 HPLC column was submitted to the UW Biotechnology Facility for MALDI-TOF MS analysis. Samples were spotted to stainless steel targets, mixing 1:1 analyte with saturated α-cyano-4-hydroxy cinnamic acid matrix in 70% acetonitrile-water, 0.2% trifluoroacetic acid. Spots were allowed to dry and then analyzed by using a 4800 TOF/TOF mass spectrometer (Applied Biosystems). For the analysis of ethyl acetate extracts from strains, MALDI-TOF MS analysis was performed using a Voyager Biospectrometry workstation (DE Pro; Applied Biosystems, Foster City, CA) in linear mode. Calibration was performed using apomyoglobin, bovine insulin, and cytochrome c (Sigma).

A portion of each sample was also submitted to the UW Biotechnology Facility for electrospray ionization mass spectrometry/mass spectrometry (ESI-MS/MS) analysis. Samples were introduced into a 3200 Q trap (Applied Biosystems) mass spectrometer by direct infusion via a 1-ml Hamilton syringe and metering pump at 30 μl min−1. The capillary was maintained at 5,500 V. Collision energy was set to 25 V for collection of doubly charged species. Spectra were collected in enhanced product ion scan mode with precursors being selected with an opened (“low”) resolution setting on the Q1 quadrupole. Linear trap functions were set to “dynamic fill” time and were optimized for peptides. The declustering potential was 15 V.

RESULTS AND DISCUSSION

In silico identification of a nonribosomal peptide biosynthetic gene cluster and prediction of the metabolite structure.

A gene cluster that may code for an NRPS that synthesizes a nonribosomal peptide was identified in the P. syringae pv. tomato DC3000 genome by searching the predicted proteome using the genomic BLAST program from the National Center for Biotechnology Information (1, 54), with the default settings for proteins similar to the Bacillus subtilis GrsA protein. GrsA contains the initiation module of the NRPS that generates the nonribosomal peptide gramicidin (28). This protein was chosen for the BLAST search because it is biochemically and structurally the most well studied NRPS subunit, and the A domain of GrsA was used as the basis for the development of the A domain amino acid specificity codes (8, 9, 59, 60). As shown in Fig. Fig.1,1, one of the gene clusters we identified contained two large genes, pspto_2829 and pspto_2830, that coded for eight NRPS modules. Immediately downstream of these two genes were two open reading frames (ORFs; pspto_2831 and pspto_2832) that showed sequence similarity to subunits of a multidrug efflux pump. These two genes were potentially in an operon with pspto_2829 and pspto_2830, based on the overlapping stop and start codons of pspto_2830 and pspto_2831 and the separation of pspto_2831 and pspto_2832 by only 3 nucleotides. On either side of this putative four-gene operon were genes coding for proteins showing sequence similarity with the LuxR family of transcriptional regulators. Thus, the production of the putative nonribosomal peptide potentially involved pspto_2828 through pspto_2833 (Fig. (Fig.11).

FIG. 1.
Schematic of the targeted biosynthetic gene cluster in P. syringae pv. tomato DC3000. (Top) Graphic representation of the targeted gene cluster spanning pspto_2828 to pspto_2833. Colors of genes indicate putative functions of coded proteins: white, NRPS; ...

Analysis of the NRPS encoded by pspto_2829 and pspto_2830 identified eight modules, suggesting an octapeptide would be biosynthesized (Fig. (Fig.1).1). The likely order of function for these two NRPS subunits was first Pspto_2829, consisting of the first three NRPS modules, and then Pspto_2830, consisting of the final five modules. This hypothesis was based on the C-terminal thioesterase domains of Pspto_2830. These domains typically catalyze the final step in nonribosomal peptide synthesis and denote the terminal module of an NRPS (reviewed in reference 25). Pspto_2830 terminated in tandem thioesterase domains that are both likely active and may function to enhance the rate of product release from the NRPS, as seen during the analysis of arthrofactin biosynthesis (53). Also of interest was the N-terminal C domain present in the initiating module of Pspto_2829. This domain organization implied that the nonribosomal peptide generated by this enzymology would contain an N-terminal lipid. This hypothesis was based on precedent whereby an N-terminal C domain recognizes a fatty acyl-coenzyme A or fatty acyl-acyl carrier protein derivative from primary metabolism and condenses the first amino acid tethered to the NRPS with the thioesterified fatty acid to generate a lipidated nonribosomal peptide (21, 38, 63).

The eight A domains of the NRPS were analyzed for their amino acid substrate specificity codes to predict the amino acid sequence of the octapeptide based on comparisons to known substrate specificity codes (Table (Table2).2). Based on this analysis, the NRPS identified by in silico analysis was predicted to generate a lipooctapeptide with the sequence lipid-Leu-Leu-Gln-Leu-Thr-Val-Leu-Leu (Fig. (Fig.1).1). Bioinformatics analysis also determined the C domains of modules 2, 3, 4, 6, and 8 were slightly larger than standard C domains, having additional amino acids at the N terminus. Work by other groups has established that these types of C domains in Pseudomonas species are bifunctional (2). These C domains catalyze not only the directional condensation between two neighboring PCP-tethered amino acids but also epimerization of the amino acid associated with that particular module. Thus, it was reasonable to hypothesize that residues 2, 3, 4, 6, and 8 of the lipooctapeptide would be d-isomers rather than the natural l-isomers.

TABLE 2.
Analysis of A domain amino acid specificity codes

Finally, the N-terminal C domain of pspto_2829 showed the highest level of amino acid sequence similarity with the N-terminal C domain of ArfA, the initiating NRPS that biosynthesizes the cyclic lipopeptide arthrofactin (52). However, the remainder of pspto_2829 showed a high level of sequence similarity to ArfB, starting with the A domain of module 4 of the arthrofactin NRPS. Thus, protein domains corresponding to the first three modules of the arthrofactin NRPS were absent. This suggested that the NRPS system targeted here in P. syringae pv. tomato DC3000 evolved from the arthrofactin system, with one of the changes being the deletion of three modules of the arthrofactin NRPS, resulting in the fusion of the N terminus of ArfA with a portion of ArfB. Importantly, the deleted modules include the module that incorporates the threonyl residue that forms the ester linkage involved in cyclization of arthrofactin (52, 53). This suggested that the NRPS from P. syringae pv. tomato DC3000 would generate a linear lipopeptide rather than a cyclic lipopeptide.

It should be noted that while the manuscript was being prepared, a paper was published that used a similar approach to identify cyclic lipopeptides in Pseudomonas species (12). In that study, the authors proposed a structure for the metabolite produced by the biosynthetic gene cluster targeted here. There are two important differences in their proposed structure and the one we propose. First, they proposed the metabolite is a cyclic lipopeptide, and our analysis suggested the molecule would be linear. Second, they predicted a different amino acid sequence for the peptide. The peptide sequence they predicted is Leu-Leu-(Glu/Asp)-Leu-Dhb-Ile-Leu-Leu, with Dhb representing 2,3-dihydroxy-2-aminobutyric acid. Thus, three of the eight proposed amino acids are different. It can be argued that the previously predicted amino acid sequence is very unlikely. First, there are no nonribosomal peptides we are aware of that contain the unusual amino acid 2,3-dihydroxy-2-aminobutyric acid. There is precedent for 2-aminobut-2-enoic acid, the chemically correct name for what has been called 2,3-dehydro-2-aminobutyric acid in syringomycin and syringopeptin (3, 16), but not 2,3-dihydroxy-2-aminobutyric acid. Second, 2,3-dihydroxy-2-aminobutyric acid is a hemiaminal, which is not a chemically stable compound. Therefore, it is unlikely it will be found as the free amino acid in the cell. This is exemplified by the fact that hemiaminal amino acids in peptides are found as intermediates in enzymatic reactions that lead to peptide cleavage (30, 56). These discrepancies in proposals highlight that while bioinformatics analysis enables predictions to be made, interpretations can differ and experimental approaches must be used to test such hypotheses before structures are proposed on uncharacterized clusters.

Mutants disrupted in pspto_2829 have phenotypes consistent with the loss of lipopeptide production.

To initiate the analysis of the targeted gene cluster, two strains mutated in pspto_2829 were constructed. The first strain contained an insertion of a pVIK112 suicide vector into the chromosome of P. syringae pv. tomato DC3000 that resulted in a promoterless lacZ being inserted into, and in the same orientation as, pspto_2829. This offered the ability to monitor expression of pspto_2829. A second mutation was constructed whereby the pspto_2829 gene was deleted from the chromosome, with pspto_2830 being positioned in place of pspto_2829. This strain provided a means of phenotypic characterization and metabolite purification that did not require the addition of antibiotic to retain the mutation, thereby simplifying metabolite profile comparisons with the wild-type strain.

The bioinformatics analysis of the targeted gene cluster suggested it was involved in the formation of a lipopeptide. One of the functions of lipopeptides is as a biosurfactant that enables the bacterium to swarm on solid medium containing a low percentage of agar (32-34). This offered a simple phenotype to investigate. The wild-type and pspto_2829::lacZ-nptII strains were spotted on KB medium containing various concentrations of agar (0.3, 0.5, 0.7, 0.9, and 1.5% [wt/vol]) and supplemented with 5-bromo-4-chloro-3-indolyl-β-d-galactopyranoside to enable the detection of lacZ expression. These plates were incubated at 30°C for 48 h, and the plates were analyzed for blue pigment formation and swarming motility. From this analysis it was determined that the pspto_2829::lacZ-nptII strain turned blue on these plates and also failed to swarm on any of the agar plates. On a control plate, the wild-type strain did not turn blue, since it does not include lacZ, but did show swarming motility on medium containing 0.5% (wt/vol) agar (data not shown). Importantly, when the pspto_2829::lacZ-nptII strain was grown on LB with various concentrations of agar, the strain did not turn blue in the presence of 5-bromo-4-chloro-3-indolyl-β-d-galactopyranoside and did not swarm. Also, the wild-type strain did not show swarming motility on LB medium (data not shown). This confirmed that on KB medium, P. syringae pv. tomato DC3000 expressed pspto_2829 and the metabolite associated with the NRPS encoded by pspto_2829 was important for swarming motility of the bacterium. Retesting the swarming phenotype with the Δpspto_2829 strain indicated that this strain also failed to swarm on KB medium containing 0.5% agar, consistent with the loss of biosurfactant production. The wild-type strain was unaffected (Fig. (Fig.2A2A).

FIG. 2.
Phenotypes associated with the pspto_2828-pspto_2833 gene cluster. (A) Motility of P. syringae pv. tomato DC3000 strains on KB medium containing 0.5% agar. Five μl of cells from each strain was separately spotted to the center of a plate ...

As a second test for the presence or absence of a biosurfactant, standard droplet collapse assays were performed (22). Briefly, aqueous solutions lacking a surfactant form domed droplets on various surfaces due to surface tension. When a surfactant is present, the surface tension is reduced and the domed droplets collapse. The cells from the swarming plates of the wild-type and mutant strains were scraped from the plate, resuspended in water, and removed by centrifugation. The resulting supernatant was analyzed for whether the supernatant stimulated droplet collapse. The supernatant of the wild-type strain caused droplet collapse, but the supernatants of the mutants did not (data not shown). This same result was observed when the strains were grown in liquid culture, the cells were removed by centrifugation, and the culture supernatant was spotted on Parafilm (Fig. (Fig.2B).2B). These data were consistent with the targeted gene cluster being involved in the production of a lipopeptide biosurfactant.

A strain disrupted in pspto_2828, but not pspto_2833, has phenotypes consistent with the loss of lipopeptide production.

ORFs coding for LuxR-like transcriptional regulators are located upstream of pspto_2829 and downstream of pspto_2832 (Fig. (Fig.1).1). This raised the possibility that either one or both genes code for a transcriptional regulator involved in the production of the metabolite with biosurfactant activity. To investigate this, spectinomycin resistance gene cassettes were introduced into pspto_2828 and pspto_2833 and the resulting mutant strains were analyzed for swarming motility and stimulation of droplet collapse. These analyses determined that an insertion in pspto_2828 resulted in the loss of swarming motility and droplet collapse, but an insertion in pspto_2833 did not have a discernible effect (Fig. 2A and B). Therefore, only pspto_2828 played a clear role in lipopeptide production.

Detection and purification of the metabolites associated with the pspto_2828-pspto_2832 gene cluster.

The wild-type P. syringae pv. tomato DC3000 was grown under a variety of conditions to determine the optimal conditions for biosurfactant activity. It was determined that small-volume (3-ml) cultures with vigorous shaking for 3 days resulted in the strongest detectable biosurfactant activity based on the droplet collapse activity of serial dilutions of cell-free culture supernatants. It was surprising that such high aeration conditions would enhance biosurfactant production, due to the role the biosurfactant plays in swarming motility. However, high levels of biosurfactant production in liquid culture are not uncommon (e.g., rhamnolipid [19], surfactin [10], and putisolvins I and II [29]). Furthermore, the regulation of biosurfactant production is also influenced by the medium in which the cells are grown, since no biosurfactant activity or pspto_2829::lacZ-nptII expression was observed on LB plates. Thus, the regulation of lipopeptide production is influenced in this bacterium by many factors besides growth on a solid support. Further investigations into the regulation of biosurfactant production by this strain and the role that the LuxR homolog, Pspto_2828, plays in this process are ongoing.

Analysis of ethyl acetate extracts of the supernatants of the aerated cultures using TLC followed by visualization by bromothymol blue staining to discern lipid-containing metabolites (64) detected a metabolite in the wild-type culture that was absent in the Δpspto_2829 mutant strain (Fig. (Fig.2C).2C). Thus, under these growth conditions, P. syringae pv. tomato DC3000 produces a metabolite that contains a lipid moiety, and production of this metabolite is associated with pspto_2829. The combination of droplet collapse assay results and TLC separation and bromothymol blue staining were used to monitor metabolite purification.

Six-hundred-milliliter cultures (200 3-ml cultures) of the wild-type, Δpspto_2829, and pspto_2828::spc strains were grown for metabolite purification and comparison. The purification scheme involved ethyl acetate extraction, silica resin chromatography, precipitation, and reverse-phase HPLC as described in Materials and Methods. The final step using HPLC purification enabled a comparison of the metabolite profiles of the wild-type strain versus the two mutant strains. From this analysis, four distinct peaks were observed in the HPLC traces of the metabolites purified from the wild-type strain that were absent from the Δpspto_2829 and pspto_2828::spc strains (Fig. (Fig.3).3). Each of these purified metabolites stimulated droplet collapse, consistent with the purification of metabolites with lipopeptide characteristics. These data suggested the targeted biosynthetic gene cluster was involved in the production of at least four metabolites. This is in contrast to the bioinformatics that predicted a single lipopeptide structure.

FIG. 3.
Reverse-phase HPLC analysis of partially purified metabolites. Trace A, wild type; trace B, pspto_2828::spc; trace C, Δpspto_2829. Metabolite elution was monitored at 210 nm. The absorption peaks of the four samples analyzed further are highlighted ...

Amino acid analysis of purified metabolites.

The metabolites corresponding to the four absorbance peaks observed from the HPLC analysis were independently collected for further characterization. The hypothesis was these metabolites would be structurally related lipooctapeptides produced by the enzymes encoded by the pspto_2828-2832 gene cluster. Each HPLC-purified sample was submitted for amino acid analysis to determine its amino acid content (Fig. (Fig.4).4). These data confirmed that each metabolite contained eight amino acids. The metabolites eluting at 24.1 and 26.5 min contained the amino acids Leu, Val, Thr, and Glx in a molar ratio of 5.0:1.0:0.9:1.1 and 5.0:1.0:0.9:1.1, respectively, with Glx representing either Gln or Glu. These amino acids and their ratios were consistent with the prediction made through bioinformatics analysis of a 5:1:1:1 ratio of Leu, Val, Thr, and Gln (Fig. (Fig.1).1). Since these two metabolites had identical amino acid content but different retention times on the reverse-phase HPLC, it was predicted these metabolites would have different N-terminal lipids. Interestingly, the metabolites eluting at 24.7 and 27.2 min from the HPLC had slightly altered amino acid content compared to the metabolites eluting at 24.1 and 26.5 min. The amino acids were Leu, Ile, Thr, and Glx in a ratio of 5.4:0.6:0.9:1.1 and 5.5:0.5:0.9:1.1 for the metabolites eluting at 24.7 and 27.2 min, respectively (Fig. (Fig.4).4). This suggested the metabolites eluting at 24.7 and 27.2 min were each a mixture of two lipopeptides that differed in having either Leu or Ile at one position, most likely the position that was Val in the metabolites eluting at 24.1 and 26.5 min. Additionally, since the retention times were different but the amino acid content was the same, it was reasonable to propose that the metabolites eluting at 24.7 min have a different fatty acid from those eluting at 27.2 min. From this analysis, it was concluded that the NRPS encoded by pspto_2829 and pspto_2830 resulted in the production of six structurally related lipooctapeptides. Thus, while bioinformatics analysis and HPLC separation suggested one or four metabolites were produced by this biosynthetic gene cluster, respectively, the amino acid analysis supports the presence of six lipopeptides.

FIG. 4.
Amino acid analysis. Data shown represent the numbers and identities of amino acids recovered after acid hydrolysis of metabolites collected at the elution times indicated. The data were normalized to a total of eight amino acids. GLX represents Gln or ...

Mass spectrometry analysis of the purified metabolites.

To gain further insights into the structures of the metabolites, each of the purified samples from the HPLC was analyzed by MALDI-TOF MS (Table (Table3).3). Interpretations of these data benefited from our knowing the amino acid content of each metabolite. MALDI-TOF MS analysis of the metabolite eluting at 24.1 min identified four masses at 1,104.8, 1,120.8, 1,126.8, and 1,142.7 Da. The 1,104.8-Da mass was consistent with a metabolite consisting of a peptide with the determined amino acid content (Fig. (Fig.4),4), an N-terminal 3-hydroxydecanoyl fatty acid, and a sodium ion (910.6 + 171.1 + 23 = 1,104.7 Da). The hypothesis that 3-hydroxydecanoate was the lipid attached to the N terminus was based on the observed mass and this lipid being a common component of other lipidated nonribosomal peptides from Pseudomonas spp. (17, 41). The three other observed masses were consistent with different adducts of the predicted lipopeptide (Table (Table3).3). Importantly, the observed masses suggested the lipopeptide was linear, not cyclic. If the lipopeptide were cyclic, the mass would have been 18 Da smaller to account for the loss of two hydrogens and one oxygen for the cyclization.

TABLE 3.
MALDI-TOF MS analysis of purified metabolites

From the amino acid analysis, the metabolite eluting at 26.5 min had the same amino acid content as that eluting at 24.1 min; therefore, they likely differed in their lipid component. Consistent with this, the masses observed for the various ion forms of the metabolites eluting at 26.5 min were 28 Da larger than those observed for the metabolite eluting at 24.1 min, suggesting a lipid component two carbon units larger (3-hydroxydodecanoyl rather than 3-hydroxydecanoyl) (Table (Table3).3). 3-Hydroxydodecanoyl is also found attached to lipopeptides produced by Pseudomonas species (15, 48), and so it was not surprising to find a change in the mass consistent with this lipid being attached to the N terminus of the purified lipopeptides.

The masses of the two remaining metabolite samples were expected to differ from the other two samples by at least 14 Da, because a Val residue was replaced by a Leu or Ile residue based on amino acid analysis (Fig. (Fig.4).4). Additionally, based on the predicted differences in the lipid present in the prior set of metabolites, it was expected that the masses between the metabolites eluting at 24.7 and 27.2 min would differ by 28 Da. Consistent with these predictions, the metabolites eluting at 24.7 and 27.2 min differed by 14 mass units from the metabolites eluting at 24.1 and 26.5 min, respectively (Table (Table3).3). Additionally, the metabolites at 24.7 and 27.2 min differed from each other by 28 Da. From these data, it was concluded that six structurally distinct lipooctapeptides had been identified, and these lipooctapeptides differ either in lipid content or in having a Val, Leu, or Ile residue at one amino acid position of the peptide. Furthermore, the masses of all of these lipopeptides suggested they were all linear, not cyclic. Importantly, MALDI-TOF MS analysis of the metabolites present after the initial ethyl acetate extraction detected metabolites with the same masses listed in Table Table33 but did not detect masses consistent with the cyclized forms of the metabolites (data not shown). Ethyl acetate extraction is a common step in cyclic lipopeptide purification (44, 45, 48, 58); therefore, it was unlikely that this purification step caused the metabolites to become linear.

To gain additional support for the conclusion that the metabolites were not hydrolyzed to the linear forms by the ethyl acetate extraction, we tested whether the cyclic lipopeptide surfactin was linearized by this purification step. Surfactin was chosen because it has a similar amino acid content as the metabolites isolated from P. syringae pv. tomato DC3000, and it is cyclized with an ester linkage (42), the linkage that would be expected if our isolated metabolites were cyclic. Surfactin was added to cell-free supernatant of wild-type P. syringae pv. tomato DC3000 after the culture had produced the metabolites we were characterizing. This mixture was then subjected to ethyl acetate extraction and subsequent MALDI-TOF MS analysis. As a control, surfactin that had not been added to the supernatant or extracted with ethyl acetate was analyzed by MALDI-TOF MS. The mass spectrometry analyses determined that surfactin was not linearized by its addition to the cell-free supernatant and subsequent ethyl acetate extraction. In the sample containing surfactin and supernatant, the linear lipopeptides listed in Table Table33 were detected (data not shown). Thus, it is unlikely that the ethyl acetate extraction method caused the linearization of the metabolites, suggesting P. syringae pv. tomato produces linear lipopeptides.

To more directly analyze the lipooctapeptides, each purified sample was analyzed by ESI-MS/MS. Using this technique, metabolite fragmentation patterns were compared with the known amino acid content and MALDI-TOF MS results to determine the likely structures of the lipooctapeptides. For this analysis, the [M + 2H]2+ ion of each sample was collected and fragmented. Figure Figure55 shows the spectrum from the ESI-MS/MS analysis of the metabolite eluting at 24.1 min, along with the identification of the b and y fragments. The combination of b and y fragments, along with the associated dehydrated (bo) and deaminated (b*) derivatives, supported the proposed linear structure of 3-hydroxydecanoyl-Leu-Leu-Gln-Leu-Thr-Val-Leu-Leu for the metabolite eluting at 24.1 min (Fig. (Fig.55 and and6).6). We propose the name syringafactin A for this metabolite. The masses of the fragments along with the MALDI-TOF data supported the conclusion that Gln was the third amino acid rather than Glu, since the masses of these compounds would be 1 Da larger if the residue were Glu. Also, since amino acid residue six was Val, this was the amino acid that was expected to vary in the metabolites eluting at 24.7 and 27.2 min from the HPLC.

FIG. 5.
Analysis of ESI-MS/MS fragmentation for the metabolite eluting at 24.1 min. (Top) A schematic is shown representing sites of cleavage in the proposed metabolite structure that would yield b and y ions. The theoretical masses of these ions are noted above ...
FIG. 6.
Proposed structures of lipopeptide metabolites. (A) Summary of ESI-MS/MS data obtained from metabolites eluting from the HPLC column at the times indicated. Shown are the observed masses of b and y and dehydration (b°, y°) and deamination ...

Similar analyses were done on the three remaining metabolite samples. The ESI-MS/MS spectra for these metabolites are included online as supplementary figures (see Fig. S1 to S3 in the supplemental material), while the mass fragments for all four samples are summarized in Fig. Fig.6A.6A. Based on these fragmentation patterns, the remaining metabolites were identified as five lipooctapeptides related in structure to syringafactin A. The metabolites eluting at 24.7 min had masses that were consistent with a mixture of lipooctapeptides with N-terminal 3-hydroxydecanoyl lipids and with residue six of the peptides being either Leu or Ile, as evident in the increase of 14 Da in the mass of the b7, bo7, b8, bo8, y3-y7, yo6, and yo7 fragments (Fig. (Fig.6A).6A). These metabolites are referred to as syringafactins B (Leu) and C (Ile) (Fig. (Fig.6B).6B). The remaining metabolite ESI-MS/MS fragmentation products were identical to those seen for syringafactins A, B, and C, with the only difference being a 28-Da increase in mass of the fragments containing the N terminus of the metabolite (Fig. (Fig.6A).6A). The observed increase in mass was consistent with a 3-hydroxydodecanoyl N-terminal lipid compared to the 3-hydroxydecanoyl lipid seen in the three prior syringafactins. These metabolites are referred to as syringafactins D, E, and F for the metabolites containing Val, Leu, or Ile at the sixth position, respectively (Fig. (Fig.6B).6B). Importantly, bioinformatics predicted the production of a single lipopeptide; however, experimentally it was determined that six lipopeptides were produced.

Finally, it was important to show that the reason the mutant strains failed to swarm on low-agar KB was that they lacked the ability to produce these six linear lipopeptides. Therefore, the purified metabolites were pooled and then spread on KB agar containing 0.5% agar. The wild-type and Δpspto_2829 strains were spotted on these plates, and the plates were analyzed for swarming motility after 48 h of incubation. Both the wild-type and the mutant strains swarmed on these plates, showing that these six metabolites complemented the swarming motility phenotype of the Δpspto_2829 strain (data not shown). Importantly, when the Δpspto_2829 strain was grown under identical conditions and cellular metabolites purified using the same protocol, samples collected from the HPLC at the same retention times as the lipopeptides from the wild-type strain failed to complement swarming motility of the Δpspto_2829 strain (data not shown). Thus, the restoration of swarming motility to the mutant strain was due to the presence of the six linear lipopeptides.

Summary and conclusions.

We have presented bioinformatic, phenotypic, chemical, and mass spectrometry data that six structurally related linear lipopeptides were produced by a single biosynthetic gene cluster in P. syringae pv. tomato DC3000. We propose that these metabolites be called syringafactins A to F (Fig. (Fig.6B)6B) and the associated genes be designated as syfR (pspto_2828), syfA (pspto_2829), syfB (pspto_2830), syfC (pspto_2831), and syfD (pspto_2832) (Fig. (Fig.1).1). The metabolites produced by the syf biosynthetic gene cluster were shown to be involved in the swarming motility of P. syringae pv. tomato DC3000.

One surprising finding from this work was that the isolated metabolites were linear rather than cyclic. Prior to this work, lipopeptides identified from Pseudomonas species that function as biosurfactants were found to be cyclic (reviewed in reference 50). Importantly, we were able to show that the purified linear lipopeptides restored swarming motility to a mutant strain. Thus, while many Pseudomonas species use cyclic lipopeptides for a variety of biological purposes, linear lipopeptides can have equivalent functions.

We, and others (12), noticed a biosynthetic gene cluster that codes for an NRPS very similar to the syringafactin NRPS in P. syringae pv. syringae B728a (ORFs psyr_2576-psyr_2577). While it was proposed that this cluster would also produce a cyclic lipopeptide with a different amino acid sequence than we determined for the syringafactins (12), the work presented here on the syringafactins suggests this NRPS generates linear lipopeptides with a similar amino acid sequence to the syringafactins. Consistent with this proposal, preliminary analysis of the metabolites isolated from P. syringae pv. syringae B728a identified metabolites with the same HPLC retention time and the same masses, based on MALDI-TOF MS, as syringafactins A, B, and C (data not shown). Interestingly, metabolites eluting with the same retention time as syringafactins D, E, and F were not observed, suggesting only a single lipid will be found at the N terminus of these metabolites. Thus, even NRPS systems showing extensive amino acid similarity may not produce the same metabolites. Further analyses of the metabolites from P. syringae pv. syringae B728a are ongoing.

The work presented here highlights the importance of coupling bioinformatics analysis with experimental and metabolite analyses to test the hypothetical structure developed by bioinformatics. We have shown that a single biosynthetic gene cluster produced not a single metabolite but six related metabolites due to the presumed substrate recognition flexibility of some of the NRPS components. The observed structural differences likely came from the substrate flexibility of the initiating C domain of Pspto_2829 (SyfA) and the A domain of module six of the NRPS. Bioinformatics, at least at this time, is not accurate enough to make these distinctions. Regardless, the data presented here show that combining genome mining and metabolite purification enables the identification of previously unknown secondary metabolites and, based on the determined structure, the biological role of the metabolite can be deciphered. This approach has enormous potential in uncovering new metabolites due to the ever-expanding pool of sequenced microbial genomes, many of which have gene clusters that potentially generate previously unknown secondary metabolites.

Supplementary Material

[Supplemental material]

Acknowledgments

This work was supported by the Wisconsin Agricultural Experimentation Station project WIS04976 from the U.S. Department of Agriculture and the Alfred Toepfer Faculty Fellow award from the College of Agriculture and Life Sciences at the University of Wisconsin—Madison (M.G.T.). Q.H.C. was supported by an Ira L. Baldwin undergraduate scholarship.

We thank Herbert P. Schweizer (Colorado State University) for generously providing plasmids and Michelle R. Rondon for critical reading of the manuscript. We thank Amy C. Harms and James F. Brown from the University of Wisconsin Biotechnology Center for their expert assistance in the mass spectrometry analyses. We thank Michael Burkart (University of California at San Diego) for discussions concerning amino acid chemistry.

Footnotes

[down-pointing small open triangle]Published ahead of print on 29 June 2007.

Supplemental material for this article may be found at http://jb.asm.org/.

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