Mechanisms of tolerance to the self-produced natural antibiotic PYO in P. aeruginosa

We started by characterizing the defense mechanisms P. aeruginosa has evolved to tolerate its most toxic self-produced phenazine, pyocyanin (PYO) [10,16]. To do so in an unbiased fashion, we performed a genome-wide transposon sequencing (Tn-seq) screen in which the mutant library was exposed to PYO under starvation to maximize PYO toxicity [16], and tolerance of the pooled mutants to PYO was assessed following regrowth (Fig 1A). This revealed 5 broad categories of genes that significantly affect tolerance to PYO: (i) efflux system repressors; (ii) protein damage responses; (iii) membrane or cell wall biosynthesis; (iv) oxidative stress responses; and (v) carbon metabolism and transport (Fig 1B). We validated the screen results by constructing and testing chromosomal clean deletion mutants for four of these genes (Fig 1C).

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Fig 1. Mechanisms of tolerance to the self-produced natural antibiotic PYO in P. aeruginosa.

(A) Genome-wide Tn-seq experimental design. Cells were incubated with and without PYO under nutrient starvation for maximum PYO toxicity [16] (see Methods for details). Bar graphs shown are hypothetical representations of the expected results for genes with different fitness effects and are not derived from the obtained data. (B) Statistically significant fitness effects of transposon insertions in different representative genes under conditions that maximize PYO toxicity (for full dataset, see S1 Table). See Methods for details on calculation of fitness. Asterisks show genes for which chromosomal clean deletion mutants were constructed and validated. (C) Tn-seq validations. Chromosomal clean deletion mutants were exposed to PYO under carbon starvation, similar to the conditions used for the Tn-seq experiment. Survival of each strain was measured by CFUs and compared to the survival of the parent Δphz strain for fitness calculation (see Methods for details). Statistical significance was calculated using 1-way ANOVA with Tukey HSD multiple comparison test, with asterisks showing significant differences relative to Δphz (*** p < 0.001). Data points represent independent replicates, and black horizontal lines mark the mean fitness for each strain. (D) Tolerance to PYO toxicity in the presence and absence of the efflux inhibitor PAβN. Each data point represents an independent biological replicate (n = 4), and the horizontal black lines mark the mean survival for each condition and time point. (E) Fitness correlation analysis between PYO tolerance Tn-seq (this study) and CIP persistence Tn-seq [24]. Efflux repressors present in both datasets are highlighted in green. For full analysis, see S1 Table. The data underlying this figure can be found in Table A in S1 Data. ANOVA, analysis of variance; CFU, colony-forming unit; CIP, ciprofloxacin; HSD, honestly significant difference; PAβN, phenylalanine-arginine β-naphthylamide; PYO, pyocyanin; Tn-seq, transposon sequencing.

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The fitness effects of different transposon insertions largely aligned with what is thought to be the primary mode of PYO toxicity, which is the generation of reactive oxygen species (ROS) [19,20]. For example, the fact that transposon insertions in different genes within the “carbon metabolism and transport” category had opposite effects on fitness likely reflects conflicting priorities for cells challenged with ROS-generating toxins: On the one hand, limiting flux through the electron transport chain decreases the potential for ROS generation, but on the other hand, proton motive force is required to pump the toxin out, and NADH is needed to power reductases involved in repair of oxidative damage. The need to counteract oxidative stress would also explain why transposon insertions in genes related to protein damage repair and glutathione synthesis or reduction led to decreased fitness in the presence of PYO (Fig 1B). Finally, for genes related to cell wall/membrane synthesis, the transposon insertions may have altered cellular permeability and thereby either increased or decreased PYO influx.

However, the strongest hits in our Tn-seq were transposon insertions in transcriptional repressors of resistance-nodulation-division (RND) efflux system genes (Fig 1B, S1 Table), which would cause overexpression of the downstream efflux pumps. These insertions dramatically increased fitness in the presence of PYO, suggesting that one of the most effective defenses against PYO toxicity is to decrease the intracellular concentration of the toxin. While transposon insertions in the genes encoding the efflux pump proteins themselves did not have strong effects in our screen (S1 Table), this is likely due to partial functional redundancy among the various efflux systems [21]. Indeed, when we challenged starved P. aeruginosa with PYO in the presence of the broad-spectrum RND efflux inhibitor phenylalanine-arginine β-naphthylamide (PAβN), cell death was accelerated, confirming that efflux pumps are necessary for minimizing PYO toxicity (Fig 1D).

Mutations in the efflux system repressors identified in our Tn-seq screen are commonly found in clinical isolates that are resistant to synthetic fluoroquinolone antibiotics, as the efflux systems regulated by these repressors efficiently export this class of drugs [22,23]. We therefore asked whether the mechanisms used by P. aeruginosa to tolerate PYO toxicity might overlap more broadly with those that confer tolerance to fluoroquinolones. To address this question, we compared our dataset to a recent Tn-seq study that screened for genes that affect P. aeruginosa survival in the presence of the broad-spectrum fluoroquinolone ciprofloxacin [24]. Across the 2 datasets, we observed similar fitness effects for insertions in a small number of genes within the “protein damage response,” “membrane/cell wall,” and “oxidative stress response” categories, but the most dramatic fitness increases in both experiments were caused by insertions in a shared set of efflux system repressors (Fig 1E, S1 Table). These results highlighted the potential for a conserved molecular route to increased tolerance against both a natural antibiotic, PYO, and a synthetic clinical antibiotic, ciprofloxacin.

PYO induces expression of specific efflux systems, conferring cross-tolerance to fluoroquinolones

Given that cellular processes involved in PYO tolerance have also been implicated in ciprofloxacin tolerance, we asked whether exposure to PYO could promote an increase in tolerance to ciprofloxacin and related clinical antibiotics, including other synthetic fluoroquinolones. Importantly, such an effect would require that PYO induces the expression of shared defense mechanisms. We have previously established that PYO up-regulates expression of not only the oxidative stress response genes ahpB (an alkyl hydroperoxide reductase) and katB (a catalase) [16], but also at least 2 efflux systems known to pump fluoroquinolones, mexEF-oprN and mexGHI-opmD [13,16]. We confirmed these expression patterns by performing quantitative reverse transcriptase PCR (qRT-PCR) on the wild-type (WT) strain that produces PYO, a Δphz mutant that does not produce PYO, and Δphz treated with exogenous PYO (S1–S3 Figs). Notably, phenazines and fluoroquinolones both contain at least 1 aromatic ring, unlike other antibiotics that are not thought to be pumped by mexEF-oprN and mexGHI-opmD, such as aminoglycosides [21] (Fig 2A). Thus, structural similarities could account for why efflux pumps that likely evolved to export natural antibiotics such as PYO can also transport certain classes of synthetic antibiotics. To determine whether PYO also induces other efflux systems known to pump clinical antibiotics besides fluoroquinolones, we performed qRT-PCR on representative genes from all 11 major RND efflux systems in the P. aeruginosa genome. These measurements confirmed that mexEF-oprN and mexGHI-opmD are the only 2 efflux systems significantly induced by PYO and that the induction is PYO dose dependent (Fig 2B, S2 and S3 Figs). The mexGHI-opmD system in particular reached expression levels comparable to the constitutively expressed mexAB-oprM efflux system (Fig 2B, S2 Fig), which plays an important role in the intrinsic antibiotic tolerance and resistance of P. aeruginosa [21].

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Fig 2. PYO induces expression of specific efflux systems, conferring cross-tolerance to fluoroquinolones.

(A) Structures of PYO, 2 representative fluoroquinolones (CIP and LVX) and 2 representative aminoglycosides (GEN and TOB). PYO and fluoroquinolones are pumped by MexEF-OprN and MexGHI-OpmD, while aminoglycosides are not [21,22]. Rings with an aromatic character are highlighted in red. (B) Normalized cDNA levels for genes within operons coding for the 11 main RND efflux systems in P. aeruginosa (left; n = 3) and PYO-dose-dependent changes in expression of mexEF-oprN and mexGHI-opmD systems (right; n = 3). For full qRT-PCR dataset, see S1–S3 Figs. (C) Effect of PYO on tolerance to CIP (1 μg/mL), LVX (1 μg/mL), and CST (16 μg/mL) in GMM (n = 4). (D) Effect of PYO on tolerance to CIP (1 μg/mL) and TOB (40 μg/mL) in SCFM (n = 4). PYO itself was not toxic under the experimental conditions [16] (S4C Fig). WT made 50–80 μM PYO as measured by absorbance of the culture supernatant at 691 nm. See S5A Fig for experimental design. (E) Effect on tolerance to CIP (1 μg/mL) in GMM caused by the presence of the 4 main phenazines produced by P. aeruginosa (PYO, PCA, PCN, and 1-OH-PHZ) (n = 4). For this experiment, a Δphz* strain that cannot produce or modify any phenazine was used (see Methods). (F, G) Effect of PYO on lag during outgrowth after exposure to CIP in GMM. A representative field of view over different time points (F; magenta = WT::mApple, green = Δphz::GFP; see S1 Movie) is shown together with the quantification of growth area on the agarose pads at time 0 hour and 15 hours (G). For these experiments, a culture of each strain tested was grown and exposed to CIP (10 μg/mL) separately, then cells of both cultures were washed, mixed, and placed together on a pad and imaged during outgrowth. The pads did not contain any PYO or CIP (see Methods and S5D Fig for details). White arrows in the displayed images point to regions with faster recovery of WT growth. The field of view displayed is marked with a black arrow in the quantification plot. The results for the experiment with swapped fluorescent proteins are shown in S4E Fig. See S4C Fig for complementary data about effects of PYO on lag. Scale bar: 20 μm. (H) Tolerance of Δphz to CIP (1 μg/mL) in GMM in the presence of different concentrations of PYO (n = 4). (G) Tolerance of Δphz to CIP (1 μg/mL) in GMM upon artificial induction of the mexGHI-opmD operon with arabinose (n = 4). The dashed green line marks the average survival of PYO-producing WT under similar conditions (without arabinose). Statistics: C, D, E, H—1-way ANOVA with Tukey HSD multiple comparison test, with asterisks showing significant differences relative to untreated Δphz (no PYO); G, I—Welch unpaired t test (* p < 0.05, ** p < 0.01, *** p < 0.001, n.s. p > 0.05). In all panels with quantitative data, black horizontal lines mark the mean value for each condition. Individual data points represent independent biological replicates, except for in panel G, where the data points represent different fields of view. The data underlying this figure can be found in Table B in S1 Data. 1-OH-PHZ, 1-hydroxyphenazine; ANOVA, analysis of variance; CIP, ciprofloxacin; CST, colistin; GEN, gentamicin; GMM, glucose minimal medium; HSD, honestly significant difference; LVX, levofloxacin; PCA, phenazine-1-carboxylic acid; PCN, phenazine-1-carboxamide; PYO, pyocyanin; qRT-PCR, quantitative reverse transcriptase PCR; RND, resistance-nodulation-division; SCFM, synthetic cystic fibrosis sputum medium; TOB, tobramycin; WT, wild-type.

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To assess whether the induction of efflux pumps and oxidative stress responses by PYO could increase the tolerance of P. aeruginosa to clinical drugs such as ciprofloxacin, we grew cultures with or without clinically relevant concentrations of PYO [25] and performed a survival assay following treatment with different antibiotics. Importantly, we hypothesized that PYO would not be a universal antagonist to all clinical antibiotics. Instead, we expected tolerance to increase only for drugs affected by the defense mechanisms induced by PYO in the cells. Indeed, compared to the non-PYO-producing Δphz mutant, the PYO-producing WT strain and PYO-treated Δphz were more tolerant to both ciprofloxacin and another fluoroquinolone, levofloxacin (Fig 2C). On the other hand, PYO did not confer increased tolerance to (i) aminoglycosides (Fig 2D, S4B Fig), which are not substrates for the efflux pumps up-regulated by PYO [21]; or (ii) colistin (polymyxin E) (Fig 2C), an antimicrobial peptide that permeabilizes the outer membrane of the cell by interacting with the lipopolysaccharide and causing displacement of divalent cations [1]. Similar to aminoglycosides, colistin is not known to be pumped by the PYO-induced efflux systems [21]; moreover, efflux rarely impacts polymyxin efficacy [26]. PYO itself was not toxic under the experimental conditions used in our tolerance assays [16] (S4C Fig). Aside from PYO, 1-hydroxyphenazine was the only other phenazine made by P. aeruginosa that increased tolerance to ciprofloxacin under our conditions, albeit to a lesser extent than PYO (Fig 2E). We also tested whether the presence of PYO could affect the minimum inhibitory concentration (MIC) for ciprofloxacin, as the classical definition of antibiotic tolerance also stipulates that increased survival in the presence of an antibiotic is not accompanied by an increase in MIC [2,8]. When we determined the MIC for ciprofloxacin according to standard clinical protocols [27] for our P. aeruginosa strain in the presence or absence of PYO, we saw no consistent difference at a detection limit of a 2-fold increase in MIC (S7 Table), supporting the interpretation that the effect of PYO on P. aeruginosa is primarily an increase in antibiotic tolerance (i.e., survival without the ability to grow) rather than phenotypic resistance. Importantly, PYO also induced ciprofloxacin tolerance when P. aeruginosa was grown in synthetic cystic fibrosis sputum medium (SCFM) (Fig 2D), suggesting that PYO production could contribute to antibiotic tolerance of this bacterium in CF patients. Together, these results indicate that PYO preferentially induces tolerance to fluoroquinolones.

Under in vitro conditions, PYO is typically produced in early stationary phase [13]. However, the heterogeneous nature of physiological conditions in infections [28,29] could lead to intermixing of PYO-producing and PYO-non-producing cells in vivo. We therefore tested whether exogenous PYO could increase the fluoroquinolone tolerance of cells harvested during log phase, which did not make PYO. To limit the growth of the no-antibiotic control, we exposed these cells to the antibiotics under nitrogen depletion. PYO still increased tolerance to both ciprofloxacin and levofloxacin under these conditions, suggesting that the induced tolerance phenotype does not depend on the previous growth phase of growth-arrested cells [16] (S4B Fig). Next, to visualize the recovery of cell growth after a transient exposure to ciprofloxacin, we performed a time-lapse microscopy assay (S4D Fig). Interestingly, WT P. aeruginosa and PYO-treated Δphz exhibited a shorter lag phase compared to non-PYO-treated Δphz following ciprofloxacin treatment (Fig 2F and 2G, S4E and S4F Fig, S1 Movie), suggesting that PYO-induced defenses may help minimize cellular damage during the antibiotic treatment. We also found that addition of PYO to Δphz increased ciprofloxacin tolerance in a dose-dependent manner (Fig 2H), mirroring the dose-dependent induction of mexEF-oprN and mexGHI-opmD (Fig 2B).

Given that PYO-induced efflux pumps transport specific substrates [21], we asked if increased drug efflux could be the primary mechanism underlying PYO-mediated tolerance to fluoroquinolones. At high concentrations of ciprofloxacin, addition of the efflux inhibitor PAβN eliminated the survival advantage of PYO-treated cells, indicating that efflux pump activity is necessary for the PYO-mediated increase in antibiotic tolerance (S4G Fig). Next, we constructed a Δphz strain with the mexGHI-opmD operon under the control of an arabinose-inducible promoter (P_ara:mexGHI-opmD). We verified that the transcription levels of mexGHI-opmD under arabinose induction were comparable to when PYO is present (S5 Fig). Indeed, arabinose induction of mexGHI-opmD expression increased ciprofloxacin tolerance to near WT levels (Fig 2I), suggesting that induction of this efflux system is sufficient to confer the PYO-mediated increase in tolerance. On the other hand, arabinose induction of the oxidative stress response genes ahpB or katB did not significantly increase tolerance of Δphz to ciprofloxacin (S6A and S6B Fig); however, the levels of induction achieved for these 2 genes with arabinose were lower than those observed in the presence of PYO (S1–S5 Figs). Importantly, the clinical relevance of mexGHI-opmD was previously not well known; as to our knowledge, there have been no reports of clinical mutants with constitutive overexpression of this efflux system. Taken together, our results demonstrate that PYO-mediated regulation of mexGHI-opmD expression modulates tolerance to a particular class of clinically used antibiotics in P. aeruginosa.

PYO promotes the evolution of antibiotic resistance in P. aeruginosa

Previous studies have demonstrated that mutations conferring antibiotic tolerance or persistence promote the evolution of antibiotic resistance [3,4]. Moreover, tolerance mutations can (i) interact synergistically with resistance mutations to increase bacterial survival during antibiotic treatment [30]; and (ii) promote the establishment of resistance mutations during combination drug therapy [31]. To assess whether antibiotic tolerance induced by PYO could similarly promote the establishment of resistance mutations in populations of P. aeruginosa undergoing extended exposure to a clinical antibiotic, we next performed a series of fluctuation tests (Fig 3A). In clinical settings, antibiotic resistance is likely to result in treatment failure if a pathogen can grow at antibiotic concentrations above a threshold commonly referred to as a “breakpoint.” We adopted this criterion by selecting mutants on antibiotic concentrations equal to or higher than the breakpoints defined by the European Committee on Antimicrobial Susceptibility Testing (EUCAST) [32]. Furthermore, we added PYO to our cultures either prior to the antibiotic selection step and/or concurrently with the antibiotic selection, in order to distinguish between the effects of preemptive versus continuous induction of PYO-regulated cellular defenses. Finally, while mutation rates inferred from fluctuation tests have sometimes been assumed to correlate with the per-base mutation rate across the genome [33,34], the results from these assays are also affected by the number of unique possible mutations that permit growth under the selection condition [35]. To encompass both possibilities in this study, we use the term μ_app (apparent rate of mutation) as a proxy for the likelihood of evolving antibiotic resistance. We calculated this parameter using standard methods for fluctuation test analysis (see Methods for details).

Regardless of whether PYO was added prior to or concurrently with the antibiotic selection, PYO significantly increased μ_app for resistance to ciprofloxacin in log-phase cultures (Fig 3B, S2 and S3 Tables). The same trends were also observed in stationary-phase cultures, albeit with smaller effect sizes (S7A Fig). These results indicate that pretreatment with PYO is sufficient but not necessary to increase μ_app for ciprofloxacin resistance. Adding PYO at both stages of the fluctuation test generally resulted in an even greater increase in μ_app (Fig 3B, S7A Fig), and the increase in μ_app when PYO was added prior to antibiotic selection was dose dependent (Fig 3C). Cultures that were selected on levofloxacin similarly displayed an increased μ_app upon PYO treatment, although the impact of pretreatment versus co-treatment with PYO varied across biological replicates (Fig 3B, S2 and S3 Tables). More importantly, PYO significantly increased μ_app for cultures that were grown in liquid SCFM and selected on SCFM plates containing ciprofloxacin (Fig 3D, S3 Table), suggesting that PYO produced by P. aeruginosa could promote mutation to antibiotic resistance in chronically infected lungs of CF patients [36].

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Fig 3. PYO increases the apparent rate of mutation to antibiotic resistance in P. aeruginosa.

(A) Experimental design for fluctuation tests to determine the effect of PYO (100 μM unless otherwise noted) on apparent mutation rates. For panels B–E, mutation rates were calculated using an established maximum likelihood-based method that accounts for the effects of plating a small proportion of the total culture volume (see Methods for details). Each data point in those panels represents a single biological replicate comprising 44 parallel cultures, and the vertical lines represent the 84% confidence intervals. Lack of overlap in these confidence intervals corresponds to statistical significance at the p < 0.05 threshold [99]. For statistical significance as determined by a likelihood ratio test, see S2 Table. In B, D, and E, the PYO treatments correspond to the following: −/− denotes no PYO pretreatment (in the liquid culture stage) or co-treatment (in the antibiotic agar plates), +/− denotes PYO pretreatment but no co-treatment, −/+ denotes PYO co-treatment without pretreatment, and +/+ denotes both PYO pretreatment and co-treatment. (B) Apparent mutation rates of log-phase Δphz grown in GMM and plated on MH agar containing CIP (0.5 μg/mL; n = 4) or LVX (1 μg/mL; n = 5), with or without pre- and/or co-exposure to PYO relative to the antibiotic selection step. (C) The apparent rate of mutation to resistance for Δphz cells that were pretreated with different concentrations of PYO in GMM and plated onto MH agar containing CIP (0.5 μg/mL). (D) Apparent mutation rates of log-phase Δphz grown in SCFM and plated on SCFM agar containing CIP (1 μg/mL; n = 4) with or without pre- and/or co-exposure to PYO. (E) Apparent mutation rates of log-phase Δphz grown in GMM and plated during onto MH agar containing GEN (16 μg/mL; n = 4) or TOB (4 μg/mL; n = 4), with or without pre- and/or co-exposure to PYO. The data underlying this figure can be found in Table C in S1 Data. CIP, ciprofloxacin; GEN, gentamicin; GMM, glucose minimal medium; LVX, levofloxacin; MH, Mueller–Hinton; PYO, pyocyanin; SCFM, synthetic cystic fibrosis sputum medium; TOB, tobramycin.

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Because PYO did not increase tolerance to aminoglycosides (Fig 2D, S4B Fig), we hypothesized that PYO would not promote mutation to aminoglycoside resistance if the induction of shared defense mechanisms was required for the observed increases in μ_app. On the other hand, if PYO affected μ_app primarily by acting as a mutagen, pretreatment with PYO before antibiotic selection would be expected to increase μ_app by a similar proportion for resistance to all classes of antibiotics. To differentiate between these modes of action, we repeated the fluctuation tests using gentamicin and tobramycin, representative members of the aminoglycoside class that disrupt protein translation [1]. Cultures that were preexposed to PYO consistently exhibited significant increases in μ_app for gentamicin resistance (Fig 3E, S2 and S3 Tables). For tobramycin resistance, on the other hand, pretreatment with PYO only significantly increased μ_app in one out of 4 biological replicates (Fig 3E, S2 and S3 Tables). In addition, for both aminoglycosides, adding PYO to the antibiotic selection plates had no effect on μ_app in most replicates (Fig 3E, S2 and S3 Tables). These differing responses to PYO depending on the choice of clinical antibiotic suggested that the observed changes in μ_app were related to PYO-induced cellular defenses more so than a mutagenic effect of PYO. In fact, previous studies have suggested that gentamicin generates ROS more readily than tobramycin [37,38]. This could account for why the effect of pre-exposure to PYO on μ_app for resistance was greater for gentamicin than for tobramycin, given that PYO primes cells to detoxify ROS by inducing oxidative stress responses (S1 Fig). For resistance to fluoroquinolones, on the other hand, simultaneous induction of multiple defenses is likely necessary to recapitulate the increases in μ_app upon exposure to PYO. Overexpression of individual oxidative stress genes induced by PYO did not increase μ_app for ciprofloxacin resistance, while overexpression of the mexGHI-opmD efflux system only mildly increased μ_app in a subset of biological replicates (S7B Fig). Interestingly, the latter result contrasted with our finding that induction of mexGHI-opmD was sufficient to recapitulate PYO-mediated increases in fluoroquinolone tolerance. Together, our data suggest that while tolerance and resistance can be mechanistically interrelated, overcoming the barrier to growing in the presence of an antibiotic in some cases requires a different or broader set of defenses than is required for temporary survival under growth-arrested conditions. Nevertheless, PYO-induced defense mechanisms appear to contribute to both types of resilience to antibiotic treatment.

We envisioned at least 3 ways in which, under antibiotic selection, PYO-induced defense mechanisms could lead to the apparent increases in mutation rates: (A) by enhancing the growth of preexisting “partially resistant” mutants during exposure to the antibiotic; (B) by increasing the proportion of cells that survive and subsequently mutate to resistance while still in the presence of the antibiotic; or (C) by a combination of A and B. To distinguish between these scenarios, we implemented a 2-pronged approach. First, to explore the possibility of scenario A, we isolated and characterized several mutants from the fluctuation test plates containing ciprofloxacin. We regrew the isolates under nonselective conditions both with and without PYO treatment and calculated the percentage of colony-forming units (CFUs) that could subsequently be recovered on ciprofloxacin plates relative to nonselective plates, as a metric for each isolate’s level of resistance. We defined as “partially resistant” those isolates for which only a subset of the population could grow under the antibiotic selection without PYO treatment, as evidenced by lower CFU counts on antibiotic plates compared to nonselective plates. Second, to determine the relative likelihoods of scenario A and scenario B, we examined the fit of our fluctuation test data to different formulations of the theoretical Luria–Delbrück (LD) distribution. Specifically, we compared mathematical models that make different assumptions regarding whether mutants arise prior to or during the antibiotic selection.

We identified multiple partially resistant mutants for which the percentage of CFUs recovered on ciprofloxacin plates following growth under nonselective conditions increased when the isolate was either preexposed or co-exposed to PYO (Fig 4A), although the trends were not always statistically significant. Importantly, CFUs for the Δphz parent strain were below the level of detection on the ciprofloxacin plates even in the presence of PYO, confirming that PYO-induced defenses alone, in the absence of a resistance mutation, were insufficient to enable growth under the selection condition used for the fluctuation tests (Fig 4A). In addition, for all characterized partially resistant mutants, the MIC for ciprofloxacin was higher than for the parent strain (S7 Table). For some of these mutants, the MIC determined according to standard clinical protocols [27] matched the ciprofloxacin concentration originally used for selection, even in the presence of PYO, but this is not surprising, as the relatively dilute inoculum (5 × 10⁵ CFU/mL) and short incubation time (18 hours) used for standard MIC assays can preclude detection of weak growth at a given antibiotic concentration. As a further validation of our assay for detection of partially resistant mutants, we also tested isolates with distinct colony morphologies that were not enriched on the PYO-containing antibiotic plates relative to PYO-free antibiotic plates in the original fluctuation tests. As expected, these mutants were fully resistant to ciprofloxacin at the original selection concentration (S8A Fig), meaning that the same number of CFUs grew on both antibiotic plates and nonselective plates even in the absence of PYO. Interestingly, the effect of PYO on ciprofloxacin resistance varied across different partially resistant isolates (Fig 4A). This suggests that PYO does not universally raise the level of resistance of the entire population, but rather interacts synergistically with specific types of mutations conferring partial resistance. Such heterogeneity could account for why the effect of PYO in the fluctuation tests varied across biological replicates, as the degree of benefit conferred by PYO would depend on the specific mutations that randomly occurred in each replicate. We also repeated the stationary-phase ciprofloxacin tolerance assay with the partially resistant isolates and found that tolerance was likewise differentially affected by PYO (S8B Fig). Interestingly, the tolerance and resistance phenotypes shared no obvious underlying pattern, again suggesting that cellular processes that affect resistance do not always equally effect tolerance and vice versa. Nevertheless, our results demonstrate that under antibiotic selection, a subset of partially resistant mutants benefits from exposure to PYO.

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Fig 4. PYO promotes the growth of partially resistant mutants and the occurrence of post-plating mutations.

(A) Putative CIP-resistant mutants of P.a. isolated from fluctuation test plates were grown to mid-log phase in liquid GMM with or without 100 μM PYO, before plating for CFUs on nonselective MH agar plates, plates containing CIP alone (0.5 μg/mL), and plates containing CIP and PYO. Plotted values represent the percentage of CFUs recovered on the CIP plates, calculated relative to total CFUs counted on nonselective plates. On the x-axis, “pre” denotes the presence of PYO in the liquid cultures, and “co” denotes the presence of PYO in the agar plates. Data points represent independent biological cultures (n = 4). Black horizontal lines mark the mean values for each condition. (B) Goodness of fit of different mathematical models for P. aeruginosa Δphz fluctuation test data. Data from the fluctuation tests performed on CIP are plotted for different combinations of PYO in liquid (pretreatment) and PYO in agar (co-exposure to antibiotic selection). The empirical CDFs of the data (black) are plotted against (1) a variation of the LD model fit with 2 parameters, m (the expected number of mutations per culture) and w (the relative fitness of mutant cells vs. WT), as implemented by Hamon and Ycart [44] (pink); (2) a mixed LD and Poisson distribution fit with 2 parameters, m and d (the number of generations that occur post-plating), allowing for the possibility of post-plating mutations, as implemented by Lang and Murray [45] (blue); and (3) the basic LD distribution model fit only with m, as implemented by Lang and Murray [45] (gray). In each condition, the plotted experimental data represent the biological replicate with the lowest chi-squared goodness of fit p-value (i.e., least good fit) for the Hamon and Ycart model, demonstrating that this model was still a reasonable fit for these samples. Statistics: A—Welch unpaired t tests with Benjamini–Hochberg correction for controlling false discovery rate (* p < 0.05, ** p < 0.01, *** p < 0.001, n.s. p > 0.05). The data underlying this figure can be found in Table D in S1 Data. CDF, cumulative distribution function; CFU, colony-forming unit; CIP, ciprofloxacin; GMM, glucose minimal medium; LD, Luria–Delbrück; P.a., P. aeruginosa; PYO, pyocyanin; WT, wild-type.

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Whole-genome sequencing revealed that the partially resistant isolates contained mutations either in the efflux pump repressors nfxB or mexS or in genes that affected growth rate, such as a ribosomal protein, a C4-dicarboxylate transporter, and a cell wall synthesis gene (S4 Table). Mutations in nfxB or mexS were also found in the fully resistant isolates (S4 Table), albeit at different loci compared to the partially resistant isolates. Notably, nfxB is considered a “pathoadaptive gene” in which mutations tend to accumulate during chronic infections [39,40]. Mutations in mexS are less common, but have also been detected in clinical isolates [41]. Slow-growing small-colony variant mutants of P. aeruginosa have likewise been isolated from patients [42,43]. Thus, the growth benefits conferred by PYO-induced defenses during antibiotic selection could be relevant to a variety of clinically adapted strains.

That PYO increases μ_app at least in part by promoting the growth of preexisting partially resistant mutants was further supported by the alternative approach of evaluating the fit of our data to different mathematical models. Specifically, Pearson chi-squared test indicated that our data closely fit the Hamon and Ycart model [44] (Fig 4B, S9 Fig, S3 Table), which allows for differential fitness of mutants compared to WT cells, but assumes that all mutants arise pre-plating. However, we could not unequivocally rule out the possibility that post-plating mutations contributed to the increases in μ_app, as a subset of our data also fit a mixed LD–Poisson model that assumes some mutations occurred during the antibiotic selection step [45] (Fig 4B, S9 Fig, S3 Table). We also performed growth curves under the culture conditions used in our fluctuation tests prior to the antibiotic selection step, with the addition of the live cell-impermeable DNA-binding dye propidium iodide (PI) as a marker for cell death. As expected from a previous study on PYO toxicity [16], cell death was undetectable prior to the sampling time point used in most of the fluctuation tests (S8D and S8E Fig). Thus, while increased population turnover due to stress can also lead to increases in μ_app [46], this is unlikely to underlie the effect of PYO on μ_app. Together, these results suggest that the most probable explanation for the PYO-mediated increases in apparent mutation rates is a combined effect of increased detection of partially resistant mutants (the proposed scenario A) and increased occurrence of post-plating mutations resulting from elevated survival on the antibiotic plates (the proposed scenario B).

Importantly, previous studies based on in vitro evolution experiments have demonstrated that even modest increases in mutation rates, in the range of 2- to 5-fold, significantly affect the maximum achievable level of antibiotic resistance for diverse bacterial pathogens [47,48]. Moreover, it is well established that partial resistance can rapidly lead to acquisition of full resistance via secondary mutations [49,50]. Indeed, several putative mutants appeared fully resistant to ciprofloxacin in our CFU recovery assay despite having been enriched by exposure to PYO in the fluctuation tests (S8C Fig). This discrepancy could be a result of acquiring secondary mutations either during growth on the original fluctuation test plates or during the pre-growth for the CFU recovery assay. Thus, our results suggest that PYO may significantly affect the rate at which high-level resistance emerges in populations of P. aeruginosa undergoing long-term antibiotic exposure.

PYO promotes antibiotic tolerance in other opportunistic pathogens

While the above experiments were performed with single species cultures, P. aeruginosa is found in polymicrobial communities in both natural environments (e.g., soil) and clinical contexts (e.g., chronic infections) [51–54]. We hypothesized that microbes that frequently interact with P. aeruginosa would have evolved inducible defense mechanisms against PYO toxicity and that production of PYO by P. aeruginosa might therefore also increase tolerance and resistance to clinical antibiotics in these community members. To test this hypothesis, we focused on the genera Burkholderia and Stenotrophomonas, both of which are (i) soil-borne gram-negative opportunistic pathogens that are frequently refractory to clinical antibiotic treatments [55–57]; and (ii) found in coinfections with P. aeruginosa, e.g., in CF patients [58]. Specifically, we tested a plant-derived strain, B. cepacia ATCC 25416; a non-CF clinical isolate of Stenotrophomonas, Stenotrophomonas maltophilia ATCC 13637; and several clinical isolates of the 3 most prevalent Burkholderia species found in CF patients [51]: Burkholderia cenocepacia, Burkholderia multivorans, and Burkholderia gladioli (for descriptions of these strains, see S5 Table).

We first assessed each strain’s intrinsic resistance to PYO (Fig 5A), as we expected that strong defenses against PYO toxicity would be required in order to benefit from exposure to this natural antibiotic. Indeed, for S. maltophilia, which was sensitive to PYO (Fig 5A), the effects of PYO on antibiotic tolerance were complex: The presence of PYO was only beneficial when ciprofloxacin levels were low (1 μg/mL) (Fig 5B). At a higher concentration of ciprofloxacin (10 μg/mL), PYO was detrimental in a dose-dependent manner (Fig 5B), suggesting that the additional stress conferred by PYO outweighed any induction of defense mechanisms against ciprofloxacin. S. maltophilia also struggled to grow with P. aeruginosa in cocultures (Fig 5C and 5D), indicating that the conditions under which this species could potentially benefit from PYO are very limited.

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Fig 5. PYO promotes antibiotic tolerance in other opportunistic pathogens.

(A) Growth in GMM + AA of several strains in the presence of different concentrations of PYO. Plotted lines represent averages of 4–6 replicates, and shaded areas in gray represent the standard deviation. Burkholderia multivorans 1 = B. multivorans AU42096. For complete information on strains, see S5 Table. (B) Tolerance of S. maltophilia to different concentrations of CIP (1 or 10 μg/mL) after growth in the presence of different concentrations of PYO (0, 10, or 50 μM) in GMM + AA (n = 4). (C) Schematic depicting the experimental design for coculture antibiotic tolerance assays (see Methods for details). (D) CFUs recovered from cocultures of P. aeruginosa (PA14 WT and Δphz) and S. maltophilia (n = 3), showing that the latter struggled to grow in the presence of P. aeruginosa in GMM + AA. (E) Effect of PYO on the tolerance to CIP (10 μg/mL) in GMM + AA of multiple Burkholderia species isolated from environmental and clinical samples (n = 4). (F) Effect of PYO produced by P. aeruginosa in cocultures on the tolerance of different Burkholderia species to CIP (10 μg/mL) in GMM + AA. PA14 is our model laboratory strain of P. aeruginosa, while PA 76–11 is a PYO-producing strain of P. aeruginosa isolated from a CF patient. The Burkholderia strains were plated separately for CFUs to assess survival following treatment with CIP in the cocultures (n = 3). (G) Tolerance of P. aeruginosa PA14 WT and Δphz to CIP (1 μg/mL) when grown in cocultures with B. multivorans 1 in GMM + AA (n = 3). (H) Effect of PYO on the tolerance to CIP (10 μg/mL) of B. multivorans 1 in SCFM (n = 4). (I) Tolerance to CIP (1 μg/mL) in SCFM of B. multivorans 1 grown in cocultures with P. aeruginosa PA14 WT, Δphz, or alone with 100 μM PYO added exogenously (n = 3). Statistics: B, E, F, G, H, I—1-way ANOVA with Tukey HSD multiple comparison test for comparisons of 3 conditions or Welch unpaired t test for comparison of 2 conditions, with asterisks showing the statistical significance of comparisons with the untreated (no PYO or Δphz) condition (* p < 0.05, ** p < 0.01, *** p < 0.001). In all panels, data points represent independent biological replicates, and black horizontal bars mark the mean values for each condition. The data underlying this figure can be found in Table E in S1 Data. AA, amino acids; ANOVA, analysis of variance; CF, cystic fibrosis; CFU, colony-forming unit; CIP, ciprofloxacin; GMM, glucose minimal medium; HSD, honestly significant difference; PYO, pyocyanin; SCFM, synthetic cystic fibrosis sputum medium; WT, wild-type.

https://doi.org/10.1371/journal.pbio.3001093.g005

B. cepacia, B. cenocepacia, and B. multivorans, on the other hand, were highly resistant to PYO (Fig 5A). For these 3 species, exogenously added PYO increased tolerance to ciprofloxacin (Fig 5E). Furthermore, for B. cepacia, we confirmed that this effect was PYO dose dependent (Fig 5E). We therefore tested whether P. aeruginosa could induce tolerance to ciprofloxacin in cocultures with these Burkholderia strains. Using liquid culture plates in which the 2 species were separated by a permeable membrane (Fig 5C), we found that PYO-producing P. aeruginosa strongly induced tolerance to ciprofloxacin in the Burkholderia species and that the observed tolerance phenotypes were recapitulated by addition of exogenous PYO to cocultures of Burkholderia and the P. aeruginosa Δphz mutant or to control cultures with Burkholderia alone in the same setup (Fig 5F). Notably, for B. cenocepacia and B. multivorans, increased ciprofloxacin tolerance was also observed in cocultures with a PYO-producing strain isolated from a CF patient, P. aeruginosa PA 76–11 (Fig 5F). In addition, even when cocultured with Burkholderia, the P. aeruginosa WT strain still showed elevated ciprofloxacin tolerance when compared to the non-PYO-producing Δphz mutant (Fig 5G). This indicates that the presence of Burkholderia did not alter P. aeruginosa tolerance patterns under our conditions. Finally, similar results were obtained for experiments performed in SCFM, where either the addition of exogenous PYO (Fig 5H) or coculture with P. aeruginosa (Fig 5I) led to increased tolerance levels in Burkholderia. Together, these results suggest that PYO produced by P. aeruginosa in CF patients may decrease the efficacy of ciprofloxacin as a treatment for multispecies infections.

PYO promotes the evolution of antibiotic resistance in a co-occurring opportunistic pathogen

We next asked whether PYO could mediate an increase in apparent mutation rate for ciprofloxacin resistance in Burkholderia species. We chose B. multivorans AU42096 (B. multivorans 1 in Fig 5) as our model strain for these experiments because, among the clinical isolates, it displayed the strongest response to PYO in the ciprofloxacin tolerance assays. Remarkably, when selecting B. multivorans mutants on ciprofloxacin, we observed PYO-mediated increases in μ_app that were far more dramatic than for P. aeruginosa: Pretreatment with PYO increased μ_app for B. multivorans approximately 10-fold, while co-exposure to PYO in the antibiotic plate without pre-exposure increased μ_app approximately 40-fold, and the combination of pre- and co-exposure to PYO increased μ_app by 230-fold (Fig 6A, S3 Table). Notably, the magnitude of the latter effect is on par with observed differences between hypermutators, such as mutants deficient in the mismatch repair pathway, and their respective parent strains [59–61]. Moreover, hypermutators of Burkholderia isolated from CF infections are associated with clinical ciprofloxacin resistance [59]. In light of these observations, our results suggest that PYO could significantly affect clinical outcomes for coinfections of P. aeruginosa and B. multivorans treated with ciprofloxacin.

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Fig 6. PYO promotes antibiotic resistance in B. multivorans.

(A) The apparent rate of mutation to resistance when log-phase B. multivorans 1 cells grown in GMM + AA were plated on MH agar containing CIP (8 μg/mL), with or without pre- and/or co-exposure to 100 μM PYO relative to the antibiotic selection step. Each data point represents a biological replicate comprising 44 parallel cultures (n = 4). The vertical lines represent 84% confidence intervals, in which lack of overlap corresponds to statistical significance at the p < 0.05 level [99]. The PYO treatments correspond to the following: −/− denotes no PYO pretreatment (in the liquid culture stage) or co-treatment (in the antibiotic agar plates), +/− denotes PYO pretreatment but no co-treatment, −/+ denotes PYO co-treatment without pretreatment, and +/+ denotes both PYO pretreatment and co-treatment. (B) The percentage of CFUs recovered on CIP plates either with or without PYO in the agar, for exponential phase cultures of different partially resistant B. multivorans 1 (B.m.) mutants that were pre-grown with or without PYO in liquid cultures in GMM + AA. Plotted values represent the percentage of CFUs recovered on the CIP plates, calculated relative to total CFUs counted on nonselective plates. On the x-axis, “pre” denotes the presence of PYO in the liquid cultures, and “co” denotes the presence of PYO in the agar plates. Data points represent independent biological replicates (n = 4), and black horizontal bars mark the mean values for each condition. (C) Goodness of fit of different mathematical models for B. multivorans 1 fluctuation test data. Data are plotted for different combinations of PYO in liquid (pretreatment) and PYO in agar (co-exposure to antibiotic selection). The empirical CDFs of the data (black) are plotted against (1) a variation of the LD model fit with 2 parameters, m (the expected number of mutations per culture) and w (the relative fitness of mutant cells vs. WT), as implemented by Hamon and Ycart [44] (pink); (2) a mixed LD and Poisson distribution fit with 2 parameters, m and d (the number of generations that occur post-plating), allowing for the possibility of post-plating mutations, as implemented by Lang and Murray [45] (blue); and (3) the basic LD distribution model fit only with m, as implemented by Lang and Murray [45] (gray). In each condition, the plotted experimental data represent the biological replicate with the lowest chi-squared goodness of fit p-value (i.e., least good fit) for the Hamon and Ycart model. Statistics: B—Welch unpaired t tests with Benjamini–Hochberg correction for controlling false discovery rate (* p < 0.05, ** p < 0.01, *** p < 0.001). The data underlying this figure can be found in Table F in S1 Data. AA, amino acids; CDF, cumulative distribution function; CFU, colony-forming unit; CIP, ciprofloxacin; GMM, glucose minimal medium; LD, Luria–Delbrück; MH, Mueller–Hinton; PYO, pyocyanin; WT, wild-type.

https://doi.org/10.1371/journal.pbio.3001093.g006

To verify that the B. multivorans colonies growing on ciprofloxacin in the presence of PYO were mutants, and to assess their responses to PYO, we isolated several putative mutants from the fluctuation test antibiotic plates and tested three in our CFU recovery assay. All three displayed unique profiles of ciprofloxacin resistance in response to PYO treatment, as well as different maximal levels of resistance. However, all were more resistant than the WT parent strain in the presence of PYO, and none were noticeably resistant to ciprofloxacin in this assay without exposure to PYO (Fig 6B). MIC tests performed according to clinical standards revealed that the MIC of CipR-1 was indistinguishable from that of the parent strain, while the MIC of CipR-2 was 2-fold higher than that of the parent strain in the absence of PYO but identical in the presence of PYO (S7 Table), reflecting the limitations of standard 2-fold antibiotic dilution series for revealing mild increases in resistance. The MIC of CipR-7, on the other hand, was 8-fold higher than that of the parent strain, although in the absence of PYO, the MIC of this mutant was still below the ciprofloxacin concentration used in the fluctuation tests. Notably, for all tested B. multivorans isolates, including the WT parent, the addition of PYO to the standard MIC tests increased the MIC for ciprofloxacin by 4- to 8-fold (S7 Table); however, even in the presence of PYO, the MIC for the parent strain was less than half of the ciprofloxacin concentration used in the tolerance assays, indicating that the observed tolerance phenotype for this strain cannot be fully explained by phenotypic resistance. Interestingly, the percentage of the parent strain population that could grow on ciprofloxacin in the presence of PYO (Fig 6B) was approximately equal to what would have been expected from the frequency of colonies detected in the fluctuation tests; moreover, when the CFU recovery assay was performed for the parent strain, the colonies that grew on ciprofloxacin in the presence of PYO exhibited diverse morphologies. This suggests that much of the parent strain growth on ciprofloxacin in the presence of PYO may have in fact reflected the growth of high-frequency spontaneous mutants, rather than background growth of the parent strain itself.

Whole-genome sequencing of the fluctuation test isolates revealed that B. multivorans CipR-1 possessed mutations in 3 uncharacterized regulatory genes (S6 Table). B. multivorans CipR-2 possessed mutations in 2 different homologs of the SpoT/RelA (p)ppGpp synthetase gene, which is known to affect antibiotic tolerance and resistance [62]. Finally, B. multivorans CipR-7 possessed a point mutation in DNA gyrase A (S83R), along with a point mutation in a malto-oligosyltrehalose synthase. Given that DNA gyrase A is the target of ciprofloxacin and that the specific mutated residue is likely homologous to the T83 residue that was mutated in a study of fluoroquinolone-resistant mutants in B. cepacia [63], it is intriguing that this mutant was not able to grow on the original selection concentration of ciprofloxacin in the absence of PYO; however, the specific amino acid (AA) substitution in this strain may have resulted in only a mild disruption of ciprofloxacin binding.

Lastly, we asked whether the B. multivorans mutants we detected primarily arose prior to or during the antibiotic selection. In all cases, the distribution of mutants closely matched the Hamon and Ycart formulation of the theoretical LD distribution, suggesting that the detected mutants arose prior to the antibiotic exposure (Fig 6C, S3 Table). Interestingly, the Hamon and Ycart model also predicted the average relative fitness of mutants detected in PYO-treated samples to be significantly lower compared to mutants detected in non-PYO-treated samples (S3 Table; p < 0.05 for all 3 comparisons between non-PYO-treated and PYO-treated sample groups, using Welch paired t test with Benjamini–Hochberg corrections for controlling the false discovery rate). In addition, unlike for P. aeruginosa, the mixed LD–Poisson distribution that allows for post-plating mutations was a poorer fit than the Hamon and Ycart model for all PYO-treated B. multivorans samples (Fig 6C, S3 Table). Together, these results suggest that in B. multivorans, PYO increases μ_app by promoting growth of a wider range of mutants that arise prior to antibiotic selection, including those with slower growth rates.

Culture media and incubation conditions

Different culture media were used for different experiments as indicated throughout the Methods. Succinate minimal medium (SMM) composition was 40 mM sodium succinate (or 20 mM, if specified), 50 mM KH₂PO₄/K₂HPO₄ (pH 7), 42.8 mM NaCl, 1 mM MgSO₄, 9.35 mM NH₄Cl, and a trace elements solution [81]. Glucose minimal medium (GMM) was identical to SMM, except with 10 or 20 mM glucose (as specified for different experiments) instead of succinate. SMM and GMM were prepared by autoclaving all components together for 20 minutes at 121°C, except for the carbon source and the 1,000× trace elements stock solution, which were filter-sterilized and added separately; interestingly, we found that autoclaving MgSO₄ with the other components was crucial for consistent PYO production by WT P. aeruginosa UCBPP-PA14 in GMM. Luria–Bertani (LB) Miller broth (BD Biosciences, San Jose, California, United States of America) and BBL cation-adjusted Mueller–Hinton (MH) II broth (BD Biosciences) were prepared according to the manufacturer’s instructions (notably with only a 10 minute autoclave step for MH medium), with the addition of 1.5% Bacto agar (BD Biosciences) to make solid media. SCFM composition was prepared as described previously [36], with 1.355 mM K₂SO₄ and no nitrate. In addition, 3.6 μM FeSO₄.7H₂O and 0.3 mM N-acetyl-glucosamine were added [82]. All components except for the latter two were dissolved together, sterilized by filtration through a 0.22 μm membrane, and stored for up to 2 weeks; FeSO₄.7H₂O and N-acetyl-glucosamine solutions were prepared fresh each time or stored at −20°C, respectively, and added to SCFM on the day of use. For SCFM agar, a 2× solution of the medium components was prepared and added to a separately autoclaved 3% molten agar solution, for a final concentration of 1× SCFM and 1.5% agar.

Antibiotics were prepared in concentrated stock solutions (100× or greater) and stored at −20°C. Ciprofloxacin was dissolved in 0.1 M or 20 mM HCl, while levofloxacin, gentamicin, tobramycin, and colistin were dissolved in sterile deionized water. PAβN dihydrochloride (MedChemExpress, Monmouth Junction, New Jersey, USA) was dissolved in sterile deionized water (50 mg/mL). PYO was synthesized and purified as previously described [83,84] and dissolved in 20 mM HCl to make 10 mM stock solutions. Experiments involving exogenous PYO always included negative controls to which an equivalent volume of 20 mM HCl was added. In addition, MH agar plates were buffered to pH 7 with 10 mM 4-morpholinepropanesulfonic acid (MOPS) to avoid any pH changes upon addition of PYO or HCl; all other media used with exogenous PYO were already inherently buffered. Incubations were always done at 37°C, with shaking for liquid cultures (250 rpm), unless mentioned otherwise.

Strain construction

In this study, we use PA14 as an abbreviation for UCBPP-PA14. P. aeruginosa PA14 was used for all experiments unless otherwise noted. For a full list of strains made in this study, see S5 Table. Three types of strains were made in different P. aeruginosa PA14 backgrounds: (i) unmarked deletions, used for Tn-seq validation experiments; (ii) fluorescent strains for time-lapse microscopy experiments; and (iii) strains overexpressing one of the following 3 systems: mexGHI-opmD, ahpB, and katB. Established protocols were used for all these procedures [85].

Briefly, for unmarked deletions, approximately 1-kb fragments immediately upstream and downstream of the target locus were cloned using Gibson assembly into the pMQ30 suicide vector [86,87]. Fragments amplified from P. aeruginosa PA14 genomic DNA (gDNA) and cleaned up using the Monarch PCR Purification kit (New England Biolabs, Ipswich, Massachusetts, USA) were used for Gibson assembly together with pMQ30 cut with SacI and HindIII. The assembled construct was then transformed into E. coli DH10B, with transformants being selected in LB with 20 μg/mL gentamicin. All correctly assembled plasmids were identified by colony PCR and verified by Sanger sequencing (Laragen, Culver City, California, USA). Next, for the insertion of the constructs into P. aeruginosa PA14 genome, triparental conjugation was performed following Choi and Schweizer [88]. All unmarked deletions were done in the P. aeruginosa PA14 Δphz background (both phzA-G1 and phzA-G2 operons are deleted in this strain [13]), allowing clean experiments by addition of exogenous phenazines. Merodiploids containing the construct integrated into their genomes were selected on VBMM medium (3 g/L trisodium citrate, 2 g/L citric acid, 10 g/L K₂HPO₄, 3.5 g/L NaNH₄PO₄·4H₂O, 1 mM MgSO₄, 100 μM CaCL₂, pH 7) with 100 μg/mL gentamicin following Choi and Schweizer [88]. Finally, merodiploids were then plated on LB lacking NaCl and containing 10% sucrose to select for colonies resulting from homologous recombination. Colonies missing the target locus (unmarked deletions) were identified by PCR. For all primers used, see S5 Table.

Fluorescent strains used in time-lapse microscopy were made using previously published plasmids [85,89]. Constructs containing GFP and mApple florescent proteins under the control of the ribosomal rpsG gene were inserted in the attTn7 site of P. aeruginosa PA14 Δphz chromosome by tetraparental conjugation, followed with selection on VBMM with 100 μg/mL gentamicin [88].

Finally, overexpressing strains were made as previously described [85]. The previously made overexpression construct (pUC18T-miniTn7T-GmR vector containing the arabinose-inducible promoter P_ara [85]) and the 3 different targets (mexGHI-opmD, ahpB, and katB) were all amplified by PCR. Next, using Gibson assembly, the targets were cloned downstream of P_ara in the pUC18T-miniTn7T-GmR vector, resulting in the 3 different overexpression constructs: P_ara:mexGHI-opmD, P_ara:ahpB, and P_ara:katB. The final constructs were introduced into the attTn7 of the P. aeruginosa PA14 Δphz background strain by tetraparental conjugation [88].

Transposon sequencing (Tn-seq) experiment

The Tn-seq experiment was performed following the design presented in Fig 1A. Two aliquots of the P. aeruginosa PA14 transposon library previously prepared [89] were thawed on ice for 15 minutes, diluted to a starting optical density (OD₅₀₀) of 0.05 in 50 mL of SMM, and grown aerobically under shaking conditions (250 rpm) at 37°C for approximately 4 to 5 generations to an OD₅₀₀ of 0.8–1. These growing conditions were used for all the stages of the experiment. After growth in SMM, each aliquot was considered an independent replicate. Cells from each replicate were pelleted, washed, and resuspended (5 mL in 18 × 150 mm glass tubes, OD₅₀₀ = 2) in minimal phosphate buffer (MPB; 50 mM KH₂PO₄/K₂HPO₄[pH 7], 42.8 mM NaCl) with and without 100 μM PYO. Cells were then incubated for 26 hours under shaking conditions at 37°C. Therefore, the experiment consisted of 4 different samples that were later sequenced: (i) “R1 No PYO”; (ii) “R1 + PYO”; (iii) “R2 No PYO”; and (iv) “R2 + PYO.” After the incubation, cultures from all treatments were pelleted, washed again to remove PYO, and resuspended in fresh SMM. Immediately, an aliquot of each sample was diluted to a starting OD₅₀₀ of approximately 0.05 in 25 mL SMM, followed by outgrowth for approximately 4 to 5 generations to an OD₅₀₀ of 0.8–1. After outgrowth, 2.5 mL of each sample was pelleted and stored at −80°C.

gDNA was extracted from the pelleted samples using the DNeasy Blood & Tissue kit (Qiagen, Valencia, California, USA). All the steps for sequencing library preparation followed exactly the protocol used by Basta and colleagues [89], including (i) DNA shearing by sonication (to produce 200- to 500-bp fragments); (ii) end repair; (iii) addition of poly(C) tail; and (iv) enrichment of transposon–genome junctions and addition of adapter for Illumina sequencing by PCR [89,90]. The resulting amplified DNA samples were sequenced using 100-bp single-end reads on the Illumina HiSeq 2500 platform at the Millard and Muriel Jacobs Genetics and Genomics Laboratory at Caltech. Data analysis also followed Basta and colleagues [89]. In summary, sequences were mapped to the P. aeruginosa UCBPP-PA14 genome sequence using Bowtie 2 [91] and were analyzed in MATLAB using the ARTIST Tn-seq analysis pipeline [92], with nonoverlapping windows of 100 bp across the genome [89,92]. Using the Mann–Whitney U statistical test, the total reads mapping for each gene in the “+PYO” samples were compared to the corresponding reads in the “No PYO” control for each replicate independently [89,92]. Next, the read ratio for each replicate was calculated within ARTIST for each gene and then log₂ transformed. Finally, the p-values for both replicates were combined using the Fisher combined probability test as done in Basta and colleagues [89], and the average of the log₂ ratios of the 2 replicates are also shown. For the log₂ ratios and p-values for all PA14 genes, see S1 Table. For heatmaps shown in Fig 1A, the average log₂ ratios (fitness) for the selected genes were plotted using the geom_tile() function from the ggplot2 package in R [93,94].

Tn-seq datasets correlation analysis

To compare the results of this Tn-seq analysis with a previously published study [24] analyzing fitness determinants for survival during ciprofloxacin treatment in the P. aeruginosa PAO1 strain background (Fig 1E, S1 Table), the data from that study’s supplemental S1 Table were used. The normalized average ratio of reads in the treated sample compared to reads in the input sample for each gene (geometric mean of 3 replicates) was log₂ transformed for comparison to the Tn-seq data described above. The list of genes was filtered to include only genes for which ratios were reported in both our PYO Tn-seq experiment and the ciprofloxacin Tn-seq study and for which there are clear orthologs in both strains (n = 4,209 genes). Orthologs were determined using the “pseudomonas.com” database [95].

Tn-seq validation experiments

To validate the Tn-seq results (Fig 1C), experiments were performed by comparing the survival of 4 different mutants (ΔphzΔackAΔpta, ΔphzΔlptA, ΔphzΔmexS, and ΔphzΔdctBD) to the survival of the Δphz strain upon exposure to PYO. The experimental design was very similar to the one used in for the Tn-seq, with minor adaptations. An overnight culture (5 mL) of each strain was grown in SMM (40 mM succinate) from LB plates. Cells were washed and resuspended at an OD₅₀₀ of 0.1 (or 0.25 for ΔphzΔdctBD) in the same medium to start the new cultures (5 mL), which were grown to OD₅₀₀ approximately 0.8–1, pelleted, washed, and resuspended in the same minimal medium without succinate (no carbon source) at OD₅₀₀ of 1. For each strain, the culture was split across 8 to 12 wells (150 μL cultures) in a 96-well plate, with 100 μM PYO added to half of the cultures. Moreover, 70 μL of mineral oil was added to the top of the wells to prevent evaporation. PI at 5 μM was also added to the cultures to monitor cell death [16]. The plate was then moved to a BioTek Synergy 4 plate reader (BioTek, Winooski, Vermont, USA) and incubated under shaking conditions at 37°C for 24 hours. After incubation, cultures were serially diluted in buffer and plated for CFUs on LB agar, and survival in the presence of PYO was compared to the no-PYO control. Plates were incubated at room temperature (RT), and CFUs were counted after 36 to 48 hours. In this study, a stereoscope was always used to count the CFUs. Survival levels were calculated for each mutant (i.e., for each replicate, the % survival for “+PYO” was calculated based on CFUs for “No PYO”). Then, the survival levels for each mutant were normalized by the survival levels of the Δphz parent strain (i.e., % survival for “+PYO” for each mutant was divided by the average % survival for “+PYO” of the Δphz strain); these “fitness” values were log₂ transformed for plotting.

PYO tolerance with efflux inhibitor

Survival assays with efflux inhibition were performed to test the importance of efflux systems in P. aeruginosa for tolerance against PYO toxicity. From a Δphz overnight culture pre-grown in SMM (20 mM succinate), a new 7-mL culture was started in fresh SMM at an OD₅₀₀ of 0.05 and was incubated for around 10 hours (enough to reach stationary phase). Cells were then pelleted, washed, and resuspended in MPB at an OD₅₀₀ of 1 (10 mL of culture was prepared). The culture was then split into 4 different treatments: (i) no PYO, no PAβN; (ii) 100 μM PYO, no PAβN; (iii) no PYO, with PAβN (50 μg/mL); and (iv) 100 μM PYO, with PAβN. Each of the treatments were split across 12 wells containing 150 μL of culture + 70 μL of mineral oil in a 96-well plate. The plate was incubated at 37°C under shaking conditions using a BioTek Synergy 4 plate reader. Samples were serially diluted in MPB and plated for CFUs on LB agar after 12, 24, and 48 hours. Survival for treatments containing PYO was calculated based on the CFUs counted for the negative control without PYO (Fig 1D). At each time point, 4 wells were sampled, with each well considered an independent replicate. The experiment was repeated twice with similar results.

Antibiotic tolerance experiments using P. aeruginosa

Time-lapse microscopy experiment and quantification

Fluorescently tagged strains of WT or Δphz were grown in GMM, and tolerance experiments were performed as shown in S4D Fig using ciprofloxacin (10 μg/mL). After the 4-hour incubation with the antibiotic, cells were washed and resuspended in GMM. The 2 different strains were then mixed and placed on an agarose pad containing GMM (no ciprofloxacin or PYO was added to the pad). Agarose pads were placed into a PELCO Clear Wall Glass Bottom Dish (Cat. No. 14023–20), and the dish was used for imaging within the microscope incubation chamber. Outgrowth was visualized using a Nikon Ti2E microscope with Perfect Focus System 4 (Nikon Instruments, Melville, New York, USA). Incubation proceeded for 12.5 to 15 hours at 37°C, with imaging every 15 minutes in bright field (phase contrast) and green and red fluorescence channels (50-ms exposure with 470-nm LED lamp and a green-FITC filter [ex = 465 to 495 nm, em = 515 to 555 nm] for GFP; 50-ms exposure with 555-nm LED lamp and a quad band filter [red ex = 543 to 566 nm, red em = 580 to 611 nm] for mApple).

For image analysis, a Fiji macro was used. Briefly, fluorescent channels (GFP/mApple) of the first and last time points were segmented using the “Auto Threshold” function and “Default” setting. The area of the segmented cells was then recorded using the “Analyze Particles” function in Fiji [96]. This allowed for quantification of the total area covered by cells within each channel, with each field of view being processed separately. After that, for each field of view, the total area covered by WT cells (or Δphz + 100 μM PYO, depending on the experiment) was divided by the area covered by Δphz cells to obtain the relative “growth area ratios”. This was done for first and last time points. Three experiments were performed, with different fluorescent protein/strain combinations: (i) WT::mApple/Δphz::GFP (n = 13 fields of view, Fig 2F and 2G, S1 Movie); (ii) Δphz::GFP+PYO /Δphz::mApple (n = 19, Fig 2G); and (iii) WT::GFP/Δphz::mApple (n = 16, S4E Fig). GFP/mApple were controlled by the rpsG promoter for all of the strains (S5 Table).

RNA extraction and quantitative reverse transcriptase PCR (qRT-PCR)

Stenotrophomonas and Burkholderia growth curves and antibiotic tolerance assays

S. maltophilia ATCC 13637, B. cepacia ATCC 25416, B. cenocepacia AU42085, B. multivorans AU42096 (B. multivorans 1), and B. gladioli AU42104 were used in the growth experiments shown in Fig 5A (for strain details, see S5 Table). Each strain was grown overnight in GMM (20 mM glucose, 5 mL culture tubes) supplemented with 1× MEM AA (MilliporeSigma, Cat. No. M5550, Burlington, Massachusetts, USA). Cells were pelleted, washed, and resuspended in new cultures at an OD₅₀₀ of 0.05 in the same medium. Cultures were then split, different concentrations of PYO were added (0, 10, 50, or 100 μM for S. maltophilia; 0, 10, or 100 μM for all others), and moved to a 96-well plate (4 to 6 wells per treatment, with each well being considered an independent replicate). Cultures within wells contained 150 μL with an additional 70 μL of mineral oil on top to prevent evaporation. The plates were incubated at 37°C under shaking conditions using a BioTek Synergy 4 plate reader with OD₅₀₀ measurements every 15 minutes for 24 hours to measure growth. Assays for tolerance to ciprofloxacin with or without exogenous PYO were performed for S. maltophilia (sensitive to PYO) and for 4 Burkholderia strains (all resistant to PYO): B. cepacia, B. cenocepacia, B. multivorans 1, and B. multivorans AU18358 (B. multivorans 4). The experiments followed exactly what was done for P. aeruginosa (S4A Fig), except that cultures were grown in GMM + AA, and are shown in Fig 4B–4E. Finally, a tolerance assay in SCFM with and without PYO was performed for B. multivorans 1 (Fig 5H) and followed what was described for the SCFM experiments in P. aeruginosa (with the only difference being the ciprofloxacin concentrations, always mentioned in the legends).

Coculture antibiotic tolerance experiments

To test how PYO produced by P. aeruginosa impacts tolerance to ciprofloxacin in other species, coculture experiments were performed using membrane-separated 12-well tissue plate cultures containing 0.1 μm pore PET membranes (VWR Cat. No. 10769–226). Briefly, overnight cultures of the P. aeruginosa strain (WT/Δphz PA14 or PA 76–11) and the respective other species tested (S. maltophilia, B. cepacia, B. cenocepacia, or B. multivorans 1) were prepared in GMM (20 mM glucose) + AA. Cells were pelleted, washed, and resuspended to different ODs as follows: (i) for any P. aeruginosa–Burkholderia assay, P. aeruginosa starting OD₅₀₀ = 0.05 and Burkholderia starting OD₅₀₀ = 0.025; and (ii) for the P. aeruginosa–S. maltophilia assay, P. aeruginosa starting OD₅₀₀ = 0.01 and S. maltophilia starting OD₅₀₀ = 0.1. P. aeruginosa was cultured in the bottom part of the well (600 μL), while the other species was cultured in the upper part of the well (100 μL), as shown in Fig 5C. B. cepacia and S. maltophilia were cultured either with WT or Δphz P. aeruginosa PA14 (with and without 100 μM PYO exogenously added).

B. cenocepacia and B. multivorans 1 were cultured either with PA 76–11 (a P. aeruginosa strain isolated from CF sputum that produced 50 to 100 μM PYO in these assays) or alone in the presence or absence of 100 μM PYO. For cases where Burkholderia was grown alone, the strain tested was grown in both the bottom and top parts of the membrane-separated wells. In all experiments, cocultures were grown for around 20 hours at 37°C under shaking conditions (175 rpm) using a benchtop incubator, followed by addition of ciprofloxacin (concentrations were either 1 or 10 μg/mL, as specified in the figure legends) and incubation for 4 hours. The membrane-separated plates were kept inside an airtight plastic container with several wet paper towels to maintain high humidity attached to the shaker. For every coculture combination in the membrane-separated plate, 3 wells were used as a negative control (no antibiotic), and 3 wells were used for ciprofloxacin treatment; each well was considered an independent replicate. After incubation with ciprofloxacin, cells were serially diluted in MPB and plated for CFUs on LB. In most cases, only Burkholderia cells were plated (Fig 5F). However, to test if our P. aeruginosa WT strain was still more tolerant than the Δphz strain when both were grown in the presence of a Burkholderia species, we performed an experiment with P. aeruginosa PA14 and B. multivorans 1 where we treated the cocultures with ciprofloxacin 1 μg/mL and plated P. aeruginosa (Fig 5G). Finally, we also performed a coculture experiment in SCFM (P. aeruginosa PA14 WT/Δphz with B. multivorans 1) to test if PYO produced by PA14 WT increases tolerance in Burkholderia in this medium (Fig 5I). This experiment in SCFM followed the same overall experimental design used before, except for using SCFM instead of GMM in all steps.

Determination of minimum inhibitory concentrations

The antibiotic concentrations used for selecting de novo antibiotic-resistant mutants in the fluctuation tests were chosen based on the results of a modified agar dilution MIC assay. Overnight cultures were grown for each strain in GMM (with 10 mM glucose) or GMM (10 mM glucose) + AA, respectively, then diluted to an OD₅₀₀ of 0.5, from which 3 μL was spotted onto MH agar containing a 2-fold dilution series of the antibiotic. After the spots dried, the antibiotic plates were incubated upside down for 48 hours at 37°C before assessing the spots for growth. We considered the MIC to be the first concentration at which there was neither a lawn of background growth nor dozens of overlapping colonies visible without magnification. We generally used 2 times this MIC as the selection condition for fluctuation tests; for P. aeruginosa, this corresponded to the EUCAST [27] resistance breakpoints for ciprofloxacin and levofloxacin, while our chosen concentrations of gentamicin and tobramycin were 2-fold higher than the EUCAST breakpoints [32]. EUCAST breakpoints are not available for Stenotrophomonas spp. or the B. cepacia complex. The appropriateness of the selection condition was additionally verified by performing a fluctuation test, as described below, and choosing the antibiotic concentration that reliably yielded a countable number of colonies (0 to several dozen, with at least several nonzero counts per 44 parallel cultures) in each well.

Ciprofloxacin MICs for parent strains and isolated mutants from the fluctuation tests were determined according to standard clinical methods for broth microdilution assays [27]. In brief, cells from either overnight cultures in MH broth or fresh streaks on LB agar (14 to 16 hours old) were resuspended to a density of 3 to 7 × 10⁵ CFUs/mL in a 2-fold dilution series of ciprofloxacin in MH broth, with or without 100 μM PYO. The dilution series were set up in a final volume of 100 μL per well in 96-well microtiter plates, with appropriate no-antibiotic and cell-free controls. Three biological replicates (independent overnight cultures or cell suspensions) were prepared for each tested strain. Following inoculation, the microtiter plates were sealed with a plastic film to prevent evaporation and incubated in a single layer at 37°C, without shaking. The wells were assessed for growth (turbidity) visible to the naked eye after 18 hours of incubation.

Fluctuation tests, calculation of mutation rates, and model fitting

For all tested strains and conditions, fluctuation tests were performed by inoculating 200-μL cultures in parallel in a flat-bottomed 96-well plate. All reported fluctuation test data for P. aeruginosa are from experiments using the Δphz strain. We also performed fluctuation tests using the P. aeruginosa PA14 WT strain and performed phenotypic and genotypic characterization of partially resistant mutants detected in those experiments (see below); however, the effect of PYO on apparent mutation rates in WT was difficult to interpret due to inconsistent PYO production in the 96-well plates. For cultures that were grown with PYO (or arabinose in the case of strains with arabinose-inducible constructs), the PYO (or 20 mM arabinose) was added to the medium before inoculation. The cultures were inoculated with a 10⁻⁶ dilution of a single overnight culture (representing a biological replicate) that had first been diluted to a standard OD₅₀₀ of 1.0, corresponding to an initial cell density of approximately 2,000 to 2,500 CFUs/mL (400 to 500 cells/culture). Each treatment condition consisted of 44 such parallel cultures.

The 96-well plates were placed inside an airtight plastic container with several wet paper towels to maintain high humidity, then incubated at 37°C with shaking at 250 rpm. For plating during log phase, the cultures were incubated until reaching approximately half-maximal density (OD₅₀₀ of 0.4 to 0.7 for P. aeruginosa in GMM with 10 mM glucose or 0.9 to 1.2 for P. aeruginosa in SCFM or B. multivorans in GMM + AA). For plating during stationary phase, the cultures were incubated for 24 hours. The cultures were then plated by spotting 40 to 50 μL per culture into single wells of 24-well plates (for any given experiment, the same volume was spotted for all parallel cultures); each well contained 1 mL of MH agar or SFCM agar plus an antibiotic, with or without 100 μM PYO (or 20 mM arabinose for strains with arabinose-inducible constructs). In the case of B. multivorans cultures that were spotted onto antibiotic plates containing 100 μM PYO, the cultures were first diluted 1:10 (if not pretreated with 100 μM PYO) or 1:100 (if pretreated with 100 μM PYO).

At the same time as plating onto the antibiotic plates, 6 representative cultures from each treatment were serially diluted and plated on LB agar plates to assess total CFUs. The antibiotic plates were incubated upside down, in stacks of no more than 8, at 37°C for 16 to 24 hours for P. aeruginosa (except for gentamicin plates, which were incubated for 40 to 48 hours) or 40 to 48 hours for B. multivorans. Subsequently, colonies were counted under a stereoscope at the highest magnification for which the field of view still encompassed an entire well; occasionally, a well contained too many colonies to count (a so-called “jackpot” culture [98]), in which case that culture was discarded from further analysis. The LB agar plates for total CFU counts were incubated for 30 to 36 hours at RT before counting colonies at the same magnification.

Mutation rates reported in the figures were calculated using the function newton.LD.plating from the R package rSalvador [99] to estimate m, the expected number of mutations per culture. This is a maximum likelihood-based method for inferring mutation rates from fluctuation test colony counts, based on the classic LD distribution with a correction to account for the effects of partial plating (i.e., plating a portion of each culture rather than the total volume) [99]. We chose this method because it has been shown to be the most accurate estimator of m when partial plating is involved [99,100]. To get μ_app (apparent mutation rate per generation) from m, we divided m by the total number of cells per parallel culture [99], as estimated from the mean number of CFUs counted for the 6 representative cultures.

To compare the fits of different formulations of the LD distribution to our data, we generated theoretical cumulative distributions using the parameter values estimated for our data. Specifically, for the Hamon and Ycart version of the LD model [44], we estimated m and w (relative fitness of mutants compared to the parent strain in the nonselective pre-plating liquid growth medium) using the function GF.est from the R script available at http://ljk.imag.fr/membres/Bernard.Ycart/LD/ (version 1.0; note that in the script, m is called alpha and 1/w is called rho); then, we used the function pLD from the same script to generate the theoretical distribution. For the mixed LD–Poisson and basic LD models, we wrote and used an R translation of the MATLAB code written by Lang and colleagues [45]; the original code is available at https://github.com/AWMurrayLab/FluctuationTest_GregLang. The basic LD model used by Lang and colleagues [45] is equivalent to that available in the rSalvador package (using the function newton.LD), except without the correction for partial plating; the latter is only important when using the estimate of m to infer the mutation rate, not when comparing the fits of different models to the empirical cumulative distribution of the raw colony counts.

Plots of the empirical cumulative distributions of our data against the theoretical models showed that the Hamon and Ycart model was a visually good fit in all cases (see Figs 4B and 6C and S9 Fig for examples). To further assess goodness of fit of the Hamon and Ycart model, we performed Pearson chi-squared test in R after binning the data and theoretical distribution such that the expected number of cultures in each bin of mutant counts was at least 5 [101]. To compare the goodness of fit of the Hamon and Ycart model to the basic LD model, we calculated the negative log likelihood for each model and performed the likelihood ratio test. To compare the Hamon and Ycart model to the mixed LD–Poisson model, we simply compared the negative log likelihoods (smaller values indicate a better fit); the likelihood ratio test was not applicable as these 2 models contain the same number of parameters. Note that although the Hamon and Ycart (or in some cases, LD–Poisson) models were often better fits than the basic LD model, we still used the basic LD model for statistical comparison of mutation rates between conditions, because an accurate method to account for partial plating has not yet been developed for the cases of post-plating mutations or differential fitness between mutants and parent strains [99]. Nevertheless, similar patterns in mutation rates were observed when using an older method of accounting for partial plating to derive μ_app from the Hamon and Ycart model [102]; the Pearson correlation coefficient was 0.98 for mutation rates calculated with the newton.LD.plating function in rSalvador versus the partial plating–corrected Hamon and Ycart method (S3 Table). We also separately performed nonparametric statistical analysis of the raw mutant frequencies (i.e., mutant colony counts divided by the number of cells per parallel culture), as such analysis is agnostic to any assumptions about the biological processes underlying the data. The statistical significance of this analysis generally corresponded with the statistical significance of a likelihood ratio test based on the newton.LD.plating model of mutation rates, indicating that the effects of PYO were robust to different mathematical approaches to analyzing the fluctuation test data (S2 Table).

Characterization of antibiotic resistance phenotypes

We defined putative ciprofloxacin-resistant mutants as “enriched” by PYO in the fluctuation tests if colonies with a given morphology were at least 2 times more numerous on the PYO-containing ciprofloxacin plate than the respective non-PYO-containing ciprofloxacin plate derived from the same 200-μL culture. These putative mutants could be either from the PYO pretreated or non-PYO pretreated branches of the fluctuation test. Putative mutants that were seemingly enriched by PYO were restreaked for purity on PYO-containing agar plates at the same ciprofloxacin concentration on which they were selected in the fluctuation test (0.5 μg/mL for PA and 8 μg/mL for B. multivorans). Putative mutants that were not enriched by PYO were restreaked on ciprofloxacin agar plates without PYO. Frozen stocks of each restreaked, visually pure isolate were prepared by inoculating cultures with single colonies in 5 mL of liquid LB, incubating to stationary phase, mixing 1:1 with 50% glycerol, and storing at −80°C.

The levels of ciprofloxacin resistance of selected isolates, as well as the parent strains, were assessed using a CFU recovery assay as follows. For each isolate, four 5 mL cultures in GMM (for P. aeruginosa) or GMM + AA (for B. multivorans) were inoculated directly from the frozen stock to minimize the number of generations in which secondary mutations could be acquired. The cultures were grown to stationary phase overnight, then subcultured to an OD₅₀₀ of 0.05 in 5 mL of fresh GMM (for P. aeruginosa) or GMM + AA (for B. multivorans), with or without 100 μM PYO. The new cultures were grown to mid-log phase, then serially diluted in GMM or GMM + AA (+/− 100 μM PYO as appropriate) and plated for CFUs (10 μL per dilution step) on (1) plain MH agar; (2) MH agar + ciprofloxacin; and (3) MH agar + ciprofloxacin + 100 μM PYO. The lowest plated dilution was 10⁻¹, making the limit of detection approximately 1,000 CFUs/mL.

Identification of mutations by whole-genome sequencing

gDNA was isolated from selected putative mutants and the parent strains using the DNeasy Blood & Tissue kit (Qiagen). Library preparation and 2 × 150 bp paired-end Illumina sequencing was performed by the Microbial Genome Sequencing Center (Pittsburgh, Pennsylvania, USA), with a minimum of 300-Mb sequencing output per sample (approximately 50 × coverage). Forward and reverse sequencing reads were concatenated into a single file for each isolate, and quality control was performed using Trimmomatic (version 0.39) [103] with the following settings: LEADING:27 TRAILING:27 SLIDINGWINDOW:4:27 MINLEN:35. Mutations were then identified using breseq (version 0.34.1) [104]. The annotated reference genome for P. aeruginosa UCBPP-PA14 was obtained from BioProject accession number PRJNA38507. For B. multivorans AU42096, no reference genome was available from NCBI. Therefore, a genome scaffold was assembled from the paired-end sequencing data for the parent strain using SPAdes (version 3.14.0) with default parameters [105]. This scaffold was then used as the reference for breseq. Differences between the parent strain and isolates were identified using the gdtools utility that comes with breseq to compare the respective breseq outputs. All sequenced P. aeruginosa mutants were derived from the Δphz strain except for CipR-33 and CipR-40, which were derived from the WT strain. In the case of B. multivorans, several dozen putative mutations were identified that were common to all 3 sequenced putative mutants. We assumed that these represented assembly errors in the parent strain genome scaffold, but even if they were genuine mutations, these would not account for the phenotypic differences between the isolates; therefore, S6 Table reports only mutations that were unique to each isolate. The genomic loci containing each putative mutation for the B. multivorans isolates were identified by retrieving the surrounding 200 bp from the parent genome scaffold and using the nucleotide BLAST tool on the MicroScope platform [106] to find the closest match in the B. multivorans ATCC 17616 genome.

Growth curves with propidium iodide

To verify that PYO did not increase the population turnover rate (i.e., cell death) in our log-phase fluctuation tests prior to the antibiotic selection step, we performed growth curves in the presence of different concentrations of PYO, with the addition of PI as a fluorescent marker for cell death. The use of PI as a marker for PYO-induced cell death has previously been validated under similar conditions [16]. The growth curves were performed in GMM and SCFM for P. aeruginosa Δphz and GMM + AA for B. multivorans 1. The cultures were prepared and incubated in the same manner as the fluctuation tests, except that 5 μM PI was added at the beginning of the experiment, and measurements for OD₅₀₀ and PI fluorescence (ex = 535 nm, em = 617 nm) were taken periodically using a Spark 10M plate reader (Tecan US, Morrisville, North Carolina, USA). Importantly, PI stock concentration (5 mM, 1,000×) was prepared in DMSO, and the final concentration of DMSO in the cultures did not exceed 0.1%. In addition, black 96-well plates with clear bottoms were used to minimize the effects of adjacent wells on fluorescence readings.

Statistical analyses

All statistical analyses were performed using R [94]. Welch unpaired t tests or 1-way analysis of variance (ANOVA) with post hoc Tukey honestly significant difference (HSD) test for multiple comparisons were used for tolerance assay data. The likelihood ratio test as implemented by the rSalvador function LRT.LD.plating was used to compare mutation rates, alongside the alternative criterion of nonoverlapping 84% confidence intervals as a proxy for the p < 0.05 threshold for statistical significance. The Mann–Whitney U test was used to compare the distributions of mutant frequencies. Welch unpaired t tests were used for comparisons of CFU recovery on ciprofloxacin plates under different PYO treatments. Benjamini–Hochberg corrections were used in all cases to control false discovery rates, except where Tukey HSD test was performed. For all antibiotic tolerance assays measured by CFUs, survival data were log₁₀ transformed before statistical analyses.