Effects of Sequential and Simultaneous Applications of ...

Effects of Sequential and Simultaneous Applications of Bacteriophages on Populations of Pseudomonas aeruginosa In Vitro and in Wax Moth Larvae Alex R. Hall,a* Daniel De Vos,b Ville-Petri Friman,a,c Jean-Paul Pirnay,b and Angus Bucklingc Department of Zoology, University of Oxford, Oxford, United Kingdoma; Laboratory for Molecular and Cellular Technology (LabMCT), Burn Centre, Queen Astrid Military Hospital, Bruynstraat, Neder-over-Heembeek, Brussels, Belgiumb; and Biosciences, University of Exeter, Cornwall Campus, Penryn, United Kingdomc

Interest in using bacteriophages to treat bacterial infections (phage therapy) is growing, but there have been few experiments comparing the effects of different treatment strategies on both bacterial densities and resistance evolution. While it is established that multiphage therapy is typically more effective than the application of a single phage type, it is not clear if it is best to apply phages simultaneously or sequentially. We tried single- and multiphage therapy against Pseudomonas aeruginosa PAO1 in vitro, using different combinations of phages either simultaneously or sequentially. Across different phage combinations, simultaneous application was consistently equal or superior to sequential application in terms of reducing bacterial population density, and there was no difference (on average) in terms of minimizing resistance. Phage-resistant bacteria emerged in all experimental treatments and incurred significant fitness costs, expressed as reduced growth rate in the absence of phages. Finally, phage therapy increased the life span of wax moth larvae infected with P. aeruginosa, and a phage cocktail was the most effective short-term treatment. When the ratio of phages to bacteria was very high, phage cocktails cured otherwise lethal infections. These results suggest that while adding all available phages simultaneously tends to be the most successful short-term strategy, there are sequential strategies that are equally effective and potentially better over longer time scales.

A

ntibiotic resistance in pathogenic bacteria is reducing our ability to control infections. The search for novel treatments has led to renewed interest in phage therapy (15, 17), which has been practiced for decades in several countries but never became fully established in western medicine (1, 2, 34, 35). Similar to their reaction to antibiotics, bacteria can rapidly evolve resistance to bacteriophages (2, 26, 32, 35). Therefore, the success of phage therapy will depend on finding treatment strategies that are effective at clearing infections and minimizing the emergence of phage-resistant bacteria (29). There is good evidence to suggest that phage cocktails that target multiple bacterial receptors will limit the evolution of resistance over the course of therapy (5, 8, 9, 26). However, precisely how these cocktails should be applied is unclear (23, 27). The development and optimization of phage therapy for future clinical use may require a different approach to that used historically for static drugs, such as antibiotics (29). Therefore, experimental work in controlled conditions is necessary to understand why some treatment types are more effective than others. For example, the simultaneous application of multiple phages may be more effective than sequential application if multiresistance is unlikely to evolve in a single step (e.g., if it requires several different mutations, as is often the case with antibiotics [18, 31]), or if multiresistance is costly to the bacteria (e.g., if generalized resistance mechanisms, such as overexpression of alginate [16], have pleiotropic effects on bacterial growth). Alternatively, if bacteria can acquire resistance to any number of phages relatively easily, sequential application may be more effective, continually exposing bacteria to phages to which they are not initially resistant and keeping bacterial population density lower for longer, even if resistance eventually appears against all phages. In this paper, we assess the impact on bacterial population sizes and resistance

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when different combinations of phages are applied simultaneously or sequentially. We used one, two, or four phage types, either sequentially or simultaneously, to treat populations of the human pathogen Pseudomonas aeruginosa PAO1 both in vitro (in liquid culture) and in vivo (in wax moth larvae [Galleria mellonella]). Phage therapy is particularly relevant for P. aeruginosa because of its high levels of antibiotic resistance (13). We expected that the differences between treatments could be affected by bacterial population size. For example, if the evolution of multiresistance is less likely in some treatments than others, this difference might only be detected in small bacterial populations where the mutation supply rate is limited. We therefore replicated our experiment with periodic bottlenecks in population size of various magnitudes (dilution factor during serial transfer). Fitness costs associated with resistance that can be expressed as reduced bacterial growth rate or virulence are important for understanding long-term resistance evolution (3, 20), hence we estimated the fitness cost of different resistance phenotypes by measuring growth rates relative to the wild type in phage-free conditions. Finally, we tested the effectiveness of phage treatments in a live-animal model (wax moth larvae Galleria mellonella).

Applied and Environmental Microbiology

Received 7 March 2012 Accepted 28 May 2012 Published ahead of print 1 June 2012 Address correspondence to Alex R. Hall, [email protected]. * Present address: Alex R. Hall, Institute of Integrative Biology, ETH Zürich, Zürich, Switzerland. Supplemental material for this article may be found at http://aem.asm.org/. Copyright © 2012, American Society for Microbiology. All Rights Reserved. doi:10.1128/AEM.00757-12

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Phage Therapy: Sequences or Cocktails?

MATERIALS AND METHODS

TABLE 1 Phages used in this studya

Strains and growth conditions. P. aeruginosa PAO1 was grown at 37°C in 200 ␮l of liquid King’s medium B (KB) throughout the experiment in 96-well microplates. We used the phages listed in Table 1, adding phages to bacterial cultures by transferring a fixed volume of phages upon transfer. Phage stock solutions were prepared by overnight growth from frozen stock with wild-type PAO1 in liquid KB medium before chloroforming and centrifugation to purify phages from bacteria and storage at 4°C. Thus, phage stock solutions were at approximately the same density as they would be in a stationary-phase culture with ancestral bacteria. For each phage, the concentrations in PFU per ml (means ⫾ standard errors [SE]), as estimated by diluting and plating three independent stock solutions of each onto lawns of PAO1, were the following: 14/1, 5.9 ⫻ 108 ⫾ 1.8 ⫻ 107; ⌽KZ, 3.1 ⫻ 108 ⫾ 9 ⫻ 106; PNM, 2.1 ⫻ 108 ⫾ 7.1 ⫻ 107; and PT7, 1.1 ⫻ 109 ⫾ 1.5 ⫻ 108. Phage therapy treatments. We tested the effectiveness of the following phage treatments: no phage (n ⫽ 1), single phages (n ⫽ 4), pairs of phages applied simultaneously (n ⫽ 6; all possible pairs), pairs of phages applied sequentially (n ⫽ 6; all possible pairs, each in one of two possible orders, chosen so that every phage was first in at least one treatment), all phages applied simultaneously (n ⫽ 1), and all phages applied sequentially in different temporal orders (n ⫽ 4; chosen so that each phage appeared once at each position in the temporal sequence). We maintained four replicate populations in every treatment, starting with ⬃5 ⫻ 106 cells of P. aeruginosa. We transferred a proportion of the total volume of each culture to fresh media every 24 h for a total of 12 transfers. Cultures were homogenized by pipette before each transfer. To manipulate population size, we replicated the experiment at four different dilution factors (d), 0.0001, 0.001, 0.01, and 0.1, where d is the proportion by volume of each culture transferred (11). Phages were added upon transfer, with a total volume of 20 ␮l added each time (i.e., 20 ␮l of a single phage, 10 ␮l of each of two phages, or 5 ␮l of each of four phages). This amounted to approximately 106 phage particles in total. Although the density of stock solutions varied among phage types (up to a 5-fold difference), this does not influence our comparison of simultaneous and sequential treatments, because the same total number of each phage type was added over the course of the experiment with both types of treatment for each combination of phages. For sequential pairs, one phage was applied for the first six transfers and the other phage for the remaining six transfers; for sequential application of all four phages, phages were changed every three transfers. At the end of the experiment, all populations were stored at ⫺80°C in 25% (vol/vol) glycerol. Measuring bacterial population size and phage resistance. We measured the effects of phage(s) on bacterial population size by recording the optical density at 600 nm (OD600) every transfer, correcting each reading by subtracting the score for sterile growth media (see Fig. S1 to S3 in the supplemental material). At the end of the experiment, we tested for resistance to each phage by isolating 10 independent colonies from each replicate population (reconditioning the population from a frozen sample by overnight growth in KB, diluting and plating on KB agar plates, and randomly picking 10 colonies). Note that in some cases, independently isolated colonies may be genetically identical. We then grew each colony isolate in liquid culture for 24 h before streaking it across a perpendicular line of each of the four phages on an agar plate. We scored bacteria as resistant to a given phage if there was no evidence of growth inhibition after 24 h (4, 6). For every population, we then calculated two measures of resistance: (i) resistance against selective phage(s), calculated as the proportion of infection assays that showed no growth inhibition, averaged across 10 bacterial isolates and measured only against the phage(s) they were exposed to during the phage therapy experiment, and (ii) total resistance, measured against all four phages and averaged across 10 bacterial isolates in each population. For example, a total resistance score of 0.5 indicates that bacteria were resistant to two out of four phages, on average. This score gives a measure of cross-resistance in populations that were exposed to one or two phage types; for populations exposed to all four

Name

Source (location and yr)

Serology group

Morphology

14/1

Sewage water, Regensburg, Germany, 2000 Sewage water, Kazakhstan, 1975 Mtkvari River, Tbilisi, Georgia, 1999 Lake Ku, Tibilisi, Georgia, 1999

E serogroup

Myoviridae A1

Untested

Myoviridae A1

PT 5, PN C101

Podoviridae C1

PT1

Myoviridae A1

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⌽KZ PNM PT7 a

All phages lyse wild-type P. aeruginosa PAO1. Morphology and serological grouping are given according to reference 22.

phages, resistance to selective phages and total resistance are equivalent. Because of the large number of assays (n ⫽ 3,520), we restricted these experiments to populations taken from a single dilution factor (d ⫽ 0.001). Measuring the cost of phage resistance. To test whether resistance incurs a fitness cost in the absence of selecting phage(s), we isolated resistant mutants after a single round of selection with each phage on its own or all phages applied simultaneously. We isolated four independent mutants in each phage treatment, using the following procedure for each treatment: four replicate populations of wild-type bacteria were exposed to phages in liquid culture for 24 h, then plated on KB agar; following overnight growth, 10 colonies were selected at random from each replicate population and screened for resistance against the relevant phage(s); a single resistant mutant was then selected at random from each population, grown overnight, and stored at ⫺80°C before later being reconditioned and assayed for cross-resistance and fitness. The wild type used in these assays was also subjected to growth and colony picking in the same way as phage-resistant mutants, but it was never exposed to phages, to account for any effect of our isolation procedure on the growth rate. We estimated fitness as growth rate relative to that of the wild type during exponential growth in phage-free KB media using Gen5 software (Biotek Instruments), taking the OD600 every hour for 24 h and with four replicates for each mutant genotype and the wild type. This method has been shown previously to detect variation in costs of resistance to antibiotics and is positively correlated with fitness measured by competition assays across a range of environments (12, 28). As a second test for costs of resistance, we measured the growth rates of bacterial colony isolates from the end of the phage therapy experiment. We chose a single multiresistant genotype from each of 15 populations that had been exposed to all four phages. We also compared multiresistant genotypes to those resistant to a subset of phages: using a pairwise design, we isolated a single genotype that was resistant to one, two, or three phages from 7 of the same 15 populations. We then measured growth rates relative to the wild type as described above. In vivo assays. We tested the effects of three phage therapy treatments on the time to death for wax moth larvae (G. mellonella; Livefood, United Kingdom) infected with P. aeruginosa PAO1. Following the methods of Harrison et al. (14), larvae were swabbed with 70% ethanol before injection on the abdomen between the first two prolegs with ⬃5 ⫻ 105 cells of PAO1 suspended in 10 ␮l 0.8% salt solution. Control caterpillars were injected with 10 ␮l salt solution, and all survived the entire experiment. We then scored larvae as either alive or dead every 12 h for 48 h at 37°C. We applied phages and bacteria at the start of infection assays and reapplied phages every 12 h by injecting 10 ␮l (1 ␮l phage lysate diluted in 9 ␮l salt solution) into the abdomen or injecting 10 ␮l salt solution in control larvae. There were 18 replicate caterpillars in each of four treatments: no phage, ⌽KZ, all phages simultaneously, and all phages sequenced in the order that was most effective in in vitro experiments in terms of reducing bacterial density and minimizing resistance (14/1-PT7-⌽KZ-PNR1). To determine the effects of phage therapy on bacterial growth during infection, we ran a further eight larvae in each treatment, which we de-

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FIG 1 Bacterial density and resistance to bacteriophages. (a to c) Density of bacteria in populations treated with single phages, pairs of phages either sequentially (filled circles) or simultaneously (open circles), or all four phages sequentially (filled circles) or simultaneously (open circles). Density is shown as means ⫾ SE for 16 replicate selection lines (four at each dilution factor) after 12 experimental transfers. (d to f) Resistance to selective phages, calculated as the proportion of infection assays that showed no evidence of inhibition by the phages to which bacteria were exposed over the phage therapy experiment; points show means ⫾ SE from four replicates in each treatment. (g and h) Total resistance, measured against all four phages; points show means ⫾ SE for four replicate populations; scores for total resistance in the all-phage treatments are not shown because they are identical to those for resistance to selective phages.

structively sampled after 12 h to measure bacterial population sizes (we homogenized larvae in M9 salt solution and then centrifuged, diluted, and plated them onto KB agar before incubating them overnight and counting colonies to estimate the total number of CFU in each larva). Statistical analyses. We tested for variation in response variables (bacterial population size, resistance against selective phages, and total resistance) separately for single-phage, two-phage, and four-phage treatments. For population size, the OD600 at the end of the experiment was taken as the response variable in analysis of variance (ANOVA), with phage species and dilution factor (in single-phage treatments) or phage pair, treatment type (sequential or simultaneous), and dilution factor (in two-phage treatments) as fixed effects. We analyzed resistance data in the same way, except that they were from a single dilution factor, and we arcsine-transformed frequencies prior to analysis. For two-phage treatments, we Box-Cox transformed the OD600 to account for heterogeneous residuals. To test for a difference between sequential and simultaneous application in four-phage treatments, we conducted pairwise tests between each of the four sequential treatments (temporal orders) and the simultaneous treatment, adjusting for multiple comparisons by sequential Bonferroni correction. To test whether resistance and population size varied among treatments with different numbers of phage types, we took the average for all

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replicate populations within a given treatment (phage combination and order) across dilution factors, excluding phage-free controls, and tested for an effect of the number of phages on population size by analysis of variance with the number of phages as a factor. For resistance, we tested resistance to selective phages or total resistance against the number of phages used during the phage therapy experiment, averaging across all replicate populations within each treatment as described above.

RESULTS

Single-phage treatments vary in effectiveness; resistance and cross-resistance are common. The population density of bacteria at the end of the experiment varied among single-phage treatments (F3,48 ⫽ 7.93 and P ⫽ 0.0002 for effect of phage treatment) (Fig. 1a), being significantly lower than that in control treatments (no phage) for phage PT7 (F1,24 ⫽ 10.55 and P ⫽ 0.003), but not for 14/1 (F1,24 ⫽ 2.55 and P ⫽ 0.12), ⌽KZ (F1,24 ⫽ 0.75 and P ⫽ 0.39), or PNM (F1,24 ⫽ 2.73 and P ⫽ 0.11). The final density of bacteria at the end of the experiment averaged across all phages was not influenced by bottleneck size (F3,48 ⫽ 0.13 and P ⫽ 0.94 for effect of dilution factor), and neither was the relative effective-

Applied and Environmental Microbiology

Phage Therapy: Sequences or Cocktails?

ness of different phages (F9,48 ⫽ 0.64 and P ⫽ 0.75 for dilution factor by phage interaction). Resistance evolved to some extent in all single-phage treatments. On average, 85% (SD, 26) of bacteria were resistant to the phage species they had been exposed to over the experiment. The frequency of resistance to selective phages was similar for different phage species (␹2 ⫽ 2.59 and P ⫽ 0.45 by Kruskal-Wallis test) (Fig. 1d). Cross-resistance was also common, and total resistance, measured across all phages, was greatest for populations exposed to 14/1 and PT7 (␹2 ⫽ 8.13 and P ⫽ 0.04 by Kruskal-Wallis test) (Fig. 1g; also see Fig. S4 in the supplemental material). We detected no resistance in bacteria from control lines. In summary, although all phages were effective over a single transfer (see Fig. S1), resistance evolved in every treatment. For three out of four phages there was not a long-term reduction in bacterial density, but with PT7, where cross-resistance was relatively common, there was a significant reduction in bacterial density relative to control treatments. Multiphage treatments are more effective. Using a greater number of phage types caused a greater reduction in bacterial density (F2,18 ⫽ 3.79 and P ⫽ 0.04) (Fig. 1a to c). The difference in bacterial density between treatments with one and two phages was also clear over the first six transfers in two-phage treatments (at this point, bacteria in sequential treatments had only been exposed to a single phage). The OD600 was much lower in simultaneous treatments (F1,144 ⫽ 79.26 and P ⬍ 0.0001) (see Fig. S2 in the supplemental material). Similarly, over the first three transfers in four-phage treatments, when bacteria in sequential treatments had only been exposed to a single phage, densities were lowest in the simultaneous treatment (all P ⬍ 0.001 for pairwise comparisons to each sequential treatment) (see Fig. S3). The frequency of resistance against selective phages (to which bacteria were exposed over the experiment) was no greater in multiphage treatments (F2,18 ⫽ 0.58 and P ⫽ 0.57) (Fig. 1d to f), and the same was true of total resistance measured against all phages (F2,18 ⫽ 1.66 and P ⫽ 0.22) (Fig. 1g, h, and f). We detected multiresistant bacteria (i.e., resistant to all four phages) in every treatment except for phage-free controls and one two-phage sequence (⌽KZ-14/1). The average frequency of multiresistance showed a qualitative pattern similar to that for total resistance across phage therapy treatments, being highest for populations exposed to all four phages (0.41 ⫾ 0.16 [means ⫾ SE] with one phage, 0.52 ⫾ 0.10 with two phages, and 0.68 ⫾ 0.10 with four phages), although this did not represent statistically significant variation (F2,18 ⫽ 0.91 and P ⫽ 0.42). Two-phage treatments: sequential and simultaneous applications are similarly effective across 12 transfers. The average density of bacteria at the end of the experiment was no different when pairs of phages were applied sequentially or simultaneously (F1,144 ⫽ 0.22 and P ⫽ 0.63) (Fig. 1b). Although there was no difference after 12 transfers, densities were lower with simultaneous application over the first six transfers; temporal dynamics under sequential treatment showed a characteristic drop in OD600 after six transfers when the second phage was introduced (see Fig. S2 in the supplemental material). The lack of a difference between simultaneous and sequential treatments at the end of the experiment was true across all dilution factors (F3,144 ⫽ 0.62 and P ⫽ 0.60 for dilution factor by treatment type interaction), and there was no effect of dilution factor on final bacterial density averaged across treatments (F3,144 ⫽ 1.78 and P ⫽ 0.15). The density of bacteria was lower for some pairs of phages than others (F5,144 ⫽

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3.42 and P ⫽ 0.006), with ⌽KZ-14/1 and ⌽KZ-PT7 being particularly effective (Fig. 1b). We note that cross-resistance was relatively rare in ⌽KZ single-phage treatments (Fig. 1g), offering a potential explanation for its effectiveness in sequential treatments. Resistance to selective phages evolved to a similar extent when pairs were applied sequentially or simultaneously (F1,31 ⫽ 0.71 and P ⫽ 0.41). Similarly, total resistance did not differ significantly between simultaneous and sequential treatments on average (F1,31 ⫽ 1.01 and P ⫽ 0.32) (Fig. 1 h), although resistance was less common for some phage pairs than others (F5,31 ⫽ 2.79 and P ⫽ 0.03), being particularly infrequent for ⌽KZ-PT7 and PNM⌽KZ. Four-phage treatments: simultaneous application is more effective than application of two out of four sequences. The effectiveness of a four-phage sequence depended on the order in which we applied them (F3,48 ⫽ 6.84 and P ⫽ 0.0005 for order effect) (Fig. 1c). Two sequential strategies were just as good at suppressing bacterial growth as adding all phages simultaneously (⌽KZ-PNM-14/1-PT7, F1,24 ⫽ 1.51 and P ⫽ 0.23; 14/1-PT7⌽KZ-PNM, F1,24 ⫽ 0.75 and P ⫽ 0.39), while bacterial density was higher for the remaining two sequences than under simultaneous treatment (PT7-⌽KZ-PNM-14/1, F1,24 ⫽ 23.57 and P ⬍ 0.0001; PNM-14/1-PT7-⌽KZ, F1,24 ⫽ 11.77 and P ⫽ 0.002). The difference between sequential and simultaneous treatments was unaffected by dilution factor in every sequence (all P ⬎ 0.05), as was the relative effectiveness of different phage sequences (F9,48 ⫽ 0.69 and P ⫽ 0.71 for order by dilution interaction). Note that these analyses are for the long-term effects of each type of treatment; over the first few transfers, simultaneous application is the most effective, and population size in these treatments increased toward the end of the experiment (see Fig. S3 in the supplemental material). Bacteria were resistant to a similar proportion of phages in simultaneous and sequential treatments (all P ⬎ 0.05 by Wilcoxon tests) (Fig. 1f). Furthermore, resistance did not vary significantly among sequential treatments (␹2 ⫽ 2.06 and P ⫽ 0.56 by KruskalWallis test for an effect of temporal order). In summary, two out of four sequential treatments were just as effective as simultaneous application in terms of both the reduction in bacterial density and the emergence of resistant bacteria; the remaining two were no different in terms of resistance evolution but were less effective at suppressing bacterial density. Resistance incurs reduced growth rate in the absence of phages. We isolated bacterial mutants that were resistant to different subsets of the four phages by growing the wild type with different combinations of phages over a single growth cycle and screening for resistant phenotypes. In phage-free conditions, 18 of the 19 resistant mutants that we tested grew more slowly than the wild type (Fig. 2). The mean relative growth rate across all five phage treatments was 0.44 (SE, 0.07). Furthermore, growth rate relative to the wild type varied among bacteria resistant to different groups of phage (F4,14 ⫽ 3.73 and P ⫽ 0.028 by one-way ANOVA) (Fig. 2). Those exposed to a four-phage cocktail had the highest overall resistance but also paid the highest fitness cost. We also tested for a cost of resistance in bacteria from the end of the main phage therapy experiment. Both resistant and multiresistant bacteria (resistant to a subset of phages or to all phages, respectively) grew more slowly than the wild type in phage-free conditions (for resistant bacteria, mean relative growth rate ⫾ SD ⫽ 0.60 ⫾ 0.30, t test [t5] ⫽ ⫺3.29, and P ⫽ 0.02; for multiresistant

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FIG 2 Relative fitness of resistant mutants isolated after a single growth cycle

FIG 3 Effects of phage therapy on life spans of infected wax moth larvae. All

of selection with each phage individually or with all phages applied simultaneously. Points show means from four independently isolated mutants, and the average total resistance (proportion of four phages resisted) is given to the right of each point. Each point shows means ⫾ SE for four resistant mutants, except for PT7, where one resistant mutant failed to grow during resistance assays.

larvae were infected with PAO1, and some were given phage therapy (x axis). Bars show means ⫾ SE for 18 larvae in each treatment.

bacteria, means ⫾ SD ⫽ 0.39 ⫾ 0.24, t14 ⫽ ⫺9.70, and P ⬍ 0.0001) (see Fig. S5 in the supplemental material). Multiresistant bacteria were not significantly less fit than resistant bacteria taken from the same populations (t5 ⫽ 1.59 and P ⫽ 0.17 for matched pairs). Multiphage therapy is also effective in vivo. Phage therapy extended the time to death for wax moth larvae infected with P. aeruginosa PAO1 (Fig. 3). This was true for all three types of therapy tested, with the average time to death increasing from 12.67 h (SE, 0.67) to 26.67 h (SE, 1.21), 27.33 h (SE, 1.30), and 33.33 h (SE, 1.21), respectively, for larvae treated with ⌽KZ, a multiphage sequence, and a multiphage cocktail (P ⬍ 0.0001 in each case by Wilcoxon signed-rank tests compared to the untreated group) (Fig. 3). Variation of the time to death among phage treatments was statistically significant (␹2 ⫽ 13.53 and P ⫽ 0.001 by KruskalWallis test), and applying all phages simultaneously was the most successful at controlling infections over this time period (Fig. 3). We note that the average time to death in these experiments meant that the multiphage sequence treatments had not been completed by the time larvae were dead. Nevertheless, the short-term superiority of a four-phage cocktail is consistent with results from the early stages of our long-term in vitro experiments (see Fig. S3 in the supplemental material). Extended time to death was associated with greatly reduced density of bacteria in all treatments (P ⬍ 0.01 by pairwise Wilcoxon tests against PAO1-infected larvae), causing an average reduction in bacterial density in excess of 2,000-fold (see Table S1). When we reduced the bacterial inoculum to ⬃5,000 cells and increased the phage dose by 10-fold (to ⬃5 ⫻ 106 phage particles), a four-phage cocktail completely cleared infections that were lethal to untreated larvae: six out of six infected-untreated larvae were dead after 24 h, while control larvae and infected-treated larvae were alive after 24 and 48 h. DISCUSSION

We used in vitro and in vivo experiments with P. aeruginosa and four different phages to test whether simultaneous or sequential application of phages was more effective at reducing bacterial densities and minimizing resistance evolution. For two-phage treatments, there was no consistent difference between sequential and

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simultaneous application in terms of clearance or resistance, although some phage pairs were more effective than others. For four-phage treatments, two sequential treatments (temporal orders) were just as effective as simultaneous application in terms of reducing bacterial population size, while the remaining two were less effective; there was no consistent difference in the frequency of resistance between treatments. Multiphage therapy was more successful than single-phage therapy for reducing bacterial densities and did not cause a significant increase in the frequency of multiresistance. We found that resistance incurred a fitness cost in the absence of phages, and this was relatively more severe for multiresistant bacteria immediately following the acquisition of resistance but not in the longer term. Our in vitro findings were supported by experiments with an animal model (wax moth larvae): the time to death of infected larvae was increased by phage therapy, and the most successful treatment was a multiphage cocktail. Two of the best strategies in our in vitro experiment were sequences of ⌽KZ-PT7 and ⌽KZ-14/1. ⌽KZ was the phage least commonly associated with cross-resistance; therefore, we suggest that the success of these strategies lies in the first phage reducing bacterial population density but preserving sensitivity to the next phage in the sequence. Similarly, one of the two best four-phage sequences began with ⌽KZ, although multiresistance eventually reached a high frequency with this sequence, presumably due to selection imposed by succeeding phages. It follows that sequences of phages that target different bacterial receptors and are less likely to generate cross-resistance to each other generally will be more successful than more similar groups of phages. Previous work shows that some of the phages in our study are morphologically distinct and differ in the range of host strains that they can infect (21, 30). Crucially, phages ⌽KZ and 14/1 appear to adsorb to different receptors on the host cell (30), consistent with the idea that phages with different receptors work well in combination therapy. Fitness costs associated with resistance can influence bacterial population size in both the presence and absence of phages. In the absence of phages, resistance is less likely to persist if it is associated with a large fitness cost (3, 14, 20). In the presence of phages, even though resistance counters the direct effects of phages on bacterial growth, a fitness cost may result in less growth than in an untreated infection. Disentangling the indirect effects of phage resistance mutations on bacterial growth from the direct effects of

Applied and Environmental Microbiology

Phage Therapy: Sequences or Cocktails?

lysis is not possible in our phage therapy experiment. Nevertheless, our finding that resistance is relatively costly in the absence of phages is consistent with the notion that even when phage therapy does not clear bacterial infection it may impair growth indirectly by causing costly resistance mutations to fix, potentially reducing bacterial growth and virulence. In agreement, phage-resistant Escherichia coli mutants that emerge during infections of mice and cattle show reduced virulence (32, 33). Bacterial growth in resistant populations could also potentially be countered by adaptation of phages to overcome resistance. This type of coevolution, with reciprocal adaptation of phages and bacteria leading to increasing infectivity and resistance, has been observed repeatedly in populations of Pseudomonas fluorescens and phage ⌽2 using the same time scale as our phage therapy experiment (19, 24, 25). While we did not test for coevolution directly, reduced bacterial population sizes despite high levels of resistance may be partially explained by the presence of phage genotypes with mutations that increase their infectivity against resistant bacteria, as well as the direct action of the ancestral phages and fitness costs associated with resistance. One limitation of our study is that we estimated bacterial population densities by optical density measurements. It is possible that the relationship between population size and optical density is altered by the evolution of phage resistance, for example, if resistant genotypes overproduce alginate or extracellular polymeric substances (EPS) that inflate OD scores. In this case, while our optical density measures still provide useful information about the density of biological material generated by the infection, we would have overestimated the density of bacteria in some phage treatments and the fitness of resistant bacteria. However, this mechanism would not influence our main conclusions regarding comparisons between simultaneous and sequential treatments, because in each case we compared the same combination of phages under both types of treatment. We applied the same total number of each phage type simultaneously or sequentially over 12 experimental transfers. This meant that, at a given transfer, a smaller absolute number of each phage type was added in simultaneous treatments than the absolute number of the focal phage type in the corresponding sequential treatments. One possible consequence of this is that the number of phage progeny produced per phage type would be lower in simultaneous compared to sequential treatments at a given time point. This could occur either through within-host competition, where different phages interfere with each other’s replicative capacity (10, 36), or because their infection probability, replication rates, or virulence is density dependent. Thus, the effectiveness of phage cocktails might be further increased by using combinations of phages that do not interact antagonistically or by using a greater number of each phage type. Additionally, the temporal dynamics of multiphage therapy can be complicated, for example, if one phage type is relatively successful before resistance appears, followed by the ascendance of a second phage type (17). We therefore note that simultaneous application of phages does not necessarily translate into simultaneous killing by each phage type. In some of our treatments, there was little difference between simultaneous and sequential treatments for the long-term reduction in bacterial density, even though simultaneous application was generally more effective over the first few transfers. Infections in clinical settings may be cleared through a combination of therapy and host immune response. If the reduction in bacterial den-

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sity caused by simultaneous phage application is sufficient to cause rapid clearance in combination with the immune response, then the evolution of resistance across longer time scales, such as those in our experiments, may be less important for clinical outcomes than the short-term impact of phage therapy on bacterial densities. This caveat highlights the importance of comparing in vitro and in vivo results, and our results from a live animal model suggest that simultaneous application can indeed be effective over short time scales, completely clearing infections when the phageto-bacteria ratio is sufficiently high. Finally, the fact that phage therapy often delayed, but did not prevent, the death of wax moth larvae could be due to either insufficient killing of bacteria or the evolution of resistance. Our in vitro work suggests that resistance evolution occurs rapidly for the bacterium-phage combinations in our experiments, and previous work with Escherichia coli in a mouse model also showed rapid resistance evolution (7). Nevertheless, further work is required to understand the relative contributions of killing efficiency and resistance to treatment outcomes. Our results suggest that if the aim is to clear bacterial infections, the most effective strategy for a given combination of phages is to apply them all simultaneously. However, by using combinations of phages that target different bacterial receptors and therefore minimizing the frequency of cross-resistance, sequential strategies can be devised that are just as effective as a cocktail in terms of clearance. It is likely that combinations of phages exist where cross-resistance is less common than in our experiments, and we suggest that sequential applications of such combinations are associated with a lower incidence of multiresistance compared to equivalent simultaneous applications. This supports the view that development of phage therapy for clinical applications may be most effective through a more responsive, “sur-mesure” (29) approach than that used for static drugs, such as antibiotics. Further work is required to show that sequences of phages with different resistance mechanisms are effective over longer time scales, and that our conclusions apply to other bacterium-phage combinations. An additional aspect for further investigation is the possibility that large fitness costs associated with phage resistance can impair bacterial growth during infection and therefore potentially interact with chemical antibiotics or the host immune response to improve treatment outcomes. ACKNOWLEDGMENTS J.-P.P. thanks the Royal High Institute for Defense, Belgium, for financial support. A.R.H. and A.B. were funded by the European Research Council. V.-P.F. was funded by a Marie Curie Intra-European Fellowship within the 7th European Community Framework Programme.

REFERENCES 1. Abedon ST, Kuhl SJ, Blasdel BG, Kutter EM. 2011. Phage treatment of human infections. Bacteriophage 1:66 – 85. 2. Alisky J, Iczkowski K, Rapoport A, Troitsky N. 1998. Bacteriophages show promise as antimicrobial agents. J. Infect. 36:5–15. 3. Andersson DI, Hughes D. 2010. Antibiotic resistance and its cost: is it possible to reverse resistance? Nat. Rev. Microbiol. 8:260 –271. 4. Brockhurst MA, Morgan AD, Fenton A, Buckling A. 2007. Experimental coevolution with bacteria and phage. The Pseudomonas fluorescens–⌽2 model system. Infect. Genet. Evol. 7:547–552. 5. Brüssow H. 2005. Phage therapy: the Escherichia coli experience. Microbiology 151:2133–2140. 6. Buckling A, Rainey PB. 2002. Antagonistic coevolution between a bacterium and a bacteriophage. Proc. R. Soc. London Ser. B Biol. Sci. 269:931– 936. 7. Bull JJ, Levin BR, DeRouin T, Walker N, Bloch CA. 2002. Dynamics of

aem.asm.org 5651

Hall et al.

8. 9. 10. 11. 12. 13. 14. 15. 16. 17. 18. 19. 20. 21. 22.

success and failure in phage and antibiotic therapy in experimental infections. BMC Microbiol. 2:35. doi:10.1186/1471-2180-2-35. Cairns BJ, Payne RJ. 2008. Bacteriophage therapy and the mutant selection window. Antimicrob. Agents Chemother. 52:4344 – 4350. Callaway TR, et al. 2008. Bacteriophage isolated from feedlot cattle can reduce Escherichia coli O157:H7 populations in ruminant gastrointestinal tracts. Foodborne Pathog. Dis. 5:183–191. Dennehy JJ, Turner PE. 2004. Reduced fecundity is the cost of cheating in RNA virus ⌽6. Proc. R. Soc. London Ser. B Biol. Sci. 271:2275–2282. Hall AR, Griffiths VF, MacLean RC, Colegrave N. 2010. Mutational neighbourhood and mutation supply rate constrain adaptation in Pseudomonas aeruginosa. Proc. R. Soc. London Ser. B Biol. Sci. 277:643– 650. Hall AR, MacLean RC. 2011. Epistasis buffers the fitness effects of rifampicin-resistance mutations in Pseudomonas aeruginosa. Evolution 65: 2370 –2379. Harper DR, Enright MC. 2011. Bacteriophages for the treatment of Pseudomonas aeruginosa infections. J. Appl. Microbiol. 111:1–7. Harrison F, Browning LE, Vos M, Buckling A. 2006. Cooperation and virulence in acute Pseudomonas aeruginosa infections. BMC Biol. 4:21. doi:10.1186/1741-7007-4-21. Housby JN, Mann NH. 2009. Phage therapy. Drug Discov. Today 14: 536 –540. Labrie SJ, Samson JE, Moineau S. 2010. Bacteriophage resistance mechanisms. Nat. Rev. Microbiol. 8:317–327. Levin BR, Bull JJ. 2004. Population and evolutionary dynamics of phage therapy. Nat. Rev. Microbiol. 2:166 –173. Livermore DM. 2002. Multiple mechanisms of antimicrobial resistance in Pseudomonas aeruginosa: our worst nightmare? Clin. Infect. Dis. 34:634 – 640. Lopez-Pascua LDC, Buckling A. 2008. Increasing productivity accelerates host-parasite coevolution. J. Evol. Biol. 21:853– 860. MacLean RC, Hall AR, Perron GG, Buckling A. 2010. The population genetics of antibiotic resistance: integrating molecular mechanisms and treatment contexts. Nat. Rev. Genet. 11:405– 414. Merabishvili M, et al. 2009. Quality-controlled small-scale production of a well-defined bacteriophage cocktail for use in human clinical trials. PLoS One 4:e4944. doi:10.1371/journal.pone.0004944. Merabishvili M, et al. 2007. Digitized fluorescent RFLP analysis (fRFLP)

5652

aem.asm.org

23. 24. 25. 26. 27. 28. 29. 30. 31. 32. 33. 34. 35. 36.

as a universal method for comparing genomes of culturable dsDNA viruses: application to bacteriophages. Res. Microbiol. 158:572–581. Merril CR, Scholl D, Adhya SL. 2003. The prospect for bacteriophage therapy in western medicine. Nat. Rev. Drug Discov. 2:489 – 497. Morgan AD, Bonsall MB, Buckling A. 2010. Impact of bacterial mutation rate on coevolutionary dynamics between bacteria and phages. Evolution 64:2980 –2987. Morgan AD, Gandon S, Buckling A. 2005. The effect of migration on local adaptation in a coevolving host-parasite system. Nature 437:253– 256. O’Flynn G, Ross RP, Fitzgerald GF, Coffey A. 2004. Evaluation of a cocktail of three bacteriophages for biocontrol of Escherichia coli O157: H7. Appl. Environ. Microbiol. 70:3417–3424. Payne RJ, Phil D, Jansen VA. 2000. Phage therapy: the peculiar kinetics of self-replicating pharmaceuticals. Clin. Pharmacol. Ther. 68:225–230. Perron GG, Hall AR, Buckling A. 2010. Hypermutability and compensatory adaptation in antibiotic-resistant bacteria. Am. Nat. 176:303–311. Pirnay JP, et al. 2011. The phage therapy paradigm: prêt-a`-porter or sur-mesure? Pharm. Res. 28:934 –937. Pleteneva EA, et al. 2008. Study of the diversity in a group of phages of Pseudomonas aeruginosa species PB1 (Myoviridae) and their behavior in adsorption-resistant bacterial mutants. Russian J. Genet. 44:150 –158. Ramaswamy S, Musser JM. 1998. Molecular genetic basis of antimicrobial agent resistance in Mycobacterium tuberculosis: 1998 update. Tubercle Lung Dis. 79:3–29. Smith HW, Huggins MB. 1982. Successful treatment of experimental Escherichia coli infections in mice using phage: its general superiority over antibiotics. J. Gen. Microbiol. 128:307–318. Smith HW, Huggins MB, Shaw KM. 1987. The control of experimental Escherichia coli diarrhoea in calves by means of bacteriophages. J. Gen. Microbiol. 133:1111–1126. Sulakvelidze A, Alavidze Z, Morris JG, Jr. 2001. Bacteriophage therapy. Antimicrob. Agents Chemother. 45:649 – 659. Summers AO. 2002. Generally overlooked fundamentals of bacterial genetics and ecology. Clin. Infect. Dis. 34:S85–S92. Turner PE, Burch CL, Hanley KA, Chao L. 1999. Hybrid frequencies confirm limit to coinfection in the RNA bacteriophage ⌽6. J. Virol. 73: 2420 –2424.

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