The path to re-evolve cooperation is constrained in Pseudomonas aeruginosa
© The Author(s). 2017
Received: 12 July 2017
Accepted: 1 September 2017
Published: 11 September 2017
A common form of cooperation in bacteria is based on the secretion of beneficial metabolites, shareable as public good among cells within a group. Because cooperation can be exploited by “cheating” mutants, which contribute less or nothing to the public good, there has been great interest in understanding the conditions required for cooperation to remain evolutionarily stable. In contrast, much less is known about whether cheats, once fixed in the population, are able to revert back to cooperation when conditions change. Here, we tackle this question by subjecting experimentally evolved cheats of Pseudomonas aeruginosa, partly deficient for the production of the iron-scavenging public good pyoverdine, to conditions previously shown to favor cooperation.
Following approximately 200 generations of experimental evolution, we screened 720 evolved clones for changes in their pyoverdine production levels. We found no evidence for the re-evolution of full cooperation, even in environments with increased spatial structure, and reduced costs of public good production – two conditions that have previously been shown to maintain cooperation. In contrast, we observed selection for complete abolishment of pyoverdine production. The patterns of complete trait degradation were likely driven by “cheating on cheats” in unstructured, iron-limited environments where pyoverdine is important for growth, and selection against a maladaptive trait in iron-rich environments where pyoverdine is superfluous.
Our study shows that the path to re-evolve public-goods cooperation can be constrained. While a limitation of the number of mutational targets potentially leading to reversion might be one reason for the observed pattern, an alternative explanation is that the selective conditions required for revertants to spread from rarity are much more stringent than those needed to maintain cooperation.
Bacterial life predominantly takes place in diverse communities, where individual cells are constantly surrounded by neighbors. While high cell density and diversity can create strong competition in the struggle for nutrients and space [1, 2], it can also promote stable networks of cooperation [3, 4]. A common way for bacteria to cooperate is through the secretion of nutrient-scavenging metabolites, which are shared as “public goods” in the community. Public goods cooperation is thought to increase nutrient uptake rate, and results in the costs and benefits of public goods being shared among producer cells. Although beneficial for the collective as a whole, public goods cooperation can select for “social cheats”: mutants that lower or abolish their investment into public good production, but still reap the benefits of nutrient uptake [5, 6].
The undermining of public goods cooperation by cheats has spurred an entire field of research, examining the conditions required for cooperation to be maintained in the population [7–12]. In contrast, the question of how public goods cooperation evolves in the first place has received much less attention. The main question is: will the conditions that have been shown to maintain cooperation also promote the evolution of cooperation? Here, we tackle this question by examining whether bacteria that have evolved low levels of cooperation in a previous experiment can evolve back to normal levels of cooperation under conditions that are known to be favorable for cooperation [13, 14]. We use pyoverdine, an iron-scavenging siderophore secreted by the opportunistic pathogen Pseudomonas aeruginosa, as our model cooperative trait. Pyoverdine is the main siderophore of P. aeruginosa, and is secreted into the environment in response to iron limitation . Pyoverdine acts as a shareable public good that can be exploited by non-producing cheats that possess the matching receptor for uptake [16, 17].
We conducted experimental evolution in replicated populations with the two types of pyoverdine deficient strains across three levels of iron limitations and two habitats, differing in their level of spatial structuring. Based on social evolution theory, we predict the reversion to full cooperation whenever Hamilton’s rule – rB > C – is satisfied . While r is the relatedness between the actor and the recipient, C is the cost to the actor performing cooperation, and B is the benefit gained by the individual receiving cooperation. In our treatments, we vary r by manipulating the degree of spatial structure and C/B by manipulating the level of iron limitation. Accordingly, we predict that increased spatial structure and/or moderate investments into pyoverdine production should be most conducive for the re-evolution of cooperation. Moreover, we also envisage the possibility of pyoverdine production to degrade even further. This seems plausible because the mutated clones still produce some amount of pyoverdine, and thus, there is room for further exploitation by de novo mutants that make even less. We predict this to happen under low spatial structure, and high pyoverdine investment levels. Finally, pyoverdine could also be degraded due to disuse , especially under conditions of high iron availability where pyoverdine is not required.
Characterization of the ancestral pyoverdine deficient strains
We first characterized the strains pvdS_gene (having a SNP in pvdS) and pvdS_prom (having a SNP in the promoter of pvdS) for their pyoverdine production and growth dynamics (Fig. 1) before they were subjected to experimental evolution. These two mutants themselves spontaneously arose and spread during a previous experimental evolution study (Granato ET, Ziegenhain C, Marvig RL & Kümmerli R, unpublished). Their entire genomes had been re-sequenced and analysed. Those analyses revealed that both pvdS_gene and pvdS_prom carried non-synonymous mutations that are directly associated with their reduced pyoverdine investment levels (Fig. 1a). Strain pvdS_gene has a point mutation (G > C) in the pvdS gene that leads to an amino acid change (Met135Ile), and thus to a modified iron-starvation sigma factor PvdS. A modified PvdS presumably has lower affinity to the RNA-polymerase, a complex that directly controls the expression of the non-ribosomal peptide synthesis machinery required to build pyoverdine. Strain pvdS_prom carries a point mutation (G > T) in the consensus sequence of the −35 element in the promoter region upstream of pvdS. This mutant produces a wildtype sigma factor, but the transcription rate of PvdS is likely reduced.
Both of these mutations show strong defects in pyoverdine production and growth under iron-limited conditions (Fig. 1b and c). Pyoverdine production of the pvdS_gene strain was only 9.4 ± 0.1% (mean ± SE) compared to the wildtype strain PAO1 (measured after 24 h), and characterized by a low but steady production rate (Fig. 1c). While pyoverdine production was also reduced in pvdS_prom (34.7 ± 1.4% relative to the ancestral wildtype strain), the production dynamic differed from pvdS_gene. The pvdS_prom strain had an extended phase, where no pyoverdine is produced, followed by a phase with a considerable production rate (Fig. 1c). Both mutant strains displayed substantial growth impairments, comparable to that of a constructed pyoverdine knockout (Fig. 1b). This indicates that the production of higher amounts of pyoverdine would be advantageous.
Further degradation and not re-evolution of pyoverdine production prevails
Frequency of non- and low-producing strains per treatment
In-depth analysis of a subset of evolved clones confirms selection against pyoverdine
Evolved pyoverdine phenotypes are not based on further mutations in pvdS
We anticipated that both restoration and further reduction of pyoverdine production could be caused by additional mutations in the pvdS gene or its promoter. However, we found no support for this hypothesis when sequencing this genetic region for the subset of 57 clones described above (Additional file 1: Table S1). All clones had retained the original, ancestral mutation inherited from their respective low-producing ancestor (SNP in the pvdS gene itself for pvdS_gene, SNP in the pvdS promoter region for pvdS_prom). One clone from the pvdS_gene line gained an additional SNP in the pvdS promoter region, which however did not affect its phenotype. No additional mutations were found in any of the clones, indicating that the observed changes in pyoverdine production either represent entirely phenotypic changes, or are caused by mutations in regions other than pvdS.
Numerous studies used microbial systems to address a key question in evolutionary biology: how can cooperation be maintained in the face of cheats that exploit the cooperative acts performed by others [9, 11, 12]. Conversely, the question of what happens after a cheat has become fixed in the population has received much less attention. Would it be possible that cooperation re-evolves if environmental conditions and thus selection pressures change [26, 27]? To tackle this question, we performed experimental evolution with P. aeruginosa cheat strains (mutants that produced greatly reduced amounts of the iron-scavenging public good pyoverdine), which had the potential to revert back to a full cooperative phenotype by a single point mutation. Despite this favourable genetic predisposition, we never observed reversion to cooperation, even under conditions that had previously been identified as being beneficial for cooperation. Instead, we observed the emergence of mutants that completely abolished pyoverdine production, with their frequency of appearance depending on both their genetic background and the environmental conditions. Taken together, our study highlights that the re-evolution of public-goods cooperation might be constrained in bacteria.
Estimation of mutation supply during experimental evolution
Population bottleneck [CFU]
Number of cell divisionsa
Expected number of mutation eventsb …
at specific nucleotide in pvdS
anywhere in pvdS locusc
5.0 × 104
1.2 × 1010
8.2 × 104
1.3 × 105
3.1 × 1010
20.6 × 104
2.8 × 105
6.8 × 1010
45.1 × 104
Obviously, not all of these mutations would be of a compensatory nature and restore PvdS functionality, but rather be synonymous or deleterious. Taken together, our calculations show that specific reversions to the ancestral cooperative state and compensatory mutations must have likely occurred during our experiment, albeit in relatively small numbers. The exact number of mutational targets outside of pvdS that would lead to a reversion are unknown but likely to be limited, given the exceptionally tight and specific regulation of pyoverdine production by PvdS . In contrast, the further decrease of pyoverdine production we observed is a much more likely event because mutations in any of numerous synthesis genes or additional regulatory elements, which we did not sequence, could have caused this decrease .
At the ultimate level, it might be that we have not chosen the appropriate environmental conditions that would select for reversion. According to Hamilton’s rule, we would expect selection for reverted cooperators when relatedness is relatively high and/or when the cost-to-benefit ratio of cooperation is relatively low. Although we have implemented experimental conditions promoting significant relatedness (through limited cell mixing in spatially structured environments) and reduced costs of pyoverdine production (at intermediate iron limitation), the chosen conditions were apparently not favourable enough to select for the re-evolution of cooperation. At first glance, this seems surprising because the chosen conditions have previously been shown to prevent the spreading of cheats and to maintain cooperation [13, 17, 19]. Our findings thus suggest that the conditions for the evolution of cooperation might be more stringent than those for the maintenance of cooperation. Indeed, social evolution theory predicts cooperation to be maintained when rB = C, as it prevents rare cheats from invading. Conversely, for cooperation to evolve from scratch, the more stringent condition of Hamilton’s rule, rB > C, must be met. The fulfilment of this latter condition might require specific conditions (e.g. very high relatedness), as reverted cooperators would have to invade from extreme rarity, while being surrounded by clones exploiting any pyoverdine molecule diffusing away from the producer.
Instead of reversion to cooperation, we observed selection for mutants that further reduced or completely abolished pyoverdine production (Figs. 3 and 4). Intriguingly, the environments that promoted the spread of these mutants differed between pvdS_gene and pvdS_prom, indicating that different selection pressures can promote the same phenotype. For the pvdS_gene background, we found that the further degradation of pyoverdine production predominantly occurred with low spatial structure and under stringent iron limitation. As pyoverdine is important for growth under these conditions but widely shared due to mixing, we assume that these mutants spread because they cheated on the residual pyoverdine produced by the ancestral pvdS_gene. This finding confirms the notion that “cheating” is context-dependent, and shows that a strain that evolved as a cheat is still susceptible to further exploitation, despite its greatly reduced investment into a cooperative trait . In contrast to this pattern, we observed further degradation of pyoverdine production in the pvdS_prom background almost exclusively in iron-rich environments regardless of spatial structure. Because pyoverdine is not needed under iron-rich conditions, yet still expressed in low amounts [14, 25], we assume that selection against pyoverdine production represents the erosion of an unnecessary trait.
We can only speculate about why the genetic background seems to matter for whether pyoverdine degradation is presumably driven by cheating or disuse. One possible explanation might reside in the different pyoverdine production profiles shown by the two strains. While pvdS_gene has a low but steady production rate, pvdS_prom delays pyoverdine production, but then produces pyoverdine at a higher rate compared to pvdS_gene. It could be that delaying the onset of pyoverdine production is a successful strategy to prevent the invasion of cheating mutants with completely abolished pyoverdine production. With regard to trait erosion, it seems possible that pvdS_prom produces higher amounts of pyoverdine compared to pvdS_gene under iron-rich conditions; this would make this strain more susceptible for trait erosion because pyoverdine production is maladaptive under these conditions. Further studies are clearly needed to elucidate these pattern at both the proximate and ultimate level. The proximate level is of special interest here because the complete loss of pyoverdine production did not involve mutations in pvdS, which has been identified as the main target of selection for the initial reduction in pyoverdine production ([21, 22]; Granato ET, Ziegenhain C, Marvig RL & Kümmerli R, unpublished).
Our findings indicate that the evolution of cooperation through mutational reversion seems to be constrained. Reasons for this could be linked to the low number of mutational targets available that can lead to reversion and/or the stringent selective conditions required to promote the spread of revertants. Thus, conditions previously shown to maintain cooperation might not be sufficient to promote the invasion of de novo re-evolved cooperators from rarity. We believe that the insights gained from our study have implications for other public good traits for two reasons. For one thing, it was shown that public goods traits can easily be lost through SNPs in their key regulators [21, 22, 34–36]. However, regulators are usually highly specialized proteins encoded by a single gene, such that mutational targets allowing reversion or compensation are likely limited [9, 37, 38]. Moreover, invasion from rarity might be generally hampered because many public-good traits are under quorum sensing-control (i.e. only expressed at high cell density). This could mean that rare co-operators cannot invade because they do not reach the quorum, and thus no public good is produced and no benefit generated [39, 40]. Important to note here is, that while we focussed on the re-evolution of cooperation via mutations, an alternative scenario under natural conditions is that cheats may revert to cooperators through horizontal gene transfer [41, 42]. This scenario has especially been advocated for cooperative traits located on plasmids [43, 44]. While this is a plausible scenario for some social traits, it is unlikely to apply to siderophores, which are typically encoded on the chromosome. To conclude, the insights gained from our study contribute to our general understanding of the conditions necessary for a cooperative trait to evolve in microorganisms.
Strains and growth conditions
We used Pseudomonas aeruginosa wildtype strain PAO1 (ATCC 15692) and a pyoverdine-negative mutant, both constitutively expressing GFP (PAO1-gfp, PAO1-ΔpvdD-gfp), as positive and negative controls for pyoverdine production, respectively. We further used PAO1-pvdS_gene and PAO1-pvdS_prom, two mutants with strongly reduced pyoverdine production, that evolved de novo from PAO1-gfp during experimental evolution in iron-limited media (2.5 gL−1 BactoPeptone, 3 gL−1 NaCl, 5 mgL−1 Cholesterol, 25 mM MES buffer pH = 6.0, 1 mM MgSO4, 1 mM CaCl2, 200 μM 2,2′-Bipyridyl (Granato ET, Ziegenhain C, Marvig RL & Kümmerli R, unpublished)). PAO1-pvdS_gene carries a non-synonymous point mutation (G > C) in the pvdS gene that leads to an amino acid change (Met135Ile). PAO1-pvdS_prom carries a point mutation (G > T) in the consensus sequence of the −35 element in the promoter region upstream of pvdS. Both mutants constitutively express GFP. Throughout this publication, the two mutants are referred to as “pvdS_gene” and “pvdS_prom”.
For overnight pre-culturing, we used Luria Bertani (LB) medium, and incubated the bacteria under shaking conditions (190–200 rpm) for 16–18 h. All experiments in this study were conducted at 37 °C. Optical density (OD) of pre-cultures was determined at a wavelength of 600 nm in a spectrophotometer. We induced strongly iron-limiting growth conditions by using casamino acids (CAA) medium (5 gL−1 casamino acids; 1.18 gL−1 K2HPO4*3H2O; 0.25 gL−1 MgSO4*7H2O) supplemented with 25 mM HEPES and 400 μM of the iron chelator 2,2′-Bipyridyl. All chemicals were purchased from Sigma-Aldrich, Switzerland.
For conditions with medium or high iron availability, we further added FeCl3 at final concentrations of 1 μM or 40 μM, respectively. These levels of iron supplementation have previously been shown to either reduce pyoverdine production to intermediate levels (1 μM FeCl3) or to completely stall pyoverdine synthesis (40 μM FeCl3) . Furthermore, competition experiments between PAO1 cooperators and their cheating isogenic knock-out mutant (PAO1 ΔpvdD ΔpchEF), deficient for siderophore production, revealed that cheats could only invade without the supplementation of extra iron .
We manipulated the spatial structure of the environment by growing bacteria either in liquid medium under shaking conditions (180 rpm; unstructured environment) or in viscous medium containing 0.1% agar under static conditions (structured environment). Competition experiments between PAO1 and its knock-out cheat, previously conduced in our laboratory, showed that cheats experienced a significant relative fitness advantage under well-mixed, but not under more viscous conditions .
Ancestral growth and pyoverdine kinetics
To measure growth and pyoverdine production kinetics of all strains in iron-limited media prior to experimental evolution, we washed bacterial pre-cultures twice with sterile NaCl (0.85%), adjusted OD600 to 1.0, and diluted 10−4 into 200 μL of iron-limited CAA (Bipyridyl 400 μM) per well in a 96-well plate. The plate was then incubated in a Tecan Infinite M-200 plate reader (Tecan Group Ltd., Switzerland) for 24 h, and OD600 and pyoverdine-specific fluorescence (emission 400 nm, excitation 460 nm) were measured every 15 min.
We conducted experimental evolution with pvdS_gene and pvdS_prom as starting points. We let each strain evolve independently under six different experimental treatments in a full-factorial design: 2 spatial structures (unstructured vs. structured) × 3 iron availabilities (low vs. medium vs. high iron availability) in three replicate independent lines (Fig. 2). At the start of the experimental evolution, overnight cultures of both clones were washed twice with NaCl (0.85%), adjusted to an OD600 of 1.0 and diluted 1:1000 into 200 μL of nutrient medium in 96-well plates. Plates were wrapped with parafilm, incubated for 24 h and subsequently diluted 1:1000 in fresh nutrient medium. We repeated this cycle for 20 consecutive transfers, allowing for approximately 200 generations of bacterial evolution (Fig. 2). At the end of the experiment, we prepared freezer stocks for each evolved population (n = 36) by mixing 100 μL of bacterial culture with 100 μL of sterile glycerol (85%). Samples were stored at −80 °C.
Isolation of single clones
To check whether evolved clones showed altered pyoverdine production levels compared to the ancestral pvdS_gene and pvdS_prom strains, we isolated a total of 720 evolved clones (20 per replicate and treatment). Specifically, we regrew evolved bacterial populations from freezer stocks in 5 mL LB medium for 16–18 h (180 rpm) and subsequently adjusted them to OD600 = 1.0. Then, 200 μL of 10−6 and 10−7 dilutions were spread on large LB agar plates (diameter 150 mm), which we incubated at 37 °C for 18–20 h. We then randomly picked twenty colonies for each of the 36 evolved populations, and immediately processed the clones for the pyoverdine measurement assay (see below).
Screen for evolved pyoverdine production levels
For each of the 720 evolved clones, we transferred a small amount of material from the agar plate directly into 200 μL of CAA + Bipyridyl (400 μM) in individual wells on a 96-well plate. We incubated plates with clones originating either from unstructured environments or structured environments for 24 h under shaken (180 rpm) or static conditions, respectively. Following incubation, we measured OD600 and pyoverdine-specific fluorescence (emission 400 nm, excitation 460 nm) in the Tecan Infinite M-200 plate reader as a single endpoint measurement. As controls, we included in three-fold replication on each plate: the high-producing PAO1 wildtype (positive control); the pyoverdine knockout mutant PAO1-ΔpvdD-gfp (negative control); the two low-producing mutants pvdS_gene and pvdS_prom; and blank growth medium. To preserve all tested clones for future experiments, we mixed 100 μL of bacterial culture with 100 μL of sterile glycerol (85%) for storage at −80 °C.
Confirmation of evolved pyoverdine phenotypes
Based on the screen above, we identified 34 clones with an altered pyoverdine production level (Additional file 1: Table S1). Specifically, we found five clones that seem to have restored pyoverdine production by roughly 50% (i.e. in terms of the difference between the low-producing ancestor cheat and the high-producing wildtype) and 29 clones that seem to produce less than 33% of pyoverdine compared to their ancestral pyoverdine low-producers (either pvdS_gene or pvdS_prom). We subjected these clones to an in-depth repeated screening of their pyoverdine phenotype. In addition, we selected two random clones per treatment (n = 24), from different evolved populations, that displayed no change in their production levels (compared to pvdS_gene or pvdS_prom). One clone had to be excluded due to contamination, so that the final sample size for this group of clones was n = 23. For all of these evolved clones (n = 57), we re-measured their pyoverdine production level in three-fold replication using the same protocol and controls as described above.
Sequencing of pvdS promoter and coding region
Since the ancestral low-producing strains (pvdS_gene or pvdS_prom) had mutations in the pvdS gene or its promoter, we were wondering whether the altered phenotypes observed in the evolved clones were based on reversion or additional mutations in this genetic region. To address this question, we PCR amplified and sequenced the pvdS gene and the upstream region containing the promoter sequence of all 57 evolved clones screened above. PCR mixtures consisted of 2 μl of a 10 μM solution of each primer, pvdS_fw (5′-GACGCATGACTGCAACATT-3′) and pvdS_rev (5′-CCTTCGATTTTCGCCACA-3′), 25 μl Quick-Load Taq 2X Master Mix (New England Biolabs), 1 μl of DMSO, and 20 μl of sterile Milli-Q water. We added bacterial biomass from glycerol stocks to the PCR mixture distributed in 96-well PCR plates. Plates were sealed with an adhesive film. We used the following PCR conditions: denaturation at 95 °C for 10 min; 30 cycles of amplification (1 min denaturation at 95 °C, 1 min primer annealing at 56 °C, and 1 min primer extension at 72 °C); final elongation at 72 °C for 5 min. The PCR products were purified and commercially sequenced using the pvdS_fw primer. While sequencing worked well for 51 clones, it failed for two clones, and resulted in partial sequences for six clones (Additional file 1: Table S1).
All statistical analyses were performed using R 3.2.2 . We tested for treatment differences in the frequency of non- or low-producing strains using Fisher’s exact test and corrected for multiple testing using the Bonferroni correction. To compare pyoverdine production of evolved clones to that of the low-producing ancestors, we used one-way analyses of variance (ANOVA) and corrected for multiple testing using Tukey’s HSD (honest significant difference) test.
We thank Chiara Rezzoagli for assistance during the experimental evolution, and two anonymous reviewers whose suggestions helped to improve and clarify this manuscript.
The work was funded by the Swiss National Science Foundation (grants no. PP00P3–139,164 and PP00P3_165835 to RK) and the Forschungskredit of the University of Zurich (to EG).
Availability of data and materials
The data sets supporting the results of this article are available in the Dryad repository, http://0-dx.doi.org.brum.beds.ac.uk/10.5061/dryad.r75qr.
EG and RK planned the experiments. EG carried out the experiments and conducted statistical analysis. EG and RK analysed and interpreted the data, and wrote the manuscript. Both authors read and approved the final manuscript.
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- Foster KR, Bell T. Competition, not cooperation, dominates interactions among culturable microbial species. Curr Biol. 2012;22:1845–50. doi:10.1016/j.cub.2012.08.005.View ArticlePubMedGoogle Scholar
- Becker J, Eisenhauer N, Scheu S, Jousset A. Increasing antagonistic interactions cause bacterial communities to collapse at high diversity. Ecol Lett. 2012;15:468–74.View ArticlePubMedGoogle Scholar
- Faust K, Raes J. Microbial interactions: from networks to models. Nat Rev Microbiol. 2012;10:538–50. doi:10.1038/nrmicro2832.View ArticlePubMedGoogle Scholar
- Inglis RF, Biernaskie JM, Gardner A, Kümmerli R. Presence of a loner strain maintains cooperation and diversity in well-mixed bacterial communities. Proc R Soc B Biol Sci. 2016;283:20152682. doi:10.1098/rspb.2015.2682.View ArticleGoogle Scholar
- Diggle SP, Griffin AS, Campbell GS, West SA. Cooperation and conflict in quorum-sensing bacterial populations. Nature. 2007;450:411–4. doi:10.1038/nature06279.View ArticlePubMedGoogle Scholar
- Griffin AS, West SA, Buckling A. Cooperation and competition in pathogenic bacteria. Nature. 2004;430:1024–7.View ArticlePubMedGoogle Scholar
- Sachs JL, Mueller UG, Wilcox TP, Bull JJ. The evolution of cooperation. Q Rev Biol. 2004;79:135–60.View ArticlePubMedGoogle Scholar
- Lehmann L, Keller L. The evolution of cooperation and altruism - a general framework and a classification of models. J Evol Biol. 2006;19:1365–76.View ArticlePubMedGoogle Scholar
- West SA, Griffin AS, Gardner A, Diggle SP. Social evolution theory for microorganisms. Nat Rev Microbiol. 2006;4:597–607. doi:10.1038/nrmicro1461.View ArticlePubMedGoogle Scholar
- West SA, Griffin AS, Gardner A. Evolutionary explanations for cooperation. Curr Biol. 2007;17:661–72. doi:10.1016/j.cub.2007.06.004.View ArticleGoogle Scholar
- Strassmann JE, Queller DC. Evolution of cooperation and control of cheating in a social microbe. Proc Natl Acad Sci U S A. 2011;108 Supplement 2:10855–62. doi:10.1073/pnas.1102451108.
- Bruger E, Waters C. Sharing the sandbox: Evolutionary mechanisms that maintain bacterial cooperation [version 1; referees: 2 approved]. F1000Research. 2015;4:1504.Google Scholar
- Leinweber A, Fredrik Inglis R, Kümmerli R. Cheating fosters species co-existence in well-mixed bacterial communities. ISME J. 2017;11:1179–88. doi:10.1038/ismej.2016.195.View ArticlePubMedGoogle Scholar
- Kümmerli R, Jiricny N, Clarke LS, West SA, Griffin AS. Phenotypic plasticity of a cooperative behaviour in bacteria. J Evol Biol. 2009;22:589–98. doi:10.1111/j.1420-9101.2008.01666.x.View ArticlePubMedGoogle Scholar
- Visca P, Imperi F, Lamont IL. Pyoverdine siderophores: from biogenesis to biosignificance. Trends Microbiol. 2007;15:22–30. doi:10.1016/j.tim.2006.11.004.View ArticlePubMedGoogle Scholar
- Kümmerli R, Santorelli LA, Granato ET, Dumas Z, Dobay A, Griffin AS, et al. Co-evolutionary dynamics between public good producers and cheats in the bacterium Pseudomonas Aeruginosa. J Evol Biol. 2015;28:2264–74. doi:10.1111/jeb.12751.View ArticlePubMedGoogle Scholar
- Dumas Z, Kümmerli R. Cost of cooperation rules selection for cheats in bacterial metapopulations. J Evol Biol. 2012;25:473–84.View ArticlePubMedGoogle Scholar
- Julou T, Mora T, Guillon L, Croquette V, Schalk IJ, Bensimon D, et al. Cell-cell contacts confine public goods diffusion inside Pseudomonas aeruginosa clonal microcolonies. Proc Natl Acad Sci U S A. 2013;110:12577–82. doi:10.1073/pnas.1301428110.View ArticlePubMedPubMed CentralGoogle Scholar
- Kümmerli R, Griffin AS, West SA, Buckling A, Harrison F. Viscous medium promotes cooperation in the pathogenic bacterium Pseudomonas Aeruginosa. Proc Biol Sci. 2009;276:3531–8. doi:10.1098/rspb.2009.0861.View ArticlePubMedPubMed CentralGoogle Scholar
- Weigert M, Kümmerli R. The physical boundaries of public goods cooperation between surface-attached bacterial cells. Proc R Soc B Biol Sci. 2017;284:20170631. doi:10.1098/rspb.2017.0631.View ArticleGoogle Scholar
- Kümmerli R, Santorelli LA, Granato ET, Dumas Z, Dobay A, Griffin AS, et al. Co-evolutionary dynamics between public good producers and cheats in the bacterium Pseudomonas aeruginosa. J Evol Biol. 2015;28:n/a-n/a. doi:10.1111/jeb.12751.
- Andersen SB, Marvig RL, Molin S, Krogh Johansen H, Griffin AS. Long-term social dynamics drive loss of function in pathogenic bacteria. Proc Natl Acad Sci. 2015;112:10756–61. doi:10.1073/pnas.1508324112.View ArticlePubMedPubMed CentralGoogle Scholar
- Schalk IJ, Guillon L. Pyoverdine biosynthesis and secretion in Pseudomonas Aeruginosa: implications for metal homeostasis. Environ Microbiol. 2013;15:1661–73. doi:10.1111/1462-2920.12013.View ArticlePubMedGoogle Scholar
- Hamilton WD. The genetical evolution of social behaviour. J Theor Biol. 1964;7:1–16. doi:10.1016/0022-5193(64)90038-4.View ArticlePubMedGoogle Scholar
- Zhang X, Rainey PB. Exploring the sociobiology of Pyoverdin-producing pseudomonas. Evolution (N Y). 2013;67:3161–74. doi:10.1111/evo.12183.Google Scholar
- Cordero OX, Ventouras L-A, DeLong EF, Polz MF. Public good dynamics drive evolution of iron acquisition strategies in natural bacterioplankton populations. Proc Natl Acad Sci U S A. 2012;109:20059–64. doi:10.1073/pnas.1213344109.View ArticlePubMedPubMed CentralGoogle Scholar
- Fiegna F, Yu Y-TN, Kadam SV, Velicer GJ. Evolution of an obligate social cheater to a superior cooperator. Nature. 2006;441:310–4. doi:10.1038/nature04677.View ArticlePubMedGoogle Scholar
- Eyre-Walker A, Keightley PD. The distribution of fitness effects of new mutations. Nat Rev Genet. 2007;8:610–8. doi:10.1038/nrg2146.View ArticlePubMedGoogle Scholar
- Yu YTN, Kleiner M, Velicer GJ. Spontaneous reversions of an evolutionary trait loss reveal regulators of a small RNA that controls multicellular development in myxobacteria. J Bacteriol. 2016;198:3142–51.View ArticlePubMedPubMed CentralGoogle Scholar
- Crill WD, Wichman HA, Bull JJ. Evolutionary reversals during viral adaptation to alternating hosts. Genetics. 2000;154:27–37.PubMedPubMed CentralGoogle Scholar
- Heineman RH, Molineux IJ, Bull JJ. Evolutionary robustness of an optimal phenotype: re-evolution of lysis in a bacteriophage deleted for its lysin gene. J Mol Evol. 2005;61:181–91.View ArticlePubMedGoogle Scholar
- McElroy KE, Hui JGK, Woo JKK, Luk AWS, Webb JS, Kjelleberg S, et al. Strain-specific parallel evolution drives short-term diversification during Pseudomonas Aeruginosa biofilm formation. Proc Natl Acad Sci U S A. 2014;111:E1419–27. doi:10.1073/pnas.1314340111.View ArticlePubMedPubMed CentralGoogle Scholar
- Ghoul M, West SA, Diggle SP, Griffin AS. An experimental test of whether cheating is context dependent. J Evol Biol. 2014;27:551–6.View ArticlePubMedGoogle Scholar
- Sandoz KM, Mitzimberg SM, Schuster M. Social cheating in Pseudomonas Aeruginosa quorum sensing. Proc Natl Acad Sci. 2007;104:15876–81. doi:10.1073/pnas.0705653104.View ArticlePubMedPubMed CentralGoogle Scholar
- Wilder CN, Diggle SP, Schuster M. Cooperation and cheating in Pseudomonas Aeruginosa: the roles of the las, rhl and pqs quorum-sensing systems. ISME J. 2011;5:1332–43. doi:10.1038/ismej.2011.13.View ArticlePubMedPubMed CentralGoogle Scholar
- Driscoll WW, Pepper JW, Pierson LS. Pierson E a. Spontaneous Gac mutants of pseudomonas biological control strains: cheaters or mutualists? Appl Environ Microbiol. 2011;77:7227–35. doi:10.1128/AEM.00679-11.View ArticlePubMedPubMed CentralGoogle Scholar
- Saha R, Saha N, Donofrio RS, Bestervelt LL. Microbial siderophores: a mini review. J Basic Microbiol. 2013;53:303–17.View ArticlePubMedGoogle Scholar
- Popat R, Cornforth DM, McNally L, Brown SP. Collective sensing and collective responses in quorum-sensing bacteria. J R Soc Interface. 2015;12:20140882-. doi:10.1098/rsif.2014.0882.
- Allen RC, McNally L, Popat R, Brown SP. Quorum sensing protects bacterial co-operation from exploitation by cheats. ISME J. 2016;1–11. doi:10.1038/ismej.2015.232.
- Pollak S, Omer-Bendori S, Even-Tov E, Lipsman V, Bareia T, Ben-Zion I, et al. Facultative cheating supports the coexistence of diverse quorum-sensing alleles. Proc Natl Acad Sci. 2016;113:2152–7. doi:10.1073/pnas.1520615113.View ArticlePubMedPubMed CentralGoogle Scholar
- Soucy SM, Huang J, Gogarten JP. Horizontal gene transfer: building the web of life. Nat Rev Genet. 2015;16:472–82. doi:10.1038/nrg3962.View ArticlePubMedGoogle Scholar
- Cordero OX, Polz MF. Explaining microbial genomic diversity in light of evolutionary ecology. Nat Rev Microbiol. 2014;12:263–73. doi:10.1038/nrmicro3218.View ArticlePubMedGoogle Scholar
- Nogueira T, Rankin DJ, Touchon M, Taddei F, Brown SP, Rocha EPC. Horizontal gene transfer of the Secretome drives the evolution of bacterial cooperation and virulence. Curr Biol. 2009;19:1683–91.View ArticlePubMedPubMed CentralGoogle Scholar
- Dimitriu T, Lotton C, Benard-Capelle J, Misevic D, Brown SP, Lindner AB, et al. Genetic information transfer promotes cooperation in bacteria. Proc Natl Acad Sci. 2014;111:11103–8. doi:10.1073/pnas.1406840111.View ArticlePubMedPubMed CentralGoogle Scholar
- R Development Core Team. R: A language and environment for statistical computing. Vienna, Austria: R Foundation for Statistical Computing; 2016.Google Scholar