Bacteria can evolve quickly, a skill that proves useful in ever-changing environments. For example, individuals in many bacterial species can start to work together under certain circumstances; this ability is underpinned by a system called quorum sensing, which allows cells to detect nearby conspecifics. However, species of harmful bacteria often lose their quorum sensing abilities when they infect humans. This is the case for Pseudomonas aeruginosa, which normally lives in the soil but can also cause deadly conditions, especially in hospital settings.

Patients often carry P. aeruginosa with mutations that disable the quorum-sensing signal receptor LasR, a molecular actor that can switch on many other genes in a cell. People who are infected with P. aeruginosa strains carrying a damaged version of the lasR gene are typically more ill and less likely to recover. Why this is the case – and in fact, why genes associated with quorum sensing often lose function during infection – is still unclear.

To investigate this question, Mould et al. used laboratory evolution experiments and computer models of P. aeruginosa growth to understand how lasR mutant cells evolve. Differences in growth rates and ways to use resources (rather than changes in cell-to-cell interactions) best explained why lasR mutants become more successful. Further experiments narrowed down the molecular cascade required for the rise of lasR mutants, identifying a pathway that regulates how P. aeruginosa switches between different nutrient sources.

This work reveals a new connection between quorum sensing genes and nutrient regulation in bacterial cells. Loss of functional LasR changes the way that cells use nutrients, and thus will reshape how they interact with host cells and other bacteria. This insight could lead to better ways to predict the outcomes of bacterial infections and how to best treat them.

Quorum sensing (QS) is a mechanism of microbial communication that regulates the expression of a suite of genes in response to diffusible autoinducers in a population (Schuster and Greenberg, 2007; Schuster et al., 2003). Despite the importance of cell-cell communication for virulence (Rumbaugh et al., 2009) and high conservation across divergent phylogenies, key QS regulators in diverse species, such as Pseudomonas aeruginosa, Vibrio cholerae, and Staphylococcus aureus, frequently lose function (Mould and Hogan, 2021), due to recent missense and nonsense mutations, indels, or genome rearrangements. These paradoxical findings suggest that there may be connections between QS and other key physiological pathways that have yet to be revealed.

In P. aeruginosa, many isolates from humans, plants, and water sources have loss-of-function mutations in the gene encoding the transcription factor LasR (Groleau et al., 2022; O’Connor et al., 2021), which is central to an interconnected QS network (Schuster et al., 2003). LasR^– isolates have been repeatedly observed in P. aeruginosa lung infections in people with cystic fibrosis (pwCF) (Smith et al., 2006), and LasR^– isolate detection is associated with more rapid lung function decline and more inflammation than in comparator populations (Hoffman et al., 2009; LaFayette et al., 2015). In a clinical study of acute corneal infections (Hammond et al., 2016), LasR^– strains also correlated with more damage and worse outcomes.

Multiple studies contribute to our understanding of the physiologies and social interactions that impact LasR LOF mutant fitness. Several studies provide evidence in support of the model that LasR^– strains are ‘social cheaters’ that reap the benefits of shared goods secreted by neighboring wild-type cells without incurring the metabolic costs (Sandoz et al., 2007). In this case, LasR^– strains grow better when the wild type is in the majority, and crash when a critical threshold of LasR^– cells is surpassed due to insufficient wild-type support (West et al., 2006). The extent of lasR mutant ‘cheating’ depends on the cost-benefit difference, and multiple shared goods, including siderophores, must be considered (Ozkaya et al., 2018). To combat the rise of cheaters, P. aeruginosa produces products such as hydrogen cyanide, rhamnolipids, or pyocyanin that inhibit the growth of quorum sensing mutants through a process known as ‘policing’ (Castañeda-Tamez et al., 2018; García-Contreras et al., 2020; Wang et al., 2015). There is evidence that the presence of LasR^– subpopulations may be beneficial (García-Contreras and Loarca, 2020) and lead to emergent properties including metabolite-driven interactions between wild type and lasR mutants that provoke the production of QS-controlled factors by the lasR mutant to levels greater than in wild-type monocultures (Mould et al., 2020). In addition to the interactions between LasR⁺ and LasR^– cells that influence the fitness and behavior of LasR^– strains described above, there are important intrinsic characteristics of LasR^– strains including increased Anr-regulated microoxic fitness (Clay et al., 2020), resistance to alkaline pH in aerobic conditions (Heurlier et al., 2005), and altered metabolism (D’Argenio et al., 2007). The metabolic advantages associated with LasR^– strains include growth on individual amino acids (D’Argenio et al., 2007). The numerous differences described between LasR⁺ and LasR^– strains indicate that an understanding of the factors that drive the rise and persistence of lasR mutants may be complex and are not yet well understood. While it is clear that there are many ways in which lasR LOF mutants differ from their LasR +progenitors, a common trait that promotes the rise of LasR^– strains in diverse environments, even in rich and minimal laboratory media (Heurlier et al., 2005; Scribner et al., 2022; O Brien et al., 2017; Luján et al., 2007; Qi et al., 2016; Robitaille et al., 2020; Sandoz et al., 2007; Wong et al., 2012; Yan et al., 2018), has not been established.

Here, we use mathematical modeling, experimental evolution-based genetic screens, phenotype profiling, and whole-genome sequencing of evolved communities in different backgrounds to understand the rise of LasR^– strains over only a few serial passages. We identified the CbrAB pathway as the strongest contributor to the rise of lasR LOF mutants, and our findings were not specific to strain background or medium. LasR^– strains are more commonly detected in samples from individuals with more severe CF lung disease (Smith et al., 2006). Analysis of bronchoalveolar lavage samples from pwCF and non-CF comparators identified several compounds that were higher in pwCF and that inversely correlated with lung function. LasR^– strains showed improved growth on the majority of these compounds, many of which were amino acids, and epistasis analysis confirmed that the improved growth was due to altered activity of the CbrB-CrcZ-Crc pathway.

Through mathematical modeling, experimental evolution, and competition assays, we found that the rise of problematic P. aeruginosa LasR^– variants frequently observed in disease could be explained by increases in yield and decreases in lag during growth on carbon sources abundant in the lung environment (Figure 5). In fact, the steady state growth rate for ∆lasR was slightly less than that for the wild type, which is consistent with the model that there are frequently tradeoffs between a shorter lag phase and overall growth rate (Basan et al., 2020). Interestingly, CF-adapted P. aeruginosa isolates have been found to have slower in vitro growth rates than other strains (Yang et al., 2008). Other factors will impact the relative fitness of LasR⁺ and LasR^– cells across different growth phases (Figure 5) including oxygen availability and pH buffering capacity, which may lead to differential lysis (Heurlier et al., 2005), or the need for (or exploitation of) proteases to gain access to growth substrates (Van Delden et al., 1998; Sandoz et al., 2007).

Figure 5

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The overlap between the model-predicted and observed percentages of LasR^– strains over the course of the evolution regime suggests that the described social advantage resulting from QS dysfunction for LasR^– strains (i.e. social cheating) is not necessary to explain the timing and kinetics of the initial rise of LasR^– strains under our conditions. However, social interactions that benefit LasR- strains may be evident where the model estimates percentages that fall below or on the lower range of that experimentally observed, such as Day 4 or 6. A more detailed discussion of when social interactions are required is found below.

The data presented support the model that that increased growth of LasR^– cells on many amino acids, sugars, and lactate is due to higher CbrAB-controlled crcZ levels which downregulates metabolism under Crc control, and these findings nicely parallel studies by D’Argenio et al. (D’Argenio et al., 2007) that found higher levels of CbrB in LasR^– isolates. In PA14 ∆cbrA and ∆cbrB mutants, lasR LOF mutations did not arise, but mutations in crc and upstream of hfq were observed. As crc mutations phenocopy some of the growth advantages of the lasR mutants (Figures 2C and 3, Figure 3—figure supplement 1), the importance of derepressed catabolism for fitness is underscored. It is interesting to note that there were differences in the relative dependence on CbrB for the selection for LasR^– between strains PA14 and DH2417, and in ASM the dependence on cbrB for the selection of LasR^– strains increased in both strains (Figure 4D) suggesting that different environments may alter the importance of different LasR- and CbrB-controlled targets important for fitness that have yet to be elucidated. Though deletion of cbrA or cbrB can have pleiotropic effects (Yeung et al., 2011), we did not observe differences in density, quorum sensing regulation, production of quorum sensing controlled factors such as proteases, lysis in stationary phase, or overall mutation accumulation between wild type, ∆cbrA, and ∆cbrB that could explain differences in the rise of LasR^– subpopulations. Furthermore, environmental modification of CbrB activity by the addition of succinate to LB (Sonnleitner et al., 2009) also suppressed the emergence of LasR^– strains in the wild type. Because CbrAB activity can still be suppressed by succinate in LasR^– cells (Figure 2E), LasR^– variants were not strictly ‘de-repressed’, and this is consistent with the fact that ∆lasR and ∆crc growth patterns were not identical. Unlike lasR mutations, crc mutations are not commonly observed in clinical isolates (Winstanley et al., 2016) and crc mutants have been shown to be under negative selection in Tn-Seq experiments (Lorenz et al., 2019).

Analysis of BAL fluid revealed higher levels of substrates such as lactate and amino acids, which require CbrB for consumption, in samples from pwCF, and these findings are consistent with other more targeted analyses of CF airway samples (Bensel et al., 2011; Twomey et al., 2013). Consistent with our finding that higher levels of certain metabolites correlated with worse CF lung disease, other studies including that of Esther et al., 2016 found a correlation between total metabolites and neutrophil counts suggesting host cell lysis, along with lysis of microbial cells, may be a major contributor to a shift in the metabolome. CF-lung derived P. aeruginosa isolates can have amino acid auxotrophies and enhanced amino acid uptake (La Rosa et al., 2019) which supports ready access to amino acids in vivo. Several CF isolates show reduced succinate assimilation to suggest the uptake of less preferred substrates over the course of adaptation, which may indicate decreased Crc activity over time (Jørgensen et al., 2015; La Rosa et al., 2018).

Our model predicts LasR^– strains benefit from growth advantages that might be present when new nutrients become available (analogous to lag phase) and in dense populations when improved yields for the ∆lasR mutant emerges; due to a slower steady state growth rate, we predict that LasR^– strains would not emerge under steady state growth conditions such as in a chemostat. Indeed, the advantages of decreased lag phase in cultures has been proposed to be a universal adaptation in dynamic environments (Basan et al., 2020; Bertrand and Margolin, 2019). Thus, the frequent emergence of LasR^– lineages in the CF lung and other disease settings suggests that P. aeruginosa often undergoes growth transitions in vivo, possibly due to fluctuating local conditions, spatial heterogeneity, or the result of complex competition between bacterial and host cell types. The CbrB-dependent rise of LasR^– strains in the complex CF mimetic medium (i.e. artificial sputum medium, ASM) alongside the positive selection observed in minimal media with CF relevant substrates (Scribner et al., 2022) shown to require CbrB for LasR^– strain growth enhancement suggests that the growth advantages of lasR mutants may be sufficient to overcome any potential negative selective pressures mediated by the host, neighboring microbes, or inaccessibility to nutrients like complex protein or adenosine. In addition, the loss of LasR function enables other inherent advantages that contribute to competitive fitness including resistance to lysis under conditions of high aeration, enhanced microoxic fitness, enhanced RhlR activity (Chen et al., 2019; Clay et al., 2020; Heurlier et al., 2005), and altered intraspecies interactions (Mould et al., 2020) which may be relevant in the complex and dynamic nutritional environment of the CF airway over the course of disease. The connection between these phenotypes and the CbrAB-crcZ-Crc pathway is not yet clear.

The increased growth in post-exponential phase cultures for LasR^– strains bears similarities to mutations that arise in other microbes. For example, the selection for rpoS mutants in stationary phase cultures of E. coli (Finkel and Kolter, 1999; Zambrano et al., 1993; Zinser and Kolter, 2000) is also dependent on nutrient accessibility (Farrell and Finkel, 2003) with enhanced amino acid catabolism as a major contributor to E. coli lineages with growth advantages in stationary phase (GASP) (Zinser and Kolter, 1999). While the rise of rpoS mutants in laboratory settings required pH-driven lysis (Farrell and Finkel, 2003), LasR^– strains still evolved in buffered medium suggesting distinct mechanisms for the metabolic advantages of lasR mutants. It is worth noting that none of the common GASP mutations (rpoS, lrp, or ybeJ-gltJKL) were identified in our in vitro evolution studies (Supplementary file 1). We considered that the enhanced growth of LasR^– strains in post-exponential growth phases may be due to differences in ppGpp signaling, given growth arrest as part of the stringent response modifies the expression of QS-regulated genes (van Delden et al., 2001). However, no mutations in relA or spoT, the two ppGpp synthases, were observed. The mechanism of increased CbrB activity in ∆lasR remains an unresolved question that is relevant to P. aeruginosa biology and may aid in the identification of the signals that activate the CbrA sensor kinase which influences clinically relevant phenotypes including virulence and antibiotic resistance (Yeung et al., 2011). Our working model is that the upregulation of CbrB transcription of crcZ increases levels of transporters and catabolic enzymes due to the release from Crc repression, and this enhanced substrate uptake alters intracellular metabolite pools driving metabolism in accordance with Le Chatelier’s principle (Monod, 1949). Thus, quorum sensing mutants can maintain higher growth rates at lower substrate concentrations than for quorum-sensing intact cells.

The repeated observation that LOF mutations readily arise in diverse settings provokes the question of how quorum sensing is maintained. Several elegant mechanisms that address this point have been described. First, the wiring of the LasR regulon is such that while there are growth advantages on many substrates present in the lung, there are growth disadvantages on other important nutrient sources (e.g. adenosine and proteins and peptides Heurlier et al., 2005). Social cheating can promote the rise of LOF mutants in protease-requiring environments (Diggle et al., 2007; Hassett et al., 1999). Second, there are quorum-sensing controlled ‘policing’ mechanisms through which LasR⁺ strains restrict the growth of LasR^– types through the release of products toxic to quorum-sensing mutants (Castañeda-Tamez et al., 2018; García-Contreras et al., 2020; Wang et al., 2015). Lastly, there are other tradeoffs such as sensitivity to oxidative stress that may limit LasR^– lineage success (Hassett et al., 1999). Quorum sensing exerts metabolic control in other diverse microbes beyond P. aeruginosa. Thus, these data provide insight into generalizable explanations for the benefits of metabolic control in dense populations and indicate drivers for frequent loss-of-function mutations in quorum-sensing genes such as agr in Staphylococcus aureus and hapR in Vibrio cholerae (Mould and Hogan, 2021).

Together, these data highlight the power of coupling in vitro evolution studies with forward and reverse genetic analyses. Other benefits to this approach include the ability to dissect subtle differences between pathway components. For example, multiple mutations in crc repeatedly rose in ∆cbrA-, but not in ∆cbrB-derived populations, and multiple mutations in hfq rose in ∆cbrB-, and not in ∆cbrA-derived populations. While CbrA and B work together as do Crc and Hfq, these observations may provide a foothold into key distinctions that could yield mechanistic insights. In the future, the ability for deep sequencing of infection populations and analysis of evolutionary trajectories may aid diagnoses and treatment decisions in beneficial ways.

See Key Resources Table in supplement for additional details on key reagents.

All sequencing data is available on the Sequence Read Archive with accession number PRJNA786588 upon publication. All data generated or analyzed and all code used during this study are included in the manuscript or associated files.

1. 1. Mould DL

   (2021) NCBI Sequence Read Archive

   ID PRJNA786588. Pool Seq of Experimentally Evolved P. aeruginosa PA14 populations in LB.

   https://www.ncbi.nlm.nih.gov/sra/?term=PRJNA786588

1. 1. Abisado RG
   2. Kimbrough JH
   3. McKee BM
   4. Craddock VD
   5. Smalley NE
   6. Dandekar AA
   7. Chandler JR

   (2021) Tobramycin Adaptation Enhances Policing of Social Cheaters in Pseudomonas aeruginosa

   Applied and Environmental Microbiology 87:e0002921.

   https://doi.org/10.1128/AEM.00029-21

   • PubMed
   • Google Scholar
2. 1. Basan M
   2. Honda T
   3. Christodoulou D
   4. Hörl M
   5. Chang Y-F
   6. Leoncini E
   7. Mukherjee A
   8. Okano H
   9. Taylor BR
   10. Silverman JM
   11. Sanchez C
   12. Williamson JR
   13. Paulsson J
   14. Hwa T
   15. Sauer U

   (2020) A universal trade-off between growth and lag in fluctuating environments

   Nature 584:470–474.

   https://doi.org/10.1038/s41586-020-2505-4

   • PubMed
   • Google Scholar
3. 1. Bensel T
   2. Stotz M
   3. Borneff-Lipp M
   4. Wollschläger B
   5. Wienke A
   6. Taccetti G
   7. Campana S
   8. Meyer KC
   9. Jensen PØ
   10. Lechner U
   11. Ulrich M
   12. Döring G
   13. Worlitzsch D

   (2011) Lactate in cystic fibrosis sputum

   Journal of Cystic Fibrosis 10:37–44.

   https://doi.org/10.1016/j.jcf.2010.09.004

   • PubMed
   • Google Scholar
4. 1. Bertrand RL
   2. Margolin W

   (2019) Lag Phase Is a Dynamic, Organized, Adaptive, and Evolvable Period That Prepares Bacteria for Cell Division

   Journal of Bacteriology 201:e00618–e00697.

   https://doi.org/10.1128/JB.00697-18

   • PubMed
   • Google Scholar
5. 1. Boyle KE
   2. Monaco HT
   3. Deforet M
   4. Yan J
   5. Wang Z
   6. Rhee K
   7. Xavier JB

   (2017) Metabolism and the Evolution of Social Behavior

   Molecular Biology and Evolution 34:2367–2379.

   https://doi.org/10.1093/molbev/msx174

   • PubMed
   • Google Scholar
6. 1. Castañeda-Tamez P
   2. Ramírez-Peris J
   3. Pérez-Velázquez J
   4. Kuttler C
   5. Jalalimanesh A
   6. Saucedo-Mora MÁ
   7. Jiménez-Cortés JG
   8. Maeda T
   9. González Y
   10. Tomás M
   11. Wood TK
   12. García-Contreras R

   (2018) Pyocyanin Restricts Social Cheating in Pseudomonas aeruginosa

   Frontiers in Microbiology 9:1348.

   https://doi.org/10.3389/fmicb.2018.01348

   • PubMed
   • Google Scholar
7. 1. Chen R
   2. Déziel E
   3. Groleau MC
   4. Schaefer AL
   5. Greenberg EP

   (2019) Social cheating in a Pseudomonas aeruginosa quorum-sensing variant

   PNAS 116:7021–7026.

   https://doi.org/10.1073/pnas.1819801116

   • PubMed
   • Google Scholar
8. 1. Clay ME
   2. Hammond JH
   3. Zhong F
   4. Chen X
   5. Kowalski CH
   6. Lee AJ
   7. Porter MS
   8. Hampton TH
   9. Greene CS
   10. Pletneva EV
   11. Hogan DA

   (2020) Pseudomonas aeruginosa lasR mutant fitness in microoxia is supported by an Anr-regulated oxygen-binding hemerythrin

   PNAS 117:3167–3173.

   https://doi.org/10.1073/pnas.1917576117

   • PubMed
   • Google Scholar
9. 1. Collier DN
   2. Spence C
   3. Cox MJ
   4. Phibbs PV

   (2001) Isolation and phenotypic characterization of Pseudomonas aeruginosa pseudorevertants containing suppressors of the catabolite repression control-defective crc-10 allele

   FEMS Microbiology Letters 196:87–92.

   https://doi.org/10.1111/j.1574-6968.2001.tb10546.x

   • PubMed
   • Google Scholar
10. 1. Crocker AW
    2. Harty CE
    3. Hammond JH
    4. Willger SD
    5. Salazar P
    6. Botelho NJ
    7. Jacobs NJ
    8. Hogan DA

    (2019) Pseudomonas aeruginosa Ethanol Oxidation by AdhA in Low-Oxygen Environments

    Journal of Bacteriology 201:23.

    https://doi.org/10.1128/JB.00393-19

    • PubMed
    • Google Scholar
11. 1. Cui Y
    2. Chen X
    3. Luo H
    4. Fan Z
    5. Luo J
    6. He S
    7. Yue H
    8. Zhang P
    9. Chen R

    (2016) BioCircos.js: an interactive Circos JavaScript library for biological data visualization on web applications

    Bioinformatics (Oxford, England) 32:1740–1742.

    https://doi.org/10.1093/bioinformatics/btw041

    • PubMed
    • Google Scholar
12. 1. Deatherage DE
    2. Barrick JE

    (2014) Identification of mutations in laboratory-evolved microbes from next-generation sequencing data using breseq

    Methods in Molecular Biology (Clifton, N.J.) 1151:165–188.

    https://doi.org/10.1007/978-1-4939-0554-6_12

    • PubMed
    • Google Scholar
13. 1. Dettman JR
    2. Sztepanacz JL
    3. Kassen R

    (2016) The properties of spontaneous mutations in the opportunistic pathogen Pseudomonas aeruginosa

    BMC Genomics 17:27.

    https://doi.org/10.1186/s12864-015-2244-3

    • PubMed
    • Google Scholar
14. 1. Diggle SP
    2. Griffin AS
    3. Campbell GS
    4. West SA

    (2007) Cooperation and conflict in quorum-sensing bacterial populations

    Nature 450:411–414.

    https://doi.org/10.1038/nature06279

    • PubMed
    • Google Scholar
15. 1. D’Argenio DA
    2. Wu M
    3. Hoffman LR
    4. Kulasekara HD
    5. Déziel E
    6. Smith EE
    7. Nguyen H
    8. Ernst RK
    9. Larson Freeman TJ
    10. Spencer DH
    11. Brittnacher M
    12. Hayden HS
    13. Selgrade S
    14. Klausen M
    15. Goodlett DR
    16. Burns JL
    17. Ramsey BW
    18. Miller SI

    (2007) Growth phenotypes of Pseudomonas aeruginosa lasR mutants adapted to the airways of cystic fibrosis patients

    Molecular Microbiology 64:512–533.

    https://doi.org/10.1111/j.1365-2958.2007.05678.x

    • PubMed
    • Google Scholar
16. 1. Esther CR
    2. Alexis NE
    3. Clas ML
    4. Lazarowski ER
    5. Donaldson SH
    6. Ribeiro CMP
    7. Moore CG
    8. Davis SD
    9. Boucher RC

    (2008) Extracellular purines are biomarkers of neutrophilic airway inflammation

    The European Respiratory Journal 31:949–956.

    https://doi.org/10.1183/09031936.00089807

    • PubMed
    • Google Scholar
17. 1. Esther CR
    2. Turkovic L
    3. Rosenow T
    4. Muhlebach MS
    5. Boucher RC
    6. Ranganathan S
    7. Stick SM
    8. AREST CF

    (2016) Metabolomic biomarkers predictive of early structural lung disease in cystic fibrosis

    The European Respiratory Journal 48:1612–1621.

    https://doi.org/10.1183/13993003.00524-2016

    • PubMed
    • Google Scholar
18. 1. Farrell MJ
    2. Finkel SE

    (2003) The growth advantage in stationary-phase phenotype conferred by rpoS mutations is dependent on the pH and nutrient environment

    Journal of Bacteriology 185:7044–7052.

    https://doi.org/10.1128/JB.185.24.7044-7052.2003

    • PubMed
    • Google Scholar
19. 1. Feltner JB
    2. Wolter DJ
    3. Pope CE
    4. Groleau MC
    5. Smalley NE
    6. Greenberg EP
    7. Mayer-Hamblett N
    8. Burns J
    9. Déziel E
    10. Hoffman LR
    11. Dandekar AA

    (2016) LasR Variant Cystic Fibrosis Isolates Reveal an Adaptable Quorum-Sensing Hierarchy in Pseudomonas aeruginosa

    MBio 7:e01516.

    https://doi.org/10.1128/mBio.01513-16

    • PubMed
    • Google Scholar
20. 1. Finkel SE
    2. Kolter R

    (1999) Evolution of microbial diversity during prolonged starvation

    PNAS 96:4023–4027.

    https://doi.org/10.1073/pnas.96.7.4023

    • PubMed
    • Google Scholar
21. 1. Fitzpatrick AM
    2. Park Y
    3. Brown LAS
    4. Jones DP

    (2014) Children with severe asthma have unique oxidative stress-associated metabolomic profiles

    The Journal of Allergy and Clinical Immunology 133:258–261.

    https://doi.org/10.1016/j.jaci.2013.10.012

    • PubMed
    • Google Scholar
22. 1. García-Contreras R
    2. Loarca D

    (2020) The bright side of social cheaters: potential beneficial roles of “social cheaters” in microbial communities

    FEMS Microbiology Ecology 97:fiaa239.

    https://doi.org/10.1093/femsec/fiaa239

    • PubMed
    • Google Scholar
23. 1. García-Contreras R
    2. Loarca D
    3. Pérez-González C
    4. Jiménez-Cortés JG
    5. Gonzalez-Valdez A
    6. Soberón-Chávez G

    (2020) Rhamnolipids stabilize quorum sensing mediated cooperation in Pseudomonas aeruginosa

    FEMS Microbiology Letters 367:fnaa080.

    https://doi.org/10.1093/femsle/fnaa080

    • PubMed
    • Google Scholar
24. 1. Groleau MC
    2. Taillefer H
    3. Vincent AT
    4. Constant P
    5. Déziel E

    (2022) Pseudomonas aeruginosa isolates defective in function of the LasR quorum sensing regulator are frequent in diverse environmental niches

    Environmental Microbiology 24:1062–1075.

    https://doi.org/10.1111/1462-2920.15745

    • PubMed
    • Google Scholar
25. 1. Hammond JH
    2. Hebert WP
    3. Naimie A
    4. Ray K
    5. Van Gelder RD
    6. DiGiandomenico A
    7. Lalitha P
    8. Srinivasan M
    9. Acharya NR
    10. Lietman T
    11. Hogan DA
    12. Zegans ME

    (2016) Environmentally Endemic Pseudomonas aeruginosa Strains with Mutations in lasR Are Associated with Increased Disease Severity in Corneal Ulcers

    MSphere 1:e00140–e00116.

    https://doi.org/10.1128/mSphere.00140-16

    • PubMed
    • Google Scholar
26. 1. Harty CE
    2. Martins D
    3. Doing G
    4. Mould DL
    5. Clay ME
    6. Occhipinti P
    7. Nguyen D
    8. Hogan DA

    (2019) Ethanol Stimulates Trehalose Production through a SpoT-DksA-AlgU-Dependent Pathway in Pseudomonas aeruginosa

    Journal of Bacteriology 201:e00794–e00718.

    https://doi.org/10.1128/JB.00794-18

    • PubMed
    • Google Scholar
27. 1. Hassett DJ
    2. Ma JF
    3. Elkins JG
    4. McDermott TR
    5. Ochsner UA
    6. West SE
    7. Huang CT
    8. Fredericks J
    9. Burnett S
    10. Stewart PS
    11. McFeters G
    12. Passador L
    13. Iglewski BH

    (1999) Quorum sensing in Pseudomonas aeruginosa controls expression of catalase and superoxide dismutase genes and mediates biofilm susceptibility to hydrogen peroxide

    Molecular Microbiology 34:1082–1093.

    https://doi.org/10.1046/j.1365-2958.1999.01672.x

    • PubMed
    • Google Scholar
28. 1. Heurlier K
    2. Dénervaud V
    3. Haenni M
    4. Guy L
    5. Krishnapillai V
    6. Haas D

    (2005) Quorum-sensing-negative (lasR) mutants of Pseudomonas aeruginosa avoid cell lysis and death

    Journal of Bacteriology 187:4875–4883.

    https://doi.org/10.1128/JB.187.14.4875-4883.2005

    • PubMed
    • Google Scholar
29. 1. Hoffman LR
    2. Kulasekara HD
    3. Emerson J
    4. Houston LS
    5. Burns JL
    6. Ramsey BW
    7. Miller SI

    (2009) Pseudomonas aeruginosa lasR mutants are associated with cystic fibrosis lung disease progression

    Journal of Cystic Fibrosis 8:66–70.

    https://doi.org/10.1016/j.jcf.2008.09.006

    • PubMed
    • Google Scholar
30. 1. Jørgensen KM
    2. Wassermann T
    3. Johansen HK
    4. Christiansen LE
    5. Molin S
    6. Høiby N
    7. Ciofu Oana

    (2015) Diversity of metabolic profiles of cystic fibrosis Pseudomonas aeruginosa during the early stages of lung infection

    Microbiology (Reading, England) 161:1447–1462.

    https://doi.org/10.1099/mic.0.000093

    • PubMed
    • Google Scholar
31. 1. La Rosa R
    2. Johansen HK
    3. Molin S

    (2018) Convergent Metabolic Specialization through Distinct Evolutionary Paths in Pseudomonas aeruginosa

    MBio 9:e00269.

    https://doi.org/10.1128/mBio.00269-18

    • PubMed
    • Google Scholar
32. 1. La Rosa R
    2. Johansen HK
    3. Molin S

    (2019) Adapting to the Airways: Metabolic Requirements of Pseudomonas aeruginosa during the Infection of Cystic Fibrosis Patients

    Metabolites 9:E234.

    https://doi.org/10.3390/metabo9100234

    • PubMed
    • Google Scholar
33. 1. LaFayette SL
    2. Houle D
    3. Beaudoin T
    4. Wojewodka G
    5. Radzioch D
    6. Hoffman LR
    7. Burns JL
    8. Dandekar AA
    9. Smalley NE
    10. Chandler JR
    11. Zlosnik JE
    12. Speert DP
    13. Bernier J
    14. Matouk E
    15. Brochiero E
    16. Rousseau S
    17. Nguyen D

    (2015) Cystic fibrosis-adapted Pseudomonas aeruginosa quorum sensing lasR mutants cause hyperinflammatory responses

    Science Advances 1:e1500199.

    https://doi.org/10.1126/sciadv.1500199

    • PubMed
    • Google Scholar
34. 1. Lorenz A
    2. Preuße M
    3. Bruchmann S
    4. Pawar V
    5. Grahl N
    6. Pils MC
    7. Nolan LM
    8. Filloux A
    9. Weiss S
    10. Häussler S

    (2019) Importance of flagella in acute and chronic Pseudomonas aeruginosa infections

    Environmental Microbiology 21:883–897.

    https://doi.org/10.1111/1462-2920.14468

    • PubMed
    • Google Scholar
35. Website

    1. Lüdecke D

    (2021) sjPlot: Data Visualization for Statistics in Social Science (Version R package version 2.8.10)

    SjPlot. Accessed November 26, 2021.

    https://CRAN.R-project.org/package=sjPlot
36. 1. Luján AM
    2. Moyano AJ
    3. Segura I
    4. Argaraña CE
    5. Smania AM

    (2007) Quorum-sensing-deficient (lasR) mutants emerge at high frequency from a Pseudomonas aeruginosa mutS strain

    Microbiology (Reading, England) 153:225–237.

    https://doi.org/10.1099/mic.0.29021-0

    • PubMed
    • Google Scholar
37. 1. McCready AR
    2. Paczkowski JE
    3. Henke BR
    4. Bassler BL

    (2019) Structural determinants driving homoserine lactone ligand selection in the Pseudomonas aeruginosa LasR quorum-sensing receptor

    PNAS 116:245–254.

    https://doi.org/10.1073/pnas.1817239116

    • PubMed
    • Google Scholar
38. 1. Monod J

    (1949) THE GROWTH OF BACTERIAL CULTURES

    Annual Review of Microbiology 3:371–394.

    https://doi.org/10.1146/annurev.mi.03.100149.002103

    • Google Scholar
39. 1. Mould DL
    2. Botelho NJ
    3. Hogan DA

    (2020) Intraspecies Signaling between Common Variants of Pseudomonas aeruginosa Increases Production of Quorum-Sensing-Controlled Virulence Factors

    MBio 11:e01865-20.

    https://doi.org/10.1128/mBio.01865-20

    • PubMed
    • Google Scholar
40. 1. Mould DL
    2. Hogan DA

    (2021) Intraspecies heterogeneity in microbial interactions

    Current Opinion in Microbiology 62:14–20.

    https://doi.org/10.1016/j.mib.2021.04.003

    • PubMed
    • Google Scholar
41. 1. Neidhardt FC
    2. Bloch PL
    3. Smith DF

    (1974) Culture medium for enterobacteria

    Journal of Bacteriology 119:736–747.

    https://doi.org/10.1128/jb.119.3.736-747.1974

    • PubMed
    • Google Scholar
42. 1. O Brien S
    2. Lujan AM
    3. Paterson S
    4. Cant MA
    5. Buckling A

    (2017) Adaptation to public goods cheats in Pseudomonas aeruginosa

    Proceedings. Biological Sciences 284:20171089.

    https://doi.org/10.1098/rspb.2017.1089

    • PubMed
    • Google Scholar
43. 1. Ozkaya O
    2. Balbontín R
    3. Gordo I
    4. Xavier KB

    (2018) Cheating on Cheaters Stabilizes Cooperation in Pseudomonas aeruginosa

    Current Biology 28:2070–2080.

    https://doi.org/10.1016/j.cub.2018.04.093

    • PubMed
    • Google Scholar
44. 1. O’Connor K
    2. Zhao CY
    3. Diggle SP

    (2021) Frequency of Quorum Sensing Mutations in Pseudomonas aeruginosa Strains Isolated from Different Environments

    Microbiology 1:365.

    https://doi.org/10.1101/2021.02.22.432365

    • Google Scholar
45. 1. O’Toole GA
    2. Gibbs KA
    3. Hager PW
    4. Phibbs PV
    5. Kolter R

    (2000) The global carbon metabolism regulator Crc is a component of a signal transduction pathway required for biofilm development by Pseudomonas aeruginosa

    Journal of Bacteriology 182:425–431.

    https://doi.org/10.1128/JB.182.2.425-431.2000

    • PubMed
    • Google Scholar
46. 1. Palmer KL
    2. Mashburn LM
    3. Singh PK
    4. Whiteley M

    (2005) Cystic fibrosis sputum supports growth and cues key aspects of Pseudomonas aeruginosa physiology

    Journal of Bacteriology 187:5267–5277.

    https://doi.org/10.1128/JB.187.15.5267-5277.2005

    • PubMed
    • Google Scholar
47. 1. Qi Q
    2. Toll-Riera M
    3. Heilbron K
    4. Preston GM
    5. MacLean RC

    (2016) The genomic basis of adaptation to the fitness cost of rifampicin resistance in Pseudomonas aeruginosa

    Proceedings. Biological Sciences 283:1822.

    https://doi.org/10.1098/rspb.2015.2452

    • PubMed
    • Google Scholar
48. Software

    1. R Development Core Team

    (2021) R: A language and environment for statistical computing, version 2.6.2

    R Foundation for Statistical Computing, Vienna, Austria.

    https://www.R-project.org/
49. 1. Robitaille S
    2. Groleau MC
    3. Déziel E

    (2020) Swarming motility growth favours the emergence of a subpopulation of Pseudomonas aeruginosa quorum-sensing mutants

    Environmental Microbiology 22:2892–2906.

    https://doi.org/10.1111/1462-2920.15042

    • PubMed
    • Google Scholar
50. 1. Rodrigue A
    2. Quentin Y
    3. Lazdunski A
    4. Méjean V
    5. Foglino M

    (2000) Two-component systems in Pseudomonas aeruginosa: why so many?

    Trends in Microbiology 8:498–504.

    https://doi.org/10.1016/s0966-842x(00)01833-3

    • PubMed
    • Google Scholar
51. 1. Rumbaugh KP
    2. Diggle SP
    3. Watters CM
    4. Ross-Gillespie A
    5. Griffin AS
    6. West SA

    (2009) Quorum sensing and the social evolution of bacterial virulence

    Current Biology 19:341–345.

    https://doi.org/10.1016/j.cub.2009.01.050

    • PubMed
    • Google Scholar
52. 1. Sandoz KM
    2. Mitzimberg SM
    3. Schuster M

    (2007) Social cheating in Pseudomonas aeruginosa quorum sensing

    PNAS 104:15876–15881.

    https://doi.org/10.1073/pnas.0705653104

    • PubMed
    • Google Scholar
53. 1. Schuster M
    2. Lostroh CP
    3. Ogi T
    4. Greenberg EP

    (2003) Identification, timing, and signal specificity of Pseudomonas aeruginosa quorum-controlled genes: a transcriptome analysis

    Journal of Bacteriology 185:2066–2079.

    https://doi.org/10.1128/JB.185.7.2066-2079.2003

    • PubMed
    • Google Scholar
54. 1. Schuster M
    2. Greenberg EP

    (2007) Early activation of quorum sensing in Pseudomonas aeruginosa reveals the architecture of a complex regulon

    BMC Genomics 8:287.

    https://doi.org/10.1186/1471-2164-8-287

    • PubMed
    • Google Scholar
55. 1. Scribner MR
    2. Stephens AC
    3. Huong JL
    4. Richardson AR
    5. Cooper VS

    (2022) The Nutritional Environment Is Sufficient To Select Coexisting Biofilm and Quorum Sensing Mutants of Pseudomonas aeruginosa

    Journal of Bacteriology 204:e0044421.

    https://doi.org/10.1128/JB.00444-21

    • PubMed
    • Google Scholar
56. 1. Shanks RMQ
    2. Caiazza NC
    3. Hinsa SM
    4. Toutain CM
    5. O’Toole GA

    (2006) Saccharomyces cerevisiae -Based Molecular Tool Kit for Manipulation of Genes from Gram-Negative Bacteria

    Applied and Environmental Microbiology 72:5027–5036.

    https://doi.org/10.1128/AEM.00682-06

    • PubMed
    • Google Scholar
57. 1. Smith EE
    2. Buckley DG
    3. Wu Z
    4. Saenphimmachak C
    5. Hoffman LR
    6. D’Argenio DA
    7. Miller SI
    8. Ramsey BW
    9. Speert DP
    10. Moskowitz SM
    11. Burns JL
    12. Kaul R
    13. Olson MV

    (2006) Genetic adaptation by Pseudomonas aeruginosa to the airways of cystic fibrosis patients

    PNAS 103:8487–8492.

    https://doi.org/10.1073/pnas.0602138103

    • PubMed
    • Google Scholar
58. 1. Sonnleitner E
    2. Abdou L
    3. Haas D

    (2009) Small RNA as global regulator of carbon catabolite repression in Pseudomonas aeruginosa

    PNAS 106:21866–21871.

    https://doi.org/10.1073/pnas.pnas.0910308106

    • PubMed
    • Google Scholar
59. 1. Sonnleitner E
    2. Prindl K
    3. Bläsi U

    (2017) The Pseudomonas aeruginosa CrcZ RNA interferes with Hfq-mediated riboregulation

    PLOS ONE 12:e0180887.

    https://doi.org/10.1371/journal.pone.0180887

    • PubMed
    • Google Scholar
60. 1. Tang Y
    2. Horikoshi M
    3. Li W

    (2016) ggfortify: Unified Interface to Visualize Statistical Results of Popular R Packages

    The R Journal 8:474.

    https://doi.org/10.32614/RJ-2016-060

    • Google Scholar
61. 1. Twomey KB
    2. Alston M
    3. An S-Q
    4. O’Connell OJ
    5. McCarthy Y
    6. Swarbreck D
    7. Febrer M
    8. Dow JM
    9. Plant BJ
    10. Ryan RP

    (2013) Microbiota and metabolite profiling reveal specific alterations in bacterial community structure and environment in the cystic fibrosis airway during exacerbation

    PLOS ONE 8:e82432.

    https://doi.org/10.1371/journal.pone.0082432

    • PubMed
    • Google Scholar
62. 1. Van Delden C
    2. Pesci EC
    3. Pearson JP
    4. Iglewski BH

    (1998) Starvation selection restores elastase and rhamnolipid production in a Pseudomonas aeruginosa quorum-sensing mutant

    Infection and Immunity 66:4499–4502.

    https://doi.org/10.1128/IAI.66.9.4499-4502.1998

    • PubMed
    • Google Scholar
63. 1. van Delden C
    2. Comte R
    3. Bally AM

    (2001) Stringent response activates quorum sensing and modulates cell density-dependent gene expression in Pseudomonas aeruginosa

    Journal of Bacteriology 183:5376–5384.

    https://doi.org/10.1128/JB.183.18.5376-5384.2001

    • PubMed
    • Google Scholar
64. 1. Wang M
    2. Schaefer AL
    3. Dandekar AA
    4. Greenberg Ep

    (2015) Quorum sensing and policing of Pseudomonas aeruginosa social cheaters

    PNAS 112:2187–2191.

    https://doi.org/10.1073/pnas.1500704112

    • PubMed
    • Google Scholar
65. 1. Wang BX
    2. Cady KC
    3. Oyarce GC
    4. Ribbeck K
    5. Laub MT

    (2021) Two-Component Signaling Systems Regulate Diverse Virulence-Associated Traits in Pseudomonas aeruginosa

    Applied and Environmental Microbiology 87:11.

    https://doi.org/10.1128/AEM.03089-20

    • PubMed
    • Google Scholar
66. 1. West SA
    2. Griffin AS
    3. Gardner A
    4. Diggle SP

    (2006) Social evolution theory for microorganisms

    Nature Reviews. Microbiology 4:597–607.

    https://doi.org/10.1038/nrmicro1461

    • PubMed
    • Google Scholar
67. 1. Whiteley M
    2. Lee KM
    3. Greenberg EP

    (1999) Identification of genes controlled by quorum sensing in Pseudomonas aeruginosa

    PNAS 96:13904–13909.

    https://doi.org/10.1073/pnas.96.24.13904

    • PubMed
    • Google Scholar
68. 1. Whiteley M
    2. Greenberg EP

    (2001) Promoter specificity elements in Pseudomonas aeruginosa quorum-sensing-controlled genes

    Journal of Bacteriology 183:5529–5534.

    https://doi.org/10.1128/JB.183.19.5529-5534.2001

    • PubMed
    • Google Scholar
69. Book

    1. Wickham H

    (2016) Ggplot2: Elegent Graphics for Data Analysis

    Cham: Springer-Verlag.

    https://doi.org/10.1007/978-3-319-24277-4

    • Google Scholar
70. 1. Winstanley C
    2. O’Brien S
    3. Brockhurst MA

    (2016) Pseudomonas aeruginosa Evolutionary Adaptation and Diversification in Cystic Fibrosis Chronic Lung Infections

    Trends in Microbiology 24:327–337.

    https://doi.org/10.1016/j.tim.2016.01.008

    • PubMed
    • Google Scholar
71. 1. Wong A
    2. Rodrigue N
    3. Kassen R

    (2012) Genomics of adaptation during experimental evolution of the opportunistic pathogen Pseudomonas aeruginosa

    PLOS Genetics 8:e1002928.

    https://doi.org/10.1371/journal.pgen.1002928

    • PubMed
    • Google Scholar
72. 1. Xia Y
    2. Wang D
    3. Pan X
    4. Xia B
    5. Weng Y
    6. Long Y
    7. Ren H
    8. Zhou J
    9. Jin Y
    10. Bai F
    11. Cheng Z
    12. Jin S
    13. Wu W

    (2020) TpiA is a Key Metabolic Enzyme That Affects Virulence and Resistance to Aminoglycoside Antibiotics through CrcZ in Pseudomonas aeruginosa

    MBio 11:e02079.

    https://doi.org/10.1128/mBio.02079-19

    • PubMed
    • Google Scholar
73. 1. Yan H
    2. Wang M
    3. Sun F
    4. Dandekar AA
    5. Shen D
    6. Li Na

    (2018) A Metabolic Trade-Off Modulates Policing of Social Cheaters in Populations of Pseudomonas aeruginosa

    Frontiers in Microbiology 9:337.

    https://doi.org/10.3389/fmicb.2018.00337

    • PubMed
    • Google Scholar
74. 1. Yang L
    2. Haagensen JAJ
    3. Jelsbak L
    4. Johansen HK
    5. Sternberg C
    6. Høiby N
    7. Molin S

    (2008) In situ growth rates and biofilm development of Pseudomonas aeruginosa populations in chronic lung infections

    Journal of Bacteriology 190:2767–2776.

    https://doi.org/10.1128/JB.01581-07

    • PubMed
    • Google Scholar
75. 1. Yeung ATY
    2. Bains M
    3. Hancock REW

    (2011) The sensor kinase CbrA is a global regulator that modulates metabolism, virulence, and antibiotic resistance in Pseudomonas aeruginosa

    Journal of Bacteriology 193:918–931.

    https://doi.org/10.1128/JB.00911-10

    • PubMed
    • Google Scholar
76. 1. Zambrano MM
    2. Siegele DA
    3. Almirón M
    4. Tormo A
    5. Kolter R

    (1993) Microbial competition: Escherichia coli mutants that take over stationary phase cultures

    Science (New York, N.Y.) 259:1757–1760.

    https://doi.org/10.1126/science.7681219

    • PubMed
    • Google Scholar
77. 1. Zinser ER
    2. Kolter R

    (1999) Mutations enhancing amino acid catabolism confer a growth advantage in stationary phase

    Journal of Bacteriology 181:5800–5807.

    https://doi.org/10.1128/JB.181.18.5800-5807.1999

    • PubMed
    • Google Scholar
78. 1. Zinser ER
    2. Kolter R

    (2000) Prolonged stationary-phase incubation selects for lrp mutations in Escherichia coli K-12

    Journal of Bacteriology 182:4361–4365.

    https://doi.org/10.1128/JB.182.15.4361-4365.2000

    • PubMed
    • Google Scholar

Decision letter after peer review:

Thank you for submitting your article "Metabolic basis for the evolution of a common pathogenic Pseudomonas aeruginosa variant" for consideration by eLife. Your article has been reviewed by 3 peer reviewers, one of whom is a member of our Board of Reviewing Editors, and the evaluation has been overseen by Gisela Storz as the Senior Editor. The following individual involved in review of your submission has agreed to reveal their identity: Karin Kram (Reviewer #3).

The reviewers have discussed their reviews with one another, and the Reviewing Editor has drafted this to help you prepare a revised submission.

Essential revisions:

1) Please clarify the open questions of the complexity of the CF nutritional environment selecting for lasR mutants, which are also selected in multiple, simpler environments.

2) It's unclear whether the QS (dys-)function of lasR- is necessary to explain these results; mutants appear to be selected independent of a social interaction with producers. Please address.

3) Figure 1E: The authors state that the model predicted the extent to which LasR- outcompeted the WT, but the model appears to have predicted slightly higher percentages for the LasR- strain than observed in the experiment. Is this difference significant? Should this finding be interpreted as evidence that other phenotypes of LasR- mutants beyond metabolic adaptations contribute to some extent to their fitness?

4) Line 213/Figure 2A: Although the selection of LasR- mutants was delayed in the CF mutant (DH2417), LasR- phenotypes still consistently occurred and rose to high frequencies. This is a major point that suggests that CbrB may play a role but is not required for LasR- mutant fitness in certain genetic backgrounds. The wording in line 213 and conclusions throughout ought to carefully reflect this point.

5) Line 404: It is curious that crc mutations occur in a ∆cbrB background but are not frequently observed in vivo or in a WT background. While it is true that the effect of a ∆crc mutant is not identical the effect of a lasR mutation, the ∆crc mutant still appears to produce many similar phenotypes but to a different magnitude. While this pathway is required for LasR- mutant fitness, is it possible that phenotypes unrelated to this pathway also contribute to LasR- mutant fitness, enabling their selection?

6) It is not clear whether or why ∆anr or ∆rhlR strains are used to compare rates of LasR- mutations.

Reviewer #2 (Recommendations for the authors):

This is an exceptionally thorough and well-written manuscript. Comments for the authors are indicated below.

Figure 1E: The authors state that the model predicted the extent to which LasR- outcompeted the WT, but the model appears to have predicted slightly higher percentages for the LasR- strain than observed in the experiment. Is this difference significant? Should this finding be interpreted as evidence that other phenotypes of LasR- mutants beyond metabolic adaptations contribute to some extent to their fitness?

Line 213/Figure 2A: Although the selection of LasR- mutants was delayed in the CF mutant (DH2417), LasR- phenotypes still consistently occurred and rose to high frequencies. This is a major point that suggests that CbrB may play a role but is not required for LasR- mutant fitness in certain genetic backgrounds. The wording in line 213 and conclusions throughout ought to carefully reflect this point.

Figure 4: Do the authors have experimental data to model growth parameters of lasR mutants in their ASM medium? This would be interesting to compare with the LB data in Figure 1.

Line 404: It is curious that crc mutations occur in a ∆cbrB background but are not frequently observed in vivo or in a WT background. While it is true that the effect of a ∆crc mutant is not identical the effect of a lasR mutation, the ∆crc mutant still appears to produce many similar phenotypes but to a different magnitude. While this pathway is required for LasR- mutant fitness, is it possible that phenotypes unrelated to this pathway also contribute to LasR- mutant fitness, enabling their selection?

Line 548: Does read trimming refer to trimming of Illumina adapters, to other quality-control related trimming procedures, or both? Please briefly indicate the software and parameters used for this step.

Line 552: Was variant calling analysis performed using the -p option to call polymorphisms? If so, please add this detail to the example command.

Supplemental Figure 6B: Lines should not connect the data points in this figure as the x-axis is categorical.

Reviewer #3 (Recommendations for the authors):

I might mention quorum sensing/the function of LasR somewhere in the abstract, especially since that is the opening paragraph in the introduction.

Figure 1A: For the model, why OD600 instead of CFU/ml or cells?

Figure 1B: y-axis should just be % LasR phenotype (since it shows both + and -).

Figure 1E is not particularly clear nor do I think this experiment adds much, since you already show that LasR- mutants can outcompete even when starting as a very low percentage of the population.

Line 244: citation twice.

Line 289: maybe you could add something to the end of this sentence like, "retains the control of Crc-Hfq mediated regulation, allowing for Crc expression, even when crcZ is repressed." The way this sentence is written currently is a bit hard to decipher.

Line 294: Do you know nothing else increases crcZ expression?

Figure 4C: It is really difficult to determine which metabolites are important or significant (the way it is right now it looks like all are labeled as significant, but that can't be true). Maybe just show the ones that are significantly different here (or a subset), and the rest in a supplemental figure? You don't explain the pvalue in the legend, or the colors.

I am not sure the experiment in 4D is particularly additive, since the LasR- phenotype does not evolve faster or more dramatically in LB. It indicates that these can still evolve in ASM, but there is nothing specific about ASM. Also, it should be clear in the text that this is the same passaging scheme as was used previously.

Why do you think no gain of function mutations, or those that lead to increased expression, are found for cbrA/cbrB? Why there may be no loss-of-function crc mutations is briefly addressed.

Essential revisions:

1) Please clarify the open questions of the complexity of the CF nutritional environment selecting for lasR mutants, which are also selected in multiple, simpler environments.

We agree that this would be a useful addition to the text. We have added the following sentence to the Introduction.

“While it is clear that there are many ways in which LasR loss-of-function mutants differ from their LasR+ progenitors, a common trait that promotes the rise of LasR– strains in diverse environments, even in rich and minimal laboratory media (Heurlier et al., 2005; Scribner, Stephens, Huong, Richardson, and Cooper, 2022) (O'Brien, Luján, Paterson, Cant, and Buckling, 2017) (Lujan, Moyano, Segura, Argarana, and Smania, 2007; Qi, Toll-Riera, Heilbron, Preston, and MacLean, 2016; Robitaille, Groleau, and Déziel, 2020; Sandoz et al., 2007; Wong, Rodrigue, and Kassen, 2012; Yan et al., 2018), has not been established.”

We also added text to the Discussion that highlights how the complex CF lung environment may provide multiple positive and negative selective pressures.

“Unlike lasR mutations, crc mutations are not commonly observed in clinical isolates (Winstanley, O'Brien, and Brockhurst, 2016) and crc mutants have been shown to be under negative selection in Tn-Seq experiments (Lorenz et al., 2019).”

2) It's unclear whether the QS (dys-)function of lasR- is necessary to explain these results; mutants appear to be selected independent of a social interaction with producers. Please address.

Overall, our data suggest that LasR- selection is independent of social interaction and driven by growth differences under the conditions assessed in this paper. The concordance between the modeling described in Figure 1A, which does not include any terms for positive or negative interactions, and the actual data shown in Figure 1B is one of the major pieces of data in support of this conclusion. We have revised our description of the motivation for the model in the revised manuscript.

“We built a mathematical model of strain competition based exclusively on experimentally-determined mono-culture growth parameters to predict the relative changes in wild type and LasR– cell numbers when grown on a common pool of growth substrates to determine how differences in growth kinetics alone would impact the rise of LasR– lineages (Figure 1A).”

We have also revised the description of Figure 1E to indicate that it does not show density dependent fitness for LasR strains under our conditions, which suggests that “cheating” does not play a driving role for the improved growth of LasR- P. aeruginosa in co-culture.

“When the ∆lasR competitor was cultured with the tagged wild type for 6 h, the percentage of ∆lasR mutant cells in the total population increased regardless of the initial percentage of ∆lasR (1% to 85%) at the time of inoculation (Figure 1E). The model successfully predicted that ∆lasR would outcompete the wild type over this range which is consistent with differential growth kinetics playing a major role (Figure 1E-dotted line). No ∆lasR advantage would be observed when it is at high initial percentages if its advantage was solely due to exploitation of common goods, as is observed when WT and ∆lasR are co-cultured on a substrate that requires WT protease production (Sandoz et al., 2007). There were differences between the best fit lines for the actual and predicted data that could be due to a variety of factors including measurement error or biological interactions between WT and ∆lasR strains (e.g. policing Castañeda-Tamez et al., 2018; M. Wang et al., 2015).”

We have also added the following text to the manuscript discussion:

“The overlap between the model-predicted and observed percentages of LasR^– strains over the course of the evolution regime suggests that the described social advantage resulting from QS dysfunction for LasR^– strains (i.e. social cheating) is not necessary to explain the timing and kinetics of the initial rise of LasR^– strains under our conditions. However, social interactions that benefit LasR- strains may be evident where the model estimates percentages that fall below or on the lower range of that experimentally observed, such as Day 4 or 6. A more detailed discussion of when social interactions are required is found below.”

3) Figure 1E: The authors state that the model predicted the extent to which LasR- outcompeted the WT, but the model appears to have predicted slightly higher percentages for the LasR- strain than observed in the experiment. Is this difference significant? Should this finding be interpreted as evidence that other phenotypes of LasR- mutants beyond metabolic adaptations contribute to some extent to their fitness?

In the revised the description of Figure 1E, we discuss the fact that the model predicts higher percentages of LasR- strains than observed. We speculate that this could be due to decreased fitness of LasR- strains in the presence of LasR+ strains. This may relate to “policing” mechanisms by which wild type strains kill LasR- strains through a variety of secreted goods as a way of controlling the levels which LasR- strains reach. We have added text to this effect to the discussion.

4) Line 213/Figure 2A: Although the selection of LasR- mutants was delayed in the CF mutant (DH2417), LasR- phenotypes still consistently occurred and rose to high frequencies. This is a major point that suggests that CbrB may play a role but is not required for LasR- mutant fitness in certain genetic backgrounds. The wording in line 213 and conclusions throughout ought to carefully reflect this point.

We agree that in LB the loss of cbrB delayed and reduced the percentage of LasR- strains in the CF isolate, but that LasR- colonies still arose (Figure 2B), and that we did not accurately note this in the referenced sentence. We have corrected this.

In addition, we added the following text to the discussion:

“It is interesting to note that there were differences in the relative dependence on CbrB for the selection for LasR^– between strains PA14 and DH2417, and in ASM the dependence on cbrB for the selection of LasR^– strains increased in both strains (Figure 4D) suggesting that different environments may alter the importance of different LasR and CbrB controlled targets important for fitness that have yet to be elucidated.

5) Line 404: It is curious that crc mutations occur in a ∆cbrB background but are not frequently observed in vivo or in a WT background. While it is true that the effect of a ∆crc mutant is not identical the effect of a lasR mutation, the ∆crc mutant still appears to produce many similar phenotypes but to a different magnitude. While this pathway is required for LasR- mutant fitness, is it possible that phenotypes unrelated to this pathway also contribute to LasR- mutant fitness, enabling their selection?

We have modified text to address this interesting point as follows:

“Because CbrAB activity can still be suppressed by succinate in LasR^– cells (Figure 2E), LasR^– variants were not strictly “de-repressed”, and this is consistent with the fact that ∆lasR and ∆crc growth patterns were not identical. Unlike lasR mutations, crc mutations are not commonly observed in clinical isolates (Winstanley, O'Brien, and Brockhurst, 2016) and crc mutants have been shown to be under negative selection in Tn-Seq experiments (Lorenz et al., 2019).”

6) It is not clear whether or why ∆anr or ∆rhlR strains are used to compare rates of LasR- mutations.

We revised the text to clarify the motivation for these experiments as follows:

“LasR^– strains rose with a similar frequency as in the wild type progenitor when evolution experiments were initiated with strains lacking the regulator Anr, important for LasR^– microoxic fitness, or the regulator RhlR, important for lasR mutant policing (Chen, Déziel, Groleau, Schaefer, and Greenberg, 2019; Clay et al., 2020) suggesting that these regulators were not major contributors to fitness under these conditions (Figure 2 —figure supplement 2B).”

Reviewer #2 (Recommendations for the authors):

This is an exceptionally thorough and well-written manuscript. Comments for the authors are indicated below.

Lines 49 and 80: Inconsistencies in citation format are present in a few instances.

Thank you. We think that this refers to the fact that the eLife Endnote output style only lists one author when the author list is long.

Figure 1E: The authors state that the model predicted the extent to which LasR- outcompeted the WT, but the model appears to have predicted slightly higher percentages for the LasR- strain than observed in the experiment. Is this difference significant? Should this finding be interpreted as evidence that other phenotypes of LasR- mutants beyond metabolic adaptations contribute to some extent to their fitness?

This comment was included in essential comments and is addressed above.

Line 213/Figure 2A: Although the selection of LasR- mutants was delayed in the CF mutant (DH2417), LasR- phenotypes still consistently occurred and rose to high frequencies. This is a major point that suggests that CbrB may play a role but is not required for LasR- mutant fitness in certain genetic backgrounds. The wording in line 213 and conclusions throughout ought to carefully reflect this point.

This comment was included in essential comments and is addressed above.

Figure 2C: It is not clear what the arrows are pointing at.

To improve clarity, we have removed the arrows and added detail about the larger colony size in text and figure legend.

Figure 4: Do the authors have experimental data to model growth parameters of lasR mutants in their ASM medium? This would be interesting to compare with the LB data in Figure 1.

We agree that this would be interesting. Because ASM is not optically clear, we cannot measure cellular numbers using the same parameters/tools used for LB (OD600). The code included in this manuscript for the mathematical model (Source Code File 1) can be used by the community and the parameters altered for growth in other conditions.

Line 404: It is curious that crc mutations occur in a ∆cbrB background but are not frequently observed in vivo or in a WT background. While it is true that the effect of a ∆crc mutant is not identical the effect of a lasR mutation, the ∆crc mutant still appears to produce many similar phenotypes but to a different magnitude. While this pathway is required for LasR- mutant fitness, is it possible that phenotypes unrelated to this pathway also contribute to LasR- mutant fitness, enabling their selection?

This comment was included in essential comments and is addressed above.

Line 548: Does read trimming refer to trimming of Illumina adapters, to other quality-control related trimming procedures, or both? Please briefly indicate the software and parameters used for this step.

Read trimming was performed with bcl2fastq (v2.20.0422) to remove Illumina adaptor sequences during the demultiplexing process. This information has been added to the methods.

Line 552: Was variant calling analysis performed using the -p option to call polymorphisms? If so, please add this detail to the example command.

Variant calling analysis was performed using the -p option to call polymorphisms. We have added this detail to the example command.

Supplemental Figure 6B: Lines should not connect the data points in this figure as the x-axis is categorical.

Agreed. We have removed the line.

Reviewer #3 (Recommendations for the authors):

I might mention quorum sensing/the function of LasR somewhere in the abstract, especially since that is the opening paragraph in the introduction.

Thank you. We have added text on the function of LasR to the abstract.

Figure 1A: For the model, why OD600 instead of CFU/ml or cells?

Given that the model was built on data obtained as OD600, as a proxy for cell number, we maintained this as an output for the model. We note that OD600 correlates with cell number shown in panel B.

Figure 1B: y-axis should just be % LasR phenotype (since it shows both + and -).

Thank you. We have corrected the y-axis in Figure 1B and Figure 2F as well.

Figure 1B legend should explain the differences in the symbols (not just the text).

We have added this detail to the legend.

Figure 1E is not particularly clear nor do I think this experiment adds much, since you already show that LasR- mutants can outcompete even when starting as a very low percentage of the population.

We revised the text describing this figure. We feel that it is useful to compare to other studies that assess social interactions, which we discuss in more detail in this manuscript (see Essential Comment 3).

Line 289: maybe you could add something to the end of this sentence like, "retains the control of Crc-Hfq mediated regulation, allowing for Crc expression, even when crcZ is repressed." The way this sentence is written currently is a bit hard to decipher.

Thank you for this suggestion. We have altered the text and figure to highlight the point that lasR mutants respond to succinate while crc mutants do not.

Line 294: Do you know nothing else increases crcZ expression?

Because we can’t rule out players other than CbrAB in the control of crcZ, we have modified this sentence to say:

“CbrAB activity induces the expression of crcZ, which sequesters Crc. We found that the ∆lasR mutant had ~ two-fold higher crcZ levels compared to wild type, suggesting higher activity of the CbrAB two component system in LasR^– strains (Figure 3A).”

Figure 4C: It is really difficult to determine which metabolites are important or significant (the way it is right now it looks like all are labeled as significant, but that can't be true). Maybe just show the ones that are significantly different here (or a subset), and the rest in a supplemental figure? You don't explain the pvalue in the legend, or the colors.

In this figure we are not comparing individual carbon sources, but rather the relative density of WT and ∆lasR across those carbon sources present on BIOLOG PM-1 and PM-2 plates that were enriched in the CF lung. We have added additional details to the text, legend and figure for clarification. Thank you.

I am not sure the experiment in 4D is particularly additive, since the LasR- phenotype does not evolve faster or more dramatically in LB. It indicates that these can still evolve in ASM, but there is nothing specific about ASM. Also, it should be clear in the text that this is the same passaging scheme as was used previously.

We retained this figure and now discuss that while the kinetics for the increase in LasR- are similar in LB and ASM, it is interesting that the dependence on cbrB for LasR- lineage selection (particularly for strain 2417) is greater in ASM. We have added this observation to the discussion to highlight how environment may affect fitness as discussed above (see Essential Comment 4).

We have added text to indicate that the evolution scheme is similar.

Why do you think no gain of function mutations, or those that lead to increased expression, are found for cbrA/cbrB? Why there may be no loss-of-function crc mutations is briefly addressed.

There is one mutation found in an upstream region of CbrB, though the impact of this mutation on expression is currently unknown. It would be interesting if it were a gain of function in terms of expression.

We have expanded our discussion of the negative fitness of crc single mutants in the revised discussion, and believe that they only frequently rise to detectable levels in cbrB mutant populations.

Author details

1. Dallas L Mould

   Department of Microbiology and Immunology, Geisel School of Medicine at Dartmouth, Hanover, United States

   Contribution

   Conceptualization, Data curation, Formal analysis, Investigation, Methodology, Supervision, Validation, Visualization, Writing - original draft, Writing – review and editing

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0001-6939-1351

2. Mirjana Stevanovic

   Department of Microbiology and Immunology, Geisel School of Medicine at Dartmouth, Hanover, United States

   Contribution

   Formal analysis, Methodology, Writing – review and editing

   Competing interests

   No competing interests declared

3. Alix Ashare

   1. Department of Microbiology and Immunology, Geisel School of Medicine at Dartmouth, Hanover, United States
   2. Department of Medicine, Dartmouth-Hitchock Medical Center, Lebanon, United States

   Contribution

   Funding acquisition, Resources, Writing – review and editing

   Competing interests

   No competing interests declared

4. Daniel Schultz

   Department of Microbiology and Immunology, Geisel School of Medicine at Dartmouth, Hanover, United States

   Contribution

   Conceptualization, Formal analysis, Funding acquisition, Methodology, Supervision, Visualization, Writing – review and editing

   Competing interests

   No competing interests declared

5. Deborah A Hogan

   Department of Microbiology and Immunology, Geisel School of Medicine at Dartmouth, Hanover, United States

   Contribution

   Conceptualization, Formal analysis, Funding acquisition, Project administration, Supervision, Validation, Writing – review and editing

   For correspondence

   dhogan@dartmouth.edu

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0002-6366-2971

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