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Pseudomonad reverse carbon catabolite repression, interspecies metabolite exchange, and consortial division of labor

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Abstract

Microorganisms acquire energy and nutrients from dynamic environments, where substrates vary in both type and abundance. The regulatory system responsible for prioritizing preferred substrates is known as carbon catabolite repression (CCR). Two broad classes of CCR have been documented in the literature. The best described CCR strategy, referred to here as classic CCR (cCCR), has been experimentally and theoretically studied using model organisms such as Escherichia coli. cCCR phenotypes are often used to generalize universal strategies for fitness, sometimes incorrectly. For instance, extremely competitive microorganisms, such as Pseudomonads, which arguably have broader global distributions than E. coli, have achieved their success using metabolic strategies that are nearly opposite of cCCR. These organisms utilize a CCR strategy termed ‘reverse CCR’ (rCCR), because the order of preferred substrates is nearly reverse that of cCCR. rCCR phenotypes prefer organic acids over glucose, may or may not select preferred substrates to optimize growth rates, and do not allocate intracellular resources in a manner that produces an overflow metabolism. cCCR and rCCR have traditionally been interpreted from the perspective of monocultures, even though most microorganisms live in consortia. Here, we review the basic tenets of the two CCR strategies and consider these phenotypes from the perspective of resource acquisition in consortia, a scenario that surely influenced the evolution of cCCR and rCCR. For instance, cCCR and rCCR metabolism are near mirror images of each other; when considered from a consortium basis, the complementary properties of the two strategies can mitigate direct competition for energy and nutrients and instead establish cooperative division of labor.

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References

  1. Law R (1979) Optimal life histories under age-specific predation. Am Nat 114(3):399–417. https://doi.org/10.1086/283488

    Article  Google Scholar 

  2. Carlson RP, Taffs RL (2010) Molecular-level tradeoffs and metabolic adaptation to simultaneous stressors. Curr Opin Biotechnol 21(5):670–676

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  3. Shoval AO, Sheftel H, Shinar G, Hart Y, Ramote O, Mayo A, Dekel E, Alon U, Science S, Series N, June N (2012) Evolutionary trade-offs, pareto optimality, and the geometry of phenotype space. Science 336(6085):1157–1160

    Article  CAS  PubMed  Google Scholar 

  4. Sandberg TE, Lloyd CJ, Palsson BO, Feist AM (2017) Laboratory evolution to alternating substrate environments yields distinct phenotypic and genetic adaptive strategies. Appl Environ Microbiol 83(13):e00410–e00417. https://doi.org/10.1128/AEM.00410-17

    Article  CAS  PubMed Central  PubMed  Google Scholar 

  5. Wang X, Xia K, Yang X, Tang C (2019) Growth strategy of microbes on mixed carbon sources. Nat Commun 10(1):1279. https://doi.org/10.1038/s41467-019-09261-3

    Article  CAS  PubMed Central  PubMed  Google Scholar 

  6. Frunzke J, Engels V, Hasenbein S, Gatgens C, Bott M (2008) Co-ordinated regulation of gluconate catabolism and glucose uptake in Corynebacterium glutamicum by two functionally equivalent transcriptional regulators, GntR1 and GntR2. Mol Microbiol 67(2):305–322. https://doi.org/10.1111/j.1365-2958.2007.06020.x

    Article  CAS  PubMed  Google Scholar 

  7. Görke B, Stülke J (2008) Carbon catabolite repression in bacteria: many ways to make the most out of nutrients. Nat Rev Microbiol 6(8):613. https://doi.org/10.1038/nrmicro1932

    Article  CAS  PubMed  Google Scholar 

  8. Rojo F (2010) Carbon catabolite repression in Pseudomonas: optimizing metabolic versatility and interactions with the environment. FEMS Microbiol Rev 34(5):658–684. https://doi.org/10.1111/j.1574-6976.2010.00218.x

    Article  CAS  PubMed  Google Scholar 

  9. Collier DN, Hager PW, Phibbs PV (1996) Catabolite repression control in the Pseudomonads. Res Microbiol 147(6):551–561. https://doi.org/10.1016/0923-2508(96)84011-3

    Article  CAS  PubMed  Google Scholar 

  10. Durica-Mitic S, Gopel Y, Gorke B (2018) Carbohydrate utilization in bacteria: making the most out of sugars with the help of small regulatory RNAs. Microbiol Spectr 6(2):1–19. https://doi.org/10.1128/microbiolspec.rwr-0013-2017

    Article  Google Scholar 

  11. Galinier A, Deutscher J (2017) Sophisticated regulation of transcriptional factors by the bacterial phosphoenolpyruvate: sugar phosphotransferase system. J Mol Biol 429(6):773–789. https://doi.org/10.1016/j.jmb.2017.02.006

    Article  CAS  PubMed  Google Scholar 

  12. Vinuselvi P, Kim MK, Lee SK, Ghim CM (2012) Rewiring carbon catabolite repression for microbial cell factory. BMB Rep 45(2):59–70. https://doi.org/10.5483/bmbrep.2012.45.2.59

    Article  CAS  PubMed  Google Scholar 

  13. Monod J (1949) The growth of bacterial cultures. Annu Rev Microbiol 3(1):371–394. https://doi.org/10.1146/annurev.mi.03.100149.002103

    Article  CAS  Google Scholar 

  14. Chu D (2017) Limited by sensing– A minimal stochastic model of the lag-phase during diauxic growth. J Theor Biol 414:137–146. https://doi.org/10.1093/bjc/azq038

    Article  PubMed  Google Scholar 

  15. Chu DF (2015) In silico evolution of diauxic growth. BMC Evol Biol 15(1):211. https://doi.org/10.1186/s12862-015-0492-0

    Article  CAS  PubMed Central  PubMed  Google Scholar 

  16. Hermsen R, Okano H, You C, Werner N, Hwa T (2015) A growth-rate composition formula for the growth of E coli on co-utilized carbon substrates. Mol Syst Biol 11(4):801. https://doi.org/10.15252/msb.20145537

    Article  CAS  PubMed Central  PubMed  Google Scholar 

  17. Solopova A, van Gestel J, Weissing FJ, Bachmann H, Teusink B, Kok J, Kuipers OP (2014) Bet-hedging during bacterial diauxic shift. Proc Natl Acad Sci. https://doi.org/10.1073/pnas.1320063111

    Article  PubMed  PubMed Central  Google Scholar 

  18. Kotte O, Volkmer B, Radzikowski JL, Heinemann M (2014) Phenotypic bistability in Escherichia coli’s central carbon metabolism. Mol Syst Biol 10:736. https://doi.org/10.15252/msb.20135022

    Article  CAS  PubMed Central  PubMed  Google Scholar 

  19. Succurro A, Segre D, Ebenhoh O (2019) Emergent subpopulation behavior uncovered with a community dynamic metabolic model of Escherichia coli diauxic growth. mSystems. https://doi.org/10.1128/msystems.00230-18

    Article  PubMed Central  PubMed  Google Scholar 

  20. Zimmermann T, Sorg T, Siehler SY, Gerischer U (2009) Role of Acinetobacter baylyi Crc in catabolite repression of enzymes for aromatic compound catabolism. J Bacteriol 191(8):2834–2842. https://doi.org/10.1128/JB.00817-08

    Article  CAS  PubMed Central  PubMed  Google Scholar 

  21. Cornforth DM, Foster KR (2013) Competition sensing: the social side of bacterial stress responses. Nat Rev Microbiol 11:285. https://doi.org/10.1038/nrmicro2977

    Article  CAS  PubMed  Google Scholar 

  22. Liu W, Roder HL, Madsen JS, Bjarnsholt T, Sorensen SJ, Burmolle M (2016) Interspecific bacterial interactions are reflected in multispecies biofilm spatial organization. Front Microbiol 7:1366. https://doi.org/10.3389/fmicb.2016.01366

    Article  PubMed Central  PubMed  Google Scholar 

  23. Ren D, Madsen JS, Sorensen SJ, Burmolle M (2015) High prevalence of biofilm synergy among bacterial soil isolates in cocultures indicates bacterial interspecific cooperation. ISME J 9(1):81–89. https://doi.org/10.1038/ismej.2014.96

    Article  CAS  PubMed  Google Scholar 

  24. Russel J, Roder HL, Madsen JS, Burmolle M, Sorensen SJ (2017) Antagonism correlates with metabolic similarity in diverse bacteria. Proc Natl Acad Sci USA 114(40):10684–10688. https://doi.org/10.1073/pnas.1706016114

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  25. Bernstein HC, Carlson RP (2012) Microbial consortia engineering for cellular factories: in vitro to in silico systems. Comput Struct Biotechnol J 3(4):e201210017. https://doi.org/10.5936/csbj.201210017

    Article  PubMed Central  PubMed  Google Scholar 

  26. Scitable by Nature EDUCATION (2005) Nature Publishing Group. www.nature.com/scitable/definition/catabolite-repression-113/. Accessed 3 Sep 2018

  27. Zhang YH, Lynd LR (2005) Cellulose utilization by Clostridium thermocellum: bioenergetics and hydrolysis product assimilation. Proc Natl Acad Sci USA 102(20):7321–7325. https://doi.org/10.1073/pnas.0408734102

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  28. Parche S, Jacobs D, Arigoni F, Titgemeyer F, Jankovic I (2006) Lactose-over-glucose preference in bifidobacterium longum NCC2705: glcP, encoding a glucose transporter, is subject to lactose repression. Society 188(4):1260–1265. https://doi.org/10.1128/JB.188.4.1260

    Article  CAS  Google Scholar 

  29. Ng TK, Zeikus JG (1982) Differential metabolism of cellobiose and glucose by Clostridium thermocellum and Clostridium thermohydrosulfuricum. J Bacteriol 150(3):1391–1399

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  30. Frimmersdorf E, Horatzek S, Pelnikevich A, Wiehlmann L, Schomburg D (2010) How Pseudomonas aeruginosa adapts to various environments: a metabolomic approach. Environ Microbiol 12(6):1734–1747. https://doi.org/10.1111/j.1462-2920.2010.02253.x

    Article  CAS  PubMed  Google Scholar 

  31. Carlson RP (2007) Metabolic systems cost-benefit analysis for interpreting network structure and regulation. Bioinformatics 23(10):1258–1264. https://doi.org/10.1093/bioinformatics/btm082

    Article  CAS  PubMed  Google Scholar 

  32. Carlson RP (2009) Decomposition of complex microbial behaviors into resource-based stress responses. Bioinformatics 25(1):90–97

    Article  CAS  PubMed  Google Scholar 

  33. Hamilton WA, Dawes EA (1959) A diauxic effect with Pseudomonas aeruginosa. Biochem J 71:25P–26P

    CAS  Google Scholar 

  34. Harwood JP, Gazdar C, Prasad C, Peterkofsky A (1976) A involvement of the glucose enzymes II of the sugar phosphotransferase system in the regulation of adenylate cyclase by glucose in Escherichia coli. J Biol Chem 25(8):2462–2468

    Google Scholar 

  35. Feucht BU, Saier MH (1980) Fine control of adenylate cyclase by the phosphoenolpyruvate:sugar phosphotransferase systems in Escherichia coli and Salmonella typhimurium. J Bacteriol 141(2):603–610

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  36. Titgemeyer F, Hillen W (2002) Global control of sugar metabolism: a Gram-positive solution. Antonie Van Leeuwenhoek 82(1):59–71. https://doi.org/10.1023/A:1020628909429

    Article  CAS  PubMed  Google Scholar 

  37. Warner JB, Lolkema JS (2003) CcpA-dependent carbon catabolite repression in bacteria. Microbiol Mol Biol Rev 67(4):475–490. https://doi.org/10.1128/MMBR.67.4.475-490.2003

    Article  CAS  PubMed Central  PubMed  Google Scholar 

  38. Henkin TM, Grundy FJ, Nicholson WL, Chambliss GH (1991) Catabolite repression of α amylase gene expression in Bacillus subtilis involves a trans-acting gene product homologous to the Escherichia coli lacl and galR repressors. Mol Microbiol 5(3):575–584. https://doi.org/10.1111/j.1365-2958.1991.tb00728.x

    Article  CAS  PubMed  Google Scholar 

  39. Djordjevic GM, Tchieu JH, Saier MH (2001) Genes involved in control of galactose uptake inlactobacillus brevis and reconstitution of the regulatory system in Bacillus subtilis. J Bacteriol 183(10):3224–3236. https://doi.org/10.1128/JB.183.10.3224-3236.2001

    Article  CAS  PubMed Central  PubMed  Google Scholar 

  40. Zomer AL, Buist G, Larsen R, Kok J, Kuipers OP (2007) Time-resolved determination of the CcpA regulon of Lactococcus lactis subsp. cremoris MG1363. J Bacteriol 189(4):1366–1381. https://doi.org/10.1128/jb.01013-06

    Article  CAS  PubMed  Google Scholar 

  41. Abdou L, Boileau C, Philip Pd, Pagès S, Fiérobe H-P, Tardif C (2008) Transcriptional regulation of the clostridium cellulolyticum cip-cel Operon: a complex mechanism involving a catabolite-responsive element. J Bacteriol 190(5):1499–1506. https://doi.org/10.1128/JB.01160-07

    Article  CAS  PubMed  Google Scholar 

  42. Arndt A, Eikmanns BJ (2007) The alcohol dehydrogenase gene adhA in corynebacterium glutamicum is subject to carbon catabolite repression. J Bacteriol 189(20):7408–7416. https://doi.org/10.1128/JB.00791-07

    Article  CAS  PubMed Central  PubMed  Google Scholar 

  43. Gerstmeir R, Cramer A, Dangel P, Schaffer S, Eikmanns BJ (2004) RamB, a novel transcriptional regulator of genes involved in acetate metabolism of Corynebacterium glutamicum. J Bacteriol 186(9):2798–2809

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  44. Müller C, Petruschka L, Cuypers H, Burchhardt G, Herrmann H (1996) Carbon catabolite repression of phenol degradation in Pseudomonas putida is mediated by the inhibition of the activator protein PhlR. J Bacteriol 178(7):2030–2036. https://doi.org/10.1128/jb.178.7.2030-2036.1996

    Article  PubMed Central  PubMed  Google Scholar 

  45. Kwakman JH, Postma PW (1994) Glucose kinase has a regulatory role in carbon catabolite repression in Streptomyces coelicolor. J Bacteriol 176(9):2694–2698. https://doi.org/10.1128/jb.176.9.2694-2698.1994

    Article  CAS  PubMed Central  PubMed  Google Scholar 

  46. Commichau FM, Stülke J (2008) Trigger enzymes: bifunctional proteins active in metabolism and in controlling gene expression. Mol Microbiol 67(4):692��702. https://doi.org/10.1111/j.1365-2958.2007.06071.x

    Article  CAS  PubMed  Google Scholar 

  47. Chu D, Barnes DJ (2016) The lag-phase during diauxic growth is a trade-off between fast adaptation and high growth rate. Sci Rep 6:25191. https://doi.org/10.1038/srep25191

    Article  CAS  PubMed Central  PubMed  Google Scholar 

  48. Wezel GPV, König M, Mahr K, Nothaft H, Thomae AW, Bibb M, Titgemeyer F (2007) A new piece of an old jigsaw: glucose kinase is activated posttranslationally in a glucose transport-dependent manner in streptomyces coelicolor A3(2). MMB 12(1–2):67–74. https://doi.org/10.1159/000096461

    Article  CAS  Google Scholar 

  49. Busby S, Ebright RH (1999) Transcription activation by catabolite activator protein (CAP). J Mol Biol 293(2):199–213. https://doi.org/10.1006/jmbi.1999.3161

    Article  CAS  PubMed  Google Scholar 

  50. Malan TP, Kolb A, Buc H, McClure WR (1984) Mechanism of CRP-cAMP activation of lac operon transcription initiation activation of the P1 promoter. J Mol Biol 180(4):881–909. https://doi.org/10.1016/0022-2836(84)90262-6

    Article  CAS  PubMed  Google Scholar 

  51. Deutscher J (2008) The mechanisms of carbon catabolite repression in bacteria. Curr Opin Microbiol 11(2):87–93. https://doi.org/10.1016/j.mib.2008.02.007

    Article  CAS  PubMed  Google Scholar 

  52. Harwood JP, Gazdar C, Prasad C, Peterkofsky A, Curtis SJ, Epstein W (1976) Involvement of the glucose enzymes II of the sugar phosphotransferase system in the regulation of adenylate cyclase by glucose in Escherichia coli. J Biol Chem 251(8):2462–2468

    CAS  PubMed  Google Scholar 

  53. Park Y-H, Lee BR, Seok Y-J, Peterkofsky A (2006) In vitro reconstitution of catabolite repression in Escherichia coli. J Biol Chem 281(10):6448–6454. https://doi.org/10.1074/jbc.M512672200

    Article  CAS  PubMed  Google Scholar 

  54. Tagami H, Aiba H (1998) A common role of CRP in transcription activation: CRP acts transiently to stimulate events leading to open complex formation at a diverse set of promoters. EMBO J 17(6):1759–1767. https://doi.org/10.1093/emboj/17.6.1759

    Article  CAS  PubMed Central  PubMed  Google Scholar 

  55. Cases I, Velázquez F, de Lorenzo V (2007) The ancestral role of the phosphoenolpyruvate–carbohydrate phosphotransferase system (PTS) as exposed by comparative genomics. Res Microbiol 158(8):666–670. https://doi.org/10.1016/j.resmic.2007.08.002

    Article  CAS  PubMed  Google Scholar 

  56. Deutscher J, Francke C, Postma PW (2006) How phosphotransferase system-related protein phosphorylation regulates carbohydrate metabolism in bacteria. Microbiol Mol Biol Rev 70(4):939–1031. https://doi.org/10.1128/MMBR.00024-06

    Article  CAS  PubMed Central  PubMed  Google Scholar 

  57. Postma PW, Lengeler JW, Jacobson GR (1993) Phosphoenolpyruvate:carbohydrate phosphotransferase systems of bacteria. Microbiol Mol Biol Rev 57(3):543–594

    CAS  Google Scholar 

  58. Hogema BM, Arents JC, Bader R, Postma PW (1999) Autoregulation of lactose uptake through the LacYpermease by enzyme IIAGlc of the PTS in Escherichia coli K-12. Mol Microbiol 31(6):1825–1833. https://doi.org/10.1046/j.1365-2958.1999.01319.x

    Article  CAS  PubMed  Google Scholar 

  59. Inada T, Kimata K, Aiba H (1996) Mechanism responsible for glucose–lactose diauxie in Escherichia coli: challenge to the cAMP model. Genes Cells 1(3):293–301. https://doi.org/10.1046/j.1365-2443.1996.24025.x

    Article  CAS  PubMed  Google Scholar 

  60. Kimata K, Takahashi H, Inada T, Postma P, Aiba H (1997) cAMP receptor protein–cAMP plays a crucial role in glucose–lactose diauxie by activating the major glucose transporter gene in Escherichia coli. Proc Natl Acad Sci 94(24):12914–12919. https://doi.org/10.1073/pnas.94.24.12914

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  61. Beckwith JR (1967) Regulation of the Lac operon. Science 156(3775):597–604

    Article  CAS  PubMed  Google Scholar 

  62. Storz G, Vogel J, Wassarman KM (2011) Regulation by small RNAs in bacteria: expanding frontiers. Mol Cell 43(6):880–891. https://doi.org/10.1016/j.molcel.2011.08.022

    Article  CAS  PubMed Central  PubMed  Google Scholar 

  63. Beisel CL, Updegrove TB, Janson BJ, Storz G (2012) Multiple factors dictate target selection by Hfq-binding small RNAs. EMBO J 31(8):1961–1974. https://doi.org/10.1038/emboj.2012.52

    Article  CAS  PubMed Central  PubMed  Google Scholar 

  64. Beisel CL, Storz G (2011) Discriminating tastes: physiological contributions of the Hfq-binding small RNA Spot 42 to catabolite repression. RNA Biol 8(5):766–770. https://doi.org/10.4161/rna.8.5.16024

    Article  CAS  PubMed Central  PubMed  Google Scholar 

  65. Beisel CL, Storz G (2011) The base-pairing RNA spot 42 participates in a multioutput feedforward loop to help enact catabolite repression in Escherichia coli. Mol Cell 41(3):286–297. https://doi.org/10.1016/j.molcel.2010.12.027

    Article  CAS  PubMed Central  PubMed  Google Scholar 

  66. You C, Okano H, Hui S, Zhang Z, Kim M, Gunderson CW, Wang YP, Lenz P, Yan D, Hwa T (2013) Coordination of bacterial proteome with metabolism by cyclic AMP signalling. Nature 500(7462):301–306. https://doi.org/10.1038/nature12446

    Article  CAS  PubMed Central  PubMed  Google Scholar 

  67. Bettenbrock K, Fischer S, Kremling A, Jahreis K, Sauter T, Gilles ED (2006) A quantitative approach to catabolite repression in Escherichia coli. J Biol Chem 281(5):2578–2584. https://doi.org/10.1074/jbc.M508090200

    Article  CAS  PubMed  Google Scholar 

  68. Bren A, Park JO, Towbin BD, Dekel E, Rabinowitz JD, Alon U (2016) Glucose becomes one of the worst carbon sources for E. coli on poor nitrogen sources due to suboptimal levels of cAMP. Sci Rep 6:24834. https://doi.org/10.1038/srep24834

    Article  CAS  PubMed Central  PubMed  Google Scholar 

  69. Deutscher J, Küster E, Bergstedt U, Charrier V, Hillen W (1995) Protein kinase-dependent HPr/CcpA interaction links glycolytic activity to carbon catabolite repression in Gram-positive bacteria. Mol Microbiol 15(6):1049–1053. https://doi.org/10.1111/j.1365-2958.1995.tb02280.x

    Article  CAS  PubMed  Google Scholar 

  70. Jones BE, Dossonnet V, Küster E, Hillen W, Deutscher J, Klevit RE (1997) Binding of the catabolite repressor protein CcpA to Its DNA target is regulated by phosphorylation of its corepressor HPr. J Biol Chem 272(42):26530–26535. https://doi.org/10.1074/jbc.272.42.26530

    Article  CAS  PubMed  Google Scholar 

  71. Galinier A, Kravanja M, Engelmann R, Hengstenberg W, Kilhoffer M-C, Deutscher J, Haiech J (1998) New protein kinase and protein phosphatase families mediate signal transduction in bacterial catabolite repression. Proc Natl Acad Sci 95(4):1823–1828. https://doi.org/10.1073/pnas.95.4.1823

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  72. Jault J-M, Fieulaine S, Nessler S, Gonzalo P, Pietro AD, Deutscher J, Galinier A (2000) The HPr kinase from Bacillus subtilis is a homo-oligomeric enzyme which exhibits strong positive cooperativity for nucleotide and fructose 1,6-bisphosphate binding. J Biol Chem 275(3):1773–1780. https://doi.org/10.1074/jbc.275.3.1773

    Article  CAS  PubMed  Google Scholar 

  73. Reizer J, Hoischen C, Titgemeyer F, Rivolta C, Rabus R, Stülke J, Karamata D, Hillen W (1998) A novel protein kinase that controls carbon catabolite repression in bacteria. Mol Microbiol 27(6):1157–1169. https://doi.org/10.1046/j.1365-2958.1998.00747.x

    Article  CAS  PubMed  Google Scholar 

  74. Schumacher MA, Allen GS, Diel M, Seidel G, Hillen W, Brennan RG (2004) Structural basis for allosteric control of the transcription regulator CcpA by the phosphoprotein HPr-Ser46-P. Cell 118(6):731–741. https://doi.org/10.1016/j.cell.2004.08.027

    Article  CAS  PubMed  Google Scholar 

  75. Seidel G, Diel M, Fuchsbauer N, Hillen W (2005) Quantitative interdependence of coeffectors, CcpA and cre in carbon catabolite regulation of Bacillus subtilis. FEBS J 272(10):2566–2577. https://doi.org/10.1111/j.1742-4658.2005.04682.x

    Article  CAS  PubMed  Google Scholar 

  76. Moreno MS, Schneider BL, Maile RR, Weyler W, Saier MH (2004) Catabolite repression mediated by the CcpA protein in Bacillus subtilis: novel modes of regulation revealed by whole-genome analyses. Mol Microbiol 39(5):1366–1381. https://doi.org/10.1111/j.1365-2958.2001.02328.x

    Article  Google Scholar 

  77. Yoshida K, Kobayashi K, Miwa Y, Kang CM, Matsunaga M, Yamaguchi H, Tojo S, Yamamoto M, Nishi R, Ogasawara N, Nakayama T, Fujita Y (2001) Combined transcriptome and proteome analysis as a powerful approach to study genes under glucose repression in Bacillus subtilis. Nucleic Acids Res 29(3):683–692. https://doi.org/10.1093/nar/29.3.683

    Article  CAS  PubMed Central  PubMed  Google Scholar 

  78. Kim SU, Kim DC, Dhurjati P (1988) Mathematical modeling for mixed culture growth of two bacterial populations with opposite substrate preferences. Biotechnol Bioeng 31(2):144–159. https://doi.org/10.1002/bit.260310208

    Article  CAS  PubMed  Google Scholar 

  79. Ramkrishna D, Kompala DS, Tsao GT (1987) Are microbes optimal strategists? Biotechnol Progr 3(3):121–126. https://doi.org/10.1002/btpr.5420030302

    Article  Google Scholar 

  80. Kim SU, Dhurjati P (1987) Analysis of two interacting bacterial populations with opposite substrate preferences. Biotechnol Bioeng 29(8):1015–1023. https://doi.org/10.1002/bit.260290813

    Article  CAS  PubMed  Google Scholar 

  81. Wolff JA, MacGregor CH, Eisenberg RC, Phibbs PV (1991) Isolation and characterization of catabolite repression control mutants of Pseudomonas aeruginosa PAO. J Bacteriol 173(15):4700–4706

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  82. Stewart PS (2003) Diffusion in Biofilms. J Bacteriol 185(5):1485–1491. https://doi.org/10.1128/JB.185.5.1485-1491.2003

    Article  CAS  PubMed Central  PubMed  Google Scholar 

  83. Sonnleitner E, Blasi U (2014) Regulation of Hfq by the RNA CrcZ in Pseudomonas aeruginosa carbon catabolite repression. PLoS Genet 10(6):e1004440. https://doi.org/10.1371/journal.pgen.1004440

    Article  CAS  PubMed Central  PubMed  Google Scholar 

  84. Sonnleitner E, Abdou L, Haas D (2009) Small RNA as global regulator of carbon catabolite repression in Pseudomonas aeruginosa. Proc Natl Acad Sci 106(51):21866–21871. https://doi.org/10.1073/pnas.0910308106

    Article  PubMed  PubMed Central  Google Scholar 

  85. Sonnleitner E, Wulf A, Campagne S, Pei XY, Wolfinger MT, Forlani G, Prindl K, Abdou L, Resch A, Allain FH, Luisi BF, Urlaub H, Blasi U (2018) Interplay between the catabolite repression control protein Crc, Hfq and RNA in Hfq-dependent translational regulation in Pseudomonas aeruginosa. Nucleic Acids Res 46(3):1470–1485. https://doi.org/10.1093/nar/gkx1245

    Article  CAS  PubMed  Google Scholar 

  86. Moreno R, Hernandez-Arranz S, La Rosa R, Yuste L, Madhushani A, Shingler V, Rojo F (2015) The Crc and Hfq proteins of Pseudomonas putida cooperate in catabolite repression and formation of ribonucleic acid complexes with specific target motifs. Environ Microbiol 17(1):105–118. https://doi.org/10.1111/1462-2920.12499

    Article  CAS  PubMed  Google Scholar 

  87. Kambara TK, Ramsey KM, Dove SL (2018) Pervasive targeting of nascent transcripts by Hfq. Cell Rep 23(5):1543–1552. https://doi.org/10.1016/j.celrep.2018.03.134

    Article  CAS  PubMed Central  PubMed  Google Scholar 

  88. Grenga L, Chandra G, Saalbach G, Galmozzi CV, Kramer G, Malone JG (2017) Analyzing the complex regulatory landscape of Hfq—an integrative, multi-omics approach. Front Microbiol 8:1784. https://doi.org/10.3389/fmicb.2017.01784

    Article  PubMed Central  PubMed  Google Scholar 

  89. Moreno R, Fonseca P, Rojo F (2012) Two small RNAs, CrcY and CrcZ, act in concert to sequester the Crc global regulator in Pseudomonas putida, modulating catabolite repression. Mol Microbiol 83(1):24–40. https://doi.org/10.1111/j.1365-2958.2011.07912.x

    Article  CAS  PubMed  Google Scholar 

  90. Liu Y, Gokhale CS, Rainey PB, Zhang XX (2017) Unravelling the complexity and redundancy of carbon catabolic repression in Pseudomonas fluorescens SBW25. Mol Microbiol 105(4):589–605. https://doi.org/10.1111/mmi.13720

    Article  CAS  PubMed  Google Scholar 

  91. Filiatrault MJ, Stodghill PV, Wilson J, Butcher BG, Chen H, Myers CR, Cartinhour SW (2013) CrcZ and CrcX regulate carbon source utilization in Pseudomonas syringae pathovar tomato strain DC3000. RNA Biol 10(2):245–255. https://doi.org/10.4161/rna.23019

    Article  CAS  PubMed Central  PubMed  Google Scholar 

  92. Garcia-Maurino SM, Perez-Martinez I, Amador CI, Canosa I, Santero E (2013) Transcriptional activation of the CrcZ and CrcY regulatory RNAs by the CbrB response regulator in Pseudomonas putida. Mol Microbiol 89(1):189–205. https://doi.org/10.1111/mmi.12270

    Article  CAS  PubMed  Google Scholar 

  93. Kremling A, Geiselmann J, Ropers D, de Jong H (2015) Understanding carbon catabolite repression in Escherichia coli using quantitative models. Trends Microbiol 23(2):99–109. https://doi.org/10.1016/j.tim.2014.11.002

    Article  CAS  PubMed  Google Scholar 

  94. Kremling A, Geiselmann J, Ropers D, de Jong H (2018) An ensemble of mathematical models showing diauxic growth behaviour. BMC Syst Biol 12(1):1–16. https://doi.org/10.1186/s12918-018-0604-8

    Article  CAS  Google Scholar 

  95. Kompala DS, Ramkrishna D, Jansen NB, Tsao GT (1986) Investigation of bacterial growth on mixed substrates: experimental evaluation of cybernetic models. Biotechnol Bioeng 28(7):1044–1055. https://doi.org/10.1002/bit.260280715

    Article  CAS  PubMed  Google Scholar 

  96. DeVilbiss F, Mandli A, Ramkrishna D (2018) Consistency of cybernetic variables with gene expression profiles: a more rigorous test. Biotechnol Prog 34(4):858–867. https://doi.org/10.1002/btpr.2654

    Article  CAS  PubMed  Google Scholar 

  97. Schuster R, Schuster S (1993) Refined algorithm and computer program for calculating all non–negative fluxes admissible in steady states of biochemical reaction systems with or without some flux rates fixed. Bioinformatics 9(1):79–85. https://doi.org/10.1093/bioinformatics/9.1.79

    Article  CAS  Google Scholar 

  98. Schuster S, Dandekar T, Fell DA (1999) Detection of elementary flux modes in biochemical networks: a promising tool for pathway analysis and metabolic engineering. Trends Biotechnol 17(2):53–60. https://doi.org/10.1016/S0167-7799(98)01290-6

    Article  CAS  PubMed  Google Scholar 

  99. Varma A, Boesch BW, Palsson BO (1993) Stoichiometric interpretation of Escherichia coli glucose catabolism under various oxygenation rates. Appl Environ Microbiol 59(8):2465–2473

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  100. Varma A, Palsson BO (1994) Stoichiometric flux balance models quantitatively predict growth and metabolic by-product secretion in wild-type Escherichia-Coli W3110. Appl Environ Microb 60(10):3724–3731

    Article  CAS  Google Scholar 

  101. Beck AE, Hunt KA, Carlson RP (2018) Measuring cellular biomass composition for computational biology applications. Processes 6(5):38. https://doi.org/10.3390/pr6050038

    Article  CAS  Google Scholar 

  102. Schuster S, Pfeiffer T, Fell DA (2008) Is maximization of molar yield in metabolic networks favoured by evolution? J Theor Biol 252(3):497–504. https://doi.org/10.1016/j.jtbi.2007.12.008

    Article  CAS  PubMed  Google Scholar 

  103. Beg QK, Vazquez A, Ernst J, de Menezes MA, Bar-Joseph Z, Barabasi AL, Oltvai ZN (2007) Intracellular crowding defines the mode and sequence of substrate uptake by Escherichia coli and constrains its metabolic activity. Proc Natl Acad Sci USA 104(31):12663–12668. https://doi.org/10.1073/pnas.0609845104

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  104. Vazquez A, Oltvai ZN (2016) Macromolecular crowding explains overflow metabolism in cells. Sci Rep 6:31007. https://doi.org/10.1038/srep31007

    Article  CAS  PubMed Central  PubMed  Google Scholar 

  105. Carlson R, Srienc F (2004) Fundamental Escherichia coli biochemical pathways for biomass and energy production: identification of reactions. Biotechnol Bioeng 85(1):1–19. https://doi.org/10.1002/bit.10812

    Article  CAS  PubMed  Google Scholar 

  106. Carlson R, Srienc F (2004) Fundamental Escherichia coli biochemical pathways for biomass and energy production: creation of overall flux states. Biotechnol Bioeng 86(2):149–162. https://doi.org/10.1002/bit.20044

    Article  CAS  PubMed  Google Scholar 

  107. Carlson RP, Beck AE, Phalak P, Fields MW, Gedeon T, Hanley L, Harcombe WR, Henson MA, Heys JJ (2018) Competitive resource allocation to metabolic pathways contributes to overflow metabolisms and emergent properties in cross-feeding microbial consortia. Biochem Soc Trans 46(2):269–284. https://doi.org/10.1042/BST20170242

    Article  CAS  PubMed  Google Scholar 

  108. Zhuang K, Vemuri GN, Mahadevan R (2011) Economics of membrane occupancy and respiro-fermentation. Mol Syst Biol 7:500. https://doi.org/10.1038/msb.2011.34

    Article  PubMed Central  PubMed  Google Scholar 

  109. Szenk M, Dill KA, de Graff AMR (2017) Why do fast-growing bacteria enter overflow metabolism? Testing the membrane real estate hypothesis. Cell Syst 5(2):95–104. https://doi.org/10.1016/j.cels.2017.06.005

    Article  CAS  PubMed  Google Scholar 

  110. Basan M, Hui S, Okano H, Zhang Z, Shen Y, Williamson JR, Hwa T (2015) Overflow metabolism in Escherichia coli results from efficient proteome allocation. Nature 528(7580):99–104. https://doi.org/10.1038/nature15765

    Article  CAS  PubMed Central  PubMed  Google Scholar 

  111. Goelzer A (1810) Fromion V (2011) Bacterial growth rate reflects a bottleneck in resource allocation. Biochim Biophys Acta 10:978–988. https://doi.org/10.1016/j.bbagen.2011.05.014

    Article  CAS  Google Scholar 

  112. Goelzer A, Fromion V (2017) Resource allocation in living organisms. Biochem Soc Trans 45(4):945–952. https://doi.org/10.1042/BST20160436

    Article  CAS  PubMed  Google Scholar 

  113. Yang L, Ma D, Ebrahim A, Lloyd CJ, Saunders MA, Palsson BO (2016) solveME: fast and reliable solution of nonlinear ME models. BMC Bioinformatics 17(1):391. https://doi.org/10.1186/s12859-016-1240-1

    Article  CAS  PubMed Central  PubMed  Google Scholar 

  114. Mahadevan R, Edwards JS, Doyle FJ (2002) Dynamic flux balance analysis of diauxic growth in Escherichia coli. Biophys J 83(3):1331–1340

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  115. Rentz JA, Alvarez PJ, Schnoor JL (2004) Repression of Pseudomonas putida phenanthrene-degrading activity by plant root extracts and exudates. Environ Microbiol 6(6):574–583. https://doi.org/10.1111/j.1462-2920.2004.00589.x

    Article  PubMed  Google Scholar 

  116. Tiwari N, Campbell J (1969) Enzymatic control of the metabolic activity of Pseudomonas aeruginosa grown in glucose or succinate media. Biochim Biophys Acta (BBA) Gen Subj 192(3):395–401. https://doi.org/10.1016/0304-4165(69)90388-2

    Article  CAS  Google Scholar 

  117. Trautwein K, Grundmann O, Wohlbrand L, Eberlein C, Boll M, Rabus R (2012) Benzoate mediates repression of C(4)-dicarboxylate utilization in “Aromatoleum aromaticum” EbN1. J Bacteriol 194(2):518–528. https://doi.org/10.1128/JB.05072-11

    Article  CAS  PubMed Central  PubMed  Google Scholar 

  118. Berger A, Dohnt K, Tielen P, Jahn D, Becker J, Wittmann C (2014) Robustness and plasticity of metabolic pathway flux among uropathogenic isolates of Pseudomonas aeruginosa. PLoS One 9(4):e88368. https://doi.org/10.1371/journal.pone.0088368

    Article  CAS  PubMed Central  PubMed  Google Scholar 

  119. Hintermayer SB, Weuster-Botz D (2017) Experimental validation of in silico estimated biomass yields of Pseudomonas putida KT2440. Biotechnol J 12(6):1600720. https://doi.org/10.1002/biot.201600720

    Article  CAS  Google Scholar 

  120. La Rosa R, Behrends V, Williams HD, Bundy JG, Rojo F (2016) Influence of the Crc regulator on the hierarchical use of carbon sources from a complete medium in Pseudomonas. Environ Microbiol 18(3):807–818. https://doi.org/10.1111/1462-2920.13126

    Article  CAS  PubMed  Google Scholar 

  121. Nikel PI, Chavarria M, Fuhrer T, Sauer U, de Lorenzo V (2015) Pseudomonas putida KT2440 strain metabolizes glucose through a cycle formed by enzymes of the entner-doudoroff, embden-meyerhof-parnas, and pentose phosphate pathways. J Biol Chem 290(43):25920–25932. https://doi.org/10.1074/jbc.M115.687749

    Article  CAS  PubMed Central  PubMed  Google Scholar 

  122. Flamholz A, Noor E, Bar-Even A, Liebermeister W, Milo R (2013) Glycolytic strategy as a tradeoff between energy yield and protein cost. Proc Natl Acad Sci USA 110(24):10039–10044. https://doi.org/10.1073/pnas.1215283110

    Article  PubMed  PubMed Central  Google Scholar 

  123. Oberhardt MA, Goldberg JB, Hogardt M, Papin JA (2010) Metabolic network analysis of Pseudomonas aeruginosa during chronic cystic fibrosis lung infection. J Bacteriol 192(20):5534–5548. https://doi.org/10.1128/JB.00900-10

    Article  CAS  PubMed Central  PubMed  Google Scholar 

  124. Xu Z, Fang X, Wood TK, Huang ZJ (2013) A systems-level approach for investigating Pseudomonas aeruginosa biofilm formation. PLoS One 8(2):e57050. https://doi.org/10.1371/journal.pone.0057050

    Article  CAS  PubMed Central  PubMed  Google Scholar 

  125. Nogales J, Palsson BO, Thiele I (2008) A genome-scale metabolic reconstruction of Pseudomonas putida KT2440: iJN746 as a cell factory. BMC Syst Biol 2:79. https://doi.org/10.1186/1752-0509-2-79

    Article  CAS  PubMed Central  PubMed  Google Scholar 

  126. Borgos SE, Bordel S, Sletta H, Ertesvag H, Jakobsen O, Bruheim P, Ellingsen TE, Nielsen J, Valla S (2013) Mapping global effects of the anti-sigma factor MucA in Pseudomonas fluorescens SBW25 through genome-scale metabolic modeling. BMC Syst Biol 7:19. https://doi.org/10.1186/1752-0509-7-19

    Article  PubMed Central  PubMed  Google Scholar 

  127. Stolyar S, Van Dien S, Hillesland KL, Pinel N, Lie TJ, Leigh JA, Stahl DA (2007) Metabolic modeling of a mutualistic microbial community. Mol Syst Biol 3:92. https://doi.org/10.1038/msb4100131

    Article  CAS  PubMed Central  PubMed  Google Scholar 

  128. Goyal A, Dubinkina V, Maslov S (2018) Multiple stable states in microbial communities explained by the stable marriage problem. ISME J 12(12):2823–2834. https://doi.org/10.1038/s41396-018-0222-x

    Article  PubMed Central  PubMed  Google Scholar 

  129. Roell GW, Zha J, Carr RR, Koffas MA, Fong SS, Tang YJ (2019) Engineering microbial consortia by division of labor. Microb Cell Fact 18(1):35. https://doi.org/10.1186/s12934-019-1083-3

    Article  PubMed Central  PubMed  Google Scholar 

  130. Shuler ML, Kargi F, DeLisa M (2017) Bioprocess engineering: basic concepts, 3rd edn. Prentice Hall, Boston

    Google Scholar 

  131. Kreft JU (2004) Biofilms promote altruism. Microbiology 150(Pt 8):2751–2760. https://doi.org/10.1099/mic.0.26829-0

    Article  CAS  PubMed  Google Scholar 

  132. Boyle KE, Monaco HT, Deforet M, Yan J, Wang Z, Rhee K, Xavier JB (2017) Metabolism and the evolution of social behavior. Mol Biol Evol 34(9):2367–2379. https://doi.org/10.1093/molbev/msx174

    Article  CAS  PubMed Central  PubMed  Google Scholar 

  133. Ng WL, Bassler BL (2009) Bacterial quorum-sensing network architectures. Annu Rev Genet 43:197–222. https://doi.org/10.1146/annurev-genet-102108-134304

    Article  CAS  PubMed Central  PubMed  Google Scholar 

  134. Sonnleitner E, Schuster M, Sorger-Domenigg T, Greenberg EP, Blasi U (2006) Hfq-dependent alterations of the transcriptome profile and effects on quorum sensing in Pseudomonas aeruginosa. Mol Microbiol 59(5):1542–1558. https://doi.org/10.1111/j.1365-2958.2006.05032.x

    Article  CAS  PubMed  Google Scholar 

  135. Zhang L, Gao Q, Chen W, Qin H, Hengzhuang W, Chen Y, Yang L, Zhang G (2013) Regulation of pqs quorum sensing via catabolite repression control in Pseudomonas aeruginosa. Microbiology 159(Pt 9):1931–1936. https://doi.org/10.1099/mic.0.066266-0

    Article  CAS  PubMed  Google Scholar 

  136. Yang N, Lan L (2016) Pseudomonas aeruginosa Lon and ClpXP proteases: roles in linking carbon catabolite repression system with quorum-sensing system. Curr Genet 62(1):1–6. https://doi.org/10.1007/s00294-015-0499-5

    Article  CAS  PubMed  Google Scholar 

  137. Parsek MR, Greenberg EP (2005) Sociomicrobiology: the connections between quorum sensing and biofilms. Trends Microbiol 13(1):27–33. https://doi.org/10.1016/j.tim.2004.11.007

    Article  CAS  PubMed  Google Scholar 

  138. Coenye T (2010) Social interactions in the Burkholderia cepacia complex: biofilms and quorum sensing. Future Microbiol 5(7):1087–1099. https://doi.org/10.2217/fmb.10.68

    Article  CAS  PubMed  Google Scholar 

  139. Hall-Stoodley L, Costerton JW, Stoodley P (2004) Bacterial biofilms: from the natural environment to infectious diseases. Nat Rev Microbiol 2(2):95–108. https://doi.org/10.1038/nrmicro821

    Article  CAS  PubMed  Google Scholar 

  140. Flemming HC, Wingender J, Szewzyk U, Steinberg P, Rice SA, Kjelleberg S (2016) Biofilms: an emergent form of bacterial life. Nat Rev Microbiol 14(9):563–575. https://doi.org/10.1038/nrmicro.2016.94

    Article  CAS  PubMed  Google Scholar 

  141. Xavier JB, Foster KR (2007) Cooperation and conflict in microbial biofilms. Proc Natl Acad Sci USA 104(3):876–881. https://doi.org/10.1073/pnas.0607651104

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  142. Nadell CD, Drescher K, Foster KR (2016) Spatial structure, cooperation and competition in biofilms. Nat Rev Microbiol 14(9):589–600. https://doi.org/10.1038/nrmicro.2016.84

    Article  CAS  PubMed  Google Scholar 

  143. Stewart PS (1998) A review of experimental measurements of effective diffusive permeabilities and effective diffusion coefficients in biofilms. Biotechnol Bioeng 59(3):261–272. https://doi.org/10.1002/(SICI)1097-0290(19980805)59:3%3c261:AID-BIT1%3e3.0.CO;2-9

    Article  CAS  PubMed  Google Scholar 

  144. Kreft JU, Bonhoeffer S (2005) The evolution of groups of cooperating bacteria and the growth rate versus yield trade-off. Microbiology 151(Pt 3):637–641. https://doi.org/10.1099/mic.0.27415-0

    Article  CAS  PubMed  Google Scholar 

  145. Liu J, Prindle A, Humphries J, Gabalda-Sagarra M, Asally M, Lee DY, Ly S, Garcia-Ojalvo J, Suel GM (2015) Metabolic co-dependence gives rise to collective oscillations within biofilms. Nature 523(7562):550–554. https://doi.org/10.1038/nature14660

    Article  CAS  PubMed Central  PubMed  Google Scholar 

  146. Liu J, Martinez-Corral R, Prindle A, Lee DD, Larkin J, Gabalda-Sagarra M, Garcia-Ojalvo J, Suel GM (2017) Coupling between distant biofilms and emergence of nutrient time-sharing. Science 356(6338):638–642. https://doi.org/10.1126/science.aah4204

    Article  CAS  PubMed Central  PubMed  Google Scholar 

  147. Johnson DI (2018) Pseudomonas spp. In: Bacterial pathogens and their virulence factors, Springer International Publishing, Cham, pp 325–338. https://doi.org/10.1007/978-3-319-67651-7_25

    Google Scholar 

  148. Pusic P, Sonnleitner E, Krennmayr B, Heitzinger DA, Wolfinger MT, Resch A, Blasi U (2018) Harnessing metabolic regulation to increase Hfq-dependent antibiotic susceptibility in Pseudomonas aeruginosa. Front Microbiol 9:2709. https://doi.org/10.3389/fmicb.2018.02709

    Article  PubMed Central  PubMed  Google Scholar 

  149. Corona F, Martinez JL, Nikel PI (2019) The global regulator Crc orchestrates the metabolic robustness underlying oxidative stress resistance in Pseudomonas aeruginosa. Environ Microbiol 21(3):898–912. https://doi.org/10.1111/1462-2920.14471

    Article  CAS  PubMed  Google Scholar 

  150. Bernal V, Castano-Cerezo S, Canovas M (2016) Acetate metabolism regulation in Escherichia coli: carbon overflow, pathogenicity, and beyond. Appl Microbiol Biotechnol 100(21):8985–9001. https://doi.org/10.1007/s00253-016-7832-x

    Article  CAS  PubMed  Google Scholar 

  151. Kelsic ED, Zhao J, Vetsigian K, Kishony R (2015) Counteraction of antibiotic production and degradation stabilizes microbial communities. Nature 521(7553):516–519. https://doi.org/10.1038/nature14485

    Article  CAS  PubMed Central  PubMed  Google Scholar 

  152. Hibbing ME, Fuqua C, Parsek MR, Peterson SB (2010) Bacterial competition: surviving and thriving in the microbial jungle. Nat Rev Microbiol 8(1):15–25. https://doi.org/10.1038/nrmicro2259

    Article  CAS  PubMed Central  PubMed  Google Scholar 

  153. Meylan S, Porter CBM, Yang JH, Belenky P, Gutierrez A, Lobritz MA, Park J, Kim SH, Moskowitz SM, Collins JJ (2017) carbon sources tune antibiotic susceptibility in Pseudomonas aeruginosa via tricarboxylic acid cycle control. Cell Chem Biol 24(2):195–206. https://doi.org/10.1016/j.chembiol.2016.12.015

    Article  CAS  PubMed Central  PubMed  Google Scholar 

  154. Yeung AT, Bains M, Hancock RE (2011) The sensor kinase CbrA is a global regulator that modulates metabolism, virulence, and antibiotic resistance in Pseudomonas aeruginosa. J Bacteriol 193(4):918–931. https://doi.org/10.1128/JB.00911-10

    Article  CAS  PubMed  Google Scholar 

  155. Hufnagel DA, Evans ML, Greene SE, Pinkner JS, Hultgren SJ, Chapman MR (2016) The catabolite repressor protein-cyclic AMP complex regulates csgD and biofilm formation in uropathogenic Escherichia coli. J Bacteriol 198(24):3329–3334. https://doi.org/10.1128/JB.00652-16

    Article  CAS  PubMed Central  PubMed  Google Scholar 

  156. Jackson DW, Simecka JW, Romeo T (2002) Catabolite repression of Escherichia coli biofilm formation. J Bacteriol 184(12):3406–3410. https://doi.org/10.1128/jb.184.12.3406-3410.2002

    Article  CAS  PubMed Central  PubMed  Google Scholar 

  157. O’Toole GA, Gibbs KA, Hager PW, Phibbs PV Jr, Kolter R (2000) The global carbon metabolism regulator Crc is a component of a signal transduction pathway required for biofilm development by Pseudomonas aeruginosa. J Bacteriol 182(2):425–431. https://doi.org/10.1128/jb.182.2.425-431.2000

    Article  PubMed Central  PubMed  Google Scholar 

  158. Pusic P, Tata M, Wolfinger MT, Sonnleitner E, Haussler S, Blasi U (2016) Cross-regulation by CrcZ RNA controls anoxic biofilm formation in Pseudomonas aeruginosa. Sci Rep 6:39621. https://doi.org/10.1038/srep39621

    Article  CAS  PubMed Central  PubMed  Google Scholar 

  159. Amador CI, Lopez-Sanchez A, Govantes F, Santero E, Canosa I (2016) A Pseudomonas putida cbrB transposon insertion mutant displays a biofilm hyperproducing phenotype that is resistant to dispersal. Environ Microbiol Rep 8(5):622–629. https://doi.org/10.1111/1758-2229.12414

    Article  PubMed  Google Scholar 

  160. Seidl K, Goerke C, Wolz C, Mack D, Berger-Bachi B, Bischoff M (2008) Staphylococcus aureus CcpA affects biofilm formation. Infect Immun 76(5):2044–2050. https://doi.org/10.1128/IAI.00035-08

    Article  CAS  PubMed Central  PubMed  Google Scholar 

  161. Sadykov MR, Hartmann T, Mattes TA, Hiatt M, Jann NJ, Zhu Y, Ledala N, Landmann R, Herrmann M, Rohde H, Bischoff M, Somerville GA (2011) CcpA coordinates central metabolism and biofilm formation in Staphylococcus epidermidis. Microbiology 157(Pt 12):3458–3468. https://doi.org/10.1099/mic.0.051243-0

    Article  CAS  PubMed Central  PubMed  Google Scholar 

  162. Ha JH, Hauk P, Cho K, Eo Y, Ma X, Stephens K, Cha S, Jeong M, Suh JY, Sintim HO, Bentley WE, Ryu KS (2018) Evidence of link between quorum sensing and sugar metabolism in Escherichia coli revealed via cocrystal structures of LsrK and HPr. Sci Adv 4(6):eaar7063. https://doi.org/10.1126/sciadv.aar7063

    Article  CAS  PubMed Central  PubMed  Google Scholar 

  163. Mitra A, Herren CD, Patel IR, Coleman A, Mukhopadhyay S (2016) Integration of AI-2 based cell-cell signaling with metabolic cues in Escherichia coli. PLoS One 11(6):e0157532. https://doi.org/10.1371/journal.pone.0157532

    Article  CAS  PubMed Central  PubMed  Google Scholar 

  164. Wang L, Hashimoto Y, Tsao CY, Valdes JJ, Bentley WE (2005) Cyclic AMP (cAMP) and cAMP receptor protein influence both synthesis and uptake of extracellular autoinducer 2 in Escherichia coli. J Bacteriol 187(6):2066–2076. https://doi.org/10.1128/JB.187.6.2066-2076.2005

    Article  CAS  PubMed Central  PubMed  Google Scholar 

  165. Novick RP, Geisinger E (2008) Quorum sensing in staphylococci. Annu Rev Genet 42:541–564. https://doi.org/10.1146/annurev.genet.42.110807.091640

    Article  CAS  PubMed  Google Scholar 

  166. Hartmann T, Baronian G, Nippe N, Voss M, Schulthess B, Wolz C, Eisenbeis J, Schmidt-Hohagen K, Gaupp R, Sunderkotter C, Beisswenger C, Bals R, Somerville GA, Herrmann M, Molle V, Bischoff M (2014) The catabolite control protein E (CcpE) affects virulence determinant production and pathogenesis of Staphylococcus aureus. J Biol Chem 289(43):29701–29711. https://doi.org/10.1074/jbc.M114.584979

    Article  CAS  PubMed Central  PubMed  Google Scholar 

  167. Chakravarthy S, Butcher BG, Liu Y, D’Amico K, Coster M, Filiatrault MJ (2017) Virulence of Pseudomonas syringae pv. tomato DC3000 is influenced by the catabolite repression control protein Crc. Mol Plant Microbe Interact 30(4):283–294. https://doi.org/10.1094/mpmi-09-16-0196-r

    Article  CAS  PubMed  Google Scholar 

  168. Tuncil YE, Xiao Y, Porter NT, Reuhs BL, Martens EC, Hamaker BR (2017) Reciprocal prioritization to dietary glycans by gut bacteria in a competitive environment promotes stable coexistence. MBio 8(5):e01068-17. https://doi.org/10.1128/mbio.01068-17

    Article  PubMed Central  PubMed  Google Scholar 

  169. Riviere A, Selak M, Geirnaert A, Van den Abbeele P, De Vuyst L (2018) Complementary mechanisms for degradation of inulin-type fructans and arabinoxylan oligosaccharides among bifidobacterial strains suggest bacterial cooperation. Appl Environ Microbiol. https://doi.org/10.1128/AEM.02893-17

    Article  PubMed Central  PubMed  Google Scholar 

  170. Foster KR, Bell T (2012) Competition, not cooperation, dominates interactions among culturable microbial species. Curr Biol 22(19):1845–1850. https://doi.org/10.1016/j.cub.2012.08.005

    Article  CAS  PubMed  Google Scholar 

  171. Orazi G, O’Toole GA (2017) Pseudomonas aeruginosa alters Staphylococcus aureus sensitivity to vancomycin in a biofilm model of cystic fibrosis infection. MBio 8(4). https://doi.org/10.1128/mbio.00873-17

  172. Rhoads DD, Wolcott RD, Sun Y, Dowd SE (2012) Comparison of culture and molecular identification of bacteria in chronic wounds. Int J Mol Sci 13(3):2535–2550. https://doi.org/10.3390/ijms13032535

    Article  CAS  PubMed Central  PubMed  Google Scholar 

  173. Dalton T, Dowd SE, Wolcott RD, Sun Y, Watters C, Griswold JA, Rumbaugh KP (2011) An in vivo polymicrobial biofilm wound infection model to study interspecies interactions. PLoS One 6(11):e27317. https://doi.org/10.1371/journal.pone.0027317

    Article  CAS  PubMed Central  PubMed  Google Scholar 

  174. Gjodsbol K, Christensen JJ, Karlsmark T, Jorgensen B, Klein BM, Krogfelt KA (2006) Multiple bacterial species reside in chronic wounds: a longitudinal study. Int Wound J 3(3):225–231. https://doi.org/10.1111/j.1742-481X.2006.00159.x

    Article  PubMed  PubMed Central  Google Scholar 

  175. Kirketerp-Moller K, Jensen PO, Fazli M, Madsen KG, Pedersen J, Moser C, Tolker-Nielsen T, Hoiby N, Givskov M, Bjarnsholt T (2008) Distribution, organization, and ecology of bacteria in chronic wounds. J Clin Microbiol 46(8):2717–2722. https://doi.org/10.1128/JCM.00501-08

    Article  PubMed Central  PubMed  Google Scholar 

  176. Korgaonkar A, Trivedi U, Rumbaugh KP, Whiteley M (2013) Community surveillance enhances Pseudomonas aeruginosa virulence during polymicrobial infection. Proc Natl Acad Sci USA 110(3):1059–1064. https://doi.org/10.1073/pnas.1214550110

    Article  PubMed  Google Scholar 

  177. Murray JL, Connell JL, Stacy A, Turner KH, Whiteley M (2014) Mechanisms of synergy in polymicrobial infections. J Microbiol 52(3):188–199. https://doi.org/10.1007/s12275-014-4067-3

    Article  PubMed  PubMed Central  Google Scholar 

  178. Siddiqui AR, Bernstein JM (2010) Chronic wound infection: facts and controversies. Clin Dermatol 28(5):519–526. https://doi.org/10.1016/j.clindermatol.2010.03.009

    Article  PubMed  Google Scholar 

  179. Sanchez CJ Jr, Mende K, Beckius ML, Akers KS, Romano DR, Wenke JC, Murray CK (2013) Biofilm formation by clinical isolates and the implications in chronic infections. BMC Infect Dis 13:47. https://doi.org/10.1186/1471-2334-13-47

    Article  PubMed Central  PubMed  Google Scholar 

  180. Noto MJ, Burns WJ, Beavers WN, Skaar EP (2017) Mechanisms of pyocyanin toxicity and genetic determinants of resistance in Staphylococcus aureus. J Bacteriol 199(17):e00221-17. https://doi.org/10.1128/jb.00221-17

    Article  CAS  PubMed Central  PubMed  Google Scholar 

  181. DeLeon S, Clinton A, Fowler H, Everett J, Horswill AR, Rumbaugh KP (2014) Synergistic interactions of Pseudomonas aeruginosa and Staphylococcus aureus in an in vitro wound model. Infect Immun 82(11):4718–4728. https://doi.org/10.1128/IAI.02198-14

    Article  CAS  PubMed Central  PubMed  Google Scholar 

  182. Peters BM, Jabra-Rizk MA, O’May GA, Costerton JW, Shirtliff ME (2012) Polymicrobial interactions: impact on pathogenesis and human disease. Clin Microbiol Rev 25(1):193–213. https://doi.org/10.1128/CMR.00013-11

    Article  PubMed Central  PubMed  Google Scholar 

  183. Brileya KA, Camilleri LB, Zane GM, Wall JD, Fields MW (2014) Biofilm growth mode promotes maximum carrying capacity and community stability during product inhibition syntrophy. Front Microbiol 5:693. https://doi.org/10.3389/fmicb.2014.00693

    Article  PubMed Central  PubMed  Google Scholar 

  184. Hillesland KL, Stahl DA (2010) Rapid evolution of stability and productivity at the origin of a microbial mutualism. Proc Natl Acad Sci USA 107(5):2124–2129. https://doi.org/10.1073/pnas.0908456107

    Article  PubMed  PubMed Central  Google Scholar 

  185. de Mazancourt C, Schwartz MW (2010) A resource ratio theory of cooperation. Ecol Lett 13(3):349–359. https://doi.org/10.1111/j.1461-0248.2009.01431.x

    Article  PubMed  Google Scholar 

  186. Wang M, Schaefer AL, Dandekar AA, Greenberg EP (2015) Quorum sensing and policing of Pseudomonas aeruginosa social cheaters. Proc Natl Acad Sci USA 112(7):2187–2191. https://doi.org/10.1073/pnas.1500704112

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  187. Allegretta G, Maurer CK, Eberhard J, Maura D, Hartmann RW, Rahme L, Empting M (2017) In-depth profiling of MvfR-regulated small molecules in Pseudomonas aeruginosa after quorum sensing inhibitor treatment. Front Microbiol 8(MAY):1–12. https://doi.org/10.3389/fmicb.2017.00924

    Article  Google Scholar 

  188. Filkins LM, Graber JA, Olson DG, Dolben EL, Lynd LR, Bhuju S, O’Toole GA (2015) Coculture of Staphylococcus aureus with Pseudomonas aeruginosa Drives S. aureus towards fermentative metabolism and reduced viability in a cystic fibrosis model. J Bacteriol 197(14):2252–2264. https://doi.org/10.1128/jb.00059-15

    Article  CAS  PubMed Central  PubMed  Google Scholar 

  189. Das T, Kutty SK, Tavallaie R, Ibugo AI, Panchompoo J, Sehar S, Aldous L, Yeung AW, Thomas SR, Kumar N, Gooding JJ, Manefield M (2015) Phenazine virulence factor binding to extracellular DNA is important for Pseudomonas aeruginosa biofilm formation. Sci Rep 5:8398. https://doi.org/10.1038/srep08398

    Article  CAS  PubMed Central  PubMed  Google Scholar 

  190. Glasser NR, Kern SE, Newman DK (2014) Phenazine redox cycling enhances anaerobic survival in Pseudomonas aeruginosa by facilitating generation of ATP and a proton-motive force. Mol Microbiol 92(2):399–412. https://doi.org/10.1111/mmi.12566

    Article  CAS  PubMed Central  PubMed  Google Scholar 

  191. Deziel E, Lepine F, Milot S, He J, Mindrinos MN, Tompkins RG, Rahme LG (2004) Analysis of Pseudomonas aeruginosa 4-hydroxy-2-alkylquinolines (HAQs) reveals a role for 4-hydroxy-2-heptylquinoline in cell-to-cell communication. Proc Natl Acad Sci USA 101(5):1339–1344. https://doi.org/10.1073/pnas.0307694100

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  192. Meirelles LA, Newman DK (2018) Both toxic and beneficial effects of pyocyanin contribute to the lifecycle of Pseudomonas aeruginosa. Mol Microbiol 110(6):995–1010. https://doi.org/10.1111/mmi.14132

    Article  CAS  PubMed Central  PubMed  Google Scholar 

  193. Hall S, McDermott C, Anoopkumar-Dukie S, McFarland AJ, Forbes A, Perkins AV, Davey AK, Chess-Williams R, Kiefel MJ, Arora D, Grant GD (2016) Cellular effects of pyocyanin, a secreted virulence factor of Pseudomonas aeruginosa. Toxins (Basel) 8(8). https://doi.org/10.3390/toxins8080236

    Article  PubMed Central  Google Scholar 

  194. Price-Whelan A, Dietrich LE, Newman DK (2006) Rethinking ‘secondary’ metabolism: physiological roles for phenazine antibiotics. Nat Chem Biol 2(2):71–78. https://doi.org/10.1038/nchembio764

    Article  CAS  PubMed  Google Scholar 

  195. Hoffman LR, Deziel E, D’Argenio DA, Lepine F, Emerson J, McNamara S, Gibson RL, Ramsey BW, Miller SI (2006) Selection for Staphylococcus aureus small-colony variants due to growth in the presence of Pseudomonas aeruginosa. Proc Natl Acad Sci 103(52):19890–19895. https://doi.org/10.1073/pnas.0606756104

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  196. Mashburn LM, Jett AM, Akins DR, Whiteley M (2005) Staphylococcus aureus serves as an iron source for Pseudomonas aeruginosa during in vivo coculture. J Bacteriol 187(2):554–566. https://doi.org/10.1128/JB.187.2.554-566.2005

    Article  CAS  PubMed Central  PubMed  Google Scholar 

  197. Kinnersley M, Wenger J, Kroll E, Adams J, Sherlock G, Rosenzweig F (2014) Ex uno plures: clonal reinforcement drives evolution of a simple microbial community. PLoS Genet 10(6):e1004430. https://doi.org/10.1371/journal.pgen.1004430

    Article  CAS  PubMed Central  PubMed  Google Scholar 

  198. Kinnersley MA, Holben WE, Rosenzweig F (2009) E Unibus plurum: genomic analysis of an experimentally evolved polymorphism in Escherichia coli. PLoS Genet 5(11):e1000713. https://doi.org/10.1371/journal.pgen.1000713

    Article  CAS  PubMed Central  PubMed  Google Scholar 

  199. Bernstein HC, Paulson SD, Carlson RP (2012) Synthetic Escherichia coli consortia engineered for syntrophy demonstrate enhanced biomass productivity. J Biotechnol 157(1):159–166. https://doi.org/10.1016/j.jbiotec.2011.10.001

    Article  CAS  PubMed  Google Scholar 

  200. Beck AE, Hunt K, Bernstein HC, Carlson RP (2016) Interpreting and designing microbial communities for bioprocess applications, from components to interactions to emergent properties. In: Eckert CA, Trinh CT (eds) Biotechnology for biofuel production and optimization. Elsevier, Amsterdam, pp 407–432

    Chapter  Google Scholar 

  201. Carlson RP, Oshota OJ, Taffs RL (2012) Systems analysis of microbial adaptations to simultaneous stresses. In: Wang X, Chen J, Quinn P (eds) Reprogramming microbial metabolic pathways. Subcellular Biochemistry, vol 64. Springer, Dordrecht. https://doi.org/10.1007/978-94-007-5055-5_7

    Chapter  Google Scholar 

  202. Schepens D, Beck AE, Heys JJ, Gedeon T, Carlson RP (2017) The Benefits of resource partitioning and division of labor in microbial consortia. In: P Amar FK, V Norris (ed) Advances in Systems and Synthetic Biology EDP Sciences Publishing, pp 137–148

  203. Schepens D, Carlson RP, Heys J, Beck AE, Gedeon T (2019) Role of resource allocation and transport in emergence of cross-feeding in microbial consortia. J Theor Biol 467:150–163. https://doi.org/10.1016/j.jtbi.2019.01.030

    Article  PubMed  Google Scholar 

  204. Patel A, Carlson RP, Henson MA (2019) In silico metabolic design of two-strain biofilm systems predicts enhanced biomass production and biochemical synthesis. Biotechnol J 14(7):e1800511. https://doi.org/10.1002/biot.201800511

    Article  CAS  PubMed  Google Scholar 

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Acknowledgements

HP was supported by the Army Research Office award W911NF-16-1-0463, SLM was supported by the National Institutes of Health award U01EB019416, ADA was supported by the National Science Foundation award OIA 1736255, and RPC was supported by National Science Foundation award 1361240.

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  1. Department of Chemical and Biological Engineering, Montana State University, Bozeman, USA

    Heejoon Park & Ross P. Carlson

  2. Department of Microbiology and Immunology, Montana State University, Bozeman, USA

    S. Lee McGill, Adrienne D. Arnold & Ross P. Carlson

  3. Center for Biofilm Engineering, Montana State University, Bozeman, USA

    Heejoon Park, S. Lee McGill, Adrienne D. Arnold & Ross P. Carlson

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Park, H., McGill, S.L., Arnold, A.D. et al. Pseudomonad reverse carbon catabolite repression, interspecies metabolite exchange, and consortial division of labor. Cell. Mol. Life Sci. 77, 395–413 (2020). https://doi.org/10.1007/s00018-019-03377-x

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