Synthetically engineered microbes reveal interesting principles of cooperation

doi:10.1007/s11705-016-1605-z

Front. Chem. Sci. Eng.

2017, Vol. 11

Issue (1) : 3-14 https://doi.org/10.1007/s11705-016-1605-z

REVIEW ARTICLE

Synthetically engineered microbes reveal interesting principles of cooperation

Michael D. Dressler^1,²,Corey J. Clark^1,²,Chelsea A. Thachettu²,Yasmine Zakaria²,Omar Tonsi Eldakar²,Robert P. Smith²(

)

¹. Department of Marine Biology and Environmental Science, Halmos College of Natural Sciences and Oceanography, Nova Southeastern University, Fort Lauderdale, FL 33314-7796, USA
². Department of Biological Sciences, Halmos College of Natural Sciences and Oceanography, Nova Southeastern University, Fort Lauderdale, FL 33314-7796, USA

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Abstract

Cooperation is ubiquitous in biological systems. However, if natural selection favors traits that confer an advantage to one individual over another, then helping others would be paradoxical. Nevertheless, cooperation persists and is critical in maintaining homeostasis in systems ranging from populations of bacteria to groupings of mammals. Developing an understanding of the dynamics and mechanisms by which cooperation operates is critical in understanding ecological and evolutionary relationships. Over the past decade, synthetic biology has emerged as a powerful tool to study social dynamics. By engineering rationally controlled and modulatable behavior into microbes, we have increased our overall understanding of how cooperation enhances, or conversely constrains, populations. Furthermore, it has increased our understanding of how cooperation is maintained within populations, which may provide a useful framework to influence populations by altering cooperation. As many bacterial pathogens require cooperation to infect the host and survive, the principles developed using synthetic biology offer promise of developing novel tools and strategies to treat infections, which may reduce the use of antimicrobial agents. Overall, the use of engineered cooperative microbes has allowed the field to verify existing, and develop novel, theories that may govern cooperative behaviors at all levels of biology.

Keywords synthetic biology engineered bacteria cooperation cheater quorum sensing

Corresponding Author(s): Robert P. Smith

Online First Date: 13 December 2016 Issue Date: 17 March 2017

Cite this article:

Michael D. Dressler,Corey J. Clark,Chelsea A. Thachettu, et al. Synthetically engineered microbes reveal interesting principles of cooperation[J]. Front. Chem. Sci. Eng., 2017, 11(1): 3-14.

URL:

https://academic.hep.com.cn/fcse/EN/10.1007/s11705-016-1605-z
https://academic.hep.com.cn/fcse/EN/Y2017/V11/I1/3

Fig.1 The dynamics of a single population cooperating. (a) The core behavior of quorum sensing. Bacteria release an autoinducer to sense cell density (triangles). At low cell density, a low concentration of autoinducer is produced and the population does not activate expression of autoinducer-regulated genes. However, at high cell density, a sufficiently high concentration of autoinducer is produced, which activates expression of autoinducer-regulated genes. Expression of this gene results in a benefit to the population; (b) to verify the core behavior of quorum sensing, Darch et al. used an engineered strain of bacteria that would gain a growth benefit by digesting BSA via quorum sensing regulated genes [30]. As the density of the bacteria was increased, there was an increased benefit from digesting BSA as evidence through increased growth; (c) the Allee effect. Below a minimal initial density, cooperation is not initiated. Here, the population has a negative growth rate and tends towards extinction [12]. Above this minimal initial cell density, cooperation is initiated and the population grows; (d) using a strain of cooperative engineered bacteria with an Allee effect, it was observed that if the dispersal rate of a population was too slow or too fast, growth of the total population was reduced, or could lead to extinction [39]. Spread and maximal growth occurred at intermediate dispersal rates

Fig.2 Cooperation between communities leads to rich dynamical behavior. (a) An engineered community that cooperates to resist antibiotics [73]. Each strain produces a different AHL (ovals and diamond shapes) that activates gene expression in the alternate strain. kanR confers that ability to break down and resist the antibiotic kanamycin. ampR confers the ability to break down and resist the antibiotics ampicillin; (b) the degree of mutualism between the strains is affected by the concentrations of the antibiotics. As the concentrations of antibiotics increases, the degree of mutualism increases until the concentration of antibiotics is too high, which leads to death of both populations; (c) previous studies have shown that crossfeeding auxotrophic strains exist in the natural environment, suggesting that this behavior might have an adaptive significance. It is hypothesized that this natural crossfeeding may reduce the metabolic burden of each individual in the crossfeeding pair. Symbols represent two different essential metabolites; (d) using engineered auxotrophic E. coli that require different amino acids, it was observed that highly cooperative partnerships involved the crossfeeding of amino acids that are metabolically expensive to produce [75]. Otherwise, the crossfeeding of amino acids that are relative inexpensive to produce did not facilitate cooperative interactions

Fig.3 Examining predator and prey interactions using engineered bacteria. (a) Engineered predator and prey bacteria [77]. The predator produces an autoinducer (triangles) that kills the prey. The prey produces an autoinducer (circles) that rescues the predator; (b) dilution rate (dispersal rate) in the experiment controls the behavior of the two strains. At low dilution rates, the predator and prey have sustained, offset oscillations. As the dilution rate is increased, the oscillations become dampened. Sufficiently high dilution rates cause population extinction

Fig.4 Understanding how competition affects the relationship between cooperator and cheater strains. (a) Celiker and Gore used a pair of cooperator and cheating yeast strains and cultured them in the presence of a competitor E. coli, that could not take advantage of the public good but would compete for nutrients (left panel) [92]. They observed that as the fraction of competitor E. coli increased, the amount of cooperators increased (right panel); (b) using a pair of cooperator and cheating yeast strains, Chen et al. observed that as a growth environment deteriorates, which was performed through extreme dilution, the fraction of cooperators increased [97]. This was due to decreased competition between cooperators and cheaters. Eventually, a sufficientdecrease in environmental quality does not allow the survival of cooperators

1	Nowak M A. Five rules for the evolution of cooperation. Science, 2006, 314(5805): 1560–1563 https://doi.org/10.1126/science.1133755
2	Axelrod R, Hamilton W D. The evolution of cooperation. Science, 1981, 211(4489): 1390–1396 https://doi.org/10.1126/science.7466396
3	Fehr E, Fischbacher U. Social norms and human cooperation. Trends in Cognitive Sciences, 2004, 8(4): 185–190 https://doi.org/10.1016/j.tics.2004.02.007
4	Shan W, Hamilton W. Country—specific advantage and international cooperation. Strategic Management Journal, 1991, 12(6): 419–432 https://doi.org/10.1002/smj.4250120603
5	Hardin G. The tragedy of the commons. Science, 1968, 162(3859): 1243–1248 https://doi.org/10.1126/science.162.3859.1243
6	Feeny D, Berkes F, McCay B J, Acheson J M. The tragedy of the commons: twenty-two years later. Human Ecology, 1990, 18(1): 1–19 https://doi.org/10.1007/BF00889070
7	Hamilton W. The evolution of altruistic behavior. American Naturalist, 1963, 97(896): 354–356 https://doi.org/10.1086/497114
8	Eldakar O T, Wilson D S. Eight criticisms not to make about group selection. Evolution, 2011, 65(6): 1523–1526 https://doi.org/10.1111/j.1558-5646.2011.01290.x
9	Wilson D S, Wilson E O. Rethinking the theoretical foundation of sociobiology. Quarterly Review of Biology, 2007, 82(4): 327–348 https://doi.org/10.1086/522809
10	Rapoport A, Chammah A M. Prisoner’s dilemma: A study in conflict and cooperation. Michigan: University of Michigan press, 1965: 31–44
11	Doebeli M, Hauert C. Models of cooperation based on the Prisoner’s Dilemma and the Snowdrift game. Ecology Letters, 2005, 8(7): 748–766 https://doi.org/10.1111/j.1461-0248.2005.00773.x
12	Allee W C. Cooperation among animals. American Journal of Sociology, 1951, 1: 93–95
13	Seger J. Cooperation and conflict in social insects. Behavioural Ecology: An Evolutionary Approach, 1991, 338–373
14	West S A, El Mouden C, Gardner A. Sixteen common misconceptions about the evolution of cooperation in humans. Evolution and Human Behavior, 2011, 32(4): 231–262 https://doi.org/10.1016/j.evolhumbehav.2010.08.001
15	Gintis H, Bowles S, Boyd R, Fehr E. Explaining altruistic behavior in humans. Evolution and Human Behavior, 2003, 24(3): 153–172 https://doi.org/10.1016/S1090-5138(02)00157-5
16	Sober E, Wilson D S. Unto others: The evolution and psychology of unselfish behavior. Massachusetts: Harvard University Press, 1999, 6–14
17	Tanouchi Y, Smith R, You L. Engineering microbial systems to explore ecological and evolutionary dynamics. Current Opinion in Biotechnology, 2012, 23(5): 791–797 https://doi.org/10.1016/j.copbio.2012.01.006
18	Benner S A, Sismour A M. Synthetic biology. Nature Reviews. Genetics, 2005, 6(7): 533–543 https://doi.org/10.1038/nrg1637
19	Jusiak B, Daniel R, Farzadfard F, Nissim L, Purcell O, Rubens J, Lu T K. Synthetic gene circuits. Reviews in Cell Biology and Molecular Medicine, 2014, 1‒56
20	Khalil A S, Collins J J. Synthetic biology: Applications come of age. Nature Reviews. Genetics, 2010, 11(5): 367–379 https://doi.org/10.1038/nrg2775
21	Bracho O R, Manchery C, Haskell E C, Blanar C A, Smith R P. Circumvention of learning increases intoxication efficacy of nematicidal engineered bacteria. ACS Synthetic Biology, 2016, 5(3): 241–249 https://doi.org/10.1021/acssynbio.5b00192
22	Escalante A E, Rebolleda-Gómez M, Benítez M, Travisano M. Ecological perspectives on synthetic biology: Insights from microbial population biology. Frontiers in Microbiology, 2015, 6: 1–10 https://doi.org/10.3389/fmicb.2015.00143
23	Pianka E R. On r- and K-selection. American Naturalist, 1970, 104(940): 592–597 https://doi.org/10.1086/282697
24	Miller M B, Bassler B L. Quorum sensing in bacteria. Annual Review of Microbiology, 2001, 55(1): 165–199 https://doi.org/10.1146/annurev.micro.55.1.165
25	Berendsen R L, Pieterse C M, Bakker P A. The rhizosphere microbiome and plant health. Trends in Plant Science, 2012, 17(8): 478–486 https://doi.org/10.1016/j.tplants.2012.04.001
26	Antunes L C M, Ferreira R B R, Buckner M M C, Finlay B B. Quorum sensing in bacterial virulence. Microbiology, 2010, 156(8): 2271–2282 https://doi.org/10.1099/mic.0.038794-0
27	De Kievit T R, Gillis R, Marx S, Brown C, Iglewski B H. Quorum-sensing genes in Pseudomonas aeruginosa biofilms: Their role and expression patterns. Applied and Environmental Microbiology, 2001, 67(4): 1865–1873 https://doi.org/10.1128/AEM.67.4.1865-1873.2001
28	De Kievit T R, Iglewski B H. Bacterial quorum sensing in pathogenic relationships. Infection and Immunity, 2000, 68(9): 4839–4849 https://doi.org/10.1128/IAI.68.9.4839-4849.2000
29	Stewart P S, Costerton J W. Antibiotic resistance of bacteria in biofilms. Lancet, 2001, 358(9276): 135–138 https://doi.org/10.1016/S0140-6736(01)05321-1
30	Darch S E, West S A, Winzer K, Diggle S P. Density-dependent fitness benefits in quorum-sensing bacterial populations. Proceedings of the National Academy of Sciences, 2012: 8259–8263
31	Pai A, Tanouchi Y, You L. Optimality and robustness in quorum sensing (QS)-mediated regulation of a costly public good enzyme. Proceedings of the National Academy of Sciences of the United States of America, 2012, 109(48): 19810–19815 https://doi.org/10.1073/pnas.1211072109
32	An J H, Goo E, Kim H, Seo Y S, Hwang I. An J H, Goo E, Kim H, Seo Y-S, Hwang I. Bacterial quorum sensing and metabolic slowing in a cooperative population. Proceedings of the National Academy of Sciences of the United States of America, 2014, 111(41): 14912–14917 https://doi.org/10.1073/pnas.1412431111
33	Allee W, Emerson A, Park O, Park T, Schmidt K. Principles of Animal Ecology. Philadelphia, Pennsylvania, USA, 1949, 416–425
34	Driscoll W W, Espinosa N J, Eldakar O T, Hackett J D. Allelopathy as an emergent, exploitable public good in the bloom-forming microalga Prymnesium parvum. Evolution, 2013, 67(6): 1582–1590 https://doi.org/10.1111/evo.12030
35	Liebhold A M, Tobin P C. Exploiting the Achilles heels of pest invasions: Allee effects, stratified dispersal and management of forest insect establishment and spread. New Zealand Journal of Forestry Science, 2010, 40: S25–S33
36	Robinet C, Lance D R, Thorpe K W, Onufrieva K S, Tobin P C, Liebhold A M. Dispersion in time and space affect mating success and Allee effects in invading gypsy moth populations. Journal of Animal Ecology, 2008, 77(5): 966–973 https://doi.org/10.1111/j.1365-2656.2008.01417.x
37	Tobin P C, Berec L, Liebhold A M. Exploiting Allee effects for managing biological invasions. Ecology Letters, 2011, 14(6): 615–624 https://doi.org/10.1111/j.1461-0248.2011.01614.x
38	Hackney E E, McGraw J B. Experimental demonstration of an Allee effect in American ginseng. Conservation Biology, 2001, 15(1): 129–136 https://doi.org/10.1111/j.1523-1739.2001.98546.x
39	Smith R, Tan C, Srimani J, Pai A, Riccione K, Song H, You L. Programmed Allee effect results in a tradeoff between population spread and survival. Proceedings of the National Academy of Sciences of the United States of America, 2014, 111(5): 1969–1974 https://doi.org/10.1073/pnas.1315954111
40	Myers R A, Hutchings J A, Barrowman N J. Why do fish stocks collapse? The example of cod in Atlantic Canada. Ecological Applications, 1997, 7(1): 91–106 https://doi.org/10.1890/1051-0761(1997)007[0091:WDFSCT]2.0.CO;2
41	Myers R, Barrowman N, Hutchings J, Rosenberg A. Population dynamics of exploited fish stocks at low population levels. Science, 1995, 269(5227): 1106–1108 https://doi.org/10.1126/science.269.5227.1106
42	Dai L, Vorselen D, Korolev K S, Gore J. Generic indicators for loss of resilience before a tipping point leading to population collapse. Science, 2012, 336(6085): 1175–1177 https://doi.org/10.1126/science.1219805
43	Dai L, Korolev K S, Gore J. Relation between stability and resilience determines the performance of early warning signals under different environmental drivers. Proceedings of the National Academy of Sciences of the United States of America, 2015, 112(32): 10056–10061 https://doi.org/10.1073/pnas.1418415112
44	Liebhold A M, Tobin P C. Population ecology of insect invasions and their management. Annual Review of Entomology, 2008, 53(1): 387–408 https://doi.org/10.1146/annurev.ento.52.110405.091401
45	Visick K L, Foster J, Doino J, McFall-Ngai M, Ruby E G. Vibrio fischeri lux genes play an important role in colonization and development of the host light organ. Journal of Bacteriology, 2000, 182(16): 4578–4586 https://doi.org/10.1128/JB.182.16.4578-4586.2000
46	Bahassi E M, O’Dea M H, Allali N, Messens J, Gellert M, Couturier M. Interactions of CcdB with DNA gyrase. Journal of Biological Chemistry, 1999, 274(16): 10936–10944 https://doi.org/10.1074/jbc.274.16.10936
47	Dai L, Korolev K S, Gore J. Slower recovery in space before collapse of connected populations. Nature, 2013, 496(7445): 355–358 https://doi.org/10.1038/nature12071
48	Ratzke C, Gore J. Self-organized patchiness facilitates survival in a cooperatively growing Bacillus subtilis population. Nature Microbiology, 2016: 16022
49	Wong C M, Zhou Y, Ng R W, Kung H F, Jin D Y. Cooperation of yeast peroxiredoxins Tsa1p and Tsa2p in the cellular defense against oxidative and nitrosative stress. Journal of Biological Chemistry, 2002, 277(7): 5385–5394 https://doi.org/10.1074/jbc.M106846200
50	Boulant J A. Hypothalamic mechanisms in thermoregulation. Federation Proceedings, 1981, 40(14): 2843-50
51	Stephens P A, Frey-Roos F, Arnold W, Sutherland W J. Model complexity and population predictions. The alpine marmot as a case study. Journal of Animal Ecology, 2002, 71(2): 343–361 https://doi.org/10.1046/j.1365-2656.2002.00605.x
52	Liermann H, Hilborn. Depensation: Evidence, models and implications. Fish and Fisheries, 2001, 2(1): 33–58 https://doi.org/10.1046/j.1467-2979.2001.00029.x
53	Aizenman E, Engelberg-Kulka H, Glaser G. An Escherichia coli chromosomal “addiction module” regulated by guanosine 3',5'-bispyrophosphate: A model for programmed bacterial cell death. Proceedings of the National Academy of Sciences of the United States of America, 1996, 93(12): 6059–6063 https://doi.org/10.1073/pnas.93.12.6059
54	Misselwitz B, Barrett N, Kreibich S, Vonaesch P, Andritschke D, Rout S, Weidner K, Sormaz M, Songhet P, Horvath P, Chabria M, Vogel V, Spori D M, Jenny P, Hardt W D. Near surface swimming of Salmonella typhimurium explains target-site selection and cooperative invasion. PLoS Pathogens, 2012, 8(7): e1002810 https://doi.org/10.1371/journal.ppat.1002810
55	Tan C, Smith R P, Srimani J, Riccione K, Prasada S, Kuehn M, You L. The inoculum effect and band-pass bacterial response to periodic antibiotic treatment. Molecular Systems Biology, 2012, 8(1): 679–688
56	Lee H H, Molla M N, Cantor C R, Collins J J. Bacterial charity work leads to population-wide resistance. Nature, 2010, 467(7311): 82–85 https://doi.org/10.1038/nature09354
57	Vega N M, Allison K R, Samuels A N, Klempner M S, Collins J J. Salmonella typhimurium intercepts Escherichia coli signaling to enhance antibiotic tolerance. Proceedings of the National Academy of Sciences of the United States of America, 2013, 110(35): 14420–14425 https://doi.org/10.1073/pnas.1308085110
58	Meredith H R, Srimani J K, Lee A J, Lopatkin A J, You L. Collective antibiotic tolerance: Mechanisms, dynamics and intervention. Nature Chemical Biology, 2015, 11(3): 182–188 https://doi.org/10.1038/nchembio.1754
59	Nedelcu A M, Driscoll W W, Durand P M, Herron M D, Rashidi A. On the paradigm of altruistic suicide in the unicellular world. Evolution, 2011, 65(1): 3–20 https://doi.org/10.1111/j.1558-5646.2010.01103.x
60	Ackermann M, Stecher B, Freed N E, Songhet P, Hardt W D, Doebeli M. Self-destructive cooperation mediated by phenotypic noise. Nature, 2008, 454(7207): 987–990 https://doi.org/10.1038/nature07067
61	Rice K C, Bayles K W. Death’s toolbox: Examining the molecular components of bacterial programmed cell death. Molecular Microbiology, 2003, 50(3): 729–738 https://doi.org/10.1046/j.1365-2958.2003.t01-1-03720.x
62	Ameisen J C. The origin of programmed cell death. Science, 1996, 272(5266): 1278–1279 https://doi.org/10.1126/science.272.5266.1278
63	Brown S P, West S A, Diggle S P, Griffin A S. Social evolution in micro-organisms and a Trojan horse approach to medical intervention strategies. Philosophical Transactions of the Royal Society of London B: Biological Sciences, 2009, 364(1533): 3157–3168
64	Moran N A, Degnan P H, Santos S R, Dunbar H E, Ochman H. The players in a mutualistic symbiosis: Insects, bacteria, viruses, and virulence genes. Proceedings of the National Academy of Sciences of the United States of America, 2005, 102(47): 16919–16926 https://doi.org/10.1073/pnas.0507029102
65	Breznak J A. Symbiotic relationships between termites and their intestinal microbiota. Symposia of the Society for Experimental Biology, 1975, 29: 559–580
66	Glaser R. The intracellular bacteria of the cockroach in relation to symbiosis. Journal of Parasitology, 1946, 32(5): 483–489 https://doi.org/10.2307/3272922
67	Uhlig H H, Powrie F. Dendritic cells and the intestinal bacterial flora: a role for localized mucosal immune responses. Journal of Clinical Investigation, 2003, 112(5): 648–651 https://doi.org/10.1172/JCI19545
68	Wintermute E H, Silver P A. Dynamics in the mixed microbial concourse. Genes & Development, 2010, 24(23): 2603–2614 https://doi.org/10.1101/gad.1985210
69	Wintermute E H, Silver P A. Emergent cooperation in microbial metabolism. Molecular Systems Biology, 2010, 6(1): 820–833
70	Shou W, Ram S, Vilar J M G. Synthetic cooperation in engineered yeast populations. Proceedings of the National Academy of Sciences of the United States of America, 2007, 104(6): 1877–1882 https://doi.org/10.1073/pnas.0610575104
71	Brenner K, Karig D K, Weiss R, Arnold F H. Engineered bidirectional communication mediates a consensus in a microbial biofilm consortium. Proceedings of the National Academy of Sciences of the United States of America, 2007, 104(44): 17300–17304 https://doi.org/10.1073/pnas.0704256104
72	Brenner K, You L, Arnold F H. Engineering microbial consortia: A new frontier in synthetic biology. Trends in Biotechnology, 2008, 26(9): 483–489 https://doi.org/10.1016/j.tibtech.2008.05.004
73	Hu B, Du J, Zou R Y, Yuan Y J. An environment-sensitive synthetic microbial ecosystem. PLoS One, 2010, 5(5): e10619 https://doi.org/10.1371/journal.pone.0010619
74	Kerner A, Park J, Williams A, Lin X N. A programmable Escherichia coli consortium via tunable symbiosis. PLoS One, 2012, 7(3): e34032 https://doi.org/10.1371/journal.pone.0034032
75	Mee M T, Collins J J, Church G M, Wang H H. Syntrophic exchange in synthetic microbial communities. Proceedings of the National Academy of Sciences of the United States of America, 2014, 111(20): 2149–2156 https://doi.org/10.1073/pnas.1405641111
76	Berryman A A. The orgins and evolution of predator-prey theory. Ecology, 1992, 73(5): 1530–1535 https://doi.org/10.2307/1940005
77	Balagadde F K, Song H, Ozaki J, Collins C H, Barnet M, Arnold F H, Quake S R, You L. A synthetic Escherichia coli predator-prey ecosystem. Molecular Systems Biology, 2008, 4: 187 https://doi.org/10.1038/msb.2008.24
78	Wangersky P J. Lotka-Volterra population models. Annual Review of Ecology and Systematics, 1978, 9(1): 189–218 https://doi.org/10.1146/annurev.es.09.110178.001201
79	Sun G Q, Jin Z, Liu Q X, Li L. Dynamical complexity of a spatial predator-prey model with migration. Ecological Modelling, 2008, 219(1-2): 248–255 https://doi.org/10.1016/j.ecolmodel.2008.08.009
80	Yuan S, Xu C, Zhang T. Spatial dynamics in a predator-prey model with herd behavior. Chaos (Woodbury, N.Y.), 2013, 23(3): 033102 https://doi.org/10.1063/1.4812724
81	Song H, Payne S, Gray M, You L. Spatiotemporal modulation of biodiversity in a synthetic chemical-mediated ecosystem. Nature Chemical Biology, 2009, 5(12): 929–935 https://doi.org/10.1038/nchembio.244
82	Yamamura N, Higashi M, Behera N, Yuichiro Wakano J. Evolution of mutualism through spatial effects. Journal of Theoretical Biology, 2004, 226(4): 421–428 https://doi.org/10.1016/j.jtbi.2003.09.016
83	Poisot T, Bever J D, Thrall P H, Hochberg M E. Dispersal and spatial heterogeneity allow coexistence between enemies and protective mutualists. Ecology and Evolution, 2014, 4(19): 3841–3850 https://doi.org/10.1002/ece3.1151
84	Park J, Kerner A, Burns M A, Lin X N. Microdroplet-enabled highly parallel co-cultivation of microbial communities. PLoS One, 2011, 6(2): e17019 https://doi.org/10.1371/journal.pone.0017019
85	Wilson W, Morris W, Bronstein J. Coexistence of mutualists and exploiters on spatial landscapes. Ecological Monographs, 2003, 73(3): 397–413 https://doi.org/10.1890/02-0297
86	Brenner K, Arnold F H. Self-organization, layered structure, and aggregation enhance persistence of a synthetic biofilm consortium. PLoS One, 2011, 6(2): e16791 https://doi.org/10.1371/journal.pone.0016791
87	Chuang J S, Rivoire O, Leibler S. Cooperation and Hamilton’s rule in a simple synthetic microbial system. Molecular Systems Biology, 2010, 6: 398 https://doi.org/10.1038/msb.2010.57
88	Chuang J S, Rivoire O, Leibler S. Simpson’s paradox in a synthetic microbial system. Science, 2009, 323(5911): 272–275 https://doi.org/10.1126/science.1166739
89	Gore J, Youk H, van Oudenaarden A. Snowdrift game dynamics and facultative cheating in yeast. Nature, 2009, 459(7244): 253–256 https://doi.org/10.1038/nature07921
90	Griffin A S, West S A, Buckling A. Cooperation and competition in pathogenic bacteria. Nature, 2004, 430(7003): 1024–1027 https://doi.org/10.1038/nature02744
91	West S A, Pen I, Griffin A S. Cooperation and competition between relatives. Science, 2002, 296(5565): 72–75 https://doi.org/10.1126/science.1065507
92	Celiker H, Gore J. Competition between species can stabilize public—goods cooperation within a species. Molecular Systems Biology, 2012, 8(1): 621
93	Bergstrom T, Blume L, Varian H. On the private provision of public goods. Journal of Public Economics, 1986, 29(1): 25–49 https://doi.org/10.1016/0047-2727(86)90024-1
94	Driscoll W W, Pepper J W. Theory for the evolution of diffusible external goods. Evolution, 2010, 64(9): 2682–2687 https://doi.org/10.1111/j.1558-5646.2010.01002.x
95	Zhang F, Kwan A, Xu A, Süel G M. A synthetic quorum sensing system reveals a potential private benefit for public good production in a biofilm. PLoS One, 2015, 10(7): e0132948 https://doi.org/10.1371/journal.pone.0132948
96	Waite A J, Shou W. Adaptation to a new environment allows cooperators to purge cheaters stochastically. Proceedings of the National Academy of Sciences of the United States of America, 2012, 109(47): 19079–19086 https://doi.org/10.1073/pnas.1210190109
97	Chen A, Sanchez A, Dai L, Gore J. Dynamics of a producer-freeloader ecosystem on the brink of collapse. Nature Communications, 2014, 5: 3713
98	Venturi V, Bertani I, Kerényi Á, Netotea S, Pongor S. Co-swarming and local collapse: Quorum sensing conveys resilience to bacterial communities by localizing cheater mutants in Pseudomonas aeruginosa. PLoS One, 2010, 5(4): e9998 https://doi.org/10.1371/journal.pone.0009998
99	Bihary D, Tóth M, Kerényi Á, Venturi V, Pongor S. Modeling bacterial quorum sensing in open and closed environments: potential discrepancies between agar plate and culture flask experiments. Journal of Molecular Modeling, 2014, 20(7): 1–6 https://doi.org/10.1007/s00894-014-2248-y
100	Pepper J W. The evolution of bacterial social life: From the ivory tower to the front lines of public health. Evolution, Medicine, and Public Health, 2014, 2014(1): 65–68 https://doi.org/10.1093/emph/eou010
101	Ross-Gillespie A, Weigert M, Brown S P, Kümmerli R. Gallium-mediated siderophore quenching as an evolutionarily robust antibacterial treatment. Evolution, Medicine, and Public Health, 2014, 2014(1): 18–29 https://doi.org/10.1093/emph/eou003
102	Hood M I, Skaar E P. Nutritional immunity: Transition metals at the pathogen-host interface. Nature Reviews. Microbiology, 2012, 10(8): 525–537 https://doi.org/10.1038/nrmicro2836
103	Skaar E P. The battle for iron between bacterial pathogens and their vertebrate hosts. PLoS Pathogens, 2010, 6(8): e1000949 https://doi.org/10.1371/journal.ppat.1000949
104	Köhler T, Buckling A, van Delden C. Cooperation and virulence of clinical Pseudomonas aeruginosa populations. Proceedings of the National Academy of Sciences of the United States of America, 2009, 106(15): 6339–6344 https://doi.org/10.1073/pnas.0811741106
105	Merlo L M F, Pepper J W, Reid B J, Maley C C. Cancer as an evolutionary and ecological process. Nature Reviews. Cancer, 2006, 6(12): 924–935 https://doi.org/10.1038/nrc2013
106	Pepper J W. Defeating pathogen drug resistance: Guidance from evolutionary theory. Evolution, 2008, 62(12): 3185–3191 https://doi.org/10.1111/j.1558-5646.2008.00525.x
107	Boehm T, Folkman J, Browder T, O’Reilly M S. Antiangiogenic therapy of experimental cancer does not induce acquired drug resistance. Nature, 1997, 390(6658): 404–407 https://doi.org/10.1038/37126
108	Folkman J. Angiogenesis. Annual Review of Medicine, 2006, 57: 1–18 https://doi.org/10.1146/annurev.med.57.121304.131306
109	Duan F, March J C. Engineered bacterial communication prevents Vibrio cholerae virulence in an infant mouse model. Proceedings of the National Academy of Sciences of the United States of America, 2010, 107(25): 11260–11264 https://doi.org/10.1073/pnas.1001294107
110	Saeidi N, Wong C K, Lo T M, Nguyen H X, Ling H, Leong S S J, Poh C L, Chang M W. Engineering microbes to sense and eradicate Pseudomonas aeruginosa, a human pathogen. Molecular Systems Biology, 2011, 7(1): 521 https://doi.org/10.1038/msb.2011.55

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