Genome-driven evolutionary game theory helps understand the rise of metabolic interdependencies in microbial communities

Nature Communications

Volumn 8, Issue 1, 2017, Pages

  Zomorrodi, Ali R ^a,b,c   Segrè, Daniel ^a,b,c  

^a BOSTON UNIVERSITY   (United States)

^b Boston University   (United States)

^c Boston University   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

AMINO ACID;

AMINO ACID; COLIFORM BACTERIUM; EPISTASIS; EVOLUTIONARY THEORY; EXPERIMENTAL STUDY; FITNESS; GAME THEORY; GENOME; METABOLISM; MICROBIAL COMMUNITY; PLEIOTROPY;

ARTICLE; BACTERIAL MICROBIOME; COMPUTER MODEL; ECOLOGY; EPISTASIS; ESCHERICHIA COLI; EVOLUTIONARY GAME THEORY; GAME; GENOMICS; HYBRID; METABOLITE; MICROBIAL COMMUNITY; MICROBIAL CONSORTIUM; MICROBIAL GENOME; MICROBIAL METABOLISM; MOLECULAR EVOLUTION; NONHUMAN; PLEIOTROPY; SPECIES COMPARISON; SPECIES DIVERSITY; YEAST; ALGORITHM; BIOLOGICAL MODEL; ECOSYSTEM; ENERGY METABOLISM; GENETICS; METAGENOME; MICROFLORA; ORGANISMAL INTERACTION;

ESCHERICHIA COLI;

ALGORITHMS; ECOSYSTEM; ENERGY METABOLISM; EVOLUTION, MOLECULAR; GAME THEORY; METAGENOME; MICROBIAL INTERACTIONS; MICROBIOTA; MODELS, GENETIC;

EID: 85034425139     PISSN: None     EISSN: 20411723     Source Type: Journal    
DOI: 10.1038/s41467-017-01407-5     Document Type: Article

References (55)

1. Zelezniak, A. et al. Metabolic dependencies drive species co-occurrence in diverse microbial communities. Proc. Natl Acad. Sci. USA 112, 6449-6454 (2015).
2. Freilich, S. et al. Competitive and cooperative metabolic interactions in bacterial communities. Nat. Commun. 2, 589 (2011).
3. Wintermute, E. H. & Silver, P. A. Emergent cooperation in microbial metabolism. Mol. Syst. Biol. 6, 407 (2010).
4. Mee, M. T., Collins, J. J., Church, G. M. & Wang, H. H. Syntrophic exchange in synthetic microbial communities. Proc. Natl Acad. Sci. USA 111, E2149-E2156 (2014).
5. Shou, W., Ram, S. & Vilar, J. M. Synthetic cooperation in engineered yeast populations. Proc. Natl Acad. Sci. USA 104, 1877-1882 (2007).
6. Harcombe, W. Novel cooperation experimentally evolved between species. Evolution 64, 2166-2172 (2010).
7. Hoek, T. A. et al. Resource availability modulates the cooperative and competitive nature of a microbial cross-feeding mutualism. PLoS Biol. 14, e1002540 (2016).
8. Zhang, X. & Reed, J. L. Adaptive evolution of synthetic cooperating communities improves growth performance. PLoS ONE 9, e108297 (2014).
9. Morris, J. J., Lenski, R. E. & Zinser, E. R. The Black Queen Hypothesis: evolution of dependencies through adaptive gene loss. MBio 3, e00036-12 (2012).
10. Morris, J. J. Black Queen evolution: the role of leakiness in structuring microbial communities. Trends Genet. 31, 475-482 (2015).
11. Oliveira, N. M., Niehus, R. & Foster, K. R. Evolutionary limits to cooperation in microbial communities. Proc. Natl Acad. Sci. USA 111, 17941-17946 (2014).
12. Mas, A., Jamshidi, S., Lagadeuc, Y., Eveillard, D. & Vandenkoornhuyse, P. Beyond the Black Queen Hypothesis. ISME J. https://doi.org/10.1038/ismej.2016.22 (2016).
13. Estrela, S., Morris, J. J. & Kerr, B. Private benefits and metabolic conflicts shape the emergence of microbial interdependencies. Environ. Microbiol 18, 1415-1427 (2016).
14. Germerodt, S. et al. Pervasive selection for cooperative cross-feeding in bacterial communities. PLoS Comput. Biol 12, e1004986 (2016).
15. Frey, E. Evolutionary game theory: theoretical concepts and applications to microbial communities. Phys. A 389, 4265-4298 (2010).
16. Hummert, S. et al. Evolutionary game theory: cells as players. Mol. Biosyst. 10, 3044-3065 (2014).
17. Blaser, M. J. & Kirschner, D. The equilibria that allow bacterial persistence in human hosts. Nature 449, 843-849 (2007).
18. Ruppin, E., Papin, J. A., de Figueiredo, L. F. & Schuster, S. Metabolic reconstruction, constraint-based analysis and game theory to probe genomescale metabolic networks. Curr. Opin. Biotechnol. 21, 502-510 (2010).
19. Tasoff, J., Mee, M. T. & Wang, H. H. An economic framework of microbial trade. PLoS ONE 10, e0132907 (2015).
20. Becker, S. A. et al. Quantitative prediction of cellular metabolism with constraint-based models: the COBRA toolbox. Nat. Protoc. 2, 727-738 (2007).
21. Stolyar, S. et al. Metabolic modeling of a mutualistic microbial community. Mol. Syst. Biol. 3, 92 (2007).
22. Klitgord, N. & Segrè, D. Environments that induce synthetic microbial ecosystems. PLoS Comput. Biol. 6, e1001002 (2010).
23. Zomorrodi, A. R. & Maranas, C. D. OptCom: a multi-level optimization framework for the metabolic modeling and analysis of microbial communities. PLoS Comput. Biol. 8, e1002363 (2012).
24. Zhuang, K. et al. Genome-scale dynamic modeling of the competition between Rhodoferax and Geobacter in anoxic subsurface environments. ISME J. 5, 305-316 (2011).
25. Khandelwal, R. A., Olivier, B. G., Röling, W. F., Teusink, B. & Bruggeman, F. J. Community flux balance analysis for microbial consortia at balanced growth. PLoS ONE 8, e64567 (2013).
26. Zomorrodi, A. R., Islam, M. M. & Maranas, C. D. d-OptCom: dynamic multilevel and multi-objective metabolic modeling of microbial communities. ACS Synth. Biol. 3, 247-257 (2014).
27. Chan, S. H. J., Simons, M. N. & Maranas, C. D. SteadyCom: predicting microbial abundances while ensuring community stability. PLoS Comput. Biol. 13, e1005539 (2017).
28. Harcombe, W. R. et al. Metabolic resource allocation in individual microbes determines ecosystem interactions and spatial dynamics. Cell Rep. 7, 1104-1115 (2014).
29. Cole, J. A., Kohler, L., Hedhli, J. & Luthey-Schulten, Z. Spatially-resolved metabolic cooperativity within dense bacterial colonies. BMC Syst. Biol. 9, 15 (2015).
30. Chen, J. et al. Spatiotemporal modeling of microbial metabolism. BMC Syst. Biol. 10, 21 (2016).
31. Zomorrodi, A. R. & Segrè, D. Synthetic ecology of microbes: mathematical models and applications. J. Mol. Biol. https://doi.org/10.1016/j.jmb.2015.10.019 (2015).
32. Großkopf, T. et al. Metabolic modelling in a dynamic evolutionary framework predicts adaptive diversification of bacteria in a long-term evolution experiment. BMC Evol. Biol. 16, 163 (2016).
33. Nowak, M. A. Evolutionary Dynamics: Exploring The Equations Of Life. (Belknap Press of Harvard University Press, Cambridge, 2006).
34. Gore, J., Youk, H. & van Oudenaarden, A. Snowdrift game dynamics and facultative cheating in yeast. Nature 459, 253-256 (2009).
35. Zomorrodi, A. R. & Maranas, C. D. Improving the iMM904 S. cerevisiae metabolic model using essentiality and synthetic lethality data. BMC Syst. Biol. 4, 178 (2010).
36. Orth, J. D. et al. A comprehensive genome-scale reconstruction of Escherichia coli metabolism-2011. Mol. Syst. Biol. 7, 535 (2011).
37. Paczia, N. et al. Extensive exometabolome analysis reveals extended overflow metabolism in various microorganisms. Microb. Cell Fact. 11, 122 (2012).
38. Valle, J. et al. The amino acid valine is secreted in continuous-flow bacterial biofilms. J. Bacteriol. 190, 264-274 (2008).
39. D'Souza, G. & Kost, C. Experimental evolution of metabolic dependency in bacteria. PLoS Genet. 12, e1006364 (2016).
40. D'Souza, G., Waschina, S., Kaleta, C. & Kost, C. Plasticity and epistasis strongly affect bacterial fitness after losing multiple metabolic genes. Evolution 69, 1244-1254 (2015).
41. Campbell, K. et al. Self-establishing communities enable cooperative metabolite exchange in a eukaryote. Elife 4, e09943 (2015).
42. Pande, S. et al. Fitness and stability of obligate cross-feeding interactions that emerge upon gene loss in bacteria. ISME J. 8, 953-962 (2014).
43. Post, D. M. & Palkovacs, E. P. Eco-evolutionary feedbacks in community and ecosystem ecology: interactions between the ecological theatre and the evolutionary play. Philos. Trans. R. Soc. Lond. B Biol. Sci. 364, 1629-1640 (2009).
44. Sanchez, A. & Gore, J. feedback between population and evolutionary dynamics determines the fate of social microbial populations. PLoS Biol. 11, e1001547 (2013).
45. Rauch, J., Kondev, J. & Sanchez, A. Cooperators trade off ecological resilience and evolutionary stability in public goods games. J. R. Soc. Interface 14, 20160967 (2017).
46. Widder, S. et al. Challenges in microbial ecology: building predictive understanding of community function and dynamics. ISME J. 10, 2557-2568 (2016).
47. Rakoff-Nahoum, S., Foster, K. R. & Comstock, L. E. The evolution of cooperation within the gut microbiota. Nature 533, 255-259 (2016).
48. Waldor, M. K. et al. Where next for microbiome research? PLoS Biol. 13, e1002050 (2015).
49. Magnúsdóttir, S. et al. Generation of genome-scale metabolic reconstructions for 773 members of the human gut microbiota. Nat. Biotechnol. 35, 81-89 (2017).
50. Elena, S. F. & Lenski, R. E. Evolution experiments with microorganisms: the dynamics and genetic bases of adaptation. Nat. Rev. Genet. 4, 457-469 (2003).
51. Orth, J. D., Thiele, I. & Palsson, B. What is flux balance analysis? Nat. Biotechnol. 28, 245-248 (2010).
52. Segrè, D., Vitkup, D. & Church, G. M. Analysis of optimality in natural and perturbed metabolic networks. Proc. Natl Acad. Sci. USA 99, 15112-15117 (2002).
53. Trinh, C. T., Wlaschin, A. & Srienc, F. Elementary mode analysis: a useful metabolic pathway analysis tool for characterizing cellular metabolism. Appl. Microbiol. Biotechnol. 81, 813-826 (2009).
54. Schuster, S., de Figueiredo, L. F., Schroeter, A. & Kaleta, C. Combining Metabolic Pathway Analysis with Evolutionary Game Theory. Explaining the occurrence of low-yield pathways by an analytic optimization approach. Biosystems 105, 147-153 (2009).
55. Cressman, R. & Tao, Y. The replicator equation and other game dynamics. Proc. Natl Acad. Sci. USA 111, 10810-10817 (2014).