Skip to main content

Thank you for visiting nature.com. You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript.

Engineering complex communities by directed evolution

Nature Ecology & Evolution volume 5, pages 1011–1023 (2021)Cite this article

Subjects

Abstract

Directed evolution has been used for decades to engineer biological systems at or below the organismal level. Above the organismal level, a small number of studies have attempted to artificially select microbial ecosystems, with uneven and generally modest success. Our theoretical understanding of artificial ecosystem selection is limited, particularly for large assemblages of asexual organisms, and we know little about designing efficient methods to direct their evolution. Here, we have developed a flexible modelling framework that allows us to systematically probe any arbitrary selection strategy on any arbitrary set of communities and selected functions. By artificially selecting hundreds of in silico microbial metacommunities under identical conditions, we first show that the main breeding methods used to date, which do not necessarily let communities reach their ecological equilibrium, are outperformed by a simple screen of sufficiently mature communities. We then identify a range of alternative directed evolution strategies that, particularly when applied in combination, are well suited for the top-down engineering of large, diverse and stable microbial consortia. Our results emphasize that directed evolution allows an ecological structure–function landscape to be navigated in search of dynamically stable and ecologically resilient communities with desired quantitative attributes.

This is a preview of subscription content, access via your institution

Relevant articles

Open Access articles citing this article.

Access options

Rent or buy this article

Prices vary by article type

from$1.95

to$39.95

Learn more

Prices may be subject to local taxes which are calculated during checkout

Fig. 1: Migrant-pool and propagule strategies are limited in their ability to find new, high-functioning microbial communities.
Fig. 2: Directed evolution as an artificial selection strategy for high-performing communities.
Fig. 3: Iteratively combining bottlenecks and migrations to optimize community function selects for high-functioning communities.
Fig. 4: Directed evolution produces communities that are resistant to ecological perturbations.

Data availability

All data generated and analysed in this paper can be found at https://doi.org/10.5281/zenodo.4608427.

Code availability

All simulations were conducted in Python using ecoprospector (https://github.com/Chang-Yu-Chang/ecoprospector). All the data analysis was conducted in R. The complete code used for this paper including the ecoprospector package can be found in the Zenodo repository (https://doi.org/10.5281/zenodo.4608427).

References

  1. Mueller, U. G. & Sachs, J. L. Engineering microbiomes to improve plant and animal health. Trends Microbiol. 23, 606–617 (2015).

    Article  CAS  PubMed  Google Scholar 

  2. Gilbert, E. S., Walker, A. W. & Keasling, J. D. A constructed microbial consortium for biodegradation of the organophosphorus insecticide parathion. Appl. Microbiol. Biotechnol. 61, 77–81 (2003).

    Article  CAS  PubMed  Google Scholar 

  3. Yoshida, S., Ogawa, N., Fujii, T. & Tsushima, S. Enhanced biofilm formation and 3-chlorobenzoate degrading activity by the bacterial consortium of Burkholderia sp. NK8 and Pseudomonas aeruginosa PAO1. J. Appl. Microbiol. 106, 790–800 (2009).

    Article  CAS  PubMed  Google Scholar 

  4. Piccardi, P., Vessman, B. & Mitri, S. Toxicity drives facilitation between 4 bacterial species. Proc. Natl Acad. Sci. USA 116, 15979–15984 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  5. Herrera Paredes, S. et al. Design of synthetic bacterial communities for predictable plant phenotypes. PLoS Biol. 16, e2003962 (2018).

    Article  PubMed  PubMed Central  Google Scholar 

  6. Minty, J. J. et al. Design and characterization of synthetic fungal-bacterial consortia for direct production of isobutanol from cellulosic biomass. Proc. Natl Acad. Sci. USA 110, 14592–14597 (2013).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  7. Jiang, Y., Dong, W., Xin, F. & Jiang, M. Designing synthetic microbial consortia for biofuel production. Trends Biotechnol. 38, 828–831 (2020).

    Article  CAS  PubMed  Google Scholar 

  8. Eng, A. & Borenstein, E. Microbial community design: methods, applications, and opportunities. Curr. Opin. Biotechnol. 58, 117–128 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  9. Fredrickson, J. K. Ecological communities by design. Science 348, 1425–1427 (2015).

    Article  CAS  PubMed  Google Scholar 

  10. Sanchez-Gorostiaga, A., Bajić, D., Osborne, M. L., Poyatos, J. F. & Sanchez, A. High-order interactions distort the functional landscape of microbial consortia. PLoS Biol. 17, e3000550 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  11. Senay, Y., John, G., Knutie, S. A. & Brandon Ogbunugafor, C. Deconstructing higher-order interactions in the microbiota: a theoretical examination. Preprint at bioRxiv https://doi.org/10.1101/647156 (2019).

  12. Gould, A. L. et al. Microbiome interactions shape host fitness. Proc. Natl Acad. Sci. USA 115, E11951–E11960 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  13. Mickalide, H. & Kuehn, S. Higher-order interaction between species inhibits bacterial invasion of a phototroph-predator microbial community. Cell Syst. 9, 521–533.e10 (2019).

    Article  CAS  PubMed  Google Scholar 

  14. Sanchez, A. Defining higher-order interactions in synthetic ecology: lessons from physics and quantitative genetics. Cell Syst. 9, 519–520 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  15. Guo, X. & Boedicker, J. Q. The contribution of high-order metabolic interactions to the global activity of a four-species microbial community. PLoS Comput. Biol. 12, e1005079 (2016).

    Article  PubMed  PubMed Central  Google Scholar 

  16. Sundarraman, D. et al. Higher-order interactions dampen pairwise competition in the zebrafish gut microbiome. mBio 11, e01667-20 (2020).

    Article  PubMed  PubMed Central  Google Scholar 

  17. Goldman, R. P. & Brown, S. P. Making sense of microbial consortia using ecology and evolution. Trends Biotechnol. 27, 3–4 (2009).

    Article  CAS  PubMed  Google Scholar 

  18. Brenner, K., You, L. & Arnold, F. H. Response to Goldman and Brown: Making sense of microbial consortia using ecology and evolution. Trends Biotechnol. 27, 4 (2009).

    Article  CAS  Google Scholar 

  19. Escalante, A. E., Rebolleda-Gómez, M., Benítez, M. & Travisano, M. Ecological perspectives on synthetic biology: insights from microbial population biology. Front. Microbiol. 6, 143 (2015).

    Article  PubMed  PubMed Central  Google Scholar 

  20. Gilmore, S. P. et al. Top-down enrichment guides in formation of synthetic microbial consortia for biomass degradation. ACS Synth. Biol. 8, 2174–2185 (2019).

    Article  CAS  PubMed  Google Scholar 

  21. Cortes-Tolalpa, L., Jiménez, D. J., de Lima Brossi, M. J., Salles, J. F. & van Elsas, J. D. Different inocula produce distinctive microbial consortia with similar lignocellulose degradation capacity. Appl. Microbiol. Biotechnol. https://doi.org/10.1007/s00253-016-7516-6 (2016).

  22. Lee, D.-J., Show, K.-Y. & Wang, A. Unconventional approaches to isolation and enrichment of functional microbial consortium - a review. Bioresour. Technol. 136, 697–706 (2013).

    Article  CAS  PubMed  Google Scholar 

  23. Lazuka, A., Auer, L., O’Donohue, M. & Hernandez-Raquet, G. Anaerobic lignocellulolytic microbial consortium derived from termite gut: enrichment, lignocellulose degradation and community dynamics. Biotechnol. Biofuels 11, 284 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  24. Puentes-Téllez, P. E. & Falcao Salles, J. Construction of effective minimal active microbial consortia for lignocellulose degradation. Microb. Ecol. 76, 419–429 (2018).

    Article  PubMed  PubMed Central  Google Scholar 

  25. He, X., McLean, J. S., Guo, L., Lux, R. & Shi, W. The social structure of microbial community involved in colonization resistance. ISME J. 8, 564–574 (2014).

    Article  PubMed  Google Scholar 

  26. Jung, J., Philippot, L. & Park, W. Metagenomic and functional analyses of the consequences of reduction of bacterial diversity on soil functions and bioremediation in diesel-contaminated microcosms. Sci. Rep. 6, 23012 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  27. Franklin, R. B. & Mills, A. L. Structural and functional responses of a sewage microbial community to dilution-induced reductions in diversity. Microb. Ecol. 52, 280–288 (2006).

    Article  PubMed  Google Scholar 

  28. Kang, D. et al. Enrichment and characterization of an environmental microbial consortium displaying efficient keratinolytic activity. Bioresour. Technol. 270, 303–310 (2018).

    Article  CAS  PubMed  Google Scholar 

  29. Goodnight, C. J. Evolution in metacommunities. Phil. Trans. R. Soc. B 366, 1401–1409 (2011).

    Article  PubMed  PubMed Central  Google Scholar 

  30. Swenson, W., Wilson, D. S. & Elias, R. Artificial ecosystem selection. Proc. Natl Acad. Sci. USA 97, 9110–9114 (2000).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  31. Jochum, M. D., McWilliams, K. L., Pierson, E. A. & Jo, Y.-K. Host-mediated microbiome engineering (HMME) of drought tolerance in the wheat rhizosphere. PLoS ONE 14, e0225933 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  32. Mueller, U. G. et al. Artificial microbiome-selection to engineer microbiomes that confer salt-tolerance to plants. Preprint at bioRxiv https://doi.org/10.1101/081521 (2016).

  33. Panke-Buisse, K., Poole, A. C., Goodrich, J. K., Ley, R. E. & Kao-Kniffin, J. Selection on soil microbiomes reveals reproducible impacts on plant function. ISME J. 9, 980–989 (2015).

    Article  CAS  PubMed  Google Scholar 

  34. Panke-Buisse, K., Lee, S. & Kao-Kniffin, J. Cultivated sub-populations of soil microbiomes retain early flowering plant trait. Microb. Ecol. https://doi.org/10.1007/s00248-016-0846-1 (2016).

  35. Arora, J., Mars Brisbin, M. A. & Mikheyev, A. S. Effects of microbial evolution dominate those of experimental host-mediated indirect selection. PeerJ 8, e9350 (2020).

    Article  PubMed  PubMed Central  Google Scholar 

  36. Swenson, W., Arendt, J. & Wilson, D. S. Artificial selection of microbial ecosystems for 3-chloroaniline biodegradation. Environ. Microbiol. 2, 564–571 (2000).

    Article  CAS  PubMed  Google Scholar 

  37. Wright, R. J., Gibson, M. I. & Christie-Oleza, J. A. Understanding microbial community dynamics to improve optimal microbiome selection. Microbiome 7, 85 (2019).

    Article  PubMed  PubMed Central  Google Scholar 

  38. Blouin, M., Karimi, B., Mathieu, J. & Lerch, T. Z. Levels and limits in artificial selection of communities. Ecol. Lett. 18, 1040–1048 (2015).

    Article  PubMed  Google Scholar 

  39. Raynaud, T., Devers, M., Spor, A. & Blouin, M. Effect of the reproduction method in an artificial selection experiment at the community level. Front. Ecol. Evol. 7, 416 (2019).

    Article  Google Scholar 

  40. Chang, C.-Y., Osborne, M. L., Bajic, D. & Sanchez, A. Artificially selecting bacterial communities using propagule strategies. Evolution https://doi.org/10.1111/evo.14092 (2020).

  41. Arias-Sánchez, F. I., Vessman, B. & Mitri, S. Artificially selecting microbial communities: if we can breed dogs, why not microbiomes? PLoS Biol. 17, e3000356 (2019).

    Article  PubMed  PubMed Central  Google Scholar 

  42. Day, M. D., Beck, D. & Foster, J. A. Microbial communities as experimental units. BioScience 61, 398–406 (2011).

    Article  PubMed  Google Scholar 

  43. Wade, M. J. Group selections among laboratory populations of Tribolium. Proc. Natl Acad. Sci. USA 73, 4604–4607 (1976).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  44. Wade, M. J. An experimental study of group selection. Evolution 31, 134–153 (1977).

    Article  PubMed  Google Scholar 

  45. Wade, M. J. A critical review of the models of group selection. Q. Rev. Biol. 53, 101–114 (1978).

    Article  Google Scholar 

  46. Goodnight, C. J. Experimental studies of community evolution I: The response to selection at the community level. Evolution 44, 1614–1624 (1990).

    Article  PubMed  Google Scholar 

  47. Guo, X. & Boedicker, J. High-order interactions between species strongly influence the activity of microbial communities. Biophys. J. 110, 143a (2016).

    Article  Google Scholar 

  48. Stein, R. R. et al. Computer-guided design of optimal microbial consortia for immune system modulation. eLife 7, e30916 (2018).

    Article  PubMed  PubMed Central  Google Scholar 

  49. Arnold, F. H. Innovation by evolution: bringing new chemistry to life (Nobel lecture). Angew. Chem. Int. Ed. 58, 14420–14426 (2019).

    Article  CAS  Google Scholar 

  50. Tracewell, C. A. & Arnold, F. H. Directed enzyme evolution: climbing fitness peaks one amino acid at a time. Curr. Opin. Chem. Biol. 13, 3–9 (2009).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  51. Williams, H. T. P. & Lenton, T. M. Artificial selection of simulated microbial ecosystems. Proc. Natl Acad. Sci. USA 104, 8918–8923 (2007).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  52. Williams, H. T. P. & Lenton, T. M. in Advances in Artificial Life ECAL 2007. Lecture Notes in Computer Science, vol. 4648 (eds Almeida e Costa, F. et al.) 93–102 (Springer, 2007).

  53. Doulcier, G., Lambert, A., De Monte, S. & Rainey, P. B. Eco-evolutionary dynamics of nested Darwinian populations and the emergence of community-level heredity. eLife 9, e53433 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  54. Xie, L., Yuan, A. E. & Shou, W. Simulations reveal challenges to artificial community selection and possible strategies for success. PLoS Biol. 17, e3000295 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  55. Wilson, D. S. Complex interactions in metacommunities, with implications for biodiversity and higher levels of selection. Ecology 73, 1984–2000 (1992).

    Article  Google Scholar 

  56. Marsland, R. III et al. Available energy fluxes drive a transition in the diversity, stability, and functional structure of microbial communities. PLoS Comput. Biol. 15, e1006793 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  57. Marsland, R., Cui, W., Goldford, J. & Mehta, P. The Community Simulator: a Python package for microbial ecology. PLoS ONE 15, e0230430 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  58. Marsland, R. III, Cui, W. & Mehta, P. A minimal model for microbial biodiversity can reproduce experimentally observed ecological patterns. Sci. Rep. 10, 3308 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  59. Advani, M., Bunin, G. & Mehta, P. Statistical physics of community ecology: a cavity solution to MacArthur’s consumer resource model. J. Stat. Mech. 2018, 033406 (2018).

    Article  PubMed  PubMed Central  Google Scholar 

  60. Goldford, J. E. et al. Emergent simplicity in microbial community assembly. Science 361, 469–474 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  61. Lu, N., Sanchez-Gorostiaga, A., Tikhonov, M. & Sanchez, A. Cohesiveness in microbial community coalescence. Preprint at bioRxiv https://doi.org/10.1101/282723 (2018).

  62. Faith, J. J., Ahern, P. P., Ridaura, V. K., Cheng, J. & Gordon, J. I. Identifying gut microbe-host phenotype relationships using combinatorial communities in gnotobiotic mice. Sci. Transl. Med. 6, 220ra11 (2014).

    Article  PubMed  PubMed Central  Google Scholar 

  63. Estrela, S. et al. Metabolic rules of microbial community assembly. Preprint at bioRxiv https://doi.org/10.1101/2020.03.09.984278 (2020).

  64. Friedman, J., Higgins, L. M. & Gore, J. Community structure follows simple assembly rules in microbial microcosms. Nat. Ecol. Evol. 1, 0109 (2017).

    Article  Google Scholar 

  65. Venturelli, O. S. et al. Deciphering microbial interactions in synthetic human gut microbiome communities. Mol. Syst. Biol. 14, e8157 (2018).

    Article  PubMed  PubMed Central  Google Scholar 

  66. Hall, B. G. Experimental evolution of a new enzymatic function. II. Evolution of multiple functions for ebg enzyme in E. coli. Genetics 89, 453–465 (1978).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  67. Smith, G. P. & Petrenko, V. A. Phage display. Chem. Rev. 97, 391–410 (1997).

    Article  CAS  PubMed  Google Scholar 

  68. Bloom, J. D. & Arnold, F. H. In the light of directed evolution: pathways of adaptive protein evolution. Proc. Natl Acad. Sci. USA 106, 9995–10000 (2009).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  69. Romero, P. A., Krause, A. & Arnold, F. H. Navigating the protein fitness landscape with Gaussian processes. Proc. Natl Acad. Sci. USA 110, E193–E201 (2013).

    Article  CAS  PubMed  Google Scholar 

  70. Ho, K.-L., Lee, D.-J., Su, A. & Chang, J.-S. Biohydrogen from cellulosic feedstock: dilution-to-stimulation approach. Int. J. Hydrog. Energy 37, 15582–15587 (2012).

    Article  CAS  Google Scholar 

  71. Shepherd, E. S., DeLoache, W. C., Pruss, K. M., Whitaker, W. R. & Sonnenburg, J. L. An exclusive metabolic niche enables strain engraftment in the gut microbiota. Nature 557, 434–438 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  72. Ting, S.-Y. et al. Targeted depletion of bacteria from mixed populations by programmable adhesion with antagonistic competitor cells. Cell Host Microbe https://doi.org/10.1016/j.chom.2020.05.006 (2020).

  73. Sheth, R. U., Cabral, V., Chen, S. P. & Wang, H. H. Manipulating bacterial communities by in situ microbiome engineering. Trends Genet. 32, 189–200 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  74. Lemon, K. P., Armitage, G. C., Relman, D. A. & Fischbach, M. A. Microbiota-targeted therapies: an ecological perspective. Sci. Transl. Med. 4, 137rv5 (2012).

    Article  PubMed  PubMed Central  Google Scholar 

  75. Harcombe, W. R. & Bull, J. J. Impact of phages on two-species bacterial communities. Appl. Environ. Microbiol. 71, 5254–5259 (2005).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  76. Chan, B. K. et al. Phage treatment of an aortic graft infected with Pseudomonas aeruginosa. Evol. Med. Public Health 2018, 60–66 (2018).

    Article  PubMed  PubMed Central  Google Scholar 

  77. Rillig, M. C., Tsang, A. & Roy, J. Microbial community coalescence for microbiome engineering. Front. Microbiol. 7, 1967 (2016).

    Article  PubMed  PubMed Central  Google Scholar 

  78. Sierocinski, P. et al. A single community dominates structure and function of a mixture of multiple methanogenic communities. Curr. Biol. 27, 3390–3395.e4 (2017).

    Article  CAS  PubMed  Google Scholar 

  79. Tilman, D. The ecological consequences of changes in biodiversity: a search for general principles. Ecology 80, 1455–1474 (1999).

    Google Scholar 

  80. Shade, A. et al. Fundamentals of microbial community resistance and resilience. Front. Microbiol. 3, 417 (2012).

    Article  PubMed  PubMed Central  Google Scholar 

  81. Kang, D. et al. Construction of simplified microbial consortia to degrade recalcitrant materials based on enrichment and dilution-to-extinction cultures. Front. Microbiol. 10, 3010 (2019).

    Article  PubMed  Google Scholar 

  82. Zanaroli, G. et al. Characterization of two diesel fuel degrading microbial consortia enriched from a non acclimated, complex source of microorganisms. Microb. Cell Factories 9, 10 (2010).

    Article  Google Scholar 

  83. Peter, H. et al. Function-specific response to depletion of microbial diversity. ISME J. 5, 351–361 (2011).

    Article  CAS  PubMed  Google Scholar 

  84. Pacheco, A. R., Moel, M. & Segrè, D. Costless metabolic secretions as drivers of interspecies interactions in microbial ecosystems. Nat. Commun. 10, 103 (2019).

    Article  PubMed  PubMed Central  Google Scholar 

  85. West, S. A., Griffin, A. S., Gardner, A. & Diggle, S. P. Social evolution theory for microorganisms. Nat. Rev. Microbiol. 4, 597–607 (2006).

    Article  CAS  PubMed  Google Scholar 

  86. Scheuerl, T. et al. Bacterial adaptation is constrained in complex communities. Nat. Commun. 11, 754 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  87. Lewontin, R. C. The units of selection. Annu. Rev. Ecol. Syst. 1, 1–18 (1970).

    Article  Google Scholar 

  88. Marsland, R., Cui, W., Goldford, J. & Mehta, P. The Community Simulator: a Python package for microbial ecology. PLoS ONE 15, e0230430 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  89. Shoemaker, W. R., Locey, K. J. & Lennon, J. T. A macroecological theory of microbial biodiversity. Nat. Ecol. Evol. 1, 0107 (2017).

    Article  Google Scholar 

  90. Degnan, P. H., Taga, M. E. & Goodman, A. L. Vitamin B12 as a modulator of gut microbial ecology. Cell Metab. 20, 769–778 (2014).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  91. Degnan, P. H., Barry, N. A., Mok, K. C., Taga, M. E. & Goodman, A. L. Human gut microbes use multiple transporters to distinguish vitamin B12 analogs and compete in the gut. Cell Host Microbe 15, 47–57 (2014).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

Download references

Acknowledgements

We thank H. Garcia and R. Phillips for inviting most of us, either as instructors or as students, to the Physical Biology of the Cell summer course at the Marine Biology Laboratory (MBL) in Woods Hole, MA, where this project was started and the first version of the ecoprospector package was coded. We also wish to thank B. Von Herzen for his input and discussion while we were at MBL. This work was supported by the National Institutes of Health through grant 1R35 GM133467-01 and by a Packard Foundation Fellowship to A.S. C.-Y.C. was supported by a graduate fellowship Government Scholarship to Study Abroad by the Government of Taiwan. M.R.-G. was supported by a Gaylord Donnelley postdoctoral fellowship through the Yale Institute for Biospheric Studies.

Author information

Author notes

  1. These authors contributed equally: Chang-Yu Chang, Jean C. C. Vila.

Authors and Affiliations

  1. Department of Ecology & Evolutionary Biology, Yale University, New Haven, CT, USA

    Chang-Yu Chang, Jean C. C. Vila, Madeline Bender, Richard Li, Juan Diaz-Colunga, Sylvie Estrela, Maria Rebolleda-Gomez & Alvaro Sanchez

  2. Microbial Sciences Institute, Yale University, New Haven, CT, USA

    Chang-Yu Chang, Jean C. C. Vila, Madeline Bender, Juan Diaz-Colunga, Sylvie Estrela, Maria Rebolleda-Gomez & Alvaro Sanchez

  3. Department of Immunobiology and Department of Laboratory Medicine, Yale University, New Haven, CT, USA

    Madeleine C. Mankowski

  4. Biomedical Sciences Graduate Program, University of California San Francisco, San Francisco, CA, USA

    Molly Bassette

  5. Department of Molecular & Cellular Biology, University of California Berkeley, Berkeley, CA, USA

    Julia Borden

  6. Max Planck Institute of Molecular Cell Biology and Genetics, and Max Planck Institute for the Physics of Complex Systems, Dresden, Germany

    Stefan Golfier

  7. European Molecular Biology Laboratory (EMBL), Developmental Biology Unit, Heidelberg, Germany

    Paul Gerald L. Sanchez

  8. Department of Developmental and Cell Biology, University of California Irvine, Irvine, CA, USA

    Rachel Waymack

  9. Department of Biomedical Engineering and the Biological Design Center, Boston University, Boston, MA, USA

    Xinwen Zhu

Authors
  1. Chang-Yu Chang
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Jean C. C. Vila
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Madeline Bender
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Richard Li
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Madeleine C. Mankowski
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Molly Bassette
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. Julia Borden
    View author publications

    You can also search for this author in PubMed Google Scholar

  8. Stefan Golfier
    View author publications

    You can also search for this author in PubMed Google Scholar

  9. Paul Gerald L. Sanchez
    View author publications

    You can also search for this author in PubMed Google Scholar

  10. Rachel Waymack
    View author publications

    You can also search for this author in PubMed Google Scholar

  11. Xinwen Zhu
    View author publications

    You can also search for this author in PubMed Google Scholar

  12. Juan Diaz-Colunga
    View author publications

    You can also search for this author in PubMed Google Scholar

  13. Sylvie Estrela
    View author publications

    You can also search for this author in PubMed Google Scholar

  14. Maria Rebolleda-Gomez
    View author publications

    You can also search for this author in PubMed Google Scholar

  15. Alvaro Sanchez
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

C.-Y.C., J.C.C.V. and A.S. conceived the idea and designed the study. C.-Y.C., J.C.C.V., M. Bassette, J.B., S.G., P.G.L.S., R.W., X.Z. and A.S. contributed to the development of ecoprospector. C.-Y.C. and J.C.C.V. carried out all simulations and made the results figures. C.-Y.C., J.C.C.V. and J.D.-C. made the diagrams. C.-Y.C, J.C.C.V., M. Bender, R.L., M.C.M., S.E., M.R.-G., J.D.-C. and A.S. discussed the results and drafted the paper. M.C.M. compiled the list of previous selection protocols from the literature. C.-Y.C., J.C.C.V. and A.S. wrote the final version of the paper.

Corresponding author

Correspondence to Alvaro Sanchez.

Ethics declarations

Competing interests

The authors declare no competing interests.

Additional information

Peer review information Nature Ecology & Evolution thanks Kevin Esvelt and the other, anonymous, reviewer(s) for their contribution to the peer review of this work. Peer reviewer reports are available.

Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Extended data

Extended Data Fig. 1 Non-additive function, costly function, and two empirically motivated functions.

a, Illustration of the different types of community function we have considered. In addition to the additive function used in the main text we have simulated four other community functions: a non-additive pairwise function, a costly function, a function that maximizes the consumption of a target resource, and a function that maximizes resistance to an invader. Panels B-F reproduce the main results reported in Figs. 1–4. b, Difference in Fmax between the artificial selection line (AS) and no-selection line (NS) for all previously published protocols, corresponding to Fig. 1f. c, Difference in Fmax between parent (before directed evolution) and offspring (after directed evolution) for the 6 types of perturbation considered in Fig. 2, this plot aggregates the results shown in Fig. 2d-i. d, Reproduction of Fig. 3e, to show that iteratively combining migrations and bottlenecks does better than either alone. Q is obtained from each of the three iterative protocols at generation 460 (e) Reproduction of Fig. 4e, where we compare Fmax of the no-selection (NS), directed evolution (DE), and synthetic communities; (F) Mean function (F*) of the DE, NS and Synthetic communities following an ecological perturbation (migration). This corresponds to the y-axis of Fig. 4f.

Extended Data Fig. 2 Alternative ecological scenarios with metabolic cross-feeding.

Besides the rich medium without cross-feeding shown in the main text, we have included two other ecological scenarios: i) rich medium with cross-feeding and ii) simple minimal medium with cross-feeding. The layout of (B-F) follows Extended Data Fig. 1b–f, reproducing the main results from Figs. 1–4.

Extended Data Fig. 3 Functional responses.

The resource import rate depends on its concentration in the environments, which can take a linear (type I), Monod (type II), or Hill (type III) form. A Type-III functional response is used in the simulation presented in the main text. The layout of (B-F) follows Extended Data Fig. 1b–f, reproducing the main results from Figs. 1–4.

Extended Data Fig. 4 Alternative Metacommunity sampling approaches.

We simulate three metacommunity sampling approaches: i) Each community is seeded with 106 cells drawn from a different regional pool, where the species abundances in each regional pool are drawn from a power-law distribution with a=0.01, ii) Each community is seeded with 106 cells drawn from a different regional, where the species abundances in each regional pool are drawn from a log-normal distribution with mean μ=8 and standard deviation σ=8, iii) Each community is seeded with a randomly chosen set of 225 species and they are all set to have the same initial abundance. The simulation in the main text adopts the power-law distribution approach. The layout of (B-F) follows Extended Data Fig. 1b–f, reproducing the main results from Figs. 1–4.

Extended Data Fig. 5 Different distributions of per capita species contribution to additive community function.

Per capita species contribution drawn from i) normal distribution centered around 0 with standard deviation sd=1, ii) normal distribution with mean=11 and sd=1, iii) uniform distribution ranged from min=0 to max=1, iiii) a sparse additive function where 20% of the species contribute to community function.In the main text, per capita species contribution uses normal distribution with mean=0 and sd=1. The layout of (B-F) follows Extended Data Fig. 1b–f, reproducing the main results from Figs. 1–4.

Supplementary information

Supplementary Information

Supplementary Discussion, Methods, Figs. 1–12 and Tables 1–5.

Reporting Summary

Peer Review Information

Rights and permissions

Reprints and Permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Chang, CY., Vila, J.C.C., Bender, M. et al. Engineering complex communities by directed evolution. Nat Ecol Evol 5, 1011–1023 (2021). https://doi.org/10.1038/s41559-021-01457-5

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1038/s41559-021-01457-5

This article is cited by

Search

Advanced search

Quick links

Nature Briefing

Sign up for the Nature Briefing newsletter — what matters in science, free to your inbox daily.

Get the most important science stories of the day, free in your inbox. Sign up for Nature Briefing