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Engineering complex communities by directed evolution

  • 1.

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

    CAS 
    Article 

    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).

    CAS 
    Article 

    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).

    CAS 
    Article 

    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).

    CAS 
    Article 

    Google Scholar 

  • 5.

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

    PubMed 
    PubMed Central 
    Article 
    CAS 

    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).

    CAS 
    Article 

    Google Scholar 

  • 7.

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

    CAS 
    Article 

    Google Scholar 

  • 8.

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

    CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 

  • 9.

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

    CAS 
    Article 

    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).

    CAS 
    PubMed 
    PubMed Central 
    Article 

    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).

    CAS 
    Article 

    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).

    CAS 
    Article 

    Google Scholar 

  • 14.

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

    CAS 
    PubMed 
    PubMed Central 
    Article 

    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).

    PubMed 
    PubMed Central 
    Article 
    CAS 

    Google Scholar 

  • 16.

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

    PubMed 
    PubMed Central 
    Article 

    Google Scholar 

  • 17.

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

    CAS 
    Article 

    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).

    CAS 
    Article 

    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).

    PubMed 
    PubMed Central 
    Article 

    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).

    CAS 
    Article 

    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).

    CAS 
    Article 

    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).

    CAS 
    PubMed 
    PubMed Central 
    Article 

    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).

    PubMed 
    PubMed Central 
    Article 
    CAS 

    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 

    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).

    CAS 
    PubMed 
    PubMed Central 
    Article 

    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 

    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).

    CAS 
    Article 

    Google Scholar 

  • 29.

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

    Article 

    Google Scholar 

  • 30.

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

    CAS 
    Article 

    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).

    CAS 
    PubMed 
    PubMed Central 
    Article 

    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).

    CAS 
    Article 

    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).

    PubMed 
    PubMed Central 
    Article 

    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).

    CAS 
    Article 

    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).

    PubMed 
    PubMed Central 
    Article 

    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 

    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).

    PubMed 
    PubMed Central 
    Article 
    CAS 

    Google Scholar 

  • 42.

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

    PubMed 
    PubMed Central 
    Article 

    Google Scholar 

  • 43.

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

    CAS 
    Article 

    Google Scholar 

  • 44.

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

    Article 

    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 

    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).

    PubMed 
    PubMed Central 
    Article 

    Google Scholar 

  • 49.

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

    CAS 
    Article 

    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).

    CAS 
    PubMed 
    PubMed Central 
    Article 

    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).

    CAS 
    Article 

    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).

    CAS 
    PubMed 
    PubMed Central 
    Article 

    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).

    CAS 
    PubMed 
    PubMed Central 
    Article 

    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).

    CAS 
    PubMed 
    PubMed Central 
    Article 

    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).

    CAS 
    PubMed 
    PubMed Central 
    Article 

    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).

    CAS 
    PubMed 
    PubMed Central 
    Article 

    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).

    PubMed 
    PubMed Central 
    Article 

    Google Scholar 

  • 60.

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

    CAS 
    PubMed 
    PubMed Central 
    Article 

    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).

    PubMed 
    PubMed Central 
    Article 
    CAS 

    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).

    PubMed 
    PubMed Central 
    Article 

    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).

    CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 

  • 67.

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

    CAS 
    Article 

    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).

    CAS 
    Article 

    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).

    CAS 
    Article 

    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).

    CAS 
    Article 

    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).

    CAS 
    PubMed 
    PubMed Central 
    Article 

    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).

    CAS 
    PubMed 
    PubMed Central 
    Article 

    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).

    PubMed 
    PubMed Central 
    Article 
    CAS 

    Google Scholar 

  • 75.

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

    CAS 
    PubMed 
    PubMed Central 
    Article 

    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).

    PubMed 
    PubMed Central 
    Article 

    Google Scholar 

  • 77.

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

    PubMed 
    PubMed Central 
    Article 

    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).

    CAS 
    Article 

    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).

    PubMed 
    PubMed Central 
    Article 

    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 

    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 
    CAS 

    Google Scholar 

  • 83.

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

    CAS 
    Article 

    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).

    PubMed 
    PubMed Central 
    Article 
    CAS 

    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).

    CAS 
    Article 

    Google Scholar 

  • 86.

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

    CAS 
    PubMed 
    PubMed Central 
    Article 

    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).

    CAS 
    PubMed 
    PubMed Central 
    Article 

    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).

    CAS 
    PubMed 
    PubMed Central 
    Article 

    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).

    CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 


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