1932

Abstract

Directed evolution is a form of artificial selection that has been used for decades to find biomolecules and organisms with new or enhanced functional traits. Directed evolution can be conceptualized as a guided exploration of the genotype–phenotype map, where genetic variants with desirable phenotypes are first selected and then mutagenized to search the genotype space for an even better mutant. In recent years, the idea of applying artificial selection to microbial communities has gained momentum. In this article, we review the main limitations of artificial selection when applied to large and diverse collectives of asexually dividing microbes and discuss how the tools of directed evolution may be deployed to engineer communities from the top down. We conceptualize directed evolution of microbial communities as a guided exploration of an ecological structure–function landscape and propose practical guidelines for navigating these ecological landscapes.

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2021-05-06
2024-06-16
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Literature Cited

  1. 1. 
    Acevedo-Rocha CG, Hoebenreich S, Reetz MT 2014. Iterative saturation mutagenesis: a powerful approach to engineer proteins by systematically simulating Darwinian evolution. Directed Evolution Library Creation: Methods and Protocols EMJ Gillam, JN Copp, D Ackerley 103–28 Berlin: Springer
    [Google Scholar]
  2. 2. 
    Ackermann M, Stecher B, Freed NE, Songhet P, Hardt W-D, Doebeli M. 2008. Self-destructive cooperation mediated by phenotypic noise. Nature 454:7207987–90
    [Google Scholar]
  3. 3. 
    Alseth EO, Pursey E, Luján AM, McLeod I, Rollie C, Westra ER. 2019. Bacterial biodiversity drives the evolution of CRISPR-based phage resistance. Nature 574:7779549–52
    [Google Scholar]
  4. 4. 
    Amor DR, Ratzke C, Gore J. 2020. Transient invaders can induce shifts between alternative stable states of microbial communities. Sci. Adv. 6:8eaay8676
    [Google Scholar]
  5. 5. 
    Arias-Sánchez FI, Vessman B, Mitri S. 2019. Artificially selecting microbial communities: If we can breed dogs, why not microbiomes?. PLOS Biol 17:8e3000356
    [Google Scholar]
  6. 6. 
    Arkin AP, Youvan DC 1992. An algorithm for protein engineering: simulations of recursive ensemble mutagenesis. PNAS 89:167811–15
    [Google Scholar]
  7. 7. 
    Arnold FH, Georgiou G. 2003. Directed Enzyme Evolution: Screening and Selection Methods Totowa, NJ: Humana Press
    [Google Scholar]
  8. 8. 
    Arora J, Brisbin MM, Mikheyev AS. 2019. The microbiome wants what it wants: microbial evolution overtakes experimental host-mediated indirect selection. PeerJ 8:e9350
    [Google Scholar]
  9. 9. 
    Axelrod K, Sanchez A, Gore J. 2015. Phenotypic states become increasingly sensitive to perturbations near a bifurcation in a synthetic gene network. eLife 4:e07935
    [Google Scholar]
  10. 10. 
    Ayed L, Achour S, Khelifi E, Cheref A, Bakhrouf A. 2010. Use of active consortia of constructed ternary bacterial cultures via mixture design for Congo Red decolorization enhancement. Chem. Eng. J. 162:2495–502
    [Google Scholar]
  11. 11. 
    Barve A, Wagner A. 2013. A latent capacity for evolutionary innovation through exaptation in metabolic systems. Nature 500:7461203–6
    [Google Scholar]
  12. 12. 
    Beaudry AA, Joyce GF. 1992. Directed evolution of an RNA enzyme. Science 257:5070635–41
    [Google Scholar]
  13. 13. 
    Ben-David Y, Moraïs S, Bayer EA, Mizrahi I. 2020. Rapid adaptation for fibre degradation by changes in plasmid stoichiometry within Lactobacillus plantarum at the synthetic community level. Microb. Biotechnol. 13:61748–64
    [Google Scholar]
  14. 14. 
    Bloom JD, Arnold FH. 2009. In the light of directed evolution: pathways of adaptive protein evolution. PNAS 106:Suppl. 19995–10000
    [Google Scholar]
  15. 15. 
    Blouin M, Karimi B, Mathieu J, Lerch TZ. 2015. Levels and limits in artificial selection of communities. Ecol. Lett. 18:101040–48
    [Google Scholar]
  16. 16. 
    Burrowes BH, Molineux IJ, Fralick JA. 2019. Directed in vitro evolution of therapeutic bacteriophages: the Appelmans protocol. Viruses 11:3241
    [Google Scholar]
  17. 17. 
    Chan BK, Turner PE, Kim S, Mojibian HR, Elefteriades JA, Narayan D. 2018. Phage treatment of an aortic graft infected with Pseudomonas aeruginosa. Evol. Med. Public Health 2018:160–66
    [Google Scholar]
  18. 18. 
    Chang C-Y, Osborne ML, Bajic D, Sanchez A. 2020. Artificially selecting microbial communities using propagule strategies. Evolution 74:2392–403
    [Google Scholar]
  19. 19. 
    Chang C-Y, Vila JCC, Bender M, Mankowski MC, Li R et al. 2020. Top-down engineering of complex communities by directed evolution. bioRxiv 214775. https://doi.org/10.1101/2020.07.24.214775
    [Crossref]
  20. 20. 
    Chen A, Sanchez A, Dai L, Gore J. 2014. Dynamics of a producer-freeloader ecosystem on the brink of collapse. Nat. Commun. 5:3713
    [Google Scholar]
  21. 21. 
    Chuang JS, Rivoire O, Leibler S. 2009. Simpson's paradox in a synthetic microbial system. Science 323:5911272–75
    [Google Scholar]
  22. 22. 
    Cortes-Tolalpa L, Jiménez DJ, de Lima Brossi MJ, Salles JF, van Elsas JD. 2016. Different inocula produce distinctive microbial consortia with similar lignocellulose degradation capacity. Appl. Microbiol. Biotechnol. 100:177713–25
    [Google Scholar]
  23. 23. 
    Dai L, Vorselen D, Korolev KS, Gore J. 2012. Generic indicators for loss of resilience before a tipping point leading to population collapse. Science 336:60851175–77
    [Google Scholar]
  24. 24. 
    Datta MS, Sliwerska E, Gore J, Polz MF, Cordero OX. 2016. Microbial interactions lead to rapid micro-scale successions on model marine particles. Nat. Commun. 7:11965
    [Google Scholar]
  25. 25. 
    Day MD, Beck D, Foster JA. 2011. Microbial communities as experimental units. Bioscience 61:5398–406
    [Google Scholar]
  26. 26. 
    Doulcier G, Lambert A, De Monte S, Rainey PB. 2020. Eco-evolutionary dynamics of nested Darwinian populations and the emergence of community-level heredity. eLife 9:e53433
    [Google Scholar]
  27. 27. 
    Driscoll CA, Macdonald DW, O'Brien SJ. 2009. From wild animals to domestic pets, an evolutionary view of domestication. PNAS 106:Suppl. 19971–78
    [Google Scholar]
  28. 28. 
    Eng A, Borenstein E. 2019. Microbial community design: methods, applications, and opportunities. Curr. Opin. Biotechnol. 58:117–28
    [Google Scholar]
  29. 29. 
    Enke TN, Datta MS, Schwartzman J, Cermak N, Schmitz D et al. 2019. Modular assembly of polysaccharide-degrading marine microbial communities. Curr. Biol. 29:91528–35.e6
    [Google Scholar]
  30. 30. 
    Estrela S, Vila JCC, Lu N, Bajic D, Rebolleda-Gomez M et al. 2020. Metabolic rules of microbial community assembly. bioRxiv 984278. https://doi.org/10.1101/2020.03.09.984278
    [Crossref]
  31. 31. 
    Esvelt KM, Carlson JC, Liu DR. 2011. A system for the continuous directed evolution of biomolecules. Nature 472:7344499–503
    [Google Scholar]
  32. 32. 
    Faith JJ, Ahern PP, Ridaura VK, Cheng J, Gordon JI. 2014. Identifying gut microbe-host phenotype relationships using combinatorial communities in gnotobiotic mice. Sci. Transl. Med. 6:220220ra11
    [Google Scholar]
  33. 33. 
    Fernández A, Huang S, Seston S, Xing J, Hickey R et al. 1999. How stable is stable? Function versus community composition. Appl. Environ. Microbiol. 65:83697–704
    [Google Scholar]
  34. 34. 
    François P, Hakim V 2004. Design of genetic networks with specified functions by evolution in silico. PNAS 101:2580–85
    [Google Scholar]
  35. 35. 
    Franklin RB, Mills AL. 2006. Structural and functional responses of a sewage microbial community to dilution-induced reductions in diversity. Microb. Ecol. 52:2280–88
    [Google Scholar]
  36. 36. 
    Friedman J, Higgins LM, Gore J. 2017. Community structure follows simple assembly rules in microbial microcosms. Nat. Ecol. Evol. 1:5109
    [Google Scholar]
  37. 37. 
    Fukami T, Dickie IA, Wilkie JP, Paulus BC, Park D et al. 2010. Assembly history dictates ecosystem functioning: evidence from wood decomposer communities. Ecol. Lett. 13:6675–84
    [Google Scholar]
  38. 38. 
    Gilmore SP, Lankiewicz TS, Wilken SE, Brown JL, Sexton JA et al. 2019. Top-down enrichment guides in formation of synthetic microbial consortia for biomass degradation. ACS Synth. Biol. 8:92174–85
    [Google Scholar]
  39. 39. 
    Goldford JE, Lu N, Bajić D, Estrela S, Tikhonov M et al. 2018. Emergent simplicity in microbial community assembly. Science 361:6401469–74
    [Google Scholar]
  40. 40. 
    Goodnight CJ. 1990. Experimental studies of community evolution I: the response to selection at the community level. Evolution 44:61614–24
    [Google Scholar]
  41. 41. 
    Goodnight CJ. 1990. Experimental studies of community evolution II: the ecological basis of the response to community selection. Evolution 44:61625–36
    [Google Scholar]
  42. 42. 
    Goodnight CJ. 2000. Heritability at the ecosystem level. PNAS 97:179365–66
    [Google Scholar]
  43. 43. 
    Goodnight CJ. 2011. Evolution in metacommunities. Philos. Trans. R. Soc. Lond. B 366:15691401–9
    [Google Scholar]
  44. 44. 
    Gore J, Youk H, van Oudenaarden A. 2009. Snowdrift game dynamics and facultative cheating in yeast. Nature 459:7244253–56
    [Google Scholar]
  45. 45. 
    Gould AL, Zhang V, Lamberti L, Jones EW, Obadia B et al. 2018. Microbiome interactions shape host fitness. PNAS 115:51E11951–60
    [Google Scholar]
  46. 46. 
    Guo X, Boedicker JQ. 2016. The contribution of high-order metabolic interactions to the global activity of a four-species microbial community. PLOS Comput. Biol. 12:9e1005079
    [Google Scholar]
  47. 47. 
    Guo X, Boedicker JQ. 2016. High-order interactions between species strongly influence the activity of microbial communities. Biophys. J. 110:3143a
    [Google Scholar]
  48. 48. 
    Hall BG. 1978. Experimental evolution of a new enzymatic function. II. Evolution of multiple functions for ebg enzyme in E. coli. Genetics 89:3453–65
    [Google Scholar]
  49. 49. 
    Hansen JJ, Sartor RB. 2015. Therapeutic manipulation of the microbiome in IBD: current results and future approaches. Curr. Treat. Opt. Gastroenterol. 13:1105–20
    [Google Scholar]
  50. 50. 
    Hauert C, Holmes M, Doebeli M. 2006. Evolutionary games and population dynamics: maintenance of cooperation in public goods games. Proc. Biol. Sci. 273:16002565–70
    [Google Scholar]
  51. 51. 
    Hauert C, Wakano JY, Doebeli M. 2008. Ecological public goods games: cooperation and bifurcation. Theor. . Popul. Biol. 73:2257–63
    [Google Scholar]
  52. 52. 
    Herrera Paredes S, Gao T, Law TF, Finkel OM, Mucyn T et al. 2018. Design of synthetic bacterial communities for predictable plant phenotypes. PLOS Biol 16:2e2003962
    [Google Scholar]
  53. 53. 
    Ho K-L, Lee D-J, Su A, Chang J-S. 2012. Biohydrogen from cellulosic feedstock: dilution-to-stimulation approach. Int. J. Hydrogen Energy 37:2015582–87
    [Google Scholar]
  54. 54. 
    Hu J, Xue Y, Li J, Wang L, Zhang S et al. 2016. Characterization of a designed synthetic autotrophic-heterotrophic consortia for fixing CO2 without light. RSC Adv 6:8178161–69
    [Google Scholar]
  55. 55. 
    Jochum MD, McWilliams KL, Pierson EA, Jo Y-K. 2019. Host-mediated microbiome engineering (HMME) of drought tolerance in the wheat rhizosphere. PLOS ONE 14:12e0225933
    [Google Scholar]
  56. 56. 
    Kang D, Jacquiod S, Herschend J, Wei S, Nesme J, Sørensen SJ. 2019. Construction of simplified microbial consortia to degrade recalcitrant materials based on enrichment and dilution-to-extinction cultures. Front. Microbiol. 10:3010
    [Google Scholar]
  57. 57. 
    Karkaria BD, Treloar NJ, Barnes CP, Fedorec AJH. 2020. From microbial communities to distributed computing systems. Front. Bioeng. Biotechnol. 8:834
    [Google Scholar]
  58. 58. 
    Kato S, Haruta S, Cui ZJ, Ishii M, Igarashi Y. 2005. Stable coexistence of five bacterial strains as a cellulose-degrading community. Appl. Environ. Microbiol. 71:117099–106
    [Google Scholar]
  59. 59. 
    Klumpp S, Zhang Z, Hwa T. 2009. Growth rate-dependent global effects on gene expression in bacteria. Cell 139:71366–75
    [Google Scholar]
  60. 60. 
    Kucharzyk KH, Crawford RL, Paszczynski AJ, Soule T, Hess TF. 2012. Maximizing microbial degradation of perchlorate using a genetic algorithm: media optimization. J. Biotechnol. 157:1189–97
    [Google Scholar]
  61. 61. 
    Kurkjian H, Akbari MJ, Momeni B. 2020. The impact of interactions on invasion and colonization resistance in microbial communities. bioRxiv 146571. https://doi.org/10.1101/2020.06.11.146571
    [Crossref]
  62. 62. 
    Lee D-J, Show K-Y, Wang A 2013. Unconventional approaches to isolation and enrichment of functional microbial consortium–a review. Bioresour. Technol. 136:697–706
    [Google Scholar]
  63. 63. 
    Lee HL, Shen H, Hwang IY, Ling H, Yew WS et al. 2018. Targeted approaches for in situ gut microbiome manipulation. Genes 9:7351
    [Google Scholar]
  64. 64. 
    Leemhuis H, Stein V, Griffiths AD, Hollfelder F. 2005. New genotype-phenotype linkages for directed evolution of functional proteins. Curr. Opin. Struct. Biol. 15:4472–78
    [Google Scholar]
  65. 65. 
    Levin BR, Kilmer WL. 1974. Interdemic selection and the evolution of altruism: a computer simulation study. Evolution 28:4527–45
    [Google Scholar]
  66. 66. 
    Lewontin RC. 1970. The units of selection. Annu. Rev. Ecol. Syst. 1:1–18
    [Google Scholar]
  67. 67. 
    Loreau M, Naeem S, Inchausti P, Bengtsson J, Grime JP et al. 2001. Biodiversity and ecosystem functioning: current knowledge and future challenges. Science 294:5543804–8
    [Google Scholar]
  68. 68. 
    Lu N, Sanchez-Gorostiaga A, Tikhonov M, Sanchez A. 2018. Cohesiveness in microbial community coalescence. bioRxiv 282723. https://doi.org/10.1101/282723
    [Crossref]
  69. 69. 
    Macia J, Manzoni R, Conde N, Urrios A, de Nadal E et al. 2016. Implementation of complex biological logic circuits using spatially distributed multicellular consortia. PLOS Comput. Biol. 12:2e1004685
    [Google Scholar]
  70. 70. 
    Maxson T, Mitchell DA. 2016. Targeted treatment for bacterial infections: prospects for pathogen-specific antibiotics coupled with rapid diagnostics. Tetrahedron 72:253609–24
    [Google Scholar]
  71. 71. 
    Mueller UG, Juenger TE, Kardish MR, Carlson AL, Burns K et al. 2016. Artificial microbiome-selection to engineer microbiomes that confer salt-tolerance to plants. bioRxiv 081521. https://doi.org/10.1101/081521
    [Crossref]
  72. 72. 
    Mueller UG, Sachs JL. 2015. Engineering microbiomes to improve plant and animal health. Trends Microbiol 23:10606–17
    [Google Scholar]
  73. 73. 
    Nale JY, Redgwell TA, Millard A, Clokie MRJ 2018. Efficacy of an optimised bacteriophage cocktail to clear Clostridium difficile in a batch fermentation model. Antibiotics 7:113
    [Google Scholar]
  74. 74. 
    Packer MS, Liu DR. 2015. Methods for the directed evolution of proteins. Nat. Rev. Genet. 16:7379–94
    [Google Scholar]
  75. 75. 
    Panke-Buisse K, Lee S, Kao-Kniffin J. 2016. Cultivated sub-populations of soil microbiomes retain early flowering plant trait. Microb. Ecol. 73:2394–403
    [Google Scholar]
  76. 76. 
    Panke-Buisse K, Poole AC, Goodrich JK, Ley RE, Kao-Kniffin J. 2015. Selection on soil microbiomes reveals reproducible impacts on plant function. ISME J 9:4980–89
    [Google Scholar]
  77. 77. 
    Puentes-Téllez PE, Falcao Salles J 2018. Construction of effective minimal active microbial consortia for lignocellulose degradation. Microb. Ecol. 76:2419–29
    [Google Scholar]
  78. 78. 
    Quistad SD, Doulcier G, Rainey PB. 2020. Experimental manipulation of selfish genetic elements links genes to microbial community function. Phil. Trans. R. Soc. B 375:20190681
    [Google Scholar]
  79. 79. 
    Raman S, Rogers JK, Taylor ND, Church GM. 2014. Evolution-guided optimization of biosynthetic pathways. PNAS 111:5017803–8
    [Google Scholar]
  80. 80. 
    Ratcliff WC, Denison RF, Borrello M, Travisano M. 2012. Experimental evolution of multicellularity. PNAS 109:51595–600
    [Google Scholar]
  81. 81. 
    Ratcliff WC, Herron MD, Howell K, Pentz JT, Rosenzweig F, Travisano M. 2013. Experimental evolution of an alternating uni- and multicellular life cycle in Chlamydomonas reinhardtii. Nat. Commun. 4:2742
    [Google Scholar]
  82. 82. 
    Rauch J, Kondev J, Sanchez A. 2017. Cooperators trade off ecological resilience and evolutionary stability in public goods games. J. R. Soc. Interface 14:12720160967
    [Google Scholar]
  83. 83. 
    Raynaud T, Devers M, Spor A, Blouin M. 2019. Effect of the reproduction method in an artificial selection experiment at the community level. Front. Ecol. Evol. 7:416
    [Google Scholar]
  84. 84. 
    Regot S, Macia J, Conde N, Furukawa K, Kjellén J et al. 2011. Distributed biological computation with multicellular engineered networks. Nature 469:7329207–11
    [Google Scholar]
  85. 85. 
    Reich PB, Tilman D, Naeem S, Ellsworth DS, Knops J et al. 2004. Species and functional group diversity independently influence biomass accumulation and its response to CO2 and N. PNAS 101:2710101–6
    [Google Scholar]
  86. 86. 
    Rillig MC, Antonovics J, Caruso T, Lehmann A, Powell JR et al. 2015. Interchange of entire communities: microbial community coalescence. Trends Ecol. Evol. 30:8470–76
    [Google Scholar]
  87. 87. 
    Rillig MC, Tsang A, Roy J. 2016. Microbial community coalescence for microbiome engineering. Front. Microbiol. 7:1967
    [Google Scholar]
  88. 88. 
    Robert L, Paul G, Chen Y, Taddei F, Baigl D, Lindner AB. 2010. Pre-dispositions and epigenetic inheritance in the Escherichia coli lactose operon bistable switch. Mol. Syst. Biol. 6:357
    [Google Scholar]
  89. 89. 
    Romero PA, Arnold FH. 2009. Exploring protein fitness landscapes by directed evolution. Nat. Rev. Mol. Cell Biol. 10:12866–76
    [Google Scholar]
  90. 90. 
    Romero PA, Krause A, Arnold FH. 2013. Navigating the protein fitness landscape with Gaussian processes. PNAS 110:3E193–201
    [Google Scholar]
  91. 91. 
    Ronda C, Chen SP, Cabral V, Yaung SJ, Wang HH. 2019. Metagenomic engineering of the mammalian gut microbiome in situ. Nat. Methods 16:2167–70
    [Google Scholar]
  92. 92. 
    Ross-Ibarra J, Morrell PL, Gaut BS. 2007. Plant domestication, a unique opportunity to identify the genetic basis of adaptation. PNAS 104:Suppl. 18641–48
    [Google Scholar]
  93. 93. 
    Sanchez A, Gore J. 2013. Feedback between population and evolutionary dynamics determines the fate of social microbial populations. PLOS Biol 11:4e1001547
    [Google Scholar]
  94. 94. 
    Sanchez-Gorostiaga A, Bajić D, Osborne ML, Poyatos JF, Sanchez A. 2019. High-order interactions distort the functional landscape of microbial consortia. PLOS Biol 17:12e3000550
    [Google Scholar]
  95. 95. 
    Senay Y, John G, Knutie SA, Ogbunugafor CB. 2019. Deconstructing higher-order interactions in the microbiota: a theoretical examination. bioRxiv 647156. https://doi.org/10.1101/647156
    [Crossref]
  96. 96. 
    Shepherd ES, DeLoache WC, Pruss KM, Whitaker WR, Sonnenburg JL. 2018. An exclusive metabolic niche enables strain engraftment in the gut microbiota. Nature 557:7705434–38
    [Google Scholar]
  97. 97. 
    Sheth RU, Cabral V, Chen SP, Wang HH. 2016. Manipulating bacterial communities by in situ microbiome engineering. Trends Genet 32:4189–200
    [Google Scholar]
  98. 98. 
    Shibasaki S, Mitri S. 2020. Controlling evolutionary dynamics to optimize microbial bioremediation. Evol. Appl. 13:2460–71
    [Google Scholar]
  99. 99. 
    Sierocinski P, Milferstedt K, Bayer F, Großkopf T, Alston M et al. 2017. A single community dominates structure and function of a mixture of multiple methanogenic communities. Curr. Biol. 27:213390–95.e4
    [Google Scholar]
  100. 100. 
    Smith GP, Petrenko VA. 1997. Phage display. Chem. Rev. 97:2391–410
    [Google Scholar]
  101. 101. 
    Stein RR, Tanoue T, Szabady RL, Bhattarai SK, Olle B et al. 2018. Computer-guided design of optimal microbial consortia for immune system modulation. eLife 7:e30916
    [Google Scholar]
  102. 102. 
    Stemmer WP. 1994. Rapid evolution of a protein in vitro by DNA shuffling. Nature 370:6488389–91
    [Google Scholar]
  103. 103. 
    Swenson W, Arendt J, Wilson DS. 2000. Artificial selection of microbial ecosystems for 3-chloroaniline biodegradation. Environ. Microbiol. 2:5564–71
    [Google Scholar]
  104. 104. 
    Swenson W, Wilson DS, Elias R. 2000. Artificial ecosystem selection. PNAS 97:169110–14
    [Google Scholar]
  105. 105. 
    Tikhonov M. 2016. Community-level cohesion without cooperation. eLife 5:e15747
    [Google Scholar]
  106. 106. 
    Tilman D. 1999. The ecological consequences of changes in biodiversity: a search for general principles. Ecology 80:51455–74
    [Google Scholar]
  107. 107. 
    Tilman D, Reich PB, Knops J, Wedin D, Mielke T, Lehman C. 2001. Diversity and productivity in a long-term grassland experiment. Science 294:5543843–45
    [Google Scholar]
  108. 108. 
    Tracewell CA, Arnold FH. 2009. Directed enzyme evolution: climbing fitness peaks one amino acid at a time. Curr. Opin. Chem. Biol. 13:13–9
    [Google Scholar]
  109. 109. 
    Vila JCC, Liu Y-Y, Sanchez A. 2020. Dissimilarity-overlap analysis of replicate enrichment communities. ISME J 14:102505–13
    [Google Scholar]
  110. 110. 
    Vorholt JA, Vogel C, Carlström CI, Müller DB. 2017. Establishing causality: opportunities of synthetic communities for plant microbiome research. Cell Host Microbe 22:2142–55
    [Google Scholar]
  111. 111. 
    Wade MJ. 1976. Group selections among laboratory populations of Tribolium. PNAS 73:124604–7
    [Google Scholar]
  112. 112. 
    Wade MJ. 1977. An experimental study of group selection. Evolution 31:1134–53
    [Google Scholar]
  113. 113. 
    Wade MJ. 1978. A critical review of the models of group selection. Q. Rev. Biol. 53:2101–14
    [Google Scholar]
  114. 114. 
    Wade MJ. 2016. Adaptation in Metapopulations: How Interaction Changes Evolution Chicago: Univ. Chicago Press
    [Google Scholar]
  115. 115. 
    Wang HH, Isaacs FJ, Carr PA, Sun ZZ, Xu G et al. 2009. Programming cells by multiplex genome engineering and accelerated evolution. Nature 460:7257894–98
    [Google Scholar]
  116. 116. 
    Westra ER, van Houte S, Oyesiku-Blakemore S, Makin B, Broniewski JM et al. 2015. Parasite exposure drives selective evolution of constitutive versus inducible defense. Curr. Biol. 25:81043–49
    [Google Scholar]
  117. 117. 
    Widder S, Allen RJ, Pfeiffer T, Curtis TP, Wiuf C et al. 2016. Challenges in microbial ecology: building predictive understanding of community function and dynamics. ISME J 10:112557–68
    [Google Scholar]
  118. 118. 
    Williams HTP, Lenton TM. 2007. Artificial selection of simulated microbial ecosystems. PNAS 104:218918–23
    [Google Scholar]
  119. 119. 
    Wilson DS. 1992. Complex interactions in metacommunities, with implications for biodiversity and higher levels of selection. Ecology 73:61984–2000
    [Google Scholar]
  120. 120. 
    Wolfe BE, Button JE, Santarelli M, Dutton RJ. 2014. Cheese rind communities provide tractable systems for in situ and in vitro studies of microbial diversity. Cell 158:2422–33
    [Google Scholar]
  121. 121. 
    Wright RJ, Gibson MI, Christie-Oleza JA. 2019. Understanding microbial community dynamics to improve optimal microbiome selection. Microbiome 7:185
    [Google Scholar]
  122. 122. 
    Wright S. 1982. The shifting balance theory and macroevolution. Annu. Rev. Genet. 16:1–20
    [Google Scholar]
  123. 123. 
    Xie L, Yuan AE, Shou W. 2019. Simulations reveal challenges to artificial community selection and possible strategies for success. PLOS Biol 17:6e3000295
    [Google Scholar]
  124. 124. 
    Yao J, Carter RA, Vuagniaux G, Barbier M, Rosch JW, Rock CO. 2016. A pathogen-selective antibiotic minimizes disturbance to the microbiome. Antimicrob. Agents Chemother. 60:74264–73
    [Google Scholar]
  125. 125. 
    Yokobayashi Y, Arnold FH. 2005. A dual selection module for directed evolution of genetic circuits. Nat. Comput. 4:3245–54
    [Google Scholar]
  126. 126. 
    Yokobayashi Y, Weiss R, Arnold FH 2002. Directed evolution of a genetic circuit. PNAS 99:2616587–91
    [Google Scholar]
  127. 127. 
    Zanaroli G, Di Toro S, Todaro D, Varese GC, Bertolotto A, Fava F. 2010. Characterization of two diesel fuel degrading microbial consortia enriched from a non acclimated, complex source of microorganisms. Microb. Cell Fact. 9:10
    [Google Scholar]
  128. 128. 
    Zhang Y-X, Perry K, Vinci VA, Powell K, Stemmer WPC, del Cardayré SB. 2002. Genome shuffling leads to rapid phenotypic improvement in bacteria. Nature 415:6872644–46
    [Google Scholar]
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