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Emergence and maintenance of stable coexistence during a long-term multicellular evolution experiment

Nature Ecology & Evolution (2024)Cite this article

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Abstract

The evolution of multicellular life spurred evolutionary radiations, fundamentally changing many of Earth’s ecosystems. Yet little is known about how early steps in the evolution of multicellularity affect eco-evolutionary dynamics. Through long-term experimental evolution, we observed niche partitioning and the adaptive divergence of two specialized lineages from a single multicellular ancestor. Over 715 daily transfers, snowflake yeast were subjected to selection for rapid growth, followed by selection favouring larger group size. Small and large cluster-forming lineages evolved from a monomorphic ancestor, coexisting for over ~4,300 generations, specializing on divergent aspects of a trade-off between growth rate and survival. Through modelling and experimentation, we demonstrate that coexistence is maintained by a trade-off between organismal size and competitiveness for dissolved oxygen. Taken together, this work shows how the evolution of a new level of biological individuality can rapidly drive adaptive diversification and the expansion of a nascent multicellular niche, one of the most historically impactful emergent properties of this evolutionary transition.

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Fig. 1: Emergence and long-term coexistence of large and small snowflake yeast phenotypes.
Fig. 2: Coexistence between Small and Large group-forming genotypes is mediated by oxygen.
Fig. 3: Coexistence arises in a general model via size-dependent trade-offs between growth rate and survival.
Fig. 4: Selection drives divergence via character displacement.
Fig. 5: The divergent traits of Large and Small genotypes appear to have arisen via character displacement.

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Data availability

Underlying data used to generate figures and raw data are available on GitHub (https://github.com/Ratcliff-Lab/coexistence-paper.git). Raw Illumina sequencing reads are available at the NIH Sequence Read Archive under accession number PRJNA1064559. Source data are provided with this paper.

Code availability

Codes used to generate the main figures are available on GitHub (https://github.com/Ratcliff-Lab/coexistence-paper.git).

References

  1. Maloof, A. C. et al. The earliest Cambrian record of animals and ocean geochemical change. GSA Bull. 122, 1731–1774 (2010).

    Article  CAS  Google Scholar 

  2. McMahon, S. & Parnell, J. The deep history of Earth’s biomass. J. Geol. Soc. 175, 716–720 (2018).

    Article  Google Scholar 

  3. Delwiche, C. F. & Cooper, E. D. The evolutionary origin of a terrestrial flora. Curr. Biol. 25, R899–R910 (2015).

    Article  CAS  PubMed  Google Scholar 

  4. Odling-Smee, F. J., Laland, K. N. & Feldman, M. W. Niche construction. Am. Nat. 147, 641–648 (1996).

    Article  Google Scholar 

  5. Boyce, C. K. The evolutionary history of roots and leaves. in Vascular Transport in Plants (eds Holbrook, N. M. & Zwieniecki, M. A.) 479–499 (Elsevier, 2005).

  6. Kenrick, P. & Strullu-Derrien, C. The origin and early evolution of roots. Plant Physiol. 166, 570–580 (2014).

    Article  PubMed  PubMed Central  Google Scholar 

  7. Rose, C. J. & Hammerschmidt, K. What do we mean by multicellularity? The evolutionary transitions framework provides answers. Front. Ecol. Evol. 9, 730714 (2021).

    Article  Google Scholar 

  8. Bonner, J. T. The Evolution of Complexity by Means of Natural Selection (Princeton Univ. Press, 1988).

  9. Bourrat, P., Doulcier, G., Rose, C. J., Rainey, P. B. & Hammerschmidt, K. Tradeoff breaking as a model of evolutionary transitions in individuality and limits of the fitness-decoupling metaphor. ELife 11, e73715 (2022).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  10. Ratcliff, W. C., Denison, R. F., Borrello, M. & Travisano, M. Experimental evolution of multicellularity. Proc. Natl Acad. Sci. USA 109, 1595–1600 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  11. Bozdag, G. O., Libby, E., Pineau, R., Reinhard, C. T. & Ratcliff, W. C. Oxygen suppression of macroscopic multicellularity. Nat. Commun. 12, 2838 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  12. Bozdag, G. O. et al. De novo evolution of macroscopic multicellularity. Nature 617, 747–754 (2023).

    Article  CAS  PubMed  Google Scholar 

  13. Pentz, J. T., Taylor, B. P. & Ratcliff, W. C. Apoptosis in snowflake yeast: novel trait, or side effect of toxic waste? J. R. Soc. Interface 13, 20160121 (2016).

    Article  PubMed  PubMed Central  Google Scholar 

  14. Jacobeen, S. et al. Cellular packing, mechanical stress and the evolution of multicellularity. Nat. Phys. 14, 286–290 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  15. Zamani-Dahaj, S. A. et al. Spontaneous emergence of multicellular heritability. Genes 14, 1635 (2023).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  16. Tong, K., Bozdag, G. O. & Ratcliff, W. C. Selective drivers of simple multicellularity. Curr. Opin. Microbiol. 67, 102141 (2022).

    Article  PubMed  Google Scholar 

  17. Knoll, A. H. The multiple origins of complex multicellularity. Annu. Rev. Earth Planet. Sci. 39, 217–239 (2011).

    Article  CAS  Google Scholar 

  18. Herron, M. D. et al. De novo origins of multicellularity in response to predation. Sci. Rep. 9, 2328 (2019).

    Article  PubMed  PubMed Central  Google Scholar 

  19. Kapsetaki, S. E. & West, S. A. The costs and benefits of multicellular group formation in algae. Evolution 73, 1296–1308 (2019).

    Article  PubMed  Google Scholar 

  20. Rebolleda-Gómez, M., Ratcliff, W. C., Fankhauser, J. & Travisano, M. Evolution of simple multicellularity increases environmental complexity. Preprint at bioRxiv https://doi.org/10.1101/067991 (2016).

  21. Ratcliff, W. C., Fankhauser, J. D., Rogers, D. W., Greig, D. & Travisano, M. Origins of multicellular evolvability in snowflake yeast. Nat. Commun. 6, 6102 (2015).

    Article  CAS  PubMed  Google Scholar 

  22. Levin, B. Frequency-dependent selection in bacterial populations. Phil. Trans. R. Soc. Lond. B 319, 459–472 (1988).

    Article  CAS  Google Scholar 

  23. Heino, M., Metz, J. A. & Kaitala, V. The enigma of frequency-dependent selection. Trends Ecol. Evol. 13, 367–370 (1998).

    Article  CAS  PubMed  Google Scholar 

  24. Chevin, L.-M. On measuring selection in experimental evolution. Biol. Lett. 7, 210–213 (2011).

    Article  PubMed  Google Scholar 

  25. Schirrmeister, B. E., de Vos, J. M., Antonelli, A. & Bagheri, H. C. Evolution of multicellularity coincided with increased diversification of cyanobacteria and the great oxidation event. Proc. Natl Acad. Sci. USA 110, 1791–1796 (2013).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  26. Szathmáry, E. & Smith, J. M. The major evolutionary transitions. Nature 374, 227–232 (1995).

    Article  PubMed  Google Scholar 

  27. Szathmáry, E. Toward major evolutionary transitions theory 2.0. Proc. Natl Acad. Sci. USA 112, 10104–10111 (2015).

    Article  PubMed  PubMed Central  Google Scholar 

  28. Simpson, G. G.Tempo and Mode in Evolution (Columbia Univ. Press, 1984).

  29. Yoder, J. et al. Ecological opportunity and the origin of adaptive radiations. J. Evol. Biol. 23, 1581–1596 (2010).

    Article  CAS  PubMed  Google Scholar 

  30. Le Hir, G. et al. The climate change caused by the land plant invasion in the Devonian. Earth Planet. Sci. Lett. 310, 203–212 (2011).

    Article  Google Scholar 

  31. Dahl, T. W. & Arens, S. K. The impacts of land plant evolution on Earth’s climate and oxygenation state–an interdisciplinary review. Chem. Geol. 547, 119665 (2020).

    Article  CAS  Google Scholar 

  32. Erwin, D. H. & Tweedt, S. Ecological drivers of the Ediacaran-Cambrian diversification of Metazoa. Evol. Ecol. 26, 417–433 (2012).

    Article  Google Scholar 

  33. Warren, L. et al. Origin and impact of the oldest metazoan bioclastic sediments. Geology 41, 507–510 (2013).

    Article  Google Scholar 

  34. McIlroy, D. & Logan, G. A. The impact of bioturbation on infaunal ecology and evolution during the Proterozoic-Cambrian transition. Palaios 14, 58–72 (1999).

    Article  Google Scholar 

  35. Billingham, J. (ed.) Life in the Universe NASA Conference Pulication 2156 (NASA, 1981).

  36. Kassen, R. The experimental evolution of specialists, generalists, and the maintenance of diversity. J. Evol. Biol. 15, 173–190 (2002).

    Article  Google Scholar 

  37. Doebeli, M., Ispolatov, Y. & Simon, B. Towards a mechanistic foundation of evolutionary theory. Elife 6, e23804 (2017).

    Article  PubMed  PubMed Central  Google Scholar 

  38. Roff, D. Evolution of Life Histories: Theory and Analysis (Springer, 1993).

  39. Stearns, S. C. Trade-offs in life-history evolution. Funct. Ecol. 3, 259–268 (1989).

    Article  Google Scholar 

  40. Sibly, R. M. in Encyclopedia of Evolution (ed. Pagel, M.) 623–627 (Oxford Univ. Press, 2002).

  41. Rainey, P. B. & Travisano, M. Adaptive radiation in a heterogeneous environment. Nature 394, 69–72 (1998).

    Article  CAS  PubMed  Google Scholar 

  42. Koza, A., Moshynets, O., Otten, W. & Spiers, A. J. Environmental modification and niche construction: developing O2 gradients drive the evolution of the wrinkly spreader. ISME J. 5, 665–673 (2011).

    Article  CAS  PubMed  Google Scholar 

  43. Kinnersley, M. et al. Ex uno plures: clonal reinforcement drives evolution of a simple microbial community. PLoS Genet. 10, e1004430 (2014).

    Article  PubMed  PubMed Central  Google Scholar 

  44. Frenkel, E. M. et al. Crowded growth leads to the spontaneous evolution of semistable coexistence in laboratory yeast populations. Proc. Natl Acad. Sci. USA 112, 11306–11311 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  45. Mizuuchi, R., Furubayashi, T. & Ichihashi, N. Evolutionary transition from a single RNA replicator to a multiple replicator network. Nat. Commun. 13, 1460 (2022).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  46. Mahler, D. L., Revell, L. J., Glor, R. E. & Losos, J. B. Ecological opportunity and the rate of morphological evolution in the diversification of Greater Antillean anoles. Evolution 64, 2731–2745 (2010).

    Article  PubMed  Google Scholar 

  47. Wellborn, G. A. & Langerhans, R. B. Ecological opportunity and the adaptive diversification of lineages. Ecol. Evol. 5, 176–195 (2015).

    Article  PubMed  Google Scholar 

  48. Dobson, A., Tilman, D. & Holt, R. D. Unsolved Problems in Ecology (Princeton Univ. Press, 2020).

  49. Godfray, H. C. J. & May, R. M. Open questions: are the dynamics of ecological communities predictable? BMC Biol. 12, 22 (2014).

    Article  PubMed  PubMed Central  Google Scholar 

  50. Godfrey-Smith, P. in From Groups to Individuals: Evolution and Emerging Individuality (eds Bouchard, F. & Huneman, P.) Ch. 1 (MIT Press, 2013).

  51. Bonner, J. T. The origins of multicellularity. Integr. Biol. 1, 27–36 (1998).

    Article  Google Scholar 

  52. Bonner, J. T. Why Size Matters: From Bacteria to Blue Whales (Princeton Univ. Press, 2007).

  53. Velicer, G. J. & Yu, Y.-tN. Evolution of novel cooperative swarming in the bacterium Myxococcus xanthus. Nature 425, 75–78 (2003).

    Article  CAS  PubMed  Google Scholar 

  54. Jeon, K. W. Development of cellular dependence on infective organisms: micrurgical studies in amoebas. Science 176, 1122–1123 (1972).

    Article  CAS  PubMed  Google Scholar 

  55. Barber, J. N. et al. The evolution of coexistence from competition in experimental co-cultures of Escherichia coli and Saccharomyces cerevisiae. ISME J. 15, 746–761 (2021).

    Article  CAS  PubMed  Google Scholar 

  56. Blount, Z. D., Borland, C. Z. & Lenski, R. E. Historical contingency and the evolution of a key innovation in an experimental population of Escherichia coli. Proc. Natl Acad. Sci. USA 105, 7899–7906 (2008).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  57. Mukherjee, A. et al. Coexisting ecotypes in long-term evolution emerged from interacting trade-offs. Nat. Commun. 14, 3805 (2023).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  58. Velicer, G. J., Kroos, L. & Lenski, R. E. Developmental cheating in the social bacterium Myxococcus xanthus. Nature 404, 598–601 (2000).

    Article  CAS  PubMed  Google Scholar 

  59. Hart, S. F. M., Pineda, J. M. B., Chen, C.-C., Green, R. & Shou, W. Disentangling strictly self-serving mutations from win-win mutations in a mutualistic microbial community. Elife 8, e44812 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  60. Harcombe, W. Novel cooperation experimentally evolved between species. Evolution 64, 2166–2172 (2010).

    PubMed  Google Scholar 

  61. Ratcliff, W. C. et al. Experimental evolution of an alternating uni- and multicellular life cycle in Chlamydomonas reinhardtii. Nat. Commun. 4, 2742 (2013).

    Article  PubMed  Google Scholar 

  62. Gietz, R. D. & Schiestl, R. H. Quick and easy yeast transformation using the LiAc/SS carrier DNA/PEG method. Nat. Protoc. 2, 35–37 (2007).

    Article  CAS  PubMed  Google Scholar 

  63. Chen, S., Zhou, Y., Chen, Y. & Gu, J. fastp: an ultra-fast all-in-one FASTQ preprocessor. Bioinformatics 34, i884–i890 (2018).

    Article  PubMed  PubMed Central  Google Scholar 

  64. Li, H. Aligning sequence reads, clone sequences and assembly contigs with bwa-mem. Preprint at https://doi.org/10.48550/arXiv.1303.3997 (2013).

  65. Li, H. et al. The sequence alignment/map format and samtools. Bioinformatics 25, 2078–2079 (2009).

    Article  PubMed  PubMed Central  Google Scholar 

  66. Barnett, D. W., Garrison, E. K., Quinlan, A. R., Strömberg, M. P. & Marth, G. T. Bamtools: a C++ API and toolkit for analyzing and managing BAM files. Bioinformatics 27, 1691–1692 (2011).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  67. Van der Auwera, G. A. et al. From FastQ data to high-confidence variant calls: the genome analysis toolkit best practices pipeline. Curr. Protoc. Bioinformatics 43, 11.10.1–11.10.33 (2013).

    PubMed  Google Scholar 

  68. Danecek, P. et al. The variant call format and vcftools. Bioinformatics 27, 2156–2158 (2011).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  69. Robinson, J. T., Thorvaldsdóttir, H., Turner, D. & Mesirov, J. P. igv.js: an embeddable Javascript implementation of the Integrative Genomics Viewer (IGV). Bioinformatics 39, btac830 (2023).

    Article  CAS  PubMed  Google Scholar 

  70. Jukes, T. H. & Cantor, C. R. et al. in Mammalian Protein Metabolism (ed. Munro, H. N.) 21–132 (Academic Press, 1969).

  71. Bouckaert, R. et al. BEAST 2: a software platform for Bayesian evolutionary analysis. PLoS Comput. Biol. 10, e1003537 (2014).

    Article  PubMed  PubMed Central  Google Scholar 

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Acknowledgements

We thank the members of the Ratcliff lab for their input on the project, as well as the GT QBioS Graduate Program for its support (this paper would not exist without the incredible work of program coordinator L. Redding). This work was supported by grants from the NIH (Grant No. 5R35GM138030) and the NSF Division of Environmental Biology (Grant No. DEB-1845363) to W.C.R. P.J.Y. acknowledges funding from the NIH National Institute of General Medical Sciences (Grant No. 1R35GM138354-01).

Author information

Authors and Affiliations

  1. School of Biological Sciences, Georgia Institute of Technology, Atlanta, GA, USA

    Rozenn M. Pineau, Dung T. Lac, Joshua S. Weitz, G. Ozan Bozdag & William C. Ratcliff

  2. Interdisciplinary Graduate Program in Quantitative Biosciences, Georgia Institute of Technology, Atlanta, GA, USA

    Rozenn M. Pineau & Pablo Bravo

  3. Integrated Science Lab, Umeå university, Umeå, Sweden

    Eric Libby

  4. Department of Mathematics and Mathematical Statistics, Umeå university, Umeå, Sweden

    Eric Libby

  5. CNRS, Sorbonne Université, USR 3579 Laboratoire de Biodiversité et Biotechnologies Microbiennes (LBBM), Observatoire Océanologique, Banyuls-sur-Mer, France

    David Demory

  6. School of Physics, Georgia Institute of Technology, Atlanta, GA, USA

    Thomas C. Day, Pablo Bravo, Peter J. Yunker & Joshua S. Weitz

  7. Department of Biological Sciences, University of Southern California, Los Angeles, CA, USA

    Thomas C. Day

  8. Department of Biology, University of Maryland, College Park, MD, USA

    Joshua S. Weitz & William C. Ratcliff

  9. Department of Physics, University of Maryland, College Park, MD, USA

    Joshua S. Weitz

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  1. Rozenn M. Pineau
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Contributions

R.M.P., W.C.R., E.L. and G.O.B. designed the study and wrote the paper. R.M.P. and D.T.L. performed the experiments and analysed the data. G.O.B. performed the evolution experiment. E.L., R.M.P., D.D., P.B., P.J.Y., T.C.D., W.C.R. and J.S.W. developed and analysed the model. All authors contributed to editing the paper.

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Correspondence to Eric Libby or William C. Ratcliff.

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Extended data

Extended Data Fig. 1 Size distributions of isolates from the ancestor and the five lines subject to long term evolution, after 715 days of serial transfers ( ~ 4,300 generations).

We observe the emergence of phenotypic diversity in lines PO-3, PO-4 and PO-5. The different colors denote different isolates.

Source Data

Extended Data Fig. 2 Standing diversity within population PO-4.

(A) Whole population size distribution (n > 1000) and b picture of PO-4 after 715 days of serial transfers ( ~ 4300 generations). Large-sized snowflake yeast were present at a mean frequency of 9.4% in the whole t715 population (Extended Data Fig. 4). This image is representative of the whole population.

Source Data

Extended Data Fig. 3 Phylogeny of independently evolved line PO-3, showing deep divergence between Small and Large isolates.

They do not share any mutations, indicating that the last common ancestor of these lineages was the genotype used to found the experiment, and these lineages have been coexisting for the full duration of the experiment. Here, the color represents the phenotype (Small or Large), and numbers PO-3-1, PO-3-2, and PO-3-3 represent three isolates of Small and Large yeast sampled from line PO-3.

Extended Data Fig. 4 Coexistence over time.

To examine the stability of coexistence throughout the experiment, we measured the frequency and group size at time 200, 400 and 600. We estimated the frequency of large and small phenotypes by segmenting microscopy images at these timepoints. (A) The large phenotype declined in frequency as a function of time, at approximately 1.3% per 100 transfers (y = 11.96 -.013x, P = 0.00017, linear regression. Adjusted R2 = 0.86). Bars represent one standard deviation. (B) There were no obvious differences in the size of large and small phenotypes over time, though the main effects of time and phenotype were highly significant (F1, 18173 = 226.8 and 6989.5, respectively, P < 10−15 for each, two-way ANOVA), as was the interaction between phenotype and time (F1,18173 = 23.7, P < 10−5). Bars represent one standard deviation. (C) Snapshots of PO-4 populations for each time point measured.

Source Data

Extended Data Fig. 5 Frequency dependence and specialization along a growth-survival trade-off.

To test for frequency dependence in the experiment, we initiated one round of growth and one round of settling selection starting from a wide range of initial 10 frequencies (from 1% to 99%). The proportion of Large clusters after 24 hours of growth (A), but not after settling (B), is frequency dependent. Linear regression fit for growth: β = -1.5, R2 = 0.6, P = 0.0002 ; Linear regression fit for selection: β = -1.2, R2 = 0.04, P = 0.2.

Source Data

Supplementary information

Supplementary Information

Supplementary Figs. 1 and 2 with model derivations.

Reporting Summary

Supplementary Data 1

List of mutations found in the Small and Large isolates from PO-3 and PO-4 populations.

Source data

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Pineau, R.M., Libby, E., Demory, D. et al. Emergence and maintenance of stable coexistence during a long-term multicellular evolution experiment. Nat Ecol Evol (2024). https://doi.org/10.1038/s41559-024-02367-y

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