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.

Light-driven eco-evolutionary dynamics in a synthetic replicator system

Nature Chemistry (2023)Cite this article

Subjects

Abstract

Darwinian evolution involves the inheritance and selection of variations in reproducing entities. Selection can be based on, among others, interactions with the environment. Conversely, the replicating entities can also affect their environment generating a reciprocal feedback on evolutionary dynamics. The onset of such eco-evolutionary dynamics marks a stepping stone in the transition from chemistry to biology. Yet the bottom-up creation of a molecular system that exhibits eco-evolutionary dynamics has remained elusive. Here we describe the onset of such dynamics in a minimal system containing two synthetic self-replicators. The replicators are capable of binding and activating a co-factor, enabling them to change the oxidation state of their environment through photoredox catalysis. The replicator distribution adapts to this change and, depending on light intensity, one or the other replicator becomes dominant. This study shows how behaviour analogous to eco-evolutionary dynamics—which until now has been restricted to biology—can be created using an artificial minimal replicator system.

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

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: Self-replication, co-factor activation and rudimentary eco-evolutionary dynamics.
Fig. 2: Characterization of replicator 13.
Fig. 3: The oxidation level of the solution controls the outcome of replicator competition.
Fig. 4: Cross-catalysis between replicators 13 and 16.
Fig. 5: Replicator-mediated activation of co-factor 2 and photocatalytic self-replication.
Fig. 6: Eco-evolutionary dynamics involving replicator-induced changes in the oxidation level of the environment that, in turn, cause evolutionary changes to the replicator mutant distribution.

Similar content being viewed by others

Data availability

The UPLC data generated and analysed in this article are included in its Supplementary Information in the form of integrated peak areas and exported traces of representative chromatograms. All other data generated or analysed during this study are included in this published article and its Supplementary Information. Waters UPLC software stores raw UPLC data in a database format from which they are not readily extractable. These data are available on request. Source data are provided with this paper.

References

  1. Miikkulainen, R. & Forrest, S. A biological perspective on evolutionary computation. Nat. Mach. Intell. 3, 9–15 (2021).

    Article  Google Scholar 

  2. De Jong, K. A. Evolutionary Computation: A Unified Approach (MIT Press, 2006).

  3. Zeymer, C. & Hilvert, D. Directed evolution of protein catalysts. Annu. Rev. Biochem. 87, 31–157 (2018).

    Article  Google Scholar 

  4. Arnold, F. H. Directed evolution: bringing new chemistry to life. Angew. Chem. Int. Ed. 57, 4143–4148 (2018).

    Article  CAS  Google Scholar 

  5. Adamski, P. et al. From self-replication to replicator systems en route to de novo life. Nat. Rev. Chem. 4, 386–403 (2020).

    Article  PubMed  Google Scholar 

  6. Jakiela, S., Kaminski, T. S., Cybulski, O., Weibel, D. B. & Garstecki, P. Bacterial growth and adaptation in microdroplet chemostats. Angew. Chem. Int. Ed. 52, 8908–8911 (2013).

    Article  CAS  Google Scholar 

  7. Behe, M. J. Experimental evolution, loss-of-function mutations, and ‘the first rule of adaptive evolution’. Q. Rev. Biol. 85, 419–445 (2010).

    Article  PubMed  Google Scholar 

  8. Mizuuchi, R., Ichihashi, N. & Yomo, T. Adaptation and diversification of an RNA replication system under initiation- or termination-impaired translational conditions. ChemBioChem 17, 1229–1232 (2016).

    Article  CAS  PubMed  Google Scholar 

  9. Govaert, L. et al. Eco-evolutionary feedbacks—theoretical models and perspectives. Funct. Ecol. 33, 13–30 (2019).

    Article  Google Scholar 

  10. Schoener, T. W. The newest synthesis: understanding the interplay of evolutionary and ecological dynamics. Science 331, 426–429 (2011).

    Article  CAS  PubMed  Google Scholar 

  11. Kosikova, T. & Philp, D. Exploring the emergence of complexity using synthetic replicators. Chem. Soc. Rev. 46, 7274–7305 (2017).

    Article  CAS  PubMed  Google Scholar 

  12. Clixby, G. & Twyman, L. Self-replicating systems. Org. Biomol. Chem. 14, 4170–4184 (2016).

    Article  CAS  PubMed  Google Scholar 

  13. Le Vay, K., Weise, L. I., Libicher, K., Mascarenhas, J. & Mutschler, H. Templated self-replication in biomimetic systems. Adv. Biosyst. 3, 1800313 (2019).

    Article  Google Scholar 

  14. Hong, J. I., Feng, Q., Rotello, V. & Rebek, J. Competition, cooperation, and mutation—improving a synthetic replicator by light irradiation. Science 255, 848–850 (1992).

    Article  CAS  PubMed  Google Scholar 

  15. Yao, S., Ghosh, I., Zutshi, R. & Chmielewski, J. A pH-modulated, self-replicating peptide. J. Am. Chem. Soc. 119, 10559–10560 (1997).

    Article  CAS  Google Scholar 

  16. Yao, S., Ghosh, I., Zutshi, R. & Chmielewski, J. A self-replicating peptide under ionic control. Angew. Chem. Int. Ed. 37, 478–481 (1998).

    Article  CAS  Google Scholar 

  17. Leonetti, G. & Otto, S. Solvent composition dictates emergence in dynamic molecular networks containing competing replicators. J. Am. Chem. Soc. 137, 2067–2072 (2015).

    Article  CAS  PubMed  Google Scholar 

  18. Dadon, Z., Samiappan, M., Wagner, N. & Ashkenasy, G. Chemical and light triggering of peptide networks under partial thermodynamic control. Chem. Commun. 48, 1419–1421 (2012).

    Article  CAS  Google Scholar 

  19. Dadon, Z., Samiappan, M., Safranchik, E. Y. & Ashkenasy, G. Light-induced peptide replication controls logic operations in small networks. Chem. Eur. J. 16, 12096–12099 (2010).

    Article  CAS  PubMed  Google Scholar 

  20. Riess, B., Grotsch, R. K. & Boekhoven, J. The design of dissipative molecular assemblies driven by chemical reaction cycles. Chem 6, 552–578 (2020).

    Article  CAS  Google Scholar 

  21. Ragazzon, G. & Prins, L. J. Energy consumption in chemical fuel-driven self-assembly. Nat. Nanotech. 13, 882–889 (2018).

    Article  CAS  Google Scholar 

  22. Merindol, R. & Walther, A. Materials learning from life: concepts for active, adaptive and autonomous molecular systems. Chem. Soc. Rev. 46, 5588–5619 (2017).

    Article  CAS  PubMed  Google Scholar 

  23. Sorrenti, A., Leira-Iglesias, J., Markvoort, A. J., de Greef, T. F. A. & Hermans, T. M. Non-equilibrium supramolecular polymerization. Chem. Soc. Rev. 46, 5476–5490 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  24. Engwerda, A. H. et al. Coupled metabolic cycles allow out-of-equilibrium autopoietic vesicle replication. Angew. Chem. Int. Ed. 59, 20361–20366 (2020).

    Article  CAS  Google Scholar 

  25. Morrow, S. M., Colomer, I. & Fletcher, S. P. A chemically fuelled self-replicator. Nat. Commun. 10, 1011 (2019).

    Article  PubMed  PubMed Central  Google Scholar 

  26. Pross, A. Seeking to uncover biology’s chemical roots. Emerg. Top. Life Sci. 3, 435–443 (2019).

    Article  PubMed  Google Scholar 

  27. Pross, A. What Is Life?: How Chemistry Becomes Biology (Oxford Univ. Press, 2016).

  28. Yang, S. et al. Chemical fueling enables molecular complexification of self-replicators. Angew. Chem. Int. Ed. 60, 11344–11349 (2021).

    Article  CAS  Google Scholar 

  29. Bandela, A. K. et al. Primitive selection of the fittest emerging through functional synergy in nucleopeptide networks. Proc. Natl Acad. Sci. USA 118, e2015285118 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  30. Wagner, N., Hochberg, D., Peacock-Lopez, E., Maity, I. & Ashkenasy, G. Open prebiotic environments drive emergent phenomena and complex behavior. Life 9, 45 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  31. Kamioka, S., Ajami, D. & Rebek, J. Autocatalysis and organocatalysis with synthetic structures. Proc. Natl Acad. Sci. USA 107, 541–544 (2010).

    Article  CAS  PubMed  Google Scholar 

  32. Arsene, S., Ameta, S., Lehman, N., Griffiths, A. D. & Nghe, P. Coupled catabolism and anabolism in autocatalytic RNA sets. Nucleic Acids Res. 46, 9660–9666 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  33. Santiago, G. M., Liu, K., Browne, W. R. & Otto, S. Emergence of light-driven protometabolism on recruitment of a photocatalytic cofactor by a self-replicator. Nat. Chem. 12, 603–607 (2020).

    Article  Google Scholar 

  34. Ottele, J., Hussain, A. S., Mayer, C. & Otto, S. Chance emergence of catalytic activity and promiscuity in a self-replicator. Nat. Catal. 3, 547–553 (2020).

    Article  CAS  Google Scholar 

  35. Black, S. P., Sanders, J. K. M. & Stefankiewicz, A. R. Disulfide exchange: exposing supramolecular reactivity through dynamic covalent chemistry. Chem. Soc. Rev. 43, 1861–1872 (2014).

    Article  CAS  PubMed  Google Scholar 

  36. Malakoutikhah, M. et al. Uncovering the selection criteria for the emergence of multi-building-block replicators from dynamic combinatorial libraries. J. Am. Chem. Soc. 135, 18406–18417 (2013).

    Article  CAS  PubMed  Google Scholar 

  37. Colomb-Delsuc, M., Mattia, E., Sadownik, J. W. & Otto, S. Exponential self-replication enabled through a fibre elongation/breakage mechanism. Nat. Commun. 6, 7427 (2015).

    Article  CAS  PubMed  Google Scholar 

  38. Rekondo, A. et al. Catalyst-free room-temperature self-healing elastomers based on aromatic disulfide metathesis. Mater. Horizons 1, 237–240 (2014).

    Article  CAS  Google Scholar 

  39. Nevejans, S., Ballard, N., Miranda, J. I., Reck, B. & Asua, J. M. The underlying mechanisms for self-healing of poly (disulfide)s. Phys. Chem. Chem. Phys. 18, 27577–27583 (2016).

    Article  CAS  PubMed  Google Scholar 

  40. Sreerama, N. & Woody, R. W. Computation and analysis of protein circular dichroism spectra. Methods Enzymol. 383, 318–351 (2004).

    Article  CAS  PubMed  Google Scholar 

  41. LeVine, H. III Thioflavine-T interaction with synthetic Alzheimers-disease β-amyloid peptides—detection of amyloid aggregation in solution. Protein Sci. 2, 404–410 (1993).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  42. Maity, S. et al. Caught in the act: mechanistic insight into supramolecular polymerization-driven self-replication from real-time visualization. J. Am. Chem. Soc. 142, 13709–13717 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  43. Pal, A. et al. Controlling the structure and length of self-synthesizing supramolecular polymers through nucleated growth and disassembly. Angew. Chem. Int. Ed. 54, 7852–7856 (2015).

    Article  CAS  Google Scholar 

  44. Otto, S., Furlan, R. L. & Sanders, J. K. Dynamic combinatorial libraries of macrocyclic disulfides in water. J. Am. Chem. Soc. 122, 12063–12064 (2000).

    Article  CAS  Google Scholar 

  45. Henrıquez, C., Bueno, C., Lissi, E. & Encinas, M. Thiols as chain transfer agents in free radical polymerization in aqueous solution. Polymer 44, 5559–5561 (2003).

    Article  Google Scholar 

  46. RÖder, B., Hanke, T., Oelckers, S., Hackbarth, S. & Symietz, C. Photophysical properties of pheophorbide a in solution and in model membrane systems. J. Porphyrins Phthalocyanines 4, 37–44 (2000).

    Article  Google Scholar 

  47. Ogilby, P. R. Singlet oxygen: there is indeed something new under the sun. Chem. Soc. Rev. 39, 3181–3209 (2010).

    Article  CAS  PubMed  Google Scholar 

  48. Semisotnov, G. et al. Study of the ‘molten globule’ intermediate state in protein folding by a hydrophobic fluorescent probe. Biopolymers 31, 119–128 (1991).

    Article  CAS  PubMed  Google Scholar 

  49. Entradas, T., Waldron, S. & Volk, M. The detection sensitivity of commonly used singlet oxygen probes in aqueous environments. J. Photochem. Photobiol. B 204, 111787 (2020).

    Article  CAS  PubMed  Google Scholar 

  50. Volterra, V. Variations and fluctuations of the number of individuals in animal species living together. ICES J. Mar. Sci. 3, 3–51 (1928).

    Article  Google Scholar 

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

    Article  Google Scholar 

  52. Markovitch, O., Ottelé, J., Veldman, O. & Otto, S. Automated device for continuous stirring while sampling in liquid chromatography systems. Commun. Chem. 3, 1–4 (2020).

    Article  Google Scholar 

  53. Valbuena, A., Maity, S., Mateu, M. G. & Roos, W. H. Visualization of single molecules building a viral capsid protein lattice through stochastic pathways. ACS Nano 14, 8724–8734 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

Download references

Acknowledgements

The authors thank W. R. Browne (University of Groningen) for the phosphorescence and EPR measurements and O. Markovitch (University of Groningen) for providing guidance on the usage of UPLC stirring device. This work was supported by the Simons Foundation (553330; K.L.), Marie Curie Individual Fellowships (PSR 786350; K.L.), the oLife Cofund programme (847675; A.B.), the ERC (AdG 741774), the China Scholarship Council (J.W.) and the Dutch Ministry of Education, Culture and Science (Gravitation programme 024.001.035; A.K. and S.O.).

Author information

Authors and Affiliations

  1. Centre for Systems Chemistry, Stratingh Institute, University of Groningen, Groningen, the Netherlands

    Kai Liu, Alex Blokhuis, Armin Kiani, Juntian Wu & Sijbren Otto

  2. Molecular Biophysics, Zernike Institute for Advanced Materials, University of Groningen, Groningen, the Netherlands

    Chris van Ewijk & Wouter H. Roos

Authors
  1. Kai Liu
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Alex Blokhuis
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Chris van Ewijk
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Armin Kiani
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Juntian Wu
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Wouter H. Roos
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. Sijbren Otto
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

K.L. and S.O. conceived the concept. K.L. designed, performed experiments and analysed the data. A.B. assisted in the data analysis and contributed to the scientific discussion. C.E. performed the experiments related to HS-AFM. C.E. and W.H.R. analysed the data related to HS-AFM. A.K. performed the experiments related to TEM. J.W. performed the experiments related to UPLC–MS. K.L. A.B. and S.O. wrote the manuscript.

Corresponding author

Correspondence to Sijbren Otto.

Ethics declarations

Competing interests

The authors declare no competing interests.

Peer review

Peer review information

Nature Chemistry thanks David Lynn, Subhabrata Maiti and the other, anonymous, reviewer(s) for their contribution to the peer review of this work.

Additional information

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

Supplementary information

Supplementary Information

Supplementary Figs. 1–58 and Tables 1 and 2.

Supplementary Video 1

HS-AFM showing the growth of 3-mer fibres from the ends liberated by fibre breakage.

Supplementary Video 2

HS-AFM showing the growth of a single 3-mer fibre.

Source data

Source Data Fig. 2

Relative peak area of components in the sample obtained from UPLC chromatograms.

Source Data Fig. 3

Relative/absolute peak areas of components in the sample obtained from UPLC chromatograms; calculation of the relative change of the peak areas, the concentration of bound precursor, the adsorption efficiency, and replication rate.

Source Data Fig. 4

Relative peak areas of components in the sample obtained from UPLC chromatograms.

Source Data Fig. 5

Raw spectral data; absolute peak areas of the monomer in UPLC chromatograms, and calculation of oxidation rates from them; Relative peak areas of components in the sample obtained from UPLC chromatograms.

Source Data Fig. 6

Relative peak areas of components in the sample in UPLC chromatograms.

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and Permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Liu, K., Blokhuis, A., van Ewijk, C. et al. Light-driven eco-evolutionary dynamics in a synthetic replicator system. Nat. Chem. (2023). https://doi.org/10.1038/s41557-023-01301-2

Download citation

  • Received:

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1038/s41557-023-01301-2

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