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.

  • Article
  • Published:

Environmental boundary conditions for the origin of life converge to an organo-sulfur metabolism

Nature Ecology & Evolution volume 3, pages 1715–1724 (2019)Cite this article

Subjects

Abstract

It has been suggested that a deep memory of early life is hidden in the architecture of metabolic networks, whose reactions could have been catalyzed by small molecules or minerals before genetically encoded enzymes. A major challenge in unravelling these early steps is assessing the plausibility of a connected, thermodynamically consistent proto-metabolism under different geochemical conditions, which are still surrounded by high uncertainty. Here we combine network-based algorithms with physico-chemical constraints on chemical reaction networks to systematically show how different combinations of parameters (temperature, pH, redox potential and availability of molecular precursors) could have affected the evolution of a proto-metabolism. Our analysis of possible trajectories indicates that a subset of boundary conditions converges to an organo-sulfur-based proto-metabolic network fuelled by a thioester- and redox-driven variant of the reductive tricarboxylic acid cycle that is capable of producing lipids and keto acids. Surprisingly, environmental sources of fixed nitrogen and low-potential electron donors are not necessary for the earliest phases of biochemical evolution. We use one of these networks to build a steady-state dynamical metabolic model of a protocell, and find that different combinations of carbon sources and electron donors can support the continuous production of a minimal ancient ‘biomass’ composed of putative early biopolymers and fatty acids.

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

Access options

Buy this article

  • Purchase on Springer Link
  • Instant access to full article PDF
Buy now

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

Fig. 1: Nitrogen is not essential for the initial expansion of metabolism.
Fig. 2: Primitive redox systems and reaction classes constrain network expansion from CO2.
Fig. 3: Systematic exploration of prebiotic scenarios reveals a core organo-sulfur network.
Fig. 4: Constraint-based modelling of plausible ancient protocells.

Similar content being viewed by others

Data availability

All data presented in this paper have been deposited in a public repository and can be accessed at https://github.com/segrelab/BoundaryConditionsForAncientMetabolism.

Code availability

All code presented in this paper can be accessed at https://github.com/segrelab/BoundaryConditionsForAncientMetabolism.

References

  1. Eck, R. V. & Dayhoff, M. O. Evolution of the structure of ferredoxin based on living relics of primitive amino acid sequences. Science 152, 363–366 (1966).

    CAS  PubMed  Google Scholar 

  2. Hartman, H. Speculations on the origin and evolution of metabolism. J. Mol. Evol. 4, 359–370 (1975).

    CAS  PubMed  Google Scholar 

  3. Hartman, H. Conjectures and reveries. Photosynth. Res. 33, 171–176 (1992).

    CAS  PubMed  Google Scholar 

  4. de Duve, C. Blueprint for a Cell: The Nature and Origin of Life (Neil Patterson Publishers, 1991).

  5. Morowitz, H. J., Kostelnik, J. D., Yang, J. & Cody, G. D. The origin of intermediary metabolism. Proc. Natl Acad. Sci. USA 97, 7704–7708 (2000).

    CAS  PubMed  Google Scholar 

  6. Smith, E. & Morowitz, H. J. The Origin and Nature of Life On Earth (Cambridge Univ. Press, 2016).

  7. Smith, E. & Morowitz, H. J. Universality in intermediary metabolism. Proc. Natl Acad. Sci. USA 101, 13168–13173 (2004).

    CAS  PubMed  Google Scholar 

  8. Sousa, F. L. et al. Early bioenergetic evolution. Phil. Trans. R. Soc. Lond. B 368, 20130088 (2013).

    Google Scholar 

  9. Lazcano, A. & Miller, S. L. The origin and early evolution of life: prebiotic chemistry, the pre-RNA world, and time. Cell 85, 793–798 (1996).

    CAS  PubMed  Google Scholar 

  10. Deamer, D. & Weber, A. L. Bioenergetics and life’s origins. Cold Spring Harb. Perspect. Biol. 2, a004929 (2010).

    PubMed  PubMed Central  Google Scholar 

  11. Martin, W. & Russell, M. J. On the origin of biochemistry at an alkaline hydrothermal vent. Phil. Trans. R. Soc. Lond. B 362, 1887–1926 (2007).

    CAS  Google Scholar 

  12. Martin, W., Baross, J., Kelley, D. & Russell, M. J. Hydrothermal vents and the origin of life. Nat. Rev. Microbiol. 6, 805–814 (2008).

    CAS  Google Scholar 

  13. Weiss, M. C. et al. The physiology and habitat of the last universal common ancestor. Nat. Microbiol. 1, 16116 (2016).

    CAS  PubMed  Google Scholar 

  14. Russell, M. J., Hall, A. J. & Martin, W. Serpentinization as a source of energy at the origin of life. Geobiology 8, 355–371 (2010).

    CAS  PubMed  Google Scholar 

  15. McDermott, J. M., Seewald, J. S., German, C. R. & Sylva, S. P. Pathways for abiotic organic synthesis at submarine hydrothermal fields. Proc. Natl Acad. Sci. USA 112, 7668–7672 (2015).

    CAS  PubMed  Google Scholar 

  16. Parker, E. T. et al. Primordial synthesis of amines and amino acids in a 1958 Miller H2S-rich spark discharge experiment. Proc. Natl Acad. Sci. USA 108, 5526–5531 (2011).

    CAS  PubMed  Google Scholar 

  17. Heinen, W. & Lauwers, A. M. Organic sulfur compounds resulting from the interaction of iron sulfide, hydrogen sulfide and carbon dioxide in an anaerobic aqueous environment. Orig. Life Evol. Biosph. 26, 131–150 (1996).

    CAS  PubMed  Google Scholar 

  18. Cody, G. D. Primordial carbonylated iron–sulfur compounds and the synthesis of pyruvate. Science 289, 1337–1340 (2000).

    CAS  PubMed  Google Scholar 

  19. Varma, S. J., Muchowska, K. B., Chatelain, P. & Moran, J. Native iron reduces CO2 to intermediates and end-products of the acetyl-CoA pathway. Nat. Ecol. Evol. 2, 1019–1024 (2018).

    PubMed  PubMed Central  Google Scholar 

  20. Huber, C. Activated acetic acid by carbon fixation on (Fe,Ni)S under primordial conditions. Science 276, 245–247 (1997).

    CAS  PubMed  Google Scholar 

  21. Wachtershauser, G. Evolution of the first metabolic cycles. Proc. Natl Acad. Sci. USA 87, 200–204 (1990).

    CAS  PubMed  Google Scholar 

  22. Fuchs, G. Alternative pathways of carbon dioxide fixation: insights into the early evolution of life? Annu. Rev. Microbiol. 65, 631–658 (2011).

    CAS  Google Scholar 

  23. Dörr, M. et al. A possible prebiotic formation of ammonia from dinitrogen on iron sulfide surfaces. Angew. Chem. Int. Ed. 42, 1540–1543 (2003).

    Google Scholar 

  24. Navarro-González, R., McKay, C. P. & Mvondo, D. N. A possible nitrogen crisis for Archaean life due to reduced nitrogen fixation by lightning. Nature 412, 61–64 (2001).

    PubMed  Google Scholar 

  25. Martin, W. F. & Thauer, R. K. Energy in ancient metabolism. Cell 168, 953–955 (2017).

    CAS  PubMed  Google Scholar 

  26. Sousa, F. L., Preiner, M. & Martin, W. F. Native metals, electron bifurcation, and CO2 reduction in early biochemical evolution. Curr. Opin. Microbiol. 43, 77–83 (2018).

    CAS  PubMed  Google Scholar 

  27. Halmann, M. in The Origin of Life and Evolutionary Biochemistry (eds Dose, K. et al.) 169–182 (Springer, 1974).

  28. Schwartz, A. W. Phosphorus in prebiotic chemistry. Phil. Trans. R. Soc. Lond. B 361, 1743–1749 (2006).

    CAS  Google Scholar 

  29. Keefe, A. D. & Miller, S. L. Are polyphosphates or phosphate esters prebiotic reagents? J. Mol. Evol. 41, 693–702 (1995).

    CAS  PubMed  Google Scholar 

  30. Goldford, J. E., Hartman, H., Smith, T. F. & Segrè, D. Remnants of an ancient metabolism without phosphate. Cell 168, 1126–1134 (2017).

    CAS  PubMed  Google Scholar 

  31. Goldford, J. E. & Segrè, D. Modern views of ancient metabolic networks. Curr. Opin. Syst. Biol. 8, 117–124 (2018).

  32. Ebenhöh, O., Handorf, T. & Heinrich, R. Structural analysis of expanding metabolic networks. Genome Inform. 15, 35–45 (2004).

    PubMed  Google Scholar 

  33. Handorf, T., Ebenhöh, O. & Heinrich, R. Expanding metabolic networks: scopes of compounds, robustness, and evolution. J. Mol. Evol. 61, 498–512 (2005).

    CAS  PubMed  Google Scholar 

  34. Raymond, J. & Segrè, D. The effect of oxygen on biochemical networks and the evolution of complex life. Science 311, 1764–1767 (2006).

    CAS  PubMed  Google Scholar 

  35. Petrov, A. S. et al. History of the ribosome and the origin of translation. Proc. Natl Acad. Sci. USA 112, 15396–15401 (2015).

    CAS  PubMed  Google Scholar 

  36. Aziz, M. F., Caetano-Anollés, K. & Caetano-Anollés, G. The early history and emergence of molecular functions and modular scale-free network behavior. Sci. Rep. 6, 25058 (2016).

    CAS  PubMed  PubMed Central  Google Scholar 

  37. Barve, A. & Wagner, A. A latent capacity for evolutionary innovation through exaptation in metabolic systems. Nature 500, 203–206 (2013).

    CAS  PubMed  Google Scholar 

  38. Szappanos, B. et al. Adaptive evolution of complex innovations through stepwise metabolic niche expansion. Nat. Commun. 7, 11607 (2016).

    CAS  PubMed  PubMed Central  Google Scholar 

  39. Pál, C. & Papp, B. Evolution of complex adaptations in molecular systems. Nat. Ecol. Evol. 1, 1084–1092 (2017).

    PubMed  PubMed Central  Google Scholar 

  40. Lipmann, F. Attempts to map a process evolution of peptide biosynthesis. Science 173, 875–884 (1971).

    CAS  PubMed  Google Scholar 

  41. Muchowska, K. B. et al. Metals promote sequences of the reverse Krebs cycle. Nat. Ecol. Evol. 1, 1716–1721 (2017).

    PubMed  PubMed Central  Google Scholar 

  42. Muchowska, K. B., Varma, S. J. & Moran, J. Synthesis and breakdown of universal metabolic precursors promoted by iron. Nature 569, 104–107 (2019).

    CAS  PubMed  PubMed Central  Google Scholar 

  43. Meringer, M. & Cleaves, H. J. Computational exploration of the chemical structure space of possible reverse tricarboxylic acid cycle constituents. Sci. Rep. 7, 17540 (2017).

    PubMed  PubMed Central  Google Scholar 

  44. Zubarev, D. Y., Rappoport, D. & Aspuru-Guzik, A. Uncertainty of prebiotic scenarios: the case of the non-enzymatic reverse tricarboxylic acid cycle. Sci. Rep. 5, 8009 (2015).

    PubMed  PubMed Central  Google Scholar 

  45. Vetsigian, K., Woese, C. & Goldenfeld, N. Collective evolution and the genetic code. Proc. Natl Acad. Sci. USA 103, 10696–10701 (2006).

    CAS  PubMed  Google Scholar 

  46. David, L. A. & Alm, E. J. Rapid evolutionary innovation during an Archaean genetic expansion. Nature 469, 93–96 (2011).

    CAS  PubMed  Google Scholar 

  47. Ochman, H., Lawrence, J. G. & Groisman, E. A. Lateral gene transfer and the nature of bacterial innovation. Nature 405, 299–304 (2000).

    CAS  PubMed  Google Scholar 

  48. Keller, M. A., Kampjut, D., Harrison, S. A. & Ralser, M. Sulfate radicals enable a non-enzymatic Krebs cycle precursor. Nat. Ecol. Evol. 1, 0083 (2017).

    PubMed Central  Google Scholar 

  49. Keller, M. A., Turchyn, A. V. & Ralser, M. Non-enzymatic glycolysis and pentose phosphate pathway-like reactions in a plausible Archean ocean. Mol. Syst. Biol. 10, 725 (2014).

    PubMed  PubMed Central  Google Scholar 

  50. Kanehisa, M. & Goto, S. KEGG: kyoto encyclopedia of genes and genomes. Nucleic Acids Res. 28, 27–30 (2000).

    CAS  PubMed  PubMed Central  Google Scholar 

  51. Dellomonaco, C., Clomburg, J. M., Miller, E. N. & Gonzalez, R. Engineered reversal of the β-oxidation cycle for the synthesis of fuels and chemicals. Nature 476, 355–359 (2011).

    CAS  PubMed  Google Scholar 

  52. Orth, J. D., Thiele, I. & Palsson, B. Ø. What is flux balance analysis? Nat. Biotechnol. 28, 245 (2010).

    CAS  PubMed  PubMed Central  Google Scholar 

  53. Henry, C. S., Broadbelt, L. J. & Hatzimanikatis, V. Thermodynamics-based metabolic flux analysis. Biophys. J. 92, 1792–1805 (2007).

    CAS  PubMed  Google Scholar 

  54. Chandru, K. et al. Simple prebiotic synthesis of high diversity dynamic combinatorial polyester libraries. Commun. Chem. 1, 30 (2018).

    Google Scholar 

  55. Forsythe, J. G. et al. Ester-mediated amide bond formation driven by wet-dry cycles: a possible path to polypeptides on the prebiotic earth. Angew. Chem. Int. Ed. 54, 9871–9875 (2015).

    CAS  Google Scholar 

  56. Wächtershäuser, G. Groundworks for an evolutionary biochemistry: the iron–sulphur world. Prog. Biophys. Mol. Biol. 58, 85–201 (1992).

    PubMed  Google Scholar 

  57. Bar-Even, A. Does acetogenesis really require especially low reduction potential? Biochim. Biophys. Acta 1827, 395–400 (2013).

    CAS  PubMed  Google Scholar 

  58. Poudel, S. et al. Origin and evolution of flavin-based electron bifurcating enzymes. Front. Microbiol. 9, 1–26 (2018).

    Google Scholar 

  59. Duval, S. et al. Electron transfer precedes ATP hydrolysis during nitrogenase catalysis. Proc. Natl Acad. Sci. USA 110, 16414–16419 (2013).

    CAS  PubMed  Google Scholar 

  60. Gogarten, J. P. & Deamer, D. Is LUCA a thermophilic progenote? Nat. Microbiol. 1, 16229 (2016).

    CAS  PubMed  Google Scholar 

  61. Segré, D., Ben-Eli, D., Deamer, D. W. & Lancet, D. The lipid world. Orig. Life Evol. Biosph. 31, 119–145 (2001).

    Google Scholar 

  62. Großkopf, T. et al. Metabolic modelling in a dynamic evolutionary framework predicts adaptive diversification of bacteria in a long-term evolution experiment. BMC Evol. Biol. 16, 163 (2016).

    PubMed  PubMed Central  Google Scholar 

  63. Ibarra, R. U., Edwards, J. S. & Palsson, B. O. Escherichia coli K-12 undergoes adaptive evolution to achieve in silico predicted optimal growth. Nature 420, 186–189 (2002).

    CAS  PubMed  Google Scholar 

  64. Andersen, J. L., Flamm, C., Merkle, D. & Stadler, P. F. A Software Package for Chemically Inspired Graph Transformation (Springer, 2016).

  65. Banzhaf, W. & Yamamoto, L. Artificial Chemistries (MIT Press, 2015).

  66. Flamholz, A., Noor, E., Bar-Even, A. & Milo, R. eQuilibrator – the biochemical thermodynamics calculator. Nucleic Acids Res. 40, 770–775 (2012).

    Google Scholar 

  67. Noor, E., Haraldsdóttir, H. S., Milo, R. & Fleming, R. M. T. Consistent estimation of Gibbs energy using component contributions. PLoS Comput. Biol. 9, e1003098 (2013).

    CAS  PubMed  PubMed Central  Google Scholar 

  68. Halevy, I. & Bachan, A. The geologic history of seawater pH. Science 355, 1069–1071 (2017).

    CAS  PubMed  Google Scholar 

  69. Bar-Even, A., Flamholz, A., Noor, E. & Milo, R. Thermodynamic constraints shape the structure of carbon fixation pathways. Biochim. Biophys. Acta 1817, 1646–1659 (2012).

    CAS  PubMed  Google Scholar 

  70. Milo, R., Jorgensen, P., Moran, U., Weber, G. & Springer, M. BioNumbers – the database of key numbers in molecular and cell biology. Nucleic Acids Res. 38, D750–D753 (2010).

    CAS  PubMed  Google Scholar 

  71. Schellenberger, J. et al. Quantitative prediction of cellular metabolism with constraint-based models: the COBRA toolbox v2.0. Nat. Protoc. 6, 1290–1307 (2011).

    CAS  PubMed  PubMed Central  Google Scholar 

  72. Ribeiro, A. J. M. et al. Mechanism and catalytic site atlas (M-CSA): a database of enzyme reaction mechanisms and active sites. Nucleic Acids Res. 46, D618–D623 (2018).

    CAS  PubMed  Google Scholar 

  73. Mall, A. et al. Reversibility of citrate synthase allows autotrophic growth of a thermophilic bacterium. Science 359, 563–567 (2018).

    CAS  PubMed  Google Scholar 

  74. Nunoura, T. et al. A primordial and reversible TCA cycle in a facultatively chemolithoautotrophic thermophile. Science 359, 559–563 (2018).

    CAS  PubMed  Google Scholar 

Download references

Acknowledgements

We thank all members of the Segrè Laboratory for helpful discussions. We acknowledge support provided by the Directorates for Biological Sciences and Geosciences at the National Science Foundation and NASA (agreement nos. 80NSSC17K0295, 80NSSC17K0296 and 1724150) issued through the Astrobiology Programme of the Science Mission Directorate; the National Science Foundation (grant no. 1457695, NSFOCE-BSF 1635070); the Human Frontiers Science Programme (grant no. RGP0020/2016) and the Boston University Hariri Institute for Computing and Computational Science and Engineering.

Author information

Authors and Affiliations

  1. Bioinformatics Program, Boston University, Boston, MA, USA

    Joshua E. Goldford & Daniel Segrè

  2. Department of Chemistry, Boston University, Boston, MA, USA

    Joshua E. Goldford

  3. Biological Design Center, Boston University, Boston, MA, USA

    Joshua E. Goldford & Daniel Segrè

  4. Earth, Atmosphere and Planetary Science Department, Massachusetts Institute of Technology, Cambridge, MA, USA

    Hyman Hartman

  5. Department of Physics, Boston University, Boston, MA, USA

    Robert Marsland III & Daniel Segrè

  6. Department of Biomedical Engineering, Department of Biology, Boston University, Boston, MA, USA

    Daniel Segrè

Authors
  1. Joshua E. Goldford
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Hyman Hartman
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Robert Marsland III
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Daniel Segrè
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

J.E.G., H.H. and D.S. designed the research. J.E.G. wrote the code, ran the simulations and performed the analysis. R.M. contributed to the non-equilibrium steady-state modelling. J.E.G. and D.S. wrote the manuscript. All authors read and approved the final manuscript.

Corresponding authors

Correspondence to Joshua E. Goldford or Daniel Segrè.

Ethics declarations

Competing interests

The authors declare no competing interests.

Additional information

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

Extended data

Extended Data Fig. 1 Enzymes in thioester-driven protometabolism are depleted in nitrogenous compounds.

(a) We classified reactions in KEGG as being plausibly pre-enzymatic (PPE) reactions if they (i) could proceed spontaneously, (ii) were associated with enzymes that contain at-least one iron–sulfur cluster or (iii) were associated with an enzyme that relied on atleast one metal (Ni, Co, Cu, Mg, Mn, Mo, Zn, Fe, W) cofactor. (b) For all scenarios resulting in expansion of  more than 100 metabolites (n = 144) we computed the fraction of PPE-reactions amongst the pre-ammonia reactions (x-axis) and post-ammonia reactions (y-axis). The frequency of PPE-reactions in the pre-ammonia reaction set was on average higher than the frequency of PPE-reactions in the post-ammonia reaction set (one-tailed Wilcoxon signed-rank test: P < 10-19). (c) We identified KEGG reactions that were dependent on at-least one of the following nitrogen-containing coenzymes: flavin, biotin, thiamine pyrophosphate (TPP) pyridoxal phosphate (PLP), haem, pterin or cobalamin. (d) We compute the fraction of pre- and post- ammonia reactions associated with nitrogen-containing coenzymes in the KEGG database, and found that a much higher proportion of post-ammonia reactions were dependent on these coenzymes relative to pre-ammonia reactions (one-tailed Wilcoxon signed-rank test: P < 10-24). (e) We parsed the catalytic active site database72 to find entries associated with pre and post-ammonia reactions, and compute the fraction of entries associated with amino acids with nitrogen-containing side-chains (Q,N,W,H,K,R). (f) For each scenario, the fraction of active sites with nitrogen-containing amino acids was significantly higher for post-ammonia reactions relative to pre-ammonia reactions one-tailed Wilcoxon signed-rank test: P < 10-24).

Extended Data Fig. 2 Enzymes catalyzing reactions before the addition of ammonia are not depleted in nitrogen-containing amino acids relative to enzymes added after ammonia.

To see if the amino acid biases in active sites of enzymes catalyzing reactions added to the network without ammonia (see Extended Data Fig. 1e, f) is confounded due to evolutionary selection for reduced nitrogen in these enzymes, we computed the fraction of nitrogen side-chains in enzymes in pre-ammonia reactions (x-axis) and in enzymes in post-ammonia reactions (y-axis). We found that enzymes in the pre-ammonia networks did not have significantly less nitrogen usage compared to enzymes in post-ammonia reactions (one-tailed Wilcoxon signed-rank test: P = 1).

Extended Data Fig. 3 Thiols are required for autotrophic expansion and fatty acid production.

(a) We grouped the n = 672 geochemical scenarios into whether a source of fixed carbon and thiols was provided in the seed set. We then plotted the empirical cumulative distributions for each group of scenarios. Notably, when thiols and fixed carbon are not supplied in the seed set, the networks are always below 100 metabolites, indicating that expansion is prohibited without either fixed carbon or thiols in the seed set. (b) We determined what geochemical parameters (x-axis) were essential for the production of important biomolecules (y-axis). For example, palmitate, a long-chain fatty acid, is producible only if thiols and reductant below 400 mV is provided in the seed set.

Extended Data Fig. 4 Network expansion with different combinations of carbon sources, thiols, generic reductants and generic oxidants.

We performed network expansion using a seed set with both a generic reductant at a fixed potential (x-axis) and a generic oxidant at a fixed potential (y-axis) with (a) no thiols or fixed carbon, (b) thiols and no fixed carbon, (c) no thiols and fixed carbon, and (d) both thiols and fixed carbon. The colour indicates the size (number of metabolites) in the final expanded network. Interestingly, a strong driving force provided by a strong oxidant ( > 0 mV) never sufficiently compensated for the weak driving force provided by a weak reductant ( > 0 mV), suggesting that oxidants have little influence on enabling expansion beyond 20 metabolites. The only conditions that led to an expansion that was greater than 20 metabolites with a weak electron donor was when the oxidant was also weak (-200 to -600 mV). We hypothesized that this was due to the ability of thiols or reduced carbon species to reduce the oxidant, enabling the production of a strong reductant. Indeed, when we removed all thiol to disulfide reactions using the generic redox system, expansion was blocked (inset).

Extended Data Fig. 5 Reduction potential of NAD(P)/FAD substitutes influences the size of expanded networks.

We plotted the size (number of metabolites, y-axis) of expanded networks as a function of reduction potential of NAD(P)/FAD substitutes (x-axis) for different physico-chemical conditions with (a) no fixed carbon or thiols, (b) fixed carbon and no thiols, (c) thiols and no fixed carbon, and (d) both fixed carbon and thiols. (e) We plot the range of physiologically feasible reduction potentials for classes of redox systems potentially relevant for early protometabolic systems, showing that dithiol/disulfide redox systems could potentially have enabled expansion under a variety of conditions.

Extended Data Fig. 6 Putative ancient catalysts.

(a) In extant biochemistry, keto acids are converted to amino acids using transamination or reductive amination reaction mechanisms, which are then polymerized using a phosphate or thioester-coupled mechanism to make polypeptides. (b) If prebiotic environments did not have a source of fixed nitrogen, then keto acids could have been reduced to -hydroxy acids, which could then be polymerized into polyesters either with4 or without55 thioester bond breaking.

Supplementary information

Supplementary Information

Supporting information text.

Reporting Summary

Supplementary Dataset 1

Excel tables containing all the network expansion data used to generate Figs. 1–4 and Extended Data Figs. 1–5.

Supplementary Software 1

Cytoscape file containing the data for the interactive visualization of the network expansion and its variability.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Goldford, J.E., Hartman, H., Marsland, R. et al. Environmental boundary conditions for the origin of life converge to an organo-sulfur metabolism. Nat Ecol Evol 3, 1715–1724 (2019). https://doi.org/10.1038/s41559-019-1018-8

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1038/s41559-019-1018-8

This article is cited by

Search

Advanced search

Quick links

Nature Briefing Microbiology

Sign up for the Nature Briefing: Microbiology newsletter — what matters in microbiology research, free to your inbox weekly.

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