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Proposal of the reverse flow model for the origin of the eukaryotic cell based on comparative analyses of Asgard archaeal metabolism

Abstract

The origin of eukaryotes represents an unresolved puzzle in evolutionary biology. Current research suggests that eukaryotes evolved from a merger between a host of archaeal descent and an alphaproteobacterial endosymbiont. The discovery of the Asgard archaea, a proposed archaeal superphylum that includes Lokiarchaeota, Thorarchaeota, Odinarchaeota and Heimdallarchaeota suggested to comprise the closest archaeal relatives of eukaryotes, has helped to elucidate the identity of the putative archaeal host. Whereas Lokiarchaeota are assumed to employ a hydrogen-dependent metabolism, little is known about the metabolic potential of other members of the Asgard superphylum. We infer the central metabolic pathways of Asgard archaea using comparative genomics and phylogenetics to be able to refine current models for the origin of eukaryotes. Our analyses indicate that Thorarchaeota and Lokiarchaeota encode proteins necessary for carbon fixation via the Wood–Ljungdahl pathway and for obtaining reducing equivalents from organic substrates. By contrast, Heimdallarchaeum LC2 and LC3 genomes encode enzymes potentially enabling the oxidation of organic substrates using nitrate or oxygen as electron acceptors. The gene repertoire of Heimdallarchaeum AB125 and Odinarchaeum indicates that these organisms can ferment organic substrates and conserve energy by coupling ferredoxin reoxidation to respiratory proton reduction. Altogether, our genome analyses suggest that Asgard representatives are primarily organoheterotrophs with variable capacity for hydrogen consumption and production. On this basis, we propose the ‘reverse flow model’, an updated symbiogenetic model for the origin of eukaryotes that involves electron or hydrogen flow from an organoheterotrophic archaeal host to a bacterial symbiont.

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Fig. 1: Metabolic potential of different Asgard phyla.
Fig. 2: Complex evolutionary history of group 3 and group 4 [NiFe]-hydrogenases in Asgard archaea.
Fig. 3: Evolution of the Asgard superphylum and selected metabolic features.
Fig. 4: Evolutionary scenarios for the origin of the eukaryotic cell.

Code availability

The small custom scripts used for genome annotation and phylogenetic analyses are made available on figshare and can be accessed at the following link: https://figshare.com/s/5f153d1dcacadd3b3ed6.

Data availability

The genomes of the herein analysed Asgard archaea have been made publicly available on NCBI previously2,4. Detailed annotations of the metabolic repertoire are provided in Supplementary Tables 13 accompanying this paper. Raw data files are made available via figshare under the following link: https://figshare.com/s/5f153d1dcacadd3b3ed6.

References

  1. 1.

    Hug, L. A. et al. A new view of the tree of life. Nat. Microbiol. 1, 16048 (2016).

    CAS  PubMed  Google Scholar 

  2. 2.

    Spang, A. et al. Complex archaea that bridge the gap between prokaryotes and eukaryotes. Nature 521, 173–179 (2015).

    CAS  PubMed  PubMed Central  Google Scholar 

  3. 3.

    Seitz, K. W., Lazar, C. S., Hinrichs, K. U., Teske, A. P. & Baker, B. J. Genomic reconstruction of a novel, deeply branched sediment archaeal phylum with pathways for acetogenesis and sulfur reduction. ISME J. 10, 1696–1705 (2016).

    CAS  PubMed  PubMed Central  Google Scholar 

  4. 4.

    Zaremba-Niedzwiedzka, K. et al. Asgard archaea illuminate the origin of eukaryotic cellular complexity. Nature 541, 353–358 (2017).

    CAS  PubMed  PubMed Central  Google Scholar 

  5. 5.

    Spang, A., Caceres, E. F. & Ettema, T. J. G. Genomic exploration of the diversity, ecology, and evolution of the archaeal domain of life. Science 357, eaaf3883 (2017).

    PubMed  Google Scholar 

  6. 6.

    Lopez-Garcia, P. & Moreira, D. Open questions on the origin of eukaryotes. Trends Ecol. Evol. 30, 697–708 (2015).

    PubMed  PubMed Central  Google Scholar 

  7. 7.

    Guy, L., Saw, J. H. & Ettema, T. J. The archaeal legacy of eukaryotes: a phylogenomic perspective. Cold Spring Harb. Perspect. Biol. 6, a016022 (2014).

    PubMed  PubMed Central  Google Scholar 

  8. 8.

    Martin, W. F., Garg, S. & Zimorski, V. Endosymbiotic theories for eukaryote origin. Phil. Trans. R. Soc. B 370, 20140330 (2015).

    PubMed  Google Scholar 

  9. 9.

    Da Cunha, V., Gaia, M., Gadelle, D., Nasir, A. & Forterre, P. Lokiarchaea are close relatives of Euryarchaeota, not bridging the gap between prokaryotes and eukaryotes. PLoS Genet. 13, e1006810 (2017).

    PubMed  PubMed Central  Google Scholar 

  10. 10.

    Spang, A. et al. Asgard archaea are the closest prokaryotic relatives of eukaryotes. PLoS Genet. 14, e1007080 (2018).

    PubMed  PubMed Central  Google Scholar 

  11. 11.

    Narrowe, A. B. et al. Complex evolutionary history of translation elongation factor 2 and diphthamide biosynthesis in archaea and parabasalids. Genome Biol. Evol. 10, 2380–2393 (2018).

    CAS  PubMed  PubMed Central  Google Scholar 

  12. 12.

    Klinger, C. M., Spang, A., Dacks, J. B. & Ettema, T. J. G. Tracing the archaeal origins of eukaryotic membrane-trafficking system building blocks. Mol. Biol. Evol. 33, 1528–1541 (2016).

    CAS  PubMed  Google Scholar 

  13. 13.

    Sousa, F. L., Neukirchen, S., Allen, J. F., Lane, N. & Martin, W. F. Lokiarchaeon is hydrogen dependent. Nat. Microbiol. 4, 16034 (2016).

    Google Scholar 

  14. 14.

    Martin, W. F., Tielens, A. G. M., Mentel, M., Garg, S. G. & Gould, S. B. The physiology of phagocytosis in the context of mitochondrial origin. Microbiol. Mol. Biol. Rev. 81, e00008-17 (2017).

    PubMed  PubMed Central  Google Scholar 

  15. 15.

    Zachar, I., Szilagyi, A., Szamado, S. & Szathmary, E. Farming the mitochondrial ancestor as a model of endosymbiotic establishment by natural selection. Proc. Natl Acad. Sci. USA 115, E1504–E1510 (2018).

    CAS  PubMed  Google Scholar 

  16. 16.

    Speijer, D. Alternating terminal electron-acceptors at the basis of symbiogenesis: how oxygen ignited eukaryotic evolution. Bioessays 39, 1600174 (2017).

    Google Scholar 

  17. 17.

    Koonin, E. V. Origin of eukaryotes from within archaea, archaeal eukaryome and bursts of gene gain: eukaryogenesis just made easier? Phil. Trans. R. Soc. B 370, 20140333 (2015).

    PubMed  Google Scholar 

  18. 18.

    Williams, T. A., Foster, P. G., Cox, C. J. & Embley, T. M. An archaeal origin of eukaryotes supports only two primary domains of life. Nature 504, 231–236 (2013).

    CAS  Google Scholar 

  19. 19.

    Lopez-Garcia, P., Eme, L. & Moreira, D. Symbiosis in eukaryotic evolution. J. Theor. Biol. 434, 20–33 (2017).

    CAS  PubMed  PubMed Central  Google Scholar 

  20. 20.

    Ragsdale, S. W. & Pierce, E. Acetogenesis and the Wood–Ljungdahl pathway of CO2 fixation. Biochim. Biophys. Acta 1784, 1873–1898 (2008).

    CAS  PubMed  PubMed Central  Google Scholar 

  21. 21.

    Schuchmann, K. & Muller, V. Autotrophy at the thermodynamic limit of life: a model for energy conservation in acetogenic bacteria. Nat. Rev. Microbiol. 12, 809–821 (2014).

    CAS  PubMed  Google Scholar 

  22. 22.

    Adam, P. S., Borrel, G., Brochier-Armanet, C. & Gribaldo, S. The growing tree of Archaea: new perspectives on their diversity, evolution and ecology. ISME J. 11, 2407–2425 (2017).

    PubMed  PubMed Central  Google Scholar 

  23. 23.

    Liu, Y. et al. Comparative genomic inference suggests mixotrophic lifestyle for Thorarchaeota. ISME J. 12, 1021–1031 (2018).

    CAS  PubMed  PubMed Central  Google Scholar 

  24. 24.

    Wagner, A. et al. Mechanisms of gene flow in archaea. Nat. Rev. Microbiol. 15, 492–501 (2017).

    CAS  PubMed  Google Scholar 

  25. 25.

    Buckel, W. & Thauer, R. K. Energy conservation via electron bifurcating ferredoxin reduction and proton/Na+ translocating ferredoxin oxidation. Biochim. Biophys. Acta 1827, 94–113 (2013).

    CAS  PubMed  Google Scholar 

  26. 26.

    Bryant, F. O. & Adams, M. W. Characterization of hydrogenase from the hyperthermophilic archaebacterium, Pyrococcus furiosus. J. Biol. Chem. 264, 5070–5079 (1989).

    CAS  PubMed  Google Scholar 

  27. 27.

    Schuchmann, K. & Muller, V. Energetics and application of heterotrophy in acetogenic bacteria. Appl. Environ. Microbiol. 82, 4056–4069 (2016).

    CAS  PubMed  PubMed Central  Google Scholar 

  28. 28.

    Stams, A. J. & Plugge, C. M. Electron transfer in syntrophic communities of anaerobic bacteria and archaea. Nat. Rev. Microbiol. 7, 568–577 (2009).

    CAS  PubMed  Google Scholar 

  29. 29.

    Greening, C. et al. Genomic and metagenomic surveys of hydrogenase distribution indicate H2 is a widely utilised energy source for microbial growth and survival. ISME J. 10, 761–777 (2016).

    CAS  PubMed  Google Scholar 

  30. 30.

    Yu, H. et al. Structure of an ancient respiratory system. Cell 173, 1636–1649.e16 (2018).

    CAS  PubMed  PubMed Central  Google Scholar 

  31. 31.

    Schut, G. J., Boyd, E. S., Peters, J. W. & Adams, M. W. The modular respiratory complexes involved in hydrogen and sulfur metabolism by heterotrophic hyperthermophilic archaea and their evolutionary implications. FEMS Microbiol. Rev. 37, 182–203 (2013).

    CAS  PubMed  Google Scholar 

  32. 32.

    Tully, B. J., Graham, E. D. & Heidelberg, J. F. The reconstruction of 2,631 draft metagenome-assembled genomes from the global oceans. Sci. Data 5, 170203 (2018).

    CAS  PubMed  PubMed Central  Google Scholar 

  33. 33.

    Adam, P. S., Borrel, G. & Gribaldo, S. Evolutionary history of carbon monoxide dehydrogenase/acetyl-CoA synthase, one of the oldest enzymatic complexes. Proc. Natl Acad. Sci. USA 115, E1166–E1173 (2018).

    CAS  PubMed  Google Scholar 

  34. 34.

    Kono, T. et al. A RuBisCO-mediated carbon metabolic pathway in methanogenic archaea. Nat. Commun. 8, 14007 (2017).

    CAS  PubMed  PubMed Central  Google Scholar 

  35. 35.

    Lang, B. F., Gray, M. W. & Burger, G. Mitochondrial genome evolution and the origin of eukaryotes. Annu. Rev. Genet. 33, 351–397 (1999).

    CAS  PubMed  Google Scholar 

  36. 36.

    Arshad, A. et al. A metagenomics-based metabolic model of nitrate-dependent anaerobic oxidation of methane by Methanoperedens-like archaea. Front. Microbiol. 6, 1423 (2015).

    PubMed  PubMed Central  Google Scholar 

  37. 37.

    Williams, T. A. et al. Integrative modeling of gene and genome evolution roots the archaeal tree of life. Proc. Natl Acad. Sci. USA 114, E4602–E4611 (2017).

    CAS  PubMed  Google Scholar 

  38. 38.

    Zachar, I. & Szathmary, E. Breath-giving cooperation: critical review of origin of mitochondria hypotheses: major unanswered questions point to the importance of early ecology. Biol. Direct 12, 19 (2017).

    PubMed  PubMed Central  Google Scholar 

  39. 39.

    Moreira, D. & Lopez-Garcia, P. Symbiosis between methanogenic archaea and delta-proteobacteria as the origin of eukaryotes: the syntrophic hypothesis. J. Mol. Evol. 47, 517–530 (1998).

    CAS  PubMed  Google Scholar 

  40. 40.

    López-García, P. & Moreira, D. Selective forces for the origin of the eukaryotic nucleus. Bioessays 28, 525–533 (2006).

    PubMed  Google Scholar 

  41. 41.

    Martin, W. & Muller, M. The hydrogen hypothesis for the first eukaryote. Nature 392, 37–41 (1998).

    CAS  PubMed  Google Scholar 

  42. 42.

    Sieber, J. R., McInerney, M. J. & Gunsalus, R. P. Genomic insights into syntrophy: the paradigm for anaerobic metabolic cooperation. Annu. Rev. Microbiol. 66, 429–452 (2012).

    CAS  PubMed  Google Scholar 

  43. 43.

    McGlynn, S. E., Chadwick, G. L., Kempes, C. P. & Orphan, V. J. Single cell activity reveals direct electron transfer in methanotrophic consortia. Nature 526, 531–535 (2015).

    CAS  PubMed  Google Scholar 

  44. 44.

    Wegener, G., Krukenberg, V., Riedel, D., Tegetmeyer, H. E. & Boetius, A. Intercellular wiring enables electron transfer between methanotrophic archaea and bacteria. Nature 526, 587–590 (2015).

    CAS  PubMed  Google Scholar 

  45. 45.

    Knittel, K. & Boetius, A. Anaerobic oxidation of methane: progress with an unknown process. Annu. Rev. Microbiol. 63, 311–334 (2009).

    CAS  PubMed  Google Scholar 

  46. 46.

    Seitz, K. W. et al. New Asgard archaea capable of anaerobic hydrocarbon cycling. Preprint at https://www.biorxiv.org/content/10.1101/527697v2 (2019).

  47. 47.

    Laso-Perez, R. et al. Thermophilic archaea activate butane via alkyl-coenzyme M formation. Nature 539, 396–401 (2016).

    CAS  PubMed  Google Scholar 

  48. 48.

    Martijn, J., Vosseberg, J., Guy, L., Offre, P. & Ettema, T. J. G. Deep mitochondrial origin outside the sampled alphaproteobacteria. Nature 557, 101–105 (2018).

    CAS  Google Scholar 

  49. 49.

    Leger, M. M., Eme, L., Stairs, C. W. & Roger, A. J. Demystifying eukaryote lateral gene transfer (response to Martin 2017 DOI: 10.1002/bies.201700115). Bioessays 40, e1700242 (2018).

    PubMed  Google Scholar 

  50. 50.

    Stairs, C. W. et al. Microbial eukaryotes have adapted to hypoxia by horizontal acquisitions of a gene involved in rhodoquinone biosynthesis. eLife 7, e34292 (2018).

    PubMed  PubMed Central  Google Scholar 

  51. 51.

    Norlund, K. L. et al. Microbial architecture of environmental sulfur processes: a novel syntrophic sulfur-metabolizing consortia. Environ. Sci. Technol. 43, 8781–8786 (2009).

    CAS  PubMed  Google Scholar 

  52. 52.

    Bose, A., Gardel, E. J., Vidoudez, C., Parra, E. A. & Girguis, P. R. Electron uptake by iron-oxidizing phototrophic bacteria. Nat. Commun. 5, 3391 (2014).

    CAS  PubMed  Google Scholar 

  53. 53.

    Eme, L., Spang, A., Lombard, J., Stairs, C. W. & Ettema, T. J. G. Archaea and the origin of eukaryotes. Nat. Rev. Microbiol. 15, 711–723 (2017).

    CAS  Google Scholar 

  54. 54.

    Ettema, T. J. Evolution: mitochondria in the second act. Nature 531, 39–40 (2016).

    CAS  PubMed  Google Scholar 

  55. 55.

    Caforio, A. et al. Converting Escherichia coli into an archaebacterium with a hybrid heterochiral membrane. Proc. Natl Acad. Sci. USA 115, 3704–3709 (2018).

    CAS  PubMed  Google Scholar 

  56. 56.

    Martin, W. et al. Gene transfer to the nucleus and the evolution of chloroplasts. Nature 393, 162–165 (1998).

    CAS  PubMed  Google Scholar 

  57. 57.

    Pittis, A. A. & Gabaldon, T. Late acquisition of mitochondria by a host with chimaeric prokaryotic ancestry. Nature 531, 101–104 (2016).

    CAS  PubMed  PubMed Central  Google Scholar 

  58. 58.

    Roger, A. J., Munoz-Gomez, S. A. & Kamikawa, R. The origin and diversification of mitochondria. Curr. Biol. 27, R1177–R1192 (2017).

    CAS  PubMed  Google Scholar 

  59. 59.

    Buchfink, B., Xie, C. & Huson, D. H. Fast and sensitive protein alignment using DIAMOND. Nat. Methods 12, 59–60 (2015).

    CAS  PubMed  PubMed Central  Google Scholar 

  60. 60.

    Makarova, K. S., Wolf, Y. I. & Koonin, E. V. Archaeal Clusters of Orthologous Genes (arCOGs): an update and application for analysis of shared features between Thermococcales, Methanococcales, and Methanobacteriales. Life (Basel) 5, 818–840 (2015).

    CAS  Google Scholar 

  61. 61.

    Saier, M. H. Jr, Tran, C. V. & Barabote, R. D. TCDB: the Transporter Classification Database for membrane transport protein analyses and information. Nucleic Acids Res. 34, D181–D186 (2006).

    CAS  PubMed  Google Scholar 

  62. 62.

    Sondergaard, D., Pedersen, C. N. & Greening, C. HydDB: a web tool for hydrogenase classification and analysis. Sci. Rep. 6, 34212 (2016).

    PubMed  PubMed Central  Google Scholar 

  63. 63.

    Bowers, R. M. et al. Minimum information about a single amplified genome (MISAG) and a metagenome-assembled genome (MIMAG) of bacteria and archaea. Nat. Biotechnol. 35, 725–731 (2017).

    CAS  PubMed  PubMed Central  Google Scholar 

  64. 64.

    Yin, Y. et al. dbCAN: a web resource for automated carbohydrate-active enzyme annotation. Nucleic Acids Res. 40, W445–W451 (2012).

    CAS  PubMed  PubMed Central  Google Scholar 

  65. 65.

    Rawlings, N. D., Barrett, A. J. & Finn, R. Twenty years of the MEROPS database of proteolytic enzymes, their substrates and inhibitors. Nucleic Acids Res. 44, D343–D350 (2016).

    CAS  PubMed  Google Scholar 

  66. 66.

    Lenfant, N. et al. ESTHER, the database of the α/β-hydrolase fold superfamily of proteins: tools to explore diversity of functions. Nucleic Acids Res. 41, D423–D429 (2013).

    CAS  PubMed  Google Scholar 

  67. 67.

    Yu, N. Y. et al. PSORTb 3.0: improved protein subcellular localization prediction with refined localization subcategories and predictive capabilities for all prokaryotes. Bioinformatics 26, 1608–1615 (2010).

    CAS  PubMed  PubMed Central  Google Scholar 

  68. 68.

    Katoh, K., Misawa, K., Kuma, K. & Miyata, T. MAFFT: a novel method for rapid multiple sequence alignment based on fast Fourier transform. Nucleic Acids Res. 30, 3059–3066 (2002).

    CAS  PubMed  PubMed Central  Google Scholar 

  69. 69.

    Criscuolo, A. & Gribaldo, S. BMGE (block mapping and gathering with entropy): a new software for selection of phylogenetic informative regions from multiple sequence alignments. BMC Evol. Biol. 10, 210 (2010).

    PubMed  PubMed Central  Google Scholar 

  70. 70.

    Nguyen, L. T., Schmidt, H. A., von Haeseler, A. & Minh, B. Q. IQ-TREE: a fast and effective stochastic algorithm for estimating maximum-likelihood phylogenies. Mol. Biol. Evol. 32, 268–274 (2015).

    CAS  PubMed  PubMed Central  Google Scholar 

  71. 71.

    Wang, H. C., Minh, B. Q., Susko, E. & Roger, A. J. Modeling site heterogeneity with posterior mean site frequency profiles accelerates accurate phylogenomic estimation. Syst. Biol. 67, 216–235 (2018).

    PubMed  Google Scholar 

  72. 72.

    Kamikawa, R. et al. Parallel re-modeling of EF-1α function: divergent EF-1α genes co-occur with EFL genes in diverse distantly related eukaryotes. BMC Evol. Biol. 13, 131 (2013).

    CAS  PubMed  PubMed Central  Google Scholar 

  73. 73.

    Lartillot, N., Rodrigue, N., Stubbs, D. & Richer, J. PhyloBayes MPI: phylogenetic reconstruction with infinite mixtures of profiles in a parallel environment. Syst. Biol. 62, 611–615 (2013).

    CAS  Google Scholar 

  74. 74.

    Susko, E. & Roger, A. J. On reduced amino acid alphabets for phylogenetic inference. Mol. Biol. Evol. 24, 2139–2150 (2007).

    CAS  Google Scholar 

  75. 75.

    Fu, L., Niu, B., Zhu, Z., Wu, S. & Li, W. CD-HIT: accelerated for clustering the next-generation sequencing data. Bioinformatics 28, 3150–3152 (2012).

    CAS  PubMed  PubMed Central  Google Scholar 

  76. 76.

    Eddy, S. R. Accelerated profile HMM searches. PLoS Comput. Biol. 7, e1002195 (2011).

    CAS  PubMed  PubMed Central  Google Scholar 

  77. 77.

    Price, M. N., Dehal, P. S. & Arkin, A. P. FastTree 2—approximately maximum-likelihood trees for large alignments. PLoS ONE 5, e9490 (2010).

    PubMed  PubMed Central  Google Scholar 

  78. 78.

    Capella-Gutierrez, S. et al. trimAl: a tool for automated alignment trimming in large-scale phylogenetic analyses. Bioinformatics 25, 1972–1973 (2009).

    CAS  PubMed  PubMed Central  Google Scholar 

  79. 79.

    Minh, B. Q., Nguyen, M. A. & von Haeseler, A. Ultrafast approximation for phylogenetic bootstrap. Mol. Biol. Evol. 30, 1188–1195 (2013).

    CAS  PubMed  PubMed Central  Google Scholar 

  80. 80.

    Guindon, S. et al. New algorithms and methods to estimate maximum-likelihood phylogenies: assessing the performance of PhyML 3.0. Syst. Biol. 59, 307–321 (2010).

    CAS  PubMed  Google Scholar 

  81. 81.

    Anantharaman, K. et al. Thousands of microbial genomes shed light on interconnected biogeochemical processes in an aquifer system. Nat. Commun. 7, 13219 (2016).

    CAS  PubMed  PubMed Central  Google Scholar 

  82. 82.

    Wrighton, K. C. et al. RuBisCO of a nucleoside pathway known from Archaea is found in diverse uncultivated phyla in bacteria. ISME J. 10, 2702–2714 (2016).

    CAS  PubMed  PubMed Central  Google Scholar 

  83. 83.

    Swigonova, Z., Mohsen, A. W. & Vockley, J. Acyl-CoA dehydrogenases: dynamic history of protein family evolution. J. Mol. Evol. 69, 176–193 (2009).

    CAS  PubMed  PubMed Central  Google Scholar 

  84. 84.

    Dibrova, D. V., Galperin, M. Y. & Mulkidjanian, A. Y. Phylogenomic reconstruction of archaeal fatty acid metabolism. Environ. Microbiol. 16, 907–918 (2014).

    CAS  PubMed  PubMed Central  Google Scholar 

  85. 85.

    Hug, L. A. et al. Overview of organohalide-respiring bacteria and a proposal for a classification system for reductive dehalogenases. Phil. Trans. R. Soc. B 368, 20120322 (2013).

    PubMed  Google Scholar 

  86. 86.

    Jugder, B. E., Ertan, H., Lee, M., Manefield, M. & Marquis, C. P. Reductive dehalogenases come of age in biological destruction of organohalides. Trends Biotechnol. 33, 595–610 (2015).

    CAS  PubMed  Google Scholar 

  87. 87.

    Neumann, A., Wohlfarth, G. & Diekert, G. Tetrachloroethene dehalogenase from Dehalospirillum multivorans: cloning, sequencing of the encoding genes, and expression of the pceA gene in Escherichia coli. J. Bacteriol. 180, 4140–4145 (1998).

    CAS  PubMed  PubMed Central  Google Scholar 

  88. 88.

    Vignais, P. M. & Billoud, B. Occurrence, classification, and biological function of hydrogenases: an overview. Chem. Rev. 107, 4206–4272 (2007).

    CAS  PubMed  Google Scholar 

  89. 89.

    Rochette, N. C., Brochier-Armanet, C. & Gouy, M. Phylogenomic test of the hypotheses for the evolutionary origin of eukaryotes. Mol. Biol. Evol. 31, 832–845 (2014).

    CAS  PubMed  PubMed Central  Google Scholar 

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Acknowledgements

This work was supported by grants from the European Research Council (ERC starting grant 310039-PUZZLE_CELL to T.J.G.E.), the Swedish Foundation for Strategic Research (SSF-FFL5 to T.J.G.E.), the Swedish Research Council (VR grant 2015-04959 to T.J.G.E. and VR starting grant 2016-03559 to A.S.), the NWO-I Foundation of the Netherlands Organisation for Scientific Research (WISE fellowship to A.S.), the European Commission (Marie Curie IEF European grants 625521 to A.S. and 704263 to L.E.), the Wenner-Gren Foundations in Stockholm (2016-0072 to J.L.), the European Molecular Biology Organization (EMBO long-term fellowship ALTF-997–2015 to C.W.S.), the Natural Sciences and Engineering Research Council of Canada (C.W.S), the Australian Research Council (DE170100310 and DP180101762 to C.G.) and the National Science Foundation (DEB: Systematics and Biodiversity Sciences; award number 1737298 to B.J.B.). We thank K. Zaremba-Niedzwiedzka and J. Saw for reconstruction of some of these genomes and helpful discussions. We also acknowledge S. L. Jørgensen, the chief scientist R. B. Pedersen, the scientific party and the entire crew on board the Norwegian research vessel G.O. Sars during the summer 2010 expedition, which allowed us access to samples from Loki’s Castle. Finally, we thank P. Offre for discussions on metabolic inferences.

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A.S. and T.J.G.E. conceived the study. A.S., C.W.S., E.F.C., J.L., C.G., B.J.B. and N.D. analysed the genomic data. A.S., C.W.S. and L.E. performed the phylogenetic analyses. A.S. and T.J.G.E. wrote the manuscript with input from all authors. A.S., C.W.S. and N.D. wrote the Supplementary Information. All documents were edited and approved by all authors.

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Correspondence to Anja Spang or Thijs J. G. Ettema.

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Supplementary Information

Supplementary Text, Supplementary References, legends for Supplementary Tables, Supplementary Figures 1–18 and Supplementary Files 1–3.

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Supplementary Tables 1–4

Overview of the presence/absence of discussed enzymes in Asgard lineages; annotations for proteins, which serve as candidate enzymes potentially involved in the various metabolic pathways discussed throughout this manuscript; automatic annotation of all genes; carbohydrate active enzymes, peptidases, esterases and information on extracellular localization.

Supplementary Table 5

Annotation of beta-oxidation genes encoded by Asgard genomes per protein family/phylogeny.

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Spang, A., Stairs, C.W., Dombrowski, N. et al. Proposal of the reverse flow model for the origin of the eukaryotic cell based on comparative analyses of Asgard archaeal metabolism. Nat Microbiol 4, 1138–1148 (2019). https://doi.org/10.1038/s41564-019-0406-9

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