Vosseberg, J. et al. The emerging view on the origin and early evolution of eukaryotic cells. Nature 633, 295–305 (2024).
Eme, L. et al. Inference and reconstruction of the heimdallarchaeial ancestry of eukaryotes. Nature 618, 992–999 (2023).
Spang, A. 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).
Tamarit, D. et al. Description of Asgardarchaeum abyssi gen. nov. spec. nov., a novel species within the class Asgardarchaeia and phylum Asgardarchaeota in accordance with the SeqCode. Syst. Appl. Microbiol. 47, 126525 (2024).
Spang, A. et al. Complex archaea that bridge the gap between prokaryotes and eukaryotes. Nature 521, 173–179 (2015).
Zaremba-Niedzwiedzka, K. et al. Asgard archaea illuminate the origin of eukaryotic cellular complexity. Nature 541, 353–358 (2017).
Martin, W. & Müller, M. The hydrogen hypothesis for the first eukaryote. Nature 392, 37–41 (1998).
López-García, P. & Moreira, D. The Syntrophy hypothesis for the origin of eukaryotes revisited. Nat. Microbiol. 5, 655–667 (2020).
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).
Imachi, H. et al. Isolation of an archaeon at the prokaryote-eukaryote interface. Nature 577, 519–525 (2020).
Rodrigues-Oliveira, T. et al. Actin cytoskeleton and complex cell architecture in an Asgard archaeon. Nature 613, 332–339 (2023).
Imachi, H. et al. Eukaryotes’ closest relatives are internally simple syntrophic archaea. Preprint at bioRxiv https://doi.org/10.1101/2025.02.26.640444 (2025).
Zhang, J. et al. Deep origin of eukaryotes outside Heimdallarchaeia within Asgardarchaeota. Nature 642, 990–998 (2025).
Mahendrarajah, T. A. et al. ATP synthase evolution on a cross-braced dated tree of life. Nat. Commun. 14, 7456 (2023).
Martijn, J., Vosseberg, J., Guy, L., Offre, P. & Ettema, T. J. G. Deep mitochondrial origin outside the sampled alphaproteobacteria. Nature 557, 101–105 (2018).
Muñoz-Gómez, S. A. et al. Site-and-branch-heterogeneous analyses of an expanded dataset favour mitochondria as sister to known Alphaproteobacteria. Nat. Ecol. Evol. 6, 253–262 (2022).
Lyons, T. W., Reinhard, C. T. & Planavsky, N. J. The rise of oxygen in Earth’s early ocean and atmosphere. Nature 506, 307–315 (2014).
Lyons, T. W., Diamond, C. W., Planavsky, N. J., Reinhard, C. T. & Li, C. Oxygenation, life, and the planetary system during earth’s middle history: an overview. Astrobiology 21, 906–923 (2021).
Bulzu, P.-A. et al. Casting light on Asgardarchaeota metabolism in a sunlit microoxic niche. Nat. Microbiol. 4, 1129–1137 (2019).
Liu, Y. et al. Expanded diversity of Asgard archaea and their relationships with eukaryotes. Nature 593, 553–557 (2021).
Muñoz-Gómez, S. A. Energetics and evolution of anaerobic microbial eukaryotes. Nat. Microbiol. 8, 197–203 (2023).
Sousa, F. L., Neukirchen, S., Allen, J. F., Lane, N. & Martin, W. F. Lokiarchaeon is hydrogen dependent. Nat. Microbiol. 1, 16034 (2016).
Dombrowski, N., Teske, A. P. & Baker, B. J. Expansive microbial metabolic versatility and biodiversity in dynamic Guaymas Basin hydrothermal sediments. Nat. Commun. 9, 4999 (2018).
Li, M. et al. Active bacterial and archaeal communities in coastal sediments: Biogeography pattern, assembly process and co-occurrence relationship. Sci. Total Environ. 750, 142252 (2021).
Guo, X., Li, Y., Song, G., Zhao, L. & Wang, J. Adaptation of Archaeal communities to summer hypoxia in the sediment of Bohai Sea. Ecol. Evol. 15, e70768 (2025).
Gong, X. et al. New globally distributed bacterial phyla within the FCB superphylum. Nat. Commun. 13, 7516 (2022).
Langwig, M. V. et al. Large-scale protein level comparison of Deltaproteobacteria reveals cohesive metabolic groups. ISME J. 16, 307–320 (2021).
Gong, X. et al. Contrasting archaeal and bacterial community assembly processes and the importance of rare taxa along a depth gradient in shallow coastal sediments. Sci. Total Environ. 852, 158411 (2022).
Köstlbacher, S. et al. Prediction of eukaryotic cellular complexity in Asgard archaea using structural modelling. Nat. Microbiol. https://doi.org/10.1038/s41564-026-02273-y (2026).
Williams, T. A., Cox, C. J., Foster, P. G., Szöllősi, G. J. & Embley, T. M. Phylogenomics provides robust support for a two-domains tree of life. Nat. Ecol. Evol. 4, 138–147 (2020).
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).
Rodriguez-R, L. M., Tsementzi, D., Luo, C. & Konstantinidis, K. T. Iterative subtractive binning of freshwater chronoseries metagenomes identifies over 400 novel species and their ecologic preferences. Environ. Microbiol. 22, 3394–3412 (2020).
Glasl, B. et al. Comparative genome-centric analysis reveals seasonal variation in the function of coral reef microbiomes. ISME J. 14, 1435–1450 (2020).
Rezaei Somee, M. et al. Distinct microbial community along the chronic oil pollution continuum of the Persian Gulf converge with oil spill accidents. Sci. Rep. 11, 11316 (2021).
Barnum, T. P. et al. Predicting microbial growth conditions from amino acid composition. Preprint at bioRxiv https://doi.org/10.1101/2024.03.22.586313 (2024).
Gawryluk, R. M. R. & Stairs, C. W. Diversity of electron transport chains in anaerobic protists. Biochim. Biophys. Acta, Bioenerg. 1862, 148334 (2021).
Stairs, C. W. et al. Chlamydial contribution to anaerobic metabolism during eukaryotic evolution. Sci. Adv. 6, eabb7258 (2020).
Geiger, O., Sanchez-Flores, A., Padilla-Gomez, J. & Degli Esposti, M. Multiple approaches of cellular metabolism define the bacterial ancestry of mitochondria. Sci. Adv. 9, eadh0066 (2023).
Mills, D. B. et al. Eukaryogenesis and oxygen in Earth history. Nat. Ecol. Evol. 6, 520–532 (2022).
Woodcroft, B. J. et al. Comprehensive taxonomic identification of microbial species in metagenomic data using SingleM and Sandpiper. Nat. Biotechnol. https://doi.org/10.1038/s41587-025-02738-1 (2025).
Yu, H., Schut, G. J., Haja, D. K., Adams, M. W. W. & Li, H. Evolution of complex I-like respiratory complexes. J. Biol. Chem. 296, 100740 (2021).
Greening, C. et al. Minimal and hybrid hydrogenases are active from archaea. Cell 187, 3357–3372.e19 (2024).
Valentin-Alvarado, L. E. et al. Asgard archaea modulate potential methanogenesis substrates in wetland soil. Nat. Commun. 15, 6384 (2024).
Yu, H. et al. Structure of an ancient respiratory system. Cell 173, 1636–1649.e16 (2018).
Zhu, J., Vinothkumar, K. R. & Hirst, J. Structure of mammalian respiratory complex I. Nature 536, 354–358 (2016).
Baradaran, R., Berrisford, J. M., Minhas, G. S. & Sazanov, L. A. Crystal structure of the entire respiratory complex I. Nature 494, 443–448 (2013).
Schuller, J. M. et al. Structural adaptations of photosynthetic complex I enable ferredoxin-dependent electron transfer. Science 363, 257–260 (2019).
Kravchuk, V. et al. A universal coupling mechanism of respiratory complex I. Nature 609, 808–814 (2022).
Chadwick, G. L., Hemp, J., Fischer, W. W. & Orphan, V. J. Convergent evolution of unusual complex I homologs with increased proton pumping capacity: energetic and ecological implications. ISME J. 12, 2668–2680 (2018).
Rinke, C. et al. A phylogenomic and ecological analysis of the globally abundant Marine Group II archaea (Ca. Poseidoniales ord. nov.). ISME J. 13, 663–675 (2019).
Banci, L., Bertini, I., Cavallaro, G. & Rosato, A. The functions of Sco proteins from genome-based analysis. J. Proteome Res. 6, 1568–1579 (2007).
Gribaldo, S., Talla, E. & Brochier-Armanet, C. Evolution of the haem copper oxidases superfamily: a rooting tale. Trends Biochem. Sci. 34, 375–381 (2009).
Alcott, L. J., Mills, B. J. W., Bekker, A. & Poulton, S. W. Earth’s Great Oxidation Event facilitated by the rise of sedimentary phosphorus recycling. Nat. Geosci. 15, 210–215 (2022).
Canfield, D. E. Carbon cycle evolution before and after the Great Oxidation of the atmosphere. Am. J. Sci. 321, 297–331 (2021).
Riedman, L. A., Porter, S. M., Lechte, M. A., dos Santos, A. & Halverson, G. P. Early eukaryotic microfossils of the late Palaeoproterozoic Limbunya Group, Birrindudu Basin, northern Australia. Pap. Palaeontol. 9, e1538 (2023).
Seitz, K. W. et al. Asgard archaea capable of anaerobic hydrocarbon cycling. Nat. Commun. 10, 1822 (2019).
Martin, W. F. et al. Late Mitochondrial Origin Is an Artifact. Genome Biol. Evol. 9, 373–379 (2017).
Pittis, A. A. & Gabaldón, T. Late acquisition of mitochondria by a host with chimaeric prokaryotic ancestry. Nature 531, 101–104 (2016).
Ettema, T. J. G. Evolution: mitochondria in the second act. Nature 531, 39–40 (2016).
Agić, H. in Prebiotic Chemistry and the Origin of Life (eds Neubeck, A. & McMahon, S.) 255–289 (Springer, 2021).
Chen, S., Zhou, Y., Chen, Y. & Gu, J. fastp: an ultra-fast all-in-one FASTQ preprocessor. Bioinformatics 34, i884–i890 (2018).
Li, D., Liu, C.-M., Luo, R., Sadakane, K. & Lam, T.-W. MEGAHIT: an ultra-fast single-node solution for large and complex metagenomics assembly via succinct de Bruijn graph. Bioinformatics 31, 1674–1676 (2015).
Alneberg, J. et al. Binning metagenomic contigs by coverage and composition. Nat. Methods 11, 1144–1146 (2014).
Kang, D. D. et al. MetaBAT 2: an adaptive binning algorithm for robust and efficient genome reconstruction from metagenome assemblies. PeerJ 7, e7359 (2019).
Uritskiy, G. V., DiRuggiero, J. & Taylor, J. MetaWRAP—a flexible pipeline for genome-resolved metagenomic data analysis. Microbiome 6, 158 (2018).
Wu, Y.-W., Simmons, B. A. & Singer, S. W. MaxBin 2.0: an automated binning algorithm to recover genomes from multiple metagenomic datasets. Bioinformatics 32, 605–607 (2016).
Sieber, C. M. K. et al. Recovery of genomes from metagenomes via a dereplication, aggregation and scoring strategy. Nat. Microbiol. 3, 836–843 (2018).
Karst, S. M., Kirkegaard, R. H. & Albertsen, M. mmgenome: a toolbox for reproducible genome extraction from metagenomes. Preprint at bioRxiv https://doi.org/10.1101/059121 (2016).
Parks, D. H., Imelfort, M., Skennerton, C. T., Hugenholtz, P. & Tyson, G. W. CheckM: assessing the quality of microbial genomes recovered from isolates, single cells, and metagenomes. Genome Res. 25, 1043–1055 (2015).
Hugoson, E., Lam, W. T. & Guy, L. miComplete: weighted quality evaluation of assembled microbial genomes. Bioinformatics 36, 936–937 (2020).
De Anda, V. et al. MEBS, a software platform to evaluate large (meta)genomic collections according to their metabolic machinery: unraveling the sulfur cycle. Gigascience 6, gix096 (2017).
Shen, W., Le, S., Li, Y. & Hu, F. SeqKit: a cross-platform and ultrafast toolkit for FASTA/Q file manipulation. PLoS ONE 11, e0163962 (2016).
Aroney, S. T. N. et al. CoverM: read alignment statics for metagenomics. Bioinformatics 41, btaf147 (2025).
Chaumeil, P.-A., Mussig, A. J., Hugenholtz, P. & Parks, D. H. GTDB-Tk: a toolkit to classify genomes with the Genome Taxonomy Database. Bioinformatics 36, 1925–1927 (2020).
Hyatt, D. et al. Prodigal: prokaryotic gene recognition and translation initiation site identification. BMC Bioinformatics 11, 119 (2010).
Rawlings, N. D., Barrett, A. J. & Bateman, A. MEROPS: the peptidase database. Nucleic Acids Res. 38, D227–D233 (2010).
Hernández-Plaza, A. et al. eggNOG 6.0: enabling comparative genomics across 12 535 organisms. Nucleic Acids Res. 51, D389–D394 (2023).
Søndergaard, D., Pedersen, C. N. S. & Greening, C. HydDB: a web tool for hydrogenase classification and analysis. Sci. Rep. 6, 34212 (2016).
Hunter, S. et al. InterPro: the integrative protein signature database. Nucleic Acids Res. 37, D211–D215 (2009).
Sayers, E. W. et al. Database resources of the National Center for Biotechnology Information. Nucleic Acids Res. 39, D38–D51 (2011).
Seemann, T. Prokka: rapid prokaryotic genome annotation. Bioinformatics 30, 2068–2069 (2014).
Parks, D. H. et al. GTDB: an ongoing census of bacterial and archaeal diversity through a phylogenetically consistent, rank normalized and complete genome-based taxonomy. Nucleic Acids Res. 50, D785–D794 (2022).
Parks, D. H. et al. Author Correction: recovery of nearly 8,000 metagenome-assembled genomes substantially expands the tree of life. Nat. Microbiol. 3, 253 (2018).
Schäffer, A. A. et al. Improving the accuracy of PSI-BLAST protein database searches with composition-based statistics and other refinements. Nucleic Acids Res. 29, 2994–3005 (2001).
Petitjean, C., Deschamps, P., López-García, P., Moreira, D. & Brochier-Armanet, C. Extending the conserved phylogenetic core of archaea disentangles the evolution of the third domain of life. Mol. Biol. Evol. 32, 1242–1254 (2015).
Deorowicz, S., Debudaj-Grabysz, A. & Gudyś, A. FAMSA: fast and accurate multiple sequence alignment of huge protein families. Sci. Rep. 6, 33964 (2016).
Katoh, K. & Standley, D. M. MAFFT multiple sequence alignment software version 7: improvements in performance and usability. Mol. Biol. Evol. 30, 772–780 (2013).
Capella-Gutiérrez, S., Silla-Martínez, J. M. & Gabaldón, T. trimAl: a tool for automated alignment trimming in large-scale phylogenetic analyses. Bioinformatics 25, 1972–1973 (2009).
Minh, B. Q. et al. Corrigendum to: IQ-TREE 2: new models and efficient methods for phylogenetic inference in the genomic era. Mol. Biol. Evol. 37, 2461 (2020).
Kalyaanamoorthy, S., Minh, B. Q., Wong, T. K. F., von Haeseler, A. & Jermiin, L. S. ModelFinder: fast model selection for accurate phylogenetic estimates. Nat. Methods 14, 587–589 (2017).
Hoang, D. T., Chernomor, O., von Haeseler, A., Minh, B. Q. & Vinh, L. S. UFBoot2: improving the ultrafast bootstrap approximation. Mol. Biol. Evol. 35, 518–522 (2018).
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).
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).
Susko, E. & Roger, A. J. On reduced amino acid alphabets for phylogenetic inference. Mol. Biol. Evol. 24, 2139–2150 (2007).
Steenwyk, J. L. et al. BioKIT: a versatile toolkit for processing and analyzing diverse types of sequence data. Genetics 221, iyac079 (2022).
Olm, M. R., Brown, C. T., Brooks, B. & Banfield, J. F. dRep: a tool for fast and accurate genomic comparisons that enables improved genome recovery from metagenomes through de-replication. ISME J. 11, 2864–2868 (2017).
Richter, D. J. EukProt: a database of genome-scale predicted proteins across the diversity of eukaryotes. Peer Commun. J. 2, e56 (2022).
Contreras-Moreira, B. & Vinuesa, P. GET_HOMOLOGUES, a versatile software package for scalable and robust microbial pangenome analysis. Appl. Environ. Microbiol. 79, 7696–7701 (2013).
Jumper, J. et al. Highly accurate protein structure prediction with AlphaFold. Nature 596, 583–589 (2021).
Evans, R. et al. Protein complex prediction with AlphaFold-Multimer. Preprint at bioRxiv https://doi.org/10.1101/2021.10.04.463034 (2022).
Mirdita, M. et al. ColabFold: making protein folding accessible to all. Nat. Methods 19, 679–682 (2022).
Mirdita, M., Steinegger, M. & Söding, J. MMseqs2 desktop and local web server app for fast, interactive sequence searches. Bioinformatics 35, 2856–2858 (2019).
Berman, H. M. et al. The Protein Data Bank. Nucleic Acids Res. 28, 235–242 (2000).
van Kempen, M. et al. Fast and accurate protein structure search with Foldseek. Nat. Biotechnol. 42, 243–246 (2024).
Dong, R., Peng, Z., Zhang, Y. & Yang, J. mTM-align: an algorithm for fast and accurate multiple protein structure alignment. Bioinformatics 34, 1719–1725 (2018).
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).
Pettersen, E. F. et al. UCSF ChimeraX: structure visualization for researchers, educators, and developers. Protein Sci. 30, 70–82 (2021).
Appler, K. E. et al. Oxygen metabolism in descendants of the archaeal-eukaryotic ancestor. Figshare https://figshare.com/s/f139faeb05653d1adf6b (2026).