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Gene-based predictive models of trophic modes suggest Asgard archaea are not phagocytotic

Nature Ecology & Evolution volume 2, pages 697–704 (2018)Cite this article

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A Publisher Correction to this article was published on 05 March 2018

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Abstract

With the current explosion of genomic data, there is a greater need to draw inference on phenotypic information based on DNA sequence alone. We considered complete genomes from 35 diverse eukaryotic lineages, and discovered sets of proteins predictive of trophic mode, including a set of 485 proteins that are enriched among phagocytotic eukaryotes (organisms that internalize large particles). Our model is also predictive of other aspects of trophic mode, including photosynthesis and the ability to synthesize a set of organic compounds needed for growth (prototrophy for those molecules). We applied our model to the Asgard archaea, a group of uncultured microorganisms that show close affinities to eukaryotes, to test whether the organisms are capable of phagocytosis, a phenotypic trait often considered a prerequisite for mitochondrial acquisition. Our analyses suggest that members of the Asgard archaea—despite having some eukaryote-specific protein families not found in other prokaryotes—do not use phagocytosis. Moreover, our data suggest that the process of phagocytosis arose from a combination of both archaeal and bacterial components, but also required additional eukaryote-specific innovations.

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Fig. 1: Models of eukaryote phagocytosis define phagocyte generalists and phagocyte specialists.
Fig. 2: Photosynthesis and prototrophy models.
Fig. 3: Prokaryote predictions.
Fig. 4: Observed phagosome proteins are not predictive.
Fig. 5: A combination of proteins derived from archaea and bacteria, along with eukaryotic innovations work in concert during phagocytosis.

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  • 05 March 2018

    In the version of this Article originally published, question marks appeared in Table 1; they should have been tick marks. This has now been corrected in all versions of the Article.

References

  1. Cavalier-Smith, T. The neomuran revolution and phagotrophic origin of eukaryotes and cilia in the light of intracellular coevolution and a revised tree of life. Cold Spring Harb. Perspect. Biol. 6, a016006 (2014).

    PubMed  PubMed Central  Google Scholar 

  2. Raven, J. A., Beardall, J., Flynn, K. J. & Maberly, S. C. Phagotrophy in the origins of photosynthesis in eukaryotes and as a complementary mode of nutrition in phototrophs: relation to Darwin’s insectivorous plants. J. Exp. Bot. 60, 3975–3987 (2009).

    CAS  PubMed  Google Scholar 

  3. Caron, D. A., Porter, K. G. & Sanders, R. W. Carbon, nitrogen, and phosphorus budgets for the mixotrophic phytoflagellate Poterioochromonas malhamensis (Chrysophyceae) during bacterial ingestion. Limnol. Oceanogr. 35, 433–443 (1990).

    CAS  Google Scholar 

  4. Anderson, O. R. in Comparative Protozoology 307–337 (Springer, Berlin, 1988).

  5. Desjardins, M., Houde, M. & Gagnon, E. Phagocytosis: the convoluted way from nutrition to adaptive immunity. Immunol. Rev. 207, 158–165 (2005).

    CAS  PubMed  Google Scholar 

  6. Falkowski, P. G. & Raven, J. A. Aquatic Photosynthesis (Princeton Univ. Press, Princeton, 2013).

  7. Archibald, J. One Plus One Equals One: Symbiosis and the Evolution of Complex Life (Oxford Univ. Press, Oxford, 2014).

  8. Payne, S. H. & Loomis, W. F. Retention and loss of amino acid biosynthetic pathways based on analysis of whole-genome sequences. Eukaryot. Cell 5, 272–276 (2006).

    CAS  PubMed  PubMed Central  Google Scholar 

  9. Guedes, R. et al. Amino acids biosynthesis and nitrogen assimilation pathways: a great genomic deletion during eukaryotes evolution. BMC Genom. 12, S2 (2011).

    CAS  Google Scholar 

  10. Helliwell, K. E., Wheeler, G. L. & Smith, A. G. Widespread decay of vitamin-related pathways: coincidence or consequence? Trends Genet. 29, 469–478 (2013).

    CAS  PubMed  Google Scholar 

  11. Burns, J. A., Paasch, A., Narechania, A. & Kim, E. Comparative genomics of a bacterivorous green alga reveals evolutionary causalities and consequences of phago-mixotrophic mode of nutrition. Genome Biol. Evol. 7, 3047–3061 (2015).

    CAS  PubMed  PubMed Central  Google Scholar 

  12. Koumandou, V. L. et al. Molecular paleontology and complexity in the last eukaryotic common ancestor. Crit. Rev. Biochem. Mol. Biol. 48, 373–396 (2013).

    CAS  PubMed  PubMed Central  Google Scholar 

  13. Yutin, N., Wolf, M. Y., Wolf, Y. I. & Koonin, E. V. The origins of phagocytosis and eukaryogenesis. Biol. Direct 4, 1 (2009).

    Google Scholar 

  14. Flannagan, R. S., Jaumouillé, V. & Grinstein, S. The cell biology of phagocytosis. Annu. Rev. Pathol. 7, 61–98 (2012).

    CAS  PubMed  Google Scholar 

  15. Maruyama, S. & Kim, E. A modern descendant of early green algal phagotrophs. Curr. Biol. 23, 1081–1084 (2013).

    CAS  PubMed  Google Scholar 

  16. Lewis, D. Concepts in fungal nutrition and the origin of biotrophy. Biol. Rev. 48, 261–277 (1973).

    Google Scholar 

  17. Katz, M. E., Fennel, K. & Falkowski, P. G. in Evolution of Primary Producers in the Sea 405–430 (Elsevier, Burlington, 2007).

  18. Boulais, J. et al. Molecular characterization of the evolution of phagosomes. Mol. Syst. Biol. 6, 423 (2010).

    PubMed  PubMed Central  Google Scholar 

  19. Wiedemann, A., Lim, J. & Caron, E. in Molecular Mechanisms of Phagocytosis 72–84 (Springer, New York, 2005).

  20. Engqvist-Goldstein, Å. E. & Drubin, D. G. Actin assembly and endocytosis: from yeast to mammals. Annu. Rev. Cell Dev. Biol. 19, 287–332 (2003).

  21. May, R. C. & Machesky, L. M. Phagocytosis and the actin cytoskeleton. J. Cell Sci. 114, 1061–1077 (2001).

    CAS  PubMed  Google Scholar 

  22. Buckley, C. M. et al. WASH drives early recycling from macropinosomes and phagosomes to maintain surface phagocytic receptors. Proc. Natl Acad. Sci. USA 113, E5906–E5915 (2016).

    CAS  PubMed  PubMed Central  Google Scholar 

  23. Cavalier-Smith, T. The origin of eukaryote and archaebacterial cells. Ann. NY Acad. Sci. 503, 17–54 (1987).

    CAS  PubMed  Google Scholar 

  24. Martijn, J. & Ettema, T. J. From archaeon to eukaryote: the evolutionary dark ages of the eukaryotic cell. Biochem. Soc. Trans. 41, 451–457 (2013).

    CAS  PubMed  Google Scholar 

  25. Gould, S. B., Garg, S. G. & Martin, W. F. Bacterial vesicle secretion and the evolutionary origin of the eukaryotic endomembrane system. Trends Microbiol. 24, 525–534 (2016).

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

  27. 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 

  28. Speijer, D. Birth of the eukaryotes by a set of reactive innovations: new insights force us to relinquish gradual models. Bioessays 37, 1268–1276 (2015).

    CAS  PubMed  Google Scholar 

  29. Pereira-Neves, A. & Benchimol, M. Phagocytosis by Trichomonas vaginalis: new insights. Biol. Cell 99, 87–101 (2007).

    CAS  PubMed  Google Scholar 

  30. Huston, C. D., Boettner, D. R., Miller-Sims, V. & Petri, W. A. Jr Apoptotic killing and phagocytosis of host cells by the parasite Entamoeba histolytica. Infect. Immun. 71, 964–972 (2003).

    CAS  PubMed  PubMed Central  Google Scholar 

  31. Powell, M. J., Letcher, P. M. & James, T. Y. Ultrastructural characterization of the host–parasite interface between Allomyces anomalus (Blastocladiomycota) and Rozella allomycis (Cryptomycota). Fungal Biol. 121, 561–572 (2017).

  32. Sherr, E. & Sherr, B. Bacterivory and herbivory: key roles of phagotrophic protists in pelagic food webs. Microb. Ecol. 28, 223–235 (1994).

    CAS  PubMed  Google Scholar 

  33. Jacobson, M. D., Weil, M. & Raff, M. C. Programmed cell death in animal development. Cell 88, 347–354 (1997).

    CAS  PubMed  Google Scholar 

  34. Stuart, L. M. & Ezekowitz, R. A. B. Phagocytosis: elegant complexity. Immunity 22, 539–550 (2005).

    CAS  PubMed  Google Scholar 

  35. Aderem, A. & Underhill, D. M. Mechanisms of phagocytosis in macrophages. Annu. Rev. Immunol. 17, 593–623 (1999).

    CAS  PubMed  Google Scholar 

  36. Caron, D. A. et al. Probing the evolution, ecology and physiology of marine protists using transcriptomics. Nat. Rev. Microbiol. 15, 6–20 (2017).

    CAS  PubMed  Google Scholar 

  37. Boettner, D. R. et al. Entamoeba histolytica phagocytosis of human erythrocytes involves PATMK, a member of the transmembrane kinase family. PLoS Pathog. 4, e8 (2008).

    PubMed  PubMed Central  Google Scholar 

  38. Corrotte, M. et al. Dynamics and function of phospholipase D and phosphatidic acid during phagocytosis. Traffic 7, 365–377 (2006).

    CAS  PubMed  Google Scholar 

  39. Cougoule, C., Wiedemann, A., Lim, J. & Caron, E. Phagocytosis, an alternative model system for the study of cell adhesion. Semin. Cell Dev. Biol. 15, 679–689 (2004).

  40. Zimmerli, S. et al. Phagosome-lysosome fusion is a calcium-independent event in macrophages. J. Cell Biol. 132, 49–61 (1996).

    CAS  PubMed  Google Scholar 

  41. Held, A. A. The zoospore of Rozella allomycis: ultrastructure. Can. J. Bot. 53, 2212–2232 (1975).

    Google Scholar 

  42. Harrison, R. E. & Grinstein, S. Phagocytosis and the microtubule cytoskeleton. Biochem. Cell Biol. 80, 509–515 (2002).

    CAS  PubMed  Google Scholar 

  43. Cotman, S. L. & Staropoli, J. F. The juvenile Batten disease protein, CLN3, and its role in regulating anterograde and retrograde post-Golgi trafficking. Clin. Lipidol. 7, 79–91 (2012).

    CAS  PubMed  PubMed Central  Google Scholar 

  44. Furukawa, R. & Fechheimer, M. Differential localization of α-actinin and the 30 kD actin-bundling protein in the cleavage furrow, phagocytic cup, and contractile vacuole of Dictyostelium discoideum. Cytoskeleton 29, 46–56 (1994).

    CAS  Google Scholar 

  45. Wehrle-Haller, B. Structure and function of focal adhesions. Curr. Opin. Cell Biol. 24, 116–124 (2012).

    CAS  PubMed  Google Scholar 

  46. Schymeinsky, J., Sperandio, M. & Walzog, B. The mammalian actin-binding protein 1 (mAbp1): a novel molecular player in leukocyte biology. Trends Cell Biol. 21, 247–255 (2011).

    CAS  PubMed  Google Scholar 

  47. Medini, D., Donati, C., Tettelin, H., Masignani, V. & Rappuoli, R. The microbial pan-genome. Curr. Opin. Genet. Dev. 15, 589–594 (2005).

    CAS  PubMed  Google Scholar 

  48. Blankenship, R. E. & Hartman, H. The origin and evolution of oxygenic photosynthesis. Trends Biochem. Sci. 23, 94–97 (1998).

    CAS  PubMed  Google Scholar 

  49. Ondov, B. D., Bergman, N. H. & Phillippy, A. M. Interactive metagenomic visualization in a Web browser. BMC Bioinform. 12, 385 (2011).

    Google Scholar 

  50. Cavalier-Smith, T. The phagotrophic origin of eukaryotes and phylogenetic classification of Protozoa. Int. J. Syst. Evolut. Microbiol. 52, 297–354 (2002).

    CAS  Google Scholar 

  51. Li, W. & Godzik, A. Cd-hit: a fast program for clustering and comparing large sets of protein or nucleotide sequences. Bioinformatics 22, 1658–1659 (2006).

    CAS  PubMed  Google Scholar 

  52. Camacho, C. et al. BLAST+: architecture and applications. BMC Bioinform. 10, 421 (2009).

    Google Scholar 

  53. Enright, A. J., Van Dongen, S. & Ouzounis, C. A. An efficient algorithm for large-scale detection of protein families. Nucleic Acids Res. 30, 1575–1584 (2002).

    CAS  PubMed  PubMed Central  Google Scholar 

  54. Katoh, K., Misawa, K., Kuma, Ki & 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 

  55. 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).

    PubMed  PubMed Central  Google Scholar 

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

    CAS  PubMed  PubMed Central  Google Scholar 

  57. Boutet, E., Lieberherr, D., Tognolli, M., Schneider, M. & Bairoch, A. UniProtKB/Swiss-Prot. Methods Mol. Biol. 406, 89–112 (2007).

  58. Newcombe, R. G. Interval estimation for the difference between independent proportions: comparison of eleven methods. Stat. Med. 17, 873–890 (1998).

    CAS  PubMed  Google Scholar 

  59. Alexa, A. & Rahnenfuhrer, J. topGO: Enrichment Analysis for Gene Ontology (Bioconductor, 2016); https://doi.org/10.18129/B9.bioc.topGO

  60. Kastenmüller, G., Schenk, M. E., Gasteiger, J. & Mewes, H.-W. Uncovering metabolic pathways relevant to phenotypic traits of microbial genomes. Genome Biol. 10, R28 (2009).

    PubMed  PubMed Central  Google Scholar 

  61. Kursa, Miron B. & Rudnicki, W. R. Feature selection with the Boruta package. J. Stat. Softw. 36, 11 (2010).

  62. Chasset, P. O. Probabilistic Neural Network For the R Statistical Language (Github, 2013); https://github.com/chasset/pnn

  63. Finn, R. D. et al. HMMER web server: 2015 update. Nucleic Acids Res. 43, W30–W38 (2015).

    CAS  PubMed  PubMed Central  Google Scholar 

  64. Gnanavel, M. et al. CLAP: A web-server for automatic classification of proteins with special reference to multi-domain proteins. BMC Bioinform. 15, 343 (2014).

    Google Scholar 

  65. Stamatakis, A. RAxML version 8: a tool for phylogenetic analysis and post-analysis of large phylogenies. Bioinformatics 30, 1312–1313 (2014).

    CAS  PubMed  PubMed Central  Google Scholar 

  66. Letunic, I. & Bork, P. Interactive tree of life (iTOL) v3: an online tool for the display and annotation of phylogenetic and other trees. Nucleic Acids Res. 44, W242–W245 (2016).

    CAS  PubMed  PubMed Central  Google Scholar 

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Acknowledgements

The work was supported by the Simons Foundation (SF-382790). The authors thank A. Heiss for sharing the draft genome of the undescribed mantamonad strain (SRT306). The authors also thank G. Torruella i Cortés for helpful discussions on phagocytosis in opisthokont lineages.

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Authors and Affiliations

  1. Sackler Institute for Comparative Genomics and Division of Invertebrate Zoology, American Museum of Natural History, New York, NY, USA

    John A. Burns, Alexandros A. Pittis & Eunsoo Kim

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  1. John A. Burns
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  2. Alexandros A. Pittis
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Contributions

J.A.B. and E.K. conceived of the project. J.A.B. and A.A.P. designed and completed the analysis. J.A.B., A.A.P. and E.K. wrote the paper.

Corresponding authors

Correspondence to John A. Burns or Eunsoo Kim.

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The authors declare no competing financial interests.

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Supplementary information

Supplementary Information

Supplementary Discussion 1–4, Supplementary Figures 1–4, and Supplementary Tables 1–7.

Life Sciences Reporting Summary

Supplementary Table 3

Analysis of a selection of eukaryotic signature proteins.

Supplementary Table 4

Full predictions table for phylum level pan-prokaryote assemblages.

Extended Data Figure 1

GO category heatmap of the phagocytosis predictive, phagocyte-generalist model. This model was trained on proteins from 14 free-living phagocyte genomes and 19 non-phagocyte genomes. The model consists of 474 proteins grouped into 86 GO biological process categories.

Extended Data Figure 2

Protein presence/absence map of the phagocytosis predictive, phagocyte-generalist model. This model was trained on proteins from 14 free-living phagocyte genomes and 19 non-phagocyte genomes. The model consists of 474 proteins.

Extended Data Figure 3

GO category heatmap of the phagocytosis predictive, phagocyte-specialist entamoebid model. During training, this model was restricted to only those proteins found in the genomes of three entamoebid organisms. Given that constraint, the model was trained on proteins from 14 free-living phagocyte genomes and 19 non-phagocyte genomes. The model consists of 111 proteins grouped into 41 GO biological process categories.

Extended Data Figure 4

Protein presence/absence map of the phagocytosis predictive, phagocyte-specialist entamoebid model. During training, this model was restricted to only those proteins found in the genomes of three entamoebid organisms. Given that constraint, the model was trained on proteins from 14 free-living phagocyte genomes and 19 non-phagocyte genomes. The model consists of 111 proteins.

Extended Data Figure 5

GO category heatmap of the phagocytosis predictive, phagocyte-specialist R. allomycis model. During training, this model was restricted to only those proteins found in the genome of Rozella allomycis. Given that constraint, the model was trained on proteins from 14 free-living phagocyte genomes and 19 non-phagocyte genomes. The model consists of 84 proteins grouped into 30 GO biological process categories.

Extended Data Figure 6

Protein presence/absence map of the phagocytosis predictive, phagocyte-specialist R. allomycis model. During training, this model was restricted to only those proteins found in the genome of Rozella allomycis. Given that constraint, the model was trained on proteins from 14 free-living phagocyte genomes and 19 non-phagocyte genomes. The model consists of 84 proteins.

Extended Data Figure 7

GO category heatmap of the photosynthesis predictive model. This model was trained on proteins from 14 photosynthetic and 19 non-photosynthetic genomes. The model consists of 243 proteins grouped into 37 GO biological process categories.

Extended Data Figure 8

Protein presence/absence map of the photosynthesis predictive model. This model was trained on proteins from 14 photosynthetic and 19 non-photosynthetic genomes. The model consists of 243 proteins.

Extended Data Figure 9

GO category heatmap of the prototrophy predictive model. This model was trained on proteins from 19 non-phagocyte and 14 phagocyte genomes, given the prior observation that phagocytotic organisms tend to have numerous auxotrophies. The model consists of 170 proteins grouped into 35 GO biological process categories.

Extended Data Figure 10

Protein presence/absence map of the prototrophy predictive model. This model was trained on proteins from 19 non-phagocyte and 14 phagocyte genomes, given the prior observation that phagocytotic organisms tend to have numerous auxotrophies. The model consists of 170 proteins.

Extended Data Figure 11

GO category heatmap of the prototrophy predictive model containing phylum-level pan-prokaryote assemblages.

Extended Data Figure 12

Protein presence/absence map of the prototrophy predictive model containing phylum-level pan-prokaryote assemblages.

Extended Data Figure 13

GO category heatmap of the photosynthesis predictive model containing phylum-level pan-prokaryote assemblages.

Extended Data Figure 14

Protein presence/absence map of the photosynthesis predictive model containing phylum-level pan-prokaryote assemblages.

Extended Data Figure 15

GO category heatmap of the phagocytosis predictive, phagocyte-generalist model containing phylum-level pan-prokaryote assemblages.

Extended Data Figure 16

Protein presence/absence map of the phagocytosis predictive, phagocyte-generalist model containing phylum-level pan-prokaryote assemblages.

Extended Data Figure 17

GO category heatmap of the phagocytosis predictive, phagocyte-specialist entamoebid model containing phylum-level pan-prokaryote assemblages.

Extended Data Figure 18

Protein presence/absence map of the phagocytosis predictive, phagocyte-specialist entamoebid model containing phylum-level pan-prokaryote assemblages.

Extended Data Figure 19.

GO category heatmap of the phagocytosis predictive, phagocyte-specialist R. allomycis model containing phylum-level pan-prokaryote assemblages.

Extended Data Figure 20

Protein presence/absence map of the phagocytosis predictive, phagocyte-specialist R. allomycis model containing phylum-level pan-prokaryote assemblages.

Extended Data Figure 21

GO category heatmap of GO biological process categories enriched in the observed phagosome.

Extended Data Figure 22

Protein presence/absence map of proteins in the observed phagosome.

Extended Data Table 1

Data table indicating the proteins associated with each GO biological process in the phagocytosis predictive, phagocyte generalist model. The table includes the GO category, the proteins associated with each category, the annotation of each protein, and the confidence of that annotation.

Extended Data Table 2

Data table indicating the proteins associated with each GO biological process in the phagocytosis predictive, phagocyte specialist entamoebid model. The table includes the GO category, the proteins associated with each category, the annotation of each protein, and the confidence of that annotation.

Extended Data Table 3

Data table indicating the proteins associated with each GO biological process in the phagocytosis predictive, phagocyte specialist R. allomycis model. The table includes the GO category, the proteins associated with each category, the annotation of each protein, and the confidence of that annotation.

Extended Data Table 4

Data table indicating the proteins associated with each GO biological process in the photosynthesis predictive model. The table includes the GO category, the proteins associated with each category, the annotation of each protein, and the confidence of that annotation.

Extended Data Table 5

Data table indicating the proteins associated with each GO biological process in the prototrophy predictive model. The table includes the GO category, the proteins associated with each category, the annotation of each protein, and the confidence of that annotation.

Extended Data Table 6

Full predictions table for 112 eukaryote test genomes. Green shading indicates a positive prediction.

Extended Data Table 7

Data table of the 54 proteins shared between the mouse phagosome and the proteins identified by comparative genomics.

Extended Data Table 8

Data table of the 431 proteins identified by comparative genomics, but not identified in phagosome isolation experiments.

Extended Data Table 9

Data table of the 705 identified in phagosome isolation experiments that do not overlap proteins enriched among phagocytes by comparative genomics.

Extended Data Table 10

Presence absence table of selected proteins from the phagocytosis predictive, phagocyte generalist model.

Extended Data Table 11

Presence absence table of selected proteins from the phagocytosis predictive, phagocyte specialist entamoebid model.

Extended Data Table 12

Presence absence table of selected proteins from the phagocytosis predictive, phagocyte specialist R. allomycis model.

Extended Data Table 13

Annotation table of 54 Asgard archaea specific proteins.

Extended Data Table 14

Annotation table of 6 Asgard archaea specific proteins that overlap with the phagocyte-predictive set.

Extended Data Table 15

Annotation table for proteins in the phagocyte models and observed phagosome. For proteins with prokaryote homologs, bacterial affinity by LCA is noted by the pale red highlight, archaeal affinity is noted by the pale blue highlight, and unclear affinity is noted by the gray highlight. Eukaryote specific proteins are highlighted in purple.

Extended Data Table 16

Data table indicating the proteins associated with each GO molecular function category enriched among proteins with bacterial affinity by LCA analysis.

Extended Data Table 17

Data table indicating the proteins associated with each GO molecular function category enriched among proteins with archaeal affinity by LCA analysis.

Extended Data Table 18

Data table indicating the proteins associated with each GO molecular function category enriched among eukaryote specific proteins.

Extended Data Table 19

Eukaryote genome versions used in this analysis and a summary of complete prokaryote genomes.

Extended Data Table 20

UniProtKB annotations for all HMMs generated for this analysis.

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Burns, J.A., Pittis, A.A. & Kim, E. Gene-based predictive models of trophic modes suggest Asgard archaea are not phagocytotic. Nat Ecol Evol 2, 697–704 (2018). https://doi.org/10.1038/s41559-018-0477-7

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