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Timing the origin of eukaryotic cellular complexity with ancient duplications

Nature Ecology & Evolution volume 5, pages 92–100 (2021)Cite this article

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Abstract

Eukaryogenesis is one of the most enigmatic evolutionary transitions, during which simple prokaryotic cells gave rise to complex eukaryotic cells. While evolutionary intermediates are lacking, gene duplications provide information on the order of events by which eukaryotes originated. Here we use a phylogenomics approach to reconstruct successive steps during eukaryogenesis. We find that gene duplications roughly doubled the proto-eukaryotic gene repertoire, with families inherited from the Asgard archaea-related host being duplicated most. By relatively timing events using phylogenetic distances, we inferred that duplications in cytoskeletal and membrane-trafficking families were among the earliest events, whereas most other families expanded predominantly after mitochondrial endosymbiosis. Altogether, we infer that the host that engulfed the proto-mitochondrion had some eukaryote-like complexity, which drastically increased upon mitochondrial acquisition. This scenario bridges the signs of complexity observed in Asgard archaeal genomes to the proposed role of mitochondria in triggering eukaryogenesis.

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Fig. 1: Characterization of duplications during eukaryogenesis.
Fig. 2: Contribution of different phylogenetic origins to duplications during eukaryogenesis.
Fig. 3: Timing of acquisitions and duplications from different phylogenetic origins during eukaryogenesis.
Fig. 4: Timing of duplications during eukaryogenesis according to function and localization.

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Data availability

Fasta files, phylogenetic trees and their annotations are available in figshare with the identifier53 https://doi.org/10.6084/m9.figshare.10069985.

Code availability

The code used to annotate the phylogenetic trees can be accessed in Github (https://github.com/JulianVosseberg/feca2leca).

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Acknowledgements

We thank K. S. Marakova and E. V. Koonin for sharing their KOG-to-COG protein clusters with us. We are grateful to T. J. P. van Dam, E. S. Deutekom and G. J. P. L. Kops for useful advice and discussions. This work is part of the research programme VICI with project number 016.160.638, which is (partly) financed by the Netherlands Organisation for Scientific Research (NWO). T.G. acknowledges support from the Spanish Ministry of Science and Innovation for grant PGC2018-099921-B-I00 and from the European Union’s Horizon 2020 research and innovation programme under grant agreement ERC-2016-724173.

Author information

Author notes

  1. Jolien J. E. van Hooff

    Present address: Ecologie Systématique Evolution, CNRS, Université Paris-Saclay, AgroParisTech, Orsay, France

  2. Anne van Vlimmeren

    Present address: Department of Biological Sciences, Columbia University, New York City, NY, USA

  3. These authors contributed equally: Julian Vosseberg, Jolien J. E. van Hooff.

Authors and Affiliations

  1. Theoretical Biology and Bioinformatics, Department of Biology, Faculty of Science, Utrecht University, Utrecht, the Netherlands

    Julian Vosseberg, Jolien J. E. van Hooff, Anne van Vlimmeren, Leny M. van Wijk & Berend Snel

  2. Centre for Genomic Regulation, The Barcelona Institute of Science and Technology, Barcelona, Spain

    Marina Marcet-Houben & Toni Gabaldón

  3. Life Sciences Department, Barcelona Supercomputing Center, Barcelona, Spain

    Marina Marcet-Houben & Toni Gabaldón

  4. Mechanisms of Disease, Institute for Research in Biomedicine, Barcelona, Spain

    Marina Marcet-Houben & Toni Gabaldón

  5. Institució Catalana de Recerca i Estudis Avançats, Barcelona, Spain

    Toni Gabaldón

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Contributions

J.J.E.v.H., T.G. and B.S. conceived the study. J.V. and J.J.E.v.H. performed the research. J.V., J.J.E.v.H., T.G. and B.S. analysed and interpreted the results. M.M.-H. performed the analysis on the human phylome. M.M.-H. and A.v.V. aided in the development of the tree analysis pipeline. L.M.v.W. implemented the ScrollSaw-based method. J.V., J.J.E.v.H. and B.S. wrote the manuscript, which was edited and approved by all authors.

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Correspondence to Toni Gabaldón or Berend Snel.

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Extended data

Extended Data Fig. 1 Estimating the number of LECA genes from the number of Pfam domains with linear regression.

Scatter plot showing the number of Pfam domains and protein-coding genes in present-day eukaryotes, with each dot representing one genome. The regression line (black) and its 95% confidence (filled grey) and prediction intervals (dashed grey) are depicted. The vertical line corresponds to the obtained number of LECA Pfam domains.

Extended Data Fig. 2 Effect of a different phylogenetic position of the eukaryotic root.

a, Number of inferred LECA families considering different root positions. These numbers are based on phylogenetic trees from Pfams that are only present in eukaryotes. Besides the Opimoda and Diphoda groups, two other group definitions were used to identify bidirectional best hits (BBHs) and select sequences for tree inference. Names of root positions indicate either the lineage at one side of the root or the position of the split (ADis-DiaM: Amorphea+Discoba – Diaphoretickes+Metamonada; AM-DiaDis: Amorphea+Metamonada – Diaphoretickes+Discoba). Excavate sequences, especially from Metamonada species, are rarely involved in BBHs, unless specifically searched for (Excavata in BBHs 5 groups; Discoba and Metamonada in BBHs 4 groups). b, Distribution of duplication lengths obtained using different root positions for eukaryote-only trees based on the four group BBHs. The difference between distributions is not statistically significant according to the Kruskal-Wallis test.

Extended Data Fig. 3 Fraction of LECA families resulting from inventions.

a, Contribution of inventions to LECA families performing different functions. 82% of pairwise comparisons were significantly different (Supplementary Fig. 3). b, Fraction of LECA families resulting from either an invention or duplication – a eukaryotic innovation – according to functional category. 84% of pairwise comparisons were significantly different (Supplementary Fig. 5). c, Contribution of inventions to LECA families performing their function in different cellular components. 51% of pairwise comparisons were significantly different (Supplementary Fig. 4). d, Fraction of LECA families resulting from an innovation according to cellular localisation. 74% of pairwise comparisons were significantly different (Supplementary Fig. 6). a–d, Dashed lines indicate the overall invented or innovated fraction.

Extended Data Fig. 4 Phylogenetic origin of acquired Pfams.

a, b, Phylogeny of the prokaryotes (a) and Asgard archaea (b) present in our dataset based on the NCBI taxonomy. The branch widths and numbers indicate the number of acquisitions from a group. c, Number of acquisitions from different alphaproteobacterial orders or a combination of multiple orders (‘Alphaproteobacteria’).

Extended Data Fig. 5 Effect of duplications on branch lengths.

a, b, Distribution of alphaproteobacterial (a) and Asgard archaeal (b) stem lengths (sl’s) for acquisitions without and with duplications. Two alphaproteobacterial sl’s from acquisitions with Magnetococcales as sister group were removed based on the previously inferred phylogenetic position of mitochondria8. c, d, Distribution of Asgard archaeal sl’s for information storage and processing (c) and cellular processes and signalling families (d), comparing those without and with duplications. Upon removal of the outliers, the difference in cellular processes and signalling families no longer reached statistical significance. e, Distribution of Asgard archaeal sl’s for duplicated acquisitions, in which homomer-to-heteromer transitions had occurred compared to the other duplicated acquisitions. f, Distribution of vertebrate sl’s for families without and with duplications. g, Distribution of duplication lengths (dl’s) grouped according to the lineage in which the duplication occurred. All pairwise comparisons were significantly different (Mann-Whitney U tests). h, Distribution of differences in log-transformed dl values for all pairwise comparisons between chordate duplications according to age and functional annotation. All groups were significantly different (Mann-Whitney U tests). a–f, P values of Mann-Whitney U tests are shown. c–e, The minimal sl via each duplication node is plotted.

Extended Data Fig. 6 Effect of branch length normalisation and functional divergence.

a, Ridgeline plot showing the distribution of uncorrected stem (rsl) or duplication lengths (rdl). Numbers indicate the number of acquisitions or duplications for which the branch lengths were included. The low peaks at very short branch lengths are an artefact from near-zero branch lengths. Groups are ordered based on the median value of rsl’s and rdl’s. b, Ridgeline plot showing the distribution of sls for non-duplicated acquisitions that share the same functional annotation of the prokaryotic sister group and are therefore expected to have undergone little functional divergence during eukaryogenesis. a, b, Branch lengths are depicted as the additive inverse of the log-transformed values. Pairwise comparisons that did not give a significant P value (Mann-Whitney U tests) are shown.

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Vosseberg, J., van Hooff, J.J.E., Marcet-Houben, M. et al. Timing the origin of eukaryotic cellular complexity with ancient duplications. Nat Ecol Evol 5, 92–100 (2021). https://doi.org/10.1038/s41559-020-01320-z

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