A geological timescale for bacterial evolution and oxygen adaptation

Adrián A Davín, Ben J Woodcroft, Rochelle M Soo, Benoit Morel, Ranjani Murali, Dominik Schrempf, James W Clark, Sandra Álvarez-Carretero, Bastien Boussau, Edmund R R Moody, Lénárd L Szánthó, Etienne Richy, Davide Pisani, James Hemp, Woodward W Fischer, Philip C J Donoghue, Anja Spang, Philip Hugenholtz, Tom A Williams, Gergely J Szöllősi

Research output: Contribution to journalArticlepeer-review

Abstract

Microbial life has dominated Earth's history but left a sparse fossil record, greatly hindering our understanding of evolution in deep time. However, bacterial metabolism has left signatures in the geochemical record, most conspicuously the Great Oxidation Event (GOE). We combine machine learning and phylogenetic reconciliation to infer ancestral bacterial transitions to aerobic lifestyles, linking them to the GOE to calibrate the bacterial time tree. Extant bacterial phyla trace their diversity to the Archaean and Proterozoic, and bacterial families prior to the Phanerozoic. We infer that most bacterial phyla were ancestrally anaerobic and adopted aerobic lifestyles after the GOE. However, in the cyanobacterial ancestor, aerobic metabolism likely predated the GOE, which may have facilitated the evolution of oxygenic photosynthesis.

Original languageEnglish
Article numbereadp1853
JournalScience (New York, N.Y.)
Volume388
Issue number6742
DOIs
Publication statusPublished - 4 Apr 2025

Data Availability Statement

All data used are described in detail in the SM and available in the figshare repository associated with the submission (92). All code used is described in detail in the SM and available either openly on github or other relevant public repository, as indicated, or included in the figshare repository (92) associated with the submission in the case of more specialized scripts.

Acknowledgements

We thank J. A. Palacios for providing R scripts to compute the D2 distance for ranked trees. We are grateful for the help and support provided by the Scientific Computing and Data Analysis section of Core Facilities at OIST.

Funding

This work was funded by the following: European Research Council (ERC) grant 714774 “GENECLOCKS” (to A.A.D., L.L.S., D.S., and G.J.S.) under the European Union’s Horizon 2020 research and innovation program; ERC grant 947317” ASymbEL” (to A.S.) under the European Union’s Horizon 2020 research and innovation program; Gordon and Betty Moore Foundation grant GBMF9741 (to T.A.W., A.S., P.C.J.D., and G.J.S.); Gordon and Betty Moore Foundation’s Symbiosis in Aquatic Systems Initiative grant GBMF9346 (to A.S.); Moore–Simons Project on the Origin of the Eukaryotic Cell, Simons Foundation 735929LPI grant 735929LP (to A.S.); Royal Society University Research Fellowship (to T.A.W.); John Templeton Foundation grant 62220 (to E.R.R.M., D.P., P.C.J.D., and T.A.W.); Leverhulme Trust Research Fellowship grant RF-2022-167 (to P.C.J.D.); Biotechnology and Biological Sciences Research Council grants BB/T012773/1 and BB/Y003624/1 (to P.C.J.D.); Australian Research Council (ARC) Future Fellowship grant FT210100521 (to B.J.W.); ARC Discovery Project grant DP230101171 (to B.J.W.); ARC Discovery Early Career Researcher Award grant DE190100008 (to R.M.S.); ARC Laureate Fellowship grant FL150100038 (to A.A.D. and P.H.); ARC Discovery Project grant DP220100900 (to A.A.D. and P.H.).

Keywords

  • Oxygen/metabolism
  • Phylogeny
  • Biological Evolution
  • Oxidation-Reduction
  • Bacteria/metabolism
  • Machine Learning
  • Photosynthesis
  • Adaptation, Physiological
  • Cyanobacteria/genetics

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