A molecular timescale for eukaryote evolution with implications for the origin of red algal-derived plastids

Jürgen F. H. Strassert, Iker Irisarri, Tom A. Williams, Fabien Burki

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180 Citations (SciVal)

Abstract

In modern oceans, eukaryotic phytoplankton is dominated by lineages with red algal-derived plastids such as diatoms, dinoflagellates, and coccolithophores. Despite the ecological importance of these groups and many others representing a huge diversity of forms and lifestyles, we still lack a comprehensive understanding of their evolution and how they obtained their plastids. New hypotheses have emerged to explain the acquisition of red algal-derived plastids by serial endosymbiosis, but the chronology of these putative independent plastid acquisitions remains untested. Here, we establish a timeframe for the origin of red algal-derived plastids under scenarios of serial endosymbiosis, using Bayesian molecular clock analyses applied on a phylogenomic dataset with broad sampling of eukaryote diversity. We find that the hypotheses of serial endosymbiosis are chronologically possible, as the stem lineages of all red plastid-containing groups overlap in time. This period in the Meso- and Neoproterozoic Eras set the stage for the later expansion to dominance of red algal-derived primary production in the contemporary oceans, which profoundly altered the global geochemical and ecological conditions of the Earth.
Original languageEnglish
Article number1879
JournalNature Communications
Volume12
Early online date25 Mar 2021
DOIs
Publication statusPublished - 25 Mar 2021

Data Availability Statement

All data needed to evaluate the conclusions of this study are present in the paper, the Supplementary Information and the Supplementary Data. Raw sequence data are available under the following web-links: https://ensemblgenomes.org, https://imicrobe.us/#/projects/104, https://ncbi.nlm.nih.gov, https://onekp.com/samples/list.php, https://doi.org/10.6084/m9.figshare.12417881.v2.

Acknowledgements

This work was supported by a grant from Science for Life Laboratory available to F.B., which covered the salary of J.F.H.S. and I.I. I.I. acknowledges support from the Spanish Ministry of Economy and Competitiveness (MINECO) (Juan de la Cierva fellowship IJCI-2016- 29566) and the European Research Council (Grant Agreement No. 852725; ERC-StG ‘TerreStriAL’ to Jan de Vries, University of Gottingen). T.A.W. was supported by a Royal Society University Research Fellowship and NERC Grant NE/P00251X/1. Computations were performed on resources provided by the Swedish National Infrastructure for Computing (SNIC) at Uppsala Multidisciplinary Center for Advanced Computational Science (UPPMAX) under Projects 2017-7-65, 2017-7-355, 2018-3-147, 2018-3-288, 2018-8-187, 2018-8-192 and 2019-3-305.

Funding

Open access funding provided by Uppsala University.

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