The Ectocarpus genome and the independent evolution of multicellularity in brown algae

Journal name:
Nature
Volume:
465,
Pages:
617–621
Date published:
DOI:
doi:10.1038/nature09016
Received
Accepted

Brown algae (Phaeophyceae) are complex photosynthetic organisms with a very different evolutionary history to green plants, to which they are only distantly related1. These seaweeds are the dominant species in rocky coastal ecosystems and they exhibit many interesting adaptations to these, often harsh, environments. Brown algae are also one of only a small number of eukaryotic lineages that have evolved complex multicellularity (Fig. 1). We report the 214million base pair (Mbp) genome sequence of the filamentous seaweed Ectocarpus siliculosus (Dillwyn) Lyngbye, a model organism for brown algae2, 3, 4, 5, closely related to the kelps6, 7 (Fig. 1). Genome features such as the presence of an extended set of light-harvesting and pigment biosynthesis genes and new metabolic processes such as halide metabolism help explain the ability of this organism to cope with the highly variable tidal environment. The evolution of multicellularity in this lineage is correlated with the presence of a rich array of signal transduction genes. Of particular interest is the presence of a family of receptor kinases, as the independent evolution of related molecules has been linked with the emergence of multicellularity in both the animal and green plant lineages. The Ectocarpus genome sequence represents an important step towards developing this organism as a model species, providing the possibility to combine genomic and genetic2 approaches to explore these and other4, 5 aspects of brown algal biology further.

At a glance

Figures

  1. Simplified representation of the evolutionary tree of the eukaryotes showing the five major groups that have evolved complex multicellularity (indicated in colour).
    Figure 1: Simplified representation of the evolutionary tree of the eukaryotes showing the five major groups that have evolved complex multicellularity (indicated in colour).

    Here we define groups showing complex multicellularity as those that include macroscopic organisms with defined, recognizable morphologies and composed of multiple cell types. Coloured bars indicate the approximate, relative times of emergence of complex multicellularity in each lineage. The inset tree to the right indicates the relationship of Ectocarpus to selected brown algal genera. Kelps are represented in the tree by the genus Laminaria.

  2. An integrated viral sequence in the Ectocarpus genome.
    Figure 2: An integrated viral sequence in the Ectocarpus genome.

    a, Representation of the linear and circular forms of the EsV-1 genome compared to the inserted viral genome. Genes on the upper and lower strands are above and below the line, respectively. A short region of the viral genome containing the putative integrase gene has been transposed to supercontig 0371. Gray parallelograms connect regions of high gene density that are also found in other phaeoviruses. These regions contain many of the genes that are thought to be important for the viral life cycle. Percent nucleotide identities between cognate regions in EsV-1 and in the integrated viral genome are indicated. Dashed line, algal DNA. ITRA and ITRA', inverted terminal repeats. b, Mean expression levels of the inserted viral genes: the graph shows the mean of the normalized expression values (4 replicates) ±s.d. of the genes that were included in the microarray experiments carried out in ref. 29. Expression data are shown for the control condition, but gene expression profiles were highly similar under stress conditions (not shown). Each bar represents the expression value for one coding sequence, the bars are in the same order as the corresponding genes along the supercontigs. Red bars correspond to virus genes, blue bars to host genes. Supercontigs 0062, 0052 and 0028 are adjacent on the genetic map, supercontig 0371 is part of another linkage group. The hybridization signals for 95% of the negative controls (median of four random probes on the same array) were between 19 and 59 (indicated by the two dotted lines).

  3. Predicted pattern of loss and gain of gene families during the evolution of a broad range of eukaryotes.
    Figure 3: Predicted pattern of loss and gain of gene families during the evolution of a broad range of eukaryotes.

    The number of gene families that were acquired (black) or lost (red) at each time point (grey circles) in the tree (Supplementary Information 1.15) was estimated using the Dollo parsimony principle. For each species, the number of orphans (genes that lacked homologues in the eukaryotic data set), the total number of gene families gained or lost and the overall gain (that is, total gain minus total loss) is indicated.

  4. Phylogenetic analysis showing the independent evolution of eukaryotic receptor kinases from the opisthokont, green plant and stramenopile lineages.
    Figure 4: Phylogenetic analysis showing the independent evolution of eukaryotic receptor kinases from the opisthokont, green plant and stramenopile lineages.

    Protein maximum-likelihood tree generated using a multiple alignment of kinase domains from eukaryotic receptor kinases and related cytosolic kinases. Bootstrap values, when above 65%, are provided at the nodes for maximum-likelihood (first value) and neighbour-joining (second value) analyses.

Accession codes

References

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Author information

Affiliations

  1. UPMC Université Paris 6, The Marine Plants and Biomolecules Laboratory, UMR 7139, Station Biologique de Roscoff, Place Georges Teissier, BP74, 29682 Roscoff Cedex, France

    • J. Mark Cock,
    • Delphine Scornet,
    • Catherine Boyen,
    • Bénédicte Charrier,
    • Ga Youn Cho,
    • Susana M. Coelho,
    • Jonas Collén,
    • Ludovic Delage,
    • Simon M. Dittami,
    • Bernhard Gschloessl,
    • Svenja Heesch,
    • Bernard Kloareg,
    • Aude Le Bail,
    • Catherine Leblanc,
    • Gurvan Michel,
    • Pi Nyvall-Collén,
    • Akira F. Peters,
    • Philippe Potin,
    • Andrés Ritter,
    • Sylvie Rousvoal &
    • Thierry Tonon
  2. CNRS, UMR 7139, Laboratoire International Associé Dispersal and Adaptation in Marine Species, Station Biologique de Roscoff, Place Georges Teissier, BP74, 29682 Roscoff Cedex, France

    • J. Mark Cock,
    • Delphine Scornet,
    • Catherine Boyen,
    • Bénédicte Charrier,
    • Ga Youn Cho,
    • Susana M. Coelho,
    • Jonas Collén,
    • Ludovic Delage,
    • Simon M. Dittami,
    • Bernhard Gschloessl,
    • Svenja Heesch,
    • Bernard Kloareg,
    • Aude Le Bail,
    • Catherine Leblanc,
    • Gurvan Michel,
    • Pi Nyvall-Collén,
    • Akira F. Peters,
    • Philippe Potin,
    • Andrés Ritter,
    • Sylvie Rousvoal &
    • Thierry Tonon
  3. Department of Plant Systems Biology, VIB, B-9052 Ghent, Belgium

    • Lieven Sterck,
    • Pierre Rouzé,
    • Grigoris Amoutzias,
    • Kenny Billiau,
    • Eric Bonnet,
    • Cindy Martens &
    • Yves Van de Peer
  4. Department of Plant Biotechnology and Genetics, Ghent University, B-9052 Ghent, Belgium

    • Lieven Sterck,
    • Pierre Rouzé,
    • Grigoris Amoutzias,
    • Kenny Billiau,
    • Eric Bonnet,
    • Cindy Martens &
    • Yves Van de Peer
  5. J. Craig Venter Institute, San Diego, California 92121, USA

    • Andrew E. Allen &
    • Jonathan H. Badger
  6. CEA, DSV, Institut de Génomique, Génoscope, 2 rue Gaston Crémieux, CP5706, 91057 Evry, France

    • Veronique Anthouard,
    • François Artiguenave,
    • Jean-Marc Aury,
    • Corinne Da Silva,
    • Kamel Jabbari,
    • Claire Jubin,
    • Julie Poulain,
    • Gaelle Samson,
    • Béatrice Ségurens &
    • Patrick Wincker
  7. Alfred Wegener Institute for Polar and Marine Research, Am Handelshafen 12, 27570 Bremerhaven, Germany

    • Bank Beszteri &
    • Klaus Valentin
  8. Queen’s University Belfast, School of Biological Sciences, 97 Lisburn Road, Belfast, BT9 7BL, UK

    • John H. Bothwell
  9. Queen’s University Marine Laboratory, Portaferry, Co. Down, BT22 1PF, UK

    • John H. Bothwell
  10. Marine Biological Association of the UK, The Laboratory, Citadel Hill, Plymouth, PL1 2PB, UK

    • John H. Bothwell,
    • Colin Brownlee,
    • Garry Farnham &
    • Declan C. Schroeder
  11. Institut de Biologie de l’Ecole Normale Supérieure (IBENS), Centre National de la Recherche Scientifique UMR8197, Ecole Normale Supérieure, 75005 Paris, France

    • Chris Bowler,
    • Kamel Jabbari,
    • Pascal J. Lopez &
    • Florian Maumus
  12. Stazione Zoologica, Villa Comunale, I 80121 Naples, Italy

    • Chris Bowler
  13. San Diego State University, 5500 Campanile Drive, San Diego, California 92182-1030, USA

    • Carl J. Carrano
  14. Computer and Genomics Resource Centre, FR 2424, Station Biologique de Roscoff, Place Georges Teissier, BP74, 29682 Roscoff Cedex, France

    • Erwan Corre
  15. Fraunhofer Institute for Cell Therapy and Immunology IZI, Perlickstrasse 1, 04103 Leipzig, Germany

    • Nicolas Delaroque
  16. IRD, IRD/CIRAD Palm Developmental Biology Group, UMR 1097 DIAPC, 911 avenue Agropolis, 34394 Montpellier, France

    • Sylvie Doulbeau &
    • James W. Tregear
  17. Charles University in Prague, Faculty of Science, Department of Botany and Department of Parasitology, Benatska 2, 128 01 Prague 2, Czech Republic

    • Marek Elias
  18. Scottish Association for Marine Science, Department of Microbial and Molecular Biology, Scottish Marine Institute, Oban, Argyll PA37 1QA, UK

    • Claire M. M. Gachon,
    • Frithjof C. Küpper &
    • Martina Strittmatter
  19. Kobe University Research Center for Inland Seas, 1-1, Rokkodai, Nadaku, Kobe 657-8501, Japan

    • Hiroshi Kawai &
    • Takahiro Yamagishi
  20. Muroran Marine Station, Field Science Center for Northern Biosphere, Hokkaido University, Muroran 051-0003, Hokkaido, Japan

    • Kei Kimura,
    • Taizo Motomura &
    • Chikako Nagasato
  21. University of Freiburg, Faculty of Biology, Hauptstr. 1, 79104 Freiburg, Germany

    • Daniel Lang &
    • Stefan A. Rensing
  22. Laboratoire Glyco-MEV EA 4358, IFRMP 23, Université de Rouen, 76821 Mont-Saint-Aignan, France

    • Patrice Lerouge
  23. Institut für Allgemeine Botanik, Johannes Gutenberg-Universität Mainz, 55099 Mainz, Germany

    • Martin Lohr
  24. Department of Haematology, Cambridge Institute for Medical Research, University of Cambridge, Hills Road, Cambridge CB2 0XY, UK

    • Diego Miranda-Saavedra
  25. UPMC Université Paris 6, UMR 7150 Mer & Santé, Equipe Traduction Cycle Cellulaire et Développement, Station Biologique de Roscoff, 29680 Roscoff, France

    • Julia Morales
  26. CNRS, UMR 7150 Mer & Santé, Station Biologique de Roscoff, 29680 Roscoff, France

    • Julia Morales
  27. Laboratoire ARAGO, BP44, 66651 Banyuls-sur-mer, France

    • Hervé Moreau
  28. Bio5 Institute and Department of Plant Sciences, University of Arizona, Tucson, Arizona 85719, USA

    • Carolyn A. Napoli
  29. University of Tennessee Health Science Center, Department of Molecular Sciences, 858 Madison Ave, Suite G01, Memphis, Tennessee 38163, USA

    • David R. Nelson
  30. Unité de Recherches en Génomique-Info (UR INRA 1164), INRA, Centre de recherche de Versailles, bat.18, RD10, Route de Saint Cyr, 78026 Versailles Cedex, France

    • Cyril Pommier &
    • Hadi Quesneville
  31. Biological Sciences, Cal State University, San Marcos, California 92096-0001, USA

    • Betsy Read
  32. Departamento de Ecología, Center for Advanced Studies in Ecology and Biodiversity, Facultad de Ciencias Biológicas, Pontificia Universidad Católica de Chile, Santiago, Chile

    • Andrés Ritter
  33. Systemix Institute, Redmond, Washington 98053, USA

    • Manoj Samanta
  34. CNRS, UMR 7144, Evolution du Plancton et PaleOceans, Station Biologique de Roscoff, Place Georges Teissier, BP74, 29682 Roscoff Cedex, France

    • Peter von Dassow
  35. Present addresses: Department of Microbiology, Oregon State University, Corvallis, Oregon 97331, USA (B.B.); WPI Immunology Frontier Research Center, Osaka University, 3-1 Yamadaoka, Suita, 565-0871, Osaka, Japan (D.M-S.); Bezhin Rosko, 28 route de Perharidy, 29680 Roscoff, France (A.F.P.).

    • Bank Beszteri,
    • Diego Miranda-Saavedra &
    • Akira F. Peters

Contributions

J.M.C. coordinated genome analysis and manuscript preparation. P.W. and Y.V.d.P. coordinated genome assembly and centralized and enabled the annotation process, respectively. P.W. and Y.V.d.P. should be considered joint last authors. L.S. and P.R. implemented the automated annotation of the genome and made substantial contributions to genome annotation and analysis. D.S. developed and implemented protocols for library construction. L.S., P.R and D.S. should be considered joint second authors. All other authors are members of the genome sequencing consortium and contributed annotation, analyses or data to the genome project.

Competing financial interests

The authors declare no competing financial interests.

Corresponding author

Correspondence to:

The annotated Ectocarpus genome sequence can be obtained through the EMBL Nucleotide Sequence Database (accession numbers CABU01000001–CABU01013533, FN647682–FN649242, FN649726–FN649760) and can be browsed at the Bogas website (http://bioinformatics.psb.ugent.be/webtools/bogas/). cDNA sequence data are available through accession numbers FP245546–FP312611 and small RNA sequences and tiling array data have been submitted to the GEO database (accession numbers ERA000209 and GSE19912, respectively). The Ectocarpus microRNAs have been submitted to miRBase (accession numbers esi-MIR3450–esi-MIR3469).

Author details

Supplementary information

PDF files

  1. Supplementary Information (1.4M)

    This file contains Supplementary Methods (1.1-1.21), Supplementary Notes comprising: Genome Structure and organization (2.1-2.18); Metabolism (2.2.1-2.2.13); Signalling and cell biology (2.3.1-2.3.16) and References.

  2. Supplementary Figures (9.1M)

    This file contains Supplementary Figures 1-55 with legends.

  3. Supplementary Tables (1.1M)

    This file contains Supplementary Tables 1-42.

Excel files

  1. Supplementary Table 43 (1.5M)

    This table shows genes predicted to be derived from an endosymbiotic event involving a red alga.

  2. Supplementary Table 44 (677K)

    This table shows genes predicted to be derived from an endosymbiotic event involving a green alga.

Additional data