# Evolutionary genomics of the emergence of brown algae as key components of coastal ecosystems

## Graphical abstract

<figure><img src="image_1.png" /></figure>

## Authors

France Denoeud, Olivier Godfroy, Corinne Cruaud, ..., Patrick Wincker, Jean-Marc Aury, J. Mark Cock

## Correspondence

kawai@kobe-u.ac.jp (H.K.), akirapeters@gmail.com (A.F.P.), hsyoon2011@skku.edu (H.S.Y.), cherve@sb-rostoff.fr (C.H.), yenh@ysfri.ac.cn (N.Y.), epbapteste@gmail.com (E.B.), valero@sb-rostoff.fr (M.V.), gabriel.markov@sb-rostoff.fr (G.V.M.), corre@sb-rostoff.fr (E.C.), susana.coelho@tuebingen.mpg.de (S.M.C.), pwincker@genoscope.cns.fr (P.W.), jmaury@genoscope.cns.fr (J.-M.A.), cock@sb-rostoff.fr (J.M.C.)

## In brief

Comparative genomics charts the evolutionary history of the brown algal lineage, identifying an early period of accelerated genome evolution followed by diversification of the major orders, and a major impact of continuous, widespread viral genome integration.

## Highlights

* An intense period of genome evolution during early emergence of the brown algae

* Gene family amplifications linked to diversification of the brown algae

* Extensive gene flow between species at the genus level in *Ectocarpus*

* Insertions of diverse *Phaeovirus* genomes are widespread in brown algae
Article

# Evolutionary genomics of the emergence of brown algae as key components of coastal ecosystems

France Denoeud,¹,⁶⁰ Olivier Godfroy,²,⁶⁰ Corinne Cruaud,³,⁶¹ Svenja Heesch,⁴,⁵¹,⁶¹ Zofia Nehr,⁴,⁶¹ Nachida Tadrent,¹,⁵²,⁶¹

Arnaud Couloux,¹,⁶¹ Loraine Brillet-Guéguen,⁵,⁶,⁶¹ Ludovic Delage,⁷,⁶¹ Dean Mckeown,⁶,⁶¹ Taizo Motomura,⁸,⁶²

Duncan Sussfeld,¹,⁹,⁶² Xiao Fan,¹⁰,¹¹,⁶² Lisa Mazéas,²,⁶² Nicolas Terrapon,¹²,¹³,⁶² Josué Barrera-Redondo,¹⁴,⁶²

Romy Petroll,¹⁴,⁶² Lauric Reynes,¹⁵,⁶² Seok-Wan Choi,¹⁶,⁶² Jihoon Jo,¹⁶,⁶² Kavitha Uthanumallian,¹⁷,⁶²

Kenny Bogaert,¹⁸,⁵³,⁶² Céline Duc,¹⁹,⁶² Pélagie Ratchinski,⁴,⁶² Agnieszka Lipinska,⁴,¹⁴,⁶² Benjamin Noel,¹,⁶²

Eleanor A. Murphy,²⁰,²¹,⁶² Martin Lohr,²²,⁶² Ananya Khatei,²³,⁵⁴,⁶² Pauline Hamon-Giraud,²⁴,⁶² Christophe Vieira,²⁵,⁶²

Komlan Avia,²⁶,⁶² Svea Sanja Akerfors,²² Shingo Akita,²⁷ Yacine Badis,⁴ Tristan Barbeyron,² Arnaud Belcour,²⁴,⁵⁵

Wahiba Berrabah,¹ Samuel Blanquart,²⁴ Ahlem Bouguerba-Collin,² Trevor Bringloe,²⁸ Rose Ann Cattolico,²⁹

Alexandre Cormier,³⁰ Helena Cruz de Carvalho,³¹,³² Romain Dallet,⁶ Olivier De Clerck,¹⁸

(Author list continued on next page)

¹Génomique Métabolique, Genoscope, Institut François Jacob, CEA, CNRS, Université Evry, Université Paris-Saclay, Evry 91057, France

²Sorbonne Université, CNRS, Integrative Biology of Marine Models Laboratory, Station Biologique de Roscoff, Roscoff, France

³Genoscope, Institut François Jacob, CEA, CNRS, Univ Evry, Université Paris-Saclay, Evry 91057, France

⁴Sorbonne Université, CNRS, Algal Genetics Group, Integrative Biology of Marine Models Laboratory, Station Biologique de Roscoff, Roscoff, France

⁵CNRS, UMR 8227, Laboratory of Integrative Biology of Marine Models, Sorbonne Université, Station Biologique de Roscoff, Roscoff, France

⁶CNRS, Sorbonne Université, FR2424, ABiMS-IFB, Station Biologique, Roscoff, France

⁷Sorbonne Université, CNRS, UMR 8227, ABIE Team, Integrative Biology of Marine Models Laboratory, Station Biologique de Roscoff, Roscoff, France

⁸Muroran Marine Station, Hokkaido University, Muroran, Japan

⁹Institut de Systématique, Evolution, Biodiversité (ISYEB), UMR 7205, Sorbonne Université, CNRS, Museum, Paris, France

¹⁰State Key Laboratory of Mariculture Biobreeding and Sustainable Goods, Yellow Sea Fisheries Research Institute, Chinese Academy of Fishery Sciences, Qingdao, Shandong 266071, China

¹¹Laboratory for Marine Fisheries Science and Food Production Processes, Laoshan Laboratory, Qingdao, Shandong 266237, China

¹²Aix Marseille University, CNRS, UMR 7257 AFMB, Marseille, France

¹³INRAE, USC 1408 AFMB, Marseille, France

¹⁴Department of Algal Development and Evolution, Max Planck Institute for Biology, Max-Planck-Ring 5, 72076 Tübingen, Germany

¹⁵IRL 3614, UMR 7144, DISEEM, CNRS, Sorbonne Université, Station Biologique de Roscoff, Roscoff 29688, France

¹⁶Department of Biological Sciences, Sungkyunkwan University, Suwon 16419, Republic of Korea

¹⁷University of Melbourne, Parkville, VIC, Australia

¹⁸Phycology Research Group, Ghent University, Krijgslaan 281 S8, 9000 Ghent, Belgium

¹⁹Nantes Université, CNRS, US2B, UMR 6286, 44000 Nantes, France

²⁰University of Bristol, Bristol, UK

²¹Marine Biological Association, Plymouth, UK

²²Johannes Gutenberg University, Mainz, Germany

²³Algal and Microbial Biotechnology Division, Nord University, Bodø, Norway

(Affiliations continued on next page)

## SUMMARY

Brown seaweeds are keystone species of coastal ecosystems, often forming extensive underwater forests, and are under considerable threat from climate change. In this study, analysis of multiple genomes has provided insights across the entire evolutionary history of this lineage, from initial emergence, through later diversification of the brown algal orders, down to microevolutionary events at the genus level. Emergence of the brown algal lineage was associated with a marked gain of new orthologous gene families, enhanced protein domain rearrangement, increased horizontal gene transfer events, and the acquisition of novel signaling molecules and key metabolic pathways, the latter notably related to biosynthesis of the alginate-based extracellular matrix, and halogen and phlorotannin biosynthesis. We show that brown algal genome diversification is tightly linked to phenotypic divergence, including changes in life cycle strategy and zoid flagellar structure. The study also showed that integration of large viral genomes has had a significant impact on brown algal genome content throughout the emergence of the lineage.
Ahmed Debit,³¹ Erwan Denis,¹ Christophe Destombe,¹⁵ Erica Dinatale,¹⁴ Simon Dittami,⁷ Elodie Drula,¹²,¹³

Sylvain Faugeron,³³ Jeanne Got,²⁴ Louis Graf,¹⁶ Agnès Groisillier,¹⁹ Marie-Laure Guillemin,¹⁵,³⁴,³⁵ Lars Harms,³⁶

William John Hatchett,³⁷ Bernard Henrissat,³⁸ Galice Hoarau,³⁷ Chloé Jollivet,² Alexander Jueterbock,²³ Ehsan Kayal,⁶,⁵⁶

Andrew H. Knoll,³⁹ Kazuhiro Kogame,⁴⁰ Arthur Le Bars,⁶,⁴¹ Catherine Leblanc,⁷ Line Le Gall,⁹ Ronja Ley,²² Xi Liu,⁶

Steven T. LoDuca,⁴² Pascal Jean Lopez,⁴³ Philippe Lopez,⁹ Eric Manirakiza,¹⁹ Marine Massau,⁶ Stéphane Mauger,¹⁵,⁵⁷

Laetitia Mest,⁴,⁵⁸ Gurvan Michel,² Catia Monteiro,⁷ Chikako Nagasato,⁸ Delphine Nègre,⁶,⁵⁹ Eric Pelletier,¹

Naomi Phillips,⁴⁴ Philippe Potin,⁷ Stefan A. Rensing,⁴⁵ Ellyn Rousselot,¹⁹ Sylvie Rousvoal,⁷ Declan Schroeder,⁴⁶

Delphine Scornet,⁴ Anne Siegel,²⁴ Leila Tirichine,¹⁹ Thierry Tonon,⁴⁷ Klaus Valentin,³⁶ Heroen Verbruggen,²⁸

Florian Weinberger,⁴⁸ Glen Wheeler,²¹ Hiroshi Kawai,⁴⁹,⁶³,\* Akira F. Peters,⁵⁰,⁶³,\* Hwan Su Yoon,¹⁶,⁶³,\* Cécile Hervé,²,⁶³,\*

Naihao Ye,¹⁰,¹¹,⁶³,\* Eric Bapteste,⁹,⁶³,\* Myriam Valero,¹⁵,⁶³,\* Gabriel V. Markov,⁷,⁶³,\* Erwan Corre,⁶,⁶³,\*

Susana M. Coelho,¹⁴,⁶³,\* Patrick Wincker,¹,⁶³,\* Jean-Marc Aury,¹,⁶³,\* and J. Mark Cock⁴,⁶⁴,\*

²⁴University of Rennes, Inria, CNRS, IRISA, Equipe Dyliss, Rennes, France

²⁵Research Institute for Basic Sciences, Jeju National University, Jeju 63243, Republic of Korea

²⁶INRAE, Université de Strasbourg, UMR SVQV, 68000 Colmar, France

²⁷Faculty of Fisheries Sciences, Hokkaido University, Minato-cho 3-1-1, Hakodate, Hokkaido 041-8611, Japan

²⁸University of Melbourne, Parkville, VIC, Australia

²⁹University of Washington, Seattle, WA, USA

³⁰Ifremer, IRSI, SeBiMER Service de Bioinformatique de l'Ifremer, 29280 Plouzané, France

³¹Institut de Biologie de l'ENS (IBENS), Département de Biologie, École normale supérieure, CNRS, INSERM, Université PSL, 75005 Paris, France

³²Université Paris Est-Créteil (UPEC), Faculté des Sciences et Technologie, 61, Avenue du Général De Gaulle, 94000 Créteil, France

³³Facultad de Ciencias Biológicas, Pontificia Universidad Católica de Chile, Santiago, Chile

³⁴Núcleo Milenio MASH, Instituto de Ciencias Ambientales y Evolutivas, Facultad de Ciencias, Universidad Austral de Chile, Valdivia, Chile

³⁵Centro FONDAP de Investigación en Dinámica de Ecosistemas Marinos de Altas Latitudes (IDEAL), Valdivia, Chile

³⁶Alfred Wegener Institute (AWI), Bremenhaven, Germany

³⁷Nord University, Bodø, Norway

³⁸Department of Biotechnology and Biomedicine, Technical University of Denmark, Kgs Lyngby, Denmark

³⁹Department of Organismic and Evolutionary Biology, Harvard University, Cambridge, MA 02138, USA

⁴⁰Biological Sciences, Faculty of Science, Hokkaido University, Sapporo 060-0810, Japan

⁴¹CNRS, Institut Français de Bioinformatique, IFB-core, Évry, France

⁴²Department of Geography and Geology, Eastern Michigan University, Ypsilanti, MI 48197, USA

⁴³Centre National de la Recherche Scientifique, UMR BOREA MNHN/CNRS-8067/SU/IRD/Université de Caen Normandie/Université des Antilles, Plouzané, France

⁴⁴Biology Department, Arcadia University, Glenside, PA, USA

⁴⁵University of Freiburg, Freiburg im Breisgau, Germany

⁴⁶University of Minnesota, St. Paul, MN, USA

⁴⁷Centre for Novel Agricultural Products (CNAP), Department of Biology, University of York, Heslington, York YO10 5DD, UK

⁴⁸GEOMAR Helmholtz Centre for Ocean Research, Kiel, Germany

⁴⁹Kobe University Research Center for Inland Seas, Kobe, Japan

⁵⁰Bezhin Rosko, 29250 Santec, France

⁵¹Present address: Applied Ecology & Phycology, Institute for Biosciences, University of Rostock, Albert-Einstein-Strasse 3, 18059 Rostock, Germany

⁵²Present address: Institut de Recherche sur la Biologie de l'Insecte, UMR 7261, CNRS, Université de Tours, Tours 37200, France

⁵³Present address: Department of Algal Development and Evolution, Max Planck Institute for Biology, Tübingen 72076, Germany

⁵⁴Present address: ICAR-Directorate of Coldwater Fisheries Research, Bhimtal, India

⁵⁵Present address: Univ. Grenoble Alpes, Inria, 38000 Grenoble, France

⁵⁶Present address: Department of Ecology, Evolution, and Organismal Biology, Iowa State University, Ames, IA, USA

⁵⁷Present address: CNRS, La Rochelle Université, UMR7266, Littoral Environnement et Sociétés, La Rochelle, France

(Affiliations continued on next page)

## INTRODUCTION

The brown algae (Phaeophyceae) are a lineage of complex multicellular organisms that emerged about 450 mya¹ from within a group of photosynthetic stramenopile taxa (derived from a secondary endosymbiosis involving a red alga²) that are either unicellular or have very simple filamentous multicellular thalli (Figure 1). The emerging brown algae acquired a number of characteristic features that are thought to have

contributed to the evolutionary success of this lineage, including complex polysaccharide-based cell walls that confer protection and flexibility in the highly dynamic intertidal environment,³ complex halogen⁴ and phlorotannin⁵ metabolisms that are thought to play important roles in multiple processes including defense, adhesion and cell-wall modification, and a remarkable diversity of life cycles and developmental body architectures adapted to diverse marine environments.⁶ As a result of these attributes, many brown algae have become
established as key components of extensive coastal ecosystems. These seaweed-based ecosystems provide high value Earth-system-scale services, including the sequestration of several megatons of carbon per year globally, comparable to values reported for terrestrial forests,⁷ but this important role of seaweed ecosystems is threatened by climate-related declines in seaweed populations worldwide.⁸ However, appropriate conservation measures, coupled with the development of seaweed mariculture as a highly sustainable and low impact approach to food and biomass production, could potentially reverse this trend, allowing seaweeds to play a significant role in mitigating the effects of climate change.⁹ To attain this objective, it will be necessary to address important gaps in our knowledge of the biology and evolutionary history of the brown algal lineage. For example, these seaweeds remain poorly described in terms of genome sequencing due, in part, to difficulties with extracting nucleic acids. The Phaeoexplorer project (https://phaeoexplorer.sb-ro Coff fr/) has generated a large dataset of genome sequences, spanning all the major orders of the Phaeophyceae.¹⁰ This extensive genomic dataset has been analyzed here to study the origin and evolution of key genomic features during the emergence and diversification of this important group of marine organisms.

## RESULTS

### In-depth sequencing of brown algal genomes

Until now, good quality genome assemblies have been obtained for only five brown algal species,¹¹⁻¹⁵ together with about 46 draft genome assemblies.¹⁶⁻²⁰ Here, we report work that has significantly expanded the genomic data available by sequencing and assembling 17 good quality genomes using long-read technology (Table S1), plus an additional 43 draft genome assemblies. These 60 genomes correspond to 40 brown algae and four closely related species, covering 16 Phaeophyceae families providing a dense coverage of this lineage (Figures S1A and S1B; Table S1A). The sequenced species include brown algae that occur at different levels of the intertidal and subtidal and are representative of the broad diversity of this group of seaweeds in terms of size, levels of multicellular complexity, biogeography and life cycle structure (Figures 1, S1C, and S1D). The analyses carried out in this study have focused principally on a set of 21 good quality reference genomes, which include four previously published genomes (Table S1B).

### Marked changes in genome content and gene structure during the emergence of the Phaeophyceae lineage

Recent evidence indicates that the brown algae emerged about 450 mya during the Great Ordovician Biodiversification Event (GOBE),¹ a conclusion that is supported by a fossil-calibrated tree built with a nuclear-gene-based phylogeny constructed using the Phaeoexplorer genome data (Figures 1 and S2A). An increase in atmospheric oxygen at the time of the GOBE, which coincided with the emergence of herbivorous marine invertebrates,²¹ is likely to have created conditions conducive to the observed transition toward increased multicellular complexity during early brown algal evolution.

To investigate genomic modifications associated with the emergence and diversification of the brown algae, we first carried out a series of genome-wide analyses aimed at identifying broad trends in genome evolution over evolutionary time (Figure 2). Dollo analysis of gain and loss of orthogroups (i.e., gene families) indicated marked gains during early brown algal evolution followed by a broad tendency to lose orthogroups later as the different brown algal orders diversified (Figures 2B and S2). Similarly, a phylostratigraphy analysis indicated that 29.6% of brown algal genes cannot be traced back to ancestors outside the Phaeophyceae, with the majority of gene founder events occurring early during the emergence of the brown algae (Figures 2E and S3A; Table S2), again indicating a burst of gene birth during the emergence of this lineage. Both the Dollo analysis and the phylostratigraphy approach indicated that the gene families acquired during early brown algal evolution were significantly enriched in genes that could not be assigned to a cluster of orthologous genes (COG) category, suggesting a burst in the acquisition of genetic novelty (Figure 2G).

One of the factors underlying the marked burst of gene gain during the emergence of the brown algae was an increase in the rate of acquisition of new genes via horizontal gene transfer (HGT). A phylogeny-based search for genes potentially derived from HGT events indicated that they constitute about 1% of brown algal gene catalogs and that the novel genes were principally acquired from bacterial genomes (Figures 2F and S3B). The proportion of class-specific HGT events compared with more ancient HGT events was greater for the brown algae (33.5% of HGT events) than for the closely related taxa Xanthophyceae (Tribonema minus) and Raphidophyceae (Heterosigma akashiwo; mean of 17.1% for the two taxa, Wilcoxon *p* = 0.021), indicating that higher levels of HGT occurred during the emergence of the brown algae than in closely related taxa (Figure 2F).

<footnote n="58">⁵⁸Present address: Vegenov, Saint Pol de Léon, France</footnote>

<footnote n="59">⁵⁹Present address: Nantes Université, Institut des Substances et Organismes de la Mer, ISOMer, UR 2160, Nantes, France</footnote>

<footnote n="60">⁶⁰These authors contributed equally</footnote>

<footnote n="61">⁶¹These authors contributed equally</footnote>

<footnote n="62">⁶²These authors contributed equally</footnote>

<footnote n="63">⁶³Senior author</footnote>

<footnote n="64">⁶⁴Lead contact</footnote>

<footnote>*Correspondence: kawai@kobe-u.ac.jp (H.K.), akirapeters@gmail.com (A.F.P.), hsyoon2011@skku.edu (H.S.Y.), cherve@sb-ro Coff fr (C.H.), yenh@ysfri.ac.cn (N.Y.), epbapteste@gmail.com (E.B.), valero@sb-ro Coff fr (M.V.), gabriel.markov@sb-ro Coff fr (G.V.M.), corre@sb-ro Coff fr (E.C.), susana.coelho@tuebingen.mpg.de (S.M.C.), pwincker@genoscope.cns.fr (P.W.), jmaury@genoscope.cns.fr (J.-M.A.), cock@sb-ro Coff fr (J.M.C.)</footnote>

<footnote>https://doi.org/10.1016/j.cell.2024.10.049</footnote>
① *Ascophyllum nodosum*

② *Scytosiphon promiscuus*

③ *Ectocarpus siliculosus*

④ *Pylaiella littoralis*

⑤ *Fucus serratus*

⑥ *Dictyota dichotoma*

⑦ *Chordaria linearis*

⑧ *Saccharina latissima*

⑨ *Undaria pinnatifida*

⑩ *Desmarestia herbacea*

⑪ *Schizocladia ischiensis*

⑫ *Discosporangium mesarthrocarpum*

Major events during evolution

<figure><img src="image_2.png" /></figure>

<figure><img src="image_3.png" /></figure>

<figure><img src="image_4.png" /></figure>

(legend on next page)
The marked increase in the rate of gene gain appears to have been a key factor in the emergence of the brown algal lineage but this was not the only process that enriched brown algal genomes during this period. Domain fusions and fissions (composite genes) were prevalent during the early stages of brown algal emergence (Figures 2D and S3C), affecting about 7% of brown algal gene complements. In contrast, gene family amplifications were most prevalent at a later stage of brown algal evolution, corresponding to the diversification of the major brown algal orders during the Mesozoic (Figures 2C, S3D, and S3E; Table S3). However, the amplified gene families were significantly enriched in genes that had been gained during the emergence of the brown algae ($\chi^2 p = 1.04 \times 10^{-15}$; Table S1C), indicating that gene gain during the early evolution of the lineage nonetheless played a crucial role by establishing the majority of the gene families that would later undergo amplifications.

Analysis of the predicted functions of the three sets of gene families identified as having been amplified, derived from domain fusions/fissions or derived from an HGT event (Figure 2G) indicated that they were enriched in several functional categories, notably carbohydrate metabolism, signal transduction, and transcription. These functional categories may have been important in the emergence of the complex brown algal cell wall or correspond to a complexification of signaling pathways as multicellular complexity increased. Interestingly, many of the genes acquired at, or shortly after, the origin of the Phaeophyceae encode secreted or membrane proteins (Figure S3A), suggesting roles in cell-cell communication that may have been important for the emergence of complex multicellularity or as components of defense mechanisms. The acquisition of plasmodesmata by brown algae directly after their divergence from their sister taxon *Schizocladia ischiensis*²² (Figure 1) underlines the importance of cell-cell communication from the outset of brown algal evolution.

The emergence of the brown algae also corresponded with changes in gene structure. On average, brown algal genes tend to be more intron-rich than those of the other stramenopile groups,²³ including closely related taxa (Figure S4A), with the notable exception of *Chrysoparadoxa australica* (Figure S4A). A comparison of orthologous genes indicated a phase of rapid intron acquisition just before the divergence of the Phaeophyceae and the Schizocladiophyceae, followed by a period of relative intron stability up to the present day (Figure S4B). This phase of accelerated intron acquisition coincided approximately with the periods of marked gene gain and domain reorganization discussed above and may have been an indirect consequence of increased multicellular complexity (Figure 1) due to a concomitant decrease in effective population size.²⁴ Once established, increased intron density may have facilitated some of the genome-wide tendencies described above, such as increased reorganization of composite genes, for example, and thereby

played an important role in a context of increasing developmental complexity.²⁵⁻²⁸

## Acquisition of key metabolic and signaling pathways during the emergence of the Phaeophyceae

The success of the brown algae as an evolutionary lineage has been attributed, at least in part, to the acquisition of several key metabolic pathways, particularly those associated with cell-wall biosynthesis, and both halogen and phlorotannin metabolism.³⁻⁵ Large complements of carbohydrate-active enzyme (CAZYme) genes (237 genes on average) were found in all brown algal orders and in their sister taxon *S. ischiensis*, but this class of gene was less abundant in the more distantly related unicellular alga *H. akashiwo* (Figures 3A, S5A, and S5B; Tables S4A and S4B). The evolutionary history of carbohydrate metabolism gene families was investigated by combining information from the genome-wide analyses of gene gain/loss, HGT and gene family amplification (Figure 3B). This analysis indicated that several key genes and gene families (mannuronan C5 epimerase [ManC5-E] and polysaccharide lyase 41 [PL41]) were acquired by the common ancestor of brown algae and *S. ischiensis*, with strong evidence in some cases that this occurred via HGT (PL41). Moreover, marked amplifications were detected for several families (AA15, ManC5-E, GH114, GT23, and PL41), indicating that both gain and amplification of gene families played important roles in the emergence of the brown algal carbohydrate metabolism gene set. Alginate is a major component of brown algal cell walls, and it plays an important role in conferring resistance to the biomechanical effect of wave action.³ It is therefore interesting that ManC5-E, an enzyme whose action modulates the rigidity of the alginate polymer, appears to have been acquired very early (Figures 3B and 3C). The acquisition of ManC5-E, together with other alginate pathway enzymes such as PL41 (Figures 3A, 3B, and 3D), was probably an important evolutionary step, enabling the emergence of large, resilient substrate-anchored multicellular organisms in the highly dynamic and stressful coastal environment (Figure 1).

Vanadium-dependent haloperoxidases (vHPOs) are a central component of brown algal halogen metabolism, which has been implicated in multiple biological processes including defense, adhesion, chemical signaling, and the oxidative stress response. All three classes of brown algal vHPO (algal types I and II and bacterial-type²⁹⁻³¹) appear to have been acquired early during the emergence of the Phaeophyceae (Figures 3A, S5C, and S5D; Tables S4C and S4D). Closely related stramenopile species do not possess any of these three types of haloperoxidase, with the exception of the sister taxon, *S. ischiensis*, which possesses three intermediate algal type (i.e., equidistant phylogenetically from class I and class II algal types) haloperoxidase genes (Figures 3A, S5C, and S5D). Algal type I and II vHPO

## Figure 1. Ecology, diversity, and evolutionary features of the brown algae

The upper panel indicates approximate positions in the intertidal of key species whose genomes have been sequenced by the Phaeoexplorer project. The lower panel illustrates the diversity of brown algae (maximal values for each taxa) and indicates a number of key evolutionary events that occurred during the emergence of the Phaeophyceae. Some lineages may have secondarily lost a characteristic after its acquisition. Note that members of the genus *Ishige* (Ishigeaceae) also exhibit desiccation tolerance (not shown). ECM, extracellular matrix; asterisk (*), these orders were not analyzed in this study; Cr, Cryogenian; Ed, Ediacaran; Ca, Cambrian; O, Ordovician; S, Silurian; D, Devonian; C, Carboniferous; P, Permian; T, Triassic; J, Jurassic; K, Cretaceous; Pg, Paleogene. See also Figures S1, S2, S4, and S5.
A

Emergence
of the
Phaeophyceae

<figure><img src="image_5.png" /></figure>

F

Genes acquired through HGT

<figure><img src="image_6.png" /></figure>

<figure><img src="image_7.png" /></figure>

B

Gene family
gain and loss

<figure><img src="image_8.png" /></figure>

C

Gene family
amplification and
reduction

<figure><img src="image_9.png" /></figure>

Origin of amplified
gene families

<figure><img src="image_10.png" /></figure>

D

Domain fusions
and fissions

<figure><img src="image_11.png" /></figure>

E

Gene family
founder events

G

No COG category
Function unknown

<figure><img src="image_12.png" /></figure>

<figure><img src="image_13.png" /></figure>

Metabolism

<figure><img src="image_14.png" /></figure>

Cell wall and
signalling

<figure><img src="image_15.png" /></figure>

Cytoskeleton and
trafficking

<figure><img src="image_16.png" /></figure>

Transcription and
translation

<figure><img src="image_17.png" /></figure>

Cell cycle and
DNA maintenance

<figure><img src="image_18.png" /></figure>

(legend on next page)
genes probably diverged from an intermediate-type ancestral gene similar to the *S. ischiensis* genes early during Phaeophyceae evolution. It is likely that the initial acquisitions of algal- and bacterial-type vHPOs represented independent events although the presence of probable vestiges of bacterial-type vHPO genes in *S. ischiensis* means that it is not possible to rule out acquisition of both types of vHPO through a single event.

Gene gain may not, however, have been the proximal factor responsible for all the key metabolic innovations that occurred in the emerging brown algal lineage. Phlorotannins are characteristic brown algal polyphenolic compounds that occur in all Phaeophyceae species, with the exception of some members of the Sargassaceae. Phlorotannins are derived from phloroglucinol and brown algae possess three classes of type III polyketide synthase, two of which (PKS1 and PKS2) were acquired prior to the emergence of the Phaeophyceae and the third (PKS3) evolving much later within the Ectocarpales (Figures 3A and 4A; Table S4E). Interestingly, PKS1 proteins from different brown algal species have been shown to have different activities leading to the production of distinct metabolites.<sup>32-34</sup> indicating that the acquisition of novel functions by this class of enzymes may have played an important role in the emergence of the brown algal capacity to produce phlorotannins. Moreover, many stramenopile PKS type III genes encode proteins with signal peptides or signal anchors (Table S4E). For the brown algae, this feature is consistent with the cellular production site of phlorotannins and the observed transport of these compounds by physodes, secretory vesicles characteristic of brown algae.<sup>35</sup> Cross-linking of phlorotannins, embedded within other brown algal cell-wall compounds such as alginates, has been demonstrated *in vitro* through the action of vHPOs<sup>36-38</sup> and indirectly suggested by *in vivo* observations colocalizing vHPOs with physode fusions at the cell periphery.<sup>39,40</sup> Consequently, vHPOs are good candidates for the enzymes that cross-link phlorotannins and other compounds, perhaps even for the formation of covalent bonds between phloroglucinol monomers and oligomers, which could occur via activation of aromatic rings through halogenation. These observations suggest that the acquisition of vHPOs by the common ancestor of brown algae and *S. ischiensis*, together perhaps with modifications of the existing PKS enzymes, triggered the emergence of new metabolic path-

ways leading to the production of the phlorotannin molecules characteristic of the Phaeophyceae lineage.

The acquisition of increased multicellular complexity and adaptation to new ecological niches during the early stages of brown algal evolution (i.e., during and immediately following the GOBE) is expected to have required modification and elaboration of signaling pathways. Membrane-localized signaling proteins (Figure 3A) are of particular interest in this context not only as potential mediators of intercellular signaling in a multicellular organism but also because of potential interactions with the elaborate brown algal extracellular matrices (cell walls).<sup>3,42</sup> A detailed analysis of the brown algal receptor kinase (RK) gene family, revealed that it actually includes two types of receptor, the previously reported leucine-rich repeat (LRR) RKs<sup>11</sup> and a newly discovered class of receptors with a beta-propeller extracellular domain (Figure 3A; Table S4F).

Major changes in epigenetic regulation also appear to have occurred during the emergence of the brown algae (see also **supplemental information**). **DNA METHYLTRANSFERASE 1 (DNMT1)** genes were identified in *Discosporangium mesarthrocarpum* and two closely related outgroup species (*S. ischiensis* and *C. australica*) but not in other brown algal genomes, indicating that the common ancestor of brown algae probably possessed *DNMT1* but that this gene was lost after divergence of the Discosporangiales from other brown algal taxa (Figure 3A; Table S4G). This is consistent with the reported absence of DNA methylation in the filamentous brown alga *Ectocarpus*<sup>11</sup> and a very low level of DNA methylation in the kelp *Saccharina japonica*<sup>43</sup> (which is thought to be mediated by DNMT2). Our analysis indicates that most brown algae either lack DNA methylation or exhibit very low levels of methylation and that this feature was acquired early during brown algal diversification.

## Impact of morphological, life cycle, and reproductive diversification during the Mesozoic on brown algal genome evolution

A second major step in the evolutionary history of the Phaeophyceae was the rapid diversification of the major brown algal orders, which began after the origin of the Fucophycidae/Dictyotales/Ishigeales (FDI) clade, here estimated at 235.97 Ma (95% highest posterior density region [HPD]: 158.88–312.48

## Figure 2. Genome-wide analyses of brown algal genome and gene content evolution

(A) Time-calibrated cladogram based on Figure S2A. The gray hatched area, which indicates key nodes corresponding to the origin and early emergence of the brown algae, is mirrored in (B)–(F).

(B) Gene family (orthogroup) gain (green) and loss (red) during the emergence and diversification of the brown algae based on a Dollo parsimony reconstruction (Figure S2B).

(C) Upper: timing of gene family amplification and reduction during the evolutionary history of the Phaeophyceae (CAFE5 analysis). Lower: time of origin (orthogroup gain, based on the Dollo parsimony reconstruction) of the 180 most strongly amplified gene families.

(D) Composite gene analysis. Proportions of gene families showing domain fusion (orange) or domain fission (yellow) at different age strata.

(E) Inferred gene family founder events after accounting for homology detection failure.

(F) Horizontal-gene-transfer-derived genes in orthologous groups and across species. The black trace represents the percentage of genes resulting from HGT events per species. Pie charts summarize the predicted origins (donor taxa) of the HGT genes. The right-hand bar graph indicates the proportions of ancestral (i.e., acquired before the root of the phylogenetic class, in gray) and class-specific (i.e., acquired within the phylogenetic class, in blue) HGT genes.

(G) Enrichment of COG categories in sets of gene families identified as being (1) gained at the four indicated early nodes by the Dollo analysis, (2) gene founder events at the four indicated phylostrata, (3) amplified in the Phaeophyceae (180 most strongly amplified families), (4) domain fusions or fissions, and (5) HGT derived. Asterisks indicate significantly enriched categories.

FDI clade, Fucophycideae/Dictyotales/Ishigeales; PS clade, Phaeophyceae plus Schizocladiophyceae; PX clade, Phaeophyceae plus Xanthophyceae. See also Figure S3.
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mya, broadly consistent with previous work¹; Figures 1 and S2A). This diversification closely followed the Permian–Triassic mass extinction event (which dramatically impacted marine ecosystems in which red and green algae played dominant roles) and was facilitated by Triassic marine environments that favored chlorophyll-c containing algae (e.g., high phosphate and low iron concentration), along with the appearance of new coastal niches created by Pangea rifting (Figure 1). This context would have facilitated the diversification of the brown algal lineage,⁴⁴,⁴⁵ resulting in organisms that now exhibit a broad range of morphological complexity (ranging from filamentous to complex parenchymatous thalli), different types of life cycle and diverse reproductive strategies and metabolic capacities³,⁶,⁴⁶,⁴⁷ (Figure 1). The Phaeoexplorer dataset was analyzed to identify genomic features associated with this diversification of phenotypic characteristics and to evaluate the impact on genome evolution and function.

We found indications that the diversification of life cycles, in some cases linked with the emergence of large, complex body architectures, impacted genome evolution through population genetic effects. Most brown algae have haploid-diploid life cycles involving alternation between sporophyte and gametophyte generations, the only exception being the Fucales, which have diploid life cycles. The theoretical advantages of different types of life cycle have been discussed in detail,⁴⁸ and one proposed advantage of a life cycle with a haploid phase is that this allows effective purifying counter-selection of deleterious alleles. When the brown algae with haploid-diploid life cycles were compared with species from the Fucales, increased rates of both synonymous and non-synonymous mutation rates were detected in the latter, consistent with the hypothesis that deleterious alleles are phenotypically masked in species where most genes function in a diploid context (Figure S5E). Comparison of non-synonymous substitution rates (dN) for genes in brown algae with different levels of morphological complexity, ranging from simple filamentous thalli though parenchymatous to morphologically complex, indicated significantly lower values of dN for filamentous species (Figure S5E). This observation suggests that the

emergence of larger, more complex brown algae may have resulted in reduced effective population sizes and consequently weaker counter-selection of non-synonymous substitutions.²⁴

The diversification of the brown algae in terms of developmental complexity and life cycle structure was associated with modifications to reproductive systems, including, for example, partial or complete loss of flagella from female gametes in oogamous species and more subtle modifications such as loss of the eyespot in several kelps or of the entire posterior flagellum in *Dictyota dichotoma*.⁴⁹,⁵⁰ Interestingly, these latter modifications are correlated with loss of the *HELMCHROME* gene, which is thought to be involved in light reception and zoid phototaxis,⁴¹ from these species (Figures 3A and 4B). In addition, an analysis of the presence of genes for 70 high-confidence flagellar proteins⁴¹ across eight species with different flagellar characteristics identified proteins that correlate with presence or absence of the eyespot or of the posterior flagellum (Figure 4B; Table S4H).

## Brown algal diversification and the emergence of marine forests was also associated with genomic changes affecting metabolic and signaling pathways

Forests of brown algae (i.e., Laminariales, Desmarestiales, Tilopteridales, and Fucales⁵¹) are a key aspect of the modern marine biosphere. One of the pivotal innovations related to their emergence was a new developmental tissue, an intercalary meristem situated in the zone between the stipe and the lamina. The presence of this tissue is an ancestral state of the brown algal crown radiation (BACR) clade, and this study indicates that the intercalary meristem was acquired as early as 190 mya (Figure 1). This type of intercalary meristem would have facilitated the transition from annual to perennial life history and would, therefore, have been important for the establishment and maintenance of marine forests, particularly when upper parts of thalli are subjected to heavy grazing pressure.¹⁰ Our results indicate that the Desmarestiales, Tilopteridales, and Fucales were all present by the early Cretaceous (Figure 1). Thus, it is possible that brown algal forests, at least at a small scale, provided both nutrients and shelter

### Figure 3. Gene family evolution during the emergence of the brown algal lineage and a focus on carbohydrate metabolism

(A) Variations in size for a broad range of key gene families in the brown algae and closely related taxa. Numbers indicate the size of the gene family. Note that the *S. ischiensis* algal-type HPOs appear to be intermediate between classes I and II. Brown tree branches, Phaeophyceae.

(B) Overview of information from the orthogroup Dollo analysis, the phylostratigraphy analysis, the horizontal gene transfer analysis and the gene family amplification analysis for a selection of cell-wall active protein (CWAP) families. Dots represents functional family/orthogroup couples, with the size being proportional to the number of proteins annotated in the orthogroup (OG), and the color representing the proportion of the functional annotation that falls into this OG. Phaeophyceae plus Schizocladiophyceae (PS) and FDI clade, identified as gene innovation stages, are highlighted in brown. Functional categories with interesting evolutionary histories are highlighted in red.

(C) Phylogenetic tree of mannuronan C5-epimerases (ManC5-E). The phylogeny on the left, with three clusters indicated, is representative of the global view on the right.

(D) Phylogenetic tree of the polysaccharide lyase 41 (PL41) family. Green squares, biochemically characterized proteins. Brown algal sequences are color-coded in relation to their taxonomy, as indicated in (C). Schizocladiophyceae sequences are shown in red and with a red circle.

P, present; A, absent; CAZymes, carbohydrate-active enzymes; HPO, vanadium haloperoxidase; PKS, type III polyketide synthase; TAPs, transcription-associated proteins; EsV-1-7, EsV-1-7 domain proteins; DNMT, DNA methyltransferase; GTs, glycosyltransferases; GHs, glycoside hydrolases; ARF, auxin response factor-related; bHLH, basic-helix-loop-helix; HMG, high mobility group; Zn-clus, zinc cluster; C2H2, C2H2 zinc finger; GNAT, Gcn5-related N-acetyltransferase; SNF2, sucrose nonfermenting 2; LRR, leucine-rich repeat; QAD, β-propeller domain; RK, membrane-localized receptor kinase; HK, histidine kinase; CHASE, cyclases/histidine kinases associated sensory extracellular domain; EBD, ethylene-binding-domain-like; MASE1, membrane-associated sensor 1 domain; DEK1, defective kernel1; MCU, mitochondrial calcium uniporter; GLR, glutamate receptor; pLGIC, pentameric ligand-gated ion channel; TRP, transient receptor potential channel; IMM, IMMEDIATE UPRIGHT; H3, histone H3; MAS, mastigoneme proteins; AA, auxiliary activity; ECT, Ectocarpales; LAM, Laminariales; FUC, Fucales; DES, Desmarestiales.

See also Figure S5.
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<figure><img src="image_24.png" /><figcaption>**Figure 4. Evolution of key gene families during the emergence of the brown algal lineage**</figcaption></figure>

(A) Evolution of type III polyketide synthase (PKS) genes in the stramenopiles (left). Right: condensed view of a phylogenetic reconstruction tree of stramenopile PKS III and closely related sequences. In brackets: number of sequences identified in each phylogenetic group. Bootstrap values are indicated.

(B) Loss of orthogroups corresponding to flagellar proteome components⁴¹ in eight brown algal species from five orders. For the zoid drawings: gray, nucleus; yellow, chloroplast; blue, anterior flagella with mastigonemes; red, eyespot. The posterior flagellum is shown either in green to indicate the presence of green autofluorescence correlated with the presence of the eyespot or in blue in species without an eyespot. Bars below the heatmap indicate gene losses associated with loss of just the eyespot (orange) or of the entire posterior flagellum (blue).

for the marine herbivorous animals that became common during the Cretaceous Period (e.g., algae-eating echinoids, sea turtles, and euteleostean fish⁵²,⁵³).

While our estimates of kelp antiquity are earlier than those of Starko et al.,⁵⁴ they are consistent with their suggestion that Cenozoic cooling facilitated the geographic expansion of the kelp forest ecosystem. Indeed, many of the animals found today

in kelp forest ecosystems originated toward the end of the Cretaceous Period, or later.⁵⁵ Currently, our understanding of Mesozoic marine noncalcified macroalgae on the basis of fossils⁵⁶⁻⁵⁸ is too poor to provide much guidance in this regard, but documentation by Kiel et al.⁵⁵ of fossil holdfasts indicates that kelp forests were present by the late Paleogene period (~32 mya). The highly complex, multi-layered, and canopy-forming kelp
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forests of today, however, seem to have emerged only relatively recently, during the mid-Neogene, following the expansion of cooler water shelf environments.⁵⁴,⁵⁵

Comparative analysis of the Phaeoexplorer genome dataset identified a number of gene family expansions that potentially played important roles in the adaptation of the brown algae to their diverse niches and, more particularly, in the emergence of large, forest-forming species such as the kelps. For example, the ManC5-E family expanded markedly in the Laminariales and Fucales (Figure 3C), the two main orders that constitute extant phaeophycean forests. The capacity of ManC5-E to modify organ flexibility³ may therefore have been an important factor for large organisms coping with the harsh hydrodynamic conditions of coastal environments.⁵⁹ In addition, five different orthogroups containing proteins with the mechanosensor wall stress-responsive component (WSC) domain were identified as having increased in size during the diversification of the brown algal lineage (Table S3), indicating that metabolic innovations affecting cell walls may have been concomitant with a complexification of associated signaling pathways.

Haloperoxidase gene families expanded independently in several brown algal orders, again with expansions being particularly marked in the Fucales and the Laminariales (Figures 3A and S5C). In the Laminariales, the algal type I family are specialized for iodine rather than bromine,⁶⁰ and this may have been an innovation that occurred specifically within the Laminariales, resulting in a halogen metabolism with an additional layer of complexity.

One of the proposed roles of halogenated molecules in brown algae is in biotic defense¹ and, clearly, an effective defense system would have been an important prerequisite for the emergence of the large, perennial organisms that constitute marine forests. Additional immunity-related families⁶¹ that expanded during the diversification of the brown algae include five orthogroups that contain either GTPases with a central Ras of complex proteins/C-terminal of Roc domain tandem (ROCO GTPases) or nucleotide-binding adaptor shared by apoptotic protease-activating factor 1, R proteins, and CED-4 tetratricopeptide repeat (NB-ARC-TPR) genes (Table S3).

Finally, one of the most remarkable gene family amplifications detected in this study was for proteins containing the EsV-1-7 domain, a short, cysteine-rich motif that may represent a novel class of zinc finger.⁶² EsV-1-7 domain proteins are completely absent from animal and land plant genomes and most stramenopiles either have just one member (oomycetes and eustigmatophytes) or entirely lack this gene family.⁶² Analysis of the Phaeoexplorer data (Figure 3A; Table S4I) indicated that the EsV-1-7 gene family started to expand in the common ancestor of the brown algae and the raphidophyte *H. akashiwo*, with 31–54 members in the non-Phaeophyceae taxa that share this ancestor. Further expansion of the family then occurred in most brown algal orders, particularly in some members of the Laminariales (234 genes in *Saccharina latissima*) and the Fucales (335 genes in *Ascophyllum nodosum*), with the genes tending to be clustered in tandem arrays (Tables S3 and S4I). These observations are consistent with the previous description of a large EsV-1-7 domain family (95 genes) in *Ectocarpus* species⁷,⁶² and with recent observations by Nelson et al.²⁰ One member

of this family, IMMEDIATE UPRIGHT (IMM), has been shown to play a key role in the establishment of the elaborate basal filament system of *Ectocarpus* sporophytes,⁶² suggesting that EsV-1-7 domain proteins may be novel developmental regulators in brown algae. Orthologs of the *IMM* gene were found in brown algal crown group taxa and in *D. dichotoma* but not in *D. mesarthrocarpum* (Figure 3A; Table S4I), indicating that this gene originated within the EsV-1-7 gene family as the first brown algal orders started to diverge.

## Recent evolutionary events within the genus *Ectocarpus*

The above analyses focused on deep-time evolutionary events related to the emergence of the Phaeophyceae and the later diversification of the brown algal orders during the Mesozoic. To complement these analyses an evaluation of relatively recent and ongoing evolutionary events in the brown algae was conducted by sequencing 22 new strains from the genus *Ectocarpus*, which originated about 19 mya (Figure S2C).

A phylogenetic tree was constructed for 11 selected *Ectocarpus* species based on 261 high-quality alignments of 1:1 orthologs (Figure 5A). The tree indicates substantial divergence between *E. fasciculatus* and two well-supported clades, designated clade 1 and clade 2. Incongruencies between the species tree and trees for individual genes indicated introgression events and/or incomplete lineage sorting across the *Ectocarpus* genus. D-statistic analysis, specifically ABBA-BABA tests, detected incongruities among species quartets, indicating potential gene flow at various times during the evolution of the *Ectocarpus* genus. Evidence for gene flow was particularly strong for clade 2 and there was also evidence for marked exchanges between the two clades (Figure 5B), suggesting that gene flow has not been limited to recently diverged species pairs. These findings suggest a complex evolutionary history involving rapid divergence, hybridization, and introgression among species within the *Ectocarpus* genus, with evidence for hybridization occurring between 10.5 (for clades 1 and 2) and 3.3 mya (for *Ectocarpus* species 5 and 7) based on the fossil-calibrated tree (Figure S2C). A similar scenario has been reported for the genus *Drosophila*,⁶³ suggesting that recurrent hybridization and introgression among species may be a common feature associated with rapid species radiations. Major environmental changes such as the expansion of cold-water coastal areas following the green-house/cold-house Eocene-Oligocene transition (~30 mya⁶⁴), and particularly the rapid climate destabilization and temperature drop associated with the end of the mid-Miocene thermal maximum (~15 mya⁶⁴), may have created many new opportunities for the rapid expansion and diversification of the *Ectocarpus* genus.

## Brown algal genomes contain large amounts of inserted viral sequences

A particularly striking result of this study was the identification of extensive amounts of integrated DNA sequence corresponding to large DNA viruses of the *Phaeovirus* family (Figure 6A; Table S5), which integrate into brown algal genomes as part of their lysogenic life cycles.⁶⁵ Analysis of 72 genomes in the Phaeoexplorer and associated public genome dataset identified a total of 792 viral regions (VRs) of *Nucleocytoviricota* (NCV) origin in 743 contigs, with a combined length of 32.3 Mbp.
<figure><img src="image_26.png" /><figcaption>**Figure 5.** Evidence for gene flow within the genus *Ectocarpus*</figcaption></figure>

(A) DensiTree visualisation of gene trees (gray lines) for 261 orthologs shared by 11 *Ectocarpus* species and the outgroup species *S. promiscuus*, together with the consensus species tree (black lines). All nodes of the species tree have posterior probabilities greater than 0.99.

(B) Boxplot reporting D-statistic (Patterson's D) values between P2 and P3 species. Within-lineage comparisons (i.e., within clades 1 and 2) and between-lineage comparisons are distinguished on the x axis. The annotation of each dot indicates species that were designated as P2 and P3. *Ectocarpus fasciculatus* was defined as the outgroup.

See also Figure S2.

Individual VRs ranged in size from two to 705 kbp, but the majority (81.3%) were between two and 50 kbp, while only 9% were longer than the expected minimum size (100 kbp) for an NCV genome. At least one flanking region could be identified for 40.8% of the VRs, providing direct evidence for insertion of the sequence in the algal genome (Table S5C). Figure 6B shows three examples of long VRs. Most genes in VRs are monooexonic and transcriptionally silent, as previously observed for the 310 kbp VR in the *Ectocarpus* species 7 strain Ec32 genome.¹¹

On average, each of the 72 analyzed genomes contained 469 kbp of VR (with a maximum of 5,614 kbp) and only two genomes contained no VRs (both from the Discosporangiales). There were a number of outlier genomes that contained more than 1 Mbp of VRs (*T. minus*, *S. latissima*, *S. japonica*, *P. fluviatile*, and *Myriotrichia clavaeformis* male and female). At least one partial provirus (a VR possessing several key NCV marker genes) was present in 39 genomes, 29 of which had at least one full provirus with a complete set of seven key NCV marker genes (Figure 6A; Table S5). In addition to the previously known infections in Ectocarpales⁶⁵ and Laminariales,⁶⁶ integrated NCV proviruses were found in all Phaeophyceae orders screened, except the Discosporangiales and Dictyotales, and were also detected in *T. minus* (Xanthophyceae). Moreover, NCV marker gene composition indicated that multiple integrated proviruses were present in 16 genomes from multiple Phaeophyceae orders (Ectocarpales, Desmarestiales, Sphacelariales, Tilopteridales, and Laminariales), and the Xanthophyceae (Figure 6A; Table S5). Phylogenetic analysis of the major capsid protein (MCP) and DNA polymerase genes indicated that the majority of the integrated NCVs belonged to the genus *Phaeovirus*, the sole viral group known to infect brown algae (Figures S6A and S6B). However, this analysis also revealed integrated sequences corresponding to other viral groups. Viral sequences in *T. minus* belonged to a putative novel genus closely related to *Phaeovirus*, for which we propose the name *Xanthovirus*. Finally, mimiviridae-related VRs were identified in *S. latissima* and *Pelvetia canaliculata*, but since they are partial proviruses

and do not appear to possess integrase genes, they may have originated from ancient endogenization events, similar to those described in chlorophytes.⁶⁷

The identification of integrated NCVs across almost all brown algal orders and in closely related outgroup taxa suggests that the lysogenic life cycle strategy of phaeoviruses is ancient and that giant viral genomes have been integrating into the genomes of brown algae throughout the latters' evolutionary history. This conclusion was supported by the phylostratigraphic analysis, which detected the appearance of many novel virus-related genes dating back to the origin of the Phaeophyceae (Figure S3A). Marked differences were detected in total VR size and NCV marker gene presence across the brown algal genome set, and large differences were even detected between strains from the same genus (between 24 and 992 kbp of VR in different *Ectocarpus* spp. for example; Figure 6A; Table S5). These differences indicate dynamic changes in VR content over evolutionary time, presumably due, at least in part, to differences in rates of viral genome integration, a process that can involve multiple, separate insertion events,⁶⁸ and rates of VR loss due to meiotic segregation.⁶⁹ In addition, the abundant presence of partial proviruses and NCV fragments in brown algal genomes indicates that inserted VRs can degenerate and fragment, probably also leading to VR loss over time. The identification of large-scale viral genome insertion events over such a long timescale (at least 450 mya¹) suggests that NCVs may have had a major impact on the evolution of brown algal genomes throughout the emergence of the lineage.

The widespread presence of large quantities of viral genes in brown algal genomes creates a favorable situation for recruitment of this genetic information by the algal host via HGT (provided the acquired genes confer a selective advantage⁷⁰), but clear evidence of this type of HGT event can be difficult to obtain. However, phylogenetic evidence indicates that several *Ectocarpus* species 7 histidine kinases (HKs) were derived by HGT from viral insertions⁷¹ and analysis of the Phaeoexplorer genomes supported this hypothesis. HKs are widespread in the
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stramenopiles but several classes of membrane-localized HK were either only found in brown algae (cyclases/histidine kinases associated sensory extracellular [CHASE] domain HKs and HKs with an extracellular domain resembling an ethylene binding motif<sup>71</sup>) or only in brown algae and closely related taxa (membrane-associated sensor 1 [MASE1] domain HKs<sup>71</sup>) and appear to be absent from other stramenopile lineages (Figure 3A; Table S4J). These classes of HK all exhibit a patchy pattern of distribution across the brown algae and are often monooexonic suggesting possible multiple acquisitions from viruses via HGT following integration of viral genomes into algal genomes (Figures 3A and S6C). Phylogenetic analysis provided further support for a HGT origin for these classes of HK (Figure S6C).

DISCUSSION

Comparative analysis of the genome resource presented in this study has provided insights into genome evolution across the entire evolutionary history of the brown algae. A period of marked genome evolution concomitant with the emergence of the brown algal lineage during the GOBE was correlated with an increase in multicellular complexity, possibly driven, at least in part, by increases in atmospheric oxygen and herbivory. During this period, the brown algae acquired key components of several metabolic pathways, notably cell-wall polysaccharide, phlorotannin, and halogen metabolisms, that were essential for their colonization of intertidal and subtidal environments. The capacity to synthesize flexible and resilient alginate-based cell walls<sup>72</sup> allows these organisms to resist the hydrodynamic forces of wave action,<sup>59</sup> whereas phlorotannins and halogen derivatives are thought to play important roles in defense.<sup>73</sup> There is also evidence that cell-wall cross-linking by phlorotannins may be important for strong adhesion to substrata, another important characteristic in the dynamic intertidal and subtidal coastal environments.<sup>74</sup> The capacity to adhere strongly and resist both biotic and abiotic stress factors would prove essential for the success of large, sedentary multicellular organisms in these intertidal niches over evolutionary time.

The period of increased gene gain during the emergence of the brown algae was followed by a period of overall gene loss that extended up until the present day (Figure S2B). Interestingly, similar periods of ancestral gene gain followed by gene loss have also been observed for both the animal and land plant lineages,<sup>75</sup> indicating that this may be a common feature of multicellular eukaryotic lineages.

About 220 mya after the emergence of the brown algae, the aftermath of the Permian-Triassic mass extinction event and the initiation of Pangea rifting appear to have created favorable environments for rapid diversification of the main brown algal orders,<sup>44,45</sup> resulting in the emergence of a diversity of developmental, life cycle, and reproductive strategies, with correlated effects on genome evolution. During this period some orders, such as the Laminariales and Fucales, acquired characteristics such as an intercalary meristems and modified metabolic, defense, and developmental processes that are predicted to have been important prerequisites for the emergence of marine forests.

Analysis of the genomes of multiple *Ectocarpus* species demonstrated that genomic modifications, including gene gain and gene loss have continued to occur up until the present time and indicated that these modifications can potentially be transmitted between species as a result of gene flow occurring within a genus due to incomplete reproductive boundaries and introgression.

Finally, one of the most surprising observations was that brown algal genomes contain many inserted viral sequences corresponding to large DNA viruses of the *Phaeovirus* family. Inserted viral sequences are widespread in eukaryotic genomes<sup>76,77</sup> and insertions corresponding to nucleocytoplasmic large DNA viruses have been found in green algal genomes<sup>67,78</sup> but the brown algal *Phaeovirus* VRs are remarkable because they are nearly ubiquitous in this lineage (being present in 67 of 69 brown algal genomes analyzed) and because individual genomes can contain several phylogenetically diverse *Phaeovirus* insertions and insertions of a broad range of different sizes. The near ubiquitous occurrence of these elements may be attributed to the capacity of phaeoviruses to insert into their hosts' genomes as part of their life cycle.

The above observations illustrate how the Phaeoexplorer genome dataset, along with the various analyses carried out in this study, can be used to link the gene content of brown algal genomes to biological processes and characteristics that have played fundamental roles during the evolution of this lineage. The establishment of this genome resource represents an important step forward for a key lineage that has remained poorly characterized at the genome level. The Phaeoexplorer dataset not only provides good quality genome assemblies for many, previously uncharacterized brown algal species but also represents a tool to explore genome function via comparative genomics approaches, adding an important evolutionary dimension to efforts to understand gene function in this lineage. The identification and analysis of key metabolic and signaling genes implicated

<caption>**Figure 6. Inserted viral regions in brown algal genomes**</caption>

(A) Annotated phylogeny summarizing key statistics of the presence of *Nucleocytoviricota* (NCV) sequences in the genomes of brown algae and closely related taxa. Eight genomes sourced from public databases are labeled with an asterisk. Outer layers around the tree are as follows (1) NCV genotypes in each genome, (2) NCV core gene count indicates the number of copies of each viral core gene (A32, A32 packaging ATPase; D5/D5p, D5 helicase/primase; MCP, major capsid protein; POLB, DNA polymerase B; SF2, superfamily 2 helicase; VL3, very late transcription factor 3; RNR, ribonucleotide reductase; inC, integrase recombinase; inS, integrase resolvase), (3) count of viral regions is the number of viral regions within each size range category as indicated, (4) count of proviruses is the estimated number of complete or partial integrated viral genomes in a genome, (5) total viral region length is the sum of the lengths in kbp of all viral regions within a genome. The outermost layer indicates the taxonomic class or order of the host clades.
(B) Three examples of contigs containing large viral insertions (pink shading). Genes (colored boxes) were classified as viral, cellular (i.e., cellular organism), known proteins of unclear origin (viral or cellular) or unknown (ORFan) based on comparisons with viral and cellular protein databases (see STAR Methods). Transcript abundances are shown with a locally estimated scatterplot smoothing (LOESS) plot. Exons, exons per gene. See also Figure S6.
in a broad range of brown algal biological functions represents an important resource for future research programs aimed at optimizing brown seaweed production in a mariculture context or at preserving and protecting natural seaweed populations in the context of climate change. Both of these approaches could potentially contribute to mitigation of the effects of climate change via multiple positive effects in terms of carbon capture, ecosystem services, and the promotion of highly sustainable cultivation practices.

To facilitate future use of this genome dataset, the annotated genomes have been made available through a website portal (https://phaeoexplorer.sb-rostoff.fr). The existing genome dataset provides very good coverage of the phylogenetic diversity of the Phaeophyceae and reasonably complete gene catalogs for each species, but future work is needed to improve further the quality of the genome assemblies described here and to add genomes for additional species, particularly members of the minor brown algal orders that are not represented in the dataset. The large proportion of genes with no predicted function in brown algal genomes is also a limitation that needs to be addressed. The recent development of CRISPR-Cas9 methodology for brown algae,⁷⁹,⁸⁰ together with the other tools and resources currently available for the model brown alga *Ectocarpus*,⁸¹ provide the means to deploy the functional genomics approaches necessary to address this question.

## RESOURCE AVAILABILITY

### Lead contact

Further information and requests for resources and reagents should be directed to and will be fulfilled by the lead contact, J. Mark Cock (cock@sb-rostoff.fr).

### Materials availability

All the laboratory-cultivated strains grown to provide material for genome sequencing can be accessed via the Roscoff Culture Collection (https://www.rostoff-culture-collection.org).

### Data and code availability

* All sequence data, including DNA and RNA sequencing data, genome assemblies, and annotations, have been deposited in the European Bioinformatics Institute/European Nucleotide Archive (EBI/ENA) database under the project accession PRJEB76691 and are publicly available. Additional data and results have been deposited in the CNRS Research Data depository (https://doi.org/10.57745/9U1J85) and are publicly available.

* This paper does not report original code.

* Any additional information required to reanalyze the data reported in this paper is available from the lead contact upon request.

## ACKNOWLEDGMENTS

This work was supported by the France Génomique National Infrastructure project Phaeoexplorer (ANR-10-INBS-09), the European Research Council project Sexsea (638240), the Investissements d'Avenir project Idealg (ANR-10-BTBR-04-01), the European BG-01 BlueGrowth H2020 project Genialg (727892), Laoshan Laboratory grants (LSKJ202203801, LSKJ202203204), the Taishan Scholars Program and China Agriculture Research System (CARS-50), the CNRS international research network DABMA (00022), the ANR projects Epicycle (ANR-19-CE20-0028-01), BrownSugar (ANR-20-CE44-0011), HaloGene (ANR-22-CE20-0025), Seabioz (ANR-20-CE43-0013) and BrownLincs (ANR-23-CE20-0048-01), the National Research Foundation

of Korea (2022R1A2B5B03002312, 2022R1A5A1031361) and Ministry of Oceans and Fisheries (KIMST-20180430) granted to H.S.Y., the projects Connect Talent EpiAlg Région Pays de la Loire-Nantes Métropole and Etoiles Montantes M-EpiCC Région Pays de la Loire, the MITI-funded project Algometa-bionte, Dr. Karl Feldbausch-Stiftung, the CNRS, and Sorbonne University. We are grateful to the Roscoff Bioinformatics platform ABiMS (http://abims.sb-rostoff.fr), which is part of the Institut Français de Bioinformatique (ANR-11-INBS-0013) and BioGenouest network, for providing both help and computing and storage resources.

## AUTHOR CONTRIBUTIONS

Software, L.B.-G., R.D., A.L.B., X.L., D.N., and E.C.; formal analysis, F.D., O.G., L.D., D.M., T.M., D. Sussfeld, X.F., L. Mazéas, N. Terrapon, J.B.-R., R.P., L.R., S.-W.C., J.J., K.U., K.B., C. Duc, P.R., A.L., E.A.M., M.L., A.K., P.H.-G., C.V., S.S.A., S.A., K.A., Y.B., T. Barbeyron, A.B., S.B., A.B.-C., A. Cormier, H.C.d.C., A.D., E. Dinatale, S.D., E. Drula, J.G., L.G., A.G., M.-L.G., L.H., B.H., A.J., E.K., A.H.K., C.L., L.L.G., R.L., S.T.L., P.J.L., E.M., S.M., G.M., C.N., S.A.R., E.R., D. Schroeder, A.S., L.T., T.T., K.V., H.V., G.W., H.K., A.F.P., H.S.Y., C.H., N.Y., E.B., M.V., G.V.M., E.C., S.M.C., J.-M.A., and J.M.C.; investigation, C.C., S.H., Z.N., N. Tadrent, A. Couloux, B.N., W.B., E. Denis, C.J., L. Mest, S.R., and D. Scornet; resources, O.G., A. Couloux, L.B.-G., T. Bringloe, R.A.C., C. Destombe, S.F., W.J.H., G.H., K.K., A.L.B., K.M., C.M., N.P., P.P., S.R., D. Scornet, H.V., F.W., H.K., A.F.P., M.V., E.C., and J.-M.A.; data curation, O.G., C.C., A. Couloux, L.B.-G., J.-M.A., and J.M.C.; writing – original draft, F.D., O.G., J.-M.A., and J.M.C.; writing – review and editing, all authors; visualization, F.D., O.G., L.B.-G., L.D., D.M., T.M., D. Sussfeld, X.F., L. Mazéas, J.B.-R., R.P., L.R., K.U., K.B., P.R., A.L., M.L., P.H.-G., C.V., A.D., A.L.B., H.K., E.C., and J.M.C.; supervision, O.G., N. Terrapon, M.L., R.A.C., H.C.d.C., O.D.C., S.D., G.H., A.J., C.B., E.P., P.L., S.A.R., A.S., L.T., H.V., G.W., H.K., H.S.Y., C.H., N.Y., E.B., M.V., G.V.M., E.C., S.M.C., J.-M.A., and J.M.C.; project administration, F.D., E.B., M.V., G.V.M., E.C., S.M.C., P.W., J.-M.A., and J.M.C.; funding acquisition, H.C.d.C., C.B., P.P., C.H., N.Y., S.M.C., and J.M.C.; conceptualization, J.M.C.

## DECLARATION OF INTERESTS

The authors declare no competing interests.

## STAR★METHODS

Detailed methods are provided in the online version of this paper and include the following:

* KEY RESOURCES TABLE

* EXPERIMENTAL MODEL DETAILS

* *Asophyllum nodosum*

* *Chordaria linearis* strain ClinC8C

* *Choristocarpus tenellus* strain KU-1152

* *Chrysoparadoxa australica* strain CS-1217

* *Cladosiphon okamuranus* strain S-strain

* *Desmarestia dudresnayi* strain DdudBR16

* *Desmarestia herbacea* strain DmunF

* *Desmarestia herbacea* strain DmunM

* *Dictyota dichotoma* strain KB07f IV

* *Dictyota dichotoma* strain ODC1387m

* *Dictyota dichotoma* strain KB07m IV

* *Dictyota dichotoma* strain KB07sp VI

* *Discosporangium mesarthrocarpum* strain MT17-79

* *Ectocarpus crouaniorum* strain Ec861

* *Ectocarpus crouaniorum* strain Ec862

* *Ectocarpus fasciculatus* strain Ec846

* *Ectocarpus fasciculatus* strain Ec847

* *Ectocarpus fasciculatus* strain EfasUO1

* *Ectocarpus fasciculatus* strain EfasUO2

* *Ectocarpus siliculosus* strain Ec863

* *Ectocarpus siliculosus* strain Ec864

* *Ectocarpus* species 1 strain Ec sil Puy CHCH Z9 G5f
* *Ectocarpus* species 1 strain Ec sil Puy CHCH Z9 G3m

* *Ectocarpus* species 1 strain Ec03

* *Ectocarpus* species 12 strain Ec fas CH92 Nie 2f

* *Ectocarpus* species 12 strain Ec fas CH92 Nie 3m

* *Ectocarpus* species 13 strain EcNAP12-S#4-19m

* *Ectocarpus* species 2 strain Ec06

* *Ectocarpus* species 3 strain Ec10

* *Ectocarpus* species 3 strain Ec11

* *Ectocarpus* species 5 strain Ec13

* *Ectocarpus* species 5 strain Ec12

* *Ectocarpus* species 6 strain EcLAC-371f

* *Ectocarpus* species 7 strain Ec32

* *Ectocarpus* species 8 strain EcLAC-412m

* *Ectocarpus* species 9 strain EcSCA-722f

* *Ectocarpus* subulatus strain Bft15b

* *Feldmannia mitchelliae* strain KU-2106 Giff mitch BNC GA

* *Fucus distichus*

* *Fucus serratus*

* *Fucus serratus*

* *Halopteris paniculata* strain Hal grac a UBK

* *Hapterophycus canaliculatus* strain Oshoro5f

* *Hapterophycus canaliculatus* strain Oshoro7m

* *Hapterophycus canaliculatus* strain Oshoro 3F x 9M

* *Hapterophycus canaliculatus* strain Oshoro 4F x 9M

* *Hapterophycus canaliculatus* strain Oshoro 6F x 6M

* *Heribaudiella fluviatilis* strain SAG. 13.90

* *Heterosigma akashiwo* strain CCMP452

* *Himanthalia elongata*

* *Laminaria digitata* strain LdigPH10-18mv

* *Laminarionema elsbetiae* strain ELsaHSoW15

* *Macrocystis pyrifera* strain P11A1

* *Macrocystis pyrifera* strain P11B4

* *Myriotrichia clavaeformis* strain Myr cla04

* *Myriotrichia clavaeformis* strain Myr cla05

* *Myriotrichia clavaeformis* strain Myr cla12

* *Pelvetia canaliculata*

* *Phaeothamnion wetherbeei* strain SAG 119.79

* *Pleurocardia lacustris* strain SAG 25.93

* *Porterinema fluviatile* strain SAG 2381

* *Pylaiella littoralis* strain U1.48

* *Pylaiella littoralis* strain F24

* *Saccharina japonica* strain Ja

* *Saccharina latissima* strain SLPER63f7

* *Saccorhiza dermatodea* strain SderLü1190fm

* *Saccorhiza polyschides* strain SpolBR94f

* *Saccorhiza polyschides* strain SpolBR94m

* *Saccorhiza polyschides*

* *Sargassum fusiforme*

* *Schizocladia ischiensis* strain KU-0333

* *Scytosiphon promiscuus* strain 000310-Muroran-5-female

* *Scytosiphon promiscuus* strain Ot110409-Otamoi-16-male

* *Scytosiphon promiscuus* strain SXS107

* *Sphacelaria rigidula* strain Sph rig Cal Mo 4-1-68b

* *Sphacelaria rigidula* strain Sph rig Cal Mo 4-1-G3b

* *Sphacelaria rigidula* strain Sph rig Cal Mo SP

* *Sphaerotrichia firma* strain ET2f

* *Sphaerotrichia firma* strain Sfir13m

* *Tribonema minus* strain UTEX B 3156

* *Undaria pinnatifida* strain Kr2015

## METHOD DETAILS

* Biological material

* DNA extraction

* Illumina library preparation and sequencing

* Oxford Nanopore library preparation and sequencing

* RNA extraction, Illumina RNA-seq library preparation and sequencing

* Assembly strategies

* Assembly decontamination

* Transcriptome assembly

* De novo transcriptomes

* Detection and masking of repeated sequences and transposons

* Gene prediction

* Annotation decontamination

* Analyses aimed at deducing functional characteristics of predicted proteins

* Detection of tandemly duplicated genes

* Relative orientation of adjacent genes and lengths of intergenic regions

* Detection of long non-coding RNAs

* Intron conservation

* Phylogenomic tree of the Phaeophyceae

* Bayesian divergence time estimation for the brown algae

* Detection of orthologous groups

* Dollo analysis of orthogroup gain and loss

* Phylostratigraphy analysis

* Detection of gene family amplifications

* Composite genes

* Horizontal gene transfer (HGT)

* Gene codon usage, functional annotation and expression

* Comparative analysis of gene sets identified by genome-wide analyses of evolutionary history

* Detection of viral genome insertions and viral regions in algal genomes

* Phylogenetic analysis of viral genes

* Metabolic networks

* CAZymes

* Sulfatases

* Haloperoxidases

* Ion channels

* Membrane-localised proteins

* Transcription-associated proteins

* EsV-1-7 domain proteins

* Histones

* DNA methyltransferases

* Spliceosome

* Flagella proteins

* Detection of *Porterinema fluviatile* genes differentially expressed in freshwater and seawater

* Identification of genes with generation-biased expression patterns

* Life cycle and thallus architecture

* Assembly and analysis of organellar genomes

* Analysis of *Ectocarpus* genome synteny

* Analysis of *Ectocarpus* gene evolution

* Phylogenetic analysis of *Ectocarpus* species

* *Ectocarpus* introgression analysis

## QUANTIFICATION AND STATISTICAL ANALYSIS

## ADDITIONAL RESOURCES

## SUPPLEMENTAL INFORMATION

Supplemental information can be found online at https://doi.org/10.1016/j.cell.2024.10.049.

Received: February 19, 2024

Revised: July 20, 2024

Accepted: October 28, 2024

Published: November 20, 2024

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## STAR★METHODS

### KEY RESOURCES TABLE

<table><thead><tr><th>REAGENT or RESOURCE</th><th>SOURCE</th><th>IDENTIFIER</th></tr></thead><tbody><tr><td colspan="3"><strong>Biological samples</strong></td></tr><tr><td>Descriptions of all sequenced samples have been deposited in the EBI/ENA database</td><td>This study.</td><td>EBI/ENA project PRJEB76691</td></tr><tr><td colspan="3"><strong>Critical commercial assays</strong></td></tr><tr><td>OmniPrep Genomic DNA Purification Kit</td><td>G Biosciences, St. Louis, MO, USA</td><td>N/A</td></tr><tr><td>Nucleospin Plant II midi DNA Extraction Kit</td><td>Macherey-Nagel, Düren, Germany</td><td>N/A</td></tr><tr><td>NEBNext DNA Modules Products</td><td>New England Biolabs, Ipswich, MA, USA</td><td>N/A</td></tr><tr><td>NEBNext Sample Reagent Set</td><td>New England Biolabs, Ipswich, MA, USA</td><td>N/A</td></tr><tr><td>Ampure XP</td><td>Beckmann Coulter Genomics, Danvers, MA, USA</td><td>N/A</td></tr><tr><td>Kapa Hifi Hotstart NGS library Amplification kit</td><td>Roche, Basel, Switzerland</td><td>N/A</td></tr><tr><td>Short Read Eliminator Kit</td><td>Pacific Biosciences, Menlo Park, CA, USA</td><td>N/A</td></tr><tr><td>1D Genomic DNA by Ligation</td><td>Oxford Nanopore Technologies Ltd, Oxford, UK</td><td>SQK-LSK109, SQK-LSK108 or SQK-LSK110</td></tr><tr><td>Qiagen RNeasy kit or the Macherey Nagel RNAplus kit</td><td>Macherey-Nagel, Düren, Germany</td><td>N/A</td></tr><tr><td>TruSeq Stranded mRNA Sample Prep</td><td>Illumina</td><td>N/A</td></tr><tr><td>NEBNext Ultra II Directional RNA Library Prep for Illumina</td><td>New England BioLabs</td><td>N/A</td></tr><tr><td colspan="3"><strong>Deposited data</strong></td></tr><tr><td>The sequence data generated by this project is described in Table S1.</td><td>This study.</td><td>EBI/ENA: PRJEB76691</td></tr><tr><td>CNRS Research Data dataset "Data for Phaeoexplorer publication: Evolutionary genomics of the emergence of brown algae as key components of coastal ecosystems"</td><td>This study.</td><td>CNRS Research Data: https://doi.org/10.57745/9U1J85</td></tr><tr><td colspan="3"><strong>Experimental models: Organisms/strains</strong></td></tr><tr><td>The strains used for genome and transcriptome sequencing are listed in Table S1A.</td><td>Culture collection references are provided where relevant.</td><td>See strain names and culture collection accessions for identifiers.</td></tr><tr><td colspan="3"><strong>Software and algorithms</strong></td></tr><tr><td>MEGAHIT version 1.1.1</td><td>Li et al.<sup>82</sup></td><td>RRID:SCR_018551 https://github.com/voutcn/megahit</td></tr><tr><td>MetaGene version 2008.8.19</td><td>Noguchi et al.<sup>83</sup></td><td>http://metagene.cb.k.u-tokyo.ac.jp/</td></tr><tr><td>BLAST</td><td>Altschul et al.<sup>84</sup></td><td>RRID:SCR_004870 http://blast.ncbi.nlm.nih.gov/Blast.cgi</td></tr><tr><td>Burrows-Wheeler Aligner</td><td>Li and Durbin<sup>85</sup></td><td>RRID:SCR_010910 http://bio-bwa.sourceforge.net/</td></tr><tr><td>Bowtie2 version 2.3.5.1</td><td>Langmead and Salzberg<sup>86</sup></td><td>RRID:SCR_016368 http://bowtie-bio.sourceforge.net/bowtie2/index.shtml</td></tr><tr><td>SPAdes assembler version 3.8.1</td><td>Bankevich et al.<sup>87</sup></td><td>RRID:SCR_000131 https://cab.spbu.ru/software/spades/</td></tr><tr><td>filtlong</td><td>Wick, R.</td><td>RRID:SCR_024020 https://github.com/rrwick/Filtlong</td></tr><tr><td>Smartdenovo</td><td>Liu et al.<sup>88</sup></td><td>RRID:SCR_017622 https://github.com/ruanjue/smartdenovo</td></tr></tbody></table>

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<table><thead><tr><td>REAGENT or RESOURCE</td><td>SOURCE</td><td>IDENTIFIER</td></tr></thead><tbody><tr><td>Redbean</td><td>Ruan and Li<sup>89</sup></td><td>N/A</td></tr><tr><td>Flye</td><td>Kolmogorov et al.<sup>90</sup></td><td>RRID:SCR_017016 https://github.com/fenderglass/Flye</td></tr><tr><td>Necat</td><td>Chen et al.<sup>91</sup></td><td>https://github.com/xiaochuanle/necat</td></tr><tr><td>Racon</td><td>Vaser et al.<sup>92</sup></td><td>RRID:SCR_017642 https://github.com/isovic/racon</td></tr><tr><td>Hapo-G</td><td>Aury et al.<sup>93</sup></td><td>https://www.genoscope.cns.fr/hapog/</td></tr><tr><td>Metabat 2</td><td>Kang et al.<sup>94</sup></td><td>RRID:SCR_019134 https://bitbucket.org/berkeleylab/metabat/src/master/</td></tr><tr><td>SortMeRNA</td><td>Kopylova et al.<sup>95</sup></td><td>RRID:SCR_014402 http://bioinfo.lifl.fr/RNA/sortmerna/</td></tr><tr><td>Velvet version 1.2.07</td><td>Zerbino and Birney<sup>96</sup></td><td>RRID:SCR_010755 http://www.molecularevolution.org/software/genomics/velvet</td></tr><tr><td>Oases version 0.2.08</td><td>Schulz et al.<sup>97</sup></td><td>RRID:SCR_011896 http://www.ebi.ac.uk/~zerbino/oases/</td></tr><tr><td>TransDecoder</td><td>Haas, B.J.</td><td>RRID:SCR_017647 https://github.com/TransDecoder/TransDecoder</td></tr><tr><td>CDDsearch</td><td>Marchler-Bauer et al.<sup>98</sup></td><td>N/A</td></tr><tr><td>Trimmomatic version 0.38 and version 0.39</td><td>Bolger et al.<sup>99</sup></td><td>RRID:SCR_011848 http://www.usadellab.org/cms/index.php?page=trimmomatic</td></tr><tr><td>Trinity version version 2.6.5</td><td>Grabherr et al.<sup>100</sup></td><td>RRID:SCR_013048 http://trinityrnaseq.sourceforge.net/</td></tr><tr><td>rnaSPAdes version version 3.13.1</td><td>Bushmanova et al.<sup>101</sup></td><td>RRID:SCR_016992 http://cab.spbu.ru/software/rnaspades/</td></tr><tr><td>RepeatMasker version 4.1.0</td><td>Smit et al.<sup>102</sup></td><td>RRID:SCR_012954 http://repeatmasker.org/</td></tr><tr><td>Tandem repeats finder</td><td>Benson et al.<sup>103</sup></td><td>RRID:SCR_022193 https://github.com/Benson-Genomics-Lab/TRF</td></tr><tr><td>REPET</td><td>Flutre et al.<sup>104</sup></td><td>N/A</td></tr><tr><td>BLAT</td><td>Kent<sup>105</sup></td><td>RRID:SCR_011919 http://genome.ucsc.edu/cgi-bin/hgBlat?command(start</td></tr><tr><td>Genewise</td><td>Birney et al.<sup>106</sup></td><td>RRID:SCR_015054 http://www.ebi.ac.uk/Tools/psa/genewise/</td></tr><tr><td>DIAMOND version 0.9.30</td><td>Buchfink et al.<sup>107</sup></td><td>RRID:SCR_009457 http://www.nitrc.org/projects/diamond/</td></tr><tr><td>Est2Genome</td><td>Mott<sup>108</sup></td><td>https://galaxy-iuc.github.io/emboss-5.0-docs/est2genome.html</td></tr><tr><td>Gmove</td><td>Dubarry et al.<sup>109</sup></td><td>RRID:SCR_019132 http://www.genoscope.cns.fr/gmove</td></tr><tr><td>votingLNC</td><td>Debit, A.</td><td>https://gitlab.com/a.debit/votinglnc</td></tr><tr><td>AliView version 1.26</td><td>Larsson<sup>110</sup></td><td>RRID:SCR_002780 https://github.com/AliView</td></tr><tr><td>RAxML version 8.2.</td><td>Stamatakis<sup>111</sup></td><td>RRID:SCR_006086 https://github.com/stamatak/standard-RAxML</td></tr><tr><td>Tracer version 1.7.2</td><td>Rambaut et al.<sup>112</sup></td><td>RRID:SCR_019121 https://bioweb.pasteur.fr/packages/pack@Tracer@v1.6</td></tr><tr><td>OrthoFinder version 2.5.2</td><td>Emms and Kelly<sup>113</sup></td><td>RRID:SCR_017118 https://github.com/davidemms/OrthoFinder</td></tr><tr><td>Count version 9.1106</td><td>Csűös<sup>114</sup></td><td>https://www.iro.umontreal.ca/~csuros/gene_content/count.html</td></tr><tr><td>MUSCLE version 3.8.1551</td><td>Edgar<sup>115</sup></td><td>RRID:SCR_011812 http://www.ebi.ac.uk/Tools/msa/muscle/</td></tr></tbody></table>

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<table><thead><tr><td>REAGENT or RESOURCE</td><td>SOURCE</td><td>IDENTIFIER</td></tr></thead><tbody><tr><td>OD-Seq version 1.0</td><td>Jehl et al.<sup>116</sup></td><td>https://bioconductor.org/packages/release/bioc/manuals/odseq/man/odseq.pdf</td></tr><tr><td>HMMER3 package versions 3.1b1 and 3.3.2</td><td>Mistry et al.<sup>117</sup></td><td>RRID:SCR_005305 http://hmmer.janelia.org/</td></tr><tr><td>GenEra</td><td>Barrera-Redondo et al.<sup>118</sup></td><td>N/A</td></tr><tr><td>MCL</td><td>Enright et al.<sup>119</sup></td><td>RRID:SCR_024109 https://micans.org/mcl/</td></tr><tr><td>Foldseek</td><td>Kempen et al.<sup>120</sup></td><td>https://search.foldseek.com/search</td></tr><tr><td>CleanBlastp</td><td>Pathmanathan et al.<sup>121</sup></td><td>N/A</td></tr><tr><td>SEED</td><td>Overbeek et al.<sup>122</sup></td><td>RRID:SCR_002129 http://www.theseed.org/wiki/Home_of_the_SEED</td></tr><tr><td>IPR2GO</td><td>Paysan-Lafosse et al.<sup>123</sup></td><td>http://www.ebi.ac.uk/interpro/search/sequence-search</td></tr><tr><td>eggNOG</td><td>Huerta-Cepas et al.<sup>124</sup></td><td>RRID:SCR_002456 http://eggnog.embl.de</td></tr><tr><td>eggNOG-mapper</td><td>Cantalapiedra et al.<sup>125</sup></td><td>RRID:SCR_021165 http://eggnog-mapper.embl.de</td></tr><tr><td>Spearman's rank correlation analysis tool version 1.1.23-r7</td><td>P. Wessa, Free Statistics Software, Office for Research Development and Education</td><td>https://www.wessa.net/</td></tr><tr><td>Prodigal version 2.6.3</td><td>Hyatt et al.<sup>126</sup></td><td>RRID:SCR_011936 https://github.com/hyattpd/Prodigal</td></tr><tr><td>ViralRecall version 2.0</td><td>Aylward et al.<sup>127</sup></td><td>https://github.com/fayward/viralrecall</td></tr><tr><td>esl-translate version 0.48</td><td>Rivas, E.</td><td>https://github.com/EddyRivasLab/easel/blob/master miniapps/esl-translate man in</td></tr><tr><td>bedtools version 2.29.2</td><td>Quinlan and Hall<sup>128</sup></td><td>RRID:SCR_006646 https://github.com/arq5x/bedtools2</td></tr><tr><td>MMseqs cluster version 13.45111</td><td>Hauser et al.<sup>129</sup></td><td>RRID:SCR_008184 https://github.com/eturro/mmseq#mmseq-transcript-and-gene-level-expression-analysis-using-multi-mapping-rna-seq-reads</td></tr><tr><td>MAFFT v7</td><td>Katoh and Standley<sup>130</sup></td><td>RRID:SCR_011811 http://mafft.cbrc.jp/alignment/server/</td></tr><tr><td>MEGA</td><td>Tamura et al.<sup>131</sup></td><td>RRID:SCR_023017 https://www.megasoftware.net/</td></tr><tr><td>NGphylogeny platform</td><td>Lemoine et al.<sup>132</sup></td><td>https://ngphylogeny.fr/</td></tr><tr><td>TrimAl</td><td>Capella-Gutiérrez et al.<sup>133</sup></td><td>RRID:SCR_017334 http://trimal.cgenomics.org/</td></tr><tr><td>TAPscan version 4</td><td>Petroll et al.<sup>134,135</sup></td><td>https://plantcode.cup.uni-freiburg.de/tapscan/</td></tr><tr><td>Expasy web translator</td><td>Duvaud et al.<sup>136</sup></td><td>RRID:SCR_024703 https://web.expasy.org/translate/</td></tr><tr><td>Geneious versions 11.0.5 and 11.1.5</td><td>Geneious</td><td>RRID:SCR_010519 http://www.geneious.com/</td></tr><tr><td>Interproscan 94.0</td><td>Jones et al.<sup>137</sup></td><td>RRID:SCR_005829 http://www.ebi.ac.uk/Tools/pfa/iprscan/</td></tr><tr><td>Clustal 2.1</td><td>Thompson et al.<sup>138</sup></td><td>RRID:SCR_001591 http://www.ebi.ac.uk/Tools/msa/clustalo/</td></tr><tr><td>Gblocks</td><td>Castresana<sup>139</sup></td><td>RRID:SCR_015945 http://molevol.cmima.csic.es/castresana/Gblocks_server.html</td></tr><tr><td>Kallisto version 0.44.0.</td><td>Bray et al.<sup>140</sup></td><td>RRID:SCR_016582 https://pachterlab.github.io/kallisto/about</td></tr><tr><td>Deseq2</td><td>Love et al.<sup>141</sup></td><td>RRID:SCR_015687 https://bioconductor.org/packages/release/bioc/html/DESeq2.html</td></tr></tbody></table>

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<table><thead><tr><th>REAGENT or RESOURCE</th><th>SOURCE</th><th>IDENTIFIER</th></tr></thead><tbody><tr><td>FastQC</td><td>Andrews<sup>142</sup></td><td>RRID:SCR_014583 http://www.bioinformatics.babraham.ac.uk/projects/fastqc/</td></tr><tr><td>Trim Galore version 0.6.5</td><td>Krueger et al.<sup>143</sup></td><td>RRID:SCR_011847 http://www.bioinformatics.babraham.ac.uk/projects/trim_galore/</td></tr><tr><td>HISAT2 version 2.1.0</td><td>Kim et al.<sup>144</sup></td><td>RRID:SCR_015530 http://ccb.jhu.edu/software/hisat2/index.shtml</td></tr><tr><td>featureCounts</td><td>Liao et al.<sup>145</sup></td><td>RRID:SCR_012919 http://bioinf.wehi.edu.au/featureCounts/</td></tr><tr><td>PAML version 4.9i (including MCMCTree)</td><td>Yang<sup>146</sup></td><td>RRID:SCR_014932 http://abacus.gene.ucl.ac.uk/software/paml.html</td></tr><tr><td>phytools R package</td><td>Revell<sup>147</sup></td><td>RRID:SCR_015502 https://cran.r-project.org/web/packages/phytools/index.html</td></tr><tr><td>VHICA package</td><td>Wallau et al.<sup>148</sup></td><td>https://github.com/cran/vhica</td></tr><tr><td>NOVOPlasty version 3.7</td><td>Dierckxsens et al.<sup>149</sup></td><td>RRID:SCR_017335 https://github.com/ndierckx/NOVOPlasty</td></tr><tr><td>SAMtools version 1.5</td><td>Li et al.<sup>150</sup></td><td>RRID:SCR_002105 http://htslib.org/</td></tr><tr><td>GeSeq version 2.03</td><td>Tillich et al.<sup>151</sup></td><td>RRID:SCR_017336 https://chlorobox.mpimp-golm.mpg.de/geseq.html</td></tr><tr><td>ARAGORN version 1.2.38</td><td>Laslett and Canback<sup>152</sup></td><td>RRID:SCR_015974 http://mbio-serv2.mbioekol.lu.se/ARAGORN/</td></tr><tr><td>ModelFinder</td><td>Kalyaanamoorthy et al.<sup>153</sup></td><td>http://www.iqtree.org/ModelFinder/</td></tr><tr><td>UFBoot2</td><td>Hoang et al.<sup>154</sup></td><td>N/A</td></tr><tr><td>SynMap</td><td>Haug-Baltzell et al.<sup>155</sup></td><td>https://genomevolution.org/SynMap.pl</td></tr><tr><td>DAGChainer</td><td>Haas et al.<sup>156</sup></td><td>https://dagchainer.sourceforge.net/</td></tr><tr><td>CodeML</td><td>Yang et al.<sup>146</sup></td><td>N/A</td></tr><tr><td>nwalign</td><td>Pedersen, B</td><td>https://pypi.org/project/nwalign/</td></tr><tr><td>BEAST version 2.7</td><td>Bouckaert et al.<sup>157</sup></td><td>RRID:SCR_010228 http://beast.bio.ed.ac.uk/</td></tr><tr><td>StarBEAST3 version 1.1.7</td><td>Douglas et al.<sup>158</sup></td><td>https://github.com/rbouckaert/starbeast3</td></tr><tr><td>bModelTest</td><td>Bouckaert et al.<sup>159</sup></td><td>N/A</td></tr><tr><td>LogCombiner version 2.4.7</td><td>Bouckaert et al.<sup>157</sup></td><td>N/A</td></tr><tr><td>TreeAnnotator version 2.4.7</td><td>Bouckaert et al.<sup>157</sup></td><td>N/A</td></tr><tr><td>SplitsTree 4 version 4.14.6</td><td>Kloepper and Huson<sup>160</sup></td><td>RRID:SCR_014734 http://www.splittree.org/</td></tr><tr><td>Hectar</td><td>Gschloessl et al.<sup>161</sup></td><td>https://webtools.sb-roscoff.fr/root?tool_id=abims_hectar</td></tr><tr><td>RShiny</td><td>R Core Team<sup>162</sup></td><td>https://github.com/rstudio/shiny</td></tr><tr><td>IQ-TREE 2</td><td>Minh et al.<sup>163</sup></td><td>https://github.com/iqtree/iqtree2</td></tr><tr><td>Computational analysis of gene family evolution 5 (CAFE5)</td><td>Mendes et al.<sup>164</sup></td><td>https://github.com/hahnlab/CAFE5</td></tr><tr><td>clusterProfiler</td><td>Yu et al.<sup>165</sup></td><td>RRID:SCR_016884 http://bioconductor.org/packages/release/bioc/html/clusterProfiler.html</td></tr><tr><td>ggplot2</td><td>Wickham et al.<sup>166</sup></td><td>RRID:SCR_014601 https://cran.r-project.org/web/packages/ggplot2/index.html</td></tr><tr><td>tidyverse</td><td>Wickham et al.<sup>167</sup></td><td>RRID:SCR_019186 https://CRAN.R-project.org/package=tidyverse</td></tr><tr><td>Other</td><td></td><td></td></tr><tr><td>Benchmarking universal single-copy orthologue (BUSCO) analysis version 5, eukaryota_odb10</td><td>Manni et al.<sup>168</sup></td><td>RRID:SCR_015008 http://busco.ezlab.org/</td></tr></tbody></table>

(Continued on next page)
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<table><thead><tr><td>REAGENT or RESOURCE</td><td>SOURCE</td><td>IDENTIFIER</td></tr></thead><tbody><tr><td>UniRef90</td><td>Suzek et al.<sup>169</sup></td><td>RRID:SCR_010646 http://www.uniprot.org/help/uniref</td></tr><tr><td>AlphaFold protein structure database</td><td>Varadi et al.<sup>170</sup></td><td>RRID:SCR_023662 https://alphafold.ebi.ac.uk/</td></tr><tr><td>NCVOG database</td><td>Yutin et al.<sup>171</sup></td><td>N/A</td></tr><tr><td>VOGDB database</td><td>Trgovec-Greif et al.<sup>172</sup></td><td>https://vogdb.org/</td></tr><tr><td>SulfAtlas database</td><td>Barbeyron et al.<sup>172</sup>; Stam et al.<sup>173</sup></td><td>https://sulfatlas.sb-roscoff.fr/</td></tr><tr><td>Pfam</td><td>Mistry et al.<sup>174</sup></td><td>RRID:SCR_004726 http://pfam.xfam.org/</td></tr><tr><td>Panther 17.0</td><td>Thomas et al.<sup>175</sup></td><td>RRID:SCR_004869 http://www.pantherdb.org/</td></tr><tr><td>Simple Modular Architecture Research Tool (SMART)</td><td>Letunic et al.<sup>176</sup></td><td>RRID:SCR_005026 http://smart.embl.de/</td></tr></tbody></table>

EXPERIMENTAL MODEL DETAILS

Ascophyllum nodosum

Species: *Ascophyllum nodosum*
Strain: field collected sperm cells
Genotype: diploid
Sex: male
Maintenance: N/A

Chordaria linearis strain ClinC8C

Species: *Chordaria linearis*
Strain: ClinC8C
Genotype: haploid
Sex: monoicous
Maintenance: Maintained in culture

Choristocarpus tenellus strain KU-1152

Species: *Choristocarpus tenellus*
Strain: KU-1152
Genotype: unknown
Sex: unknown
Maintenance: Maintained in culture

Chrysoparadoxa australica strain CS-1217

Species: *Chrysoparadoxa australica*
Strain: CS-1217
Genotype: unknown
Sex: unknown
Maintenance: Maintained in culture

Cladosiphon okamuranus strain S-strain

Species: *Cladosiphon okamuranus*
Strain: S-strain
Genotype: diploid
Sex: n/a
Maintenance: N/A

Desmarestia dudresnayi strain DdudBR16

Species: *Desmarestia dudresnayi*
Strain: DdudBR16
Genotype: haploid
Sex: monoicous

Maintenance: Maintained in culture

### Desmarestia herbacea strain DmunF

Species: *Desmarestia herbacea*

Strain: DmunF

Genotype: haploid

Sex: female

Maintenance: Maintained in culture

### Desmarestia herbacea strain DmunM

Species: *Desmarestia herbacea*

Strain: DmunM

Genotype: haploid

Sex: male

Maintenance: Maintained in culture

### Dictyota dichotoma strain KB07f IV

Species: *Dictyota dichotoma*

Strain: KB07f IV

Genotype: haploid

Sex: female

Maintenance: Maintained in culture

### Dictyota dichotoma strain ODC1387m

Species: *Dictyota dichotoma*

Strain: ODC1387m

Genotype: haploid

Sex: male

Maintenance: Maintained in culture

### Dictyota dichotoma strain KB07m IV

Species: *Dictyota dichotoma*

Strain: KB07m IV

Genotype: haploid

Sex: male

Maintenance: Maintained in culture

### Dictyota dichotoma strain KB07sp VI

Species: *Dictyota dichotoma*

Strain: KB07sp VI

Genotype: diploid

Sex: n/a

Maintenance: Maintained in culture

### Discosporangium mesarthrocarpum strain MT17-79

Species: *Discosporangium mesarthrocarpum*

Strain: MT17-79

Genotype: unknown

Sex: unknown

Maintenance: Maintained in culture

### Ectocarpus crouaniorum strain Ec861

Species: *Ectocarpus crouaniorum*

Strain: Ec861

Genotype: haploid

Sex: female

Maintenance: Maintained in culture
### Ectocarpus crouaniorum strain Ec862

Species: *Ectocarpus crouaniorum*

Strain: Ec862

Genotype: haploid

Sex: male

Maintenance: Maintained in culture

### Ectocarpus fasciculatus strain Ec846

Species: *Ectocarpus fasciculatus*

Strain: Ec846

Genotype: haploid

Sex: female

Maintenance: Maintained in culture

### Ectocarpus fasciculatus strain Ec847

Species: *Ectocarpus fasciculatus*

Strain: Ec847

Genotype: haploid

Sex: male

Maintenance: Maintained in culture

### Ectocarpus fasciculatus strain EfasUO1

Species: *Ectocarpus fasciculatus*

Strain: EfasUO1

Genotype: diploid

Sex: n/a

Maintenance: Maintained in culture

### Ectocarpus fasciculatus strain EfasUO2

Species: *Ectocarpus fasciculatus*

Strain: EfasUO2

Genotype: diploid

Sex: n/a

Maintenance: Maintained in culture

### Ectocarpus siliculosus strain Ec863

Species: *Ectocarpus siliculosus*

Strain: Ec863

Genotype: haploid

Sex: female

Maintenance: Maintained in culture

### Ectocarpus siliculosus strain Ec864

Species: *Ectocarpus siliculosus*

Strain: Ec864

Genotype: haploid

Sex: male

Maintenance: Maintained in culture

### Ectocarpus species 1 strain Ec sil Puy CHCH Z9 G5f

Species: *Ectocarpus species 1*

Strain: Ec sil Puy CHCH Z9 G5f

Genotype: haploid

Sex: female

Maintenance: Maintained in culture
### Ectocarpus species 1 strain Ec sil Puy CHCH Z9 G3m

Species: *Ectocarpus* species 1
Strain: Ec sil Puy CHCH Z9 G3m
Genotype: haploid
Sex: male
Maintenance: Maintained in culture

### Ectocarpus species 1 strain Ec03

Species: *Ectocarpus* species 1
Strain: Ec03
Genotype: haploid
Sex: male
Maintenance: Maintained in culture

### Ectocarpus species 12 strain Ec fas CH92 Nie 2f

Species: *Ectocarpus* species 12
Strain: Ec fas CH92 Nie 2f
Genotype: diploid
Sex: female
Maintenance: Maintained in culture

### Ectocarpus species 12 strain Ec fas CH92 Nie 3m

Species: *Ectocarpus* species 12
Strain: Ec fas CH92 Nie 3m
Genotype: diploid
Sex: n/a
Maintenance: Maintained in culture

### Ectocarpus species 13 strain EcNAP12-S#4-19m

Species: *Ectocarpus* species 13
Strain: EcNAP12-S#4-19m
Genotype: diploid
Sex: n/a
Maintenance: Maintained in culture

### Ectocarpus species 2 strain Ec06

Species: *Ectocarpus* species 2
Strain: Ec06
Genotype: haploid
Sex: male
Maintenance: Maintained in culture

### Ectocarpus species 3 strain Ec10

Species: *Ectocarpus* species 3
Strain: Ec10
Genotype: haploid
Sex: female
Maintenance: Maintained in culture

### Ectocarpus species 3 strain Ec11

Species: *Ectocarpus* species 3
Strain: Ec11
Genotype: haploid
Sex: male
Maintenance: Maintained in culture
### Ectocarpus species 5 strain Ec13

Species: *Ectocarpus* species 5
Strain: Ec13
Genotype: haploid
Sex: female
Maintenance: Maintained in culture

### Ectocarpus species 5 strain Ec12

Species: *Ectocarpus* species 5
Strain: Ec12
Genotype: haploid
Sex: male
Maintenance: Maintained in culture

### Ectocarpus species 6 strain EcLAC-371f

Species: *Ectocarpus* species 6
Strain: EcLAC-371f
Genotype: diploid
Sex: n/a
Maintenance: Maintained in culture

### Ectocarpus species 7 strain Ec32

Species: *Ectocarpus* species 7
Strain: Ec32
Genotype: haploid
Sex: male
Maintenance: N/A

### Ectocarpus species 8 strain EcLAC-412m

Species: *Ectocarpus* species 8
Strain: EcLAC-412m
Genotype: diploid
Sex: n/a
Maintenance: Maintained in culture

### Ectocarpus species 9 strain EcSCA-722f

Species: *Ectocarpus* species 9
Strain: EcSCA-722f
Genotype: haploid
Sex: female
Maintenance: Maintained in culture

### Ectocarpus subulatus strain Bft15b

Species: *Ectocarpus subulatus*
Strain: Bft15b
Genotype: haploid
Sex: male
Maintenance: N/A

### Feldmannia mitchelliae strain KU-2106 Giff mitch BNC GA

Species: *Feldmannia mitchelliae*
Strain: KU-2106 Giff mitch BNC GA
Genotype: haploid
Sex: monoicous
Maintenance: Maintained in culture
### Fucus distichus

Species: *Fucus distichus*

Strain: field collected meristem

Genotype: diploid

Sex: n/a

Maintenance: N/A

### Fucus serratus

Species: *Fucus serratus*

Strain: field collected ovule cells

Genotype: diploid

Sex: female

Maintenance: N/A

### Fucus serratus

Species: *Fucus serratus*

Strain: field collected sperm cells

Genotype: diploid

Sex: male

Maintenance: N/A

### Halopteris paniculata strain Hal grac a UBK

Species: *Halopteris paniculata*

Strain: Hal grac a UBK

Genotype: haploid

Sex: monoicous

Maintenance: Maintained in culture

### Hapterophycus canaliculatus strain Oshoro5f

Species: *Hapterophycus canaliculatus*

Strain: Oshoro5f

Genotype: haploid

Sex: female

Maintenance: Maintained in culture

### Hapterophycus canaliculatus strain Oshoro7m

Species: *Hapterophycus canaliculatus*

Strain: Oshoro7m

Genotype: haploid

Sex: male

Maintenance: Maintained in culture

### Hapterophycus canaliculatus strain Oshoro 3F x 9M

Species: *Hapterophycus canaliculatus*

Strain: Oshoro 3F x 9M

Genotype: diploid

Sex: n/a

Maintenance: Maintained in culture

### Hapterophycus canaliculatus strain Oshoro 4F x 9M

Species: *Hapterophycus canaliculatus*

Strain: Oshoro 4F x 9M

Genotype: diploid

Sex: n/a

Maintenance: Maintained in culture
### Hapterophycus canaliculatus strain Oshoro 6F x 6M

Species: *Hapterophycus canaliculatus*

Strain: Oshoro 6F x 6M

Genotype: diploid

Sex: n/a

Maintenance: Maintained in culture

### Heribaudiella fluviatilis strain SAG. 13.90

Species: *Heribaudiella fluviatilis*

Strain: SAG. 13.90

Genotype: unknown

Sex: unknown

Maintenance: Maintained in culture

### Heterosigma akashiwo strain CCMP452

Species: *Heterosigma akashiwo*

Strain: CCMP452

Genotype: unknown

Sex: unknown

Maintenance: Maintained in culture

### Himanthalia elongata

Species: *Himanthalia elongata*

Strain: field meristem

Genotype: diploid

Sex: n/a

Maintenance: N/A

### Laminaria digitata strain LdigPH10-18mv

Species: *Laminaria digitata*

Strain: LdigPH10-18mv

Genotype: haploid

Sex: male

Maintenance: Maintained in culture

### Laminarionema elsbetiae strain ELsaHSoW15

Species: *Laminarionema elsbetiae*

Strain: ELsaHSoW15

Genotype: unknown

Sex: unknown

Maintenance: Maintained in culture

### Macrocystis pyrifera strain P11A1

Species: *Macrocystis pyrifera*

Strain: P11A1

Genotype: haploid

Sex: female

Maintenance: Maintained in culture

### Macrocystis pyrifera strain P11B4

Species: *Macrocystis pyrifera*

Strain: P11B4

Genotype: haploid

Sex: male

Maintenance: Maintained in culture
### Myriotrichia clavaeformis strain Myr cla04

Species: *Myriotrichia clavaeformis*
Strain: Myr cla04
Genotype: haploid
Sex: female
Maintenance: Maintained in culture

### Myriotrichia clavaeformis strain Myr cla05

Species: *Myriotrichia clavaeformis*
Strain: Myr cla05
Genotype: haploid
Sex: male
Maintenance: Maintained in culture

### Myriotrichia clavaeformis strain Myr cla12

Species: *Myriotrichia clavaeformis*
Strain: Myr cla12
Genotype: diploid
Sex: n/a
Maintenance: Maintained in culture

### Pelvetia canaliculata

Species: *Pelvetia canaliculata*
Strain: field collected meristem
Genotype: diploid
Sex: n/a
Maintenance: N/A

### Phaeothamnion wetherbeei strain SAG 119.79

Species: *Phaeothamnion wetherbeei*
Strain: SAG 119.79
Genotype: unknown
Sex: unknown
Maintenance: Maintained in culture

### Pleurocardia lacustris strain SAG 25.93

Species: *Pleurocardia lacustris*
Strain: SAG 25.93
Genotype: unknown
Sex: unknown
Maintenance: Maintained in culture

### Porterinema fluviatile strain SAG 2381

Species: *Porterinema fluviatile*
Strain: SAG 2381
Genotype: unknown
Sex: unknown
Maintenance: Maintained in culture

### Pylaiella littoralis strain U1.48

Species: *Pylaiella littoralis*
Strain: U1.48
Genotype: haploid
Sex: unknown
Maintenance: Maintained in culture
### Pylaiella littoralis strain F24

Species: *Pylaiella littoralis*

Strain: F24

Genotype: diploid

Sex: n/a

Maintenance: Maintained in culture

### Saccharina japonica strain Ja

Species: *Saccharina japonica*

Strain: Ja

Genotype: haploid

Sex: male

Maintenance: N/A

### Saccharina latissima strain SLPER63f7

Species: *Saccharina latissima*

Strain: SLPER63f7

Genotype: haploid

Sex: female

Maintenance: Maintained in culture

### Saccorhiza dermatodea strain SderLü1190fm

Species: *Saccorhiza dermatodea*

Strain: SderLü1190fm

Genotype: haploid

Sex: monoicous

Maintenance: Maintained in culture

### Saccorhiza polyschides strain SpolBR94f

Species: *Saccorhiza polyschides*

Strain: SpolBR94f

Genotype: haploid

Sex: female

Maintenance: Maintained in culture

### Saccorhiza polyschides strain SpolBR94m

Species: *Saccorhiza polyschides*

Strain: SpolBR94m

Genotype: haploid

Sex: male

Maintenance: Maintained in culture

### Saccorhiza polyschides

Species: *Saccorhiza polyschides*

Strain: field collected sample (young sporophytes ~2-10cm)

Genotype: diploid

Sex: n/a

Maintenance: N/A

### Sargassum fusiforme

Species: *Sargassum fusiforme*

Strain: unknown

Genotype: diploid

Sex: n/a

Maintenance: N/A
### Schizocladia ischiensis strain KU-0333

Species: Schizocladia ischiensis
Strain: KU-0333
Genotype: unknown
Sex: unknown
Maintenance: Maintained in culture

### Scytosiphon promiscuus strain 000310-Muroran-5-female

Species: Scytosiphon promiscuus
Strain: 000310-Muroran-5-female
Genotype: haploid
Sex: female
Maintenance: Maintained in culture

### Scytosiphon promiscuus strain Ot110409-Otamoi-16-male

Species: Scytosiphon promiscuus
Strain: Ot110409-Otamoi-16-male
Genotype: haploid
Sex: male
Maintenance: Maintained in culture

### Scytosiphon promiscuus strain SXS107

Species: Scytosiphon promiscuus
Strain: SXS107
Genotype: diploid
Sex: n/a
Maintenance: Maintained in culture

### Sphacelaria rigidula strain Sph rig Cal Mo 4-1-68b

Species: Sphacelaria rigidula
Strain: Sph rig Cal Mo 4-1-68b
Genotype: haploid
Sex: female
Maintenance: Maintained in culture

### Sphacelaria rigidula strain Sph rig Cal Mo 4-1-G3b

Species: Sphacelaria rigidula
Strain: Sph rig Cal Mo 4-1-G3b
Genotype: haploid
Sex: male
Maintenance: Maintained in culture

### Sphacelaria rigidula strain Sph rig Cal Mo SP

Species: Sphacelaria rigidula
Strain: Sph rig Cal Mo SP
Genotype: diploid
Sex: n/a
Maintenance: Maintained in culture

### Sphaerotrichia firma strain ET2f

Species: Sphaerotrichia firma
Strain: ET2f
Genotype: haploid
Sex: female
Maintenance: Maintained in culture
### Sphaerotrichia firma strain Sfir13m

Species: *Sphaerotrichia firma*

Strain: Sfir13m

Genotype: haploid

Sex: male

Maintenance: Maintained in culture

### Tribonema minus strain UTEX B 3156

Species: *Tribonema minus*

Strain: UTEX B 3156

Genotype: unknown

Sex: unknown

Maintenance: N/A

### Undaria pinnatifida strain Kr2015

Species: *Undaria pinnatifida*

Strain: Kr2015

Genotype: diploid

Sex: n/a

Maintenance: N/A

## METHOD DETAILS

### Biological material

Sequencing brown algal genomes has been hampered by the significant challenges involved, including inherent problems with growing brown algae, the presence of molecules that interfere with sequencing reactions and complex associations with microbial symbionts. To address these problems, cultured, unialgal filamentous gametophyte material was used whenever possible (i.e. for species with haploid-diploid life cycles) and the extraction methodology was adapted for each species.

The algal strains analysed in this study are listed in Table S1A, which provides information about the sampling site for each strain. The sampling sites are shown on a world map in Figure S1D.

All strains except those belonging to the Fucales were grown under laboratory conditions. The latter cannot be maintained long-term in the laboratory so field material was harvested for extractions. The haploid gametophyte generation was grown in culture for species with characterised haploid-diploid life cycles, with the exception of *Ectocarpus* strains, for which haploid partheno-sporophytes or diploid sporophytes were cultivated. All cultures were grown either in 140 mm diameter Petri dishes or in 2–10 L bottles, the latter aerated by bubbling with sterile air. Most cultures were grown in Provasoli-enriched¹⁰⁴ natural seawater (PES medium) under fluorescent white light (10–30 µM photons/m²·s) at 13°C (or at 10°C for *Hapterophycus canaliculatus* and *Chordaria linearis* or 20°C for *Sphacelaria rigidula*, *Dictyota dichotoma*, *Schizocladia ischiensis* and *Chrysoparadoxa Australica*). Exceptions included the freshwater species *Pleurocladia lacustris*, *Porterinema fluviatile* and *Heribaudiella fluviatilis*, which were grown in natural seawater that had been diluted to 5% with distilled water (i.e., 95% distilled water / 5% seawater) before addition of ES medium (http://sagdb.uni-goettingen.de/culture_medi a/01%20Basal%20Medium.pdf) micronutrients (at 20°C for *P. lacustris*) and *Phaeothamnion wetherbeei*, which was grown in MIEB12 (medium 7 in Letunic et al.¹⁷⁷). Whole thallus was extracted for all species except the Fucales, where either dissected meristematic regions or released male gametes were extracted. Tissue samples were frozen in liquid nitrogen and stored at -80°C before extraction.

### DNA extraction

DNA was extracted using either the OmniPrep Genomic DNA Purification Kit (G Biosciences, St. Louis, MO, USA) or the Nucleospin Plant II midi DNA Extraction Kit (Macherey-Nagel, Düren, Germany). DNA quality was assessed using a Qubit fluorometer (Themo Fisher Scientific, Waltham, MA, USA), and fragment length was assessed by migration on a 1% agarose gel for some of the samples.

### Illumina library preparation and sequencing

Libraries were prepared using the NEBNext DNA Modules Products (New England Biolabs, Ipswich, MA, USA) with an ‘on bead’ protocol developed by Genoscope, starting with 100 ng of genomic DNA. DNA was sonicated to a 100–800 bp size range using a Covaris E220 sonicator (Covaris, Woburn, MA, USA), end-repaired and 3’-adenylated. Illumina adapters (Bioo Scientific, Austin, TX, USA) were then added using the NEBNext Sample Reagent Set (New England Biolabs, Ipswich, MA, USA) and the DNA purified using Ampure XP (Beckmann Coulter Genomics, Danvers, MA, USA). Adapted fragments were amplified with 12 cycles of PCR using the Kapa Hifi Hotstart NGS library Amplification kit (Roche, Basel, Switzerland), followed by 0.8x AMPure XP (Beckman Coulter Genomics, Danvers, MA, USA) purification. Libraries were sequenced with Illumina MiSeq, HiSeq 4000 or NovaSeq 6000 instruments (Illumina, San Diego, CA, USA) in paired-end mode, 150 base read-length.
## Oxford Nanopore library preparation and sequencing

Some samples were first purified using the Short Read Eliminator Kit (Pacific Biosciences, Menlo Park, CA, USA). All libraries were prepared using the protocol "1D Genomic DNA by Ligation" provided by Oxford Nanopore Technologies (Oxford Nanopore Technologies Ltd, Oxford, UK). Most of the libraries were prepared with the SQK-LSK109 kit (Oxford Nanopore Technologies), a few with the SQK-LSK108 or SQK-LSK110 kits (Oxford Nanopore Technologies). Three flow cells were loaded with barcoded samples. The samples were mainly sequenced on R9.4.1 MinION or PromethION flow cells.

## RNA extraction, Illumina RNA-seq library preparation and sequencing

RNA was extracted using either the Qiagen RNeasy kit or the Macherey Nagel RNAplus kit (Macherey-Nagel, Düren, Germany). RNA-seq libraries were prepared using the TruSeq Stranded mRNA Sample Prep (Illumina) according to the manufacturer's protocol, starting with 500 ng to 1 µg of total RNA, or using the NEBNext Ultra II Directional RNA Library Prep for Illumina (New England BioLabs) according to the manufacturer's protocol, starting with 100 ng of total RNA. The libraries were sequenced with Illumina HiSeq 2500, HiSeq 4000 or NovaSeq 6000 instruments (Illumina, San Diego, CA, USA), in paired-end mode, 150 base read-length.

## Assembly strategies

Two assembly strategies were employed: one was designed for genomes exclusively sequenced using short reads with Illumina technology, while the other was designed for genomes that underwent sequencing using a combination of long and short reads, using respectively the Nanopore and Illumina technologies.

### Short-read-based genome assembly

When sequencing was performed exclusively using short reads, reads corresponding to bacterial contaminants were filtered out early in the assembly process because, typically, the initial datasets were too large to run assemblers like SPAdes. To remove bacterial contaminants, an assembly based on the initial illumina dataset was first generated for each strain using a fast and non-greedy algorithm, MEGAHIT<sup>82</sup> version 1.1.1 with the parameters -k-min 101 -k-max 131 -k-step 10. Assigning taxonomy is easier when working with contigs than with reads. Contigs exceeding 500 bp in each preliminary assembly underwent taxonomic classification based on gene models predicted using the *ab initio* software MetaGene<sup>83</sup> version 2008.8.19 with default parameters and then aligning proteins against UniprotKB using BLASTp (e-value <10<sup>-4</sup>). A superkingdom (Eukaryota, Archaea or Bacteria) was assigned to each gene based on the best alignment (selected using the BLASTp score). Contigs that contained more than 50% of their genes assigned to Bacteria and with at least one gene every 10 kbp were classified as bacterial sequences. For each strain, the initial Illumina sequencing reads were aligned against the corresponding bacterial sequences using latest version of the Burrows-Wheeler Aligner<sup>85</sup> (BWA) with default parameters and mapped short-reads were labelled as contaminants, and assembled for the purpose of obtaining more contiguous contigs. These bacterial contigs were then used to build a contaminant sequence database. Finally, the clean subset of reads was obtained by aligning the whole Illumina dataset against this strain-specific bacterial contig database, using Bowtie2<sup>86</sup> version 2.2.9 with default parameters. A final assembly was then generated for each strain using the contaminant-free read datasets and the SPAdes<sup>87</sup> assembler version 3.8.1 with the parameters -k 21,57,71,99,127 -m 2000 -only-assembler -careful. Genome assemblies based only on short-reads were more fragmented (N50 ranged from 3 kbp to 31 kbp) than assemblies that used long reads but the sizes of the former were consistent with expectations.

### Long-read-based genome assemblies

A subset of the strains produced DNA of both adequate quality and quantity, enabling successful long-read sequencing. In these cases, long reads were assembled directly and the detection of possible bacterial contigs was carried out after the assembly step. To produce long-read-based genome assemblies we generated three samples of reads i) all reads, ii) 30X coverage of the longest reads and iii) 30X coverage of the filtlong (https://github.com/rrwick/Filtlong) highest-score reads. The three samples were used as input data for four different assemblers, Smartdenovo,<sup>88</sup> Redbean,<sup>89</sup> Flye<sup>90</sup> and Necat.<sup>91</sup> Based on the cumulative size and contiguity, we selected the best assembly for each strain. This assembly was then polished three times using Racon<sup>92</sup> with nanopore reads, and twice with Hapo-G<sup>93</sup> and Illumina PCR-free reads.

## Assembly decontamination

Contigs from the short- and long-read genome assemblies were inspected for potential bacterial sequences. This process was carried out using a combination of several analysis and tools: GC composition, read coverage, Metabat 2 (for tetramer composition and clustering)<sup>94</sup> and Metagene (for gene prediction and taxonomic identification, as described previously). Contigs were manually removed based on their characteristics.

## Transcriptome assembly

Ribosomal-RNA-like reads were detected using SortMeRNA<sup>95</sup> and filtered out. The Illumina RNA-seq short reads from each strain were assembled using Velvet<sup>96</sup> version 1.2.07 and Oases<sup>97</sup> version 0.2.08 with kmer sizes of 61, 63 and 65 bp. BUSCO<sup>168</sup> analysis (v5, eukaryota_odb10) was then performed on the three resulting assemblies for each strain in order to select the best assembly, i.e. the most complete at the gene level. Reads were mapped back to the contigs with BWA-mem, and only consistent paired-end reads were retained. Uncovered regions were detected and used to identify chimeric contigs. In addition, open reading frames (ORF) and domains were identified using TransDecoder (Haas, B.J., https://github.com/TransDecoder/TransDecoder) and CDDsearch,<sup>98</sup>
respectively. Contigs were broken into uncovered regions outside ORFs and domains. In addition, read strand information was used to correctly orient RNA-seq contigs.

## De novo transcriptomes

The RNA-seq data was also used to generate *de novo* transcriptomes. For each strain, all the RNA-seq data available was cleaned to remove poor quality sequence and adapter sequences using Trimmomatic⁹⁹ v0.39 prior to being assembled using either Trinity¹⁰⁰ version v2.6.5 or rnaSPAdes¹⁰¹ version v3.13.1. The strandness and Kmer-length parameters of the assemblers were adjusted to take into account RNA-seq read characteristics. The *de novo* transcriptomes represented an alternative source to identify and characterise genes if they were not detected in the genome assemblies. The *de novo* transcriptomes are available from the CNRS Research Data dataset (https://doi.org/10.57745/9U1J85) and from the Phaeoexplorer website (https://phaeoexplorer.sb-rocoff.fr/).

## Detection and masking of repeated sequences and transposons

Prior to gene annotation, each genome assembly was masked based on the repeat library from *Ectocarpus* species 7 (formerly *Ectocarpus siliculosus*)¹¹ and using RepBase with RepeatMasker¹⁰² version v4.1.0, default parameters. Tandem repeats finder (TRF)¹⁰³ was also used to mask tandem repeat duplications. In addition, transposons were annotated in ten species using REPET¹⁰⁴ and the transposons detected were used as a reference to mask all genomes with RepeatMasker¹⁰² version v4.1.0, default parameters.

## Gene prediction

For each strain, gene prediction was performed using both homologous proteins and RNA-seq data. Proteins from *Ectocarpus* species 7 (https://bioinformatics.psb.ugent.be/orcae/overview/EctsiV2)¹⁷⁸ and UniRef90 (https://www.uniprot.org/uniref/) were aligned against each genome assembly. First, BLAT¹⁰⁵ with default parameters was used to quickly localise putative genes corresponding to the *Ectocarpus* species 7 proteins. The best match and matches with a score ≥ 90% of the best match score were retained. Second, the alignments were refined using Genewise¹⁰⁶ with default parameters, which is more precise for intron/exon boundary detection. Alignments were retained if more than 80% of the length of the protein was aligned to the genome. To detect conserved proteins and allow detection of horizontal gene transfer, UniRef90 proteins (without *E. siliculosus* sequences) were aligned with DIAMOND¹⁰⁷ (v0.9.30 with parameters -evalue 0.001 -more-sensitive) to genomic regions lacking alignments with an *Ectocarpus* species 7 protein. Only the five best matches per locus were retained, based on their bitscore. Selected proteins from UniRef90 were aligned to the whole genome using Genewise as described previously, and alignments with at least 50% of the aligned protein length were retained. The assembled transcriptome for each strain was aligned to the strain's genome assembly using BLAT¹⁰⁵ with default parameters. For each transcript, the best match was selected based on the alignment score, with an identity greater or equal to 90%. Selected alignments were refined using Est2Genome¹⁰⁸ in order to precisely detect intron boundaries. Alignments were retained if more than 80% of the length of the transcript was aligned to the genome with a minimal identity of 95%. Finally, the protein homologies and transcript mapping were integrated using a combiner called Gmove.¹⁰⁹ This tool can find coding sequences (CDSs) based on genome-located evidence without any calibration step. Briefly, putative exons and introns, extracted from the alignments, were used to build a simplified graph by removing redundancies. Then, Gmove extracted all paths from the graph and searched for open reading frames (ORFs) consistent with the protein evidence. Translated proteins of predicted genes were then aligned against NR prot (release 19/02/2019) and the *Ectocarpus* species 7 version v2 proteome¹⁷⁸ (https://bioinformatics.psb.ugent.be/orcae/overview/EctsiV2) using DIAMOND BLASTp with parameters -evalue 10-5 -more-sensitive -unal 0. All predicted genes with significant matches (the smallest protein had to be aligned for at least 50% of its length) were retained. In addition to these genes, we also retained genes with CDS size greater than 300 bp and with a coding ratio (CDS size / mRNA size) greater or equal to 0.5.

## Annotation decontamination

After predicting the genes, an additional analysis was carried out to detect bacterial sequences. If a contig did not contain any genes, it was analysed with MetaGene and the predicted proteins added to the gene catalogue for the purpose of detecting bacterial sequences. Proteins generated from predicted genes (Gmove plus MetaGene) were then aligned against UniprotKB using BLASTp (e-value < 10⁻⁴) and superkingdom (Eukaryota, Archaea or Bacteria) was assigned to each gene based on the best alignment (selected using the BLASTp score). Contigs that contained more than 80% of their genes assigned to bacteria, Archaea or viruses were classified as bacterial sequences and removed from the final assembly file. Genes belonging to these contigs were also removed from the final gene catalogue. Finally, completeness of each predicted gene catalogue was assessed using BUSCO¹⁶⁸ (v5.0.0; eukaryota_odb10).

In addition, the quality of the annotations was assessed by comparing the length of coding regions in pairs of orthologous proteins (best reciprocal hits) between each genome and *Ectocarpus* species 7, which was used as a reference because its high-quality annotation has been extensively curated.¹⁷⁸ The correlation between orthologous CDS lengths was higher for genomes sequenced with long reads than for genomes only sequenced with short reads (Figure S1B). This difference was probably principally due to a higher proportion of underestimated protein lengths in the latter (Table S1B) which likely corresponded to fragmented genes. The qualities of Ectocarpales genome annotations were very high (BUSCO and length of predicted CDS) even when the genomes were sequenced
<figure><img src="image_31.png" /></figure>

using only short reads, probably because their phylogenetic proximity to *Ectocarpus* species 7 facilitated the building of good quality gene models.

## Analyses aimed at deducing functional characteristics of predicted proteins

Several different analyses of the predicted proteomes of each species were carried out to provide information about the cellular functions of the encoded proteins. These included eggNOG-mapper¹²⁵ analyses (v2.1.8 or v2.0.1, with emapperDB v5.0.2 or v4.5.1) to provide multiple functional annotations (Gene Ontology, Kyoto Encyclopedia of genes and genomes, Clusters of Orthologous Genes, Pfam), Interproscan¹³⁷ analyses (versions v5.55-88.0, v5.51-85.0 or v5.36-75.0) to detect functional domains, Hectar¹⁶¹ (v1.3) predictions of protein subcellular localisation and various DIAMOND¹⁰⁷ (v2.0.15 vs UniRef90 2022_03, with parameter “evalue” set to 10e⁻⁵) sequence similarity searches aimed at identifying homologous proteins with functional annotations.

## Detection of tandemly duplicated genes

Starting with the protein alignments that had been constructed to build the orthogroups, matches between proteins within the same genome with an e-value of ≤ 10⁻²⁰ and which covered at least 80% of the smallest protein were extracted. Two genes were considered to be tandemly duplicated if they were localised on the same genomic contig separated by five or less intervening genes, regardless of their orientation. The tandemly-duplicated genes were clustered using a single linkage clustering approach. A contingency test was applied to compare the proportion of tandemly-duplicated genes in each orthogroup with the global proportion of tandemly-duplicated genes (p=0.0532792). The p-values are shown in Table S1.

## Relative orientation of adjacent genes and lengths of intergenic regions

For each species, the proportion of pairs of adjacent genes localized on opposite strands was compared to the expected proportion of 0.5 using a binomial test (with p=0.5). The p-values are shown in Table S1B (p-values of <0.05 correspond to cases where the proportion is significantly higher than 0.5).

The lengths of intergenic regions between pairs of adjacent genes located on opposite strands (i.e. divergently or convergently transcribed) were compared with the lengths of intergenic regions between genes located on the same strand (i.e. transcribed in the same direction). Contingency tables were constructed for each species using a threshold of 1000 bp for the intergenic length and the number of intergenic regions in each of four categories were counted: 1) same strand genes, intergenic <1000 bp, 2) opposite strand genes, intergenic <1000 bp, 3) same strand genes, intergenic ≥ 1000 bp, 4) opposite strand genes, intergenic ≥ 1000 bp. Fisher exact tests were applied to the contingency tables (alternative hypothesis: true odds ratio is greater than 1). The p-values are shown in Table S1. When p-values are <0.05, short intergenic lengths are significantly associated with pairs of genes on opposite strands. All calculations were performed with R¹⁶² (version 4.3.0).

## Detection of long non-coding RNAs

Transcriptome data for 11 species (Table S1F), including nine brown algal strains and two outgroup taxa, was analysed to identify IncRNAs. Any transcripts with invalid nucleotide DNA symbols were discarded and sequences shorter than 200 nucleotides were removed to avoid the detection of small RNA transcripts. The transcriptome sequences in Fasta format were analysed with votingLNC (https://gitlab.com/a.debit/votinglnc) to detect IncRNA transcripts and assign a confidence level for each transcript. A similar approach was used to detect IncRNAs in the IncPlankton database.¹⁷⁹ VotingLNC is a meta-classifier combining the predictions of the ten most commonly used coding potential tools. Based on a majority voting ensemble procedure, the meta-tool assigns the final coding potential class to a transcript as the class label predicted most frequently by the ten classification models included in the ensemble. Alongside the majority voting class, a reliability score was calculated for each transcript. A cut-off non-coding reliability score of p > 0.5 was chosen to treat a transcript as IncRNA and to decrease false-positive identification. The set of transcripts predicted as IncRNA by the majority-voting procedure and having an ORF(s) encoding peptide(s) with length ≥ 100aa were discarded. IncRNA transcripts that had significant matches in either the Pfam¹⁷⁴ (hmmscan e-value < 0.001) or SwissProt (BLASTp e-value < 1e⁻⁵ and similarity ≥ 90%) databases were removed from the dataset. Transcript length, GC content, and the length of the longest ORF were compared between IncRNAs and protein-coding RNAs. The comparison was carried out using a Wilcoxon test. R version V.4.1.2 was used for all the analyses and ggplot2¹⁶⁶ (version 3.4.0) for plotting.

## Intron conservation

Intron positions were compared in a set of single copy genes that are conserved across all the Phaeophyceae and the outgroup species. The analysis focused on the 21 reference genomes (Table S1F) and on orthogroups that occurred exactly once in at least 20 of the 21 genomes, allowing the gene to be absent from only one of the 21 genomes. In addition, orthogroups were discarded if more than three copies had been annotated in the other Phaeophyceae genomes. These filters produced a set of 235 conserved (ancestral) orthogroups. Multiple alignments were carried out for each orthogroup using MUSCLE¹¹⁵ version 3.8.1551 with default parameters and conserved blocks were identified with Gblocks¹³⁹ version 0.91b with the parameters -p=t -s=n -b5=a -b2=[nsp] -b1=[nsp] -b3=6, where “nsp” is equal to 90% of the number of proteins aligned. A shell script was then used to compare intron positions in the alignments. For each intron in the multiple sequence alignment, we obtained a corresponding conservation profile listing which species contains an intron at that position. The profiles obtained for the 949 introns that are in conserved blocks of the multiple alignments are
shown in Figure S4B. Both phase and length of ancestral introns (e.g. that were conserved in most Phaeophyceae and at least two sister clades) were compared to the phase and length of *Ectocarpus* species 7 introns as a reference. The same approach was used to compare intron positions across 11 *Ectocarpus* species, with *Scytosiphon promiscuus* as an outgroup, by selecting 831 conserved monocopy orthogroups. The number of introns per gene in brown algae and in closely-related outgroup species were compared using a contingency test (Table S1C).

## Phylogenomic tree of the Phaeophyceae

To provide a phylogenetic framework for the analyses of the Phaeoexplorer genome dataset, the 41-species phylogenomic tree reported by Akita et al.<sup>180</sup> was updated by adding 15 additional species using the same methodology. Briefly, for the additional species, amino acid sequences were recovered for the 32 single-copy orthologous genes used to construct the published tree and these were aligned manually with the existing sequences using the alignment software AliView<sup>110</sup> v.1.26. The aligned sequences of the final 56 species were concatenated and maximum likelihood analysis was carried out with 10,000 rapid bootstraps using RAxML<sup>111</sup> v.8.2.9 and the gamma model. The best-fit evolutionary model for each gene was determined using AIC.

## Bayesian divergence time estimation for the brown algae

An estimation of brown algal divergence time was carried out using the 32 orthologous nuclear genes (see above and ) for 51 brown algae and five non-brown species (16,185 amino acids, 56 spp.) and MCMCTree (PAML package v4.9j) with the approximate likelihood method. The WAG protein model was selected based on the AIC and BIC criteria of ModelFinder.<sup>153</sup> The independent clock model was selected based on previous work on the brown algal timeline by Choi et al.<sup>1</sup> One hundred million years was set to correspond to 1 in the MCMCTree calculation. A secondary calibration for the root was based on Choi et al.<sup>1</sup> using a gamma distribution of 70.2 alpha and 10.22 beta. A kelp holdfast fossil<sup>55</sup> was used to date the crown node of kelps with a minimum bound of 0.31, and a *Julescaneia* fossil<sup>181</sup> for the *Macrocystis/Saccharina* clade with a minimum bound of 0.13 (Figure S2A). MCMC chains were run 1.5 million generations, with the first 200,000 MCMC chains being discarded as burn-in, and the convergence of MCMC chains was checked with Tracer v1.7.2.<sup>112</sup> This analysis estimated that Schizocladiophyceae and brown algae diverged 457.88 Mya (95% HPD: 321.29-592.66 Ma), similar to (about 8 Mya older than) the previous estimate using plastid genes<sup>1</sup> and that diversification of the major brown algal linages began about 220 million years later, after the origin of DFI clade (235.97 Mya, 95% HPD: 158.88-312.48 Mya), about 12 Ma earlier than the previous estimate.<sup>1</sup> The fossil-calibrated phylogenetic tree for 11 *Ectocarpus* species (Figure S2C) was extracted from the brown algal tree (Figure S2A).

## Detection of orthologous groups

Predicted proteins from the 60 strains sequenced in Phaeoexplorer complemented with 16 public proteomes covering the Ochrophytina subphylum and the terrestrial oomycetes were clustered using OrthoFinder<sup>113</sup> v2.5.2 with default parameters. This generated 56,340 orthogroups that contained 90.1% of the proteins (1,415,341 of the 1,571,648). Seventy-one of the 76 strains had more than 75% of their proteins in an orthogroup shared with at least one other strain. The orthogroups contain between 2 and 6,220 proteins with a mean of 25.1 proteins and a median of three.

## Dollo analysis of orthogroup gain and loss

An analysis of evolutionary events of gene family gain and loss was carried out on a selection of strains covering the brown algal phylogeny and sister groups as distant as the Raphidophyceae under the Dollo parsimony law using orthogroups as proxies for gene families. To limit possible problems due to the fragmentation of predicted proteins in some assemblies, we selected 24,410 orthogroups present in at least one of 17 strains that had both good quality genome assembly and good quality gene predictions. Dollo parsimony analysis was then run using Count<sup>114</sup> version v9.1106 based on a cladogram of a subset of 24 species representative of the Phaeoexplorer project and excluding all public outgroups more distant than *Heterosigma akashiwo*. The cladogram was based on the topology of the brown algae phylogenetic tree published by Akita et al.<sup>182</sup>

## Phylostratigraphy analysis

GenEra<sup>118</sup> was used to estimate gene family founder events for each genome assembly by running DIAMOND<sup>107</sup> in ultra-sensitive mode against the Phaeoexplorer protein dataset and the NCBI non-redundant database. All sequence matches with e-values < 10<sup>-5</sup> were treated as being homologous with the query genes in the target genomes. The NCBI taxonomy was used as an initial template to infer the evolutionary relationships of each query gene with their matches in the sequence database but taxonomic assignments within the PX clade and Phaeophyceae were then modified to reflect the evolutionary relationships that were inferred in the maximum likelihood tree. Gene families were predicted based on a clustering analysis of the query proteins against themselves using an e-value cutoff of 10<sup>-5</sup> in DIAMOND and an inflation parameter of 1.5 with MCL.<sup>119</sup> Estimated evolutionary distances were extracted for each pair of species from the maximum likelihood species tree (substitutions/site) to calculate homology detection failure probabilities.<sup>183</sup> Taxonomic sampling of the species tree enabled homology detection failure tests to be carried out within the PX clade. Gene families whose ages could not be explained by homology detection failure were analysed by inspecting the functional and domain annotations for *Ectocarpus* species.<sup>7,179</sup> Structural alignments were performed using Foldseek<sup>120</sup> against the AlphaFold protein structure database.<sup>170</sup>
## Detection of gene family amplifications

A binomial test with a parameter of 17/21 was carried out to detect gene families (OGs) that had significantly expanded in 17 Phaeophyceae reference genomes compared with four closely-related outgroup species (Schizocladia ischiensis, Tribonema minus, Chrysoparadoxa australica and Heterosigma akashiwo; Table S1F). Expanded gene families deviated significantly from the expected proportion (17/21 under the null hypothesis where there are equal gene numbers in all species). Benjamini-Hochberg FDR correction for multiple testing was then applied and 233 candidate OGs with corrected *p*-values of < 0.001 were retained. All calculations were performed with R (version 4.1.0).

The set of 233 candidate OGs was then filtered to limit counting errors due to annotation artefacts (e.g. genes missed or fragmented) using the following procedure:

1. A protein consensus was first deduced for each orthogroup. Protein sequences representative of all lineages were extracted and aligned using MUSCLE<sup>115</sup> version 3.8.1551 with default parameters and the multiple alignments were filtered using OD-Seq<sup>116</sup> version 1.0 to remove outlier sequences, with parameter -score set to 1.5. The consensus sequences were then extracted from the multiple alignments of non-outlier sequences using hmmemit in the HMMER3<sup>117</sup> package version 3.1b1 with default parameters.

2. In order to estimate gene family copy number independently of the assembly and annotation processes, short read sequences for each genome were mapped onto the orthogroup consensus sequences using DIAMOND.<sup>107</sup> Unique matches were retained for each read and depth of coverage was calculated for each consensus orthogroup. The depth obtained for each orthogroup was normalised for each species by dividing by the depth obtained on a set of conserved single-copy genes, so that the final value obtained was representative of the gene copy number. Then, for each candidate amplified orthogroup, the average depth for the 17 Phaeophyceae species and the average depth for the four outgroup species was calculated and OGs where the depth for outgroups was more than half the depth for the Phaeophyceae were discarded. We retained 227 out of 233 orthogroups after this step.

3. Finally, functional annotations were used to remove orthogroups that were likely to correspond to transposable elements. A final list of 180 OGs was retained (Table S3).

The amplified gene families were manually categorised into functional classes based on the output of automatic functional annotation programs (InterProScan,<sup>137</sup> EggNOG,<sup>124</sup> nr BLASTp) and an amplification profile was assigned to each orthogroup by identifying the taxonomic group where the amplification of the family was most marked (Table S3).

In addition to the binomial tests, we also ran CAFE5<sup>164</sup> to reconstruct the history of gene family amplifications. Such reconstructions rely on a species tree and require that all gene families are present at the root of the tree. However, of the 180 amplified OGs that were strongly amplified in Phaeophyceae (see above and listed in Table S3) only 19 were present at the ancestral node. The majority (161) of the 180 families were gained during the early evolution of the lineage, most (105) at the origin of the PX clade (i.e. a collapsed node corresponding to nodes n1 and n2 in Figure S2B) or of the Phaeophyceae/FDI clades (i.e. a collapsed node corresponding to nodes n5 and n6; Figure S2B). To determine whether the 180 amplified OGs were significantly enriched in genes that were gained early during Phaeophyceae evolution (i.e. at nodes n1/n2, n4, n5/n6 in Figure S2B), a Chi-squared test was carried out using the R *chisq.test* function on a contingency table containing the proportions of OGs gained at various periods during brown algal evolution for both the amplified OGs and for the entire set of OGs as a reference dataset (Table S1C). Twelve independent CAFE5 reconstructions were carried out on the OG subsets gained at 12 different nodes (n0, n1/2, n4, n5/n6, n8, n9, n10/n11, n13, n15, n18, n19, n20), using the subtrees rooted at these nodes so that the sets of OGs gained at each node would be placed at the root of the tree for one of the 12 analyses (Figure S3E). The analysis focused on the 19 highest quality genomes (Table S1F), which is why some pairs of nodes were collapsed (e.g. nodes n1 and n2 to give n1/n2). Several parameters were tested for CAFE5: the -p option (Poisson distribution) resulted in better likelihood scores than default, but we observed a weak effect when increasing the value of lambda (-k). Consequently, all reconstructions were performed with -p (and no k, i.e. k=1) for efficiency purposes. As recommended by Mendes et al.,<sup>164</sup> very large gene families were discarded as these can cause the program to fail to initialize the parameters. The twelve reconstructions were then aggregated and the proportions of amplified and reduced gene families were calculated for each node (Table S3). Only results on internal nodes were considered, since leaves are more subject to artefactual amplifications/reductions due to genes being missed, fused or split in the annotations.

## Composite genes

The amino-acid sequences of all 530,598 genes present in the selected genomes were compared in an all-against-all pairwise alignment using DIAMOND BLASTp<sup>107</sup> version 2.0.11; "very-sensitive" mode; e-value threshold 1e-5. This raw alignment was then filtered using CleanBlastp, from the CompositeSearch suite,<sup>121</sup> to remove sequence alignments with under 30% residue identity and produce the final sequence similarity network. CompositeSearch was then used on this network to identify putative composite gene families among the orthologous groups (OGs) previously computed by OrthoFinder.<sup>113</sup> Composite OGs containing two or more genes and having non-overlapping regions aligned to their component OGs were retained for further analysis, while singleton composite OGs and composites with overlapping component regions were discarded. A phylogeny-based approach,<sup>184</sup> which uses information from extant genomes to apply a Dollo parsimony model in Count,<sup>114</sup> was used to reconstruct the evolutionary events
(domain fusions and fissions) that led to structural rearrangements of composite genes, allowing them to be labelled as fusion or fission events (or as complex events when sequentiality could not be clearly deduced).

## Horizontal gene transfer (HGT)

### Dataset and experimental approach

Uneven data collection across taxa can impact HGT identification. The phylogeny-based HGT screening approach used here requires the establishment of a comprehensive and taxonomically diverse reference dataset. The analysis focused on the Phaeoexplorer genomes using a background database called REFAL and an automated bioinformatics tool called RoutineTree, which screens for HGTs using phylogenetics. The background database was built using a starting database, GNM1157, which includes a diverse set of 17,250,679 protein sequences from 1157 genomes spanning various prokaryotic and eukaryotic lineages (540 bacteria, 45 archaea, 431 Opisthokonta, 15 Rhodophyta, 83 Viridiplantae, and 43 genomes from CRASH lineages). Data from NCBI RefSeq (updated as of May 2020) and MMETSP were integrated into GNM1157 to form the background database REFAL. To enhance data quality and reduce redundancy, CD-HIT version 4.5.4 was used to remove highly similar sequences (with sequence identity ≥90%) within each taxonomic order. This curation process resulted in a protein database consisting of 39.9 million sequences, representing over 7,786 taxa and providing comprehensive coverage across the diverse branches of the tree of life. To obtain the best assembled genome within a genus, the latest version was selected if multiple versions were available. In addition, the dataset was expanded by searching for genomes in other repositories such as the Joint Genome Institute. Special attention was paid to achieving balanced representation of the Rhodophyta and Viridiplantae, which are particularly crucial for HGT analysis within the Chromalveolate group. To accomplish this, protein data from six red algal transcriptomes sourced from MMETSP was added. The HGT search was applied to 72 Stramenopile genomes, including 45 newly sequenced and 27 public genomes.

### Phylogenetic Tree Reconstruction

The pipeline for constructing phylogenetic trees splits fasta files into individual sequence files and then carries out a search for homologous sequences, followed by multiple sequence alignment and tree-building. Nested positions within the trees were identified using artificial intelligence and hU and hBL methods were used for HGT verification. Instead of using all available sequences, sequences with the best BLAST hit scores from each kingdom, phylum, and class were used for tree construction to expedite tree-building and enhance clarity. Each gene, regardless of whether it was a copy or not, was used as a query for tree construction. To improve precision, four different methods were used for tree building: neighbour-joining, maximum parsimony, maximum likelihood and Bayesian. As a result, each node within a tree was associated with four support values. To create single-gene phylogenetic trees, a BLASTp⁸⁴ search was carried out against the background database, employing an e-value cutoff of 1e⁻⁰⁵. For each query, the top 1,000 significant matches were sorted by bit-score in descending order as the default criterion. Matching sequences were then retrieved from the database, with a constraint of no more than three sequences per genus and no more than 12 sequences per phylum. To further refine the selection, significant matches with a query-subject alignment length of at least 120 amino acids were re-sorted based on query-subject identity in descending order. A second set of homologous sequences was then retrieved from the database following the same procedure. These two sets of homologous sequences, along with the query, were merged and aligned using MUSCLE¹¹⁵ version 3.8.31 with default settings. The resulting alignments, trimmed to a minimum length of 50 amino acids using TrimAl¹³³ version 1.2 in automated mode (-automated1), were used to construct phylogenetic trees with FastTree version 2.1.7, with the 'WAG + CAT' model and four rounds of minimum-evolution SPR moves (-spr4) along with exhaustive ML nearest-neighbour interchanges (-mlacc 2 -slownni). Branch supports were estimated using the Shimodaira-Hasegawa (SH)-test.

### Inferring HGT based on tree topology

Phylogenetic trees were examined to identify specific topologies where Phaeoexplorer query sequences were nested among other sequences, defined as a situation where two or more monophyletic clades consist of both queries and prokaryotic sequences, supported by distinct nodes within the tree. These monophyletic clades are considered to group together if they share the same set of prokaryotic sequences but differ in sequences from optional taxa. Singletons for both the donor and receptor genes were excluded to minimise contamination and recent HGT interference. To retain only robustly supported nested positions, positions were required to be multiply supported, with a minimum of ≥0.70 for the SH-test and aByes-test support from at least two Phaeoexplorer receptor nodes and three donor supporting nodes. Furthermore, queries that displayed significantly different amino acid compositions (P < 0.05) compared to the remaining sequences in the alignment were discarded. Queries from the CRASH category that nested among sequences from other kingdoms (supported by >70% UFBoot at one or more supporting nodes) were retained.

### Enhancing accuracy and establishing the timing of HGTs

To enhance accuracy, a minimum requirement was imposed for all supporting nodes and for strongly supported nodes that indicate query-donor monophyly. To determine the timing of HGT events, temporal information, primarily derived from the timetree database, was incorporated into each node. We assigned the "smallest boundary" role to pinpoint the most recent common ancestor at the time of the HGT event. Essentially, if all descendants of a given query protein sequence can be traced back to the initial HGT event, a common ancestral node can be identified whose occurrence time can be inferred using a molecular clock approach based on archaeological and fossil evidence. The taxonomy boundaries of HGT descendants were determined by identifying the smallest ancestor shared by both the donor and receptor taxa from the monophyletic clades within the tree. By considering the emergence
times of both taxa, the timing of the transfer of genes from earlier taxa to later taxa can be determined, as the reverse scenario is not considered plausible.

## Verification of HGTs

Verification of HGT used the following contamination assessment criteria: i) HGT candidates were excluded if they were located in a contig where 50% of the genes had better matches with other kingdoms, ii) HGT candidates were excluded if they were located in a contig where 50% of the genes were primarily identified as HGT genes, iii) HGT candidates were excluded if one of their five closest flanking genes, both upstream and downstream, had a better match with other kingdoms. AI, hU and the hBL value were used to further validate HGT events. This process was supplemented with annotation and functional predictions for the identified HGTs.

Further validation was based on the following concepts:

**OUTGROUP.** This comprises all biological donors present in a tree, excluding the query species if it belongs to biological donors.

**SKIP.** This includes all biological receptors (species belonging to optional taxa) in a tree, again excluding the query species if it belongs to biological receptors.

**INGROUP.** This encompasses species from SKIP's upper level, excluding SKIP itself and the query species (if it belongs to biological receptors).

**AI (Alien Index).** computed for each query gene using e-values from BLAST hits:

$$ AI = (E - \text{value of best BLAST hit in the INGROUP lineage}) / (E - \text{value of best BLAST hit in the OUTGROUP lineage}) $$

The AI score quantifies how similar queries are to their homologs in the OUTGROUP compared to homologs in the INGROUP. We apply a relatively lenient cut-off (AI > 0) for initial screening, which can be adjusted in the second screening as needed.

**hU (HGT Score Support Index).** calculated for each query gene based on the best bit scores of INGROUP vs. OUTGROUP:

$$ hU = (\text{Best} - \text{hit bitscore of OUTGROUP}) - (\text{Best} - \text{hit bitscore of INGROUP}) $$

A lenient cut-off (hU > 0) is used for initial screening, with flexibility for adjustment in the second screening.

**hBL (HGT Branch Length Support Index).** calculated based on the minimum branch length to the query within INGROUP vs. OUTGROUP:

$$ hBL = (\text{Minimum branch length to the query within INGROUP}) - (\text{Minimum branch length to the query within OUTGROUP}) $$

A lenient cut-off (hBL > 0) is applied initially, with the option for modification in the second screening.

**CHE, CHS, CHBL (Consensus Hit Support).** To mitigate the possibility that the best bit score for either INGROUP or OUTGROUP is influenced by contamination, we consider alternative matches. We introduce consensus hit support (CHE, CHS, and CHBL) to assess the reliability of AI, hU, and hBL, respectively.

For example, if AI > 0, CHE evaluates the likelihood that "AI remains greater than 0" when using the e-value of each sequence in OUTGROUP instead of the e-value of the best BLAST hit in the OUTGROUP lineage (bbhO). A similar approach applies to CHS for hU and CHBL for hBL. This additional layer of evaluation helps ensure the robustness of the HGT verification process.

## Gene codon usage, functional annotation and expression

Indices of codon usage and GC content were calculated using Codonw 1.4.4 (http://codonw.sourceforge.net). Gene functions were assigned by searching against the Gene Ontology (GO) database using blast2GO (ref blast2GO 08) and the KEGG database using blastKOALA (http://www.KEGG.jp/blastkoala/) with default parameters. The full gene sets of each species were set as the background for KEGG and GO enrichment analyses by applying Student's t-test (p-value cutoff = 0.01). HGTs were also analysed with SEED (http://www.theseed.org/wiki/Home_of_the_SEED), IPR2GO (http://www.ebi.ac.uk/interpro/search/sequence-search), eggNOG¹²⁴ (http://eggnogdb.embl.de/#/app/home) and Pfam.¹⁷⁵ For each species, the differences between mean gene expression levels for HGTs and non-HGT genes with common GO terms were accessed using Student's t-test. Go terms with less than five genes in either gene category were ignored. The differences in expression dispersal (coefficient of variation: standard deviation across genes or samples / mean value) and expression specificity (frequencies of a gene to be detected as unexpressed, defined as transcripts per kilobase million (TPM) = 2, in any condition) were accessed in a similar manner. Given the variable experimental conditions associated with different transcriptome data for each species, gene expression values for a gene were used indiscriminately regardless of the conditions. Correlation tests between the codon adaptation index (CAI) and gene expression were carried out using the Spearman's rank correlation analysis tool (P. Wessa, Free Statistics Software, Office for Research Development and Education, version 1.1.23-r7, https://www.wessa.net/).

## Comparative analysis of gene sets identified by genome-wide analyses of evolutionary history

Genes identified as belonging to orthogroups that were predicted to be gained at specific nodes of the phylogenetic tree based on the Dollo parsimony analysis, to belong to either significantly amplified gene families (binomial analysis) or to belong to gene families that have significantly changed in size over evolutionary time (CAFE5 analysis), to correspond to founder events (Phylostratigraphy analysis), to have been remodelled (composite gene analysis) or to have been derived from an HGT (HGT analysis) were extracted from the output of each of these analysis and aggregated in a single datatable. Correspondences were established manually between phylogenetic tree nodes and phylostrata and this information was integrated into the datatable. Counting and calculations of the