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Acknowledgements: Acknowledgements: We acknowledge support from the Carlsberg Foundation for logistics to carry out two expeditions to Kap København in 2006 and 2012 (S. Funder, principal investigator for Carlsberg foundation grant to LongTerm and Kap København—the age). The fieldwork in 2016 was supported by a grant to N.K.L. from the Villum Foundation. We highly appreciate the collaborative support by Illumina Inc. that was crucial for the success of the project. E.W. and K.H.K. thank the Danish National Research Foundation (DNRF) and the Lundbeck Foundation (R302-2018-2155) for providing long-term funds to develop the necessary DNA technology that eventually made it possible to retrieve environmental DNA from these ancient deposits in the Kap København Formation. E.W. also acknowledges the Wellcome Trust (UNS69906), the Carlsberg Foundation (CF18-0024), Novo Foundation (NNF18SA0035006), Leverhume (RPG-2016-235) and GRF EXC CRS Chair - Cluster of Excellence (44113220) for their support. M.W.P. acknowledges support from the Carlsberg Foundation (CF17-0275). K.K.S. and S.J. acknowledge support from VILLUM FONDEN (00025352). I.G.A. and E.C. have received funding from the European Research Council (ERC) under the European Union’s Horizon 2020 research and innovation programme (grant agreement no. 819192). B.D.S. acknowledges support from the Wellcome Trust programme in Mathematical Genomics and Medicine (WT220023). J.Å.K. was supported by the Carlsberg Foundation (CF20-0238). C.B. acknowledges ERC Advanced Award Diatomic (grant agreement no. 835067). J.C.G. was supported by Natural Science and Engineering Research Council of Canada–Discovery Grant 06785 and Canada Foundation for Innovation grant 21305. M.J.C. acknowledges support from the Danish National Research Foundation DNRF128. We thank G. Yang for cosmogenic isotope AMS target chemistry; S. Funder for introducing us to the Kap København Formation and generating much of the platform that enabled us to conduct our research; T. O. Delmont for providing data and guidance on the SMAGs analysis; Minik Rosing for providing talc minerals; T. B. Zunic for providing tremolite, orthoclase and chlorite; Z. Vardanyan for help with the DNA extractions and library build; and L. B. Levy and D. Skov for their help collecting samples in 2016. This work was prepared in part by Lawrence Livermore National Laboratory under contract DE-AC52-07NA27344; LLNL-JRNL-830653. E.W. thanks St Johns College, Cambridge for providing him with a stimulating environment for scientific thoughts and discussion., Late Pliocene and Early Pleistocene epochs 3.6 to 0.8 million years ago1 had climates resembling those forecasted under future warming2. Palaeoclimatic records show strong polar amplification with mean annual temperatures of 11-19 °C above contemporary values3,4. The biological communities inhabiting the Arctic during this time remain poorly known because fossils are rare5. Here we report an ancient environmental DNA6 (eDNA) record describing the rich plant and animal assemblages of the Kap København Formation in North Greenland, dated to around two million years ago. The record shows an open boreal forest ecosystem with mixed vegetation of poplar, birch and thuja trees, as well as a variety of Arctic and boreal shrubs and herbs, many of which had not previously been detected at the site from macrofossil and pollen records. The DNA record confirms the presence of hare and mitochondrial DNA from animals including mastodons, reindeer, rodents and geese, all ancestral to their present-day and late Pleistocene relatives. The presence of marine species including horseshoe crab and green algae support a warmer climate than today. The reconstructed ecosystem has no modern analogue. The survival of such ancient eDNA probably relates to its binding to mineral surfaces. Our findings open new areas of genetic research, demonstrating that it is possible to track the ecology and evolution of biological communities from two million years ago using ancient eDNA.

S8 Data from the publication of Nef et al. in PLOS Biology entitled "Whole-genome scanning reveals environmental selection mechanisms that shape diversity in populations of the epipelagic diatomChaetoceros". (A) Pairwise FST values of ARC_116. (B) Pairwise FST values of ARC_217. (C) Pairwise FSTvalues of ARC_232. (D) Pairwise FST values of PSW_256. (E) Pairwise FST values of SOC_37. (F)Median pairwise FST matrix of ARC_116. (G) Median pairwise FST matrix of ARC_217. (H) Medianpairwise FST matrix of ARC_232. (I) Median pairwise FST matrix of PSW_256. (J) Median pairwiseFST matrix of SOC_37. (K) Mean median pairwise FST matrix of the MAG populations located inArctic Ocean regions.

Supplementary Data from the publication of Nef et al. in PLOS Biology entitled "Whole-genome scanning reveals environmental selection mechanisms that shape diversity in populations of the epipelagic diatomChaetoceros". S10 Data. (A) LK parameters of ARC_116. (B) LK parameters of ARC_217. (C) LK parameters of SOC_37. S17 Data.(A) PCA data of ARC_116 before cleaning. (B) PCA data of ARC_116 after cleaning.

S6 Data from the publication of Nef et al. in PLOS Biology entitled "Whole-genome scanning reveals environmental selection mechanisms that shape diversity in populations of the epipelagic diatom Chaetoceros". Consists in thename, percent identity and location of the reads for the 11 Chaetoceros MAGs.&nbsp

S6 Data from the publication of Nef et al. in PLOS Biology entitled "Whole-genome scanning reveals environmental selection mechanisms that shape diversity in populations of the epipelagic diatom Chaetoceros". Consists in the name, percent identity and location of the reads for the 11 Chaetoceros MAGs.

S8 Data from the publication of Nef et al. in PLOS Biology entitled "Whole-genome scanning reveals environmental selection mechanisms that shape diversity in populations of the epipelagic diatom Chaetoceros". (A) Pairwise FST values of ARC_116. (B) Pairwise FST values of ARC_217. (C) Pairwise FST values of ARC_232. (D) Pairwise FST values of PSW_256. (E) Pairwise FST values of SOC_37. (F) Median pairwise FST matrix of ARC_116. (G) Median pairwise FST matrix of ARC_217. (H) Median pairwise FST matrix of ARC_232. (I) Median pairwise FST matrix of PSW_256. (J) Median pairwise FST matrix of SOC_37. (K) Mean median pairwise FST matrix of the MAG populations located in Arctic Ocean regions.

Supplementary Data from the publication of Nef et al. in PLOS Biology entitled "Whole-genome scanning reveals environmental selection mechanisms that shape diversity in populations of the epipelagic diatom Chaetoceros". S10 Data. (A) LK parameters of ARC_116. (B) LK parameters of ARC_217. (C) LK parameters of SOC_37. S17 Data. (A) PCA data of ARC_116 before cleaning. (B) PCA data of ARC_116 after cleaning.

Biogeographical studies have traditionally focused on readily visible organisms, but recent technological advances are enabling analyses of the large-scale distribution of microscopic organisms, whose biogeographical patterns have long been debated. Here we assessed the global structure of plankton geography and its relation to the biological, chemical, and physical context of the ocean (the ‘seascape’) by analyzing metagenomes of plankton communities sampled across oceans during the Tara Oceans expedition, in light of environmental data and ocean current transport. Using a consistent approach across organismal sizes that provides unprecedented resolution to measure changes in genomic composition between communities, we report a pan-ocean, size-dependent plankton biogeography overlying regional heterogeneity. We found robust evidence for a basin-scale impact of transport by ocean currents on plankton biogeography, and on a characteristic timescale of community dynamics going beyond simple seasonality or life history transitions of plankton., We thank the commitment of the following people and sponsors who made this expedition possible: CNRS (in particular Groupement de Recherche GDR3280), European Molecular Biology Laboratory (EMBL), Genoscope/CEA, Fund for Scientific Research – Flanders, VIB, Stazione Zoologica Anton Dohrn, UNIMIB, Paris Sciences et Lettres (PSL) Research University (ANR-11-IDEX-0001–02), the French Government ANR (projects FRANCE GENOMIQUE/ANR-10-INBS-09, MEMO LIFE/ANR-10-LABX-54, POSEIDON/ANR-09-BLAN-0348, PROMETHEUS/ANR-09-PCS-GENM-217, MAPPI/ANR-2010-COSI-004, TARA-GIRUS/ANR-09-PCS-GENM-218), US NSF grant DEB-1031049, FWO, BIO5, Biosphere 2, Agnès b., the Veolia Environment Foundation, Région Bretagne, World Courier, Illumina, Cap L’Orient, the EDF Foundation EDF Diversiterre, FRB, the Prince Albert II de Monaco Foundation, Etienne Bourgois, the Tara schooner and its captain and crew. We thank MERCATOR-CORIOLIS and ACRI-ST for providing daily satellite data during the expedition. The bulk of genomic computations were performed using the Airain HPC machine provided through GENCI- [TGCC/CINES/IDRIS] (grants t2011076389, t2012076389, t2013036389, t2014036389, t2015036389 and t2016036389). We are also grateful to the French Ministry of Foreign Affairs for supporting the expedition and to the countries who granted us sampling permissions.

The main objective of the Tara Antarctic leg was two-fold: to sample across well-established physical and biogeochemical gradients to conduct a multi-day process study around an iceberg Both are concerned with capturing the response of the microbiome to variability in the seascape introduced by topography, frontal systems and freshwater (both melting sea ice, glaciers and icebergs). These scientific objectives are motivated by the urgent need to understand how the microbiome of the Antarctic seas will respond to climate change, as well as to generate baseline understanding in this under-sampled region.

This article is contribution number 131 of Tara Oceans.-- 11 pages, 5 figures, 1 table, 1 box, Microbial communities have essential roles in ocean ecology and planetary health. Microbes participate in nutrient cycles, remove huge quantities of carbon dioxide from the air and support ocean food webs. The taxonomic and functional diversity of the global ocean microbiome has been revealed by technological advances in sampling, DNA sequencing and bioinformatics. A better understanding of the ocean microbiome could underpin strategies to address environmental and societal challenges, including achievement of multiple Sustainable Development Goals way beyond SDG 14 ‘life below water’. We propose a set of priorities for understanding and protecting the ocean microbiome, which include delineating interactions between microbiota, sustainably applying resources from oceanic microorganisms and creating policy- and funder-friendly ocean education resources, and discuss how to achieve these ambitious goals, We thank R. Zaayman-Gallant, T. Rauscher and F. Ibarbalz for preparation of the figures, and the European Union’s Horizon 2020 research and innovation project AtlantECO, under grant agreement no. 862923, With the institutional support of the ‘Severo Ochoa Centre of Excellence’ accreditation (CEX2019-000928-S)

Despite low iron availability in the ocean, marine phytoplankton require considerable amounts of iron for their growth and proliferation. While there is a constantly growing knowledge of iron uptake and its role in the cellular processes of the most abundant marine photosynthetic groups, there are still largely overlooked branches of the eukaryotic tree of life, such as the chlorarachniophytes., The productivity of the ocean is largely dependent on iron availability, and marine phytoplankton have evolved sophisticated mechanisms to cope with chronically low iron levels in vast regions of the open ocean. By analyzing the metabarcoding data generated from the Tara Oceans expedition, we determined how the global distribution of the model marine chlorarachniophyte Bigelowiella natans varies across regions with different iron concentrations. We performed a comprehensive proteomics analysis of the molecular mechanisms underpinning the adaptation of B. natans to iron scarcity and report on the temporal response of cells to iron enrichment. Our results highlight the role of phytotransferrin in iron homeostasis and indicate the involvement of CREG1 protein in the response to iron availability. Analysis of the Tara Oceans metagenomes and metatranscriptomes also points to a similar role for CREG1, which is found to be widely distributed among marine plankton but to show a strong bias in gene and transcript abundance toward iron-deficient regions. Our analyses allowed us to define a new subfamily of the CobW domain-containing COG0523 putative metal chaperones which are involved in iron metabolism and are restricted to only a few phytoplankton lineages in addition to B. natans. At the physiological level, we elucidated the mechanisms allowing a fast recovery of PSII photochemistry after resupply of iron. Collectively, our study demonstrates that B. natans is well adapted to dynamically respond to a changing iron environment and suggests that CREG1 and COG0523 are important components of iron homeostasis in B. natans and other phytoplankton. IMPORTANCE Despite low iron availability in the ocean, marine phytoplankton require considerable amounts of iron for their growth and proliferation. While there is a constantly growing knowledge of iron uptake and its role in the cellular processes of the most abundant marine photosynthetic groups, there are still largely overlooked branches of the eukaryotic tree of life, such as the chlorarachniophytes. In the present work, we focused on the model chlorarachniophyte Bigelowiella natans, integrating physiological and proteomic analyses in culture conditions with the mining of omics data generated by the Tara Oceans expedition. We provide unique insight into the complex responses of B. natans to iron availability, including novel links to iron metabolism conserved in other phytoplankton lineages.

Diverse microbial ecosystems underpin life in the sea. Among these microbes are many unicellular eukaryotes that span the diversity of the eukaryotic tree of life. However, genetic tractability has been limited to a few species, which do not represent eukaryotic diversity or environmentally relevant taxa. Here, we report on the development of genetic tools in a range of protists primarily from marine environments. We present evidence for foreign DNA delivery and expression in 13 species never before transformed and for advancement of tools for eight other species, as well as potential reasons for why transformation of yet another 17 species tested was not achieved. Our resource in genetic manipulation will provide insights into the ancestral eukaryotic lifeforms, general eukaryote cell biology, protein diversification and the evolution of cellular pathways.

An amendment to this paper has been published and can be accessed via a link at the top of the paper.

Ocean microbial communities strongly influence the biogeochemistry, food webs, and climate of our planet. Despite recent advances in understanding their taxonomic and genomic compositions, little is known about how their transcriptomes vary globally. Here, we present a dataset of 187 metatranscriptomes and 370 metagenomes from 126 globally distributed sampling stations and establish a resource of 47 million genes to study community-level transcriptomes across depth layers from pole-to-pole. We examine gene expression changes and community turnover as the underlying mechanisms shaping community transcriptomes along these axes of environmental variation and show how their individual contributions differ for multiple biogeochemically relevant processes. Furthermore, we find the relative contribution of gene expression changes to be significantly lower in polar than in non-polar waters and hypothesize that in polar regions, alterations in community activity in response to ocean warming will be driven more strongly by changes in organismal composition than by gene regulatory mechanisms. VIDEO ABSTRACT.

35 pages, 18 figures, 1 table, supplementary information https://doi.org/10.1016/j.cell.2019.10.008.-- Raw reads of Tara Oceans are deposited at the European Nucleotide Archive (ENA). In particular, newly released 18S rRNA gene metabarcoding reads are available under the number ENA: PRJEB9737. ENA references for the metagenomics reads corresponding to the size fraction < 0.22 μm (for prokaryotic viruses) analyzed in this study are included in Gregory et al. (2019); see their Table S3. ENA references for the metagenomics reads corresponding to the size fraction 0.22-1.6/3 μm (for prokaryotes and giruses) correspond to Salazar et al. (2019) (see https://zenodo.org/record/3473199). Imaging datasets from the nets are available through the collaborative web application and repository EcoTaxa (Picheral et al., 2017) under the address https://ecotaxa.obs-vlfr.fr/prj/412 for regent data, within the 3 projects https://ecotaxa.obs-vlfr.fr/prj/397, https://ecotaxa.obs-vlfr.fr/prj/398, https://ecotaxa.obs-vlfr.fr/prj/395 for bongo data, and within the 2 projects https://ecotaxa.obs-vlfr.fr/prj/377 and https://ecotaxa.obs-vlfr.fr/prj/378 for WP2 data. A table with Shannon values and multiple samples identifiers, plus a table with flow cytometry data split in six groups are available (https://doi.org/10.17632/p9r9wttjkm.1). Contextual data from the Tara Oceans expedition, including those that are newly released from the Arctic Ocean, are available at https://doi.org/10.1594/PANGAEA.875582, The ocean is home to myriad small planktonic organisms that underpin the functioning of marine ecosystems. However, their spatial patterns of diversity and the underlying drivers remain poorly known, precluding projections of their responses to global changes. Here we investigate the latitudinal gradients and global predictors of plankton diversity across archaea, bacteria, eukaryotes, and major virus clades using both molecular and imaging data from Tara Oceans. We show a decline of diversity for most planktonic groups toward the poles, mainly driven by decreasing ocean temperatures. Projections into the future suggest that severe warming of the surface ocean by the end of the 21st century could lead to tropicalization of the diversity of most planktonic groups in temperate and polar regions. These changes may have multiple consequences for marine ecosystem functioning and services and are expected to be particularly significant in key areas for carbon sequestration, fisheries, and marine conservation, Tara Oceans (which includes both the Tara Oceans and Tara Oceans Polar Circle expeditions) would not exist without the leadership of the Tara Ocean Foundation and the continuous support of 23 institutes (https://oceans.taraexpeditions.org/). We further thank the commitment of the following sponsors: CNRS (in particular Groupement de Recherche GDR3280 and the Research Federation for the Study of Global Ocean Systems Ecology and Evolution FR2022/Tara Oceans-GOSEE), the European Molecular Biology Laboratory (EMBL), Genoscope/CEA, the French Ministry of Research, and the French Government “Investissements d’Avenir” programs OCEANOMICS (ANR-11-BTBR-0008), FRANCE GENOMIQUE (ANR-10-INBS-09-08), MEMO LIFE (ANR-10-LABX-54), the PSL∗ Research University (ANR-11-IDEX-0001-02), as well as EMBRC-France (ANR-10-INBS-02). Funding for the collection and processing of the Tara Oceans data set was provided by NASA Ocean Biology and Biogeochemistry Program under grants NNX11AQ14G, NNX09AU43G, NNX13AE58G, and NNX15AC08G (to the University of Maine); the Canada Excellence research chair on remote sensing of Canada’s new Arctic frontier; and the Canada Foundation for Innovation. We also thank agnès b. and Etienne Bourgois, the Prince Albert II de Monaco Foundation, the Veolia Foundation, Region Bretagne, Lorient Agglomeration, Serge Ferrari, Worldcourier, and KAUST for support and commitment. The global sampling effort was enabled by countless scientists and crew who sampled aboard the Tara from 2009–2013, and we thank MERCATOR-CORIOLIS and ACRI-ST for providing daily satellite data during the expeditions. We are also grateful to the countries who graciously granted sampling permission. We thank Stephanie Henson for providing ocean carbon export data and are also grateful to the other researchers who kindly made their data available. We thank Juan J. Pierella-Karlusich for advice regarding single-copy genes. C.d.V. and N.H. thank the Roscoff Bioinformatics platform ABiMS (http://abims.sb-roscoff.fr) for providing computational resources. C.B. acknowledges funding from the European Research Council (ERC) under the European Union’s Horizon 2020 Research and Innovation Program (grant agreement 835067) as well as the Radcliffe Institute of Advanced Study at Harvard University for a scholar’s fellowship during the 2016-2017 academic year. M.B.S. thanks the Gordon and Betty Moore Foundation (award 3790) and the National Science Foundation (awards OCE#1536989 and OCE#1829831) as well as the Ohio Supercomputer for computational support. S.G.A. thanks the Spanish Ministry of Economy and Competitiveness (CTM2017-87736-R), and J.M.G. is grateful for project RT2018-101025-B-100. F.L. thanks the Institut Universitaire de France (IUF) as well as the EMBRC platform PIQv for image analysis. M.C.B., D.S., and J.R. received financial support from the French Facility for Global Environment (FFEM) as part of the “Ocean Plankton, Climate and Development” project. M.C.B. also received financial support from the Coordination for the Improvement of Higher Education Personnel of Brazil (CAPES 99999.000487/2016-03)

Dinoflagellates are algae of tremendous importance to ecosystems and to public health. The cell biology and genome organization of dinoflagellate species is highly unusual. For example, the plastid genomes of peridinin-containing dinoflagellates encode only a minimal number of genes arranged on small elements termed "minicircles". Previous studies of peridinin plastid genes have found evidence for divergent sequence evolution, including extensive substitutions, novel insertions and deletions, and use of alternative translation initiation codons. Understanding the extent of this divergent evolution has been hampered by the lack of characterized peridinin plastid sequences. We have identified over 300 previously unannotated peridinin plastid mRNAs from published transcriptome projects, vastly increasing the number of sequences available. Using these data, we have produced a well-resolved phylogeny of peridinin plastid lineages, which uncovers several novel relationships within the dinoflagellates. This enables us to define changes to plastid sequences that occurred early in dinoflagellate evolution, and that have contributed to the subsequent diversification of individual dinoflagellate clades. We find that the origin of the peridinin dinoflagellates was specifically accompanied by elevations both in the overall number of substitutions that occurred on plastid sequences, and in the Ka/Ks ratio associated with plastid sequences, consistent with changes in selective pressure. These substitutions, alongside other changes, have accumulated progressively in individual peridinin plastid lineages. Throughout our entire dataset, we identify a persistent bias toward non-synonymous substitutions occurring on sequences encoding photosystem I subunits and stromal regions of peridinin plastid proteins, which may have underpinned the evolution of this unusual organelle.

Dinoflagellates are a group of unicellular protists with immense ecological and evolutionary significance and cell biological diversity. Of the photosynthetic dinoflagellates, the majority possess a plastid containing the pigment peridinin, whereas some lineages have replaced this plastid by serial endosymbiosis with plastids of distinct evolutionary affiliations, including a fucoxanthin pigment-containing plastid of haptophyte origin. Previous studies have described the presence of widespread substitutional RNA editing in peridinin and fucoxanthin plastid genes. Because reports of this process have been limited to manual assessment of individual lineages, global trends concerning this RNA editing and its effect on the biological function of the plastid are largely unknown. Using novel bioinformatic methods, we examine the dynamics and evolution of RNA editing over a large multispecies data set of dinoflagellates, including novel sequence data from the peridinin dinoflagellate Pyrocystis lunula and the fucoxanthin dinoflagellate Karenia mikimotoi. We demonstrate that while most individual RNA editing events in dinoflagellate plastids are restricted to single species, global patterns, and functional consequences of editing are broadly conserved. We find that editing is biased toward specific codon positions and regions of genes, and generally corrects otherwise deleterious changes in the genome prior to translation, though this effect is more prevalent in peridinin than fucoxanthin lineages. Our results support a model for promiscuous editing application subsequently shaped by purifying selection, and suggest the presence of an underlying editing mechanism transferred from the peridinin-containing ancestor into fucoxanthin plastids postendosymbiosis, with remarkably conserved functional consequences in the new lineage.

Figure S4. PCA based on codon usage frequency of dGC flip over region on chromosome 10. PCA based on codon usage frequency of dGC flip over region on chromosome 10. 10_Fso_h and _l represents the PCA result of all homoeologous genes on chromosome 10 with our tentative classification described in the Results section. These values are identical to “10” represented in Fig. 2. 10_A and 10_B represents the PCA results of chromosome 10 (high global GC content), _A includes the homoeologous genes located in the upstream region of dGC flip and _B includes the homoeologous genes located downstream. 10′_A and 10’_B represents the PCA result of corresponding region in chromosome 10′ (low global GC content). (PDF 37 kb)

Figure S7. Expression and functional differences depending on existence of motif TABASTA in promoter region. (A) Figure shows the mean number of homoeologous gene pairs which showing homoeolog expression bias towards Fso_h or Fso_l (n = 3, ±S.D.). Bars were separated into three groups depend on whether the motif exist in promoter region of both gene of the pair, only Fso_h gene of the pair or only Fso_l gene of the pair. Asterisk show significant differences (p

Figure S2. Codon usage frequency of subgenomes Fso_h and Fso_l. (A) Codon usage frequency of each of the subgenomes are compared with genomes of the model diatoms P. tricornutum and T. pseudonana. Codon usage of P. tricornutum and T. pseudonana were calculated from the nucleotide FASTA file of the newest version of filtered gene models (v2.0 for P. tricornutum (Phatr2) and v3.0 for T. pseudonana (Thaps3)) downloaded from the genome portal of the Department of Energy Joint Genome Institute (JGI) [80]. (B) Codon usage frequency of the subgenomes ordered in coding amino acids and (C) ratio of usage between Fso_h and Fso_l. Dots show the ratio of codon usage (right y-axis). Red line indicates the equal ratio, therefore the codons with the usage ratio above the red line are more frequently used in Fso_h subgenome, whereas codons with the usage ratio beneath the red line are more frequently used in Fso_l subgenome. (PDF 110 kb)

Figure S1. Structure of dGC flip over region on chromosome 10. (A) Orange and blue colors of the pentagon arrows represent the different dGC values in corresponding homoeologous genes; orange indicates the genes with relatively higher dGC and blue indicates the genes with relatively lower dGC within the pair. Each homoeologous gene pair are connected with dotted lines. The horizontal dotted line at the ends of some of the scaffolds indicates that the scaffold continues beyond what is shown in the figures. The gene map is not drawn to scale. (B) Scaffold assembly of dGC flip over region in chromosome 10â ˛, which is rounded by red line in (A), was validated by PCR. The schematic figure represents the 8 amplified regions in PCR experiment. Vertically printed numbers indicate the base position within the scaffold. Each amplified region has overlapped region to the next. Primers for this experiment was designed to annealed on either non-coding region or non-homoeologous gene region, to avoid the non-specific amplification of paired homoeologous gene. (C) Length of PCR products were analyzed by electrophoresis through a 0.8% (wt/vol) agarose gel. Indicated lane numbers are corresponding to the amplified region shown in (B). (PDF 69 kb)

Figure S10. Comparison of GC content ratio between orthologous pair of Thalassiosira oceanica and T. pseudonana. 6463 orthologs of T. oceanica and T. pseudonana were discovered by sequence homology analysis and GC content ratio (dGC) were calculated. Vertical axis represents dGC value of each orthologs. The dGC values of orthologs along the T. oceanica and T. pseudonana were indicated as red and green line, respectively. The dGC plots for individual chromosomes were not prepared because the genome sequence of T. oceanica was not assembled into each chromosome. (PDF 151 kb)

Figure S5. Homoeolog expression bias in highly expressing genes. (A) Box plot showing RPKM values of homoeologous genes at each time point. The minimum value of outlier (represented as a circle in the figure) is approximately 50 at every time point. (B-D) Histograms show number of homoeologous genes expressing in a biased fashion towards a specific subgenome. Each figure includes the homoeologous genes expressing within a certain range of RPKM values; (B) RPKM >â 50, (C) >â 100 and (D) >â 500. N values in figure represent the number of genes expressing preferentially in subgenome A or B. S values in the figure indicate the number of genes counted as biased expression due to silencing of the other homoeologous pair (not included in histograms). (PDF 97 kb)

Figure S9. Expression profile of homoeologous genes involved in lipid metabolism at the 48, 96, and 144Â h time points. Orange and blue lines represent the subgenomes of each homoeologous gene belonging to Fso_h and Fso_l, respectively. (PDF 151 kb)

Figure S6. GO terms of consistently biased homoeologous genes. Figure shows the frequency of GO terms, annotated to the second depth, within the group of homoeologous genes which were consistently biased towards Fso_h or Fso_l (corresponding to the genes in the histogram of Fig. 3b). Orange and blue colors represent the direction of homoeolog expression bias of the group, Fso_h and Fso_l, respectively. Dots in the figure represent the log ratio of frequency of corresponding GO terms between Fso_h-biased group and Fso_l-biased group. (PDF 279 kb)

Figure S8. Range of homoeolog expression bias in metabolic pathways. Box plot of log2 showing the expression ratio of homoeologous gene pairs in each metabolic pathway. Red dotted line indicates the equal ratio log2. Black dotted line indicates the threshold of expression ratio where the homoeologous gene pair is defined as biased (log2FCâ >â 1). The orange and blue colors of the box indicate the direction of homoeolog expression bias, towards Fso_h and Fso_l, respectively. (PDF 37 kb)

Figure S3. Comparison of GC content at the third codon position (dGC3) between pairs of homoeologous genes in each homoeologous chromosome. The vertical axis represents the dGC3 value of each homoeologous gene, and horizontal axis indicates the relative number of homoeologous genes on each chromosome. The dGC3 value indicates the ratio of GC content at the third codon position in each homoeologous gene with respect to its corresponding pair, so if the dGC3 value is over 1 (above the horizontal axis), its GC content is higher than the other pair. Red and green lines correspond to the global GC content of chromosome; red lines indicate the dGC3 value of homoeologous gene along the homoeologous chromosome with higher global GC content and green lines indicate the dGC3 value of the gene along the homoeologous chromosome with lower global GC content. The profile of dGC3 showed perfect consistency with dGC (Fig. 1) and approximately twice more difference than dGC. We also calculated the GC content ratio of first and second codon positions; however consistency to dGC was low. This suggests that the differences in dGC of the two subgenomes was mainly derived from the GC content ratio of the third codon position. (PDF 137 kb)

While our knowledge about the roles of microbes and viruses in the ocean has increased tremendously due to recent advances in genomics and metagenomics, research on marine microbial eukaryotes and zooplankton has benefited much less from these new technologies because of their larger genomes, their enormous diversity, and largely unexplored physiologies. Here, we use a metatranscriptomics approach to capture expressed genes in open ocean Tara Oceans stations across four organismal size fractions. The individual sequence reads cluster into 116 million unigenes representing the largest reference collection of eukaryotic transcripts from any single biome. The catalog is used to unveil functions expressed by eukaryotic marine plankton, and to assess their functional biogeography. Almost half of the sequences have no similarity with known proteins, and a great number belong to new gene families with a restricted distribution in the ocean. Overall, the resource provides the foundations for exploring the roles of marine eukaryotes in ocean ecology and biogeochemistry.

Single-celled eukaryotes (protists) are critical players in global biogeochemical cycling of nutrients and energy in the oceans. While their roles as primary producers and grazers are well appreciated, other aspects of their life histories remain obscure due to challenges in culturing and sequencing their natural diversity. Here, we exploit single-cell genomics and metagenomics data from the circumglobal Tara Oceans expedition to analyze the genome content and apparent oceanic distribution of seven prevalent lineages of uncultured heterotrophic stramenopiles. Based on the available data, each sequenced genome or genotype appears to have a specific oceanic distribution, principally correlated with water temperature and depth. The genome content provides hypotheses for specialization in terms of cell motility, food spectra, and trophic stages, including the potential impact on their lifestyles of horizontal gene transfer from prokaryotes. Our results support the idea that prominent heterotrophic marine protists perform diverse functions in ocean ecology.

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Comparison of ferritin iron loading in O. tauri and C. reinhardtii. Cells of the two species (including the ferritin KO mutant of O. tauri, used as a control) were cultured as described in the methods, with different concentrations of Cu (−: no Cu added; +: 0.1 μM (O. tauri) or 1 μM (C. reinhardtii) CuSO4) and Fe (−: 1 nM (O. tauri) or 50 nM (C. reinhardtii) ferric citrate; +: 1 μM ferric citrate). After 5 days, the cells were harvested, and resuspended in iron-free Mf medium (O. tauri) or iron-free TAP medium (C. reinhardtii) supplemented with 5 μM 55Fe(III)-citrate. The cells were incubated for 3 h and then harvested by centrifugation. Whole-cell extracts were obtained and subjected to native PAGE (25 μg/lane), as described in the methods. A comparison of autoradiographs of dried gels shows that O. tauri ferritin was maximally loaded with iron when the cells had previously been grown in iron-rich conditions, whereas the main ferritin of C. reinhardtii was maximally loaded with iron when the cells were grown in iron-deficient conditions. The addition of Cu to the medium resulted in some intracellular iron redistribution in O. tauri (although Cu did not affect iron uptake): a band that we previously identified as a probable nitrate reductase (Botebol et al.) was more intense in the presence of copper. We are currently characterizing this band further. (PPTX 3065 kb)

Domain organization of multicopper oxidase and iron permease homologs in C. reinhardtii and O. tauri. (A) The closest homolog to the C. reinhardtii multicopper oxidase in O. tauri has a different organization of multicopper oxidase domains (ostta14g01670) and was not significantly induced by iron deficiency in our study. (B) The only O. tauri gene (ostta10g02530) displaying some sequence identity to the Ftr1 iron permease in HMM-HMM analysis had an entirely different domain organization. Domains were identified with the Pfam database and the Phobius transmembrane topology and signal peptide predictor. (PPTX 60 kb)

The Ot-Fea1 protein contains two Fea1 domains. (A) Multiple alignment of the N- and C-terminal OtFea1 domains and the C. reinhardtii Fea1 protein. R/K-E/D-X-X-E motifs are framed. (B) Domain organization of O. tauri OtFea1, P. tricornutum Isip2a and C. reinhardtii Fea1 as revealed by HMM-HMM comparison, with the Pfam database and the Phobius transmembrane topology and signal peptide predictor. (PPTX 781 kb)

Schematic representation of the experimental design used for analysis of the short-term and long-term adaptive responses of the cells to iron deprivation. In this study, 16 different samples were analyzed with RNAseq technology. They are represented here with gray circles. Transcriptome states were determined in O. tauri as a function of three different factors: i) iron deprivation, (−Fe) or control (+Fe), ii) light or dark exposure and iii) short- or long-term adaptive response (condition 1 and condition 2). Experiments comparing conditions 1 and 2 were repeated three times, with three biological replicates for each sample. (PPTX 70 kb)

PhytoCRISP-Ex efficacy. The scatter plot depicts that most of the predicted Cas9 targets per gene in Thalassiosira pseudonana and Phaeodactylum tricornutum, respectively, are potential candidates (passing both PhytoCRISP-Ex filter). X-axis represents the percent efficiency of each gene in terms of having high number of potential Cas9 targets compared to the total number of targets. Y-axis represents the number of all potential targets per gene. (PDF 182 kb)

A. thaliana IRT1 homolog in O. tauri contains three His-rich motifs. Pairwise alignment of A. thaliana Irt1 and its iron-regulated homolog from O. tauri (ostta16g02300). Transmembrane regions were predicted with TMpred software. Blue strips correspond to transmembrane helices. Histidine-rich motifs are framed. The figure was generated with Geneious version 7.1 (Biomatters). (PPTX 615 kb)

Domain organization of ferric reductase and cytochrome b561 homologs in C. reinhardtii and O. tauri. The ferrireductase homolog (ostta09g01890) was slightly repressed, whereas the two cytochrome b561 homologs (ostta16g00370 and ostta04g02840) were significantly upregulated by iron deprivation in O. tauri cells. Domains were identified by HMM-HMM comparison, with the Pfam database. (PPTX 64 kb)

There is a huge abundance of viruses and membrane vesicles in seawater. We describe a new full-field, incoherently illuminated, shot-noise limited, common-path interferometric detection method that we couple with the analysis of Brownian motion to detect, quantify, and differentiate biotic nanoparticles. We validated the method with calibrated nanoparticles and homogeneous DNA or RNA.viruses. The smallest virus size that we characterized with a suitable signal-to-noise ratio was around 30 nm in diameter. Analysis of Brownian motions revealed anisotropic trajectories for myoviruses.We further applied the method for vesicles detection and for analysis of coastal and oligotrophic samples from Tara Oceans circumnavigation., Comment: 11 pages 4 figures

Global comparison of the O. tauri and C. reinhardtii transcriptional responses to iron limitation. Gene expression data were collected from the article of Urzica et al. [72] and orthologous relationships between genes were inferred with the INPARANOID program, using default parameters. (A) Statistics regarding the number of genes for which expression had been quantified, the number of genes with LogFC values > 0.5 or

Domain organization of ferric reductase and cytochrome b561 homologs in C. reinhardtii and O. tauri. The ferrireductase homolog (ostta09g01890) was slightly repressed, whereas the two cytochrome b561 homologs (ostta16g00370 and ostta04g02840) were significantly upregulated by iron deprivation in O. tauri cells. Domains were identified by HMM-HMM comparison, with the Pfam database. (PPTX 64 kb)

Gel filtration elution profile (proteins and iron) of a whole-cell extract of O. tauri cells. O. tauri cells were grown for one week with 0.1 μM 55Fe(III) citrate as an iron source. The cells were disrupted by sonication in the presence of 0.5 % digitonin, and the soluble fraction was subjected to gel filtration (FPLC) as described in the methods. The black curve shows the elution profile of the proteins, and the red curve shows the elution profile of 55Fe. Iron was associated with three main fractions: i) a very high-MW fraction, corresponding to the high-MW complexes of the photosystems and respiratory chain, ii) a high-MW fraction corresponding to ferritin, and other large iron complexes, such as nitrate reductase, and iii) a very low-MW fraction corresponding to the unknown iron pool. This last fraction accounted for most of the intracellular iron, probably stored in the form of polyphosphate complexes. (PPTX 157 kb)

PhytoCRISP-Ex efficacy. The scatter plot depicts that most of the predicted Cas9 targets per gene in Thalassiosira pseudonana and Phaeodactylum tricornutum, respectively, are potential candidates (passing both PhytoCRISP-Ex filter). X-axis represents the percent efficiency of each gene in terms of having high number of potential Cas9 targets compared to the total number of targets. Y-axis represents the number of all potential targets per gene. (PDF 182 kb)

Global comparison of the O. tauri and C. reinhardtii transcriptional responses to iron limitation. Gene expression data were collected from the article of Urzica et al. [72] and orthologous relationships between genes were inferred with the INPARANOID program, using default parameters. (A) Statistics regarding the number of genes for which expression had been quantified, the number of genes with LogFC values > 0.5 or

Effect of copper (A) and iron (B) on the growth rate of O. tauri and C. reinhardtii, and iron associated to the cells of both species as a function of iron concentration in the medium (C). A, B: O. tauri (red circles) and C. reinhardtii (black squares) cells were grown with a gradient of CuSO4 (0.01 nM-50 μM) and Fe(III)-citrate (1 nM-10 μM) concentrations as described in Methods. Results are expressed as a % of the maximal growth rate in exponential growth phase. Means ± SD from 4 experiments. C: Intracellular iron content was determined as described in Methods after growing O. tauri (red circles) and C. reinhardtii (black squares) for 5 days in the presence of 10 nM-10 μM 55Fe-ferric citrate (open symbols) or 55Fe(III)-EDTA (closed symbols)..Means ± SD from 3 experiments. The maximal growth rates of O. tauri and C. reinhardtii were reached with 50 nM and >1 μM iron in the medium, respectively (A). Copper also had very different effects on the growth of the two species: the addition of copper to the medium did not improve the growth of O. tauri (B), even when the cells were repeatedly used to re-inoculate copper-free medium (i.e. in medium with no added copper) over a period of several months. Thus, contaminating copper was sufficient to meet the copper requirements of the cells; when copper was added to the medium, it became toxic at concentrations greater than 1 μM (B). By contrast, the addition of copper to the medium at concentrations of up to 5 μM improved the growth of C. reinhardtii, and toxicity was observed only at concentrations exceeding this value (B). These striking differences probably reflect fundamental aspects of the iron-copper connection in the two species, and differences in the response of cells to iron deprivation.The iron content per cell volume was 10 to 100-fold greater in O. tauri than in C. reinhardtii, suggesting that the iron uptake systems of O. tauri are much more efficient than those of C. reinhardtii. (PPTX 272 kb)

Effect of metals in the medium on iron uptake by O. tauri. The cells were maintained in Mf medium without zinc and copper (and 0.1 μM iron) for 3 months. The cells were then harvested and used to inoculate Mf medium with or without supplementation with zinc (0 or 1 μM), iron (1 or 100 nM) and copper (0 or 1 μM). After one week of growth, the cells were harvested and iron uptake kinetics were recorded with 1 μM 55Fe(III)-EDTA (1:10). Mean ± SD from 3 experiments. (PPTX 122 kb)

Effect of metals in the medium on iron uptake by O. tauri. The cells were maintained in Mf medium without zinc and copper (and 0.1 μM iron) for 3 months. The cells were then harvested and used to inoculate Mf medium with or without supplementation with zinc (0 or 1 μM), iron (1 or 100 nM) and copper (0 or 1 μM). After one week of growth, the cells were harvested and iron uptake kinetics were recorded with 1 μM 55Fe(III)-EDTA (1:10). Mean ± SD from 3 experiments. (PPTX 122 kb)

Mass spectrometry analysis of the proteins present in the iron-containing band of the native gel shown in Fig. 5. A: LC-MSMS-based Ostreococcus Protein identification from in-gel trypsin digestions of 55Fe-labelled proteins (from the iron band shown in Fig. 5). Right and left panels show the results of two independent experiments. Identifications were sorted by descending Mascot scores. Data presented include the Mascot score, the protein sequence coverage (as %), the number of proteins in the identified protein groups, the number of unique peptides, and of peptide search matches, together with the description of the protein in terms of number of aminoacid residues, molecular mass and calculated isoelectric point. B: MSMS-based OtFea sequence coverage. Peptides identified at a 1 % FDR are highlighted in green. C: Representative annotated fragmentation spectrum of an OtFea peptide (Charge: +2, Monoisotopic m/z: 795.88043 Da (−0.27 mmu/-0.34 ppm), MH+: 1590.75359 Da, RT: 27.91 min) and D: corresponding sequence attribution (AQALVADGGVCETGSR, C11-Carbamidomethyl (57.02146 Da)). The peptide was identified with Mascot (v1.30) with an ion Score of 83 and an e-value of 8.2E-008. Fragment match tolerance used for search was 0.02 Da. Fragments used for search were: a; a-H2O; a-NH3; b; b-H2O; b-NH3; y; y-H2O; y-NH3. (PPTX 659 kb)

Effect of copper (A) and iron (B) on the growth rate of O. tauri and C. reinhardtii, and iron associated to the cells of both species as a function of iron concentration in the medium (C). A, B: O. tauri (red circles) and C. reinhardtii (black squares) cells were grown with a gradient of CuSO4 (0.01 nM-50 μM) and Fe(III)-citrate (1 nM-10 μM) concentrations as described in Methods. Results are expressed as a % of the maximal growth rate in exponential growth phase. Means ± SD from 4 experiments. C: Intracellular iron content was determined as described in Methods after growing O. tauri (red circles) and C. reinhardtii (black squares) for 5 days in the presence of 10 nM-10 μM 55Fe-ferric citrate (open symbols) or 55Fe(III)-EDTA (closed symbols)..Means ± SD from 3 experiments. The maximal growth rates of O. tauri and C. reinhardtii were reached with 50 nM and >1 μM iron in the medium, respectively (A). Copper also had very different effects on the growth of the two species: the addition of copper to the medium did not improve the growth of O. tauri (B), even when the cells were repeatedly used to re-inoculate copper-free medium (i.e. in medium with no added copper) over a period of several months. Thus, contaminating copper was sufficient to meet the copper requirements of the cells; when copper was added to the medium, it became toxic at concentrations greater than 1 μM (B). By contrast, the addition of copper to the medium at concentrations of up to 5 μM improved the growth of C. reinhardtii, and toxicity was observed only at concentrations exceeding this value (B). These striking differences probably reflect fundamental aspects of the iron-copper connection in the two species, and differences in the response of cells to iron deprivation.The iron content per cell volume was 10 to 100-fold greater in O. tauri than in C. reinhardtii, suggesting that the iron uptake systems of O. tauri are much more efficient than those of C. reinhardtii. (PPTX 272 kb)