Richard G Dorrell

Yue Liang

Nolwenn Gueguen

Tomomi Nonoyama

Youjun Zhang

Dany Croteau

Mathias Penot

Sandrine Adiba

Benjamin Bailleul

Valérie Gros

Juan José

Pierella Karlusich

Nathanaël Zweig

Alisdair R Fernie

Juliette Jouhet

Eric Maréchal

Chris Bowler

A Plastidial Glycolytic-Gluconeogenic Switch of Mitochondrial Origin Enables Diatom Adaptations to High Latitudes

Organic carbon fixed through the Calvin Cycle can be diverted towards different metabolic fates within and beyond the plastids of photosynthetic eukaryotes. These include export to the cytoplasm and mitochondrial respiration; gluconeogenesis of storage compounds; and the anabolic synthesis of lipids, amino acids and cofactors via the plastidial pyruvate hub. In plants, pyruvate is principally synthesised via the lower half of glycolysis-gluconeogenesis in the cytoplasm, although a secondary plastid-targeted pathway in non-photosynthetic tissue directly links glyceraldehyde-3phosphate to the pyruvate hub. Here, we characterize a complete plastidial lower half glycolyticgluconeogenic pathway in the photosynthetic plastids of diatoms, obligately photosynthetic eukaryotic algae that are important contributors to marine primary production. We show that the two enzymes required to complete plastidial glycolysis-gluconeogenesis, plastidial Enolase and PGAM (bis-phospho-glycerate mutase), originated through recent duplications of mitochondriatargeted respiratory glycolytic isoforms. Through CRISPR-Cas9 mutagenesis and integrative 'omic analyses in the diatom Phaeodactylum tricornutum, we present evidence that this pathway .

Introduction

The photosynthetic assimilation of carbon dioxide into glyceraldehyde-3-phosphate via the Calvin Cycle is fundamental to plant and algal biology [START_REF] Raines | The Calvin cycle revisited[END_REF][START_REF] Martin | The evolution of the Calvin cycle from prokaryotic to eukaryotic chromosomes: a case study of functional redundancy in ancient pathways through endosymbiosis[END_REF][START_REF] Hügler | Beyond the Calvin cycle: autotrophic carbon fixation in the ocean[END_REF]. Over 100 billion tonnes of inorganic carbon is fixed each year through this pathway, with effectively equal division of activity between terrestrial plants and aquatic cyanobacteria and eukaryotic algae, maintaining planetary climate homeostasis and supporting the entire Earth ecosystem [START_REF] Hügler | Beyond the Calvin cycle: autotrophic carbon fixation in the ocean[END_REF][START_REF] Malhi | Tropical forests and atmospheric carbon dioxide[END_REF]. Following its synthesis, glyceraldehyde-3phosphate may be converted into larger carbon sugars (gluconeogenesis), broken down into smaller carbon sugars (via glycolysis), or used for the anabolic synthesis of other metabolites. In photosynthetic eukaryotes, which possess plastids (or chloroplasts), these metabolic pathways typically involve the export of glyceraldehyde-3-phosphate or one of its derivatives to the cytosol, or eventually the mitochondria, for subsequent anabolic and respiratory metabolism [START_REF] Moog | Localization and evolution of putative triose phosphate translocators in the diatom Phaeodactylum tricornutum[END_REF].

Alongside the photosynthetic production of glyceraldehyde-3-phosphate, plastids engage in multiple other carbon metabolism activities (Fig. 1A). In plants, these include the synthesis of starch from glucose-6-phosphate [START_REF] Scialdone | Arabidopsis plants perform arithmetic division to prevent starvation at night[END_REF], produced directly in the chloroplasts of leaves via the gluconeogenic conversion of glyceraldehyde-3-phosphate into glucose-6-phosphate; or in non-photosynthetic plastids (amyloplasts) found in root and other storage tissue via the uptake of cytoplasmic hexose phosphates (Fig. 1A) [START_REF] Carrera | Distinct plastid fructose bisphosphate aldolases function in photosynthetic and nonphotosynthetic metabolism in Arabidopsis[END_REF][START_REF] Anoman | The specific role of plastidial glycolysis in photosynthetic and heterotrophic cells under scrutiny through the study of glyceraldehyde-3-phosphate dehydrogenase[END_REF]. Further metabolites are synthesised in the plastids from shorter-chain sugars, including: free fatty acids and lipids from acetyl-coA [START_REF] Troncoso-Ponce | Molecular and biochemical characterization of the sunflower (Helianthus annuus L.) cytosolic and plastidial enolases in relation to seed development[END_REF][START_REF] Flores-Tornero | Overexpression of the triose phosphate translocator (TPT) complements the abnormal metabolism and development of plastidial glycolytic glyceraldehyde-3phosphate dehydrogenase mutants[END_REF]; aromatic amino acids (via the shikimate pathway), aspartate and lysine from phospho-enol-pyruvate (PEP); small hydrophic amino acids (alanine, leucine, valine) from pyruvate; serine and cysteine from 3-phosphoglycerate [START_REF] Troncoso-Ponce | Molecular and biochemical characterization of the sunflower (Helianthus annuus L.) cytosolic and plastidial enolases in relation to seed development[END_REF][START_REF] Igamberdiev | The glycerate and phosphorylated pathways of serine synthesis in plants: The branches of plant glycolysis linking carbon and nitrogen metabolism[END_REF][START_REF] Muñoz-Bertomeu | A critical role of plastidial glycolytic glyceraldehyde-3-phosphate dehydrogenase in the control of plant metabolism and development[END_REF][START_REF] Bromke | Amino acid biosynthesis pathways in diatoms[END_REF]; chlorophyll (via the tetrapyrrole pathway) from glutamate and carotenoids (via the nonmevalonate pathway) from pyruvate [START_REF] Coesel | Evolutionary origins and functions of the carotenoid biosynthetic pathway in marine diatoms[END_REF][START_REF] Tanaka | Tetrapyrrole biosynthesis in higher plants[END_REF]. The diverse plastidial anabolic reactions that utilize pyruvate, or its adjacent metabolic precursors and products (PEP, acetyl-coA) are frequently grouped together under the umbrella term "pyruvate hub" [START_REF] Shtaida | The role of pyruvate hub enzymes in supplying carbon precursors for fatty acid synthesis in photosynthetic microalgae[END_REF].

The mechanisms through which pyruvate hub intermediates are supplied to the plastid is complicated, with plants classically thought to import precursors from the cytoplasm via dedicated pyruvate and PEP transporters (Fig. 1A) [START_REF] Moog | Substrate specificity of plastid phosphate transporters in a non-photosynthetic diatom and its implication in evolution of red alga-derived complex plastids[END_REF]. Alongside this, however, certain plant plastids may directly synthesize PEP and then pyruvate from glyceraldehyde-3-phosphate in a complete plastidial lower half glycolytic pathway. This occurs via the activities of enzymes associated with the Calvin cycle (glyceraldehyde-3-phosphate dehydrogenase and phospho-glycerate kinase) and two others: plastid-targeted enolase and phospho-glycerate mutase (henceforth referred to as ptEnolase and ptPGAM), which allow the conversion of 1,3-bis-phosphoglycerate from the Calvin cycle to PEP, and subsequent incorporation into the pyruvate hub (Fig. 1A). Both ptEnolase and ptPGAM have bidirectional functions due to reversible reaction kinetics, which we henceforth refer to globally as "glycolysis-gluconeogenesis", contrasting with "glycolysis" and "gluconeogenesis" to signify metabolic activities in one direction only. In plants, ptEnolase and ptPGAM enzymes are characteristically associated with non-photosynthetic tissue such as seeds and roots [START_REF] Troncoso-Ponce | Molecular and biochemical characterization of the sunflower (Helianthus annuus L.) cytosolic and plastidial enolases in relation to seed development[END_REF][START_REF] Andriotis | Plastidial glycolysis in developing Arabidopsis embryos[END_REF][START_REF] Prabhakar | Molecular and functional characterization of the plastid-localized Phosphoenolpyruvate enolase (ENO1) from Arabidopsis thaliana[END_REF][START_REF] Fukayama | Characterization and expression analyses of two plastidic enolase genes in rice[END_REF].

. CC-BY-NC-ND 4.0 International license available under a (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made

The copyright holder for this preprint this version posted September 9, 2022. ; https://doi.org/10.1101/2022.09.08.507166 doi: bioRxiv preprint Moreover, Arabidopsis mutant lines for ptEnolase and ptPGAM have, respectively, limited and null single phenotypes under replete growth conditions [START_REF] Anoman | The specific role of plastidial glycolysis in photosynthetic and heterotrophic cells under scrutiny through the study of glyceraldehyde-3-phosphate dehydrogenase[END_REF][START_REF] Andriotis | Plastidial glycolysis in developing Arabidopsis embryos[END_REF][START_REF] Prabhakar | Molecular and functional characterization of the plastid-localized Phosphoenolpyruvate enolase (ENO1) from Arabidopsis thaliana[END_REF], and the broader significance of this pathway to plant physiology remains unknown. Genes for ptEnolase and ptPGAM are believed to be absent from primary red or green algae, the closest relatives of land plants [START_REF] Polle | Carbon partitioning in green algae (chlorophyta) and the enolase enzyme[END_REF][START_REF] Moriyama | Subcellular distribution of central carbohydrate metabolism pathways in the red alga Cyanidioschyzon merolae[END_REF].

Diatoms are a eukaryotic algal group, positioned within the stramenopile lineage that is distantly related to plants, with plastids originating from the secondary endosymbiosis of a red algal ancestor or endosymbiotic derivative thereof, separate from the primary endosymbiotic origin of plant plastids [START_REF] Dorrell | Chimeric origins of ochrophytes and haptophytes revealed through an ancient plastid proteome[END_REF][START_REF] Bowler | Oceanographic and biogeochemical insights from diatom genomes[END_REF]. Diatoms are extraordinarily successful in the modern ocean, comprising nearly half of total algal abundance in the Tara Oceans dataset, and performing about one-fifth of total planetary photosynthesis [START_REF] De Vargas | Ocean plankton. Eukaryotic plankton diversity in the sunlit ocean[END_REF][START_REF] Malviya | Insights into global diatom distribution and diversity in the world's ocean[END_REF]. The contributions of diatoms to primary production is even greater in specific oceanic regions, including the nutrient-rich and highly productive waters at high latitudes (i.e., the Arctic and Antarctic Southern Oceans) [START_REF] Gilbertson | Diatoms and their microbiomes in complex and changing polar oceans[END_REF]. Diatoms within polar environments are exposed to multiple photo-physiological stressors such as low environmental temperatures, limitations of key nutrients including iron, zinc, and cobalamin, and elongated photoperiods ranging from continuous illumination in the polar summer to continuous darkness in the winter [START_REF] Bowler | Oceanographic and biogeochemical insights from diatom genomes[END_REF][START_REF] Malviya | Insights into global diatom distribution and diversity in the world's ocean[END_REF][START_REF] Ye | The role of zinc in the adaptive evolution of polar phytoplankton[END_REF][START_REF] Browning | Nutrient co-limitation at the boundary of an oceanic gyre[END_REF]. Previous genomic and physiological studies of model diatoms, such as the transformable species Phaeodactylum tricornutum, have identified multiple strategies that allow diatoms to tolerate photostress, including extensive capacities for non-photochemical quenching pathways displaying different features than in plants or in primary red algae [START_REF] Bailleul | Energetic coupling between plastids and mitochondria drives CO2 assimilation in diatoms[END_REF][START_REF] Buck | Lhcx proteins provide photoprotection via thermal dissipation of absorbed light in the diatom Phaeodactylum tricornutum[END_REF], and complex strategies for the acquisition, storage and recycling of cellular iron [START_REF] Gao | Iron metabolism strategies in diatoms[END_REF], which may provide new strategies for metabolic engineering of enhanced photosynthetic activity in plants [START_REF] Cardona | Enhancing photosynthesis in plants: the light reactions[END_REF].

Comparative genomic analyses have revealed the presence of a different organization of carbon metabolism in diatoms to plants [START_REF] Kroth | A model for carbohydrate metabolism in the diatom Phaeodactylum tricornutum deduced from comparative whole genome analysis[END_REF]. These include the storage of sugars in the form of chrysolaminarin in cytosolic vacuoles, as opposed to plastidial starch [START_REF] Zhu | Silencing UDP-glucose pyrophosphorylase gene in Phaeodactylum tricornutum affects carbon allocation[END_REF], and the probable absence of plastidial hexose-phosphate (e.g., glucose-6-phosphate) transporters [START_REF] Moog | Substrate specificity of plastid phosphate transporters in a non-photosynthetic diatom and its implication in evolution of red alga-derived complex plastids[END_REF][START_REF] Liu | An integrative environmental atlas of diatom chloroplast transporters[END_REF]. These features seem consistent with the accumulation of photosynthetically acquired carbon in the form of fatty acids rather than glucose in the stroma; and the export of three-carbon metabolites to the cytosol for the subsequent gluoconeogenesis of storage carbons, alongside plastidial membrane galactolipids (eg., MGDG, DGDG) and sulfoquinovosyl-diacylglycerols (SQDG) synthesised, as per in plants, from cytoplasmic UDP-galactose and UDP-glucose respectively [START_REF] Zhu | Silencing UDP-glucose pyrophosphorylase gene in Phaeodactylum tricornutum affects carbon allocation[END_REF][START_REF] Liu | An integrative environmental atlas of diatom chloroplast transporters[END_REF][START_REF] Norambuena | Transport of UDP-galactose in plants. Identification and functional characterization of AtUTr1, an Arabidopsis thaliana UDP-galactos/UDP-glucose transporter[END_REF]. Moreover, the lower half of respiratory glycolysis-gluconeogenesis in diatoms (and photosynthetic and nonphotosynthetic relatives of the stramenopiles) occurs in the mitochondria, as opposed to the cytosolic pathways found in plants, red and green algae (Fig. 1A) [START_REF] Kroth | A model for carbohydrate metabolism in the diatom Phaeodactylum tricornutum deduced from comparative whole genome analysis[END_REF][START_REF] Bártulos | Mitochondrial glycolysis in a major lineage of eukaryotes[END_REF].

A second, complete lower half of glycolysis-gluconeogenesis is predicted to occur in the diatom plastid, based on plastid-targeted ptEnolase and ptPGAM1A proteins identified in silico in multiple species [START_REF] Dorrell | Chimeric origins of ochrophytes and haptophytes revealed through an ancient plastid proteome[END_REF][START_REF] Kroth | A model for carbohydrate metabolism in the diatom Phaeodactylum tricornutum deduced from comparative whole genome analysis[END_REF][START_REF] Smith | Comparative analysis of diatom genomes reveals substantial differences in the organization of carbon partitioning pathways[END_REF][START_REF] Hippmann | Proteomic analysis of metabolic pathways supports chloroplast-mitochondria cross-talk in a Cu-limited diatom[END_REF]. Thus, diatom plastids possess independent and separate pathways for the conversion of glyceraldehyde-3-phosphate to pyruvate in plastid and for mitochondrial respiration (Fig. 1A). While this metabolic arrangement has also been observed in plants, as described above, it is likely to have independently originated in diatoms (e.g., given the absence of reported plastidial glycolysis pathways in primary red algae). Moreover, while the plant plastidial glycolysis pathway is principally associated with non-photosynthetic tissue, diatoms (as obligately photosynthetic and unicellular species) presumably perform photosynthesis and plastidial glycolysis in the same organelle [START_REF] Andriotis | Plastidial glycolysis in developing Arabidopsis embryos[END_REF]. Diatom ptEnolase and ptPGAM genes have further been shown to be transcriptionally co-regulated with photosynthetic carbon assimilation, suggesting metabolically important functions linked to photosynthetic activity [START_REF] Ait-Mohamed | PhaeoNet: a holistic RNAseq-based portrait of transcriptional coordination in the model diatom Phaeodactylum tricornutum[END_REF]. Finally, ptPGAM proteins have recently been shown to overaccumulate under copper starvation, in the open-water diatom Thalassiosira oceanica, suggesting a possible connection between its function and photo-stress [START_REF] Hippmann | Proteomic analysis of metabolic pathways supports chloroplast-mitochondria cross-talk in a Cu-limited diatom[END_REF].

Here, we use combined bioinformatic profiling of large-scale sequence datasets from P. tricornutum, the broader algal tree of life, and the Tara Oceans dataset, alongside integrative functional characterization of P. tricornutum CRISPR-CAS9 knockout mutants, to reveal heretofore unknown functions of diatom ptEnolase and ptPGAM enzymes in adaptations to high latitudes. Using phylogenetic techniques, we demonstrate that the genes encoding these enzymes arise from a recent duplication and retargeting of the mitochondria-targeted and respiratory isoform in diatoms and their closest relatives within the stramenopiles. Using meta-genomic data from Tara Oceans, we further show these genes are most highly expressed in high latitudes. Considering the growth and physiological phenotypes of knockout lines lacking plastidial glycolysis-gluconeogenesis proteins, we show that this pathway has augmented importance in cells grown under continuous illumination as opposed to light-dark cycling, and present evidence that this may be due to diminished capacities to convert glyceraldehyde-3-phosphate assimilated by the Calvin cycle into plastidial amino acids and lipid pools, which may be compensated by a redirection of flux through mitochondrial glycolysis and mitochondrial: plastid amino acid shuttling. We also show that the growth phenotype of diatom plastid glycolysis mutants is counterbalanced by low temperatures, which may relate to enhanced mitochondrial respiration and impeded gluconeogenesis of plastidial phospho-enol-pyruvate. Overall, our data position plastidial glycolysis-gluconeogenesis as a key adaptive modulator of plastidial metabolic poise in diatoms, with particular importance to the light stresses associated with high latitudes, and reveals a new candidate pathway for the optimization of photosynthetic productivity in plants.

Materials and Methods

Culture conditions

Phaeodactylum tricornutum strain Pt1.86 was grown in enhanced seawater (ESAW) medium supplemented with vitamins, but without silicon or added antibiotics, in 50 μE m -2 s -1 white light, as measured with a photofluorometer, under one of four light, temperature and shaking regimes: either 19°C with 12h light: 12 dark cycling, shaken at 100 rpm (for general molecular work and transformation); 19°C with 12h light: 12 dark cycling without shaking (« LD » growth conditions and physiological analysis); 19°C with 24h continuous light and without shaking (« CL » growth conditions and physiological analysis); or 8°C with 24h continuous light and without shaking (« 8C » growth conditions and physiological analysis). Mutant Phaeodactylum lines were maintained on ½ ESAW 1% agarose plates, supplemented by 100μg ml -1 each ampicillin and streptomycin, and 30μg ml -1 chloramphenicol, and either 100μg ml -1 zeocin (single transformants), or 100μg ml -1 zeocin and 4μg ml -1 blasticidin (complementation lines), as previously described [START_REF] Falciatore | Transformation of nonselectable reporter genes in marine diatoms[END_REF][START_REF] Buck | Blasticidin-S deaminase, a new selection marker for genetic transformation of the diatom[END_REF]. All functional analyses of transformant lines were performed on transformant lines grown in the absence of antibiotic selection, to avoid secondary effects of antibiotics on growth or physiology.

Phylogenetic analysis

The peptide sequences of all genes with PFAM annotations coherent with either Enolase (PF00113) or Phospho-glycerate mutase (PGAM; taking histidine phosphatase, PF00300, as a proxy) encoded in the version 3 annotation of the P. tricornutum genome were extracted, and searched against a library of 289 complete cryptomonad, haptophyte and stramenopile genomes and transcriptomes by reciprocal BLASTp/ BLASTp best-hit search with threshold e-value 10 -05 , as previously described [START_REF] Nonoyama | Metabolic innovations underpinning the origin and diversification of the diatom chloroplast[END_REF][START_REF] Rastogi | Integrative analysis of large scale transcriptome data draws a comprehensive landscape of Phaeodactylum tricornutum genome and evolutionary origin of diatoms[END_REF][START_REF] Mistry | Pfam: The protein families database in 2021[END_REF]. Sequences that retrieved a reciprocal best hit against a Phaeodactylum enolase or PGAM gene were trimmed to the first encoded methionine, and then screened through three targeting predictors: ASAFind v 2.0, used with SignalP v 3.0 [START_REF] Gruber | Plastid proteome prediction for diatoms and other algae with secondary plastids of the red lineage[END_REF][START_REF] Bendtsen | Improved prediction of signal peptides: SignalP 3.0[END_REF], HECTAR, integrated into the Roscoff Galaxy platform, under the default settings [START_REF] Gschloessl | HECTAR: a method to predict subcellular targeting in heterokonts[END_REF][START_REF] Afgan | The Galaxy platform for accessible, reproducible and collaborative biomedical analyses: 2016 update[END_REF], and MitoFates, run with threshold value 0.35 [START_REF] Dorrell | Chimeric origins of ochrophytes and haptophytes revealed through an ancient plastid proteome[END_REF][START_REF] Fukasawa | MitoFates: Improved prediction of mitochondrial targeting sequences and their cleavage Sites[END_REF]. Sequences that retrieved a plastid, mitochondrial or dual-targeting prediction (defined as plastid-targeted with at least one programme, and mitochondria-targeted with at least one another) were annotated as such and retained.

A parallel set of reciprocal BLAST searches, with threshold e-value 10 -05 , were performed against 85 prokaryotic and eukaryotic genomes sampled with taxonomic balance from across the remainder of the tree of life [START_REF] Dorrell | Chimeric origins of ochrophytes and haptophytes revealed through an ancient plastid proteome[END_REF], and were pooled with the organelle-targeted cryptomonad, haptophyte and stramenopile proteins identified above. The pooled set of sequences were aligned first with MAFFT v 7.0 under the --auto setting, followed by the in-built alignment programme in GeneIOUS v 10.0.9, under default settings [START_REF] Kearse | Geneious Basic: an integrated and extendable desktop software platform for the organization and analysis of sequence data[END_REF][START_REF] Katoh | MAFFT online service: multiple sequence alignment, interactive sequence choice and visualization[END_REF], and incomplete and poorly aligned sequences were manually removed. The curated alignment was trimmed with trimal with setting -gt 0.5, and phylogenetic analyses were performed with MrBayes v 3.2 and RAxML v 8, integrated into the CIPRES webserver [START_REF] Miller | A RESTful API for access to phylogenetic tools via the CIPRES science gateway[END_REF][START_REF] Stamatakis | RAxML version 8: a tool for phylogenetic analysis and post-analysis of large phylogenies[END_REF][START_REF] Capella-Gutiérrez | trimAl: a tool for automated alignment trimming in large-scale phylogenetic analyses[END_REF]. MrBayes trees were run for 10,000,000 generations with the GTR, Jones and WAG substitution matrices, 4 starting chains and sumt and sump burnin fractions set to -0.5; all cold chains were confirmed to have reached a pvalue plateau below 0.1 prior to the start of the consensus building. RAxML trees were run with GTR, JTT and WAG substitution matrices, 350-400 ML generations, and automatic bootstopping. Alignments and individual tree topologies are provided in Table S1.

Co-regulation analysis

Transcriptional co-regulation of P. tricornutum genes to ptEnolase and ptPGAM1A were assessed using a composite dataset consisting of two meta-studies of Phaeodactylum gene expression: DiatomPortal, based on microarray data generated in particular from cultures exposed to different light regimes and organic and inorganic stresses (57); and PhaeoNet, constructed from RNAseq data generated from N, P and Fe-starved cultures [START_REF] Ait-Mohamed | PhaeoNet: a holistic RNAseq-based portrait of transcriptional coordination in the model diatom Phaeodactylum tricornutum[END_REF](58)(59)(60).

To reconcile both datasets, which are based on the versions 1 and 3 annotations of the P. tricornutum genome, respectively, each DiatomPortal version 1 gene number was remapped to a PhaeoNet version 3 gene number by reciprocal BLASTp/ BLASTp best hit search [START_REF] Rastogi | Integrative analysis of large scale transcriptome data draws a comprehensive landscape of Phaeodactylum tricornutum genome and evolutionary origin of diatoms[END_REF]. Version 3 genes that were retrieved as reciprocal BLAST best hits by >1 version 1 gene (typically resulting from merged gene annotations between the two genome versions) were assigned the median of all matching DiatomPortal relative fold expression changes. The raw normalized expression abundances (PhaeoNet) and fold-expression changes (DiatomPortal) for each constituent library used within each study were converted into percentile rank values to normalize them, with 100 equating to the most highly expressed or upregulated gene, and 0 to the most lowly expressed or downregulated gene. Genes lacking expression data for a particular study were marked N/A. Finally, correlation calculations of ranked gene expression (i.e., Spearman correlations) were performed for each gene in the P. tricornutum version 3 genome against ptEnolase (Phatr3_J41515), ptPGAM1A (Phatr3_J17086), and ptPGAM2 (Phatr3_J37201). Finally, the correlation coefficients were identified for proteins implicated in core plastid and mitochondrial metabolism pathways as per [START_REF] Ait-Mohamed | PhaeoNet: a holistic RNAseq-based portrait of transcriptional coordination in the model diatom Phaeodactylum tricornutum[END_REF], with the highest (strongest positive) value taken in the case of proteins with multiple homologues. Full data pertaining to these outputs are provided in Table S2, sheets 1-3.

Nucleic acid isolation

For DNA isolation, 150 ml early stationary phase P. tricornutum culture, grown under 19°C with 12h light: 12h dark cycling, and shaken at 100 rpm as described above, was centrifuged at 4000 rpm for 10 minutes. The resulting cell pellet was washed in sterile growth medium three times, and incubated for four hours in 5 ml TEN buffer (0.1M NaCl, 0.01M Tris pH8, 0.001M EDTA) supplemented with 2% volume: volume SDS, and 1U μl -1 proteinase K (Fisher Scientific). Lysed cell fractions were used for phenol: chloroform precipitation of cellular DNA, as previously described (61), prior to dissolution in 50 μl nuclease-free water, and quantification with a nanodrop photospectrometer.

For RNA isolations, 10 5 stationary phase P. tricornutum cells, as calculated with cell densities counted from a Malassez haemocytometer were inoculated in a 250 ml conical Erlenmeyer containing 80 ml ESAW without antibiotics. Cell cultures were harvested in late exponential phase, at counted densities of between 1 and 2 1-2 x 10 6 cells ml -1 . CL cultures were typically harvested eight days postinoculation, LD cultures nine days post-inoculation, and 8C culture seventeen days post-inoculation, in agreement with growth curve dynamics. Cells were harvested at the mid-point of the lightinduction phase of the LD growth condition (15:00 CET), per previous gene expression studies in Phaeodactylum (58), and per the sampling parameters used within Tara Oceans [START_REF] Pesant | Open science resources for the discovery and analysis of Tara Oceans data[END_REF]. Cell cultures were harvested at densities that were insufficient to provoke N or P limitation of the ESAW medium [START_REF] Abida | Membrane glycerolipid remodeling triggered by nitrogen and phosphorus starvation in Phaeodactylum tricornutum[END_REF].

RNA was isolated from 10 8 cells from each culture, pelleted and washed as before, and snap-frozen in liquid nitrogen. Frozen cell suspensions were re-equilibrated with 1 ml Trizol reagent (Invivogen) and 200 μl chloroform (Honeywell), prior to phenol: chloroform extraction as previously described [START_REF] Dorrell | Chimeric origins of ochrophytes and haptophytes revealed through an ancient plastid proteome[END_REF]. An additional separation step was performed in 500 μl pure chloroform to remove any residual phenol traces from the aqueous phase, and purified nucleic acids were precipitated overnight in 500 μl isopropanol at -20°C. Precipitated RNA samples were collected by centrifugation at 10,000 rpm for 30 minutes, washed with 900 μl 100% ethanol, and resupended in 50 μl RNAse-free water (Qiagen).

2 μg RNA, as quantified with a nanodrop photospectrometer, was subsequently treated with 2U RNAse-free DNAse (Promega) for 30 minutes at 37°C, with the reaction arrested with 1 μl 0.5M EDTA. The digested RNA sample was reprecipitated in isopropanol for one hour at -20°C, washed in ethanol, and resuspended in 20 μl RNAse-free water. Purified RNA sample concentrations were quantified with a nanodrop spectrometer, and a 3 μl aliquot was migrated on an RNAse-free 1% agarose gel stained with 0.2 μg ml -1 ethidium bromide to confirm RNA integrity prior to all downstream applications.

GFP localization

Full length mRNA sequences of ptEnolase, ptPGAM1A and ptPGAM2 were amplified from P. tricornutum RNA libraries grown under 19°C, light: dark cycling and replete nutrient conditions as described above, by reverse transcription with RT Maxima First Strand synthesis kit (Thermo Fisher) from 200 ng template RNA, following the manufacturer's instructions; and gene-specific primers as shown in Table S2, sheet 4. PCRs were performed using Pfu high-fidelity DNA polymerase, in 50 μl total reaction volume, including 1 μl cDNA template and 2 μl each specific primer, following the manufacturer's protocol. Typical PCR conditions were: 10 minutes at 95°C; followed by 35 cycles of 45 seconds at 95°C, 45 seconds at 55°C, and 2 minutes at 72°C; followed by a terminal elongation phase of 5 minutes at 72°C. Amplified products were migrated on a 1% agarose gel stained with ethidium bromide, cut out, and purified using a MinElute PCR purification kit (Qiagen).

Purified products were cloned into linearised versions of pPhat vectors containing eGFP and a zeocin resistance gene (SHBLE). These products were amplified using an analogous PCR protocol as above, with 1 ng purified plasmid DNA, and outward-directed PCR primers extending from the start of the fluorescence protein gene sequence to the end of the FcpA promoter region (Table S2, sheet 4); cut, purified, and treated with 1U FastDigest DpnI (Thermo Fisher) to remove any residual plasmid DNA. The gene-specific primers for each ptEnolase and ptPGAM construct were modified with 15 5' nucleotides complementary to the terminal regions of the FcpA and GFP sequences, allowing cloning of complete vector sequences using a HiFi DNA assembly kit (NEB), following the manufacturer's instructions. Assembled vectors were transformed into chemically competent Top10 E. coli, and positive clones (as identified by Sanger sequencing of positive colony PCR products) were used to generate purified plasmid DNA with a Plasmid Midi Kit (Qiagen). Subcellular localization constructs were transformed into wild-type P. tricornutum by biolistic transformation, as previously described [START_REF] Falciatore | Transformation of nonselectable reporter genes in marine diatoms[END_REF]. 5 x 10 7 mid-exponential phase cells were plated on a ½ ESAW-1% agarose plate, and left to recover for two days, prior to bombardment with 10 mg 1 μm tungsten beads treated with 5 μg purified plasmid DNA in a Helios gene gun (BioRad) at 1,550 PSI. Cells were left to recover for two days, prior to replating on ½ ESAW-1% agarose plates supplemented with 100 μg ml -1 ampicillin, 100 μg ml -1 streptomycin, 30 μg ml -1 chloramphenicol and 100 μg ml -1 zeocin. Plates post-bombardment and for the first two days post-selection were maintained in a low light environment (10 μE m -2 s -1 ) prior to return to standard growth conditions.

Positive transformant colonies, as verified by Western Blot with a mouse anti-GFP antibody (Promega), were visualised using a SP8 inverted spinning disc confocal microscopy (Leica) under 400 x magnification, with excitation wavelength 485 nm and emission wavelength filters 500-550 nm.

Tara Oceans Analysis

The complete Tara Oceans and Tara Oceans Polar Circle libraries of meta-genome and metatranscriptome diversity [START_REF] Carradec | A global ocean atlas of eukaryotic genes[END_REF][START_REF] Royo-Llonch | Compendium of 530 metagenome-assembled bacterial and archaeal genomes from the polar Arctic Ocean[END_REF] were searched for orthologues of diatom ptEnolase, ptPGAM1A and ptPGAM2 sequences via a phylogenetic reconciliation approach. First, a HMM (hidden Markov model) was constructed for all diatom plastidial-targeted sequences in the untrimmed alignments for each phylogeny, as detailed above, and searched into the complete Tara Oceans catalog by hmmer (http://hmmer.org) with evalue 10 -10 . Matching sequences were extracted, and searched by BLASTp against both the complete copy of the P. tricornutum genome [START_REF] Rastogi | Integrative analysis of large scale transcriptome data draws a comprehensive landscape of Phaeodactylum tricornutum genome and evolutionary origin of diatoms[END_REF] and the complete untrimmed alignment, and sequences that retrieved a best hit against (i) an Enolase or PGAM sequence and (ii) a diatom plastidial-targeted isoform in each search respectively were retained. Retained sequences were combined with the untrimmed alignment of each gene and realigned using the same MAFFT, GeneIOUS and trimal pipeline as defined above. Curated alignments were finally screened by RAxML tree with the JTT substitution matrix, as above, and the monophyletic clade of Tara Oceans sequences and diatom plastid-targeted proteins, defined as all sequences that position closer on a midpoint rooting of the tree to diatom plastid-targeted proteins than to any non-diatom or nonplastid targeted sequences, was extracted for further analyses.

Relative abundances were calculated for the total occurrence of all phylogenetically verified diatom plastid-targeted proteins in both meta-transcriptome and meta-genome data. Relative expression levels of each gene were estimated by reconciling the calculated meta-transcriptome abundances either to total diatom meta-transcriptome sequences using the formula 10 ^6(Σ metaT / Σ DiatomT ), i.e. expressed per million reconciled diatom reads, or to calculated meta-genome abundances, using the formula and log 10 (1+ Σ metaT ) -log 10 (1+ Σ metaG ), to allow inclusion of null values. Pearson correlations were calculated between relative abundances and all quantitative measured environmental variables associated with Tara Oceans samples as stored within the PANGAEA repository [START_REF] Pesant | Open science resources for the discovery and analysis of Tara Oceans data[END_REF]. To assess the possibility of non-linear correlations, ranked differences (i.e., Spearman correlation) were also tested for each variable with broadly consistent results. All calculations were repeated independently for each depth (surface, or deep chlorophyll maximum/ DCM) and size fraction (0.8-2000 μm, 0.8-5 μm, 3/5-20 μm, 20-180 μm, and 180-2000 μm), with 3 and 5 μm filters viewed as equivalent to allow reconciliation of Arctic and non-Arctic data, respectively. All Tara Oceans meta-gene assignations, alongside individual and total abundance calculations are provided in Table S3.

CRISPR mutagenesis

. CC-BY-NC-ND 4.0 International license available under a (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made

The copyright holder for this preprint this version posted September 9, 2022. ; https://doi.org/10.1101/2022.09.08.507166 doi: bioRxiv preprint CRISPR target sequences for ptEnolase and ptPGAM1A genes were identified using PhytoCRISP-Ex [START_REF] Rastogi | PhytoCRISP-Ex: a web-based and standalone application to find specific target sequences for CRISPR/CAS editing[END_REF], privileging positions in the N-terminal region of the conserved domain to minimize the probability of enzyme functionality in knockout lines, and uniqueness across the entire P. tricornutum genome within the final 11 bp of the target sequence to minimize off-target effects. Primers were designed for each target sequence, and introduced into a pu6:SG1 CRISPR target sequence plasmid by PCR, as previously described [START_REF] Nymark | A CRISPR/Cas9 system adapted for gene editing in marine algae[END_REF]. 2 μg insertion-positive pu6:SG1 plasmids, as confirmed by Sanger sequencing and purified using a Qiagen Midiprep kit were co-introduced into wild-type P. tricornutum cells by bombardment along with 2 μg HA-tagged Cas9 and pPhat vectors, as described above. Mutant colonies were genotyped using a DNA lysis buffer containing 0.14M NaCl, 5mM KCl, 10mM Tris-HCl pH 7.5, 1% v/v NP40 to generate crude DNA extracts, followed by PCR amplification across the CRISPR target sequences with DreamTaq PCR reagent (Promega) and Sanger sequencing (Eurofins genomics). Mixed mutant: wild-type colonies were segregated via repeated dilution on ESAW: zeocin plates until only homozygous mutant genotypes were detected (59, 67). Empty vector control lines were generated using the same protocol, except with only HA-Cas9 and pPhat plasmids, cotransformed without a CRISPR target sequence. Primers used for each mutagenesis are provided in Table S2, sheet 4; tabulated cleaned knockout mutants and their associated genotypes are shown in Table S4, sheet 1; and effective expression levels of mutated gene copies as assessed by qRT-PCR are shown in Table S4, sheet 2. Mutant colony genotypes were periodically confirmed (approx. once per month) by PCR and Sanger sequencing throughout the duration of all subsequent experiments, and found to remain stable.

Complementation of knockout lines

Knockout lines were complemented with pPhat:GFP vectors carrying overexpressing copies (under an FcpA promoter) of ptEnolase and ptPGAM1A synthetic constructs with all CRISPR target sequences replaced with silent mutations (Eurofins). Vectors were identical to those previously used for localization, but with a blasticidin S-deaminase gene in lieu of SHBLE [START_REF] Karas | Designer diatom episomes delivered by bacterial conjugation[END_REF] introduced by NEB Hi-Fi kit as before. Complementation constructs were transformed via bombardment, and cells were replated ½ ESAW-1% agarose plates supplemented with 100 μg ml -1 ampicillin, 100 μg ml -1 streptomycin, 30 μg ml -1 chloramphenicol, 100 μg ml -1 zeocin, 4 μg ml -1 blasticidin, and left to recover at low light for two days followed by standard growth conditions until the appearance of colonies.

For each complementation, three ptEnolase and ptPGAM1A knockout lines (including at least one for each CRISPR target sequence) were complemented both with the conjugate complementation construct, and an empty blasticidin resistance vector as a placebo; and two empty vector lines were further complemented with both ptEnolase and ptPGAM1A overexpressor constructs, plus an empty blasticidin resistance vector, to exclude possible effects from ectopic overexpression of each gene on cell physiology. A total of 47 colonies, with a minimum of 6 colonies for each knockout: complementation combination, including lines complemented from at least two distinct primary knockout mutant genotypes, were selected for subsequent study (Table S4, sheet 7). The retention of the primary knockout mutant genotype in each complemented line was verified by colony PCR and sequencing as above, and the overexpression and correct localization of the complementing protein sequence (i.e., to the chloroplast for ptEnolase:GFP and ptPGAM1:GFP, or the cytoplasm for ectopic GFP) was verified by western blot with an anti-GFP antibody (Promega) and a previously published protocol [START_REF] Erdene-Ochir | Identification and characterisation of the novel endogenous promoter HASP1 and its signal peptide from Phaeodactylum tricornutum[END_REF], and confocal microscopy.

Growth rate measurements

A starting density of 10 4 ml -1 stationary phase P. tricornutum cells of a given culture line, as verified with a Malassez haemocytometer, were inoculated into a 15 ml volume antibiotic-free ESAW within a sterile, ventilated cap plastic culture flask (Celltreat) and grown under LD, CL, or 8C culture conditions as described. Cell densities were recorded: every day from one day post-inoculation (CL); every day from two days post-inoculation (LD); or every two days from five days post-inoculation (8C) at the mid-point of the LD light induction phase, as defined above, using a counting CyFlow Cube 8 cytometer (ParTec).

Typically, 15 μl cell culture, passed at 0.5 μl s -1 , were used for each measurement, with three technical replicates performed for each culture of which the first (enriched in non-cellular particles) was excluded from downstream calculations. Cytometer particle counts were correlated to actual cell densities using a calibration curve realised from haemocytometer counted densities of wild-type cell culture, and cultures with observed densites > 2 x 10 6 cells ml -1 were diluted ten-fold in blank growth media to avoid saturation of the cytometer.

Cell densities were measured daily from one day post-inoculation (19C conditions) and every second day from five days post-inoculation (8C CL condition only), until cell lines were confirmed to have exited log phase (i.e., reached a stable stationary phase plateau). Primary knockout mutant growth curves were repeated a minimum of six times (three biological replicates per-inoculation, with two independent repetitions) for each independent mutant line studied, providing a minimum of 24 measurements (i.e., four distinct mutant lines) per each genotype studied (ptEnolase knockout, ptPGAM1A knockout and empty vector control lines).

To avoid artifacts based on the proximity of the seed cell culture to exponential phase at the time of inoculation (which may impact on lag phase length) or the relative diameter of each cell in culture (which may impact on carrying capacity), cell growth rates were measured exclusively from the logphase relative division rate. This was calculated via considering Δlog 2 (cell density) / Δlog 2 (time) for the time period over which a linear correlation (r> 0.99) was observed between both values, typically equating to densities between 10 5 and 2x 10 6 cells ml -1 , and covering a minimum of three and in most cases five successive measurements of each individual growth curve. Three exemplar growth curve outputs are provided in Table S4, sheets 3-5; and an overview of relative growth rates expressed as a function of mean empty vector control growth rates are provided in Table S4, sheet 6.

Complementation growth curves were repeated with at least two independent repetitions for each cell line, with five timepoints taken to project growth rates, and therefore a minimum of sixty independent measurements for each mutant: complementation genotype under each growth condition, with the average of the two fastest growth rates of each culture calculated as estimates for the growth rate. A heatmap of all estimated complementation line growth rates is provided in Table S4, sheet 7.

Photophysiology

Cultures for photophysiological analysis were grown in 10ml ventilated plastic flasks, without shaking, under 19C CL and 19C LD conditions as described above. Cultures were grown from a starting inoculum of 10 5 cells ml -1 as measured with a Malassez haemocytometer, and refreshed into fresh media when they had reached a measured density of 10 6 cells ml -1 at the initial starting concentration of 10 5 cells ml -1 to allow a prolonged adaptation to each photophysiological condition under a continuous exponential phase. Cells from refreshed culture lines were harvested in exponential phase (between 1 and 3 × 10 6 cells ml -1 , as verified by Fv/Fm measurements > 0.6 across all measured lines (Table S4, sheet 8). Cell cultures were concentrated to between 2 and 5 × 10 7 cells ml -1 before photophysiology measurements. Parameters measured with the miniFIRe fluorometer (as defined below) were also quantified for cultures grown under 8C CL conditions, as the measurements were sufficiently rapid to allow the culture to be maintained at growth temperatures [START_REF] Gorbunov | A multi-spectral fluorescence induction and relaxation (FIRe) technique for physiological and taxonomic analysis of phytoplankton communities[END_REF]. Steady-state light curves (SSLC) were conducted with a fluorescence CCD camera recorder (SpeedZen, JBeamBio, France). Samples were exposed to 7 increasing light steps of 3 min each (5 min for the first light step), from 35 to 750 µmol photons m -2 s -1 (green actinic light, 532 nm). The experiment was repeated with 2-4 independent replicates for all 19 strains (n ≥ 6 per construct under each growth condition). Minimum (F 0 ) and maximum (F M ) fluorescence were measured in darkadapted (at least 1 min) samples, before and at the end of a saturating pulse of light (532 nm, 5000 µmol photons m -2 s -1 ) and the maximum quantum yield of PSII in the dark was calculated as F V /F M = (F M -F 0 )/F M . Every minute of light exposure, steady-state fluorescence (F S ) and maximum fluorescence under light (F M ' ) were measured.

PSII quantum yield (φPSII) and non-photochemical quenching (NPQ) were calculated on the last time point of each light step as φPSII = (F M '-F s )/F M ' and NPQ = F M /F M '-1. The whole NPQ vs E curve was fitted as NPQ = NPQ M ×E n /(E50NPQ n +E n ), where NPQ M is the maximal NPQ value, E 50 NPQ is the halfsaturation intensity for NPQ and n is the sigmoidicity coefficient [START_REF] Serôdio | A model for describing the light response of the nonphotochemical quenching of chlorophyll fluorescence[END_REF]. The PSII functional absorption cross-section, σPSII, was calculated from the fluorescence induction upon a single turnover flash of blue light (100 µs, 455 nm, 60 nm bandwidth). The induction curve was measured on 20 min darkacclimated samples before centrifugation (average of 2-4 independent replicates) with a Fluorescence Induction and Relaxation (miniFIRe) fluorometer [START_REF] Gorbunov | A multi-spectral fluorescence induction and relaxation (FIRe) technique for physiological and taxonomic analysis of phytoplankton communities[END_REF]. The relative electron transport (rETR) was then calculated as rETR = φPSII.σPSII/(F V /F M ).E and was called relative because the light qualities for φPSII and σPSII were different. The rETR vs E curve was fitted as rETR = rETR M .(1-exp(α.E/rETR M )) where rETR M is the maximum rETR and α is the light-limited slope of rETR vs E. Only rETR values from 0 to 450 µmol photons m -2 were used for the fit because the values from 600 and 750 µmol photons m -2 were too noisy. The light saturation parameter E K was calculated as rETR M /α and the fitted values of the parameters were used to estimate the rETR under the growth light intensity of 60 µmol photons m -2 s -1 as E60rETR = rETR M .(1-exp(-α.60/rETR M )) [START_REF] Bailleul | Energetic coupling between plastids and mitochondria drives CO2 assimilation in diatoms[END_REF]. Measured photophysiological values are tabulated in Table S4, sheets 8-11.

Gene expression analysis

RNA sequencing libraries were prepared from 200 ng DNAse-treated RNA for each mutant line and treatment condition, with a minimum of three replicates per sample. RNA sequencing was performed by Fasteris (Plan-les-Ouates, Switzerland). After initial quality control checks, stranded Illumina mRNA libraries were prepared with a Novaseq V1.5 kit and sequenced with an SP-flow cell with 2x 100 bp over 200 cycles, yielding approximately 130-160 Gb sequence data with ≥85% of bases higher than Q30 per sample.

Output fastQ files were mapped using Nextflow's RNA sequencing assembly pipeline https://nfco.re/rnaseq/usage, and gff3 annotations of the P. tricornutum version 3 genome [START_REF] Lataretu | RNAflow: An effective and simple RNA-Seq differential gene expression pipeline using Nextflow[END_REF]. Total mapped read counts were then compared between all biological and technical replicates for (i) each mutant line sequenced, (ii) each genotype (ptEnolase knockout, ptPGAM1A knockout, control), and (iii) each treatment condition performed (LD, CL, 8C) by principal component analysis (PCA) using the R package factoextra, with highly variant libraries removed [START_REF] Kassambara | Package 'factoextra'. Extract and visualize the results of multivariate data analyses[END_REF]. The final dataset included 63 RNAseq libraries, including five ptEnolase and five ptPGAM1A knockout lines and four empty vector controls, and a minimum of four RNA libraries prepared from at least two genetically distinct mutant constructs for each genotype (ptEnolase, ptPGAM1A and control) considered (Table S5, sheets 1-2)., Differentially expressed genes (DEGs) were then calculated between each genotype for each treatment condition using DESeq2 with cutoff fold-change 2 and P-value 0.05 (74) (Table S5, sheets 2-3).

The mean transcript abundances of DEGs in knockout compared to control lines were first assessed in normalised [START_REF] Ait-Mohamed | PhaeoNet: a holistic RNAseq-based portrait of transcriptional coordination in the model diatom Phaeodactylum tricornutum[END_REF] RNAseq data of N (59) and P-limited (58) Phaeodactylum cell lines under two and nine time-points respectively (Table S5, sheet 4). No significant differences were found between DEGs and other genes in the P. tricornutum genome (one-way ANOVA, P > 0.05; S5, sheets 6-10) [START_REF] Ait-Mohamed | PhaeoNet: a holistic RNAseq-based portrait of transcriptional coordination in the model diatom Phaeodactylum tricornutum[END_REF][START_REF] Dorrell | Phylogenomic fingerprinting of tempo and functions of horizontal gene transfer within ochrophytes[END_REF]. Tested traits included: PFAM and KEGG from the P. tricornutum version 3 genome annotation [START_REF] Rastogi | Integrative analysis of large scale transcriptome data draws a comprehensive landscape of Phaeodactylum tricornutum genome and evolutionary origin of diatoms[END_REF]; GO, annotated using combined BLASTkoala and GHOSTkoala annotations [START_REF] Kanehisa | BlastKOALA and GhostKOALA: KEGG Tools for Functional Characterization of Genome and Metagenome Sequences[END_REF]; WGCNA coregulated gene module, as defined within the PhaeoNet meta-analysis of P. tricornutum RNAseq co-expression [START_REF] Ait-Mohamed | PhaeoNet: a holistic RNAseq-based portrait of transcriptional coordination in the model diatom Phaeodactylum tricornutum[END_REF]; and localization, predicted in silico using ASAFind v 1.0 with SignalP v 3.0 [START_REF] Gruber | Plastid proteome prediction for diatoms and other algae with secondary plastids of the red lineage[END_REF][START_REF] Bendtsen | Improved prediction of signal peptides: SignalP 3.0[END_REF], HECTAR under default conditions [START_REF] Gschloessl | HECTAR: a method to predict subcellular targeting in heterokonts[END_REF], and MitoFates with threshold value 0.35 [START_REF] Fukasawa | MitoFates: Improved prediction of mitochondrial targeting sequences and their cleavage Sites[END_REF]. Functional enrichments were tested by two-tailed chi-square (P < 0.05) of a differentially expressed gene occurring in either one (ptEnolase v control; ptPGAM1A v control) knockout-versus-control line tests, or in both tests realised under each physiological condition. Finally, the distribution of DEGs across Phaeodactylum core plastid and mitochondrial metabolism pathways were mapped onto a previously defined proteomic model of each organelle [START_REF] Ait-Mohamed | PhaeoNet: a holistic RNAseq-based portrait of transcriptional coordination in the model diatom Phaeodactylum tricornutum[END_REF]; with the strongest DEG enrichment taken in the case of enzymes with multiple homologues (Table S5, sheet 11).

Quantitative RT-PCR (qRT-PCR) validations were performed using cDNA synthesised from 5 ng DNase-treated RNA (per qRT-PCR reaction) and a RT Maxima First Strand synthesis kit (Thermo Fisher), following the manufacturer's instruction; using a 384-well Lightcycler (Roche) and Takyon™ No ROX SYBR 2X MasterMix (Eurogentec), following the manufacturers' protocols. Typical amplification conditions were: 10 minutes at 95°C, followed by 40 cycles of 30 seconds at 95°C, 30 seconds at 55°C, and 30 seconds at 72°C. Primer pairs for qRT-PCR amplifications were designed using NCBI Primer-BLAST (77), privileging unique amplification targets within the genomic sequence, an amplicon size of 100 to 150 base pairs, primer positions at different regions of the gene studied, and a 3' terminal G or C on each primer. Primer efficiencies were tested by qPCR with serial dilutions of P. tricornutum gDNA, with only primer pairs that yielded a Cp increment of between 0.9 and 1.1 per half dilution of DNA retained for qRT-PCR analysis. qRT-PCRs were performed a minimum of three times for each amplicon: sample pair, with at least three RT-equivalents performed to subtract residual genomic DNA from each Cp value obtained, and using two housekeeping genes (Ribosomal protein S1, RPS; and TATA binding protein, TBP) previously shown to have conditionally invariant expression patterns in P. tricornutum used as quantification references (78). Tabulated qRT-PCR outputs are shown in Table S5, sheet 12.

Metabolite analysis

Culture volumes containing 1.5 x 10 8 exponential P. tricornutum cells (counted density 1-2 x 10 6 cells ml -1 ), inoculated and grown in antibiotic-free ESAW under LD, CL or 8C conditions and at the time points described above for RNA library preparation, were calculated using a Malassez cytometer and harvested by centrifugation as described above. Cell pellets were washed three times in sterile growth medium, then transferred to a pre-weighed, double-pierced and double-autoclaved 1.5 ml Eppendorf tube for lyophilisation. Cell pellet masses were recorded for each sample, and samples were snap-frozen in liquid nitrogen and stored at -80°C for subsequent analysis.

Metabolite profiling was carried out by gas chromatography-mass spectrometry (ChromaTOF software, Pegasus driver 1.61; LECO) as described previously (79). The chromatograms and mass spectra were evaluated using TagFinder software (80). Metabolite identification was manually checked by the mass spectral and retention index collection of the Golm Metabolome Database (81). Peak heights of the mass fragments were normalized successively on the basis of the fresh weight of the sample, the added amount of an internal standard (ribitol) and values obtained for loading column controls obtained from the same experiment. Normalised metabolite abundances were subsequently screened by PCA, as per the RNAseq analysis above, and outliers and biologically nonrepresentative samples were removed. The final datasets consist of 139 libraries (metabolite GC-MS), 55 libraries (lipid GC-MS) and 49 libraries (lipid LC-MS), with a minimum of three RNA libraries prepared from at least two genetically distinct mutant constructs for each genotype considered (Table S6, sheet 1).

Glycerolipid analysis

Glycerolipids were extracted from 1.5 x 10 8 exponential P. tricornutum cells (counted density 1-2 x 10 6 cells ml -1 ), with cell pellets prepared and freeze-dried as per the metabolite analysis above. Pellets were suspended in 4 mL of boiling ethanol for 5 minutes to prevent lipid degradation and lipids were extracted by addition of 2 mL methanol and 8 mL chloroform at room temperature (82). The mixture was then saturated with argon and stirred for 1 hour at room temperature. After filtration through glass wool, cell remains were rinsed with 3 mL chloroform/methanol 2:1, v/v and 5 mL of NaCl 1% was added to the filtrate to initiate biphase formation. The chloroform phase was dried under argon and stored at -20°C. The lipid extract was resuspended in pure chloroform when needed.

Total glycerolipids were quantified from their fatty acids: in an aliquot fraction, 5 µg of 15:0 was added and the fatty acids present were converted to methyl esters (FAME) by a 1-hour incubation in 3 mL 2.5% H 2 SO 4 in pure methanol at 100°C (83). The reaction was stopped by addition of 3 mL water and 3 mL hexane. The hexane phase was analyzed by a gas chromatography-flame ionization detector (GC-FID) (Perkin Elmer) on a BPX70 (SGE) column. FAMEs were identified by comparison of their retention times with those of standards (Sigma) and quantified using 15:0 for calibration.

Glycerolipids were further analyzed by high pressure liquid chromatography-tandem mass spectrometry (HPLC-MS/MS), based on a previously described procedure (84). The lipid extracts corresponding to 25 nmol of total fatty acids were dissolved in 100 µL of chloroform/methanol [2/1, (v/v)] containing 125 pmol of each internal standard. Internal standards used were phosphatidylethanolamine (PE) 18:0-18:0 and diacylglycerol (DAG) 18:0-22:6 from Avanti Polar Lipid, and sulfoquinovosyldiacylglycerol (SQDG) 16:0-18:0 extracted from spinach thylakoid (85) and hydrogenated (86). Lipid classes were separated using an Agilent 1200 HPLC system using a 150 mm × 3 mm (length × internal diameter) 5 µm diol column (Macherey-Nagel), at 40°C. The mobile phases consisted of hexane/isopropanol/water/1 M ammonium acetate, pH 5.3 [625/350/24/1, (v/v/v/v)] (A) and isopropanol/water/1 M ammonium acetate, pH 5.3 [850/149/1, (v/v/v)] (B). The injection volume was 20 µL. After 5 min, the percentage of B was increased linearly from 0% to 100% in 30 min and kept at 100% for 15 min. This elution sequence was followed by a return to 100% A in 5 min and an equilibration for 20 min with 100% A before the next injection, leading to a total runtime of 70 min. The flow rate of the mobile phase was 200 µL min -1 . The distinct glycerophospholipid classes were eluted successively as a function of the polar head group. Mass spectrometric analysis was performed on a 6460 triple quadrupole mass spectrometer (Agilent) equipped with a Jet stream electrospray ion source under following settings: drying gas heater at 260°C, drying gas flow at 13 L•min -1 , sheath gas heater at 300°C, sheath gas flow at 11 L•min -1 , nebulizer pressure at 25 psi, capillary voltage at ± 5000 V and nozzle voltage at ± 1,000 V. Nitrogen was used as collision gas. The quadrupoles Q1 and Q3 were operated at widest and unit resolution respectively. Phosphatidylcholine (PC) and diacylglyceryl hydroxymethyltrimethyl-β-alanine (DGTA) analyses were carried out in positive ion modes by scanning for precursors of m/z 184 and 236 respectively at a collision energy (CE) of 34 and 52 eV. SQDG analysis was carried out in negative ion mode by scanning for precursors of m/z -225 at a CE of -56eV. PE, phosphatidylinositol (PI), phosphatidylglycerol (PG), monogalactosyldiacylglycerol (MGDG) and digalactosyldiacylglycerol (DGDG) measurements were performed in positive ion modes by scanning for neutral losses of 141 Da, 277 Da, 189 Da, 179 Da and 341 Da at CEs of 20 eV, 12 eV, 16 eV, 8 eV and 8 eV, respectively. DAG and triacylglycerol (TAG) species were identified and quantified by multiple reaction monitoring (MRM) as singly charged ions [M+NH4] + at a CE of 16 and 22 eV respectively. Quantification was done for each lipid species by multiple reaction monitoring (MRM) with 50 ms dwell time with the various transitions previously recorded [START_REF] Abida | Membrane glycerolipid remodeling triggered by nitrogen and phosphorus starvation in Phaeodactylum tricornutum[END_REF]. Mass spectra were processed using the MassHunter Workstation software (Agilent) for identification and quantification of lipids. Lipid amounts (pmol) were corrected for response differences between internal standards and endogenous lipids as described previously (87).

Data deposition

RNAseq data associated with this project is deposited with NCBI BioProject with project number PRJNA788211. All remaining supporting data not provided directly in paper supporting tables are provided in the linked Open Science Foundation Supporting database https://osf.io/89vm3/ (88). Project contents are ordered hierarchically by theme, with an overview of all contents provided on the site "wiki" page. A dedicated README file in each project folder explains the data presented and provides detailed methodology for each analysis.

Results

Phylogeny and localization of ptEnolase and ptPGAM sequences suggest recent recruitments of mitochondrial glycolytic enzymes to the diatom chloroplast Previous comparative genomic studies of diatom carbon metabolism have identified multiple isoforms of core glycolysis-gluconeogenesis enzymes, targeted to both mitochondria and chloroplasts [START_REF] Kroth | A model for carbohydrate metabolism in the diatom Phaeodactylum tricornutum deduced from comparative whole genome analysis[END_REF][START_REF] Bártulos | Mitochondrial glycolysis in a major lineage of eukaryotes[END_REF][START_REF] Smith | Comparative analysis of diatom genomes reveals substantial differences in the organization of carbon partitioning pathways[END_REF]. To place these data into a phylogenetic context, we considered the evolutionary diversity and history of organelle-(plastid-and mitochondria-) targeted Enolase and PGAM sequences within the diatoms alongside related algae in the stramenopiles and two other groups, the cryptomonads and haptophytes. These algal groups all possess plastids of secondary or higher red endosymbiotic origin, surrounded by four membranes, and the plastid-targeting sequences associated with nucleus-encoded proteins in each group are sufficiently well conserved to be identified through common in silico prediction pipelines [START_REF] Dorrell | Chimeric origins of ochrophytes and haptophytes revealed through an ancient plastid proteome[END_REF][START_REF] Gruber | Plastid proteome prediction for diatoms and other algae with secondary plastids of the red lineage[END_REF]. Single-gene trees were made for all organelle-targeted Enolase and PGAM sequences from 289 cryptomonad, haptophyte, and stramenopile (including diatom) genomes and transcriptomes, plus all orthologues from 85 further genomes selected from across the tree of life, based on RbH (reciprocal BLASTp best-hit) searches with all Enolase and PGAM sequences in the P. tricornutum version 3 genome annotation as queries [START_REF] Rastogi | Integrative analysis of large scale transcriptome data draws a comprehensive landscape of Phaeodactylum tricornutum genome and evolutionary origin of diatoms[END_REF]. Figs. 1B and1C show consensus MrBayes trees realised with three substitution matrices (GTR, Jones and WAG) for selected sequences from each alignment, retaining only species with both identifiable plastid-and mitochondria-targeted orthologues of each protein. Figs. S1-S3 provide consensus RAxML trees realised with three substitution matrices (GTR, JTT and WAG) for the complete aligned sequences for Enolase, and two non-homologous protein families annotated as PGAM (PGAM1 and PGAM2).

The obtained tree topologies revealed multiple evolutionary origins for plastidial Enolase and PGAM sequences across the cryptomonads, haptophytes and stramenopiles via the duplication of mitochondria-targeted (respiratory) enzymes, with diatom plastidial isoforms typically having recent and/or diatom-specific evolutionary origins. Diatom ptEnolase sequences resolve in a well-supported clade with plastid-targeted enzymes from bolidophytes, dictyochophytes and pelagophytes, which are sisters to diatoms in the stramenopile tree [START_REF] Dorrell | Chimeric origins of ochrophytes and haptophytes revealed through an ancient plastid proteome[END_REF][START_REF] Dorrell | Phylogenomic fingerprinting of tempo and functions of horizontal gene transfer within ochrophytes[END_REF], and in turn as a sister-group to mitochondriatargeted proteins from these groups (MrBayes PP = 1.0 under all studied matrices, Fig. 1B), followed by mitochondria-targeted proteins from other photosynthetic (chrysophytes) and nonphotosynthetic stramenopiles (oomycetes; MrBayes PP = > 0.95 under GTR and Jones matrices, Fig. 1B). This topology indicates a duplication and recruitment of the host-derived mitochondrial targeted protein to the plastid within a common ancestor of the diatoms, pelagophytes and dictyochophytes. The plastid-targeted isoforms identified for haptophytes positioned within the diatom/ pelagophyte/ dictyochophyte clade, consistent with a partial pelagophyte/ dictyochophyte origin of the haptophyte plastid [START_REF] Dorrell | Chimeric origins of ochrophytes and haptophytes revealed through an ancient plastid proteome[END_REF], and independent duplications and plastid retargeting of mitochondriatargeted enolase proteins were identified in both the chrysophytes and cryptomonads (Fig. S1), suggesting recurrent origins of ptEnolase functions across the secondary red plastid tree of life.

The phylogenetic origins of diatom ptPGAM genes were more complex, typified by multiple origins and plastidial recruitments of mitochondria-targeted enzymes. Two distinct clades of diatom ptPGAM proteins were identified, with ptPGAM1A (typified by the Phaeodactylum protein Phatr3_J17086) closely related to mitochondrial-targeted proteins found across the stramenopiles (MrBayes PP = 1.0 under all studied matrices, Fig. 1C), and ptPGAM1B (typified by the Phaeodactylum protein Phatr3_J50414) closely related to mitochondrial-targeted proteins found at least within pelagophytes and dictyochophytes (MrBayes > = 0.85 under all studied matrices, Fig. 1C).

More exhaustively sampled trees indicated deeper orthologies of each plastid-targeted family, respectively, to plastid-targeted proteins from the raphidophytes and chrysophytes and mitochondria-targeted oomycete proteins (ptPGAM1A, Fig. S2); and to plastid-and mitochondriatargeted enzymes from the chrysophytes and pelagophytes (ptPGAM1B, Fig. S2). This topology suggests ancestral presence and mitochondrial origins of both plastid-targeted protein families, with possible differential retention in different photosynthetic stramenopile groups. Possible duplications and plastid recruitments of mitochondria-targeted PGAM proteins were also visible in the haptophytes and cryptomonads, again positioning plastid-targeted glycolysis-gluconeogenesis as a potentially widespread innovation across the secondary red chloroplast tree of life.

A final plastid-targeted protein annotated as PGAM in the version 3 Phaeodactylum genome [START_REF] Rastogi | Integrative analysis of large scale transcriptome data draws a comprehensive landscape of Phaeodactylum tricornutum genome and evolutionary origin of diatoms[END_REF], hereafter termed PGAM2, was identified exclusively in diatoms, pelagophytes, and haptophytes (of inferred pelagophyte origin; Fig. S3), with limited homology to ptPGAM1 enzymes (BLASTp e-value > 1.0 in pairwise protein-protein searches). Only PGAM1 enzymes contain an annotated phosphoglyceromutase active site (IPR005952), while both PGAM1 and PGAM2 belong to the same PFAM superfamily (histidine phosphatase, PF03000) as inferred using InterProScan and PFAMscan respectively [START_REF] Mistry | Pfam: The protein families database in 2021[END_REF]89). This isoform was predominantly mitochondria-targeted, with plastid-or dualtargeted isoforms only identified in P. tricornutum (Phatr3_J37201) and a small number of other pennate diatoms (Attheya septentrionalis, Amphiprora coffeaformis, Entomoneis sp. CCMP2396) and haptophytes (Phaeocystis antarctica, Phaeocystis rex sp. CCMP2000) (Fig. S3).

To confirm plastidial localization of Phaeodactylum enolase and PGAM proteins, three proteins (Phatr3_J41515, ptEnolase; Phatr3_J17086, ptPGAM1A; Phatr3_J37201, ptPGAM2) were cloned into pPhat: eGFP vector sequences and transformed into wild-type P. tricornutum Pt1.86 cells by biolistic transformation (Figs. 1D,S4). These proteins were selected based on co-regulation analysis, suggesting tight transcriptional linkage to one another and core chloroplast metabolic pathways (Fig. S5, see below). The observed fluorescence patterns were coincident with chlorophyll, confirming plastid targeting.

Physiological roles of diatom ptEnolase and ptPGAM identified from cellular and environmental sequence expression data Next, we considered the mRNA abundances of diatom ptEnolase, ptPGAM1A and ptPGAM2 sequences, both in published microarray (57) and RNAseq data for P. tricornutum [START_REF] Ait-Mohamed | PhaeoNet: a holistic RNAseq-based portrait of transcriptional coordination in the model diatom Phaeodactylum tricornutum[END_REF] (Figs. S5-S7, Table S2), and more broadly within the Tara Oceans dataset (64) (Fig. 2, S8-10, Table S3), as proxies of their physiological function. First, considering general patterns of transcriptional co-regulation across published P. tricornutum data, we noted strong positive correlations (r > 0.75) between the sole gene encoding ptEnolase (Phatr3_J41515) and two of the genes encoding plastid-targeted PGAM: ptPGAM1A (Phatr3_J17086) and ptPGAM2 (Phatr3_J37201), with weaker (< 0.5) or zero correlations observed for genes encoding ptPGAM1B (Phatr3_J51404, Phar3_J5629) proteins (Fig. S5).

Next, considering patterns of transcriptional coregulation to other core components of Phaeodactylum chloroplast and mitochondrial metabolism [START_REF] Ait-Mohamed | PhaeoNet: a holistic RNAseq-based portrait of transcriptional coordination in the model diatom Phaeodactylum tricornutum[END_REF], we noted that both ptPGAM1A and ptEnolase showed consistently positive transcriptional coregulation coefficients against other components of the lower half of plastidial glycolysis-gluconeogenesis, alongside most of the genes encoding Lhcr and Lhcf proteins involved in photosynthetic light assimilation, core components of the photosynthetic electron transport chain pathway, and enzymes involved in shikimate, alanine and tetrapyrrole (chlorophyll and heme) synthesis. Both ptPGAM1A and ptEnolase genes further showed weaker positive or negative correlations to plastid fatty acid and lipid synthesis, alongside LhcX genes implicated in photo-protection (Fig. S6). Finally, considering the raw and relative abundances in both RNAseq and microarray data, we noted elevated relative abundances of ptEnolase and ptPGAM1A respectively in samples exposed to >16h and >12h continuous illumination, consistent with a photosynthesis-associated phenotype (Fig. S7; Table S2, sheet 1). We noted no effects of phosphorus or nitrogen starvation, which are typically associated with lipid production in Phaeodactylum, on the mRNA abundances of either gene (58, 59, 63).

Within Tara Oceans data, we noted high relative abundances of diatom ptEnolase and ptPGAM1 sequences in meta-transcriptome (metaT) data in stations from both high northern and southern latitudes, in both surface (Fig. 2,S8) and deep chlorophyll maximum (DCM) samples (Fig. S9). This accumulation was visible across all size fractions (Figs. 2A, S8A, S8C, S9A, S9C) and was notably greater than equivalent accumulations in meta-genome (metaG) data (Figs. 2B, S8B, S8D, S9B, S9D).

Normalisation of metaT abundances calculated for each gene in the 0.8-2000 μm size fraction against all diatom metaT sequences (Fig. S10A) both in surface (ptEnolase R 2 = 0.18, P < 10 -05 , ptPGAM1A R 2 = 0.23, P < 10 -05 ) and deep chlorophyll maximum (DCM) depths (ptEnolase R 2 = 0.53, P < 10 -05 , ptPGAM1A R 2 = 0.59, P < 10 -05 ), suggesting that plastidial glycolysis plays a more central role in diatom cellular transcription at high latitudes. Similar, albeit weaker, positive correlations were observed when normalising the metaT abundances against metaG relative abundances calculated for each gene (Fig. S10B), in surface depths for ptEnolase (R 2 = 0.10, P < 0.05) and DCM for both genes (ptEnolase R 2 = 0.28, P < 0.05, ptPGAM1 R 2 = 0.29, P< 0.05), further suggesting that latitude positively impacts on the relative expression of diatom plastidial glycolysis genes. Further significant positive correlations to with latitude, both considering absolute (Pearson) and ranked (Spearman) correlation values, were detected in multiple individual size fractions (0.8-5, 3/5-20, 20-180, 180-2000 μm) at each depth, including for ptPGAM1 metaT normalised against metaG in surface 3/5-20 (P < 10 -05 ), 20-180 (P < 10 -05 ) and 180-2000 (P < 0.05) μm fractions (Table S3, sheet 10), indicating these results are relevant across the diversity of marine diatom functional groups. Finally, recurrent positive correlations in the expression of plastid glycolysis genes to day length and negative correlations to measured water temperatures and the calculated length of the ice-free period at each station, were found considering multiple normalisations, depth and size fraction combinations (Table S3, sheet 10), whereas weaker and inconsistent correlations were found for measured nutrients (N, P, Si, Fe) that also vary across oceanic latitudes. We observed some negative correlations between ptEnolase or ptPGAM1 transcript abundance and salinity, particularly when normalised against meta-gene abundance (Table S3, sheet 10), supporting recent reports of differential expression of diatom glycolysis genes across a Baltic Sea salinity gradient, although we note that the range of salinities sampled across Tara Oceans stations (23.0 -39.4 per mille) are somewhat higher than those within the Baltic (i.e., 0-25 per mille) [START_REF] Pesant | Open science resources for the discovery and analysis of Tara Oceans data[END_REF]90).

The transcriptional preference of diatom ptEnolase and ptPGAM1 for long days and low temperatures contrasted strongly with PGAM2, which showed global expression trends, including effectively equivalent relative abundance in some stations from the temperate South Pacific and Atlantic as stations from the Arctic and Southern Oceans (Fig. S11A; Table S3, sheet 10). Although a positive correlation between relative PGAM2 accumulation and latitude was observed in metaT data normalised against total diatom metaT abundances and 0.8-2000 μm size fractions (surface R 2 = 0.20, DCM R 2 = 0.37, P< 0.05), no such correlation was observed for metaT data normalised against PGAM2 metaG relative abundances (surface R 2 = 0.00062, DCM R 2 = 0.027, P> 0.05) suggesting that latitude does not directly define PGAM2 expression (Fig. S11B,C). In certain size fraction and depth combinations (e.g., DCM 0.8-3, and 3/5-20 μm fractions, normalised against metaG abundances; and surface and DCM 180-2000 μm fractions normalised against all diatom metaT abundances) PGAM2 metaT abundances even demonstrated significant negative correlations to latitude (Table S3, sheet 10). In total, the Tara Oceans expression abundances, alongside Phaeodactylum transcriptome and microarray data, suggest diatom plastidial glycolysis to be of particular functional importance in high environmental latitudes, characterised by their extreme photoperiods and chronically low temperatures, with ptEnolase and ptPGAM1A being likely the principal drivers of this association within the lower half reactions (Figs. 2, S7-S10)

Growth phenotypes of ptEnolase and ptPGAM1A knockout and complementation lines reveal modular roles in adaptation to high latitudes Next, we generated homozygous CRISPR knockout lines for both ptEnolase and ptPGAM1A, selected because of their clear transcriptional co-regulation to one another (Fig. S5, in contrast to ptPGAM1B) and latitudinal expression correlation in Tara Oceans (Figs. 2, S10, in contrast to ptPGAM2). Two distinct CRISPR regions with unique sequences in the P. tricornutum genome targeted for each gene, and multiple CRISPR knockout lines were generated in each case (ptEnolase CRISPR region 1 n= 4, CRISPR region 2 n= 3; ptPGAM1A CRISPR region 1 n= 2, CRISPR region 2 n= 3) (Fig. S12A). Each CRISPR line was verified by sequencing to be homozygous and to contain a frame-shift mutation sufficient to impede successful translation of the ptEnolase or ptPGAM1A gene sequence (Fig. S12A).

We were unable to directly test the accumulation of ptEnolase and ptPGAM1A protein levels in knockout lines by western blot due to the absence of antibodies specific to each protein, but confirmed the effective knockdown of expression levels of mutated genes in knockout lines compared to empty vector control lines (n= 4) by qRT-PCR (measured knockout mRNA abundance 1.8-39 % mean empty vector control mRNA abundance under 19°C and 12h light: 12h dark (« 19C LD ») conditions, Fig. S12B), consistent with non-sense mediated decay of non-translatable mRNA sequences [START_REF] Chang | The nonsense-mediated decay RNA surveillance pathway[END_REF].

Next, we performed growth curves of ptEnolase and ptPGAM1A knockout lines compared to empty vector controls under three growth conditions (Fig. 3; Table S4, sheets 3-6). We chose to target changes in light and temperature, given the associations observed between these parameters and ptPGAM1A and ptEnolase expression both in Phaeodactylum gene expression and Tara Oceans data (Fig. 2; Fig. S7). The conditions were: 19°C and 12h: 12h light: dark cycling ("19C LD"), reflecting default growth conditions used in multiple other Phaeodactylum mutant and comparative physiology studies (e.g., (58, 59, 63)); 19°C and 24h continuous light ("19C CL") to test the effects of photoperiod; and 8°C and 24h continuous light ("8C CL") to test the effects of temperature. Growth curves were tested for seven ptEnolase knockout, five ptPGAM1A knockout and four empty vector control lines, with a minimum of two biological replicates and three independent repeats (i.e., six measurements total) per line.

Under 19C LD growth conditions, plastid glycolysis-gluconeogenesis knockout lines showed an approximately 15% reduction in relative growth rate compared to empty vector controls (ptEnolase relative growth rate 0.92± 0.03 cells day -1; ptPGAM1A relative growth rate 0.98± 0.04 cells day -1; empty vector growth rate 1.10 ± 0.03 cells day -1 ; Fig. 3A,3D; Table S4, sheet 3; ANOVA P < 0.05).

Under 19C CL conditions, knockout lines showed a more severe (approximately 30%) reduction in relative growth rate compared to controls (ptEnolase relative growth rate 1.02± 0.08 cells day -1 ; ptPGAM1A relative growth rate 1.00± 0.05 cells day -1 ; empty vector growth rate 1.45 ± 0.05 cells day - ; Fig. 3B,3D; Table S4, sheet 4; ANOVA P < 0.01), suggesting compromised ability to capitalize on the greater photosynthetic potential afforded by continuous illumination. For lines grown under 8C CL conditions, we observed in contrast an overall reduction in the difference in growth rate between knockout and control lines, with equivalent growth observed for ptEnolase (0.54± 0.02 cells day -1 ) and ptPGAM1A knockouts (0.56± 0.03 cells day -1 ), compared to empty vector controls (0.56± 0.03 cells day -1 ; Fig. 3C,3D; Table S4, sheet 5).

To test the possibility of off-target activity of CRISPR constructs, we complemented mutant lines with blasticidin-resistant overexpressor lines of either ptEnolase or ptPGAM1A genes modified to remove all CRISPR target sequences (Table S4, sheet 2). Constructs were built using the constitutive FcpA promoter, and the genes were linked to GFP [START_REF] Buck | Blasticidin-S deaminase, a new selection marker for genetic transformation of the diatom[END_REF]59), allowing the verification of protein expression and localization by confocal microscopy. To account for the inherent effects of (i) overexpression of complementation constructs and (ii) GFP and blasticidin deaminase gene expression in complementation lines, additional transformations were generated using CRISPR knockout lines, and placebo (cytoplasmic GFP) blasticidin-resistance vectors; and using zeocin-resistant empty vector lines transformed with each blasticidin-resistance complementation construct.

We then performed comparative growth curves of 47 complemented versus placebo transformed mutant lines (Fig. S13A; Table S4, sheet 7) under the 19C CL and 19C LD conditions in which the primary knockout lines showed slower growth rates than empty vector controls. We noted a general reduction in growth rates of blasticidin complemented lines compared to primary zeocin-resistant transformants, which may suggest secondary effects of the ectopic overexpression of the blasticidin deaminase and GFP-link constructs in the complemented lines, alongside some within-population variation. Nonetheless, a significantly increased growth rate was observed for complemented versus blank transformed knockout lines, as inferred by one-way ANOVA of separation between complemented and blank transformed mean growth rates. This was observed under both 19C LD (ptEnolase complemented knockout growth rate 1.07± 0.03 cells day -1 , blank transformed growth rate 1.00± 0.07 cells day -1 , ANOVA Pvalue= 7.4 x 10 -05; ptPGAM1A complemented knockout growth rate 1.12± 0.12 cells day -1 , blank transformed growth rate 1.02± 0.10 cells day -1 , P = 2 x 10 -04 ) and CL conditions (ptEnolase complemented knockout growth rate 1.36± 0.13 cells day -1 , blank transformed growth rate 1.29± 0.08 cells day -1 , P = 5 x 10 -03 ; ptPGAM1A complemented knockout growth rate 1.43± 0.15 cells day -1 , blank transformed growth rate 1.23± 0.14 cells day -1 , P = 5 x 10 -06 ). In contrast, complemented knockout line growth rates overlapped with blank transformed empty vector controls either transformed with ptEnolase (19C LD growth rate 1.07± 0.04 cells day -1 , 19C CL growth rate 1.37± 0.12 cells day -1 ), ptPGAM1A (19C LD growth rate 1.04± 0.09 cells day -1 , 19C CL growth rate 1.28± 0.08 cells day -1 ) or blank complementing vectors (19C LD growth rate 1.04± 0.03 cells day -1 , 19C CL growth rate 1.36± 0.12 cells day -1 ), indicating effective recovery of mutant phenotypes (ANOVA P > 0.05; Fig. S13B; Table S4, sheet 7).

Limited photo-physiological effects of ptEnolase and ptPGAM1A knockout regardless of light condition

To understand the distinctive growth phenotypes of ptEnolase and ptPGAM1A knockouts, we performed comparative photo-physiological analysis of knockout lines in the two conditions where they presented a growth phenotype, i.e. under 19C LD and 19C CL (Table S4, sheets 8-11). We measured the light dependence of photosynthesis and photo-protection (see Methods) in a selected set of control lines (n=2), ptPGAM (n=3) and ptEnolase knockouts (n=6), as well in complemented ptEnolase (n=2) and ptPGAM1A (n=3) knockout lines in which we observed a suppression of the knockout growth defect compared to complemented control lines. We further availed of a Fluorescence Induction and Relaxation (FIRe) fluorometer to measure the absorption cross-section of photosystem II, σPSII, and Fv/Fm parameters in glycolysis mutant lines under all three growth conditions (i.e., including 8C CL) [START_REF] Gorbunov | A multi-spectral fluorescence induction and relaxation (FIRe) technique for physiological and taxonomic analysis of phytoplankton communities[END_REF]. Measurements were repeated a minimum of two and in most cases four times per line and treatment condition, with a minimum of six unique measurements performed for each genotype and treatment combination (Table S4, sheets 8, 9). First, considering light-dependent changes in photophysiology, we found no significant difference between lines in rETR(II), which is the electron transfer rate through photosystem II (PSII), whatever the light irradiance (Fig. S14A; Table S4, sheet 10). We furthermore did not observe any change in the light dependency of the NPQ (non-photochemical quenching versus chlorophyll fluorescence) in each construct (Fig. S14B, Table S4, sheet 10). We observed a slight but significant (one-way ANOVA, P < 0.05) increase in the σPSII in ptEnolase (318.5± 35.3) and ptPGAM1A knockouts (307.4± 16.7) compared to empty vector control lines (292.3± 8.8) under 19C CL conditions exclusively (Fig. S15A; Table S4, sheet 11) but not in complemented lines; which may suggest that the photosystem II of plastidial glycolysis mutants has an enhanced capacity to harvest light. We further noted significantly greater maximal non-photochemical quenching rates (NPQ m ) in complemented ptEnolase mutants compared to empty vector controls under both 19C CL and 19C LD conditions (Fig. S15B; Table S4, sheet 11). Overall, our results indicate that suppression of plastidial glycolysis has limited direct effects on diatom photophysiology, even in the 19 °C CL conditions where substantial growth defects are observed.

Primary metabolic functions of ptEnolase and ptPGAM1A inferred from comparative gene expression analysis of P. tricornutum knockout lines Given the limited direct effects of plastidial glycolysis absence on photosynthesis, we considered the growth rates of mutant lines were due to broader impacts in primary metabolism. To do this, we performed quantitative RNA-seq analysis using 63 RNA samples drawn from five ptEnolase and five ptPGAM1A knockout lines alongside four empty vector controls under all three conditions (Table S5, sheet 1; Materials and Methods). Differentially expressed genes were identified by DESeq with folddifference threshold 2 and P-value 0.05. Complete results are provided in Table S5, sheets 5-11.

Exemplar Volcano plots of two comparisons (ptEnolase v control lines, and ptPGAM1A v control under 19C CL conditions) are shown in Fig. S16, confirming under-accumulation of ptEnolase and ptPGAM1A mRNA in the corresponding knockout lines.

Genome-scale enrichment analyses of the in silico localizations of proteins encoded by differentially expressed genes revealed distinctive changes in glycolysis knockout organelle metabolism compared to control lines. These effects were most strongly observed in 19C CL conditions, in which of the 239 genes differentially upregulated (mean fold-change >2, P-value < 0.05) in both ptEnolase and ptPGAM1A knockout lines compared to controls, 85 (36%) were inferred to possess chloroplast targeting peptides based on ASAFind (47) and 63 (26%) were found to inferred to possess chloroplast targeting peptides based on HECTAR predictions [START_REF] Gschloessl | HECTAR: a method to predict subcellular targeting in heterokonts[END_REF], compared respectively to 13% and 4.8% of genes globally encoded across the entire genome (chi-squared P < 10 -05 ; Fig. 4A). Less dramatic changes were evident in 19C LD and 8C CL conditions, reflecting the less exaggerated growth phenotypes of glycolysis-gluconeogenesis knockout lines under each condition, although we noted that 13 of the 51 genes (25%) inferred to be downregulated in both ptEnolase and ptPGAM1A knockout lines under 8C CL conditions were inferred to encode chloroplast-targeted proteins by ASAFind, representing likewise an enrichment compared to all genes within the genome (chi-squared P < 0.05; Fig. 4A).

Considering the KEGG, PFAM, GO and transcriptional repartition of the differentially expressed genes in each knockout line (Table S5, sheets 5-8), we noted clear patterns of differential upregulation of genes encoding photosynthesis-related proteins in both ptEnolase and ptPGAM1A knockout lines, specifically under 19C CL conditions. These included (chi-squared P < 0.05) GO enrichments in lightharvesting complex (GO:0030076), photosynthesis (GO:0009765) and protein-chromophore linkage (GO:0018298) GO terms, the chlorophyll-binding (PF00504) PFAM domain. We additionally considered the accumulation of genes that are differentially expressed in knockout lines in PhaeoNet, a WGCNA (Weighted Genome Correlation Network Analysis)-based division of the entire Phaeodactylum genome into thirty co-regulated gene modules [START_REF] Ait-Mohamed | PhaeoNet: a holistic RNAseq-based portrait of transcriptional coordination in the model diatom Phaeodactylum tricornutum[END_REF]; and noted a chi-squared P < 0.05 enrichment of differentially expressed genes in the lightcyan1 module, which has been linked to core processes in light harvesting and photosynthesis [START_REF] Ait-Mohamed | PhaeoNet: a holistic RNAseq-based portrait of transcriptional coordination in the model diatom Phaeodactylum tricornutum[END_REF]. Detailed resolution of gene expression patterns underpinning core organelle metabolism pathways [START_REF] Ait-Mohamed | PhaeoNet: a holistic RNAseq-based portrait of transcriptional coordination in the model diatom Phaeodactylum tricornutum[END_REF] in RNAseq data from ptEnolase (Fig. S17A) and ptPGAM1A (Fig. S17B) knockout lines under 19C CL conditions confirmed, a concerted upregulation of genes encoding light-harvesting complexes and photosynthesis machinery and plastidial fatty acid synthesis machinery, alongside a probable upregulation of mitochondrial respiratory complex I and ATP synthase, suggesting that these transcriptional changes reflect concerted changes in both plastid and mitochondrial metabolism, and potentially plastidmitochondrion crosstalk [START_REF] Broddrick | Cross-compartment metabolic coupling enables flexible photoprotective mechanisms in the diatom Phaeodactylum tricornutum[END_REF].

To gain a more precise insight into the effects of plastidial glycolysis-gluconeogenesis on Phaeodactylum chloroplast and mitochondria-targeted metabolism, we additionally validated by qPCR the differential expression of eleven exemplar genes encoding chloroplast-and mitochondriatargeted proteins in knockout and empty vector control lines across all three conditions (Fig. 4B; Table S5, sheet 12). These data confirmed a relatively limited difference in chloroplast metabolism under 19C LD conditions, limited to a slight (~50% downregulation, t-test P < 0.05) depression in the accumulation of Lhcf1 (Phatr3_J18049) and chorismate mutase (Phatr3_J43277) mRNA in both ptEnolase and ptPGAM1A knockouts compared to control lines (Fig. 4B). Both knockout lines were found to over-accumulate (>600%; t-test P < 10 -05 ) mRNAs encoding mitochondrial phosphoglycerate mutase (Phatr3_J33839) under 19C LD conditions compared to control lines (Fig. 4B).

Under 19C CL conditions, we observed more significant changes in plastid metabolism, including the over-accumulation of mRNAs encoding Lhcf1 (~150%, t-test P < 0.05) and a plastid-targeted petBtype protein presumably involved in cytochrome b 6 f metabolism (Phatr3_J13558, ~90%, t-test P < 0.05) in both ptEnolase and ptPGAM1A knockout lines, consistent again with the increased σPSII observed (Fig. 4B; Fig. S15). We further noted a significant over-accumulation in either ptEnolase or ptPGAM1A lines of mRNAs encoding plastidial signal processing peptidase (Phatr3_J10319), alanine transaminase (Phatr3_J34010) or copropophyrinogen oxygenase (Phatr3_J12186; Fig. 4B), consistent with a broader upregulation of plastid metabolic activity. We additionally noted a strong (>100%, P < 10 -05 ) increase in mRNA abundance of the gene encoding lysophosphatidyl acyltransferase (Phatr3_J20640), which diverts plastidial glyceraldehyde-3-phosphate into lipids via 3phosphoglycerate, in both ptEnolase and ptPGAM1A knockout lines. Concerning mitochondrial metabolism, we noted a strong increase (>250%, P < 10 -05 ) in mRNA for NDH dehydrogenase subunit 1 (Phatr3_J43944), involved in mitochondrial oxidative phosphorylation, but a corresponding decrease (>40%, P < 10 -05 ) in mRNA for citrate synthase within the TCA cycle (Phatr3_J30145).

Finally, under 8C and CL conditions, we noted contrasting and complementary changes: upregulation (>60%; P < 10 -05 ) of genes encoding both the plastidial signal processing peptidase and petB-related protein, and mitochondrial PGAM and citrate synthases in both knockout lines compared to controls (Fig. 4B). These data suggest both enhanced flux through photosystems and through mitochondrial carbon metabolism in the absence of plastidial glycolysis-gluconeogenesis. Of note, Lhcf1 mRNA was found to severely under-accumulate (>90%; P < 10 -05 ), but instead Lhcx4 (Phatr3_J38720), encoding a dark-expressed homologue of the Lhcx1 protein implicated in photo-protection although of unknown direct function [START_REF] Buck | Lhcx proteins provide photoprotection via thermal dissipation of absorbed light in the diatom Phaeodactylum tricornutum[END_REF], was found to substantially over-accumulate in both ptEnolase and ptPGAM1A knockout lines (Fig. 4B).

Metabolite profiling indicates potential systemic outputs of disrupted plastidial glycolysisgluconeogenesis

Next, we considered the likely metabolic consequences for the differential gene expression patterns observed in ptEnolase and ptPGAM1A knockouts under each environmental condition studied via GC-MS profiling of 32 soluble sugar and amino acids (Fig. 5; Fig. S18) across 139 samples drawn from seven ptEnolase and five ptPGAM1A knockout lines alongside four empty vector controls under 19C LD, 19C CL and 8C CL conditions. Complete tabulated outputs are provided in Table S6, sheets 1-2.

Accounting for within-line variance, we noted multiple concordances in the metabolites differentially accumulated in ptEnolase and ptPGAM1A knockout lines, with significant correlations between the P-values for metabolite accumulation in each knockout line observed under 19C CL (p= 0.448, t-test P-value < 0.01), and 8C CL conditions (p= 0.324, t-test P-value < 0.05), with an effectively zero correlation (p= 0.017, non-significant) observed in 19C LD conditions. We were unable to directly measure the accumulation of any of the products or substrates of either ptPGAM1A or ptEnolase (3phosphoglycerate, 2-phosphoglycerate, PEP), although we detected significantly diminished (ANOVA P-value < 10 -05 ) pyruvate accumulation in ptPGAM1A knockouts under all three conditions, and in ptEnolase knockouts under 8C CL (Fig. 5,S18).

In all three conditions, we noted significant reductions (ANOVA P-value < 0.01 in both ptEnolase and ptPGAM1A knockout lines) in cytoplasmic sugars and sugar derivatives (glucose, sucrose, histidine, myo-inositol) in ptEnolase and ptPGAM1A knockouts compared to control lines (Fig. 5). Despite a high overall variance between samples, we noted a probable over-accumulation of phosphoric acid in all knockout lines except ptPGAM1A under 19C CL conditions (Fig. 5,S18). We further noted an under-accumulation in both ptEnolase and ptPGAM1A knockout lines of citric acid in all three conditions, and malic acid in 8C CL (Fig. 5). Finally, we identified significant (ANOVA P-value < 10 -05 ) over-accumulations of valine in ptEnolase and ptPGAM1A knockouts under 19C CL and 8C CL conditions; in methionine and ornithine in 19C CL conditions only; and an under-accumulation of arginine under 19C CL conditions only (Fig. 5).

We also noted specific differences in the metabolite accumulation patterns observed in ptEnolase and ptPGAM1A knockout lines (Fig. 5; S18). These include a significant (ANOVA P < 10 -05 ) over- accumulation of three amino acids (aspartate, leucine and phenylalanine) and one sugar phosphate (glycerol-3-phosphate) specifically in ptEnolase knockout lines under all three conditions, and in serine under 19C CL and 8C CL conditions only; which contrasts to ptPGAM1A knockouts in which no significant changes were observed, or (in the case of glycerol-3-phosphate and serine) these metabolites were found to significantly under-accumulate under all three conditions compared to controls (Fig. 5; S18). These differences may be related to a greater severity of the disruption of plastidial glycolysis in ptEnolase knockouts, for which there is only one non-redundant enzyme predicted in the Phaeodactylum genome, compared to redundant ptPGAM enzymes that may partly compensate for ptPGAM1A activity (Fig. S5). It is furthermore possible that some of these metabolites relate to the specific activities of ptEnolase and ptPGAM1A in both the glycolytic and gluconeogenic directions, notably ptEnolase whose glycolytic product and gluconeogenic substrate (PEP) is a direct precursor of both aspartate and phenylalanine synthesis [START_REF] Bromke | Amino acid biosynthesis pathways in diatoms[END_REF].

Contrasting impacts of plastidial glycolysis-gluconeogenesis on lipid profiles depend on temperature Finally, to gain a greater insight into the different effects of fluxes through ptEnolase and ptPGAM enzymes under different light and temperature conditions, we performed GC-MS (55 samples) and LC-MS (49 samples) of five ptEnolase and five ptPGAM1A knockout lines alongside four empty vector controls under 19C LD, 19C CL and 8C CL conditions (Fig. 6; Fig. S19). Outputs are tabulated in Table S6, sheets 1, 3-5. Lipid quality may be influenced by multiple metabolites impacted by plastidial glycolytic-gluconeogenic flux, including pyruvate and acyl-coA pools (in the glycolytic-gluconeogenic direction), glycerol-3-phosphate (synthesised from glyceraldehyde-3-phosphate via glycerol-3phosphate dehydrogenase [START_REF] Kroth | A model for carbohydrate metabolism in the diatom Phaeodactylum tricornutum deduced from comparative whole genome analysis[END_REF]) and glucosyl-1-phosphate (synthesised from exported glucosyl-1phosphate generated in the gluconeogenic direction, via UDP-glucosyl PPiase and UDP-glucose epimerase, as described above [START_REF] Zhu | Silencing UDP-glucose pyrophosphorylase gene in Phaeodactylum tricornutum affects carbon allocation[END_REF]), with compound effects on fatty acid chain length, saturation state, and lipid head group dependent on the stoichiometric availability of each metabolite [START_REF] Maréchal | Relationship between acyl-lipid and sterol metabolisms in diatoms[END_REF]. Lipid remodelling has also previously been implicated in diatom adaptations to light and temperature, considering crude amounts [START_REF] Araújo | Growth and biochemical composition of the diatom Chaetoceros cf. wighamii Brightwell under different temperature, salinity and carbon dioxide levels. I. Protein, carbohydrates and lipids[END_REF], fatty acid chain length [START_REF] Rousch | Changes in fatty acid profiles of thermointolerant and thermo-tolerant marine diatoms during temperature stress[END_REF], and relative investments in triacylglycerol to plastid membrane (galacto-) lipids [START_REF] Sicko-Goad | Effect of growth and light/ dark cycles on diatom lipid content and composition[END_REF][START_REF] Lepetit | Molecular dynamics of the diatom thylakoid membrane under different light conditions[END_REF]. Growth conditions were observed to have strong effects on fatty acid profiles observed through GC-MS, with a significant (ANOVA P < 0.05) replacement of C20:5 side chain lipids with C16:1 side chain lipids in 19C CL compared to both 19C LD and 8C CL conditions; and over-accumulation of C16:3 side chain lipids under 19C LD, and C18:0 side chain lipids under 8C CL conditions, respectively (Fig. S19).

The enhanced accumulation of short-chain saturated lipid side chains under continuous illumination may reflect enhanced flux into fatty acid synthesis over elongation and desaturation, e.g., for the synthesis of storage triacylglycerols, whereas the enhanced accumulation of long-chain and unsaturated lipid side chains at 8C may serve to enhance membrane fluidity in response to low temperature (58, [START_REF] Abida | Membrane glycerolipid remodeling triggered by nitrogen and phosphorus starvation in Phaeodactylum tricornutum[END_REF][START_REF] Maréchal | Relationship between acyl-lipid and sterol metabolisms in diatoms[END_REF]. We noted no substantial differences in fatty acid profiles between ptEnolase, ptPGAM1A and control lines under any conditions studied (Fig. S19).

In contrast to the relatively limited effects on fatty acid profiles, we noted substantial changes in lipid class distributions in plastidial glycolysis-gluconeogenesis knockout lines compared to controls (Fig. 6; Table S6, sheet 4), as identified by LC-MS. Both ptEnolase and ptPGAM1A knockouts were found to under-accumulate triacylglycerols (TAG) compared to control lines under 19C LD (ptEnolase 2.72 ± 1.45%, ptPGAM1A 3.39 ± 1.79%, control 8.33 ± 2.36; ANOVA P separation of means between knockout and control lines < 0.001) and 19C CL conditions (ptEnolase 21.23 ± 3.58%, ptPGAM1A 20.16 ± 6.99%, control 30.35 ± 3.76%; ANOVA P < 0.001). We further noted that both ptEnolase and potPGAM1A knockout lines significantly over-accumulate mono-galactosyl-diacylglycerol (MGDG; ptEnolase 64.77 ± 2.52%, ptPGAM1A 62.75 ± 4.45%, control 53.92 ± 2.77%; ANOVA P < 10 -05 ) and under-accumulate di-galactosyl-diacylglycerol (DGDG; ptEnolase 7.62 ± 0.38%, ptPGAM1A 7.57 ± 1.12%, control 10.05 ± 0.38%; ANOVA P < 10 -05 ) and sulfoquinovosyl diacylglycerol (SQDG; ptEnolase 9.37 ± 0.61%, ptPGAM1A 9.22 ± 1.82%, control 11.80 ± 1.75%; ANOVA P < 0.01) in 19C CL conditions only (Fig. 6; Table S6, sheet 4).

Considering the individual profiles of lipid side chains under 19C conditions (Table S6, sheet 5), we noted increased levels of C16:1 sn-1 fatty acids in glycolysis-gluconeogenesis knockouts compared to control lines under 19C conditions (Figs. S20,S21). These included a conserved and significant increase (ANOVA P < 10 -05 ) under 19C CL in SQDG-16-1_16-0 in both ptEnolase (25.6± 1.17%) and ptPGAM1A knockouts (26.1± 1.93%) compared to control lines (20.8± 0.73%), and more broadly encompassed C16:1 sn-1 TAG, DGDG and PC (Fig. S21). In contrast, both ptEnolase and ptPGAM1A knockouts were found to have a significant (P < 10 -05 ) corresponding increase in 20:5 fatty acids in the sn-1 positions of the betaine lipid diacylglyceryl hydroxymethyltrimethyl-β-alanine (DGTA), implicated in lipid catabolism, recycling and TAG synthesis in Phaeodactylum under 19C CL conditions (Fig. S21) [START_REF] Popko | Metabolome analysis reveals betaine lipids as major source for triglyceride formation, and the accumulation of sedoheptulose during nitrogen-starvation of Phaeodactylum tricornutum[END_REF][START_REF] Dolch | Inventory of fatty acid desaturases in the pennate diatom Phaeodactylum tricornutum[END_REF]. We additionally observed significant (P < 0.05) under-accumulations of C14:0, C16:0 and C16:1 sn-1 fatty acids in glycolysis knockout MGDG pools under both 19C CL and 19C LD conditions (Fig. S21B; Table S6, sheet 5). These data in total suggest recycling of 20:5 fatty acids and replacement with shorter chain equivalents, although the accumulation of C16:1 sn-1 SQDG may also be driven by under-accumulation of C14:0 equivalents and rerouting of short-chain DAG from MGDG into SQDG synthesis under both 19C LD and 19C CL conditions (Figs. S20B, S21B), (100).

Under 8C CL conditions, we observed quite different trends in fatty acid accumulation specifically in ptEnolase knockouts, compared to both ptPGAM1A knockouts and controls. Considering lipid class, these changes related principally to over-accumulations of TAG (ptEnolase 29.2 ± 3.05%, ptPGAM1A 9.19 ± 4.35%, control 5.27 ± 2.30%; ANOVA P < 10 -10 ) and SQDG (ptEnolase 12.0 ± 1.98%, ptPGAM1A 8.96 ± 2.05%, control 7.79 ± 0.73%; ANOVA P < 0.01) in lieu of MGDG (ptEnolase 31.3 ± 3.17%, ptPGAM1A 44.3 ± 3.36%, control 51.3 ± 1.34%; ANOVA P < 10 -10 ; Fig. 6). Within the side-chain distributions of individual lipid classes, we further noted a significant (ANOVA P < 10 -05 ) overaccumulation of short-chain (C14:0, C16:1) and sn-1 fatty acids in ptEnolase knockout plastid lipid pools (i.e., MGDG, DGDG; Fig. S22A), although the sn-1 most abundant fatty acid in each lipid group remained C20:5 (Fig. S22B). We finally noted a probable exchange of very long-chain sn-2 fatty acids in SQDG pools in ptEnolase knockouts, with significant (ANOVA P < 10 -05 ) increases in SQDG 14-0_16-0 and SQDG-14_0-16-1 in lieu of SQDG-16-2_24-0 in in ptEnolase knockouts compared to ptPGAM1A and control lines (Fig. S22; Table S6, sheet 5).

Discussion

In this paper, we present the evolutionary history (Figs. 1, S1-S3), gene expression characteristics (Figs. 2, S4-S11), functional mutagenesis (Figs. 3, S12-13) and physiological analysis (Figs. [START_REF] Malhi | Tropical forests and atmospheric carbon dioxide[END_REF]-6, S14-22) of a lower half glycolytic-gluconeogenic pathway associated with diatom chloroplasts, relating specifically to two plastid-targeted proteins, ptEnolase and ptPGAM1A, in the model species P. tricornutum. Our data position plastid glycolysis-gluconeogenesis as arising recurrently across secondary red chloroplasts, via the duplication of genes encoding respiratory proteins (Fig. 1), placing this as one of many pathways of host evolutionary origin that have been recruited to support the diatom plastid [START_REF] Dorrell | Chimeric origins of ochrophytes and haptophytes revealed through an ancient plastid proteome[END_REF]101,102). This innovation sits alongside others associated with diatom carbon metabolism, including mitochondrial targeting of respiratory glycolysis and synthesis of storage sugars in cytosolic vacuoles as chrysolaminarin [START_REF] Zhu | Silencing UDP-glucose pyrophosphorylase gene in Phaeodactylum tricornutum affects carbon allocation[END_REF][START_REF] Bártulos | Mitochondrial glycolysis in a major lineage of eukaryotes[END_REF]; although in contrast to these features (which are respectively shared with all stramenopiles, and with the primary red algal ancestor of the diatom chloroplast, as floridoside storage (103)) the plastidial glycolysis-gluconeogenesis isoforms associated with diatoms are uniquely shared with their closest relatives (e.g., pelagophytes, dictyochophytes) and potential endosymbiotic derivatives (haptophytes) [START_REF] Dorrell | Chimeric origins of ochrophytes and haptophytes revealed through an ancient plastid proteome[END_REF].

Considering previously published transcriptomic data from P. tricornutum, we show that plastidial glycolysis-gluconeogenesis genes are co-expressed with those encoding core plastidial and mitochondrial carbon metabolism (Fig. S6), with more distant regulatory connections to other biosynthetic pathways (e.g., fatty acid synthesis; Figs. S20-22). We note that the role of plastidial glycolysis-gluconeogenesis is likely to not directly impact photosynthetic efficiency, given the limited photo-physiological phenotypes of mutant lines (Figs. S14-S15), although may indirectly impact on photosynthesis gene expression (Fig. 4; Fig. S17). From both P. tricornutum and Tara Oceans data, we show evidence that the functions of this pathway may be particularly pertinent at high latitudes (Figs. 2, S7-S10), e.g., under elongated photoperiods and low temperatures. This phenotype reflects the growth rates observed for complemented knockout mutant lines of P. tricornutum ptEnolase and ptPGAM1A, where we note a retardation in growth in glycolysis-gluconeogenesis knockout lines under 19C continuous light compared to 19C light-dark conditions (Figs. 3, S12-13). Despite clear physiological defects observed in glycolysis-gluconeogenesis knockout line under 8C continuous light conditions (e.g., upregulation of Lhcx4 and downregulation of Lhcf1 genes, and upregulation of mitochondrial TCA cycle and glycolysis genes, Fig. 4B; and differential accumulation of multiple metabolites and lipids; Figs. 56), we note equivalent growth rates in knockout and control lines (Fig. 3), suggesting additional physiological roles of plastidial glycolysis-gluconeogenesis at low temperatures may compensate for the growth defects observed due to continuous illumination.

Considering both gene expression dynamics (Figs. [START_REF] Malhi | Tropical forests and atmospheric carbon dioxide[END_REF]; S15-17) and metabolite profiles (Figs. 5,6; S18-S22) in knockout and control lines grown under 19C LD, 19C CL and 8C CL conditions, we reconstruct a map of the potential functions contributed by the lower half of plastidial glycolysis-gluconeogenesis in P. tricornutum, shown in Fig. S23. Under 19C LD conditions, we observe limited gene expression changes in ptPGAM1A and ptEnolase knockout lines, except (as inferred from qPCR) a downregulation in plastidial shikimate biosynthesis and upregulation of mitochondrial lower-half glycolysis-gluconeogenesis pathways (Fig. 4B). The downregulation of a shikimate biosynthesis enzyme intermediate (which might typically consume plastidial PEP ( 13)) might suggest a primary function of plastid glycolysis-gluconeogenesis in the glycolytic direction for augmenting plastidial PEP for shikimate biosynthesis, whereas upregulation of mitochondrial glycolysis-gluconeogenesis may reflect an enhanced flux of plastidial glyceraldehyde-3-phosphate to the mitochondrion in the absence of a direct plastidial route for its metabolism. We additionally note diminished levels of cytoplasmic sugars and TCA cycle intermediates (i.e., citric acid) in ptEnolase and ptPGAM1A knockout lines (Fig. 5) (104), whereas the overexpression of mitochondrial phospho-glycerate mutase observed in qRT-PCR and RNAseq data (Figs. 4B, S17) does not suggest substrate limitation of the TCA cycle in glycolysis knockout lines. Thhe diminished accumulation of TCA cycle intermediates and cytoplasmic sugars might instead suggest a diversion of glycolytic intermediates into anabolic activities in the Phaeodactylum mitochondrion. Finally, we note in both ptEnolase and ptPGAM1A mutants an over-accumulation of phosphoric acid, which may point to decreased phosphate usage (e.g., in phospholipid or nucleic acid synthesis) and increased storage as compatible solutes. An overaccumulation of phosphoric acid may equally relate to changes in the cellular ADP/ ATP ratio (105), although we did not identify any ATP-associated GOs amongst those enriched in glycolysisgluconeogenesis knockout DEGs under any condition (Table S4, sheet 6) Under 19C CL conditions, we observed much more dramatic remodelling of chloroplast and mitochondrial metabolic architecture, consistent with reduced growth rates in knockout lines (Figs.

3, 4A, S15, S17, S21). These include greater overall investment in photosynthesis gene expression and carbon metabolism (including the Calvin Cycle) compared to control lines (Figs. 4B,S17). We did not observe consistent differences in the expression of nitrogen or phosphorus stress metabolism in knockout lines compared to controls, suggesting that these difference do not relate to difference in N or P limitation between knockout and the faster-growing control lines under 19C CL conditions (Table S5, sheets 4-5) (59, 104, 106). These changes are supported by the greater absorption cross-section σPSII identified in photo-physiological analyses, but do not appear to impact substantially on measured Fv/ Fm (Figs. S13; S14), and we propose that any changes to photosynthesis machinery expression in glycolysis mutant lines are likely to occur proportionally to one another and to downstream carbon metabolism pathways, without apparent changes in photosynthetic efficiency. In contrast, we observed an apparent disequilibration in the expression of respiration-associated proteins (i.e., upregulation of NDH dehydrogenase and downregulation of TCA cycle enzymes), and probable upregulation of mitochondrial and peroxisomal proteins implicated in alternative electron flow in glycolysis knockout lines (Fig. S17). Previous studies have noted the important role of diatom mitochondria in dissipating excess plastidial reducing potential [START_REF] Bailleul | Energetic coupling between plastids and mitochondria drives CO2 assimilation in diatoms[END_REF][START_REF] Broddrick | Cross-compartment metabolic coupling enables flexible photoprotective mechanisms in the diatom Phaeodactylum tricornutum[END_REF], and it remains to be determined to what extent the export of plastidial NADPH to the mitochondria in glycolysisgluconeogenesis knockout lines differs compared to controls under continuous light.

Under both 19C LD and 19C CL conditions, we note probable changes in the stoichiometry of lipid classes (i.e., MGDG in lieu of DGDG, TAG and SQDG production in 19C LD; MGDG and SQDG in lieu of TAG and DGDG production in 19C CL; Fig. 6); and in fatty acid chain length in specific lipid pools (e.g., greater accumulation of 16:0 and 16:1 fatty acids in plastidial lipid pools, and 20:5 fatty acids in DGTA pools, Figs. S20,S21). These trends contrast with Arabidopsis ptEnolase and ptPGAM mutants, which present relatively limited lipid accumulation phenotypes [START_REF] Andriotis | Plastidial glycolysis in developing Arabidopsis embryos[END_REF][START_REF] Prabhakar | Molecular and functional characterization of the plastid-localized Phosphoenolpyruvate enolase (ENO1) from Arabidopsis thaliana[END_REF]. The relative over-accumulation of TAG in control lines under 19C conditions may be an indirect product of the diminished growth rates in glycolysis knockout lines, insofar as TAG accumulation is typically associated with late-over earlyexponential phase Phaeodactylum lines (Fig. 4) [START_REF] Abida | Membrane glycerolipid remodeling triggered by nitrogen and phosphorus starvation in Phaeodactylum tricornutum[END_REF]107). However, neither ptEnolase nor ptPGAM1A knockout lines show apparent changes in the expression of nitrogen or phosphate starvationassociated genes, which as described above are typically viewed to provoke TAG over-accumulation, compared to control lines (59, 104, 106). The relative change in the equilibrium of MGDG to TAG might suggest acyl-coA limitation of lipid synthesis, or greater investment into MGDG and SQDG synthesis from UDP-glucose produced from exported plastidial glyceraldehyde-3-phosphate (85) (Fig. 6). Supporting this, our RNAseq and qRT-PCR data suggest an overexpression of enzymes involved in fatty acid incorporation (acyl carrier protein, enoyl-ACP reductase, lysophospholipid acyltransferase I; Figs. 4,S17) despite limited transcriptional changes to genes encoding lipid head group remodelling and cytoplasmic sugar biosynthesis (e.g., UTP-glucose-1-phosphate uridylyltransferase, Phatr3_J32708; and UDP-Glc epimerases, Phatr3_J46785, Phatr3_J44401; Table S5, sheet 3) (35 [START_REF] Zhu | Silencing UDP-glucose pyrophosphorylase gene in Phaeodactylum tricornutum affects carbon allocation[END_REF] in glycolysis knockout lines under 19C CL conditions. The relative under-accumulation of glycose and sucrose, and overexpression of respiratory glycolysis enzymes in glycolysis knockout lines (Figs. [START_REF] Malhi | Tropical forests and atmospheric carbon dioxide[END_REF][START_REF] Moog | Localization and evolution of putative triose phosphate translocators in the diatom Phaeodactylum tricornutum[END_REF] is further coherent with a redirection of cytoplasmic triose phosphate from gluconeogenesis towards acyl-coA production, i.e. if plastidial glycolytic activity would otherwise function to directly replenish plastidial acyl-coA pools from Calvin cycle intermediates (Fig. S23). We note, however, from transcriptional coregulation data an apparent lack of coordination between the expression patterns of ptEnolase, ptPGAM1A and enzymes involved in fatty acid synthesis (Fig. S6); and it is possible that this pathway plays a secondary role to others, such as shikimate biosynthesis, for the consumption of plastidial pyruvate and PEP (Fig. S23).

It remains to be determined what routes enable the supply of pyruvate hub intermediates, to the Phaeodactylum plastid in the absence of plastidial glycolytic activity. Previous studies have noted the dual affinity of diatom triose phosphate transporters for glyceraldehyde-3-phosphate/dihydroxyacetone phosphate and PEP [START_REF] Moog | Substrate specificity of plastid phosphate transporters in a non-photosynthetic diatom and its implication in evolution of red alga-derived complex plastids[END_REF][START_REF] Liu | An integrative environmental atlas of diatom chloroplast transporters[END_REF]. One of the two annotated plastid triose phosphate transporters in the Phaeodactylum genome (Phatr3_J54017) is upregulated in both ptEnolase and ptPGAM1A knockout lines under 19C CL conditions only (Table S5, sheet 3), which may suggest some degree of direct PEP uptake from the cytoplasm [START_REF] Liu | An integrative environmental atlas of diatom chloroplast transporters[END_REF]. In both ptEnolase and ptPGAM1A knockout lines we further note an overexpression of the glycine cleavage system H protein (Phatr3_J32847) and underexpression of ammonia carbamoyl-phosphate synthase (Phatr3_J24195; Fig. S16), which may reflect an augmented importance of the mitochondrial glycine cycle in recovering photorespired glycolate in glycolysis mutant lines.

Elsewhere our data suggest an enhanced importance of organelle amino acid metabolism in the absence of plastidial glycolysis. These include an overexpression of plastidial alanine transaminase (Phatr3_J34010; Fig. 4B,S16); and differential accumulation of multiple amino acids (overaccumulation of valine, ornithine and methionine and under-accumulation of arginine in both mutant lines; overaccumulation of aspartate in ptEnolase knockout lines; and overaccumulation of glutamate in ptPGAM1A knockout lines; Fig. 5) under 19C CL conditions. Alanine synthesised in the Phaeodactylum mitochondrion could, for example, be imported into the plastid and deaminated to supply pyruvate (104), and an over-accumulation of pyruvate might in turn lead to an increased synthesis of valine [START_REF] Broddrick | Cross-compartment metabolic coupling enables flexible photoprotective mechanisms in the diatom Phaeodactylum tricornutum[END_REF]108). We noted an over-accumulation of mRNA for plastidial branched chainamino acid aminotransferase (Phatr3_J10779, involved in isoleucine: valine transamination) in both ptEnolase and ptPGAM1A knockouts in 19CL RNAseq data (Fig. S17); which may be related to dysregulation in branched-chain amino acid synthesis or catabolism (108). Aspartate, in contrast, might be synthesised either in the Phaeodactylum mitochondria from oxaloacetate or peri-plastidial compartment (via PEP, from PEP carboxylase (109), and its overaccumulation outside the plastid might in turn augment flux into methionine synthesis (via homocysteine) [START_REF] Bromke | Amino acid biosynthesis pathways in diatoms[END_REF]. It is unlikely that aspartate would directly supply PEP to the Phaeodactylum plastid due to the absence of known plastidial C4 decarboxylases in this species, but its synthesis might enable the effective recycling of extra-plastidial PEP into pyruvate pools (110, 111). Ornithine, synthesised in the Phaeodactylum plastid at the expense of plastidial glutamate, might be used to potentiate the deamination of alanine or aspartate via a keto-glutarate shuttle (104, 112), prior to being converted into arginine (113), or dissipated via a potential urea cycle in the mitochondria (114). The depletion of both arginine and citrate in both glycolysis knockout lines, a further intermediate in the proposed mitochondrial urea cycle, and corresponding accumulation of glutamate in ptPGAM1A knockout lines, suggests that ornithine over-accumulation likely relates to enhanced plastidial ornithine synthesis as opposed to diminished mitochondrial dissipation (Fig. 5,S23).

Under 8C CL conditions, we note congruent and contrasting changes in mutant phenotypes to those identified at 19C (Fig. S23). As per 19C conditions, these include over-accumulation of mRNAs encoding photosystem subunit, plastid biogenesis and mitochondrial glycolytic proteins, and an overaccumulation of short-chain amino acids (valine) and under-accumulation of cytoplasmic sugars and amino acids (glucose, histidine) in ptEnolase and ptPGAM1A knockouts relative to empty vector controls (Figs. 4B,5). Confirming the explicit significance of plastidial glycolysis for carbon metabolism under 8C CL conditions, we note under-accumulations both pyruvate and in glycerol, synthesised typically from plastidial triose-phosphate via 3-phosphoglycerate dehydrogenase in both mutant lines (Fig. 5). The absence of a specific growth defect under 8C conditions is therefore perhaps surprising (Fig. 3), although additional phenotypic differences in knockout lines unique to 8C may suggest more complicated physiological consequences for the absence of plastidial glycolysis. These include an overall enrichment in downregulated genes encoding plastid-targeted proteins (Fig. 4A); a specific over-accumulation of TCA cycle (citrate synthase, Phatr3_J30145) and a possible nonphotochemical quenching-associated mRNA (LhcX4, Phatr3_J38720) (Fig. 4B) [START_REF] Bailleul | Energetic coupling between plastids and mitochondria drives CO2 assimilation in diatoms[END_REF]115); and an overaccumulation of TAGs and SGDQs over glucosyl-lipids (Fig. 6) in both ptEnolase and ptPGAM1A knockout lines at 8C only. We tentatively propose that some of the contrasting investments observed in glycolysis knockout lines under 8C (e.g., diversion of fixed carbon either into mitochondrial respiration or anabolic pathways) conditions may to some extent compensate for phenotypes (i.e., inhibited flux into pyruvate and remodelling of carbon metabolism) observed in 19C CL conditions, thus yielding equivalent growth rates between mutant and control lines (Fig. 3; Fig. S23).

Finally, across all three conditions, we note specific phenotypic differences between ptEnolase and ptPGAM1A knockout lines, including the ptEnolase-specific accumulation of several amino acids (aspartate, phenylalanine, serine, leucine) across all three studied conditions (Fig. 5; Fig. S18). The overaccumulation of phenylalanine is particularly striking given the apparent downregulation of mRNA of chorismate mutase, (Phatr3_J43277) in both RNAseq and qRT-PCR data (Fig. 4B; Fig. 5; Fig. S17) [START_REF] Bromke | Amino acid biosynthesis pathways in diatoms[END_REF], suggesting strong metabolic forcing of its synthesis. We further notea predominant accumulation of short-chain sn-1 fatty acids in ptEnolase plastid lipid pools under 8C CL conditions (Fig. S22), which may act as a further electron sink in response to low temperature and continuous illumination (116). The exact reasons for these contrasting phenotypes remain to be determined, although may relate to the fact ptEnolase is projected to be the only plastid-targeted enolase protein encoded in the Phaeodactylum genome, whereas alternative phospho-glycerate mutases to exist that can compensate for ptPGAM1A (e.g., ptPGAM1B, ptPGAM2; Figs. S4,S6); and each enzyme performs different metabolic activities, with ptPGAM1A converting 3-phosphoglycerate to 2phosphoglycerate, and ptEnolase converting 2-phosphoglycerate to PEP in the glycolytic direction, and the reverse fluxes anticipated in the gluconeogenic direction (Fig. 1A). The absence of ptEnolase activity in a gluconeogenic direction, in particular, would lead to an over-accumulation of PEP in the Phaeodactylum plastid, which could explain the over-accumulation of aspartate (synthesised via PEP carboxylase (13, 104, 109)); phenylalanine (if excess PEP were diverted into shikimate biosynthesis) and potentially even leucine (via pyruvate) [START_REF] Bromke | Amino acid biosynthesis pathways in diatoms[END_REF]. In contrast, the excess of serine and short-chain sn-1 amino acids might relate to an absence of activity in the glycolytic direction (i.e., diminished flux of plastidial triose phosphate into acyl-coA pools from either plastidial or exported mitochondrial pyruvate [START_REF] Maréchal | Relationship between acyl-lipid and sterol metabolisms in diatoms[END_REF], and increased flux into glycerol-3-phosphate synthesis [START_REF] Bromke | Amino acid biosynthesis pathways in diatoms[END_REF]). The relevance of plastidial glycolysis in the gluconeogenic direction is particularly relevant under 8C CL conditions, where several of the phenotypes common to both ptEnolase and ptPGAM1A knockouts (including increased investment into respiration and neutral lipid accumulation, and diminished investment into plastid biogenesis and core plastid metabolism enzymes) might equally well be explained by a disruption of mitochondrial-to-plastid metabolic flux to support plastid primary carbon metabolism (Fig. S23). Ultimately, the relative importance of metabolic flux in each direction of each diatom plastid glycolysis enzyme will be best determined by kinetic studies, e.g. radio-labelled substrate tracing, in mutant lines under each growth condition.

Concluding Remarks

Overall, our data position the lower half of glycolysis-gluconeogenesis as providing a functionally redundant, but nonetheless significant component of the diatom plastid proteome, evidenced by the presence of viable mutants with observable growth and physiological phenotypes. We infer using combined growth, gene expression, photo-physiology and metabolomics analysis that plastidial glycolysis permits more efficient cycling of glyceraldehyde-3-phosphate from the Calvin cycle to the pyruvate hub, for a wide range of downstream pathways, with particular importance under continuous illumination such as the tested 19C CL condition. We additionally provide some more tentative evidence for functions of this pathway in the gluconeogenic direction, particularly in the context of the differential accumulation of metabolites specific to ptEnolase knockout and 8C CL conditions. The poise and interactions between the glycolytic and gluconeogenic functions of diatom ptEnolase and ptPGAM1A may in part contribute to the functional success of diatoms at high latitudes [START_REF] Gilbertson | Diatoms and their microbiomes in complex and changing polar oceans[END_REF], where they must tolerate extreme annual variances in photoperiod and low temperatures [START_REF] De Vargas | Ocean plankton. Eukaryotic plankton diversity in the sunlit ocean[END_REF][START_REF] Malviya | Insights into global diatom distribution and diversity in the world's ocean[END_REF]. The glycolytic functions of diatom plastidial glycolysis-gluconeogenesis may enable diatoms to dominate in the initial spring pulse of biomass that accompanies photoperiod induction (117, 118), and across the light-saturated conditions of the continuous polar summer by allowing augmented metabolic activity through the Calvin cycle (119). In contrast, a potential gluconeogenic activity may have relevance throughout the polar night (120) where photosynthesis is not possible, and might potentially induce photosynthetic activity via replenishment of the Calvin cycle from mitochondrial intermediates, in response to transient and seasonal enrichments in nutrients (e.g., in autumnal polar blooms (121)). It remains to be determined what roles plastidial glycolysis plays in other habitats where diatoms dominate phytoplankton biomass, e.g. in upwelling cycles where diatoms may preciously upregulate components of their core metabolism to rapidly avail of optimal light and nutrient conditions (119, 122) It furthermore remains to be determined what functions a lower half of plastidial glycolysisgluconeogenesis perform elsewhere across the tree of life. This question is pertinent not only to other eukaryotic algal groups (Figs. 1; S1-S2); but also in plants, where the presence of plastidial glycolysis-gluconeogenesis is to our knowledge uncharacterized in photosynthetic cells. Plastid glycolysis-gluconeogenesis in leaves has been proposed to be involved in the metabolism of imported sugars at night (123), and the synthesis of fatty acids and isoprenoids from excess glyceraldehyde-3-phosphate during the day (124), and lower half glycolysis-gluconeogenesis proteins have further been proposed to perform a secondary role as a structural component of protein contact points between plant plastids and mitochondria (125); but the eco-physiological roles of plant plastid glycolysis-gluconeogenesis remain underexplored. Notably, cytoplasmic phosphogycerate mutase (126-128) and enolase enzymes (129-131) have been identified in proteomic surveys of plant and algal cold-responsive genes, but it remains to be determined whether plastidial versions of these enzymes possess functions relevant to high latitudes. Our data shed light into functions of a poorly understood metabolic resource that may facilitate diatom success at high latitudes, and that may serve as a candidate for metabolic engineering in crop species for enhanced photosynthetic activity under continuous light and low temperatures [START_REF] Cardona | Enhancing photosynthesis in plants: the light reactions[END_REF].

No. 835067), and from the ANR 'BrownCut' project (ANR-19-CE20-0020). CB, EM and JJ were supported by ANR 'DIM' (ANR-21-CE02-0021). The lipid analysis were performed at the LIPANG (Lipid analysis in Grenoble) platform hosted by the LPCV (UMR 5168 CNRS-CEA-INRAE-UGA) and supported by the Rhône-Alpes Region, the European Regional Development Fund (ERDF), Institut Carnot 3BCAR and Labex GRAL, financed within the University Grenoble Alpes graduate school (Ecoles Universitaires de Recherche) CBH-EUR-GS (ANR-17-EURE-0003). YZ Total meta-transcriptome (top) and meta-genome (bottom) relative abundances for Tara Oceans meta-genes phylogenetically resolved to diatom ptEnolase and ptPGAM1A families [START_REF] Carradec | A global ocean atlas of eukaryotic genes[END_REF], sampled from all size fractions and surface layer stations, and demonstrating higher meta-transcript abundance without commensurate increases in meta-gene abundance at high northern and southern latitudes. Individual abundance maps for each size fraction and depth, and the correlations of meta- ptEnolase and ptPGAM1A knockout compared to control lines under 19C LD, 19C CL and 8C CL conditions. Localizations that are significantly enriched in gene expression categories (chi-squared P < 0.05), including a significant over-accumulation of genes with chloroplast-and mitochondriatargeting sequences upregulated in knockout lines under 19C CL conditions, are asterisked. Volcano plots and detailed organelle metabolism maps for genes differentially expressed under 19C CL in ptEnolase and ptPGAM1A lines are provided in Figs. S16-S17. B: relative mRNA abundances of eleven genes encoding exemplar chloroplast-and mitochondria-targeted proteins, verified by qRT-PCR. Abundances were calculated using two sets of qRT-PCR primers per gene and normalised against two housekeeping genes (Ribosomal protein S1, and TATA binding protein (78) ); across five ptEnolase, seven ptPGAM1A and four empty vector control lines, with four reverse transcriptase positive and four reverse transcriptase negative replicates each. Each bar plots thus the mean and standard deviation of expression ratio of all knockout genotypes and replicates for each line. Genes differentially expressed (t-test, P < 0.05) in each condition are asterisked. normalised abundances of 39 sugar and amino acid metabolites in ptEnolase and ptPGAM1A knockout compared to empty vector control lines, measured by GC-MS in all three experimental conditions tested. Metabolites that show a differential accumulation in each plot (P < 10 -05 ) are labelled, and metabolites that show a differential accumulation in both knockout lines in each condition are shown in black text, and five metabolites that are uniquely over-accumulated in ptEnolase knockout lines under all three conditions are shown in dark red text. Bar plots showing relative differences in metabolite abundance are shown in Fig. S18. Fig. 6. Violin plots of LC-MS lipid distributions in glycolysis-gluconeogenesis mutant lines. This figure shows plots of the observed frequencies (short bars), mean (long bars) and inferred normal distribution of the % of lipids found to belong to one of four lipid categories in ptEnolase and ptPGAM1A knockout and empty vector control lines under 19C LD, 19C CL and 8C CL conditions. Significant differences between knockout and control lines (ANOVA, P < 0.05) are asterisked. Equivalent plots of GC-MS fatty acid distributions, showing limited differences between knockout and control lines are provided in Fig. S19. ANOVA P-value distributions of sn-1 and sn-2 fatty acids associated with different lipid classes are shown in Figs. S20-S22.

/ d i c t y o c h o p h y t e / h a p t o p h y t e p l a s t i d P h a t r 3 _ J 4 1 5 1 5 -C h r y s o p h y t e p l a s t i d C r y p t o m o n a d m i t o c h o n d r i o n / p l a s t i d

Chrysophyte mitochondrion

Pelagophyte plastid D i a t o m / p e l a g o p h y t e / d i c t y o c h o p h y t e m i t o c h o n d r i o n

P h a t r 3 _ d r a f t J 1 1 7 2 ; P h a t r 3 _ d r a f t J 1 1 9 2 Fig. S1.Consensus topology of a 380 taxa x 413 aa alignment of Enolase sequences, sampling all organelle-targeted isoforms from cryptomonads, haptophytes and ochrophytes and representatives from a densely-sampled dataset of 151 taxonomic groups $across the tree of life [START_REF] Dorrell | Phylogenomic fingerprinting of tempo and functions of horizontal gene transfer within ochrophytes[END_REF]. The tree topology shown is the consensus of the best-scoring RAxML trees identified using three substitution matrices: GTR, JTT, and WAG. Branch thickness corresponds to frequency of topology recovery in the consensus of consenus trees; branches are coloured by taxonomic affiliation; and tips (cryptomonads, haptophytes and ochrophytes only) by predicted in silico localisation. Individual clades (considering both taxonomic origins and inferred localisation) of organelle-targeted enolase isoform are labelled with coloured brackets. scatterplot of maximum growth rates, calculated from two replicates, under 19C LD cycling (horizontal) and 19C CL conditions (vertical axis) for 47 total ptEnolase (i) and ptPGAM1A mutants (ii) complemented with blasticidin-resistant and GFP-linked ptEnolase, ptPGAM1 or blank constructs, compared to empty vector zeocin-resistant control lines transformed with the same constructs. The distribution of individual growth rates within complemented and blank transformed populations are shown using solid coloured, and grey dashed line circles respectively. Vectors plot the difference between the mean growth rates calculated for blank transformed versus complemented mutant lines, alongside ANOVA P-values of separation. B: mean growth rates calculated for blank transformed and complemented mutant lines, alongside empty vector primary transformants complemented either with blank, ptEnolase or ptPGAM1A constructs, showing effective overlap between complemented mutant and empty vector primary transformant line growth rates.

Key

Diatom mitochondrion C h r y s o p h y t e / p e l a g o p h y t e / h a p t o p h y t e m i t c h o n d r i o n / p l a s t i d

Di

  and ARF acknowledge funding from the European Union's Horizon 2020 research and innovation program, project PlantaSYST (SGA-CSA No. 739582 under FPA No. 66462). D.C and B.B acknowledge the support by the European Research Council (ERC) under the European Union's Horizon 2020 research and innovation program (PhotoPHYTOMIX project, grant agreement No. 715579). The authors acknowledge Giselle McCallum,Elena Kazamia and Xia Gao (IBENS) for assistance with the optimization of cell cytometry protocols; Amandine Baylet (Lycée ENCPB-Pierre Gilles de Gennes), Quentin Caris (Lycée de la Vallée de Chevreuse) and Yonna Lauruol (Ecole Sup'BioTech) for assistance with growth measurements, Frédy Barneche and Clara Richet-Bourbousse (IBENS) for the use of culture facilities; Nathalie Joli (IBENS) for aid with RNAseq library production; Catherine Cantrel and Priscillia Pierre-Elies (IBENS), Pauline Clémente (Lycée ENCPB-Pierre Gilles de Gennes) and Shun-Min Yang (University of South Bohemia) for the preparation of media substrates and aid with biolistic transformations; Max Gorbunov (Rutgers University, NJ, USA) for the provision of the miniFIRE fluorometer used for photophysiological assays; and Mattia Storti (CEA Grenoble) for cell outlines used in the production of Fig.1A. This paper is contribution XXX of Tara Oceans.

Fig. 1 .

 1 Fig. 1. Metabolic context and evolution of the lower half of diatom plastidial glycolysisgluconeogenesis. A: schematic comparison of diatom and plant core carbon metabolism, adapted

Fig. 2 .

 2 Fig. 2. Environmental distributions of diatom plastidial glycolysis-gluconeogenesis meta-genes.

Fig. 3 .Fig. 4 .

 34 Fig. 3. Growth phenotypes of ptEnolase and ptPGAM1A CRISPR-Cas9 knockout mutant and zeocinresistant empty vector control P. tricornutum lines. A: exemplar growth curves from single experiments realised for P. tricornutum lines in 50 μE m -2 s -1 illumination, non-shaken cultures and replete ESAW media, under three conditions-(i) 19°C and 12h light: 12h dark Circadian cycles (« 19C LD »); (ii) 19°C and 24h continuous light (« 19C CL »); and (iii) 8°C and 24h continuous light (« 8C CL »). B: mean relative log phase growth rates of each genotype under each condition, measured through a minimum of three biological replicates and two technical repetitions (six measurements per line, minimum 24 measurements per genotype). Plastid glycolysis-gluconeogenesis knockout lines show a substantial retardation in growth rate under 19C CL compared to 19C LD conditions, which is compensated by 8C CL conditions. Equivalent growth curves realised with compensated and blasticidin-resistant lines are shown in Fig. S13.

Fig. 5 .

 5 Fig. 5. Volcano plots of differentially accumulated metabolites assessed by GC-MS. Scatterplots of the log 2 accumulation ratios -log 10 P-values of difference in the mass, ribitol and quality-control-

P e d o s p u m e ll a _ e n c y s ta n s 5 96 E c t o c a r p u s _ g e n 1 C1 1 8 9 6 3 2 4 6 _ R h iz o c h r o m u li n a _ m a r in a _ c f _ C C M P 1 2 4 3 S 7 V S 0 2 _ W in o g r a d s k y e ll a _ p s y c h r o to le r a n s _ R S _ 3

 596163723 Photosynthesis light reactions

13 1 2 C 1 CAMPE

 1321 P E P _0 18 10 36 89 6_ M in ut oc el lu s_ po ly m or ph us _N H 8B 0P 4_ C an d id at u s_ P o ri b ac te ri a_ b ac te ri u _0 19 98 85 90 8_ As ter ion ell op sis _g lac ial is_ CC MP 13 4 CA MP EP _01 978 312 86_ Eu cam pia _an tar cti ca_ CC MP 145 _0 18 48 84 49 2_ As te rio ne llo ps is_ gl ac ial is CA MP EP _01 921 493 14_ Ske leto nem a_d ohr nii_ Ske lB C A M P E P _ 0 1 7 2 0 0 8 7 0 2 _ H e m i s e l m i s _ a n d e r s e n i i _ C C M P ry m n e s iu m _ p a rv u m _ T e x o m a p u m e ll a _ li k e _ fl a g e ll a te _ J B C h r o o m o n a s _ m e s o s

8 N e m a to s te ll a _ g e n o m ic _ 1 5 6 3 8 3 5 7 0 c o m p 3 7 0 0 9 _ P e d o s p u m e ll a _ e n c y s ta n s _ 2 Q 6 A M 9 7 _ D e s u lf o ta le a _ p s y c h ro p h il a _ s tr a in _ L S v 5 4 _ _ D S M _ 1 2 3 4 3 CA 1 Q1 ISS 7_ Ko rib ac ter _v ers ati lis _s tra in_ Ell in3 45 Pl as mo diu m_ viv ax _g en om ic_ 15 60 96 87 5 CAMP 93 CC 1 CAMPE 4 CFragil ariop sis_k ergu elens is_L2 6 Q 74 Ae p to c y li n d ru s _ d a n ic u s _ v a r_ a p o ra _ B 6 5 1 CA 8 S ch iz os ac ch ar om yc es _g en om ic _1 91 12 69 5 CAM 2 CAMPEA u r e o c o c c u s _ a n o p h a g e f f e r e na c ty li o s o le n _ fr a g il is s im u s B ig e lo w ie ll a _ g e n o m ic _ 9 A u r e o c o c c u s _ a n o p h a g e f f e r e ns e u d o _ n it z s c h ia _ p u n g e n s _ c f_ p u n g e n s g 3 2 _ F i s t u l i f e r a _ s o l a r i s A 1 C h o n d ru s _ c ri s p u s _ g e n o m ic _ 5 5 c o m p 3 7 0 0 9 _ P e d o s p u m e ll a _ e n c y s ta n s _ 7 AA 2 co m p 37 00 9_ P ed o sp u m el la _e n cy st an s_ 6 C 2 AC o r e t h r o n _ h y s t r i x _ 3 0 8 CAM PEP_ 0113 3950 08_S kelet onem a_co statu m_17 16 CP h a e o d a c t y lu m _ d r a f t J 1 1 9 2 7 2 5 2 9 _ A u r e o c o c c u s _ a n o p h a g e f e r r e n 3 CA 93 CS ch iz o sa cc h ar o m yc es _g en o m ic _1 62 31 21 41 P y th iu m _ v e x a n s _ g e n o m ic _ 0 3 20 C 3 _ D y a d o b a c te r _ p s y c h r o p h il u s C y a n o p h o r a _ g e n o m ic _ 5 u b te rc o la _ b o re u s P h y to p h th o ra _ ra m o ru m _ g e n o m ic _ 7 2 9 8 9 _ fg e n e s h 1 _ p g C _ s c a ff o ld _ 2 0 0 0 0 5 4 C y a n o p h o r a _ g e n o m ic _ 3 P e d o s p u m e ll a _ e n c y s ta n s _ 4 CAMPEP_0192957046_Thalassiosira_rotula_CCMP3096U 4 T 5 L 3 _ P s y c h r o b a c t e r _ a q u a t i c u 1 D r o s o p h il a _ g e n o m ic _ 2

 8092674314559314674185292s155972628162939341320359143445312 RT R2 _C an di da tu s_ Po rib ac te ria _b ac te riu m To xo pl as m a_ ge no m ic _2 37 83 25 61 CA MP EP _0 17 22 70 72 2_ Cy clo tel la_ me ne gh ini an a_ CC MP 33 MP EP _0 19 82 48 82 8_ Ch ae toc ero s_ dic ha eta _C CM P1 75 PE P_ 02 00 43 43 72 _E xt ub oc el lu lu s_ sp in ife r_ C C M P3 96 CA MP EP _01 922 721 92_ Sk ele ton em a_m en zel ii_C CM P7 y l i n d r o t h e c a _ c l o s t e r i u m A0 A2 E3 HX X0 _C an di da tu s_ Po rib ac te ria _b ac te riu m _0 18 57 66 54 8_ Di ct yo ch a_ sp ec ul um _C CM P1 38 e m is e lm is _ a n d e r s e n ii _ C C M P 6 4 A M PE P_ 01 72 99 06 38 _P ha eo cy st is _a nt ar ct ic a_ C ar on _L ab _I so la te c o m p 1 3 0 6 8 _ P o te r io s p u m e ll a _ la c u s tr is r c ti c ib a c te r iu m _ lu te if lu v ii s ta ti o R 6_ G eo b ac te r_ su lf u rr ed u ce n s_ st ra in _A T C C _5 15 73 __ D S PEP _019 2889 466_ Tha lass iosi ra_o cean ica_ CCM P10 05 CAMPE P_0172 310306_ Detonu la_conf ervacea _CCMP 353 c o m p 2 9 3 6 4 _ S p u m e ll a _ li k e _ fl a g e ll a te _ J B C S 2 3 _ T h al as si o th ri x_ an ta rc ti ca _L 6_ D 1 C A M P E P _0 11 92 98 48 8_ S cy p h o sp h ae ra _a p st ei n ii_ R C _C an di da tu s_ Po rib ac te ria _b ac te riu m A0A1 F7V8 W7_ Cand idatu s_Uh rbac teria _bac teriu m_R IFCS PLO WO2 _02_ FULL 0A 6L 8E V5 7_ C an di da tu s_ Po rib ac te ria _b ac te riu m h a t t o n e ll a _ s u b s a ls a _ C C M P 2 1 9 P E P _0 19 07 95 98 0_ P le u ro ch ry si s_ ca rt er ae _C C M P 64 ann och loro psis _ga dita na CA MP EP _0 18 50 24 86 6_ Pa ra ph ys om on as _b an da ien sis _C ar on _L ab _Is ola te a n d id a tu s _ M a ri n im ic ro b ia _ b a c te ri u m _ M T S A G P E P _0 18 58 11 86 8_ M in u to ce llu s_ p o ly m o rp h u s_ R C C 22 70 CA MP EP _0 19 84 27 80 6_ Sy nc hr om a_ pu sil lu m _C CM P3 07 P E P _0 18 09 84 35 0_ D it yl u m _b ri g h tw el lii _P o p 2_ _S S s CA M PE P_ 01 97 14 40 84 _A st er io ne llo ps is _g la ci al is _C CM P1 58 1 D r o s o p h il a _ g e n o m ic _ 2 s e u d o _ n it z s c h ia _ a re n y s e n s is _ B 5 9 MP EP _02 010 895 34_ Tha las sio sira _m inis cul a_C CM P10 _0 11 90 04 54 0_ Sy ne dro ps is_ rec ta_ cf_ CC MP 16 P s e u d o _ n it z s c h ia _ d e li c a ti s s im a _ U N C 1 2 0 5 CA MP EP _0 19 14 43 10 0_ Ps eu do pe din ell a_ ela sti ca _C CM P7 16 A 0A 49 6B D Q 7_ C an di da tu s_ P or ib ac te ri a_ ba ct er iu m s e u d o _ n it z s c h ia _ p u n g e n s _ c f_ c in g u la ta CA M PE P_ 01 80 84 60 92 _S ke le to ne m a_ m ar in oi _F E7 C A M P E P _0 18 33 51 36 8_ C oc co lit hu s_ pe la gi cu s_ ss p_ br aa ru di _P LY 18 p u m e ll a _ e lo n g a ta _ C C A P _ 9 5 5

96 C 6 A 7 C 4 CAMPEP_0 4 Ck e le to n e m a _ c o s ta tu m _ 1 7 1 6 C 4 C 5 Ced os pu me lla _e nc ys tan s_ 2 Cisel mis _an der sen ii_C CM P43 9 P h y s c o m it re ll a _ g e n o m ic _ 1 CCP ar ap hy so m on as _i m pe rf or at a_ PA 2 C 2 C 2 CAM 4 P a r a m e c iu m _ g e n o m ic _ 1 13 CAMP 2 CAM 38 E 9 _ C a n d id a tu s _ L o k ia r c h a e o ta _ a r c h a e o n _ L o k i_ b 3 1 CA MP EP _0 17 31 33 69 6_ 2 A 0 A 2 E 9 F 6 S 7 _ 8 CAMPEP_a n n o c h lo ro p s is _ g a d it a n a _ g e n o m ic D ic t y o s t e li u m _ g e n o m ic _ 9 3 9 1 6 CT h a la s s io s ir a _ o c e a n ic a _ C C M P 1 0 0 5 CA 1 CAMPEP_01922 4 C 4 T o x o p la s m a _ g e n o m ic _ 2 P h y s c o m it re ll a _ g e n o m ic _ 1 C 71 B 1 X 7 8 6 _ E s c h e r ic h ia _ c o li _ s tr a in _ K 1 2 _ 1 D ro s o p h il a _ g e n o m ic _ 2 55 F 1 V T Z 9 _ O x a lo b a c te r a c e a e _ b a c te r iu m _ IM C C 9 h a la s s io s ir a _ p u n c ti g e ra _ T p u n c t2 0 0 5 C 2 P 6 2 7 0 7 _ E s c h e r ic h ia _ c o li _ s tr a in _ K 1 2 CA 16 D ro s o p h il a _ g e n o m ic _ 2 93 CAMPEP_01870 56 PP h y s c o m it re ll a _ g e n o m ic _ 1 6 C 6 CA 93 C h lo re lla _v ar ia b ili s_ g en o m ic _3 84 25 7 T e tr a h y m e n a _ g e n o m ic _ 2 3 Chrysophyte mitochondrion/ plastid D i a t o m / p e l a g o p h y t e / d i c t y o c h o p h y t e m i t o c h o n d r i o n P h a t r 3 _ J 3 3 8 1 9 D i a t o m p l a s t i d P h a t r 3 _

 966744645291222411323891692029678651442171162125519927216293561669325723393 _ C la d o s ip h o n _ o k a m u r a n s is _ g e n o ar ap hy so m on as _b an da ie ns is _C ar on _L ab _I so la te P ha eo cy st is _a nt ar ct ic a_ C ar on _L ab _I so la is tu li fe ra _ s o la ri s _ g e n o m ic C A M P E P _0 19 68 04 68 0_ L ep to cy lin d ru s_ d an ic u s_ C C M P 18 56 CA MP EP _0 11 61 00 94 0_ Ps eu do _n itz sc hia _d eli ca tis sim a_ B5 H e li c o th e c a ta m e n s is C C M P 8 2 P E P _0 11 93 77 95 8_ C al ci di sc us _l ep to po ru s_ R C C 11 30 CA MP EP _0 18 25 57 11 4_ Fl or en cie lla _s p_ RC C1 58 it y lu m _ b r ig h tw e ll ii _ G S O 1 0 A s te ri o n e ll o p s is _ g la c ia li s _ C C M P 1 3 A M P E P _0 18 10 66 89 6_ M in u to ce llu s_ p o ly m o rp h u s_ N H 13 CA MP EP _01 725 731 96_ Th ala ssi os ira _p un cti ge ra_ Tp un ct2 005 C2 CA M PE P_ 01 93 21 12 74 _U nd es cr ib ed _s p_ CC M P2 09 8 CA M PE P_ 01 98 27 98 10 _A tth ey a_ se pt en tri on al is _C CM P2 08 o li d o m o n a s _ p a c if ic a _ R C C 2 0 8 C A M PE P_ 01 82 42 84 10 _M al lo m on as _S p_ C C M P3 27 P E P _0 18 33 59 06 4_ C o cc o lit h u s_ p el ag ic u s_ ss p _b ra ar u d i_ P LY 18 2g A s te ri o n e ll o p s is g la c ia li s CA M PE P_ 02 00 18 81 92 _T ha la ss io ne m a_ ni tz sc hi oi de s_ L2 6_ B CA MP EP _0 18 48 97 75 8_ As ter ion ell op sis gla cia lis G e o b a c te r_ s u lf u rr e d u c e n s _ s tr a in _ A T C C _ 5 _0 11 61 55 58 6_ Ps eu do _n itz sc hi a_ ar en ys en sis _B 59 3 CA MP EP _01 993 344 06_ Nit zsc hia _pu nct ata _C CM P56 1 49 61 4_ Th al as si os ira _o ce an ic a_ ge no m ic C A M PE P_ 01 98 15 44 90 _E nt om on ei s_ sp _C C P_ 01 93 56 75 48 _U nd es cr ib ed _u nd es cr ib ed _C CM P2 29 8_ h a la s s io s ir a _ ro tu la _ G S O 1 0 PEP_ 0188 1706 76_G ephy roca psa_ ocea nica_ RCC 1303 CAM PEP _01 928 995 84_ Tha lass iosi ra_o cea nica _CC MP1 005 P h ys co m it re lla _g en o m ic _1 80 38 20 3 C A M PE P_ 01 73 02 05 82 _P ha eo cy st is _a nt ar ct ic a_ C ar on _L ab _I so la te P y th iu m _ u lt im u m _ g e n o m ic _ 0 um ell a_ lik e_ fla ge lla te_ JB L1 M in u to ce llu s_ p o ly m o rp h u s_ N H 13 C A M PE P_ 01 72 45 08 14 _A m ph ip ro ra _p al ud os a_ C C M P1 25 C A M P E P _0 18 10 50 76 0_ M in ut oc el lu s_ po ly m or ph us _N H d o n te ll a _ s in e n s is _ G ru n o w 1 ul ac os ei ra _s ub ar ct ic a_ C C A P 10 02 5 CA MP EP_ 017 257 288 8_T hal ass ios ira_ pun ctig era _Tp unc t20 05C P_ 01 15 84 90 56 _C hr ys oc hr om ul in a_ ro ta lis _U IO 04 ro bo sc ia _i ne rm is _C C A P1 06 41 C A M PE P_ 01 72 28 34 84 _C yc lo te lla _m en eg hi ni an a_ C C M P3 Oc hr om on as _s p_ BG _1 C A M P E P _0 19 81 74 20 4_ P ha eo cy st is _a nt ar ct ic a_ C C M P 13 74 C A M P E P _0 18 81 95 48 8_ G ep hy ro ca ps a_ oc ea ni ca _R C PE P_ 01 18 83 09 80 _P ha eo cy st is _S p_ C C M P2 71 0 CA M PE P_ 01 99 64 63 10 _P se ud o_ ni tz sc hi a_ au st ra lis _1 02 T h a la s s io s ir a _ a n ta rc ti c a _ C C M P 9 8 C a n d id a tu s _ P o r ib a c te r ia _ b a c te r iu m CA MP EP _0 11 33 93 14 2_ Sk ele ton em a_ co sta tum _1 se ud o_ ni tz sc hi a_ ar en ys en si s_ B 59 3 CA MP EP _0 11 33 93 97 2_ Sk ele ton em a_ co sta tum _1 B o li d o m o n a s _ p a c if ic a _ R C C 2 0 to p h th o ra _ ra m o ru m _ g e n o m ic _ 7 2 1 0 0 _ fg e n e s h 1 _ p m C _ s c a ff o ld _ it y lu m _ b r ig h t w e ll ii _ P o p 1 _ S S 4 CA MP EP _01 971 430 98_ As ter ion ello ps is_ gla cia lis_ CC MP 158 1 C A M P E P _0 17 29 66 25 8_ P h ae o cy st is _a n ta rc ti ca _C ar o n _L a b _I so la te M PE P_ 01 93 56 75 48 _U nd es cr ib ed _u nd es cr ib ed _C CM P2 29 8_ 3053 172_ Tha lass iosir a_w eiss flog ii_C CMP 1010 CA MP EP _01 998 906 70_ As ter ion ello psi s_g lac iali s_C CM P13 PE P_ 01 19 57 26 94 _S ta ur on ei s_ co ns tr ic ta _C C M Sp um ell a_ lik e_ fla ge lla te_ JB L1 ke le to ne m a_ m ar in oi _F E6 0 CA MP EP _0 19 71 44 35 4_ As ter ion ell op sis _g lac ial is_ CC MP 15 81 P h y to p h th o ra _ ra m o ru m _ g e n o m ic _ 7 1 9 4 7 _ fg e n e s h 1 _ p m C _ s c a ff o ld _ 6 ha eo cy st is _a nt ar ct ic a_ C ar on _L ab _I so la te 03 26 71 1_ Pe do sp um ell a_ en cy sta ns GM E1 15 16 _N an no ch lo ro ps is _o ce an ic a_ ge no m ic CA M PE P_ 02 00 14 60 36 _P ro bo sc ia _a la ta _P I_ D3 Fl or en ci el la _p ar vu la _C C M P 24 _ C la d o s ip h o n _ o k a m u ra n s is _ g e n o m ic C h lo re ll a _ v a ri a b il is _ g e n o m ic _ 2 it y lu m _ b r ig h tw e ll ii _ P o p 2 ib r o c a p s a _ ja p o n ic a CA MP EP _01 983 055 50_ Att hey a_s ept ent rio nal is_ CC MP 208 ke le to ne m a_ m ar in oi _U NC 12 01 10 00 50 g2 41 _N an no ch lo ro ps is _g ad ita na _g en om ic C A M P E P _0 18 71 69 83 4_ C ha tt on el la _s ub sa ls a_ C elago mon as_c alceo lata_ CCM P175 6_2 CA MP EP _01 849 798 60_ Sp um ella _el on gat a_C CA P_ 955 cy p h o sp h ae ra _a p st ei n ii_ R C C 14 s te ri o n e ll o p s is _ g la c ia li s _ C C M P 1 MP EP _01 996 981 28_ Pel ago mo nas _ca lce ola ta_ CC MP 175 6 CA M PE P_ 01 16 12 14 60 _P se ud o_ ni tz sc hi a_ ar en ys en si s_ B5 93 CA MP EP _0 18 25 26 79 8_ Fl or en cie lla _p ar vu la_ RC C1 69 3 A0A3E0VM60_Subte rcola_boreus CA MP EP _0 11 60 92 41 4_ Ps eu do _n itz sc hia _d eli ca tis sim a_ B5 96 CA MP EP_ 018 109 333 2_T hal ass ios ira_ sp_ NH P_ 01 16 13 99 88 _P se ud o_ ni tz sc hi a_ ar en ys en si s_ B5 _01 848 771 14_ As ter ion ello psi sgl aci alis B 2 K B U 4 _ E lu s im ic ro b iu m _ m in u tu m _ s tr a in _ P e ed os pu me lla_ en cys tan s_1 CA M PE P_ 01 15 84 90 56 _C hr ys oc hr om ul in a_ ro ta lis _U IO 04 4_ 2 C A M P E P _0 19 05 31 67 8_ P el ag o co cc u s_ su b vi ri d is _C C M P 14 29 C A M P E P _0 18 64 97 75 0_ A m ph ip ro ra _s p P h a e o d a c ty lu m _ J 5 6 0 5 s0 02 _2 72 t1 _C la do si ph on _o ka m ur an si s_ ge no m N a n n o c h lo r o p s is _ g a d it a n a _ g e n Bo lid om on as _p ac ific a_ CC MP _1 86 6 C A M P E P _0 19 96 96 91 0_ P el ag o m o n as _c al ce o la ta _C C M P 17 to p h th o ra _ s o ja e _ g e n o m ic _ 5 P E P _0 20 04 82 18 6_ E xt u b o ce llu lu s_ sp in if er _C C M P 39 6 CA MP EP _0 17 89 50 67 4_ Rh izo so len ia_ se tig era _C CM P1 69 4 C A M P E P _0 20 04 86 91 6_ E xt u b o ce llu lu s_ sp in if er _C C M P 39 H e li c o th e c a ta m e n s is C C M P 8 2 MP EP _0 19 02 80 36 6_ Oc hr om on as _s p_ CC MP 13 in u to ce llu s_ p o ly m o rp h u s_ N H 13 CA MP EP _0 11 59 22 25 4_ Sk ele to ne m a_ m ar in oi _S M1 01 2H els _0 ele to ne ma _m en ze lii_ CC MP 79

1 ; J4 70 96 P e l a g o p h y t e / h a p t o p h y t e m i t o c h o n d r i o n / d u a

 196 Fig. S3. Consensus phylogeny of a 235 aa x 66 taxa alignment of PGAM isoform 2 sequences, shown as per fig. S1.

Fig. S5 .

 S5 Fig. S5. Transcriptional coordination of plastidial enolase, PGAM1 and PGAM2 from RNAseq data. This figure shows Spearman coefficients of all genes across the version 3 annotation of the Phaeodactylum tricornutum genome against two query genes: Phatr3_J41515, plastid-targeted enolase; and Phatr3_J17086, plastid-targeted PGAM1; calculated from ranked mRNA abundances within PhaeoNet (Ait-Mohamed et al., 2020). Genes are coloured by inferred in silico localisation; and other annotated enolase and PGAM enzymes in the Phaeodactylum genome are shown as large, labelled points.

Fig. S5 .

 S5 Fig. S5. Transcriptional coordination of plastidial enolase, PGAM1 and PGAM2 from published gene expression data. This figure shows Spearman coefficients of all genes across the version 3 annotation of the Phaeodactylum tricornutum genome against two query genes: Phatr3_J41515, plastid-targeted enolase; and Phatr3_J17086, plastid-targeted PGAM1; calculated from ranked mRNA abundances within normalised RNAseq (PhaeoNet, Ait-Mohamed et al., 2020) and microarray (DiatomPortal, Ashworth et al., 2016) meta-datasets. Genes are coloured by inferred in silico localisation; and other annotated enolase and PGAM enzymes in the Phaeodactylum genome are shown as large, labelled points.

  ig. S6. Transcriptional coordination of plastidial enolase, PGAM1 and PGAM2 from microarray data. This figure shows Spearman coefficients of ll genes across the version 3 annotation of the Phaeodactylum tricornutum genome against two query genes: Phatr3_J41515, plastid-targeted enolase; d Phatr3_J17086, plastid-targeted PGAM1; calculated from ranked relative fold changes from DiatomPortal (Ashworth et al., 2016). Genes are coloured by inferred in silico localisation; and other annotated enolase and PGAM enzymes in the Phaeodactylum genome are shown as large, labelled points. Ashworth et al., 2016)

Fig. S8 .Fig. S9 .

 S8S9 Fig. S8. Relative abundances of Tara Oceans meta-genes that resolve phylogenetically with (top) diatom plastidial enolase and (bottom) diatom plastidial PGAM1 sequences over all size fractions of surface meta-transcriptome (left) and meta-genome (right) data, shown as per Fig. 2.

  Fig. S13. Growth phenotypes of complemented glycolysis mutants. A:scatterplot of maximum growth rates, calculated from two replicates, under 19C LD cycling (horizontal) and 19C CL conditions (vertical axis) for 47 total ptEnolase (i) and ptPGAM1A mutants (ii) complemented with blasticidin-resistant and GFP-linked ptEnolase, ptPGAM1 or blank constructs, compared to empty vector zeocin-resistant control lines transformed with the same constructs. The distribution of individual growth rates within complemented and blank transformed populations are shown using solid coloured, and grey dashed line circles respectively. Vectors plot the difference between the mean growth rates calculated for blank transformed versus complemented mutant lines, alongside ANOVA P-values of separation. B: mean growth rates calculated for blank transformed and complemented mutant lines, alongside empty vector primary transformants complemented either with blank, ptEnolase or ptPGAM1A constructs, showing effective overlap between complemented mutant and empty vector primary transformant line growth rates.

Fig. S14 .

 S14 Fig. S14. Photophysiology curves in glyclolysis mutant lines for (i-ii) relative electron transport (rETR) of photosystem II fitted as a function of light intensity (E) and (iii-iv) photoprotective non-photochemical quenching (NPQ)fitted as a function of E. Separate values are shown for cultures acclimated under CL (i, iii) and LD (ii, iv) growth conditions. Data points are the mean between the average values (n=2-4) measured in each strain within a mutant line (number of lines per genotype; Control = 2, ptEnolase complemented = 2, ptPGAM1A complemented = 3, ptEnolase knockout = 6, ptPGAM1A knockout = 3).

  Fig.S15.Boxplots of measured photophysiological parameters in glycolysis mutants lines for cultures acclimated under 19C CL (left panels) and LD (right panels) growth conditions: (i-ii) maximum quantum yield of photosystem (PSII) in the dark (Fv/Fm) (iii-iv) PSII functional absorbtion cross-section (σPSII), (v-vi) PSII quantum yield under the growth light of 50 µmol photons m -2 s -1 (ΦPSIIE50) (vii-viii) maximal relative electron transport at PSII and (ix-x) maximum non-photochemical quenching (NPQm). Significantly different values observed for knkockout and complementation mutants relative to control lines (one-way ANOVA, P < 0.05) are asterisked, with asterisk colour corresponding to the mutant line considered. Each boxplot includes all measured/fitted values for each strain within a mutant line (number of lines per genotype; Control = 2, ptEnolase complemented = 2, ptPGAM1A complemented = 3, ptEnolase knockout = 6, ptPGAM1A knockout = 3). .

  Fig. S16. Volcano plots of differentially expressed genes in ptEnolase (left) or ptPGAM1A mutants (ight) compared to empty vector control lines under 19C CL conditions. Genes are labelled by consensus localisation prediction, and differentially expressed Enolase and PGAM isoforms are enlarged as per Fig.S6. Of note, ptEnolase (PhatrJ_41515) and ptPGAM1A (PhatrJ_17086) are significantly less expressed in the corresponding mutant lines than the controls, consistent with effective transcriptional suppression of the mutated genes.

  Photosynthesis

17

 17

  Fig. S18. Differentially accumulated metabolites in glycolysis mutant lines.This figure shows bar plots of the mean and standard deviation of the ratios of 39 metabolites assessed by GC-MS in plastid glycolysis mutant lines under the three tested experimental conditions. Metabolites are sorted in ranked decreasing accumulation in mutant lines over all three conditions. Metabolites inferred to be differentially accumulated in each mutant line and condition are asterisked: ** denotesANOVA P < 10 -05 ; * denotes P < 0.01.

Fig. S19 .

 S19 Fig. S19. Differential accumulation of fatty acids across experimental conditions.This figure shows violin plots as per Fig.6of the frequency, mean and normal distributions of the relative proportions of four fatty acids assessed by GC mass spectrometry in glycolysis mutant and lines across three experimental conditions. Despite substantial variation in fatty acid profile across experimental treatments (e.g., overaccumulation of C16:1 in lieu of C20:5 under 19C CL; C16:3 overaccumulation specifically in 19C LD; and C18:0 overaccumulation specifically in 8C CL), little difference is observed between glycolysis mutant and control lines under each condition.

Fig. S20 ...

 S20 Fig. S20. Differentially accumulated lipids in 19C LD condition. A:Volcano plots shows (horizontal axis) log 2 accumulation ratios and (vertical axis) -log 10 ANOVA Pvalues for separation of mean proportions of specific fatty acids, across all fatty acids observed in a specific lipid class in glycolysis mutants versus control lines, harvested under 19C LD conditions. Specific lipids that show extreme (P < 10 -05 ) differences in accumulation between both mutant genotypes and control lines are labelled, and coloured by lipid class. B: Bar plots showing total DGTA lipid class distributions in all three lines under these conditions. These data suggest limited changes in glycolysis mutant lipid architecture, barring a probable overaccumulation of sn-1 C16 in ptEnolase mutant MGDG and DGDG pools, a corresponding underaccumulation of sn-1 C20 in ptEnolase mutant DGTA pools, and a conserved underaccumulation of DGTA-16-1_18-3 in both ptEnolase and ptPGAM1A mutants.

  (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made

	bioRxiv preprint	doi:	; https://doi.org/10.1101/2022.09.08.507166	this version posted September 9, 2022.	The copyright holder for this preprint
			. CC-BY-NC-ND 4.0 International license available under a
	functional enrichments in DEGs from previously tabulated values for the entire P. tricornutum
	genome (Table

Table S5, sheet 5), confirming that the RNAseq samples were not generated from N-or P-limited cultures. Next,

60% ii) HECTAR Plastid Mitochondrion Signal peptide Signal anchor Other

		i) ASAFind
	60%
	50%				50%
	40%				40%
	30%				30%			*	*
	20%				20%			*		*
	10%				10%		*	**
	0%				0%
	Plastid	Endomembrane	Other
	19C LD 19C CL	8C CL	19C LD 19C CL	8C CL	19C LD	19C CL	8C CL	19C LD 19C CL	8C CL
	19C LD 19C CL	8C CL	19C LD 19C CL	8C CL	19C LD 19C CL	8C CL	19C LD 19C CL	8C CL
	19C LD 19C CL	8C CL	19C LD 19C CL	8C CL	19C LD 19C CL	8C CL

_0 18 09 69 93 0_ Di ty lu m _b

		C A M P E P _0 19 55 17 21 0_ S te p h an o p yx is _t u rr is _C C M P 81 5 3 _ e s _ b is M s M li ia c il la g d _ is ro to a 6 5 0 P M C A P E _ 0 2 0 8 3 2 s p e o c ll e e n o h ri C te _ s A _ 8 7 C A M P E P _ 0 1 8 4 8 9 0 0 5	CA MP EP rig ht we llii _P op 2_ _S S1 0_
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Chloroplast Dual Chloroplast/ Mitochondria Endomembrane Dual Endomembrane/ Mitochondria Mitochondria

							A) Enolase	Plastid
				Light-harvesting						Protein import
				Photosynthesis								tRNA synthetases
	PsbM	PsbU	Psb27	PetB	PetJ	PsaO	PetH	AtpC			Chl synthesis		A	C	D
	PsbO	PsbP	PsbQ								GluRS		E	F	G
	PsbW						Alternative electron flow		GluRR		H	I	K
							NDH	PTOX	SOD		GSA		L	M	N
			Carbon metabolism			PMP2	Mpv17	TRF		PBS		P	E*	R
		SBP	RPE	rpiA	PRK				TrX		HMBS		S	T	V
		TKT	TAL				Carbonic anhydrases			UPS		W	Y	S*
		FBP		TPI			CAB	CAT			UPD		U
	PGluM	FBA	ALDO	GAPDH	PGK				Ornithine cycle		CPO
	G6PE	PFK1			PGAM				ACGS		PPO		Ribosome
				pckA	Enol	PPC	AspAT		ACGK		FcH		rps15	rpl15	rpl7
					PyrK	PPDK			NAGPR		ChlH		rpl17	rpl10	rpl28
			MCM	DLAD	PDH				ACOAT		ChlD
											MPoM		Fe stress
	Fatty Acids		Lipid synthesis			Amino acid assimilation		PCR		ISIP1	ISIP3
	AcCOA		GPAT	PLA2	Shikimate metabolism	GS	GOGAT		CBR		FvDX	Fer
	ACPMT		AGPAT	LPIN		DPHS		AAL	AlaT		DPAR
	ACP		LPAT	DGDP		DHQS					ChlS		Riboflavin synthesis
	ACPS		DGAT	PLPL		DHQD		Ser, Thr metabolism				DMRS	RibS	RibK
	ACPR		MGAT	PGP		SK		SOAT	CBL		Isoprenoid synthesis
	ACPD		PCT	SQD1		PSCVT		CysS	THFCM		DXS		Carotenoid synthesis
	EACPR		CDGP	SQD2		ChrS			GHMT		DXR		HGS	PS
	SCD					AS	CM		ThAL		MEPC		MBMM	PDS1
	LCS					APRT	PpDH				DMEK		HGPT	PDS2
		Lysine synthesis				IGPS	POGTA				MECS		TocC	PLI
		ASPK					PA4H	Sulfate assimilation		HMPS		TocOMT	ZEP
		ASDH				TrpS	KGTA		AdK		HMPR		NQDH	VDE
		DHPS		Branched chain AA				SulR		IDI Fig. S8. Relative transcriptional regulation of PPP
		DHPR		ACLS	2IPS		Fe-S synthesis		SQOR		FDS plastidial enolase and PGAM under nutrient and light PSMT
		DAPA		KARO	3IPY		SufS		BPN		GDS stress conditions. A-C: the relative ratio of plastid to FCL
		DAPE		DHAR	3IPO		SufE		SAT		GDR mitochondria-targeted gene isoform expression for PPMT
		DAPC		BCAAT			SufD		TMST				MPBM	NS
											enolase (vertical axis), PGAM1 (horizontal axis), and
											PGAM2 (bubble size) in published Phaeodactylum
							Mitochondrion
		Carbon metabolism		Lysine catabolism						Division RNAseq datasets subject to nitrate limitation (McCarthy Protein import
	SBP				DAPE		Urea cycle/ Arginine		Tom40 et al., 2017), phosphate starvation and addition (Cruz de Erv1 Tim17 FtsZ
	FBP	GAPDH	TPI		DAPD			OCT	ASS		Tom70 Carvalho et al., 2016) and Circadian Fe enrichment and Oxa1 Tim44
		PGK					ACGS		ASL		Sam50 limitation (Smith et al., 2016), respectively. While limited Pam16 Tim50
		PGAM					URE	ARG			MPPa transcriptional responses are identifiable in response to Tim22
		Enol									ClpB changing N, P or Fe availability, the relative transcription MPPb Tim10
		PyrK					Amino acid assimilation	Carbonic anhydrase		Tim9
	PyrC	PDHA	PDHB	DHLD	CitS		AlaT			CAT	of plastidial to mitchondrial-targeted enolase is
			PDH2		ICD		GS	GOGAT			Lipid catabolism substantially greater in RNA sampled in long day (> 12h Ribosome
	MDH2	MDH1			OGR						ACCO post-illumination) conditions in C. D: relative fold changes rpl3 rpl22 rps9
		MDH3			AKDE1		Other Cysteine catabolism	Glycine/ Serine		ECH in plastidial enolase and PGAM1 expression in published rpl4 rpl24 rps15
		FUMH	SDHF	SCSA	AKDE2		PSAT		CPS		ECH/D		rpl7	rpl27	rps16
			SDHS	SCSB			PSP		GlyD		3HCD microarray data assembled in Ashworth et al., 2016, rpl11 rpl28 rps17
		MCM	SDHC				SHMT		AMM		ACCA under different illumination conditions. Both Enolase and rpl15 rpl33
							IPMS		GlyH		rpl20 PGAM1 show substantial downregulation in dark-rpl34
	Complex I									incubated (48h) and short post-illumination-incubated (<
	NDHA5	NDH6		Complex III		ATP synthase		tRNA synthetases 0.5h) cultures, and show the greatest positive fold Cofactor metabolism
	NDHA9	NDH7		UCF	COX6A	FNR	ATPA		A	C	D		HBPPT DHPRD	RFK
	NDH1	NDH8		UCC	COX6B		ATPB		E*	F	G expression changes respectively > 16h and > 12h post-ICS AdK
	NDHA13	NDHF1		UC7	COX11		ATPC		H	I	K		illumination.
	NDHB7	NDHF2			COX15		ATPD		L	fM	M		Fe-S
	NDHB9								N	P	R		IscS	IscA
			Alternative electron flow				S	T	V		IscU	HscB
		AOX	TR	PMP2	PMP4	PRX5			W	Y			SulO
													Srp54
										ClpB	ClpC
	PsbM	PsbU	Psb27	PetB	PetJ	PsaO	PetH	AtpC			Chl synthesis		A	C	D
	PsbO	PsbP	PsbQ								GluRS		E	F	G
	PsbW										GluRR		H	I	K
							NDH	PTOX	SOD		GSA		L	M	N
							PMP2	Mpv17	TRF		PBS		P	E*	R
		SBP	RPE	rpiA	PRK				TrX		HMBS		S	T	V
		TKT	TAL								UPS		W	Y	S*
		FBP		TPI			CAB	CAT			UPD		U
	PGluM	FBA	ALDO	GAPDH	PGK				Ornithine cycle		CPO
	G6PE	PFK1			PGAM				ACGS		PPO
				pckA	Enol	PPC	AspAT		ACGK		FcH		rps15	rpl15	rpl7
					PyrK	PPDK			NAGPR		ChlH		rpl17	rpl10	rpl28
			MCM	DLAD	PDH				ACOAT		ChlD
											MPoM
	Fatty Acids										PCR		ISIP1	ISIP3
	AcCOA		GPAT	PLA2	Shikimate metabolism	GS	GOGAT		CBR		FvDX	Fer
	ACPMT		AGPAT	LPIN		DPHS		AAL	AlaT		DPAR
	ACP		LPAT	DGDP		DHQS					ChlS
	ACPS		DGAT	PLPL		DHQD							DMRS	RibS	RibK
	ACPR		MGAT	PGP		SK		SOAT	CBL		Isoprenoid synthesis
	ACPD		PCT	SQD1	PSCVT		CysS	THFCM		DXS
	EACPR		CDGP	SQD2		ChrS			GHMT		DXR		HGS	PS
	SCD					AS	CM		ThAL		MEPC		MBMM	PDS1
	LCS					APRT	PpDH				DMEK		HGPT	PDS2
	Lysine synthesis				IGPS	POGTA				MECS		TocC	PLI
		ASPK					PA4H	Sulfate assimilation		HMPS		TocOMT	ZEP
		ASDH				TrpS	KGTA		AdK		HMPR		NQDH	VDE
		DHPS							SulR		IDI		PPP
		DHPR		ACLS	2IPS		Fe-S synthesis		SQOR		FDS		PSMT
		DAPA		KARO	3IPY		SufS		BPN		GDS		FCL
		DAPE		DHAR	3IPO		SufE		SAT		GDR		PPMT
		DAPC		BCAAT			SufD		TMST				MPBM	NS
				Lysine catabolism								Division
	SBP				DAPE						Tom40	Erv1	Tim17	FtsZ
	FBP	GAPDH	TPI		DAPD			OCT	ASS		Tom70	Oxa1	Tim44
		PGK					ACGS		ASL		Sam50	Pam16	Tim50
		PGAM					URE	ARG				MPPa	Tim22
		Enol									ClpB	MPPb	Tim10
		PyrK							Carbonic anhydrase		Tim9
	PyrC	PDHA	PDHB	DHLD	CitS		AlaT			CAT
			PDH2		ICD		GS	GOGAT			Lipid catabolism
	MDH2	MDH1			OGR						ACCO		rpl3	rpl22	rps9
		MDH3			AKDE1	Cysteine catabolism		Glycine/ Serine		ECH		rpl4	rpl24	rps15
		FUMH	SDHF	SCSA	AKDE2		PSAT		CPS		ECH/D		rpl7	rpl27	rps16
			SDHS	SCSB			PSP		GlyD		3HCD		rpl11	rpl28	rps17
		MCM	SDHC				SHMT		AMM		ACCA		rpl15	rpl33
							IPMS		GlyH				rpl20	rpl34
	NDHA5	NDH6					ATP synthase
	NDHA9	NDH7		UCF	COX6A	FNR	ATPA		A	C	D		HBPPT DHPRD	RFK
	NDH1	NDH8		UCC	COX6B		ATPB		E*	F	G		ICS	AdK
	NDHA13	NDHF1		UC7	COX11		ATPC		H	I	K
	NDHB7	NDHF2			COX15		ATPD		L	fM	M
	NDHB9								N	P	R		IscS	IscA
									S	T	V		IscU	HscB
		AOX	TR	PMP2	PMP4	PRX5			W	Y			SulO
	Scale
	>0.8
	0.6-0,6
	0.4-0,6
	0.2-0,4
	0-0,2
	< -0.2

Fig. S6. Transcriptional coregulation of plastid-targeted Enolase and PGAM1 to other core organelle metabolism pathways. These heatmaps show the Spearman correlation coefficients calculated between ptEnolase and ptPGAM1 against genes encoding other core Phaeodactylum plastid-and mitochondrial-targeted proteins, as annotated in Ait-Mohamed et al. 2020, considering a merged and ranked RNAseq and microarray dataset. Where more than one homologue of a particular enzyme exist, the highest correlation coefficient is shown. Complete gene lists, including abbreviations, are provided in Table

S2

.

Acknowledgments RGD acknowledges a CNRS Momentum Fellowship, awarded from 2019-2021, an ANR JCJC Grant (« PanArctica » ANR-21-CE02-0014-01) and an ANR t-ERC Grant ("ChloroMosaic"), awarded from 2021-2022. CB acknowledges support from FFEM -French Facility for Global Environment, French Government 'Investissements d'Avenir' programs OCEANOMICS (ANR-11-BTBR-0008), FRANCE GENOMIQUE (ANR-10-INBS-09-08), MEMO LIFE (ANR-10-LABX-54), and PSL Research University (ANR-11-IDEX-0001-02), the European Research Council (ERC) under the European Union's Horizon 2020 research and innovation program (Diatomic; grant agreement

CAMPEP_0199698136_Pelagomonas_calceolata_CCMP1756

C4ZXS6_Esc her ic hia_c oli _str ai n_K12_M C4100_BW2952 11830_Fistulifera_solaris_genomic 3177_Fistulifera_solaris_genomic CAMPEP_0113426974_Sk el et onem a_c ostatum _1716 Physcomitrella_genomic_18043327 Arabidopsis_genomic_AT1G780501_PACid19651046 CAMPEP_0113538428_Odontella CAMPEP_0193207640_Undes cr ibed_sp_CCMP2098 Physcomitrella_genomic_18038205 CAMPEP_0199656752_Pseudo_nitzschia_australis_1024910AB A0A3E0VCM9_Subtercola_b oreus Phytophthora_sojae_genomic_560339_ CAMPEP_0113474528_Ni tzs chia_s p 0316911_Pedospumella_encystans_JBCS23 CA MPEP_0113394156_Skeletonem a_cos tatum _1716 Drosophila_genomic_28571817 CAMPEP_0181093332_Thal ass i osi ra_s p_NH16 C AMPEP_0113484306_N it zschia_sp CAMPEP_0113405834_Skel etonem a_c ostatum _1716 Bolidophytes_CAMPEP_0182463636_Bolidomonas_pacifica_RCC208 CAMPEP_0199670936_Pseudo_nitzschia_aus tralis _1024910AB CAMPEP_0113386616_Sk eletonem a_cos tatum _1716 CAMPEP_0113408816_Skel etonem a_c ost atum _1716 Physcomitrella_genomic_18038202 CAMPEP_0116155586_Pseudo_nitzschia_arenysensis_B593 CA MPEP_0181115316_Thal as si osi r a_s p_NH16 Drosophila_genomic_24646216 Drosophila_genomic_24648979 CAMPEP_0181066896_Minutocellus_polymorphus_NH13 CA MPEP_0192898420_Thal as si osi r a_oc eani c a_CCM P1005 CAMPEP_0116092414_Pseudo_nitzschia_delicatissima_B596 CAMPEP_0200482186_Extubocellulus_spinifer_CCMP396 14178_Fistulifera_solaris_genomic CAMPEP_0199654926_Pseudo_nitzschia_aus tralis _1024910AB CA MPEP_0113540330_Odontella CAMPEP_0113393972_Skelet onem a_costatum _1716 Bolidophytes_CAMPEP_0182463846_Bolidomonas_pacifica_RCC208 Drosophila_genomic_24650981 Phytophthora_ramorum_genomic_71947_fgenesh1_pmC_scaffold_62000010 CAMPEP_0193252644_U ndesc r ibed_sp_CCMP2098 CAMPEP_0113452528_Nit zschia_sp CAMPEP_0192905390_Thalassiosira_oceanica_CCMP1005 CAMPEP_0192926984_Thalassiosira_oceanica_CCMP1005 CAMPEP_0192899584_Thalassiosira_oceanica_CCMP1005 CAMPEP_0200481196_Extubocellulus_spinifer_CCMP396 CAMPEP_0116139988_Pseudo_nitzschia_arenysensis_B593 CAMPEP_0190537572_Pelagococcus_subviridis_CCMP1429 Physcomitrella_genomic_18047834 CAMPEP_0116141812_Ps eudo_nitzschia_arenysensis_B593 CAMPEP_0113540000_Odontella CAMPEP_0116141910_Ps eudo_nitzschia_arenysensis_B593 Arabidopsis_genomic_AT1G221701_PACid19651998 Phys comi tr el l a_genomi c _18065529 Chlamydomonas_genomic_27580320 Phys com it rell a_genom i c_18065128 CAMPEP_0181092044_Thal ass iosi r a_sp_NH16 CAMPEP_0113486434_Ni tzsc hia_sp P62707_Escher ic hia_col i _s tr ai n_K12 CAMPEP_0113436208_Skeletonema_costatum_1716 Phy scom i tr el la_genom i c_18056772 CA MPEP_0199698128_Pelagomonas_calceolata_CCMP1756 4133_Fi stul if er a_sol ar i s_genomi c CAMPEP_0116100940_Pseudo_nitzschia_delicatis sima_B596 Phys com i tr el l a_genom i c_18038204 CAMPEP_0200486916_Extubocellulus_spinifer_CCMP396 0316921_Pedospumella_encystans_JBCS23 CAM PEP_0113439040_Skel etonem a_cost at um _1716 B1X786_Escherichia_coli_strain_K12_DH10B CAMPEP_0181023382_Minutocellus_polymorphus_NH13 0.1 CAMPEP_0172526538_Thal assiosi ra_puncti ger a_Tpunct2005C2 Drosophi la_genomic_24580918 Schizosacchar omyces_genomic_19112695 CAMPEP_0183295722_Odontella_Sinensi s_Grunow_1884 CAMPEP_0113461464_Ni tzschia_sp CAMPEP_0197831286_Eucampia_antarctica_CCMP1452 A0A3E0VRT2_Subter cola_bor eus B1L7C2_Kor archaeum_cryptofi lum_strain_OPF8 CAMPEP_0113653824_Cylindrotheca_closterium CAMPEP_0113516612_Nitzschi a_sp 7970t1_Cladosiphon_okamuransis CAMPEP_0197201954_Skel etonema_mar inoi _UNC1201 CAMPEP_0192928158_Thalassiosi ra_oceani ca_CCMP1005 Phytophthora_sojae_genomic_358108_ CAMPEP_0192237144_Skel etonema_mar inoi_Skel A CAMPEP_0199655126_Pseudo_ni tzschia_austr alis_10249_10_AB CAMPEP_0195517210_Stephanopyxi s_tur ris_CCMP815 CAMPEP_0113453192_Ni tzschia_sp CAMPEP_0113619370_Cylindrotheca_closterium CAMPEP_0197180992_Pseudo_ni tzschia_heimi i_UNC1101 Arabidopsis_genomic_AT2G365301_PACi d19638768 CAMPEP_0172532910_Thalassi osira_punctigera_Tpunct2005C2 CAMPEP_0193016966_Thalassiosir a_r otula_GSO102 Porphyr idi um_genomic_20922 Phaeodactyl um_J41515 CAMPEP_0113490184_Nitzschia_sp comp33495_Dinobryon_LO226KS Bol idophytes_CAMPEP_0197546546_Boli domonas_RCC2347 CAMPEP_0113395008_Skel etonema_costatum_1716 B1XDI9_Escheri chia_coli _strain_K12__DH10B Phytophthora_ramorum_genomic_72151_fgenesh1_pmC_scaffold_94000001 Bolidophytes_CAMPEP_0182463700_Boli domonas_pacifi ca_RCC208 Ph aeo dac t y l um_dr af t J 1192 CAMPEP_0200128516_Thalassiosir a_antarctica_CCMP982 CAMPEP_0201029412_Thalassi osir a_miniscula_CCMP1093 CAMPEP_0196808828_Leptocyli ndr us_danicus_CCMP1856 5581_Mini discus_RCC1721 CAMPEP_0192952172_Thalassiosir a_r otul a_CCMP3096 CAMPEP_0172423650_Aulacoseir a_subar cti ca_CCAP_10025 Chondr us_cr ispus_genomic_507112763 3663_Haslea_RCC2667 CAMPEP_0201075264_Thal assiosi ra_mi niscul a_CCMP1093 Pythium_vexans_genomi c_0227538543328016 Phaeodactyl um_dr aftJ1572 CAMPEP_0172420494_Aul acoseir a_subarctica_CCAP_10025 CAMPEP_0172265532_Cycl otell a_meneghi niana_CCMP338 CAMPEP_0181036896_Minutocellus_polymorphus_NH13 CAMPEP_0172445878_Amphi pror a_pal udosa_CCMP125 CAMPEP_0119015580_Synedropsis_r ecta_cf_CCMP1620 CAMPEP_0172342760_Thalassi osir a_FW CAMPEP_0184966570_Spumel la_el ongata_CCAP_9551 Drosophi la_genomic_24580920 CAMPEP_0199328838_Nitzschi a_punctata_CCMP561 5147_Minidi scus_RCC1721 Physcomitrella_genomic_18069857 CAMPEP_0172266710_Cyclotell a_meneghi niana_CCMP338 CAMPEP_0200203114_Thal assionema_nitzschioides_L26_B CAMPEP_0197216538_Skeletonema_mari noi _UNC1201 CAMPEP_0172310306_Detonula_confervacea_CCMP353 P37869_Baci ll us_subtili s_strain_168 CAMPEP_0172442930_Amphi pror a_pal udosa_CCMP125 Esi0100_65_Ectocar pus_si liculosus Dr osophi la_genomi c_17137654 A0A3R9WYJ6_Candidatus_Korarchaeum_cr yptofilum CAMPEP_0119033250_Ochromonas_sp_CCMP1899 CAMPEP_0200510662_Extubocell ulus_spi nifer_CCMP396 CAMPEP_0115914938_Skeletonema_mari noi _SM1012Hels_07 CAMPEP_0184969028_Spumella_elongata_CCAP_9551 CAMPEP_0185811868_Mi nutocell us_polymorphus_RCC2270 CAMPEP_0197144084_Asterionellopsis_glaciali s_CCMP1581 comp43015_Spumella_vulgaris_1 CAMPEP_0192272192_Skeletonema_menzel ii_CCMP793 CAMPEP_0200075262_Duri nskia_baltica_CSIRO_CS_38 CAMPEP_0187024446_Chaetocer os_affinis_CCMP159 CAMPEP_0113468814_Ni tzschia_sp CAMPEP_0200080304_Dur inskia_balti ca_CSIRO_CS_38 comp32731_Dinobryon_LO226KS CAMPEP_0192924946_Thalassiosi ra_oceani ca_CCMP1005 Chondrus_cr ispus_genomic_507109774 CAMPEP_0182437918_Mal lomonas_Sp_CCMP3275 Chondrus_cri spus_genomic_507111147 CAMPEP_0113530300_Odontella Physcomitr el la_genomic_18069627 CAMPEP_0182427858_Mallomonas_Sp_CCMP3275 CAMPEP_0116130796_Pseudo_ni tzschia_arenysensi s_B593 CAMPEP_0172309706_Detonula_confervacea_CCMP353 CAMPEP_0190296504_Ochr omonas_sp_CCMP1393 CAMPEP_0180926850_Di tyl um_brightwel li i_Pop1__SS4_ CAMPEP_0116032262_Leptocyl indrus_dani cus_var_danicus_B650 CAMPEP_0199352364_Fragi lari opsis_ker guelensi s_L2_C3 Porphyri dium_genomic_20574 Ar abidopsis_genomic_AT1G740301_PACi d19656394 CAMPEP_0184884492_Asteri onel lopsi s_gl aci alis CAMPEP_0183327450_Odontell a_Si nensis_Grunow_1884 CAMPEP_0181108372_Thal assi osir a_NH16 CAMPEP_0188090942_Fragi lari opsis_ker guelensi s_L26 Esi 0349_4_Ectocarpus_sili culosus CAMPEP_0180984350_Ditylum_br ightwell ii _Pop2__SS10_ A0A3E0WA15_Subtercola_bor eus CAMPEP_0172334706_Thalassiosi ra_FW Physcomitrella_genomic_18073479 CAMPEP_0184890058_Asteri onell opsis_glacial is CAMPEP_0119033826_Ochr omonas_sp_CCMP1899 CAMPEP_0199361788_Fragi lari opsis_ker guelensi s_L2_C3 CAMPEP_0115917532_Skeletonema_mari noi _SM1012Hels_07 CAMPEP_0186528530_Amphora_coffeaeformi s_CCMP127 A0A3E0VC86_Subtercola_boreus CAMPEP_0180918536_Ditylum_bri ghtwell ii_Pop1__SS4_ CAMPEP_0192992674_Thalassi osi ra_rotula_GSO102 Schi zosacchar omyces_genomic_162312141 8432_Hasl ea_RCC2667 CAMPEP_0197183162_Pseudo_ni tzschia_hei mii_UNC1101 CAMPEP_0200121408_Thalassiosi ra_antar cti ca_CCMP982 89649_Navicul a_RCC3092 CAMPEP_0185766548_Dictyocha_speculum_CCMP1381 Bol idophytes_CAMPEP_0182455002_Boli domonas_pacifica_RCC208 A0A6D2WVP5_Escheri chia_coli _str ai n_K12 CAMPEP_0199328708_Ni tzschia_punctata_CCMP561 CAMPEP_0186518368_Amphor a_coffeaefor mi s_CCMP127 A0A3E0VK77_Subtercola_boreus Boli dophytes_CAMPEP_0197552110_Bol idomonas_RCC2347 CAMPEP_0185765686_Dictyocha_speculum_CCMP1381 CAMPEP_0190274542_Ochromonas_sp_CCMP1393 CAMPEP_0113505192_Nitzschi a_sp CAMPEP_0192889466_Thalassiosir a_oceani ca_CCMP1005 CAMPEP_0197824626_Eucampi a_antarctica_CCMP1452 CAMPEP_0113384394_Skeletonema_costatum_1716 CAMPEP_0200457130_Extubocell ulus_spi nifer_CCMP396 C4ZZT2_Escher ichi a_col i_str ai n_K12__MC4100__BW2952 CAMPEP_0195516712_Stephanopyxis_turri s_CCMP815 CAMPEP_0199646738_Pseudo_nitzschia_australis_10249_10_AB CAMPEP_0200196302_Thalassi onema_nitzschi oides_L26_B CAMPEP_0115960440_Skeletonema_mari noi _SM1012Den_03 CAMPEP_0113528262_Odontel la g19837_Fi stulifer a_sol ar is A0A3E0VVN2_Subter cola_bor eus A0A6M4JPA4_Baci ll us_subtil is_str ai n_168 CAMPEP_0180969930_Dityl um_bri ghtwell ii_Pop2__SS10_ CAMPEP_0200434372_Extubocellul us_spini fer_CCMP396 CAMPEP_0201089534_Thalassiosi ra_mini scula_CCMP1093 CAMPEP_0181101612_Thalassiosir a_NH16 Dr osophi la_genomi c_281360527 CAMPEP_0196811502_Leptocyli ndrus_danicus_CCMP1856 CAMPEP_0187023688_Chaetoceros_affi ni s_CCMP159 CAMPEP_0188082742_Fr agil ariopsi s_kerguelensis_L26 CAMPEP_0197145576_Asterionel lopsi s_glaciali s_CCMP1581 Physcomitrella_genomic_18067289 g1457_Fi stul ifera_solari s CAMPEP_0192233468_Skel etonema_marinoi_Skel A Drosophi la_genomic_24580914 CAMPEP_0116026756_Leptocylindrus_danicus_var_dani cus_B650 comp43015_Spumella_vulgar is_2 g4149_Fistul ifer a_sol aris CAMPEP_0116118980_Pseudo_ni tzschia_arenysensi s_B593 5866t1_Cladosi phon_okamur ansi s CAMPEP_0113614376_Cyli ndrotheca_cl oster ium g32_Fistuli fer a_sol aris CAMPEP_0192267724_Skeletonema_menzeli i_CCMP793 CAMPEP_0181030296_Minutocel lus_polymorphus_NH13 CAMPEP_0192957046_Thalassi osir a_r otula_CCMP3096 Dr osophi la_genomi c_24580916 CAMPEP_0119004540_Synedr opsis_recta_cf_CCMP1620 Asp/ Glu shuttle 0.2 Phytophthora_ramorum_genomic_72100_fgenesh1_pmC_scaffold_86000004 CAMPEP_0199696910_Pelagomonas_calceolata_CCMP1756 CAMPEP_0190531678_Pelagococcus_subviridis_CCMP1429 CAMPEP_0181022636_Minutocellus_polymorphus_NH13 Physcomitrella_genomic_18065127 CAMPEP_0113542736_Odontella Drosophila_genomic_85725272 Phytophthora_sojae_genomic_360745_ Physcomitrella_genomic_18047833 Drosophila_genomic_85725270 A0A3E0VVI4_Subtercola_boreus Phaeodactylum_J33839 Phaeodactylum_J17086 CAMPEP_0199646310_Pseudo_nitzschia_australis_1024910AB CAMPEP_0200453364_Extubocellulus_spinifer_CCMP396 A0A4S5ATD1_Escherichia_coli_strain_K12 CAMPEP_0116121460_Pseudo_nitzschia_arenysensis_B593 CAMPEP_0113458330_Nitzschia_sp CAMPEP_0113539650_Odontella 46_Fistulifera_solaris_genomic Drosophila_genomic_28571815 CAMPEP_0181096924_Thalassiosira_sp_NH16 CAMPEP_0113484756_Nitzschia_sp CAMPEP_0113393142_Skeletonema_costatum_1716 A0A3E0VM60_Subtercola_boreus Physcomitrella_genomic_18043326 Phaeodactylum_J51404 CAMPEP_0193567548_Undescribed_undescribed_CCMP2298 A0A3E0WA22_Subtercola_boreus Physcomitrella_genomic_18038203 CAMPEP_0181050760_Minutocellus_polymorphus_NH13 CAMPEP_0199698136_Pelagomonas_calceolata_CCMP1756 C4ZXS6_Escherichia_coli_strain_K12_MC4100_BW2952 11830_Fistulifera_solaris_genomic 3177_Fistulifera_solaris_genomic CAMPEP_0113426974_Skeletonema_costatum_1716 Physcomitrella_genomic_18043327 Arabidopsis_genomic_AT1G780501_PACid19651046 CAMPEP_0113538428_Odontella CAMPEP_0193207640_Undescribed_sp_CCMP2098 Physcomitrella_genomic_18038205 CAMPEP_0199656752_Pseudo_nitzschia_australis_1024910AB A0A3E0VCM9_Subtercola_boreus Phytophthora_sojae_genomic_560339_ CAMPEP_0113474528_Nitzschia_sp 0316911_Pedospumella_encystans_JBCS23 CAMPEP_0113394156_Skeletonema_costatum_1716 Drosophila_genomic_28571817 CAMPEP_0181093332_Thalassiosira_sp_NH16 CAMPEP_0113484306_Nitzschia_sp CAMPEP_0113405834_Skeletonema_costatum_1716 Bolidophytes_CAMPEP_0182463636_Bolidomonas_pacifica_RCC208 CAMPEP_0199670936_Pseudo_nitzschia_australis_1024910AB CAMPEP_0113386616_Skeletonema_costatum_1716 CAMPEP_0113408816_Skeletonema_costatum_1716 Physcomitrella_genomic_18038202 CAMPEP_0116155586_Pseudo_nitzschia_arenysensis_B593 CAMPEP_0181115316_Thalassiosira_sp_NH16 Drosophila_genomic_24646216 Drosophila_genomic_24648979 CAMPEP_0181066896_Minutocellus_polymorphus_NH13 CAMPEP_0192898420_Thalassiosira_oceanica_CCMP1005 CAMPEP_0116092414_Pseudo_nitzschia_delicatissima_B596 CAMPEP_0200482186_Extubocellulus_spinifer_CCMP396 14178_Fistulifera_solaris_genomic CAMPEP_0199654926_Pseudo_nitzschia_australis_1024910AB CAMPEP_0113540330_Odontella CAMPEP_0113393972_Skeletonema_costatum_1716 Bolidophytes_CAMPEP_0182463846_Bolidomonas_pacifica_RCC208 Drosophila_genomic_24650981 Phytophthora_ramorum_genomic_71947_fgenesh1_pmC_scaffold_62000010 CAMPEP_0193252644_Undescribed_sp_CCMP2098 CAMPEP_0113452528_Nitzschia_sp CAMPEP_0192905390_Thalassiosira_oceanica_CCMP1005 CAMPEP_0192926984_Thalassiosira_oceanica_CCMP1005 CAMPEP_0192899584_Thalassiosira_oceanica_CCMP1005 CAMPEP_0200481196_Extubocellulus_spinifer_CCMP396 CAMPEP_0116139988_Pseudo_nitzschia_arenysensis_B593 CAMPEP_0190537572_Pelagococcus_subviridis_CCMP1429 Physcomitrella_genomic_18047834 CAMPEP_0116141812_Pseudo_nitzschia_arenysensis_B593 CAMPEP_0113540000_Odontella CAMPEP_0116141910_Pseudo_nitzschia_arenysensis_B593 Arabidopsis_genomic_AT1G221701_PACid19651998 Physcomitrella_genomic_18065529 Chlamydomonas_genomic_27580320 Physcomitrella_genomic_18065128 CAMPEP_0181092044_Thalassiosira_sp_NH16 CAMPEP_0113486434_Nitzschia_sp P62707_Escherichia_coli_strain_K12 CAMPEP_0113436208_Skeletonema_costatum_1716 Physcomitrella_genomic_18056772 CAMPEP_0199698128_Pelagomonas_calceolata_CCMP1756 4133_Fistulifera_solaris_genomic CAMPEP_0116100940_Pseudo_nitzschia_delicatissima_B596 Physcomitrella_genomic_18038204 CAMPEP_0200486916_Extubocellulus_spinifer_CCMP396 0316921_Pedospumella_encystans_JBCS23 CAMPEP_0113439040_Skeletonema_costatum_1716 B1X786_Escherichia_coli_strain_K12_DH10B CAMPEP_0181023382_Minutocellus_polymorphus_NH13 0.1 CAMPEP_0172526538_Thalassiosira_punctigera_Tpunct2005C2 Drosophila_genomic_24580918 Schizosaccharomyces_genomic_19112695 CAMPEP_0183295722_Odontella_Sinensis_Grunow_1884 CAMPEP_0113461464_Nitzschia_sp CAMPEP_0197831286_Eucampia_antarctica_CCMP1452 A0A3E0VRT2_Subtercola_boreus B1L7C2_Korarchaeum_cryptofilum_strain_OPF8 CAMPEP_0113653824_Cylindrotheca_closterium CAMPEP_0113516612_Nitzschia_sp 7970t1_Cladosiphon_okamuransis CAMPEP_0197201954_Skeletonema_marinoi_UNC1201 CAMPEP_0192928158_Thalassiosira_oceanica_CCMP1005 Phytophthora_sojae_genomic_358108_ CAMPEP_0192237144_Skeletonema_marinoi_SkelA CAMPEP_0199655126_Pseudo_nitzschia_australis_10249_10_AB CAMPEP_0195517210_Stephanopyxis_turris_CCMP815 CAMPEP_0113453192_Nitzschia_sp CAMPEP_0113619370_Cylindrotheca_closterium CAMPEP_0197180992_Pseudo_nitzschia_heimii_UNC1101 Arabidopsis_genomic_AT2G365301_PACid19638768 CAMPEP_0172532910_Thalassiosira_punctigera_Tpunct2005C2 CAMPEP_0193016966_Thalassiosira_rotula_GSO102 Porphyridium_genomic_20922 Phaeodactylum_J41515 CAMPEP_0113490184_Nitzschia_sp comp33495_Dinobryon_LO226KS Bolidophytes_CAMPEP_0197546546_Bolidomonas_RCC2347 CAMPEP_0113395008_Skeletonema_costatum_1716 B1XDI9_Escherichia_coli_strain_K12__DH10B Phytophthora_ramorum_genomic_72151_fgenesh1_pmC_scaffold_94000001 Bolidophytes_CAMPEP_0182463700_Bolidomonas_pacifica_RCC208 Phaeodactylum_draftJ1192 CAMPEP_0200128516_Thalassiosira_antarctica_CCMP982 CAMPEP_0201029412_Thalassiosira_miniscula_CCMP1093 CAMPEP_0196808828_Leptocylindrus_danicus_CCMP1856 5581_Minidiscus_RCC1721 CAMPEP_0192952172_Thalassiosira_rotula_CCMP3096 CAMPEP_0172423650_Aulacoseira_subarctica_CCAP_10025 Chondrus_crispus_genomic_507112763 3663_Haslea_RCC2667 CAMPEP_0201075264_Thalassiosira_miniscula_CCMP1093 Pythium_vexans_genomic_0227538543328016 Phaeodactylum_draftJ1572 CAMPEP_0172420494_Aulacoseira_subarctica_CCAP_10025 CAMPEP_0172265532_Cyclotella_meneghiniana_CCMP338 CAMPEP_0181036896_Minutocellus_polymorphus_NH13 CAMPEP_0172445878_Amphiprora_paludosa_CCMP125 CAMPEP_0119015580_Synedropsis_recta_cf_CCMP1620 CAMPEP_0172342760_Thalassiosira_FW CAMPEP_0184966570_Spumella_elongata_CCAP_9551 Drosophila_genomic_24580920 CAMPEP_0199328838_Nitzschia_punctata_CCMP561 5147_Minidiscus_RCC1721 Physcomitrella_genomic_18069857 CAMPEP_0172266710_Cyclotella_meneghiniana_CCMP338 CAMPEP_0200203114_Thalassionema_nitzschioides_L26_B CAMPEP_0197216538_Skeletonema_marinoi_UNC1201 CAMPEP_0172310306_Detonula_confervacea_CCMP353 P37869_Bacillus_subtilis_strain_168 CAMPEP_0172442930_Amphiprora_paludosa_CCMP125 Esi0100_65_Ectocarpus_siliculosus Drosophila_genomic_17137654 A0A3R9WYJ6_Candidatus_Korarchaeum_cryptofilum CAMPEP_0119033250_Ochromonas_sp_CCMP1899 CAMPEP_0200510662_Extubocellulus_spinifer_CCMP396 CAMPEP_0115914938_Skeletonema_marinoi_SM1012Hels_07 CAMPEP_0184969028_Spumella_elongata_CCAP_9551 CAMPEP_0185811868_Minutocellus_polymorphus_RCC2270 CAMPEP_0197144084_Asterionellopsis_glacialis_CCMP1581 comp43015_Spumella_vulgaris_1 CAMPEP_0192272192_Skeletonema_menzelii_CCMP793 CAMPEP_0200075262_Durinskia_baltica_CSIRO_CS_38 CAMPEP_0187024446_Chaetoceros_affinis_CCMP159 CAMPEP_0113468814_Nitzschia_sp CAMPEP_0200080304_Durinskia_baltica_CSIRO_CS_38 comp32731_Dinobryon_LO226KS CAMPEP_0192924946_Thalassiosira_oceanica_CCMP1005 Chondrus_crispus_genomic_507109774 CAMPEP_0182437918_Mallomonas_Sp_CCMP3275 Chondrus_crispus_genomic_507111147 CAMPEP_0113530300_Odontella Physcomitrella_genomic_18069627 CAMPEP_0182427858_Mallomonas_Sp_CCMP3275 CAMPEP_0116130796_Pseudo_nitzschia_arenysensis_B593 CAMPEP_0172309706_Detonula_confervacea_CCMP353 CAMPEP_0190296504_Ochromonas_sp_CCMP1393 CAMPEP_0180926850_Ditylum_brightwellii_Pop1__SS4_ CAMPEP_0116032262_Leptocylindrus_danicus_var_danicus_B650 CAMPEP_0199352364_Fragilariopsis_kerguelensis_L2_C3 Porphyridium_genomic_20574 Arabidopsis_genomic_AT1G740301_PACid19656394 CAMPEP_0184884492_Asterionellopsis_glacialis CAMPEP_0183327450_Odontella_Sinensis_Grunow_1884 CAMPEP_0181108372_Thalassiosira_NH16 CAMPEP_0188090942_Fragilariopsis_kerguelensis_L26 Esi0349_4_Ectocarpus_siliculosus CAMPEP_0180984350_Ditylum_brightwellii_Pop2__SS10_ A0A3E0WA15_Subtercola_boreus CAMPEP_0172334706_Thalassiosira_FW Physcomitrella_genomic_18073479 CAMPEP_0184890058_Asterionellopsis_glacialis CAMPEP_0119033826_Ochromonas_sp_CCMP1899 CAMPEP_0199361788_Fragilariopsis_kerguelensis_L2_C3 CAMPEP_0115917532_Skeletonema_marinoi_SM1012Hels_07 CAMPEP_0186528530_Amphora_coffeaeformis_CCMP127 A0A3E0VC86_Subtercola_boreus CAMPEP_0180918536_Ditylum_brightwellii_Pop1__SS4_ CAMPEP_0192992674_Thalassiosira_rotula_GSO102 Schizosaccharomyces_genomic_162312141 8432_Haslea_RCC2667 CAMPEP_0197183162_Pseudo_nitzschia_heimii_UNC1101 CAMPEP_0200121408_Thalassiosira_antarctica_CCMP982 89649_Navicula_RCC3092 CAMPEP_0185766548_Dictyocha_speculum_CCMP1381 Bolidophytes_CAMPEP_0182455002_Bolidomonas_pacifica_RCC208 A0A6D2WVP5_Escherichia_coli_strain_K12 CAMPEP_0199328708_Nitzschia_punctata_CCMP561 CAMPEP_0186518368_Amphora_coffeaeformis_CCMP127 A0A3E0VK77_Subtercola_boreus Bolidophytes_CAMPEP_0197552110_Bolidomonas_RCC2347 CAMPEP_0185765686_Dictyocha_speculum_CCMP1381 CAMPEP_0190274542_Ochromonas_sp_CCMP1393 CAMPEP_0113505192_Nitzschia_sp CAMPEP_0192889466_Thalassiosira_oceanica_CCMP1005 CAMPEP_0197824626_Eucampia_antarctica_CCMP1452 CAMPEP_0113384394_Skeletonema_costatum_1716 CAMPEP_0200457130_Extubocellulus_spinifer_CCMP396 C4ZZT2_Escherichia_coli_strain_K12__MC4100__BW2952 CAMPEP_0195516712_Stephanopyxis_turris_CCMP815 CAMPEP_0199646738_Pseudo_nitzschia_australis_10249_10_AB CAMPEP_0200196302_Thalassionema_nitzschioides_L26_B CAMPEP_0115960440_Skeletonema_marinoi_SM1012Den_03 CAMPEP_0113528262_Odontella g19837_Fistulifera_solaris A0A3E0VVN2_Subtercola_boreus A0A6M4JPA4_Bacillus_subtilis_strain_168 CAMPEP_0180969930_Ditylum_brightwellii_Pop2__SS10_ CAMPEP_0200434372_Extubocellulus_spinifer_CCMP396 CAMPEP_0201089534_Thalassiosira_miniscula_CCMP1093 CAMPEP_0181101612_Thalassiosira_NH16 Drosophila_genomic_281360527 CAMPEP_0196811502_Leptocylindrus_danicus_CCMP1856 CAMPEP_0187023688_Chaetoceros_affinis_CCMP159 CAMPEP_0188082742_Fragilariopsis_kerguelensis_L26 CAMPEP_0197145576_Asterionellopsis_glacialis_CCMP1581 Physcomitrella_genomic_18067289 g1457_Fistulifera_solaris CAMPEP_0192233468_Skeletonema_marinoi_SkelA Drosophila_genomic_24580914 CAMPEP_0116026756_Leptocylindrus_danicus_var_danicus_B650 comp43015_Spumella_vulgaris_2 g4149_Fistulifera_solaris CAMPEP_0116118980_Pseudo_nitzschia_arenysensis_B593 5866t1_Cladosiphon_okamuransis CAMPEP_0113614376_Cylindrotheca_closterium g32_Fistulifera_solaris CAMPEP_0192267724_Skeletonema_menzelii_CCMP793 CAMPEP_0181030296_Minutocellus_polymorphus_NH13 CAMPEP_0192957046_Thalassiosira_rotula_CCMP3096 Drosophila_genomic_24580916 CAMPEP_0119004540_Synedropsis_recta_cf_CCMP1620 Fig. S2. Consensus topology of a 220 aa x 560 taxa alignment of PGAM isoform 1 sequences, shown as per Fig. S1. 2.0 CAMPEP_0119234076_Pelagomonas_calceolata_Strain_RCC969 CAMPEP_0190804714_Pleurochrysis_carterae_CCMP645 CAMPEP_0188144420_Fragilariopsis_kerguelensis Toxoplasma_genomic_237841561 1900_Haslea_RCC2667 CAMPEP_0180916266_Ditylum_brightwellii Phytophthora_sojae_genomic_480534_e_gw12134551 CAMPEP_0116039132_Leptocylindrus_danicus_var_danicus Phaeodactylum_J37201 CAMPEP_0198276650_Chaetoceros_dichaeta CAMPEP_0200108416_Thalassiosira_antarctica CAMPEP_0113668510_Cryptomonas_paramecium CAMPEP_0190556586_Pelagococcus_subviridis_CCMP1429 82848_Navicula_RCC3092 CAMPEP_0172402950_Pseudo_nitzschia_pungens Trichoplax_genomic_5385_gw172631 CAMPEP_0188114000_Fragilariopsis_kerguelensis Phaeodactylum_J47096 CAMPEP_0186509620_Amphiprora_sp_ CAMPEP_0119274224_Genus_nov_species_nov_Strain_RCC1024 Pythium_vexans_genomic_00112107623318392 Drosophila_genomic_18543249 CAMPEP_0114539506_Goniomonas_sp_Strain_m CAMPEP_0193362700_Undescribed_sp_CCMP2293 010225301_Poterioochromonas_sp_DS CAMPEP_0190557306_Pelagococcus_subviridis_CCMP1429 CAMPEP_0196809402_Leptocylindrus_danicus CAMPEP_0186723814_Aureoumbra_lagunensis_CCMP1510 CAMPEP_0113525262_Odontella_ CAMPEP_0201034824_Thalassiosira_miniscula Plasmodium_vivax_genomic_156097969 CAMPEP_0193053136_Thalassiosira_weissflogii_Chloroplast CAMPEP_0198307064_Attheya_septentrionalis Pythium_vexans_genomic_0573667972984241 CAMPEP_0172542864_Thalassiosira_punctigera CAMPEP_0119098982_unid_sp_Strain_CCMP2000 CAMPEP_0200146406_Proboscia_alata CAMPEP_0119226862_Pelagomonas_calceolata_Strain_RCC969 Drosophila_genomic_665389201 162358_Guillardia_genomic CAMPEP_0198137338_Entomoneis_sp Drosophila_genomic_24762634 CAMPEP_0180989872_Ditylum_brightwellii CAMPEP_0199689196_Pelagomonas_calceolata__Strain_CCMP1756 CAMPEP_0193635710_Isochrysis_galbana_CCMP1323 Phytophthora_ramorum_genomic_83223_fgenesh1_pgC_scaffold_84000049 CAMPEP_0200980208_Thalassiothrix_antarctica CAMPEP_0201702882_Skeletonema_grethea CAMPEP_0172099162_Rhodomonas_salina_Strain_CCMP1319 CAMPEP_0198165208_Phaeocystis_antarctica_Strain_CCMP1374 CAMPEP_0169501078_Hanusia_phi_Strain_CCMP325 CAMPEP_0198253752_Chaetoceros_dichaeta CAMPEP_0116014898_Leptocylindrus_danicus_var_danicus Trichoplax_genomic_5303_gw172571 CAMPEP_0193061542_Thalassiosira_weissflogii_Chloroplast Pythium_ultimum_genomic_0398218387330754 CAMPEP_0186669414_Aureococcus_anophagefferens_CCMP1850 Phytophthora_sojae_genomic_553473_ Pythium_vexans_genomic_0146567856875515 CAMPEP_0188824638_Isochrysis_sp_CCMP1324 CAMPEP_0198645506_unid_sp_Strain_CCMP2135 CAMPEP_0199670898_Pseudo_nitzschia_australis 68902_Aureococcus_genomic CAMPEP_0119323122_Scyphosphaera_apsteinii_Strain_RCC1455 A0A086E184_Cryobacterium_sp_MLB_32 CAMPEP_0200888188_Chaetoceros_debilis

Division Lhcf1 Lhcf7 Lhcf13 Lhcr1 Lhcr5 Lhcr11 Lhcx1 Lhc6062 Hsp70_2 sDer1-1 sDer1-2 sUba1 Drp1 Lhcf2 Lhcf8 Lhcf14 Lhcr2 Lhcr6 Lhcr12 Lhcx2 Lhc13877 sCdc48 sUfd1 sUbc6 FtsZ Lhcf3 Lhcf9 Lhcf15 Lhcr3 Lhcr7 Lhcr13 Lhcx3 Lhc15820 SPP TPP sUbi FtsH Lhcf4 Lhcf10 Lhcf16 Lhcr4 Lhcr8 Lhcr14 Lhcx4 Lhc17513 cpOmp85 Oxa1 Tic62L Lhcf5 Lhcf11 Lhcf17 Lhcr9 Lhc24119 Tic20 Tic21 Tic22 TatA SecA Lhcf6 Lhcf12 Lhcr10 Lhc48798 Tic55 Tic62 Tic110 cpFtsY

Alternative electron flow B) PGAM1 Plastid