dataesr/scientific-paragraphs-categorization huggingface dataset:
, 2013) ; and cofactor (e. g. , isopentenyl pyrophosphate and protoporphyrin/haem) biosynthetic pathways, which resolve principally either with dinoflagellates or sources suggesting horizontal acquisition  (Matsuo and Inagaki, 2018). To date, a reconstruction of the entire fucoxanthin plastid has yet to be attempted, and its overall metabolic composition and specific evolutionary origin within the haptophytes remain unresolved  (Yoon et al. , 2002; Takahashi et al. , 2019; Leblond et al. , 2022). Here, we present a holistic reconstruction of the pan-kareniacean plastid proteome across seven model species, integrating newly sequenced transcriptomic datasets for two additional strains of Karenia and Karlodinium and the first transcriptome for the genus Takayama, in silico identification and phylogenomic analysis of genes encoding plastid-targeted proteins.
Pan-transcriptomics reveals the core fucoxanthin plastid proteome Five new transcriptomic datasets were produced for fucoxanthin dinoflagellates: Karenia mikimotoi and Karenia papilionacea, the RCC3446 strain of Karlodinium micrum and its distant relative Karlodinium armiger, and for Takayama helix, a predator of other dinoflagellates more closely related to Karlodinium than Karenia  (Jeong et al. , 2016) , for which to our knowledge this is the first reported transcriptomic dataset. These libraries, alongside four previously sequenced MMETSP libraries for Karenia brevis, one for Karlodinium micrum strain CCMP2283, and an independently sequenced transcriptome for RSD, represent the pan-kareniacean library used for all downstream analyses. The raw dataset sizes range from around 200 to 660 Mbp but contain large amounts of redundant or partially redundant transcripts, reflecting the extensive paralogization and pseudogenization associated with dinoflagellate nuclear genomes  (Stern et al.
Guided in silico prediction reveals within-lineage divergence in fucoxanthin plastid proteome contents A modified version of ASAFind  (Gruber et al. , 2015) , which has previously been used to reconstruct in silico the haptophyte plastid proteome  (Dorrell et al. , 2017) , was constructed with a custom scoring matrix for fucoxanthin dinoflagellate plastids (see Materials and Methods) and used in combination with SignalP 5. 0 in the final prediction pipeline. After preliminary automatic annotation of the retrieved in silico plastidial proteomes of all studied organisms by KAAS (https://www. genome. jp/kegg/kaas/; Moriya et al. , 2007) , we noticed that some of the integral plastidial proteins missing from these datasets could be captured by an alternative prediction approach combining PrediSI  (Hiller et al.
, 2004)  and ChloroP  (Emanuelsson et al. , 1999)  employed previously for Euglena gracilis data  (Ebenezer et al. , 2017) , and so included these as well. The proportion of redundancy-treated translated transcripts predicted as plastidtargeted ranged from 7. 5 to 14. 5 % in K. micrum and K. brevis, respectively (~9. 5 % being both average and modal value). Of these, most have a targeting sequence predictable by modified ASAFind, while between 16 to 22% represent additions based solely on the abovementioned alternative signal. The putative phylogenetic origin of all predicted plastid proteins was investigated via a custom pipeline, integrating homology mining, single-gene tree construction and sorting based on topology and its statistical support (Figure  S1 ). Between 40 and 55 % of each proteome dataset had some homologs in our reference database containing representatives for most major eukaryotic groups, particularly enriched in haptophyte and dinoflagellate transcriptomes (see Materials and Methods).
Of all the proteins with homologs in the database, 65-80% were categorized as "plastid-early" in all studied organisms (Figure  1A ). The proportion of proteins categorized as "plastid-late" is generally in accord with the previously published estimates  (Burki et al. , 2014)  but there was a noticeable difference between the genera, with approximately 15% noted in all three species of Karenia, 10% in Karlodinium, and 6% in Takayama. The proportion of proteins shared and clustering with ciliates, i. e. , primarily heterotrophic alveolates ("non-plastid signal"), is almost negligible in all studied organisms and so is the contribution of prokaryotic or "green" LGT (cases of which do not exceed 20 per organism). The amount of "brown" LGT is slightly higher than the amount of non-plastid signal (varying from 12 to 107 per organism).
We further noted a strong secondary signal to the family Isochrysidales in all kareniacean species including the RSD, even normalizing for dataset size (Figure  S2 ), although many were specific to individual genera (Figure  S3 ). Finally, we searched the new transcriptomes for contigs potentially representing plastid transcripts (recovering 13 to 39 genes in each case) and reconstructed a preliminary plastid phylogeny based concatenated nucleotide sequences. Consistent with previous studies, the fucoxanthin plastid genomes resolved as a long-branch sister group to all Prymnesiophyceae, although with a notable separation of Takayama from Karenia and Karlodinium (Figure  S4 )  (Choi et al. , 2017; Kawachi et al. , 2021). Finally, we used the whole transcriptomes as input for the PhyloFisher pipeline  (Tice et al.
3-5). These genes were frequently detected across all fucoxanthin genera with strong bootstrap support and are therefore unlikely to result from contamination in individual transcriptomes or phylogenetic artifact. Some otherwise broadly conserved proteins were not detected in any kareniacean transcriptomes, most notably SQD1, one of the two enzymes for the synthesis of essential plastidial glycerolipid sulfoquinovosyldiacylglycerol (SQDG), and the delta subunit of the photosynthetic ATP synthase (atpH, K02113). The former may be due to a very low expression level as it was previously missed in various algae even in dedicated studies  (Riccio et al. , 2020). The latter is more baffling as this conserved and essential subunit is usually plastid-encoded but was not retrieved as such in the previous plastid genome analyses  (Gabrielsen et al.
, 2011; Dorrell et al. , 2016)  and was not identified in any of the full transcriptomes either. However, an unrelated and structurally dissimilar but functionally analogous subunit of mitochondrial ATPase (ATP5D, K02134) seems to be duplicated and retargeted to the plastid (Figure  S6. 4. 1) and it is possible that this is in fact related to the absence of the gamma subunit.
Targeting sequences of kareniacean plastid proteins vary with their evolutionary origin Next, we wished to test if there are differences in the plastid targeting pre-sequences of plastid-late and plastid-early proteins, by comparison to analogous protein regions from peridinin dinoflagellate and haptophyte references. The signal peptides of kareniacean plastid-targeted proteins were found to typically contain a central LACLAC motif and a terminal GHG motif directly preceding the cleavage site regardless of whether they were of plastid-late or plastid-early origin (Figure  4A-B ), whereas these motifs do not occur in haptophytes nor peridinin dinoflagellates (Figure  4C-D ). Comparisons of the unique three-letter motifs associated with the plastid-targeting sequences of each group revealed particularly strong enrichments in the LACLAC and GHG motif and its variants in kareniacean proteins of plastid-late origin, compared to proteins of plastid-early origin and haptophyte and dinoflagellate equivalents (Figure  4E ), despite similar overall amino acid composition of signal peptides across all datasets (Figure  S7 ).
Global distribution of fucoxanthin dinoflagellates reveals Karlodinium as an outlier and a negative co-occurrence with their haptophyte progenitors To provide biogeographical and ecological context for the interpretation of our transcriptomic and phylogenetic results we investigated the distribution of Karenia, Karlodinium, and Takayama in the Tara Oceans database using V9 18S metabarcoding (data available in Tables  S5-8 ). Our analysis shows that Karlodinium clearly differs from the other two genera in its higher overall abundance and station occupancy and more cosmopolitan distribution, which is noticeable especially in the Indian and Arctic Oceans (Figure  5 ). Partial Least Square (PLS) analysis shows a set of environmental variables (salinity, silicate, iron) positively correlated with abundances of both Karenia and Takayma and also haptophytes as a whole, but at the same time negatively correlated to Karlodinium (Figure  S8 ), further illustrating that the latter genus is quite distant from the rest in its biogeographical pattern.
Conclusions Seven in silico plastid proteomes of fucoxanthin plastid-bearing kareniaceans were retrieved from already available and newly generated transcriptomic data and phylogenetically analysed using large-scale automated tree building and sorting. The ratio between plastid-late and plastid-early phylogenetic signal differs between the three studied genera and its uneven distribution among functionally annotated plastid enzymes in individual lineages suggests that the serial plastid establishment and formation of its evolutionarily mosaic proteome was a relatively long and relaxed process with room for significant diversification. Phylogenomic analyses of the haptophyte-like sequences reveal Chrysochromulinales as the closest relatives and possible donor lineage of the fucoxanthin plastid for the first time but at the same time suggests that its evolutionary history inside Kareniaceae was not straightforward and may have involved extensive horizontal gene flow or even additional serial endosymbiosis.
Reference database preparation An in-house protein database was used for homology searches throughout this project. This database consisted of 155 transcriptomic and genomic datasets fetched from NCBI (https://www. ncbi. nlm. nih. gov), 1KP (https://db. cngb. org/onekp/; Leebens-Mack et al. , 2019)  and decontaminated MMETSP  (Keeling et al. , 2014)  as described and in  Dorrell et al. , 2021: 30  for eubacteria, 10 for archaea, and 115 for eukaryotes with sampling spanning most of the diversity with focus on plastid-bearing groups (100 datasets) and particularly enriched in dinoflagellate and haptophyte sequences (37 and 34 datasets, respectively). More details and list of used datasets is available in Table  S2 ).
Data acquisition and de novo transcriptome sequencing The previously published peptide sequences for Karenia brevis, Karlodinium micrum (syn. veneficum) and Ross Sea Dinoflagellate (RSD) were downloaded from their respective public repositories  (Keeling et al. , 2014; Ryan et al. , 2014 , Hehenberger et al. , 2019)  and decontaminated as described above. In case of K. brevis, the four available assemblies from four clones were pooled together and treated as a single dataset with redundant sequences removed randomly. Karenia mikimotoi RCC1513, Karenia papilionacea RCC6516 and Karlodinium sp. RCC3446 were obtained from Roscoff Culture Collection (https://roscoff-culture-collection. org), Takayama helix CCMP3082 was obtained from the Bigelow National Center for Marine Algae and Microbiota (https://ncma.
, 2005) , and by removing reads potentially representing rRNA contamination (default settings were used for this step). Filtered reads were then assembled by Trinity  (Grabherr et al. , 2011)  with --NO_SEQTK and -trimmomatic options and the resulting assembly was selected for the longest isoforms by the respective script from the Trinity toolkit. The quality and completeness of the transcriptomic datasets were assessed by TrinityStats and BUSCO (eukaryota_odb10, n:255)  (Simão et al. , 2015). The datasets were translated to proteins by TransDecoder (with default settings; https://github. com/TransDecoder/TransDecoder) and subsequently decontaminated by homology search using LAST  (Kiełbasa et al. , 2011)  against the in-house database and removing sequences homologous to eubacterial, archaeal, or metazoan sequences with evalue of 1E-10 or lower.
Plastid signal prediction SignalP 5. 0  (Almagro Armenteros et al. , 2019)  was used for prediction of N-terminal signal peptides as it was determined the version providing the most specific plastid prediction during preliminary optimization. Both original sequences and sequences cut to the first methionine were analysed to accommodate for potential translated UTRs or spliced leaders, and the results were pooled afterwards prioritizing the positive signal peptide prediction in case of different results. The obtained SignalP results were converted into a tabular format compatible with SignalP versions 3. 0-4. 1 for downstream applications with a custom script (SP5toSP4. py). For the second step predicting the rest of the bipartite targeting signal, a modified version of ASAFind  (Gruber et al.
, 2015; Füssy et al. , 2019)  was used with a custom scoring matrix (modASAFind. py, Table  S9 ) that reflects the differences in sequence surrounding the signal peptide cleavage site of kareniaceans (for instance, instead of the phenylalanine motif, multiple arginine residues are typically present). The matrix was prepared based on sequence logos prepared by Seq2Logo  (Thomsen and Nielsen, 2012 ; Figure  S10 ) from model datasets of Karenia brevis and Karlodinium micrum proteins determined as having exclusively plastidial, mitochondrial, nuclear, or endomembrane functions, e. g. , photosynthetic core machinery proteins for plastidial functions, and identified from reciprocal BLAST against well-curated proteomic datasets from other organisms  (Butterfield et al. , 2013; Klinger et al. , 2013; Dorrell et al.
, 2017; Bannerman et al. , 2018; Beauchemin and Morse, 2018) , the queries and retrieved model sequences are available in Tables  S10-11. The modified script and custom scoring matrix were subsequently tested and optimized for sensitivity and specificity, and in the final version represented an improvement of 56. 4 and 0. 5 percentage points, respectively over unmodified ASAFind. The detection thresholds used with SignalP were also tested, with no apparent improvement in specificity/sensitivity tradeoffs over the default settings; as well as prediction by HECTAR and combination of PrediSI and ChloroP  (Gschloessl et al. , 2008; Dorrell et al. , 2019; Ebenezer et al. , 2019; Figure S11-12, Tables   S12-16 ). Additionally, we employed an alternative prediction, previously tested on the unrelated eukaryote Euglena gracilis combining PrediSI  (Hiller et al.
, 2004)  and ChloroP  (Emanuelsson et al. , 1999)  and proteins judged negative by modified ASAFind but positive by this approach were included in the predicted plastid proteomes as well albeit with prefix "SPTP-" denoting the presence of an alternative signal. The presence of large numbers of highly similar sequences caused by very recent paralog duplication and/or alternative splicing is common in dinoflagellates. To reduce this complexity for phylogenetic analyses, all datasets were additionally clustered by cd-hit with an identity threshold of 0. 9. This was performed after signal prediction to avoid accidentally discarding an isoform/paralog with predictable targeting signal in favor of one without it.
Phylogenetic analysis Each of the in silico plastid proteome datasets was processed independently in order not to omit any variant signals in individual proteins. First, homologs were mined by LAST  (Kiełbasa et al. , 2011)  search with threshold e-value 1E-10 against the in-house database plus all the fucoxanthin dinoflagellate transcriptomes. The best hit from all organisms in the in-house dataset were retained alongside the best five hits from each other fucoxanthin dinoflagellate library to account for potential paralogs in fucoxanthin dinoflagellate transcriptomes. Proteins with no hits whatsoever or with no homologs in any taxa other than dinoflagellates (Kareniaceae or other) were annotated as "lineage-specific" or "dinoflagellate-specific" respectively and their phylogeny was not investigated further.
The remaining homolog sets were aligned by  MAFFT (version 7. 310, Mar 17 2017; Katoh and Standley, 2013)  with default settings and trimmed by  TrimAl (version 1. 4, Dec 17 2013; Capella-Gutierrez et al. , 2009)  withgappyout parameter. The trimmed alignments were then filtered by a custom python script that discarded sequences comprising of more than 75% gaps and then rejected alignments shorter than 100 positions or containing less than 10 taxa. The satisfactory alignments were then used for maximum-likelihood tree construction by IQ-TREE (multicore version 2. 0. 5, May 15 2020;  Minh et al. , 2020)  with automatic model selection and 1000 ultrafast bootstraps. For graphical summary of this pipeline, see Figure  S2. All trees are available at https://figshare. com/articles/dataset/all-automatically-built-trees_tar/21647768; in-house scripts used in the pipeline are available among supplementary files (scripts.
tar); as well as the final predicted plastid protein datasets with their phylogenetic annotations (phylo-annotated_proteins_sets. tar). TreeSorter, a freeware program written in Python (https://github. com/vanclcode/treesorter/) builds abstract tree structure from a tree file in nexus format, takes arguments defining sets of taxa and criteria based on those sets that determine what constitutes a valid subtree. Each tree is traversed and the highest bootstrap value of an edge (bipartition) that produces a subtree containing the taxon of interest and matching the defined criteria is reported. Criteria allow for further quantification of taxon sets, such that minimum and/or maximum number or proportion of taxa from a particular set per subtree. TreeSorter was used for large-scale analysis of the tree topologies and sorting and annotating the seed plastid proteins by their probable evolutionary origin into custom categories: haptophyte (plastid-late), dinoflagellate (plastidearly), alveolate (non-plastid), green (i.
Bootstrap scores of 75 were generally considered as the lower threshold in interpreting the evolutionary origins, however under specific circumstances (e. g. when the same protein was assigned the same origin with score ≥75 in other studied species) lower scoring results are reported as well. The in silico plastid-targeted proteomes were automatically annotated by KAAS and major metabolic pathways were reconstructed, either using KEGG Mapper (https://www. genome. jp/kegg/mapper/reconstruct. html), or manually by targeted HMMER search for missing enzymes or enzymes without representation in KEGG database and/ or based on their conserved domains as detected by PfamScan  (Finn et al. , 2014).
Phylogenomic analysis A concatenated phylogenetic matrix for plastid proteins was prepared using 22 genes that passed our criteria of a) tree-based evidence of plastid-late origins, b) occurrence in at least 5 of the 7 organisms, and c) manually verified functional annotations and plastid-associated functions: 1-acyl-sn-glycerol-3phosphate acyltransferase, 3-hydroxyacyl-ACP dehydratase, 3-oxoacyl-ACP synthase, ACP, cysteinyl-tRNA synthetase, digalactosyldiacylglycerol synthase, glutaminyl-tRNA synthetase, haem oxygenase, chlorophyll a synthase, chlorophyllide b reductase, lycopene beta cyclase, magnesium chelatase subunit H, magnesium-protoporphyrin O-methyltransferase, protochlorophyllide reductase, PsbO, PsbP, PsbU, RP-L1, RP-L13, SecA, SufC, and SufD. Preliminary single-gene trees were manually checked to remove paralogs, very long branches, and likely cases of LGT, if present; these trees with the removed taxa highlighted, as well as the original and trimmed datasets, and final matrix are available as supplementary files (plastid_protein_concatenation_files.
Signal peptide analysis Four experimental datasets for this analysis consisted of two sets of plastid-targeted plastid-early (264) and plastid-late (437) proteins with assigned KEGG annotation with uniquely plastidial function and two analogous sets selected from MMETSP transcriptomes for five peridinin dinoflagellates (358) and five haptophytes (272) (details in Table  S17 ) based on presence of a) detectable signal peptide and b) the same KEGG annotations to produce comparable datasets without functional bias. Signal peptide regions of these proteins were determined by SignalP 5. 0 (the region of approximately 25 positions immediately following the predicted cleavage site was considered as putative partial transit peptide region). The regions were aligned by ClustalW, manually curated to keep their conserved part and used to create sequence logos for each dataset using WebLogo (https://weblogo.
Meta-barcoding analysis of Kareniaceae distributions To explore the distribution of the three Kareniaceae genera, the V9 18S rDNA metabarcoding data from Tara Oceans corresponding to the genera Karenia, Karlodinium and Takayama were extracted from  Ibarbalz et al. , 2019  (available at https://zenodo. org/record/3768510#. Ye-xqpHMJzp). Overall, a total of 69 barcodes were extracted for Karenia, 76 for Karlodinium and 131 for Takayama. Their respective distributions (at least equal to three reads) in the eukaryotic-enriched size fractions  (Pierella Karlusich et al. , 2019) (0. 8-5 µm; 5-20 µm; 20-180 µm and 180-2000 µm)  and two depths (surface and deepchlorophyll maximum) were pooled together and plotted using the R package 'mapdata' v2. 3. 0 (http://cran. nexr. com/web/packages/mapdata/index.
html). The occupancy was estimated as the percentage of stations in which their respective barcodes were identified. A correlation analysis was conducted on the pooled Kareniaceae size fractions and depths, including a total of 75 stations in which at least one of the organisms was retrieved, with the R package 'corrplot' (https://github. com/taiyun/corrplot; Wei and Simko, 2021). Two final Partial Least Square analyses were implemented with the R package 'plsdepot' (https://github. com/gastonstat/plsdepot) to explore the correlation between: 1) kareniacean abundances and the abundance of the 5 haptophyte taxa Chrysochromulinaceae, Coccolithales, Isochrysidales, Pavlovophyceae and Phaeocystales (V9 18S rDNA metabarcoding data extracted as above); 2) global kareniacean and haptophyte (all haptophyte taxa pooled) abundances compared with a diverse set of environmental variables (median values of nutrient concentrations of iron, nitrate, silicate, phosphate; and oxygen, salinity, temperature, all extracted from the PANGAEA database;  Ardyna et al.
Figure4: Sequence logos for aligned signal peptides of the four datasets of different evolutionary identity: haptophyte, peridinin dinoflagellate, plastid-late and plastid-early kareniacean (A-D), occurences of threeletter sequence motifs in signal peptides of each dataset, represented as a percentage of signal peptides in which at least one such motif occurs (E), and sequence logo of the signal peptide and partial transit peptide for kareniacean plastidial proteins regardless of their phylogenetic origin with red arrow denoting the signal peptide cleavage site (F).
Acknowledgements RGD and ANV acknowledge supporting from a  CNRS Momentum Fellowship  awarded to RGD,  2019-2021 , and an  ANR JCJC  (" PanArctica ",  ANR-21-CE02-0014 ) awarded to RGD,  2021-2022. RGD further acknowledges an  ANR t-ERC  ("ChloroMosaic"), awarded 2022. CB acknowledges funding from the  European Research Council (ERC)  under the European Union's  Horizon 2020 research and innovation programme (Diatomic ; grant agreement No. 835067 ), the  French Government 'Investissements d'Avenir' programmes MEMO LIFE  ( ANR-10-LABX-54 ) and  PSL* Research University  ( ANR-1253 11-IDEX-0001-02 ), and from the  Agence Nationale de la Recherche (BrownCut ; grant agreement  ANR-19-CE20-0020 ). The authors thank  Elisabeth Hehenberger  for sharing translated and decontaminated RSD data and consultation regarding their analysis,  Lukáš Novák  for consultation regarding phylogenomics, and  Emmanuel Alastra  for aid in media substrate preparation.