dataesr/scientific-paragraphs-categorization huggingface dataset:
, 2016] ). No such bias was observed in any other CASH lineage, in which invariably a significantly smaller proportion of proteins were found to resolve with hypogyristean lineages (chi-squared p<0. 01; Figure  8 -figure supplement 1; Table  S4 -sheet 5  [Dorrell et al. , 2016] ). We additionally explored whether there might be unique synapomorphies shared between one ochrophyte lineage and the haptophytes. We found 53 ASAFind-generated HPPGs that contained a majority (!2/3) of the haptophyte sub-categories and contained at least one member of the hypogyristea, but contained no other ochrophyte orthologues (Figure  8 , panel B; Table  S2 -sheet 2, section 3  [Dorrell et al. , 2016] ). This was significantly more than would be expected (28. 3, chi-squared p=0. 00013) through a random assortment of all HPPGs that were uniquely shared between haptophytes and one ochrophyte lineage, corrected for the relative size of each dataset (Materials and methods).
DOI: 10. 7554/eLife. 23717. 035 The following figure supplements are available for figure  7 :  proteins with haptophyte BLAST top hits (48/86 sequences total) had next best BLAST hits against diatoms (Figure  8 -figure supplement 4, panel B). We additionally tabulated the earliest and latest possible origin points of amino acid residues that were uniquely shared between haptophytes and some but not all ochrophyte lineages, from a dataset of 37 HPPGs for which there was a clear evolutionary affinity between haptophytes and ochrophytes and strict subsequent vertical inheritance (   S6 -sheets 3, 4  [Dorrell et al. , 2016] ). A greater number of the uniquely shared residues were conserved between the haptophytes and the common ancestor of hypogyristea and diatoms, than were specifically only shared with pelagophyte and dictyochophyte sequences, both per the latest possible origin (139 residues shared with hypogyristea and diatoms; 99 residues with pelagophytes and dictyochophytes; Figure  8 , panel D; Table  S7 -sheets 2, 3  [Dorrell et al.
, 2016] ) and per the earliest possible origin (46 residues shared with hypogyristea and diatoms; 41 residues with pelagophytes and dictyochophytes; Figure  8 -figure supplement 5; Table  S7 -sheets 2, 3  [Dorrell et al. , 2016] ). This specifically supports a transfer of plastid-targeted proteins from an ancestor of the pelagophyte/dictyochophyte clade into the haptophytes, rather than the other way around. Finally, we tested whether these proteins were likely to have been acquired through an endosymbiotic event. We reasoned that the genes acquired by haptophytes through endosymbiotic events should encode a greater proportion of plastid-targeted proteins than would be observed with genes of alternative origin. We accordingly constructed a dataset of 12,728 non-redundant gene families that were broadly distributed across the haptophytes (Table  S14 -sheet 1  [Dorrell et al.
A similar graph, showing the earliest possible inferred origin of each uniquely shared residue, is shown in Figure  8 -figure supplement 4. (Panel E) shows the number of the 12728 conserved gene families inferred to have been present in the last common haptophyte ancestor that are predicted by ASAFind to encode proteins targeted to the plastid, subdivided by probable evolutionary origin, and the number expected to be present in each category assuming a random distribution of plastid-targeted proteins across the entire dataset, independent of evolutionary origin. Evolutionary categories of proteins found to be significantly more likely (chi-squared test, p=0. 05) to encode plastid-targeted proteins than would be expected by random distribution are labelled with black arrows. The evolutionary origins of the ancestral gene families are shown in an endosymbiotic origin.
We additionally generated and inspected single-gene tree topologies for each of the constituent genes used to generate each concatenated multigene alignment, and could not find any that confidently resolved a sister-group relationship between haptophytes and the pelagophyte/dictyochophyte clade (Figure  9 -figure supplement 3; Table  S15 -sheet 3  [Dorrell et al. , 2016] ). Finally, we found only three residues in the alignment that were uniquely shared among all four haptophytes and the sole representative of pelagophytes and dictyochophytes (Aureococcus) in the gene-rich dataset, and no residues that were shared between a majority of the haptophytes and at least one pelagophyte or dictyochophyte sequence in the taxon-rich dataset (Figure  8 , panel C; Table  S17 -sheet 4  [Dorrell et al.
Discussion In this study, we have reconstructed an experimentally verified dataset of 770 plastid-targeted proteins that were present in the last common ancestor of all ochrophytes (Figures  1  and 2 ). Our dataset accordingly provides windows into the evolutionary origins of the ochrophyte plastid lineage. These include evidence for a green algal contribution to ochrophyte plastid evolution and a late acquisition of the ochrophyte plastid following divergence of the ochrophyte lineage from oomycetes (Figures  3  and 4 ). Although each of these findings have been previously suggested by studies of whole stramenopile genomes  (Moustafa et al. , 2009; Stiller et al. , 2009)  our data represent to our knowledge the first large-scale verification from studies of plastid targeted proteins for both of these important events in the origins of the ochrophyte plastid.
The relatively late origin of the ochrophyte plastid is particularly interesting as molecular divergence estimates place the ochrophytes as diverging from the oomycetes no more than 90 million years prior to the radiation of ochrophyte lineages  (Brown and Sorhannus, 2010; Matari and Blair, 2014). Assuming that these estimates are reliable, our dataset represents some of the earliest proteins to support the ochrophyte plastid following its endosymbiotic uptake. We also provide evidence for widespread mixing of proteins of different evolutionary origin in the ancestral ochrophyte plastid (Figure  5 ), including evidence for the formation of new fusion proteins through the recombination of domains of different evolutionary origins (Figure  6 ), and a bidirectional interaction between proteins derived from the endosymbiont with proteins from host organelles via dual-targeting (Figure  7 ).
A schematic outline of these results is shown in Figure  10. Many questions nonetheless remain to be answered. It remains to be determined whether the in silico prediction facilitated by programmes such as ASAFind and HECTAR are sufficient to enable the identification of all ochrophyte plastid proteins  (Gruber et al. , 2015; Gschloessl et al. , 2008). This is particularly pertinent in the context of dual-targeted proteins, insofar as the dataset of 34 potentially ancestrally dual-targeted proteins identified in this study may not include proteins that are dual-targeted to the plastid and other cellular organelles, such as the ER  (Porter et al. , 2015) , cytoplasm  (Pham et al. , 2014) , or nucleus  (Krause et al. , 2012). We note also that, based on the fluorescence patterns observed with the exemplar proteins within this study (Figures  2  and 7 ), ASA-Find and HECTAR may identify proteins targeted to the periplastid compartment, as well as to the plastid stroma.
For this, sequence data from early-diverging members of the cryptomonads and haptophytes will be particularly important  (Yabuki et al. , 2014; Choi et al. , 2017; Kim et al. , 2011). It also remains to be determined whether other CASH lineage plastids, such as the peridinin-type plastids found in most photosynthetic alveolates, originate within the ochrophytes (S ˇevcˇı ´kovaet al. , 2015;  Dorrell and Howe, 2015). Similar plastid proteome reconstructions, using bespoke datasets for these species, will be particularly useful in unravelling their disparate evolutionary origins. Overall, our dataset provides valuable and deep insights into the chimeric origins and complex fates of a major group of eukaryotic algae. Further studies using more sensitive pipelines, or using analogous datasets from other major CASH lineages, may elucidate the evolutionary and physiological diversification of plastids across the eukaryote tree of life.
Next, plastid-targeted protein sequences were sought from all available ochrophyte sequence data. A search database was built from all eleven completed ochrophyte genomes, 147 ochrophyte sequence libraries from the Marine Microeukaryote Transcriptome Sequence Project (MMETSP)  (Keeling et al. , 2014) , eleven further ochrophyte transcriptome sequencing projects  (Matasci et al. , 2014; Mangot et al. , 2017; Kessenich et al. , 2014)  and uniref. Cross-contamination was removed from MMETSP transcriptomes as previously described  (Marron et al. , 2016). Briefly, this procedure compares the nucleotide sequences of contigs assembled from each MMETSP library by pairwise BLAST, and defines a separate cross-contamination threshold for each pair of MMETSP libraries based on their distribution of BLAST percent identities.
e. , were annotated to function in photosynthesis, to constitute integral parts of the plastid thylakoid or inner membranes, or corresponded to the expression products of genes that are plastid-encoded in red algae but have been apparently relocated to the ochrophyte nucleus  [Green, 2011]  or that corresponded to proteins previously verified experimentally to localise to ochrophyte plastids  [Gruber et al. , 2015; Gschloessl et al. , 2008; Huesgen et al. , 2013; Grouneva et al. , 2011[) , and thus should contain relatively fewer examples of mispredicted proteins within the dataset. At the same time, conservation patterns were selected that minimised the number of HPPGs identified as conserved from a negative control dataset (consisting of HPPGs assembled using seed sequences from the published genome sequences of the cryptomonad Guillardia theta  (Curtis et al.
e. , the tree only contained one evolutionarily distinct group for each lineage, which could be unambiguously allocated one category over all others). Each tree annotation was repeated three times independently, and only tree annotations that were recorded consistently in each case were retained for further analysis. To identify plastid-targeted proteins that were uniquely shared between haptophytes and other lineages, every HPPG initially generated was screened for the inclusion of only two of five different lineages (diatoms including dinotoms, hypogyristea, chrysista, haptophytes, and cryptomonads; Table  S2 -sheet 2, section 3; Table  S3 -sheet 2, section 3  [Dorrell et al. , 2016] ). The frequencies of these proteins were then compared to the numbers expected in a random distribution of all uniquely shared HPPGs across the entire dataset: for example, if half of all uniquely shared HPPGs were shared with diatoms and one other lineage, and half were shared with haptophytes and one other lineage, then one-quarter of all uniquely shared HPPGs should be shared between haptophytes and diatoms.
Identification of uniquely shared residues in multigene HPPG datasets To identify residues that are uniquely shared between ochrophytes and other lineages, multigene datasets were constructed of a) ancestral HPPGs of green algal origin, and b) ancestral HPPGs for which haptophytes show origins within the ochrophytes. To minimise the incorporation of sequences of misidentified origin, in each case only the HPPGs for which the proposed evolutionary origin were identified both by BLAST top hit and single-gene tree analysis were included. To avoid introducing artifacts due to lineage-specific gene transfers, paralogy events, or other phylogenetic incongruencies that could otherwise bias the eventual results  (Qiu et al. , 2012; Leigh et al. , 2008) , the singlegene tree generated for each HPPG was manually inspected to exclude any that contain multiple clades (defined as monophyletic groups containing more than one sequence from a particular lineage, separated from one another by at least two sequences from outside that particular lineage) for each of the major lineages of interest within the tree:.
For the green gene dataset, HPPG trees containing more than one clade of ochrophyte, cryptomonad, haptophyte, red algal, or green algal sequences were excluded. To account for the possibility that CASH lineage sequences might originate from within the green algae, the green algae were allowed to be paraphyletic with regard to the cryptomonad, haptophyte and ochrophyte sequences, but were not allowed to incorporate sequences from other lineages. Similarly, to account for the possibility that subsequent gene transfers may have occurred from ochrophytes into other CASH lineages, the ochrophytes were allowed to be paraphyletic with regard to cryptomonad and haptophyte sequences, but not to any other lineages. For the haptophyte gene dataset, HPPG trees containing more than one clade of ochrophyte, haptophyte, diatom, hypogyristean, or chrysistan sequences were excluded.
To account for the possibility that haptophytes arose within the ochrophytes, the ochrophyte, diatom, hypogyristean and chrysistan sequences were allowed to incorporate sequences from haptophytes. Similarly, due to the paraphyly of hypogyristea with regard to diatoms, the hypogyristean sequences were allowed to incorporate sequences from diatoms, but not from other lineages. In all cases, sequences from chlorarachniophytes, euglenids, and alveolates were not incorporated into any of the clade assessments, due to uncertainty over the gene transfer events that have occurred in each lineage  (S ˇevcˇı ´kovaet al. , 2015; Maruyama et al. , 2011; Archibald et al. , 2003). This left datasets consisting of 32 HPPGs for which the ochrophytes were of clear green algal origin, and 37 HPPGs in which the haptophytes were of clear ochrophyte origin, with no conflicting phylogenetic signal.
Next, each of the component sequences within each cleaned alignment were searched against all other component sequences within the same alignment using BLASTp, and the top ten hits within the alignment were ranked. In each case, sequences were only approved for incorporation into the multigene dataset if the first non-self hit was to a different sub-category within the same lineage, e. g. if a query sequence from a red alga yielded a top hit against a red algal sequence from a different red sub-category. To allow for possible cases of paraphyly and/or absence of sequences within each alignment, the following modifications were applied:. Green algal sequences within the confirmed green origin alignments were allowed to yield top hits against ochrophytes, cryptomonads, and haptophytes, but were required to yield a best hit against another green alga with an expect value lower than the top hit against red algal or glaucophyte sequences.
Tabulated outputs for each BLAST analysis are provided in Table  S6 , sheets 2 and 4. Finally, each dataset was reduced to leave only one randomly selected sequence for each given sub-category within each HPPG alignment. The number of residues that were uniquely shared between ochrophytes and green algae in the green gene dataset, and haptophytes and ochrophytes in the haptophyte dataset, were then tabulated (Table  S7   [Dorrell et al. , 2016] ). Briefly, residues were inferred to be uniquely shared between ochrophytes and green algae if they were present in at least 2/3 of the ungapped ochrophyte sequences, one or more green algal sequence, and if none of the red algal or glaucophyte sequences shared the residue in question, but at least one of these sequences had a non-matching (i. e. non-gapped) residue at that position (Table  S7 -sheet 1, section 2  [Dorrell et al.
, 2016] ). Expression dynamics for each ancestral HPPG within the genomes of the model diatoms Phaeodactylum tricornutum and Thalassiosira pseudonana were inferred using microarray data integrated into the DiatomPortal server  (Ashworth et al. , 2016)  (Table  S10 -sheets 1,2  [Dorrell et al. , 2016] ). Correlation coefficients were calculated between each pair of P. tricornutum and T. pseudonana genes that were incorporated into an ancestral HPPG, across all microarray libraries within the dataset (Table  S10 -sheets 3,4  [Dorrell et al. , 2016] ), with average values being calculated from all pairwise correlations for different evolutionary categories of protein (Table  S10 -sheet 5  [Dorrell et al. , 2016] ). Possible chimeric proteins, resulting from the fusion of proteins of different evolutionary origins, were identified in the dataset using a modified version of a previously published protocol (Meheust et al.
, 2016) (Table  S9 -sheet 1, sections 4,5; Table  S11   [Dorrell et al. , 2016] ). Each protein within each HPPG was searched using BLASTp against the composite outgroup MMETSPenriched library, using the same taxonomic classification used for the identification of the evolutionary origin of each seed protein within the dataset, and all hits with an expect value of 1 Â 10 À05. Component sequences were then grouped into component families according to the following rule: if two component sequences overlapped by more than 70% of their lengths on the protein composite, they belonged to the same component family. Overlapping and/ or nested component families were additionally merged if one family was included by more than 70% of its length into the other one. Component families were then assigned a broad evolutionary origin corresponding to their taxonomic composition.
If the three best component sequences, according to their BLAST bitscore against the composite gene, matched with the same lineage (e. g. , green algae, red algae, aplastidic stramenopiles, or other eukaryotes), the component was considered to have originated from that lineage. Possible dual-targeted proteins were identified within the dataset by screening all possible plastid-targeted proteins with Mitofates, using a cut-off targeting threshold of 0. 35  (Fukasawa et al. , 2015) , which was inferred to be more effective in identifying experimentally verified ochrophyte mitochondria-targeted proteins (Figure  7 -figure supplement 2)  (Gruber et al. , 2015)  than other threshold values or targeting prediction programmes such as TargetP  (Emanuelsson et al. , 2007)  or Mitoprot  (Claros, 1995).
Tree outputs have been made publically accessible through the University of Cambridge dSpace server (https://www. repository. cam. ac. uk/handle/1810/261421)  (Dorrell et al. , 2016). Modified alignments were generated for both of the plastid concatenated multigene datasets from which individual clades of organisms (diatoms, hypogyristea, chrysista, haptophytes, cryptomonads, red algae, and different combinations of green algae) had been removed (Table  S15 -sheet 2  [Dorrell et al. , 2016] ). Fast-site removal was performed using TIGER  (Cummins and McInerney, 2011). Site rate evolution characteristics were calculated for each alignment using the -b 100 option, and modified alignments were constructed from which the rate categories corresponding to the fastest evolving 40-50% of sites were serially removed (Table  S15 -sheet 2  [Dorrell et al.
(Panel C) shows the complete HPPG assembly, alignment and phylogenetic pathway used to identify conserved plastidtargeted proteins. (Panel D) tabulates the number of HPPGs built using ASAFind and HECTAR predictions, and the number of non-redundant HPPGs identified in the final dataset. The final total represents the pooled total of non-redundant HPPGs identified with both ASAFind and HECTAR. DOI: 10. 7554/eLife. 23717. 003The following figure supplement is available for figure1:.
Finally, we considered whether the green genes that function in ochrophyte plastids were more likely to have been acquired through endosymbiosis, or through lateral gene transfers, for example from a food organism (Keeling and Palmer, 2008; Doolittle, 1998)  or other intracellular symbiont(Dorrell and Howe, 2012a). We reasoned that if the green genes in ochrophytes were predominantly of endosymbiotic origin, they should encode more plastid-targeted proteins than genes of alternative origin, in the same manner as genes of cyanobacterial origin retained in archaeplastid genomes are biased towards encoding proteins with plastid functions (Dorrell and Howe, 2015). We accordingly constructed a secondary dataset, consisting of 7140 non-redundant gene families that are broadly distributed across the ochrophytes, and tested the targeting preferences of proteins from each HPPG (Figure4, panel E; Figure4-figure supplement 5; TableS8-sheet 1 [Dorrell et al.
, 2016] ). 871 gene families resolved with the green algae per BLAST top hit analysis (Figure4-figure supplement 5; TableS8-sheet 2 [Dorrell et al. , 2016] ). Using both ASAFind (Gruber et al. , 2015)  and HECTAR (Gschloessl et al. , 2008) , gene families of predicted green algal origin were significantly more likely to encode proteins with plastid-targeting predictions than the dataset as a whole (chi-squared, p<1E À03 ; Figure4, panel E; Figure4-figure supplement 5; TableS8-sheet 3 [Dorrell et al. , 2016) ]. We also observed a similar, though stronger, bias towards.
(Panel D) lists the number of residues inferred from a dataset of 32 ochrophyte HPPGs of verified green origin, which have been subsequently entirely vertically inherited in all major photosynthetic eukaryotic lineages, to be uniquely shared between ochrophytes and some but not all green lineages, hence might represent specific synapomorphic residues. Residues are categorized by inferred origin point within the tree topology shown in panel A, i. e. , each of the five ancestral nodes labelled. A final category shows all of the residues inferred to be specifically shared with one green sub-category, and not with any other. The distribution of residues based on the earliest possible origin point (taking into account gapped and missing residues in each HPPG alignment) is shown in Figure4-figure supplement 4.
(Panel E) shows the number of the 7140 conserved gene families inferred to have been present in the last common ochrophyte ancestor that are predicted by ASAFind to encode proteins targeted to the plastid, subdivided by probable evolutionary origin, and the number expected to be present in each category assuming a random distribution of plastid-targeted proteins across the entire dataset, independent of evolutionary origin. Evolutionary categories of proteins found to be significantly more likely (chi-squared test, p=0. 05) to encode plastid-targeted proteins than would be expected are labelled with black arrows. An equivalent distribution of plastid-targeted proteins inferred using HECTAR is shown in Figure 4-figure supplement 5. DOI: 10. 7554/eLife. 23717. 015 The following figure supplements are available for figure 4:.
Overviews of the broader KOG classes that are enriched either in the ancestral HPPG dataset, or in specific evolutionary categories of ancestral HPPG, are shown in Figure 5-figure supplement 7. (Panel C) tabulates the number of ancestral HPPGs performing consecutive metabolic functions, or that are likely to have direct regulatory interactions, alongside the number of these protein pairs in which both members are of verified evolutionary origin; the number observed where both members possess the same evolutionary origin; the expected number of protein pairs where both members possess the same evolutionary origin; and the chi-squared probability of similarity between the observed and expected values. (Panel D) shows heatmaps for the pairwise correlation coefficients of expression for genes encoding different evolutionary categories, as verified using combined BLAST top hit and single-gene tree analysis, of ancestral HPPGs in the model diatoms Phaeodactylum tricornutum (i) and Thalassiosira pseudonana (ii).