The evolutionary history of haptophytes and cryptophytes: phylogenomic evidence for separate origins

Abstract

An important missing piece in the puzzle of how plastids spread across the eukaryotic tree of life is a robust evolutionary framework for the host lineages. Four assemblages are known to harbour plastids derived from red algae and, according to the controversial chromalveolate hypothesis, these all share a common ancestry. Phylogenomic analyses have consistently shown that stramenopiles and alveolates are closely related, but haptophytes and cryptophytes remain contentious; they have been proposed to branch together with several heterotrophic groups in the newly erected Hacrobia. Here, we tested this question by producing a large expressed sequence tag dataset for the katablepharid Roombia truncata, one of the last hacrobian lineages for which genome-level data are unavailable, and combined this dataset with the recently completed genome of the cryptophyte Guillardia theta to build an alignment composed of 258 genes. Our analyses strongly support haptophytes as sister to the SAR group, possibly together with telonemids and centrohelids. We also confirmed the common origin of katablepharids and cryptophytes, but these lineages were not related to other hacrobians; instead, they branch with plants. Our study resolves the evolutionary position of haptophytes, an ecologically critical component of the oceans, and proposes a new hypothesis for the origin of cryptophytes.

1. Introduction

Eukaryotes first acquired photosynthesis through endosymbiosis, where a heterotrophic cell engulfed and retained a photosynthetic prokaryote related to modern-day Cyanobacteria, ultimately integrating it to form the highly specialized plastid organelles we see today [1–3]. This crucial event in eukaryote evolution is generally seen as unique: primary plastids probably evolved only once in the common ancestor of glaucophytes, red algae and green plants (green algae + land plants), together making the Plantae supergroup [4] (but see [5]). A much more recent case of cyanobacterium to eukaryote endosymbiosis has been reported in the rhizarian Paulinella chromatophora [6], but this event appears to have had less impact on the diversification of plastids. Photosynthesis spread further to other eukaryotic lineages by means of secondary endosymbioses, when other eukaryotes subsequently engulfed green or red algae, and, in dinoflagellates, tertiary endosymbioses [7]. On the green side, two independent cases of secondary endosymbioses are known, leading to chlorarachniophyte and euglenid algae, respectively [8]. On the red side, the situation is much more contentious.

The chromalveolate hypothesis has been regarded as a likely evolutionary framework for explaining the origin and distribution of red secondary plastids [9,10]. It posits that a single secondary endosymbiosis with a red alga gave rise to plastids in stramenopiles (or heterokonts), alveolates, haptophytes and cryptophytes, altogether forming the Chromalveolata supergroup [11]. This hypothesis is based on the fact that complex events are necessary to establish a plastid, so it is more parsimonious to limit the number of plastid origins, regardless of the number of plastid losses this implies [12]. Thus far, plastid data have generally supported the monophyly of some or all of the chromalveolate lineages where plastids are known. Molecular evidence for this includes multi-gene phylogenies [13,14], shared evolutionary history of several nucleus-encoded plastid-targeted genes [15–18], and a rare lateral gene transfer in the plastids of haptophytes and cryptophytes [19].

The chromalveolate hypothesis also predicts that the host nuclear lineages are monophyletic; so far, however, this has proven impossible to verify despite the use of substantial alignments (in the range of 30 000 amino acids). Nuclear-based phylogenomics have consistently shown that stramenopiles and alveolates are closely related, and that they form a strongly supported group with Rhizaria, altogether making the so-called SAR group [20,21]. At the same time, haptophytes and cryptophytes generally appeared together, albeit with less support and only when relatively large alignments are used [21–24]. Based on congruent plastid and nuclear data, these were proposed to be a second chromalveolate lineage, Hacrobia [25]. Other lineages that were not originally included in the chromalveolate hypothesis have since been suggested to be members of Hacrobia (namely telonemids, centrohelids, katablepharids, picobiliphytes, Palpitomonas and rappemonads), but the support for these is variable, and typically few data are available, from only a single representative of these lineages [22,25–32].

The large variations in the phylogenetic signal between plastid and nuclear data have recently been formalized in a ‘phylogenomic falsification’ of the chromalveolate hypothesis, which concluded that red algal plastids were acquired separately in different lineages [18,33]. Several alternative scenarios to the chromalveolate hypothesis have also been formulated, all attempting to explain the data by suggesting that plastids in ‘chromalveolate’ lineages originated through a single secondary endosymbiosis within a subgroup of chromalveolates, and then spread to other subgroups by multiple tertiary endosymbioses [34–36]. In these complex alternatives, haptophytes and cryptophytes emerged as key players in early plastid dissemination. Cryptophytes are also peculiar in that they are the only known lineage that still harbours the red algal endosymbiont nucleus (the nucleomorph), making them of pivotal significance to study endosymbiosis [37]. In addition, haptophytes include some of the most successful marine primary producers, which have a profound impact on global biogeochemical equilibria [38]. Despite the ongoing interest in these organisms, their phylogenetic position remains among the most uncertain of any eukaryote.

Here, we have addressed this problem by deep-sequencing a cDNA library for the last hacrobian taxon for which no genome-wide sequence data are available, the katablepharid Roombia truncata, and at the same time considerably extended the length of previously published alignments by taking advantage of newly sequenced genomes (in particular that of the cryptophyte Guillardia theta). We assembled a supermatrix to test the hacrobian question, consisting of 258 genes and 68 carefully selected operational taxonomic units (OTUs), and used it to assess the monophyly of Hacrobia and the relationships of its constituent lineages to other eukaryotes.

2. Material and methods

Roombia truncata culturing and cDNA construction, sequencing and contig assembling are described in the electronic supplementary material.

(a). Sequence alignment construction

With the presence of G. theta mandatory in each single gene, a two-step strategy was adopted to maximize the number of genes entering the final concatenated alignment (supermatrix). First, we used a dataset of 162 genes described by Burki et al. [39] and added six important newly available taxa: one katablepharid (R. truncata, this study), one cryptophyte (Rhodomonas salina, this study), one picobiliphyte (cell MS584-11) and three red algae (Calliarthron tuberculosum, Porphyridium cruentum and Eucheuma denticulatum). We also reduced the amount of missing data for G. theta as well as for the rhizarian Bigelowiella natans by using the complete nuclear genome information for these two organisms, both recently sequenced by the US Department of Energy Joint Genome Institute. Second, we used the 24 480 predicted proteins (filtered model v. 1.0) of G. theta to find additional genes suitable for phylogenomics to extend our supermatrix. In order to enrich the pool of possible new genes with those containing key taxa, we applied the constraint that both G. theta and R. truncata be present in each gene, in addition to a selection of complete genomes and expressed sequence tag (EST) datasets corresponding to the taxon sampling in figure 1. This approach led to 224 potential new genes, but only 96 remained after the quality validation (i.e. did not show any evidence for deep paralogy, lateral gene transfer or extensive lineage sorting). Overall, the dataset presented here is composed of 258 genes. See the electronic supplementary material for a detailed procedure of the alignment construction, and tables presenting the missing data information and descriptions of all genes (electronic supplementary material, table S1 and S4, respectively).

Figure 1.

Phylogeny inferred with the CAT + Γ4 model, based on the most complete taxa sampling. Support values are indicated for relevant nodes or when not maximal (PP/CAT-BP). No value at a node means support equal to 1.0 PP/100% CAT-BP. Dots mark OTUs with complete genome data available. Numbers after the OTU names are the sequence lengths. Scale bar, substitutions per position.

(b). Phylogenetic analyses

The fit on the data of two evolutionary models—the site-homogeneous LG model and the site-heterogeneous mixture CAT model—was evaluated by cross-validation (CV) as implemented in PhyloBayes v. 3.3b [40]. A learning set and a test set were generated by randomly splitting (without replacement) the original alignment into 10 replicates made of 90 per cent (50 293 amino acids) and 10 per cent (5588 amino acids) of the original sites, respectively. For each replicate, a Markov chain Monte Carlo run was then performed for a total of 5000 cycles (CAT) or 1100 cycles (LG), the lower number of cycles under LG being due to a much greater computational time per cycle. The first 1000 and 100 points were discarded as burn-in for the CAT and LG runs, respectively, and the remaining points used to compute the cross-validation log-likelihood. Bayesian inferences using the best tested model (CAT) in combination with four gamma categories for handling the rate heterogeneity across sites (Γ4) were performed in PhyloBayes v. 3.3b for all datasets. In each analysis, two independent chains were run for a minimum of 5000 cycles or until convergence of the chains was reached, removing the first 1000 cycles as burn-in and calculating the posterior consensus on the remaining trees. The Dayhoff 6 classes was used for recoding the amino acids, and analysed as above. Convergence between the chains was ascertained by examining the difference in frequency for all their bipartitions (less than 0.15 in all analyses). Bootstrap CAT proportions were computed with 100 pseudo-replicates generated with Seqboot from the Phylip v. 3.69 package [41], and run for 5000 cycles under the CAT + Γ4 model with a burn-in of 1000 cycles. For each replicate, trees were collected after the burn-in period and the resulting 100 consensus trees fed to Consense (Phylip package) to calculate the bootstrap support. Because of the extreme computational burden associated with this analysis, only two datasets could be tested with CAT bootstrap (corresponding to figures 1 and 2). The less adequate model (LG) was also evaluated for aln68 in a maximum likelihood (ML) inference using RAxML v. 7.2.8 [42]. The best ML tree was determined with the PROTGAMMA + F implementation in multiple inferences using eight randomized parsimony starting trees. Statistical support was evaluated with 100 bootstrap replicates. Fast-evolving sites were identified using the slow–fast method [43] as implemented in Slowfaster [44]. The eight most trimmed alignments were analysed with RAxML under the LG + PROTCAT + F model and 100 bootstrap replicates. The amino acid composition was visualized by assembling a 20 × 64 matrix of the frequency of each amino acids per species, and represented as a two-dimensional plot in a principal component analysis (PCA) with the R package.

Figure 2.

Phylogeny inferred with the CAT + Γ4 model, with telonemids, centrohelids and picobiliphytes removed. Support values are indicated for relevant nodes or when not maximal (PP/CAT-BP). No value at a node means support equal to 1.0 PP/100% CAT-BP. Numbers after the OTU names are the sequence lengths. Scale bar, substitutions per position.

3. Results and discussion

(a). EST sequencing of Roombia truncata and assembly of a large dataset

Katablepharids are the closest known relatives to cryptophytes [30], and one of the last hacrobian groups from which no genome-wide data are available. Katablepharid phylogenomic data are important to aid in the detection of hidden multiple substitutions that could have occurred along the branch leading to cryptophytes, a common source of non-phylogenetic signal, as well as the detection of endosymbiont-derived genes, since no plastid has been reported in katablepharids. Accordingly, we developed the first genome-wide survey of the katablepharid lineage, a transcriptome of the deep-branching R. truncata [25]. Using a fractionation method to remove this predator from its diatom prey (Navicula sp.), we isolated highly enriched R. truncata RNA and carried out cDNA sequencing using both 454 and Illumina (see the electronic supplementary material). To assess the purity of this dataset with respect to potential diatom contamination, the R. truncata transcriptome was examined by BLAST comparison against GenBank and searching for diatom-derived sequences. This analysis revealed only 14 contigs with best hits against a diatom homologue (E-value < 1e–20), specifically the centric diatom Thalassiosira pseudonana and the pennate diatom Phaeodactylum tricornudum, but none of them could be clearly assigned to be a diatom sequence.

The transcriptome was also searched for evidence of a cryptic plastid or genes derived from a lost photosynthetic endosymbiont in three ways: (i) we specifically looked by BLAST for genes related to plastid pathways known from other non-photosynthetic chromalveolates (e.g. isoprenoid, fatty acid and heme biosynthesis [45,46]); (ii) we scanned the BLAST output against Genbank for candidate plastid-derived genes by closest sequence similarity to plant, algal and/or cyanobacterial genes; and (iii) we examined the position of R. truncata in our single-gene trees that could be indicative of a red algal ancestry (see §2), as the close relationship between katablepharids and cryptophytes predicts that endosymbiotic gene transfers would be from red algae if their ancestor was plastid-bearing. Interestingly, not a single gene that could be unambiguously attributed to a cryptic plastid or derived from an ancestral plastid was identified. Altogether, the R. truncata transcriptome contained no clear evidence for an ancestral endosymbiont.

To infer a global phylogeny for eukaryotes including this new data, we developed an alignment that is based on more than twice the number of genes than the largest published phylogenomic dataset previously used to investigate similar questions [22]. Importantly, the complete genome of G. theta allowed us to concomitantly reduce the missing data for cryptophytes, resulting in an alignment characterized by 0 per cent and 14 per cent missing genes for G. theta and R. truncata, respectively, out of a total of 258 genes (55 881 amino acids). Our taxon sampling also encompasses all known other ‘hacrobian’ lineages: haptophytes, telonemids, centrohelids and the recently available picobiliphytes [22,47]. Red algae, which are another pivotal lineage for investigating plastid evolution, were also heavily sampled, with eight species included (notably the deeply diverging mesophylic P. cruentum). Finally, we used the newly sequenced genome of the rhizarian B. natans to obtain for the first time a pan-eukaryotic concatenated alignment of hundreds of genes that features at least one species with complete genome data in each major group, with the exception of glaucophytes (figure 1). This is important because each major eukaryotic group is now anchored around one or several taxa with few missing data, which increases the global phylogenetic signal contained in our alignment (see electronic supplementary material, table S1 for a detailed report on the proportion of missing data).

(b). Phylogenetic relationships of the hacrobian taxa

The complete dataset (68 OTUs, 55 881 amino acids; hereafter denoted aln68) was first analysed by ML with the LG + Γ4 model and 100 bootstrap replicates. The resulting phylogeny recovered most of the major eukaryotic groups with maximal support (electronic supplementary material, figure S1), including opisthokonts, Amoebozoa, excavates and the SAR group of stramenopiles, alveolates and Rhizaria (where alveolates and rhizarians were sisters, possibly due to the fast-evolving OTUs found in these groups). The exception, however, was Hacrobia, which was found to be polyphyletic. Specifically, this tree strongly confirmed the close relationship between katablepharids and cryptophytes with 100 per cent bootstrap (henceforth the KC group), but the KC group branched within Plantae (78% bootstrap), as did another hacrobian taxa, the picobiliphytes. The positions of the three remaining hacrobian taxa were unsupported. The general lack of support for the most ancient nodes in this tree, as opposed to robust artefactual support, can be interpreted as the result of competing signals: the genuine phylogenetic signal is diluted by equivalent non-phylogenetic signals, such as undetected homoplasy, not correctly inferred by the LG model of evolution [48]. In an attempt to counter this mutational saturation, we first conducted a site removal analysis in which the fastest-evolving sites were progressively removed from the original alignment [49]. This approach led to no improvement regarding the position of the hacrobian taxa (see electronic supplementary material, figure S2).

Complex models of evolution that detect multiple substitutions at sites with better accuracy than classical models such as LG have been shown to be more powerful for investigating difficult phylogenetic questions [50]. In fact, a CV test showed that the site-heterogeneous CAT + Γ4 model fitted our alignment significantly better than the site-homogeneous LG + Γ4 model used in the ML reconstruction, with a scored average over 10 replicates of 6215 + 145, and, accordingly, Bayesian inferences with the CAT + Γ4 model were used in all subsequent analyses of our data. When applied to the same alignment (aln68), the CAT + Γ4 model provided a similar picture overall, but with several critical differences (figure 1). The KC group remained sister to the Plantae and picobiliphytes supported by 1.0 PP and 57 per cent CAT-BP, with picobiliphytes again closer to Plantae with affinities to glaucophytes (0.92 PP; 50% CAT-BP). The remaining hacrobians—haptophytes, centrohelids and telonemids—branched with the SAR group (1.0 PP; 51% CAT-BP), this time with telonemids possibly the closest lineage to SAR. It is also worth noting that, within SAR, the CAT + Γ4 model recovered alveolates and stramenopiles together with maximal support. This difference compared with the LG + Γ4 model is probably due to better prediction of homoplasic positions that have accumulated on the fast-evolving alveolates and rhizarians.

(c). How robust is this new topology?

The case for a monophyletic Hacrobia has mostly rested on the photosynthetic haptophytes and cryptophytes [25], so to test their possible polyphyly suggested in figure 1 we removed taxa in several permutations. The rationale behind this is twofold: removing unstable or deviating taxa might help to identify artefactual groupings and at the same time improve the resolution across the tree in general. We first removed the three heterotrophic hacrobian lineages (telonemids, centrohelids and picobiliphytes) that were represented by a single member in our dataset and failed to show clear evolutionary affinities in figure 1. In this analysis (figure 2), the KC group remained closely related to plants but the CAT bootstrap for this relationship increased to 79 per cent. More strikingly, the haptophytes–SAR grouping received near-maximal support (1.0 PP; 98% CAT-BP), which demonstrates the negative effect of the three removed lineages on the general stability of the tree while providing a clear evolutionary framework for haptophytes.

Second, the addition of katablepharids to a phylogenomic dataset and its strong grouping with cryptophytes created the opportunity to test the position of the KC group by alternatively removing one of its members, which is of interest since cryptophytes branched with haptophytes in previous analyses of smaller datasets without katablepharids [22,24]. The removal of R. truncata led to no topological change, suggesting that the polyphyly of hacrobians was not simply due to adding katablepharid data (electronic supplementary material, figure S3). However, the converse did not hold: when cryptophytes (and picobiliphytes) were removed, R. truncata branched specifically with glaucophytes (electronic supplementary material, figure S4, 0.98 PP). One model violation that may be responsible for this grouping is amino acid compositional heterogeneity. To verify that this was not biasing our inference, we measured the compositional deviation of each taxon in a posterior predictive test (electronic supplementary material, table S2 and figure S4). Interestingly, among the taxa that did not significantly deviate from the global empirical frequencies were R. truncata and the glaucophyte Glaucocystis nostochinearum, while the second glaucophyte, Cyanophora paradoxa, was only slightly below the 5 per cent threshold. The amino acid composition of this dataset was also visualized as a two-dimensional plot in a PCA (electronic supplementary material, figure S5), altogether demonstrating that the composition of R. truncata and both glaucophytes are not similar. However, when the amino acids were recoded into six biochemically similar categories and the resulting alignment analysed as above, the R. truncata–glaucophyte association was not recovered (electronic supplementary material, figure S6). Data recoding have been used to weaken possible compositional biases [48], so this analysis suggests that this association may be due in part to compositional heterogeneity undetected in the PCA.

Another characteristic that may account for the grouping of katablepharids and glaucophytes is their relatively slow rate of evolution—in a tree hypothetically rooted between unikonts and bikonts, R. truncata and glaucophytes indeed displayed the shortest branches (electronic supplementary material, figure S4 and table S3). It has been shown that sequences with lower-than-average rates can artificially group together based on shared ancestral positions lost from other taxa, an artefact known as ‘short-branch exclusion’ [51]. Thus, it was important to confirm that this pattern can be overcome when either branch leading to R. truncata or glaucophytes is interrupted by faster-evolving lineages. Reintroducing cryptophytes to this dataset appeared to reduce the attraction between R. truncata and glaucophytes, so that the KC group branched as sister to Plantae (electronic supplementary material, figure S7). Reintroducing picobiliphytes appeared to have the same effect by breaking the short branch leading to glaucophytes, and also showed R. truncata as sister to Plantae (electronic supplementary material, figure S8).

To evaluate whether the relationship between the KC group and Plantae is itself a result of the artefactual attraction between R. truncata and glaucophytes, we tested the consistency of the inferred relationships after the removal of glaucophytes from three datasets: from that shown in figure 1, and two alignments corresponding to figure 2 but with cryptophytes or katablepharids alternately removed. The first dataset met the expectations: the KC group branched with red algae + green plants (and picobiliphytes), distantly related to haptophytes, centrohelids and telonemids (electronic supplementary material, figure S9). Similarly, with only cryptophytes or R. truncata included in the absence of glaucophytes, we recovered fully resolved trees in which either was sister to red algae and green plants, again distantly related from haptophytes (figure 3). This illustrates the general consistency of the dataset throughout the taxa removal experiments, by showing both plastid-lacking (katablepharids) and photosynthetic (cryptophytes) representatives occupying the very same phylogenetic position.

Figure 3.

Phylogenies inferred with the CAT + Γ4 model, based on the dataset shown in figure 2, but with glaucophytes and either cryptophytes (right) or katablepharids (left) removed. Support values are indicated when not maximal (PP). No value at a node means support equal to 1.0 PP. All branches are drawn to scale, except the branches leading to Ciliates and Foraminifera, which were shortened for practical reasons. Scale bar, substitutions per position.

(d). Implication of a polyphyletic Hacrobia on plastid evolution

Overall, two significant conclusions emerge from this work. First, after more than doubling the amount of data, our analyses show that nuclear gene data do not support the Hacrobia hypothesis. Second, the phylogenetic position of a major eukaryotic lineage, haptophytes, is now robustly inferred to be closely related to the SAR group. This is the strongest case, receiving nearly maximal support when the more unstable lineages were removed (figure 2), which is noteworthy because this is the largest and most ecologically significant of all the hacrobian lineages. The relationship between the KC group and the Plantae is unfortunately less solid, yet is highly consistent in all datasets analysed. Importantly, regardless of where this group will finally branch, these analyses suggest that a monophyletic cryptophyte–haptophyte host lineage is unlikely. Telonemids, centrohelids and picobiliphytes remain of uncertain evolutionary origin, and sequencing more from related species is now crucial.

This result contrasts with some plastid phylogenies that showed the monophyly of photosynthetic hacrobians [13], but is congruent with others where stramenopiles and haptophytes branched together [8,14,52]. Regardless of which plastid phylogeny is correct, however, they all favour a unique secondary endosymbiosis giving rise to plastids in hacrobians and SAR taxa (because these plastids are monophyletic to the exclusion of the red algal plastids). Moreover, the monophyly of hacrobian plastids is also supported by their shared possession of a horizontally transferred ribosomal protein-encoding gene rpl36, which is found in no other plastid lineages [19]. Squaring this plastid data with that of their hosts has long been the challenge to sorting out the history of red algal plastids. In general, the evolution of the host lineages has proven not so much contradictory as difficult to resolve [22,33,53]. Now, however, our analysis of the largest collection of host-derived data with the most sophisticated models converged towards a scenario at the very least inconsistent with a simple vertical inheritance of secondary red plastids in hacrobians, and by extension in chromalveolates as a whole.

There are two obvious possibilities to explain the discrepancy between the host and plastid data. The first is simply that one set of phylogenies is misleading. The monophyly of chromalveolate plastids might be due to very poor red algal or haptophyte and cryptophyte sampling, as well as the deceiving behaviour of alveolate plastid data making it mostly unusable in phylogenies. Conversely, the polyphyly of hacrobians based on nuclear data might also be the result of limited taxon sampling or model misspecifications. The other possibility, however, is that the plastids are genuinely monophyletic and the hosts are not, a situation that could have arisen if higher-order endosymbioses took place, as predicted by the phylogenomic falsification of the chromalveolate hypothesis [33] or the recent study of endoplasmic reticulum-associated degradation (ERAD) components implicated in protein import across plastid membranes [54]. In this latter study, however, the host ERAD copies point to a common origin for haptophytes and cryptophytes, a scenario that we did not recover here. Yet any models that reconcile plastid and host data by means of additional layers of endosymbiosis should explain several observations. On the plastid side, these include: monophyletic hacrobian plastids (in particular, the shared rpl36 transfer in hacrobians), monophyletic alveolate and stramenopile plastids, and likely monophyletic chromalveolate plastids as a whole. On the host side, these now include: monophyletic SAR and haptophyte hosts, and the possibly independent origin of the KC host component. The emerging central difficulties with the most simple version of the chromalveolate hypothesis are therefore all related to the KC group, which should serve to refocus our efforts on the evolution of this lineage.

While the topology of the host lineages may complicate some aspects of plastid distribution in the ways noted earlier, it can also simplify others. In particular, many lineages of chromalveolates lack plastids, but in some alveolates where plastid ancestry can be reasonably inferred, evidence for this ancestry has been found in their nuclear genomes (e.g. Perkinsus and Oxyrrhis [6,45]). Many hacrobian lineages also lack plastids, but based on the host phylogeny described here at least some of these are not necessarily secondarily plastid-lacking; instead their ancestors may have never possessed the capacity to use light to produce energy. For example, we found no molecular evidence in the R. truncata transcriptome for a cryptic plastid or plastid-derived genes, and similarly no such genes were unambiguously identified in the metagenome of picobiliphytes [47], suggesting the ancestral state of the KC clade (and picobiliphytes) was non-photosynthetic.

Developing a robust integrative theory for plastid evolution conciliating plastid and host data could come from expanding taxon sampling in two ways. First, adding more sampling to sparsely sampled groups, including host data from new KC lineages, glaucophytes, telonemids, centrohelids and haptophytes, and plastid data from red algae. Second, new lineages with potentially intermediate positions can be very important, as the R. truncata transcriptome shows, and Palpitomonas or Rappemonads are already identified candidates [28,32]. We expect that several such new lineages with close affinities to ‘hacrobian’ taxa will be found, and it remains to be seen how such data will affect the larger picture of plastid evolution.