Filter-feeding sponges and tiny gliding, pancake-like animals called placozoans are the only two major groups of animals that lack muscles, nerves and an internal gut. Sponges have historically been seen as the first to have branched off in animal phylogeny – the family tree of living organisms that shows how species are related. This is because it is assumed that they split from the other animals before features including muscles, nerves and internal guts evolved.

Sequences of their genetic material (the genome) support this view, although some argue that jellyfish-like animals called ctenophores branched first. One explanation for this disagreement is that ctenophores use different proportions of amino acids in their proteins, known as compositional heterogeneity. Computer algorithms that assume amino acid usage is the same universally throughout evolution may therefore place ctenophores incorrectly. In contrast, so far the only genome from a placozoan shows that they are equally closely related to jellyfish and corals (cnidarians) and bilaterians, which includes worms, insects and vertebrates.

To test whether this view of the first branches of the animal tree of life is correct, Laumer et al. included the genomes from several undescribed species of placozoans in a phylogenetic analysis. These analyses showed a relationship that had not previously been seen. The placozoans were the closest living relative to cnidarians. However, when looking at the level of genes rather than whole genomes, the more usual relationship of placozoans being equally related to cnidarians and bilaterians re-emerged. To resolve this conflict, Laumer et al. focused on the genes that had the least compositional heterogeneity. When doing this, the relationship appeared to be the newly identified one of placozoans being most closely related to cnidarians.

Researchers studying cnidarians often hope to find some clues as to how the complex features they seem to share with bilaterians originated. The findings of Laumer et al. may suggest that the ancestors of the placozoans did in fact have muscles, nerves and guts, but they lost these traits in favor of a simpler lifestyle. An alternative, but controversial possibility is that the ancestor of cnidarians and bilaterians was a simple organism like a placozoan, and the two evolved their complex traits independently. The findings show a complex picture of early animal evolution. Further study of placozoans may well clarify this picture.

The discovery (Schulze, 1883) and mid-20^th century rediscovery (Grell and Benwitz, 1971) of the enigmatic, amoeba-like placozoan Trichoplax adhaerens did much to ignite the imagination of zoologists interested in early animal evolution (Bütschli, 1884). As microscopic animals adapted to extracellular grazing on the biofilms over which they creep (Wenderoth, 1986), placozoans have a simple anatomy suited to exploit passive diffusion for many physiological needs, with only six morphological cell types discernible even to intensive microscopical scrutiny (Grell and Ruthmann, 1991; Smith et al., 2014), albeit a greater diversity of cell types is apparent through single-cell RNA-seq (Sebé-Pedrós, 2018a). They have no conventional muscular, digestive, or nervous systems, yet show tightly-coordinated behaviour regulated by peptidergic signaling (Smith et al., 2015; Senatore et al., 2017; Varoqueaux, 2018; Armon et al., 2018). In laboratory conditions, they proliferate through fission and somatic growth. Evidence for sexual reproduction remains elusive, despite genetic evidence of recombination (Srivastava et al., 2008) and descriptions of early abortive embryogenesis (Eitel et al., 2011; Grell, 1972), with the possibility that sexual phases of the life cycle may occur only under poorly understood field conditions (Pearse and Voigt, 2007; McFall-Ngai et al., 2013)

Given their simple, puzzling morphology and dearth of embryological clues, molecular data are crucial in placing placozoans phylogenetically. The position of Placozoa in the animal tree proved recalcitrant to early standard-marker analyses (Kim et al., 1999; Silva et al., 2007; Wallberg et al., 2004), although this paradigm did reveal a large degree of molecular diversity in placozoan isolates from around the globe, clearly indicating the existence of many cryptic species (Pearse and Voigt, 2007; Eitel et al., 2013; Signorovitch et al., 2007) with up to 27% genetic distance in 16S rRNA alignments (Eitel and Schierwater, 2010). An apparent answer to the question of placozoan affinities was provided by analysis of a nuclear genome assembly (Srivastava et al., 2008), which strongly supported a position as the sister group of a clade of Cnidaria + Bilateria (sometimes called Planulozoa). However, this effort also revealed a surprisingly bilaterian-like (Dunn et al., 2015) developmental gene toolkit in placozoans, a paradox for such a simple animal.

As metazoan phylogenetics has pressed onward into the genomic era, perhaps the largest controversy has been the debate over the identity of the sister group to the remaining metazoans, traditionally thought to be Porifera, but considered to be Ctenophora by Dunn et al (Dunn et al., 2008). and subsequently by additional studies (Hejnol et al., 2009; Moroz et al., 2014; NISC Comparative Sequencing Program et al., 2013; Whelan et al., 2015; Whelan et al., 2017). Others have suggested that this result arises from artifacts with potentially additive effects, such as inadequate taxon sampling, flawed matrix husbandry (undetected paralogy or contamination), and use of poorly fitting substitution models (Philippe et al., 2009; Pick et al., 2010; Pisani et al., 2015; Simion et al., 2017; Feuda et al., 2017). A third view has emphasized that using different sets of genes can lead to different conclusions, with only a small number sometimes sufficient to drive one result or another (Nosenko et al., 2013; Shen et al., 2017). This controversy, regardless of its eventual resolution, has spurred serious contemplation of possibly independent origins of several hallmark traits such as striated muscles, digestive systems, and nervous systems (Moroz et al., 2014; Dayraud et al., 2012; Hejnol and Martín-Durán, 2015; Liebeskind et al., 2017; Moroz and Kohn, 2016; Presnell et al., 2016; Steinmetz et al., 2012).

Driven by this controversy, new genomic and transcriptomic data from sponges, ctenophores, and metazoan outgroups have accrued, while new sequences and analyses focusing on the position of Placozoa have been slow to emerge. Here, we provide a novel test of the phylogenetic position of placozoans, adding draft genomes from three putative species that span the root of this clade’s known diversity (Eitel et al., 2013) (Table 1), and critically assessing the role of systematic error in placing of these enigmatic organisms (Laumer, 2018).

Orthology assignment on sets of predicted proteomes derived from 59 genome and transcriptome assemblies yielded 4294 gene trees with at least 20 sequences each, sampling all five major metazoan clades and outgroups, from which we obtained 1388 well-aligned orthologues. Within this set, individual maximum-likelihood (ML) gene trees were constructed, and a set of 430 most-informative orthologues were selected on the basis of tree-likeness scores (Misof et al., 2013). This yielded an amino-acid matrix of 73,547 residues with 37.55% gaps or missing data, with an average of 371.92 and 332.75 orthologues represented for Cnidaria and Placozoa, respectively (with a maximum of 383 orthologues present for the newly sequenced placozoan H4 clade representative; Figure 1).

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Our Bayesian analyses of this matrix place Cnidaria and Placozoa as sister groups with full posterior probability under the general site-heterogeneous CAT + GTR + Г4 model (Figure 1). Under ML inference with the C60 +LG + FO + R4 profile mixture model (Wang et al., 2018) (Figure 1—figure supplement 1), we again recover Cnidaria + Placozoa, albeit with more marginal resampling support. Both Bayesian and ML analyses show little internal branch diversity within Placozoa. Accordingly, deleting all newly-added placozoan genomes from our analysis has no effect on topology and only a marginal effect on support in ML analysis (Figure 1—figure supplement 2). Quartet-based concordance analyses (Zhou, 2017) show no evidence of strong phylogenetic conflicts among ML gene trees in this 430-gene set (Figure 1), although internode certainty metrics are close to 0 for many key clades including Cnidaria + Placozoa, indicating that support for some ancient relationships may be masked by gene-tree estimation errors, emerging only in combined analysis (Gatesy and Baker, 2005).

Compositional heterogeneity of amino-acid frequencies along the tree is a source of phylogenetic error not modelled by even complex site-heterogeneous substitution models such as CAT+GTR (Blanquart and Lartillot, 2008; Foster, 2004; Lartillot and Philippe, 2004; Lartillot et al., 2013). Furthermore, previous analyses (Nosenko et al., 2013) have shown that placozoans and choanoflagellates in particular, both of which taxa our matrix samples intensively, deviate strongly from the mean amino-acid composition of Metazoa, perhaps as a result of genomic GC content discrepancies. As a measure to at least partially ameliorate such nonstationary substitution, we recoded the amino-acid matrix into the 6 ‘Dayhoff’ categories, a common strategy previously shown to reduce the effect of compositional variation among taxa, albeit the Dayhoff-6 groups represent only one of many plausible recoding strategies, all of which sacrifice information (Feuda et al., 2017; Nesnidal et al., 2010; Rota-Stabelli et al., 2013; Susko and Roger, 2007). Analysis of this recoded matrix under the CAT + GTR model again recovered full support (pp = 1) for Cnidaria + Placozoa (Figure 2). Indeed, under Dayhoff-6 recoding, the only major change is in the relative positions of Ctenophora and Porifera, with the latter here constituting the sister group to all other animals with full support. Similar recoding-driven effects on relative positions of Porifera and Ctenophora have also been seen in other recent work (Feuda et al., 2017), and have been interpreted to indicate a role for compositional bias in misplacing Ctenophora as sister group to all other animals

Figure 2

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Many research groups, using good taxon sampling and genome-scale datasets, and even recently including data from a new divergent placozoan species (Whelan et al., 2017; Feuda et al., 2017; Eitel, 2017), have consistently reported strong support for Planulozoa under the CAT + GTR model. Indeed, when we construct a supermatrix from our predicted peptide catalogues using a different strategy, relying on complete sequences of 303 pan-eukaryote ‘Benchmarking Universal Single-Copy Orthologs’ (BUSCOs) (Simão et al., 2015), we also see full support in a CAT + GTR + Г analysis for Planulozoa, in both amino-acid (Figure 3a) and Dayhoff-6 recoded alphabets (Figure 3b). Which phylogeny is correct, and what process drives support for the incorrect topology? Posterior predictive tests, which compare the observed among-taxon usage of amino-acid frequencies to expected distributions simulated using the sampled posterior distribution and a single composition vector, may provide insight (Feuda et al., 2017; Lartillot and Philippe, 2004). Both the initial 430-gene matrix and the 303-gene BUSCO matrix fail these tests, but the BUSCO matrix fails it more profoundly, with z-scores (measuring mean-squared across-taxon heterogeneity) scoring in the range of 330–340, in contrast to the range of 176–187 seen in the 430-gene matrix (Table 2). Furthermore, inspecting z-scores for individual taxa in representative chains from both matrices shows that a large amount of this global difference in z-scores can be attributed to placozoans, with additional contributions from choanoflagellates and select isolated representatives of other clades (Figure 3C).

Figure 3

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As a final measure to describe the influence of compositional heterogeneity in this dataset, we applied a null-simulation test for compositional bias to each alignment in our set of 1388 orthologues. This test, which compares the real data to a null distribution of amino-acid frequencies simulated along assumed gene trees with a substitution model using a single composition vector, is less prone to Type II errors than the more conventional X (Grell and Benwitz, 1971) test (Foster, 2004). Remarkably, at a conservative significance threshold of α = 0.10, the majority (764 genes or ~55%) of this gene set is identified as compositionally biased by this test, highlighting the importance of using appropriate statistical tests to control this source of systematic error, rather than applying arbitrary heuristic cutoffs (Kück and Struck, 2014). Building informative matrices from gene sets on either side of this significance threshold, and again applying both CAT + GTR mixture models and ML profile mixtures, we see strong support for Cnidaria + Placozoa in the test-passing supermatrix, and conversely, strong support for Cnidaria + Bilateria in the test-failing supermatrix (Figure 4, Figure 4—figure supplement 1, Figure 4—figure supplement 2). Interestingly, in trees built through CAT + GTR + Г4 analysis of the test-failing supermatrix (Figure 4A,C), in both amino-acid and Dayhoff-6 alphabets, we also observe full support for Porifera as sister to all other animals. In contrast, analysis of this amino acid matrix under a profile mixture model recovers support for Ctenophora in this position (Figure 4—figure supplement 1), indicating that, at least for this alignment, compositional heterogeneity need not be invoked to explain why outcomes differ among analyses, as some have argued (Feuda et al., 2017): both CAT + GTR and the C60 +LG + FO + R4 profile mixture model assume a single composition vector over time, but the CAT + GTR model is better able to accommodate site-heterogeneous substitution patterns (Lartillot et al., 2013; Quang et al., 2008). In the context of this experiment, Dayhoff-6 recoding appears impactful only for the test-passing supermatrix (Figure 4B,D), where it obviates support for Ctenophora-sister (Figure 4B, Figure 4—figure supplement 2) in favour of (albeit, with marginal support) Porifera-sister (Figure 4D), and also diminishes support for Placozoa + Cnidaria (in contrast to the 430-gene matrix; Figure 2), perhaps reflecting the inherent information loss of using a reduced amino-acid alphabet for this relatively shorter matrix.

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A possible hidden variable related to the phylogenetic discordance we describe, the precise significance of which remains unclear, is mean trimmed alignment length: both the test-passing and the original 430-gene matrix are composed of considerably shorter alignments than the test-failing and the 303-gene BUSCO matrix (see Materials and methods). Indeed, alignment length has been previously shown to be predictive of a number of other metrics of phylogenetic relevance (Shen et al., 2016); the generality and directionality of such relationships in empirical datasets at varying scales of divergence is clearly worthy of further investigation.

The previously cryptic phylogenetic link between cnidarians and placozoans seen in gene sets less influenced by compositional bias will require further testing with other analyses and data modalities, such as rare genomic changes, which should be ever more visible as highly contiguous assemblies continue to be reported from non-bilaterian animals (Eitel et al., 2018; Kamm et al., 2018; Jiang, 2018; Leclère, 2018). However, if validated, this relationship must continue to raise questions on the homology of certain traits across non-bilaterians. Many workers, citing the incompletely known development (Eitel et al., 2011; Pearse and Voigt, 2007) and relatively bilaterian-like gene content of placozoans (Srivastava et al., 2008; Eitel, 2017), presume that these organisms must have a still-unobserved, more typical development and life cycle (DuBuc et al., 2018), or else are merely oddities that have experienced wholesale secondary simplification, having scant significance to any evolutionary path outside their own. Indeed, it is tempting to interpret this new phylogenetic position as further bolstering such hypotheses, as much work on cnidarian models in the evo-devo paradigm is predicated on the notion that cnidarians and bilaterians share, more or less, many homologous morphological features, viz. axial organization (Genikhovich and Technau, 2017; DuBuc et al., 2018), nervous systems (Liebeskind et al., 2017; Moroz and Kohn, 2016; Kelava et al., 2015; Kristan, 2016; Arendt et al., 2016), basement-membrane lined epithelia (Fidler et al., 2017; Leys and Riesgo, 2012), musculature (Steinmetz et al., 2012), embryonic germ-layer organisation (Steinmetz et al., 2017), and internal digestion (Presnell et al., 2016; Putnam et al., 2007; Hejnol and Martindale, 2008; Martindale and Hejnol, 2009). While we do not argue, as some have done (Schierwater, 2005; Syed and Schierwater, 2002), that placozoans resemble hypothetical metazoan ancestors, we hesitate to dismiss them a priori as irrelevant to understanding early bilaterian evolution in particular: although apparently simpler and less diverse, placozoans nonetheless have equal status to cnidarians as an immediate extant outgroup. Rather, we see value in testing assumed hypotheses of homology, character by character, by extending pairwise comparisons between bilaterians and cnidarians to include placozoans, an agenda which demands reducing the large disparity in embryological, physiological, and molecular genetic knowledge between these taxa, towards which recent progress has been made using both established methods such as in situ hybridization (DuBuc et al., 2018) and image analysis (Varoqueaux, 2018), as well as new technologies such as single-cell RNA-seq (Sebé-Pedrós, 2018a; Sebé-Pedrós et al., 2018b). Conversely, we emphasize another implication of this phylogeny: characters that can be validated as homologous at any level between Bilateria and Cnidaria must have originated earlier in animal evolution than previously appreciated, and should either cryptically occur in modern placozoans or else have been lost at some point in their ancestry. In this light, paleobiological scenarios of early animal evolution founded on inherently phylogenetically-informed interpretations of Ediacaran fossil forms (Cavalier-Smith, 2018; Cavalier-Smith, 2017; Dufour and McIlroy, 2018; Sperling and Vinther, 2010; Evans et al., 2017) and molecular clock estimates (Cunningham et al., 2017; dos Reis et al., 2015; Dohrmann and Wörheide, 2017; Erwin et al., 2011) may require re-examination.

SRA accession codes, where used, and all alternative sources for sequence data (e.g. individually hosted websites, personal communications), are listed above in the Materials and Methods section. A DataDryad accession is available at https://doi.org/10.5061/dryad.6cm1166, which makes available all helper scripts, orthogroups, multiple sequence alignments, phylogenetic program output, and raw host proteomes inputted to OrthoFinder. Metagenomic bins containing placozoan host contigs and raw RNA reads used to derive gene annotations from H4, H6 and H11 isolates are also provided in this accession. PhyloBayes. chain files, due to their large size, are separately accessioned at in Zenodo at https://doi.org/10.5281/zenodo.1197272. Raw RNA-seq data from three separate isolates of Placozoan haplotype H4 have been deposited in NCBI BioProject under accession code PRJNA505163.

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Author details

1. Christopher E Laumer

   1. Wellcome Trust Sanger Institute, Hinxton, United Kingdom
   2. European Molecular Biology Laboratories-European Bioinformatics Institute, Hinxton, United Kingdom

   Contribution

   Conceptualization, Resources, Data curation, Software, Formal analysis, Validation, Investigation, Visualization, Methodology, Writing—original draft, Project administration, Writing—review and editing

   For correspondence

   claumer@ebi.ac.uk

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0001-8097-8516

2. Harald Gruber-Vodicka

   Max Planck Institute for Marine Microbiology, Bremen, Germany

   Contribution

   Resources, Formal analysis, Supervision, Investigation, Writing—review and editing

   Competing interests

   No competing interests declared

3. Michael G Hadfield

   Kewalo Marine Laboratory, Pacific Biosciences Research Center and the University of Hawaii-Manoa, Honolulu, United States

   Contribution

   Resources, Writing—review and editing

   Competing interests

   No competing interests declared

4. Vicki B Pearse

   Institute of Marine Sciences, University of California, Santa Cruz, United States

   Contribution

   Conceptualization, Resources, Writing—review and editing

   Competing interests

   No competing interests declared

5. Ana Riesgo

   Invertebrate Division, Life Sciences Department, The Natural History Museum, London, United Kingdom

   Contribution

   Resources, Data curation, Writing—review and editing

   Competing interests

   No competing interests declared

6. John C Marioni

   1. Wellcome Trust Sanger Institute, Hinxton, United Kingdom
   2. European Molecular Biology Laboratories-European Bioinformatics Institute, Hinxton, United Kingdom
   3. Cancer Research UK Cambridge Institute, University of Cambridge, Cambridge, United Kingdom

   Contribution

   Resources, Supervision, Writing—review and editing

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0001-9092-0852

7. Gonzalo Giribet

   Museum of Comparative Zoology, Department of Organismic and Evolutionary Biology, Harvard University, Cambridge, United States

   Contribution

   Conceptualization, Resources, Funding acquisition, Writing—review and editing

   Competing interests

   No competing interests declared

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