When living things made from many cells evolved from single-celled ancestors, it was a breakthrough in the history of life—and one that has occurred more than once. In fact, multicellular life has evolved independently at least 25 times, in groups as diverse as animals, fungi, plants, slime molds and seaweeds. There are broadly two ways to become multicellular. The most complex multicellular species, such as animals, will replicate a single cell, over and over, without separating the resultant cells. However, in species that are only occasionally multicellular, free-living cells tend instead to join together in one mass of many cells.

Evolution is constrained by its raw materials; so looking at the living relatives of a given species, or group, can lead to a better understanding of its evolution because its relatives contain clues about its ancestors. To gain insights into how animal multicellular life might have began; Sebé-Pedrós et al. studied the life cycle of the amoeboid organism Capsaspora owczarzaki. Found within the bodies of freshwater snails, this single-celled amoeba is a close relative of multicellular animals and could resemble one of their earliest ancestors.

At certain stages of the life cycle Sebé-Pedrós et al. noticed that individual amoebae gathered together to form a multicellular mass—something that had not been seen before in such a close relative of the animals. Moreover, the genes that ‘switched on’ when the amoebae began to aggregate are also found in animals; where, together with other genes, they control development and the formation of tissues. Sebé-Pedrós et al. suggest that the first multicellular animals could have recycled the genes that control the aggregation of single-celled species: in other words, genes that once controlled the changes that happen at different times in a life cycle, now control the changes that develop between different tissues at the same time.

Sebé-Pedrós et al. also observed that alternative splicing—a process that allows different proteins to be made from a single gene—occurs via two different mechanisms during the life cycle of Capsaspora. Most of the time Capsaspora employs a form of alternative splicing that is often seen in plants and fungi, and only rarely in animals; for the rest of the time it uses a form of alternative splicing similar to that used by animal cells.

The evolution of complex alternative splicing mechanisms is a hallmark feature of multicellular animals. The exploitation of two major forms of alternative splicing in Capsaspora could thus reflect an important transition during evolution that resulted in an increased diversity of proteins and in more complex gene regulation. Such a transition may ultimately have paved the way for the increased specialization of cell types seen in animals.

This glimpse into the possible transitions in gene regulation that contributed to the birth of multicellular animals indicates that the single-celled ancestor of the animals was likely more complex than previously thought. Future analyses of the animals’ close relatives may further improve our understanding of how single-celled organisms became multicellular animals.

Living organisms emerged from the integration of multiple levels of organization. These levels were shaped by both physiochemical constraints and historical circumstances, the latter being more important in more complex systems (Jacob, 1977). Therefore, it is important to identify the phylogenetic inertia (sensu Burt, 2001) imposed by the raw starting material in order to properly understand major evolutionary transitions, such as the origin of metazoan multicellularity (Knoll, 2011). Examination of both the genetic repertoire (King, 2004; Ruiz-Trillo et al., 2007; Rokas, 2008) and the cell types present in the immediate unicellular relatives of metazoans can provide insights into this evolutionary transition, as they reveal the historical constraints in early metazoan evolution. In this regard, the analyses of unicellular holozoan genomes, that is choanoflagellates and filastereans, have shown that the genetic repertoire of the metazoan unicellular ancestor was much more complex than previously thought (Abedin and King, 2008; King et al., 2008; Sebé-Pedrós et al., 2010; Suga et al., 2013).

Multicellularity has been independently acquired multiple times during the evolution of eukaryotes, in more than 20 different lineages including animals, plants, fungi, slime molds, green and brown algae, and several other eukaryotes (King, 2004; Parfrey and Lahr, 2013). Multicellular organisms evolved through two major strategies: aggregation of different cells or clonal division of a single cell. Multi-level selection theory has proposed that the most complex multicellular organisms likely arose through clonal development rather than by aggregation of genetically diverse cells, since intra-organismal competition in the latter might be expected to be evolutionarily unstable (Grosberg and Strathmann, 2007; Michod, 2007; Newman, 2012). Accordingly, eukaryotic lineages that attained the most complex multicellular lifestyles (i.e., plants and metazoans) arose through clonal cell division (Grosberg and Strathmann, 2007). In contrast to clonal multicellularity, aggregative cell behavior typically represents a transient life cycle stage. This type of multicellularity arose within several eukaryotic clades, including the dictyostelids (Amoebozoa) (Schaap, 2011), acrasid amoebas (Heterolobosea, Discicristata, Discoba) (Brown et al., 2011), Guttulinopsis vulgaris (Cercozoa, Rhizaria) (Brown et al., 2012), the genus Sorogena (Ciliata, Alveolata) (Lasek-Nesselquist and Katz, 2001), the holomycota Fonticula alba (Opisthokonta) (Brown et al., 2009) and the genus Sorodiplophrys (Labyrinthulomycetes, Heterokonta) (Dykstra and Olive, 1975) (Figure 1).

Figure 1

Download asset Open asset

Within the opisthokont clade that comprises Metazoa, Fungi and their unicellular relatives (Cavalier-Smith, 2003; Steenkamp et al., 2006; Ruiz-Trillo et al., 2008), so far only a single taxon has been described to have aggregative behavior, which is F. alba (Brown et al., 2009), a close relative of Fungi. Moreover, among close unicellular relatives of Metazoa, clonal development is the only known multicellular behavior, as described in choanoflagellates and ichthyosporeans (Dayel et al., 2011; Suga and Ruiz-Trillo, 2013). Within metazoans, which have largely clonal development, some cells show aggregative behaviors; for example, mesenchymal (O’Shea, 1987) and germ line cells (Savage and Danilchik, 1993) during development, sponge cells after cell dissociation (Wilson, 1907) and arthropod blood cells through active amoeboid movement (Loeb, 1903, 1921).

To gain deeper insight into the possible transitions that arose during the emergence of metazoan multicellularity, we have performed a detailed examination of the life cycle and associated transcriptomic changes of Capsaspora owczarzaki, one of the closest known unicellular relatives of metazoans (Figure 1). Isolated decades ago as an endosymbiont of the fresh-water snail Biomphalaria glabrata (Owczarzak et al., 1980), C. owczarzaki belongs to the clade Filasterea, the sister-group of Metazoa and choanoflagellates (Torruella et al., 2012). Filasterea also includes a free-living marine unicellular species known as Ministeria vibrans (Shalchian-Tabrizi et al., 2008).

We analyzed the C. owczarzaki life cycle and its regulation using electron microscopy, flow cytometry and high-throughput RNA sequencing (RNA-Seq). Through these analyses, we show that C. owczarzaki life cycle is tightly regulated at the level of gene expression and alternative splicing (AS). Moreover, we demonstrate the existence of an aggregative multicellular stage in C. owczarzaki in which many orthologs of genes important for metazoan clonal multicellularity are upregulated.

Under initial culture conditions (‘Materials and methods’), C. owczarzaki differentiates into an amoeba that crawls over substrate (Video 1), surveying its environment with its filopodia (Sebé-Pedrós et al., 2013). At this stage, active DNA replication occurs (with >10% of the cells in S-phase) and, within 48 hr, the cells enter an exponential growth phase (Figures 2 and 3). Subsequently, the cells start to detach from the surface and begin to retract their filopodia and encyst (Figure 3B,C). After 8 days, no attached amoebas remain and growth is stabilized (Figures 2 and 3). This cystic stage may represent a dispersal resistance form. Strikingly, we observe an alternative path to this process involving the active formation of cell aggregates (Videos 2 and 3). In these aggregates, the cells attach to each other and produce cohesive extracellular material (Figure 3D) until a compact cell aggregate, in which cells no longer bear filopodia, is formed (Figure 3E). Transmission electron microscopy demonstrates the presence of a thick, unstructured, extracellular material within the aggregates that appears to prevent direct contact between cells (Figure 3F). Clusters of cells appear to occur at random under normal culture conditions.

Video 1

Download asset

Figure 2

Download asset Open asset

Figure 3

Download asset Open asset

Video 2

Download asset

Video 3

Download asset

To investigate whether C. owczarzaki cell clusters are formed through aggregation or clonal division, we first mixed two differentially stained populations of cells (‘Materials and methods’) and induced aggregate formation, resulting in dual-colored cell clusters (Figure 4A). This indicates that cell clusters are not composed of daughter cells resulting from successive cell divisions, which would result in single-color cell clusters, but instead by aggregation of multiple cells. We also observed that aggregates could form efficiently even when cell division was blocked by two different inhibitors, hydroxyurea and aphidicolin (Figure 4B). Finally, by flow cytometry, we observed that the proliferation rate of aggregative cells (Figure 4—figure supplement 1) is extremely low (compared with the observations in Figure 2). Overall, these results show that C. owczarzaki cell clusters form by active cell aggregation, not by clonal cell division. This observation represents the first reported case of aggregative multicellularity in a close unicellular relative of Metazoa.

Figure 4 with 1 supplement see all

Download asset Open asset

The aggregative multicellularity observed in C. owczarzaki adds an additional cell behavior to those already known among extant close unicellular relatives of Metazoa (i.e., choanoflagellates, ichthyosporeans, and filastereans [Torruella et al., 2012]). We can then infer that multiple cell types and behaviors (including aggregative behavior, flagellar motility, amoeboid movement, clonal colony formation, etc) were most likely present among the unicellular ancestors of metazoans. This range of cell behaviors may have provided the basis for the evolution of the diverse cell types seen in animals (Arendt, 2008; Arendt et al., 2009). Interestingly, each one of the three known unicellular lineages closely related to Metazoa (choanoflagellates, ichthyosporeans and filastereans) has some kind of simple multicellularity. Moreover, the tight regulation observed in C. owczarzaki (see below) emphasizes that a regulated temporal cell differentiation was already in place among unicellular ancestor of animals. This reinforces the view that, during the transition to animal multicellularity, ancestral premetazoan cell types may have been integrated into a single multicellular entity by means of controlling cell differentiation spatially, rather than temporally (Mikhailov et al., 2009). An alternative explanation is that some of the cell behaviors observed in extant unicellular relatives of Metazoa may had evolved independently in some particular unicellular holozoan lineages or species, and do not represent ancestral states. The limited taxon sampling in many of these poorly studied lineages makes it difficult to reliably assess whether these are derived or ancestral characters and this situation is especially dramatic in the case of filastereans, in which only two species have been described so far (C. owczarzaki and the free-living M. vibrans).

To investigate the molecular composition and regulation of the distinct life cycle stages of C. owczarzaki, we isolated filopodiated amoebae, aggregates, and cysts, and analyzed their transcriptomes using RNA-Seq. Of 8637 annotated genes, 4486 showed statistically significant differential regulation (‘Materials and methods’) in one or more pair-wise comparisons between life cycle stages, including 1354 changes between filopodial and aggregate stages, 3227 between filopodial and cystic stages, and 3096 between aggregate and cystic stages. Moreover, when performing one-versus-all comparisons, each cell stage had a specific transcriptomic profile (Figure 5A), indicating tight regulation at the level of gene expression. Using both pairwise and one-versus-all comparisons, we identified significantly enriched gene ontology (GO) categories (Figures 5 and 6) and Pfam protein domains (Figure 7) in each set of differentially expressed (both up and down-regulated) genes (p<0.01 for each significant category; Fisher’s exact test). Genes upregulated in the filopodial stage were enriched in signalling functions, such as tyrosine kinase activity and G-protein-coupled receptor activity, as well as in transcription factors, especially of the Basic Leucine Zipper Domain (bZIP) superfamily (Figures 5 and 6). Genes involved in protein synthesis and DNA replication were also significantly upregulated, consistent with the rapid cell proliferation at this stage observed by flow cytometry (Figure 2), and further suggesting a high metabolic rate.

Figure 5

Download asset Open asset

Figure 6

Download asset Open asset

Figure 7

Download asset Open asset

When compared to filopodial and aggregative cells, cystic cells showed significant downregulation of genes associated with myosin transport, translation, DNA replication and metabolic activities (especially mitochondrial energy production). However, genes involved in vesicle transport and autophagy were significantly upregulated at this stage (Figure 5). These differences may reflect recycling of intracellular components triggered by starvation or other adverse conditions, as has been observed under conditions of adaptive cell survival in other eukaryotes (Kiel, 2010). Protein domains involved in the ubiquitin pathway (e.g., UQ_con, zf-RING2 and Cullin domains) and in synaptic cell–cell communication, such as SNARE, synaptobrevin and syntaxin, as well specific transcription factor families (e.g., bHLH transcription factors), were also significantly upregulated in the cystic cells (Figure 7). Altogether, these results suggest that major cytosolic rearrangement and protein turnover occur at the cystic stage.

Remarkably, the aggregative stage showed strong upregulation of the components of the integrin adhesome and associated signalling and cell-adhesion proteins (Figures 5 and 8A,B), such as the LamininG domain-containing protein CAOG_07351 (which contains a N-terminal signal peptide sequence and therefore is likely to be secreted) (Figure 8C), the IPP complex (ILK-PINCH-Parvin) signalling module, G-protein α-13 (Gong et al., 2010), several cytoplasmatic tyrosine kinases (Hamazaki et al., 1998) and two receptor tyrosine kinases (which possess extracellular DERM [Lewandowska et al., 1991] and fibronectin_3 domains, known to interact with integrins [Figure 8C]). These observations strongly suggest that the integrin adhesome and the likely associated tyrosine kinase signalling genes play an important role in the formation of the C. owzarzaki aggregates. Furthermore, we also observed upregulation of genes involved in mitosis and in the tubulin cytoskeleton (e.g., kinesins) at the aggregative stage (Figure 5). These results indicate that a molecular repertoire associated with animal multicellularity, could function either in aggregative or in clonal multicellularity and in different phylogenetic contexts, in line with previous hypotheses (Newman, 2012).

Figure 8

Download asset Open asset

A hallmark feature of the evolution of metazoan multicellularity and cell type diversity is the expansion of AS complexity and regulation through exon skipping, which has entailed the formation of cell type-specific networks of co-regulated exons belonging to functionally related or pathway-specific genes (Irimia and Blencowe, 2012). In contrast, differential intron retention is the most widespread form of AS found in non-metazoan eukaryotic species (McGuire et al., 2008). To assess the extent to which these forms of AS may contribute to gene regulation in C. owczarzaki, we systematically mapped reads from each life cycle stage to a comprehensive set of intron–exon and exon–exon junctions (i.e., formed by exon/intron inclusion and skipping) to score their differential usage. Of 25,677 introns with sufficient RNA-Seq read coverage across the three life cycle stages, 2986 (11.6%) showed ≥20% PSI (Percent Spliced In, percent of transcripts from a given gene in which the intron sequence is present) in at least one stage, and approximately a third of genes had at least one such intron retention event. Moreover, we observed marked differences in the extent to which detected intron retention is differentially regulated between the different life cycle stages (Figure 9A). In particular, 797 retained introns (in 441 genes) and 259 retained introns (in 232 genes) display differential PSI (dPSI) values of 25% or more in the filopodial and cystic stages compared to the other stages, respectively (Figure 9B). In contrast, no retained introns were found to be differentially spliced at the aggregative stage. Most (12 out of 15, 80%) of the analyzed cases of differentially retained introns were validated by RT-PCR (Figure 9C and Figure 9—figure supplement 1).

Figure 9 with 1 supplement see all

Download asset Open asset

GO enrichment analysis for the two sets of differentially retained introns showed distinct gene function enrichment (e.g., protein kinase activity and intracellular targeting in the filopodial stage, and histone modification and myosin complex in the cystic stage) (Figure 6), implying that regulated intron retention plays different roles at these stages. A low fraction of read-through introns (with length-multiples of three and no in-frame stop codons) were found among the two sets of differentially retained introns, suggesting that most if not all of these retained introns act by reducing the levels of spliced mRNAs exported from the nucleus and translated into protein, as has been observed previously for regulated retained introns in metazoan species (Yap et al., 2012). Moreover, we observe that multiple introns belonging to a gene can be retained in a stage-specific manner. For instance, >73% and >29% of multi-intronic genes with one differentially retained intron had at least one additional differentially retained intron at the filopodial or cystic-specific stages, respectively, and 22% and 5% of genes at these stages showed evidence of high retention for all introns in the same genes. Furthermore, RT-PCR analyses and mate information from paired-end read analyses suggested that multiple intron retention events often occur in a combinatorial manner (Figure 9—figure supplement 1), thereby increasing the potential impact of intron retentions on mRNA regulation. All of the above observations were highly consistent across three biological replicates (Figure 9B), and not observed for neighbouring genes, ruling out contamination of genomic DNA.

We analyzed different features of differentially retained introns that may account for their stage-specific regulation. First, we compared intron lengths. While filopodial-specific differentially retained introns have a similar length distribution to constitutive (PSI less than 2% across all stages) introns, cystic stage-specific introns were significantly longer (p=1.7e⁻¹⁴ Wilcoxon rank sum test) (Figure 9D). In line with this observation, the average level of intron retention increased steadily with intron length only in the cystic stage (Figure 9E). Furthermore, cystic stage-specific retained introns harbored significantly weaker canonical 5´ and 3´ splice site signals than other intron sets (p<0.0013 Wilcoxon rank sum test for all comparisons). Collectively, these data suggest that differential intron retention in the cystic stage may be associated with suboptimal introns (i.e., long and with weak splice sites) that are more efficiently spliced in the other cell stages. In the case of the filopodial-specific differentially retained introns, analyses of sequence motif enrichment with MEME show enrichment of a long T/G-rich motif that highly resembles a recently identified consensus binding site for Elav-like protein in mammals (Ince-Dunn et al., 2012) (Figure 10). Interestingly, the single ortholog for this gene in C. owczarzaki shows a highly-regulated expression pattern, with lowest expression in the filopodial stage (Figure 10C). Therefore, it is tempting to speculate that Elav-like protein may negatively regulate filopodial-specific intron retention of some introns. Experimental depletion of Elav-like protein in C. owczarzaki will require the development of RNAi or gene-targeting methods in this species before this hypothesis can be tested.

Figure 10

Download asset Open asset

Next, we investigated differential exon splicing and identified 191 cassette exons with PSIs <95% in at least one life cycle stage, 39 of which display PSIs <85%. 29 of these exons showed a ≥15% PSI difference in pairwise comparisons between the cell stages, with lower PSIs typically associated with the filopodial stage (Supplementary file 1); RT-PCR assays confirmed skipping for 7 out of 8 tested cases (Figure 11A, and Figure 11—figure supplement 1). Most (∼60%) of these exons maintain an open reading frame when skipped. In contrast to previous reports demonstrating that differentially-regulated exons are significantly under represented in modular, folded domains in metazoans (Romero et al., 2006; Ellis et al., 2012), two thirds of the differently regulated exons in C. owczarzaki overlap annotated domains (Supplementary file 1). Furthermore, genes with differential exon skipping are statistically significantly enriched in protein kinase activity, impacting both tyrosine and serine/threonine kinases (Figure 11B). This observation strongly suggests a role for coordinated exon skipping in the modulation of cell signaling in C. owczarzaki. To our knowledge, this represents the first example of a regulated exon network linked to a specific biological function in a unicellular organism.

Figure 11 with 1 supplement see all

Download asset Open asset

In summary, our results offer new insight into the origin of metazoan multicellularity. In particular, the observation of an aggregative multicellular stage in C. owczarzaki represents the first example of such cellular behavior in a close unicellular relative of metazoans. This observation therefore adds to the repertoire of reported complex cellular behaviors among extant unicellular relatives of metazoans—including clonal colony formation in choanoflagellates and sporangia formation by hypertrophic syncytial growth in ichthyosporeans (Dayel et al., 2011; Suga and Ruiz-Trillo, 2013), thus expanding the potential starting ‘raw material’ available for the evolution of animal multicellularity. We note that the current evolutionary framework on the opisthokonts, based on phylogenomic analyses (Steenkamp et al., 2006; Ruiz-Trillo et al., 2008; Shalchian-Tabrizi et al., 2008; Torruella et al., 2012; Paps et al., 2013), discards the possibility that C. owczarzaki (or choanoflagellates) derives from a more complex multicellular lineage. The sister-group of opisthokonts is the unicellular biflagellates Apusozoa, and complex multicellularity has not yet been observed in any of the non-metazoan holozoan lineages.

Furthermore, we show that the complex, metazoan-like genetic ‘toolkit’ of C. owczarzaki (Sebé-Pedrós et al., 2010, 2011; Suga et al., 2012) is dynamically deployed during its highly-regulated life cycle, with upregulation of integrin adhesome and signalling genes linked to multicellularity in metazoans during the aggregative stage. Extensive differential AS between the C. owczarzaki life cycle stages likely further contributes to the dynamic gene regulation observed in this species, with differential intron retention likely acting as an important mechanism in the control of transcript levels between life cycle stages, probably through triggering non-sense mediated decay (NMD). Our discovery of an exon network associated with tyrosine kinase genes in C. owczarzaki further adds to the metazoan-like features of this species. Together with genes resembling those that function in metazoan multicellular processes, the emergence of an exon network that functions in conjunction with differentially-regulated intron retention may have provided a degree of proteomic and regulatory complexity that was key in the evolution of cell type complexity in metazoans (Nilsen and Graveley, 2010). Based on the collective results from our investigation of C. owczarzaki, it is intriguing to consider that the integration of regulatory innovations involving differential expression and splicing of metazoan-like genes set the stage for the evolution of cell specialization in the common ancestors of metazoans and C. owczarzaki.

1. 1. Russ C
   2. Nusbaum C
   3. Sebé-Pedrós A

   (2013) RNAseq data from Capsaspora owczarzaki

   Publicly available at NCBI (http://www.ncbi.nlm.nih.gov/).

   http://www.ncbi.nlm.nih.gov/biosample/?term=txid595528[Organism:noexp]

1. 1. Abedin M
   2. King N

   (2008) The premetazoan ancestry of cadherins

   Science 319:946–948.

   https://doi.org/10.1126/science.1151084

   • Google Scholar
2. 1. Anders S
   2. Huber W

   (2010) Differential expression analysis for sequence count data

   Genome Biology 11:R106.

   https://doi.org/10.1186/gb-2010-11-10-r106

   • Google Scholar
3. 1. Arendt D

   (2008) The evolution of cell types in animals: emerging principles from molecular studies

   Nature Reviews Genetics 9:868–882.

   https://doi.org/10.1038/nrg2416

   • Google Scholar
4. 1. Arendt D
   2. Hausen H
   3. Purschke G

   (2009) The “division of labour”model of eye evolution

   Philosophical Transactions of the Royal Society of London B Biological Sciences 364:2809.

   https://doi.org/10.1098/rstb.2009.0104

   • Google Scholar
5. 1. Bailey TL
   2. Boden M
   3. Buske FA
   4. Frith M
   5. Grant CE
   6. Clementi L
   7. Ren J
   8. Li WW
   9. Noble WS

   (2009) MEME Suite: tools for motif discovery and searching

   Nucleic Acids Research 37:W202–W208.

   https://doi.org/10.1093/nar/gkp335

   • Google Scholar
6. 1. Barbosa-Morais NL
   2. Irimia M
   3. Pan Q
   4. Xiong HY
   5. Gueroussov S
   6. Lee LJ
   7. Slobodeniuc V
   8. Kutter C
   9. Watt S
   10. Colak R
   11. Kim T
   12. Misquitta-Ali CM
   13. Wilson MD
   14. Kim PM
   15. Odom DT
   16. Frey BJ
   17. Blencowe BJ

   (2012) The evolutionary landscape of alternative splicing in vertebrate species

   Science 338:1587–1593.

   https://doi.org/10.1126/science.1230612

   • Google Scholar
7. 1. Bauer S
   2. Grossmann S
   3. Vingron M
   4. Robinson PN

   (2008) Ontologizer 2.0—a multifunctional tool for GO term enrichment analysis and data exploration

   Bioinformatics 24:1650–1651.

   https://doi.org/10.1093/bioinformatics/btn250

   • Google Scholar
8. 1. Brown MW
   2. Silberman JD
   3. Spiegel FW

   (2011) A contemporary evaluation of the acrasids (Acrasidae, Heterolobosea, Excavata)

   European Journal of Protistology 48:103–123.

   https://doi.org/10.1016/j.ejop.2011.10.001

   • Google Scholar
9. 1. Brown MW
   2. Spiegel FW
   3. Silberman JD

   (2009) Phylogeny of the “forgotten” cellular slime mold, Fonticula alba, reveals a key evolutionary branch within Opisthokonta

   Molecular Biology and Evolution 26:2699–2709.

   https://doi.org/10.1093/molbev/msp185

   • Google Scholar
10. 1. Brown MW
    2. Kolisko M
    3. Silberman JD
    4. Roger AJ

    (2012) Aggregative multicellularity evolved independently in the eukaryotic supergroup Rhizaria

    Current Biology 22:1–5.

    https://doi.org/10.1016/j.cub.2012.04.021

    • Google Scholar
11. 1. Burt DB

    (2001) Evolutionary stasis, constraint and other terminology describing evolutionary patterns

    Biol J Linn Soc Lond 72:509–517.

    https://doi.org/10.1111/j.1095-8312.2001.tb01334.x

    • Google Scholar
12. 1. Cavalier-Smith T

    (2003) Phylogeny of choanozoa, apusozoa, and other protozoa and early eukaryote megaevolution

    Journal of Molecular Evolution 56:540–563.

    https://doi.org/10.1007/s00239-002-2424-z

    • Google Scholar
13. 1. Conesa A
    2. Götz S
    3. García-Gómez JM
    4. Terol J
    5. Talón M
    6. Robles M

    (2005) Blast2GO: a universal tool for annotation, visualization and analysis in functional genomics research

    Bioinformatics 21:3674–3676.

    https://doi.org/10.1093/bioinformatics/bti610

    • Google Scholar
14. 1. Curtis BA
    2. Tanifuji G
    3. Burki F
    4. Gruber A
    5. Irimia M
    6. Maruyama S
    7. Arias MC
    8. Ball SG
    9. Gile GH
    10. Hirakawa Y
    11. Hopkins JF
    12. Kuo A
    13. Rensing SA
    14. Schmutz J
    15. Symeonidi A
    16. Elias M
    17. Eveleigh RJ
    18. Herman EK
    19. Klute MJ
    20. Nakayama T
    21. Oborník M
    22. Reyes-Prieto A
    23. Armbrust EV
    24. Aves SJ
    25. Beiko RG
    26. Coutinho P
    27. Dacks JB
    28. Durnford DG
    29. Fast NM
    30. Green BR
    31. Grisdale CJ
    32. Hempel F
    33. Henrissat B
    34. Höppner MP
    35. Ishida K
    36. Kim E
    37. Kořený L
    38. Kroth PG
    39. Liu Y
    40. Malik SB
    41. Maier UG
    42. McRose D
    43. Mock T
    44. Neilson JA
    45. Onodera NT
    46. Poole AM
    47. Pritham EJ
    48. Richards TA
    49. Rocap G
    50. Roy SW
    51. Sarai C
    52. Schaack S
    53. Shirato S
    54. Slamovits CH
    55. Spencer DF
    56. Suzuki S
    57. Worden AZ
    58. Zauner S
    59. Barry K
    60. Bell C
    61. Bharti AK
    62. Crow JA
    63. Grimwood J
    64. Kramer R
    65. Lindquist E
    66. Lucas S
    67. Salamov A
    68. McFadden GI
    69. Lane CE
    70. Keeling PJ
    71. Gray MW
    72. Grigoriev IV
    73. Archibald JM

    (2012) Algal genomes reveal evolutionary mosaicism and the fate of nucleomorphs

    Nature 492:59–65.

    https://doi.org/10.1038/nature11681

    • Google Scholar
15. 1. Darzynkiewicz Z
    2. Huang X

    (2004) Analysis of cellular DNA content by flow cytometry

    Current Protocols in Immunology 60:5.7.1–5.7.18.

    https://doi.org/10.1002/0471142735.im0507s60

    • Google Scholar
16. 1. Dayel MJ
    2. Alegado RA
    3. Fairclough SR
    4. Levin TC
    5. Nichols SA
    6. McDonald K
    7. King N

    (2011) Cell differentiation and morphogenesis in the colony-forming choanoflagellate Salpingoeca rosetta

    Developmental Biology 357:73–82.

    https://doi.org/10.1016/j.ydbio.2011.06.003

    • Google Scholar
17. 1. Dykstra MJ
    2. Olive LS

    (1975) Sorodiplophrys: an unusual Sorocarp-producing protist

    Mycologia 67:873–879.

    https://doi.org/10.2307/3758346

    • Google Scholar
18. 1. Ellis JD
    2. Barrios-Rodiles M
    3. Colak R
    4. Irimia M
    5. Kim T
    6. Calarco JA
    7. Wang X
    8. Pan Q
    9. O’Hanlon D
    10. Kim PM
    11. Wrana JL
    12. Blencowe BJ

    (2012) Tissue-specific alternative splicing remodels protein-protein interaction networks

    Molecular Cell 46:884–892.

    https://doi.org/10.1016/j.molcel.2012.05.037

    • Google Scholar
19. 1. Gong H
    2. Shen B
    3. Flevaris P
    4. Chow C
    5. Lam S
    6. Voyno-Yasenetskaya TA
    7. Kozasa T
    8. Du X

    (2010) G protein subunit Galpha13 binds to integrin alphaIIbBeta3 and mediates integrin “Outside-In” signaling

    Science 327:1–4.

    https://doi.org/10.1126/science.1174779

    • Google Scholar
20. 1. Grosberg RK
    2. Strathmann RR

    (2007) The evolution of multicellularity: a Minor major transition?

    Annual Review of Ecology, Evolution, and Systematics 38:621–654.

    https://doi.org/10.1146/annurev.ecolsys.36.102403.114735

    • Google Scholar
21. 1. Hamazaki Y
    2. Kojima H
    3. Mano H
    4. Nagata Y
    5. Todokoro K
    6. Abe T
    7. Nagasawa T

    (1998) Tec is involved in G protein-coupled receptor- and integrin-mediated signalings in human blood platelets

    Oncogene 16:2773–2779.

    https://doi.org/10.1038/sj.onc.1201799

    • Google Scholar
22. 1. Han H
    2. Irimia M
    3. Ross PJ
    4. Sung H-K
    5. Alipanahi B
    6. David L
    7. Golipour A
    8. Gabut M
    9. Michael IP
    10. Nachman EN
    11. Wang E
    12. Trcka D
    13. Thompson T
    14. O’Hanlon D
    15. Slobodeniuc V
    16. Barbosa-Morais NL
    17. Burge CB
    18. Moffat J
    19. Frey BJ
    20. Nagy A
    21. Ellis J
    22. Wrana JL
    23. Blencowe BJ

    (2013) MBNL proteins repress ES-cell-specific alternative splicing and reprogramming

    Nature 498:241–245.

    https://doi.org/10.1038/nature12270

    • Google Scholar
23. 1. Ince-Dunn G
    2. Okano H
    3. Jensen KB
    4. Park WY
    5. Zhong R
    6. Ule J
    7. Mele A
    8. Fak JJ
    9. Yang C
    10. Zhang C
    11. Yoo J
    12. Herre M
    13. Okano H
    14. Noebels JL
    15. Darnell RB

    (2012) Neuronal Elav-like (Hu) proteins regulate RNA splicing and abundance to control glutamate levels and neuronal excitability

    Neuron 75:1067–1080.

    https://doi.org/10.1016/j.neuron.2012.07.009

    • Google Scholar
24. 1. Irimia M
    2. Blencowe BJ

    (2012) Alternative splicing: decoding an expansive regulatory layer

    Current Opinion In Cell Biology 24:323–332.

    https://doi.org/10.1016/j.ceb.2012.03.005

    • Google Scholar
25. 1. Jacob F

    (1977) Evolution and tinkering

    Science 196:1161–1166.

    https://doi.org/10.1126/science.860134

    • Google Scholar
26. 1. Khare T
    2. Pai S
    3. Koncevicius K
    4. Pal M
    5. Kriukiene E
    6. Liutkeviciute Z
    7. Irimia M
    8. Jia P
    9. Ptak C
    10. Xia M
    11. Tice R
    12. Tochigi M
    13. Moréra S
    14. Nazarians A
    15. Belsham D
    16. Wong AH
    17. Blencowe BJ
    18. Wang SC
    19. Kapranov P
    20. Kustra R
    21. Labrie V
    22. Klimasauskas S
    23. Petronis A

    (2012) 5-hmC in the brain is abundant in synaptic genes and shows differences at the exon-intron boundary

    Nature structural & molecular biology 19:1037–1043.

    https://doi.org/10.1038/nsmb.2372

    • Google Scholar
27. 1. Kiel JA

    (2010) Autophagy in unicellular eukaryotes

    Philosophical Transactions of the Royal Society of London B Biological Sciences 365:819–830.

    https://doi.org/10.1098/rstb.2009.0237

    • Google Scholar
28. 1. King N
    2. Westbrook MJ
    3. Young SL
    4. Kuo A
    5. Abedin M
    6. Chapman J
    7. Fairclough S
    8. Hellsten U
    9. Isogai Y
    10. Letunic I
    11. Marr M
    12. Pincus D
    13. Putnam N
    14. Rokas A
    15. Wright KJ
    16. Zuzow R
    17. Dirks W
    18. Good M
    19. Goodstein D
    20. Lemons D
    21. Li W
    22. Lyons JB
    23. Morris A
    24. Nichols S
    25. Richter DJ
    26. Salamov A
    27. Sequencing JG
    28. Bork P
    29. Lim WA
    30. Manning G
    31. Miller WT
    32. McGinnis W
    33. Shapiro H
    34. Tjian R
    35. Grigoriev IV
    36. Rokhsar D

    (2008) The genome of the choanoflagellate Monosiga brevicollis and the origin of metazoans

    Nature 451:783–788.

    https://doi.org/10.1038/nature06617

    • Google Scholar
29. 1. King N

    (2004) The unicellular ancestry of animal development

    Developmental Cell 7:313–325.

    https://doi.org/10.1016/j.devcel.2004.08.010

    • Google Scholar
30. 1. Knoll AH

    (2011) The multiple origins of complex multicellularity

    Annual Review of Earth and Planetary Science 39:217–239.

    https://doi.org/10.1146/annurev.earth.031208.100209

    • Google Scholar
31. 1. Labbé RM
    2. Irimia M
    3. Currie KW
    4. Lin A
    5. Zhu SJ
    6. Brown DD
    7. Ross EJ
    8. Voisin V
    9. Bader GD
    10. Blencowe BJ
    11. Pearson BJ

    (2012) A comparative transcriptomic analysis reveals conserved features of stem cell pluripotency in planarians and mammals

    Stem Cells 30:1734–1745.

    https://doi.org/10.1002/stem.1144

    • Google Scholar
32. 1. Lasek-Nesselquist E
    2. Katz L

    (2001) Phylogenetic position of Sorogena stoianovitchae and relationships within the class Colpodea (Ciliophora) based on SSU rDNA sequences

    The Journal of Eukaryotic Microbiology 48:604–607.

    https://doi.org/10.1111/j.1550-7408.2001.tb00197.x

    • Google Scholar
33. 1. Lewandowska K
    2. Choi HU
    3. Rosenberg LC
    4. Sasse J
    5. Neame PJ
    6. Culp LA

    (1991)

    Extracellular matrix adhesion-promoting activities of a dermatan sulfate proteoglycan-associated protein (22K) from bovine fetal skin

    Journal of Cell Science 99:657–668.

    • Google Scholar
34. 1. Loeb L

    (1903) On the coagulation of the blood of some Arthropods and on the influence of pressure and traction on the protoplasm of the blood cells of Arthropods

    The Biological Bulletin 4:301–318.

    https://doi.org/10.2307/1535851

    • Google Scholar
35. 1. Loeb L

    (1921) Amœboid movement, tissue formation and consistency of protoplasm

    Science 53:261–262.

    https://doi.org/10.1126/science.53.1368.261

    • Google Scholar
36. 1. McGuire AM
    2. Pearson MD
    3. Neafsey DE
    4. Galagan JE

    (2008) Cross-kingdom patterns of alternative splicing and splice recognition

    Genome Biology 9:R50.

    https://doi.org/10.1186/gb-2008-9-3-r50

    • Google Scholar
37. 1. Merico D
    2. Isserlin R
    3. Stueker O
    4. Emili A
    5. Bader GD

    (2010) Enrichment map: a network-based method for gene-set enrichment visualization and interpretation

    PLOS ONE 5:e13984.

    https://doi.org/10.1371/journal.pone.0013984

    • Google Scholar
38. 1. Michod RE

    (2007) Evolution of individuality during the transition from unicellular to multicellular life

    Proceedings of the National Academy of Sciences of the United States of America 104:8613–8618.

    https://doi.org/10.1073/pnas.0701489104

    • Google Scholar
39. 1. Mikhailov K
    2. Konstantinova A
    3. Nikitin M

    (2009) The origin of Metazoa: a transition from temporal to spatial cell differentiation

    Bioessays 31:758–768.

    https://doi.org/10.1002/bies.200800214

    • Google Scholar
40. 1. Newman SA

    (2012) Physico-genetic determinants in the evolution of development

    Science 338:217–219.

    https://doi.org/10.1126/science.1222003

    • Google Scholar
41. 1. Nilsen TW
    2. Graveley BR

    (2010) Expansion of the eukaryotic proteome by alternative splicing

    Nature 463:457–463.

    https://doi.org/10.1038/nature08909

    • Google Scholar
42. 1. O’Shea KS

    (1987)

    Differential deposition of basement membrane components during formation of the caudal neural tube in the mouse embryo

    Development 99:509–519.

    • Google Scholar
43. 1. Owczarzak A
    2. Stibbs HH
    3. Bayne CJ

    (1980) The destruction of Schistosoma mansoni mother sporocysts in vitro by amoebae isolated from Biomphalaria glabrata: an ultrastructural study

    Journal of Invertebrate Pathology 35:26–33.

    https://doi.org/10.1016/0022-2011(80)90079-8

    • Google Scholar
44. 1. Paps J
    2. Medina-Chacón LA
    3. Marshall W
    4. Suga H
    5. Ruiz-Trillo I

    (2013) Molecular phylogeny of unikonts: new insights into the position of apusomonads and ancyromonads and the internal relationships of opisthokonts

    Protist 164:2–12.

    https://doi.org/10.1016/j.protis.2012.09.002

    • Google Scholar
45. 1. Parfrey LW
    2. Lahr DJG

    (2013) Multicellularity arose several times in the evolution of eukaryotes

    Bioessays pp. 339–347.

    https://doi.org/10.1002/bies.201200143

    • Google Scholar
46. 1. Robinson MD
    2. McCarthy DJ
    3. Smyth GK

    (2010) edgeR: a Bioconductor package for differential expression analysis of digital gene expression data

    Bioinformatics 26:139–140.

    https://doi.org/10.1093/bioinformatics/btp616

    • Google Scholar
47. 1. Rokas A

    (2008) The molecular origins of multicellular transitions

    Current Opinion In Genetics and Development 18:472–478.

    https://doi.org/10.1016/j.gde.2008.09.004

    • Google Scholar
48. 1. Romero PR
    2. Zaidi S
    3. Fang YY
    4. Uversky VN
    5. Radivojac P
    6. Oldfield CJ
    7. Cortese MS
    8. Sickmeier M
    9. LeGall T
    10. Obradovic Z
    11. Dunker AK

    (2006) Alternative splicing in concert with protein intrinsic disorder enables increased functional diversity in multicellular organisms

    Proceedings of the National Academy of Sciences of the United States of America 103:8390–8395.

    https://doi.org/10.1073/pnas.0507916103

    • Google Scholar
49. 1. Ruiz-Trillo I
    2. Burger G
    3. Holland PWH
    4. King N
    5. Lang BF

    et al. (2007) The origins of multicellularity: a multi-taxon genome initiative

    Trends in Genetics 23:113–118.

    https://doi.org/10.1016/j.tig.2007.01.005

    • Google Scholar
50. 1. Ruiz-Trillo I
    2. Roger AJ
    3. Burger G
    4. Gray MW
    5. Lang BF

    (2008) A phylogenomic investigation into the origin of Metazoa

    Molecular Biology and Evolution 25:664–672.

    https://doi.org/10.1093/molbev/msn006

    • Google Scholar
51. 1. Savage RM
    2. Danilchik MV

    (1993) Dynamics of germ plasm localization and its inhibition by ultraviolet irradiation in early cleavage Xenopus embryos

    Developmental Biology 157:371–382.

    https://doi.org/10.1006/dbio.1993.1142

    • Google Scholar
52. 1. Schaap P

    (2011) Evolutionary crossroads in developmental biology: Dictyostelium discoideum

    Development 138:387–396.

    https://doi.org/10.1242/dev.048934

    • Google Scholar
53. 1. Sebé-Pedrós A
    2. Burkhardt P
    3. Sánchez-Pons N
    4. Fairclough SR
    5. Lang BF
    6. King N
    7. Ruiz-Trillo I

    (2013) Insights into the origin of metazoan filopodia and microvilli

    Molecular Biology and Evolution 30:2013–2023.

    https://doi.org/10.1093/molbev/mst110

    • Google Scholar
54. 1. Sebé-Pedrós A
    2. de Mendoza A
    3. Lang BF
    4. Degnan BM
    5. Ruiz-Trillo I

    (2011) Unexpected repertoire of metazoan transcription factors in the unicellular holozoan Capsaspora owczarzaki

    Molecular Biology and Evolution 28:1241–1254.

    https://doi.org/10.1093/molbev/msq309

    • Google Scholar
55. 1. Sebé-Pedrós A
    2. Roger A
    3. Lang FB
    4. King N
    5. Ruiz-Trillo I

    (2010) Ancient origin of the integrin-mediated adhesion and signaling machinery

    Proceedings of the National Academy of Sciences of the United States of America 107:10142–10147.

    https://doi.org/10.1073/pnas.1002257107

    • Google Scholar
56. 1. Shalchian-Tabrizi K
    2. Minge MA
    3. Espelund M
    4. Orr R
    5. Ruden T
    6. Jakobsen KS
    7. Cavalier-Smith T
    8. Aramayo R

    (2008) Multigene phylogeny of choanozoa and the origin of animals

    PLOS ONE 3:e2098.

    https://doi.org/10.1371/journal.pone.0002098

    • Google Scholar
57. 1. Steenkamp ET
    2. Wright J
    3. Baldauf SL

    (2006) The protistan origins of animals and fungi

    Molecular Biology and Evolution 23:93–106.

    https://doi.org/10.1093/molbev/msj011

    • Google Scholar
58. 1. Suga H
    2. Chen Z
    3. de Mendoza A
    4. Sebé-Pedrós A
    5. Brown MW
    6. Kramer E
    7. Carr M
    8. Kerner P
    9. Vervoort M
    10. Sánchez-Pons N
    11. Torruella G
    12. Derelle R
    13. Manning G
    14. Lang BF
    15. Russ C
    16. Haas BJ
    17. Roger AJ
    18. Nusbaum C
    19. Ruiz-Trillo I

    (2013) The Capsaspora genome reveals a complex unicellular prehistory of animals

    Nature Communications 4:1–9.

    https://doi.org/10.1038/ncomms3325

    • Google Scholar
59. 1. Suga H
    2. Dacre M
    3. de Mendoza A
    4. Shalchian-Tabrizi K
    5. Manning G
    6. Ruiz-Trillo I

    (2012) Genomic survey of premetazoans shows deep conservation of cytoplasmic tyrosine kinases and multiple radiations of receptor tyrosine kinases

    Science Signaling 5:ra35–ra35.

    https://doi.org/10.1126/scisignal.2002733

    • Google Scholar
60. 1. Suga H
    2. Ruiz-Trillo I

    (2013) Development of ichthyosporeans sheds light on the origin of metazoan multicellularity

    Developmental Biology 377:284–292.

    https://doi.org/10.1016/j.ydbio.2013.01.009

    • Google Scholar
61. 1. Tarazona S
    2. Furio-Tari P
    3. Ferrer A
    4. Conesa A

    (2012)

    NOISeq: exploratory analysis and differential expression for RNA-seq data

    R package version 1.0.0.

    • Google Scholar
62. 1. Torruella G
    2. Derelle R
    3. Paps J
    4. Lang BF
    5. Roger AJ
    6. Shalchian-Tabrizi K
    7. Ruiz-Trillo I

    (2012) Phylogenetic relationships within the Opisthokonta based on phylogenomic analyses of conserved single-copy protein domains

    Molecular Biology and Evolution 29:531–544.

    https://doi.org/10.1093/molbev/msr185

    • Google Scholar
63. 1. Trapnell C
    2. Hendrickson DG
    3. Sauvageau M
    4. Goff L
    5. Rinn JL
    6. Pachter L

    (2012) Differential analysis of gene regulation at transcript resolution with RNA-seq

    Nature Biotechnology 31:46–53.

    https://doi.org/10.1038/nbt.2450

    • Google Scholar
64. 1. Trapnell C
    2. Roberts A
    3. Goff L
    4. Pertea G
    5. Kim D
    6. Kelley DR
    7. Pimentel H
    8. Salzberg SL
    9. Rinn JL
    10. Pachter L

    (2012) Differential gene and transcript expression analysis of RNA-seq experiments with TopHat and Cufflinks

    Nature Protocols 7:562–578.

    https://doi.org/10.1038/nprot.2012.016

    • Google Scholar
65. 1. Wilson HV

    (1907) On some phenomena of coalescence and regeneration in sponges

    Journal of Experimental Zoology 5:245–258.

    https://doi.org/10.1002/jez.1400050204

    • Google Scholar
66. 1. Yap K
    2. Lim ZQ
    3. Khandelia P
    4. Friedman B
    5. Makeyev EV

    (2012) Coordinated regulation of neuronal mRNA steady-state levels through developmentally controlled intron retention

    Genes & Development 26:1209–1223.

    https://doi.org/10.1101/gad.188037.112

    • Google Scholar

Author details

1. Arnau Sebé-Pedrós

   1. Department of Functional Genomics and Evolution, Institut de Biologia Evolutiva (CSIC–Universitat Pompeu Fabra), Barcelona, Spain
   2. Departament de Genètica, Facultat de Biologia, Universitat de Barcelona, Barcelona, Spain

   Contribution

   AS-P, Conception and design, Acquisition of data, Analysis and interpretation of data, Drafting or revising the article

   Competing interests

   No competing interests declared.

2. Manuel Irimia

   Donnelly Centre, University of Toronto, Toronto, Canada

   Contribution

   MI, Conception and design, Acquisition of data, Analysis and interpretation of data, Drafting or revising the article

   Competing interests

   No competing interests declared.

3. Javier del Campo

   Department of Functional Genomics and Evolution, Institut de Biologia Evolutiva (CSIC–Universitat Pompeu Fabra), Barcelona, Spain

   Contribution

   JC, Acquisition of data, Analysis and interpretation of data, Drafting or revising the article

   Competing interests

   No competing interests declared.

4. Helena Parra-Acero

   Department of Functional Genomics and Evolution, Institut de Biologia Evolutiva (CSIC–Universitat Pompeu Fabra), Barcelona, Spain

   Contribution

   HP-A, Acquisition of data, Analysis and interpretation of data, Drafting or revising the article

   Competing interests

   No competing interests declared.

5. Carsten Russ

   Genomics Platform, Broad Institute of Harvard and the Massachusetts Institute of Technology, Cambridge, United States

   Contribution

   CR, Acquisition of data, Drafting or revising the article

   Competing interests

   No competing interests declared.

6. Chad Nusbaum

   Genomics Platform, Broad Institute of Harvard and the Massachusetts Institute of Technology, Cambridge, United States

   Contribution

   CN, Conception and design, Acquisition of data, Drafting or revising the article

   Competing interests

   No competing interests declared.

7. Benjamin J Blencowe

   Donnelly Centre, University of Toronto, Toronto, Canada

   Contribution

   BJB, Conception and design, Analysis and interpretation of data, Drafting or revising the article

   For correspondence

   b.blencowe@utoronto.ca

   Competing interests

   BJB: Reviewing editor, eLife.

8. Iñaki Ruiz-Trillo

   1. Department of Functional Genomics and Evolution, Institut de Biologia Evolutiva (CSIC–Universitat Pompeu Fabra), Barcelona, Spain
   2. Departament de Genètica, Facultat de Biologia, Universitat de Barcelona, Barcelona, Spain
   3. Institució Catalana de Recerca i Estudis Avançats (ICREA), Barcelona, Spain

   Contribution

   IR-T, Conception and design, Analysis and interpretation of data, Drafting or revising the article

   For correspondence

   inaki.ruiz@multicellgenome.org

   Competing interests

   No competing interests declared.

• 5,168

  views

• 607

  downloads

• 148

  citations

Views, downloads and citations are aggregated across all versions of this paper published by eLife.