Skip to main content

The origin of multicellularity in cyanobacteria



Cyanobacteria are one of the oldest and morphologically most diverse prokaryotic phyla on our planet. The early development of an oxygen-containing atmosphere approximately 2.45 - 2.22 billion years ago is attributed to the photosynthetic activity of cyanobacteria. Furthermore, they are one of the few prokaryotic phyla where multicellularity has evolved. Understanding when and how multicellularity evolved in these ancient organisms would provide fundamental information on the early history of life and further our knowledge of complex life forms.


We conducted and compared phylogenetic analyses of 16S rDNA sequences from a large sample of taxa representing the morphological and genetic diversity of cyanobacteria. We reconstructed ancestral character states on 10,000 phylogenetic trees. The results suggest that the majority of extant cyanobacteria descend from multicellular ancestors. Reversals to unicellularity occurred at least 5 times. Multicellularity was established again at least once within a single-celled clade. Comparison to the fossil record supports an early origin of multicellularity, possibly as early as the "Great Oxygenation Event" that occurred 2.45 - 2.22 billion years ago.


The results indicate that a multicellular morphotype evolved early in the cyanobacterial lineage and was regained at least once after a previous loss. Most of the morphological diversity exhibited in cyanobacteria today —including the majority of single-celled species— arose from ancient multicellular lineages. Multicellularity could have conferred a considerable advantage for exploring new niches and hence facilitated the diversification of new lineages.


Cyanobacteria are oxygenic phototrophic prokaryotes from which chloroplasts, the light harvesting organelles in plants, evolved. Some are able to convert atmospheric nitrogen into a form usable for plants and animals. During Earth history, cyanobacteria have raised atmospheric oxygen levels starting approximately 2.45 - 2.22 billion years ago and provided the basis for the evolution of aerobic respiration [17]. Cyanobacteria have also evolved extensive morphological diversity. Various patterns of cell organization exist, ranging from single-celled to differentiated multicellular forms with branching patterns. Species of this phylum occupy various habitats. They can be found in marine, freshwater or terrestrial environments, ranging from polar to tropical climate zones. Based on their morphology, they have been divided into five sections [8, 9] (Table 1). Sections I and II comprise single-celled bacteria, whereas sections III to V comprise multicellular forms. The latter sections are distinguished according to their level of organization. Section III is multicellular and undifferentiated, sections IV and V are multicellular and differentiated. The latter have the ability to produce heterocysts for nitrogen fixation and akinetes (climate-resistant resting cells). In addition, species in section V have the ability to branch in multiple dimensions.

Table 1 Subset of cyanobacterial taxa used for the analyses with GenBank accession numbers for 16S rDNA sequences

Different interpretations of multicellularity are currently used [1012]. For cyanobacteria, characterization of multicellularity has been described in previous studies [1316]. Cell to cell adhesion, intercellular communication, and for more complex species, terminal cell differentiation seem to be three essential processes that define multicellular, prokaryotic organisms on this planet [16]. Some forms of complexity found in several multicellular eukaryotes are not present in prokaryotes, but simple forms of multicellularity can be identified in three sections of the phylum cyanobacteria. Multicellular patterns comprise basic filamentous forms as found for section III, as well as more complex forms involving terminal differentiation, present in sections IV and V. In eukaryotes, multicellular complexity ranges from what is comparable to cyanobacteria to cases with up to 55 cell types as estimated for higher invertebrates such as arthropods or molluscs [17]. Considering that cyanobacterial sections III, IV and V resemble some of the first forms of multicellular filaments on Earth, knowing when and how these shapes evolved would further our understanding of complex life forms.

Some of the oldest body fossils unambiguously identified as cyanobacteria have been found in the Kasegalik and McLeary Formations of the Belcher Subgroup, Canada, and are evaluated to be between 1.8 billion and 2.5 billion years old [6, 18]. Studies from ~ 2.0 billion year old formations [18, 19] contain both unicellular and multicellular morphotypes of cyanobacteria. Cyanobacteria certainly existed as early as 2.32 billion years ago, if one accepts the assumption that they were responsible for the rapid accumulation of oxygen levels, known as the "Great Oxygenation Event" [13, 5, 7]. Multicellular fossils belonging to the cyanobacteria are well known from the late Precambrian [12, 20, 21] and possibly already existed 2.32 billion years ago. Other microbe-like multicellular filaments even older than 3.0 billion years have been found several times [2226]. Some of the latter fossils are morphologically similar to species from the cyanobacterial order Oscillatoriales [27, 28], but no clear evidence has been adduced yet. Although biogenicity of some of the oldest fossils has been questioned [29, 30], a large variety of bacteria including anoxic phototrophs already existed by the time cyanobacteria evolved oxygenic photosynthesis [26]. Though impressive for prokaryotes, the fragmentary fossil record alone is not sufficient to disentangle the origin of cyanobacteria and their morphological phenotypes. Therefore, additional methods such as phylogenetic analysis provide a promising possibility to gather further clues on the evolution of such a complex phylum.

Phylogenetic analyses of cyanobacteria have gained in quantity over the past 20 years [4, 3139]. These studies have shown that morphological characterization does not necessarily reflect true relationships between taxa, and possibly none of the five traditional morphological sections is monophyletic. Similar morphologies must have evolved several times independently, but details on this morphological evolution are scarce. Analyses assessing characteristics of cyanobacterial ancestors [37, 39] provide not only fundamental information on the history of cyanobacteria, but also on the evolution of life forms in the Archean Eon.

If one studies phylogenetic relationships based on protein coding genes in bacteria, it is possible to encounter the outcome of horizontal gene transfer (HGT) [40]. This issue is not as problematic for ribosomal DNA [41]. Nonetheless, the problem could be potentially reduced by analyzing datasets of concatenated conserved genes. Identification of these genes for phylogenetic analyses is not without difficulty, and requires in an ideal case comparison of complete genome data [42]. In cyanobacteria, many phylogenetic studies have concentrated on specific clades or smaller subsets of known species in this diverse phylum [39, 4348]. Therefore the genomic data presently available are strongly biased towards certain groups. In particular, genomic studies in cyanobacteria have emphasized marine species from Section I. Marine microphytoplankton (Synechococcus and Prochlorococcus) are a particularly well studied group [43, 45, 47, 48], reflected by 19 sequenced genomes out of 41 cyanobacterial genomes sequenced to date (, accessed in January 2011). From species belonging to section III only two genomes (Trichodesmium erythraeum and Arthrospira platensis) are known. For sections IV (four genomes known) and V (no genomes known) molecular data are rare or missing. As genomic data accumulate, promising phylogenomic approaches to cyanobacteria are being established [3739, 47]. Despite these advances, it is at present difficult to obtain sequences other then 16S rDNA to cover a representative sample of species from all five sections.

The aim of this paper is to use molecular phylogenetic methods to address the evolutionary history of cyanobacteria and the evolution of multicellularity. For this purpose, we established a phylogeny based on 16S rDNA sequences belonging to 1,254 cyanobacterial taxa. From that phylogeny we sampled 58 cyanobacterial taxa that represent all main clades obtained and all five sections described by Castenholz et al. [8, 9], and feature a 1:1 ratio of unicellular to multicellular species. We used several methods to reconstruct the morphological evolution of ancestral lineages, and compared our results to known fossil data. Since the fossil record is inconclusive on the timing and taxonomic position of multicellular cyanobacteria, our study provides independent evidence on the first appearance and evolution of multicellularity among the ancestors of living cyanobacteria.

Results and Discussion

Phylogenetic analysis

Phylogenetic analyses of all identified cyanobacteria

To infer the evolution of multicellularity in cyanobacteria we carried out several phylogenetic analyses. To ensure a correct taxon-sampling, a phylogeny containing 1,254 16S rDNA sequences of cyanobacteria obtained from GenBank was reconstructed (Figure 1). Cyanobacterial morphotypes were assigned to four groups (A-D) which correlate to the five sections described by Castenholz et al. [9]. Using this nomenclature, sub-groups in the phylogeny were assigned to one of the four different morphological groups (A-D) according to their dominant shape. In total 14 sub-groups were identified for the phylogenetic tree. Five sub-groups consist of unicellular species from section I (A1-A5), two sub-groups are composed of single celled section II bacteria (B1, B2), four sub-groups are made up of multicellular species belonging to section III (C1-C4) and two sub-groups cover differentiated species from section IV and V (D1-D2). One sub-group contains both species from section I and III and is therefore designated as AC1. The phylogeny further contains six chloroplast genomes from the eukaryotic phyla Glaucophyta, Rhodophyta and the division Chlorophyta. Chloroplast sequences branch close to the bases and form a sister group to the cyanobacterial sub-groups mentioned. Furthermore six different Eubacteria were included in the phylogeny. They appear to form a distinct outgroup to the cyanobacteria and chloroplasts.

Figure 1

Phylogenetic tree of 1,254 cyanobacterial species. Maximum likelihood phylogram of cyanobacteria, based on GTR+G+I substitution model. Six eubacterial species form an outgroup. The ingroup contains 1,254 cyanobacterial strains and six different chloroplast sequences. Bootstrap values (> 50%) calculated from 100 re-samplings are displayed at the nodes. Colors define major morphological characters in the groups. Yellow are single-celled cyanobacteria of section I; orange single-celled from section II; green are multicellular, undifferentiated cyanobacteria from section III; blue are multicellular and differentiated bacteria from section IV; and pink from section V. Sections as described by Castenholz 2001 [9]. Different sub-groups (AC1;A1-A5;B1, B2;C1-C4;D1-D2) are defined for the phylogeny. Nomenclature of groups correlates with morphological sections as illustrated in the legend. From these sub-groups taxa were sampled for further analyses. A complete list with species included in the analysis can be found in Additional File 7.

Phylogenetic analyses to identify an outgroup

Rooted and unrooted phylogenetic analyses reconstructed with maximum likelihood and Bayesian inference and based on 16S rRNA gene sequences of 27 eubacterial species, including 5 cyanobacteria revealed congruent results. Cyanobacteria form a monophyletic group. Figure 2 shows the unrooted Bayesian consensus tree which supports cyanobacterial monophyly with posterior probabilities (PP)/bootstrap values (BV) of 1.0/100%. Phylogenetic trees constructed with an archaean outgroup support cyanobacterial monophyly with PP/BV of 1.0/98% (Additional File 1). In both cases, Plantomyces brasilienses and Chlamydia trachomatis, both gram negative bacteria, form a sister group to the cyanobacteria. This does not agree with other studies [4952], where Deinococcus-thermus was suggested to be the closest eubacterial relative to cyanobacteria. These discrepancies may be due to a lack of information when solely using 16S rRNA gene sequences for such distant relations. Furthermore, our results confirm the basal position of Gloeobacter violaceus, closest to the rest of the eubacteria, as found elsewhere [51]. This supports previous findings which state that Gloeobacter violaceus diverged very early from cyanobacteria living today [32, 33, 53, 54]. Gloeobacter shows differences in cell structure and metabolism that clearly distinguish it from the rest of extant cyanobacteria [55, 56]. It lacks thylacoid membranes and many genes from Photosystems I and II. Phylogenetic relations of the other eubacterial species show only weak support and are therefore not discussed further.

Figure 2

Unrooted Bayesian consensus tree of Eubacteria including five cyanobacterial species. Unrooted phylogenetic tree of 16S rRNA gene sequences from 27 eubacterial species reconstructed using Bayesian methods. Posterior probabilities (black) and bootstrap values (red) from 100 re-samplings are displayed at the nodes. Cyanobacteria, represented by 5 species, form a monophyletic group with Gloeobacter violaceus being closest to other eubacterial species.

We separately tested each of the 22 eubacterial species originating from a diverse set of non-cyanobacterial phyla, with a subset of the cyanobacteria (58 taxa). The latter were chosen from the large dataset containing 1,254 taxa, and cover all sub-groups of the tree (Table 1). This subset was used for all subsequent phylogenetic analyses. Though multicellular species seem to dominate the known cyanobacteria, we chose to sample a taxa set containing unicellular and multicellular morphotypes in a 1:1 ratio. That way biases towards certain character states would be excluded. Furthermore, taxa used in the analyses should represent species from all five sections described by Castenholz et al. [9]. Given our interest in the base of the phylogeny, a greater number of taxa were sampled from basal sub-groups. Due to a lack of data available on GenBank at the present state of research, efforts to build a phylogenetic reconstruction of this size (58 species) using additional ribosomal protein sequences failed. But genomic data are accumulating (57 genomes in progress according to GenBank) and will soon offer possibilities for further extensive analyses.

Results of six phylogenetic trees are displayed in Figure 3 (Additional file 2: Newick format of all trees). The majority of the trees exhibit a topology that agrees with Figure 2, with the position of Gloeobacter violaceus close to the outgroup. Strong differences are found in group support within the trees. In 14 of the 22 trees, three nodes could be identified which lead to three clades, named here E (Entire five sections(A-D)), AC and C (nomenclature as described for the large tree; Figure 1). Gloebacter violaceus and Synechococcus P1 are found at the base of the cyanobacterial phylogeny in 16 trees, from which 7 trees exhibit Gloeobacter violaceus closest to the eubacterial outgroup.

Figure 3

Bayesian consensus trees of cyanobacterial subset using dierent eubacterial outgroups. Six out of 22 phylogenetic trees reconstructed with Bayesian inference. For each tree an outgroup from a different eubacterial phylum was chosen. Posterior probabilities are displayed at the nodes. Green color represents multicellular cyanobacteria from section III, green-yellow gradient covers species from unicellular section I and multicellular section III, and purple depicts all five different morphological sections present in cyanobacteria. The majority of outgroups exhibits a similar tree topology. For further analyses Beggiatoa sp. was selected as an outgroup.

In total 14 trees showed congruent topologies. From the 14 eubacteria which have been used as an outgroup in these trees, we chose Beggiatoa sp. as an outgroup for further analyses because its 16S rRNA gene sequence exhibits the shortest distance to the cyanobacteria.

Phylogenetic analyses of a cyanobacterial subset

Phylogenetic analyses of 16S rRNA gene sequences from a subset of 58 cyanobacterial taxa were conducted using maximum likelihood (Additional File 3) and Bayesian inference (Figure 4). For taxa that diverged a long time ago, there is a possibility of sequence saturation, in which case further mutations would have no effect on the distance between sequences any more. We could significantly reject the possibility of sequence saturation for our alignment (Additional File 4).

Figure 4

Phylogenetic tree of a cyanobacterial subset. Bayesian consensus cladogram of 16S rDNA sequences from 58 cyanobacterial strains, based on GTR+G+I substitution model, with Beggiatoa sp. used as outgroup. Posterior probabilities (> 0.9) are shown in black at nodes and bootstrap values (> 50%) in red. Posterior probabilities were calculated from 265,858 trees and bootstrap values from 500 re-samplings of the original data set. Colors define groups: yellow are single-celled cyanobacteria of section I; orange single-celled from section II; green are multicellular, undifferentiated cyanobacteria from section III; blue are multicellular and differentiated bacteria from section IV; and pink from section V. Sections as described by Castenholz 2001 [9]. AC, B, C, E and E1 denote phylogenetic clades described in the text.

A general substitution model (GTR+G+I) was applied for both analyses. Results of the maximum likelihood and Bayesian methods are highly congruent. Result of the Bayesian analysis with posterior probabilities (black) and bootstrap values (red) displayed at the nodes is pictured in Figure 4. Posterior probabilities above 0.95 and bootstrap values over 70% are considered to represent a high phylogenetic support. Bootstrap values between 50% and 70% are considered weak support. Posterior probabilities below 0.90 and bootstrap values below 50% are not displayed. At deep nodes, the tree topology is fully resolved with high posterior probabilities. Apart from section V, none of the morphological sections described by Castenholz et al. [9] is monophyletic. Compared to the outgroup Beggiatoa sp., branch lengths are relatively short, which seems surprising given the old age of the phylum. Rates of evolution in cyanobacteria are extremely slow. This so called "hypobradytelic" tempo would explain their short evolutionary distances [20, 57, 58].

Cyanobacteria form three distinct clades mentioned earlier (Figure 3). Clades E, AC and C exhibit posterior probabilities (PP)/bootstrap values (BV) of 1.0/51%, 0.99/-, and 1.0/97% respectively (no support: "-"). Clade E comprises all taxa analyzed from section II, some from section I (Synechocystis, Microcystis, Gloeothece and others), some from section III (Oscillatoria, Trichodesmium, Arthrospira, Lyngbya, Microcoleus, Spirulina and others) and all from sections IV and V. Within clade E two subclades, E1 (species from section II; PP/BV = 1.0/81%) and B (species from sections IV and V among others; PP/BV = 1.0/100%), are found. Clade AC contains species from section I and III (among others, species from the genera Synechococcus, Prochlorococcus, Oscillatoria, Plectonema). Clade C consists of Pseudanabaena species, Arthronema gygaxiana and Phormidium mucicola belonging to section III. Gloeobacter violaceus is placed closest to the outgroup. Several phylogenetic studies were conducted showing approximate agreement with the tree topology generated here [4, 3139, 54]. To check the consistency of results from the maximum likelihood and Bayesian analysis to previous studies, we compare our results to the trees produced by Honda et al. [32], Turner et al. [33] who used 16S rDNA sequences, and Swingley et al. [38] who used a genomic approach.

The tree from Figure 2 in Honda et al. [32] shows overall strong congruences with our tree. The only exception is that in Honda et al. [32] "Synechococcus elongatus Toray" is placed separately between Gloeobacter and the rest of the cyanobacteria. We found that "Synechococcus elongatus Toray" (identical to Thermosynechococcus elongatus BP1) is located within clade AC in our study and not next to Gloeobacter violaceus.

In Turner et al. [33], the major clades are congruent with those inferred in our study, but there are a few differences in the relationships among these clades. In that study, the analog of clade E1 is sister to clade AC, which is not the case in our consensus tree. Furthermore, Synechococcus C9 is grouped with Synechococcus P1, which might be due to long branch attraction. In our phylogenetic tree, Synechococcus C9 is grouped within clade AC, a relationship supported by high posterior probabilities and bootstrap values (1.0/99%). Clade C in our study is placed in the same position as in the tree from Turner et al. [33].

Swingley et al. [38], used a phylogenomic approach to investigate cyanobacterial relationships. Due to limited, biased genome data available at present, some clades present in our tree are missing in that study. Even so, the main clades retrieved in that study are mostly congruent with clades in our tree.

Monophyly of section V (the branching, differentiated cyanobacteria) shown in our tree agrees with Turner et al. [33] and other studies [36, 54]. Nonetheless it is possible that the monophyly of section V bacteria is due to limited taxon sampling, since polyphyly has been detected for section V in another study [59]. Gloeobacter violaceus is placed as the first diverging lineage in the phylogeny after the outgroup, as suggested by previous studies [4, 3235, 37, 39, 54]. Our phylogenetic reconstruction also confirms the placement of taxa belonging to section I and III throughout the tree [4, 3137, 39, 54]. The finding that possibly none of the traditional morphological sections are monophyletic, clearly indicates that similar morphologies have been gained and lost several times during the evolutionary history of living cyanobacteria. Overall, the strong phylogenetic agreement between this and earlier studies confirms the suitability of the tree presented here for further analyses of morphological evolution.

Ancestral character state reconstruction

Our analysis indicates that multicellularity is a phylogenetically conservative character (p-value < 0.01). If the terminal taxa of the Bayesian consensus tree are randomly re-shuffled, a count through 1,000 re-shuffled trees gives an average of 20 transition steps. However an average of only nine parsimonious transitions was observed in a count through 10,000 randomly sampled trees of our ancestral character state reconstruction.

Results of the character state reconstruction using the AsymmMK model with transition rates estimated by Mesquite 2.71 [60] are displayed in Figure 5. Using maximum likelihood analysis, average frequencies of the characters were counted across 10,000 trees randomly sampled from the two Metropolis-coupled Markov Chain Monte Carlo (MC3) searches of the Bayesian tree reconstruction.

Figure 5

Ancestral character state reconstruction using maximum likelihood. Ancestral character state reconstruction with maximum likelihood analysis, using the "Asymmetrical Markov k-state 2 parameter"(AsymmMk) model implemented in Mesquite 2.71 [60]. Transition rates were estimated by the program (Table 2). Analysis was run over 10,000 randomly sampled trees from the Bayesian analysis and plotted on the Bayesian consensus tree. Possible states are unicellular (yellow) and multicellular (black). Relative likelihood probabilities for each character state are represented with a pie chart at nodes. The white part in the pie charts indicates the fraction of trees where the node was absent. Posterior probabilities (black) and bootstrap values (red) from the phylogenetic analyses are displayed at the nodes. Asterisks denote supported nodes for which posterior probabilities and bootstrap values are presented in Figure 4. At nodes 3, 4 and 5 a multicellular ancestry is very likely. Back mutations to unicellularity occur at least five times. A back mutation to multicellularity occurs at least once. Clades where transitions occurred are labelled.

Cyanobacteria share a unicellular ancestor, but multicellularity evolved early in the cyanobacterial lineage. We identified multicellular character states for three basic ancestors leading to clades E, AC and C in our tree. Together, these clades encompass the entirety of the morphological sections II, III, IV and V. Additionally character states were reconstructed using maximum likelihood analysis and fixed transition rates to analyze properties of the data set. Transition rates are presented in Table 2. Probabilities for character states at nodes 3, 4 and 5 were examined in detail (Table 3). A multicellular ancestry is very likely for these three nodes. For node 3 the relative probabilities of a multicellular ancestor range from 0.79 to 1.00, depending on the probability of the transition rates. For node 4 with varying transition rates, the relative probabilities of a multicellular ancestor range from 0.83 to 1.00. For node 5 the probabilities for multicellularity range from 0.90 to 1.00.

Table 2 Different Transition rates with whom ancestral character states were estimated.
Table 3 Ancestral character states of nodes 3, 4 and 5 using different transition rates and methods.

The maximum likelihood analysis is not contradicted by a Maximum Parsimony optimization (Table 3 and Additional File 5). Applying maximum parsimony as a reconstruction method, the uniquely best states were counted across 10,000 trees randomly sampled from the two (MC3) runs of the Bayesian tree reconstruction. The relative probabilities for a multicellular ancestor at nodes 3, 4 and 5 are 0.68, 0.68 and 0.69, respectively. In contrast, the relative probabilities for a unicellular ancestor at nodes 3, 4 and 5 under parsimony reconstruction are 0.0013, 0.0014 and 0.0014, respectively.

Using Bayesian methods, a similar pattern is observed for these nodes. As an evolutionary model, BayesFactors revealed that a "hyperprior" approach with exponential prior distributions, whose means were sampled from a uniform distribution between 0 and 10 gave the best fit. Transition rates were estimated to be almost equal. Figure 6 displays the posterior probability distributions of character states at these three nodes as they were estimated over 10,000 randomly sampled trees. At nodes 3 and 5 posterior probabilities of a multicellular character state display values above 0.90 for most of the trees. At node 4 a multicellular state is more likely as well. Posterior probabilities at node 4 are above 0.75 for most of the trees.

Figure 6

Ancestral character states of nodes 3, 4 and 5 using Bayesian analysis. Posterior probability distribution for a unicellular character state (yellow) and a multicellular character state (black) at nodes 3, 4 and 5 from 10,000 Bayesian trees. 2× 5,000 trees were randomly sampled from 2 MC3-searches. Analysis was performed using BayesTraits. Posterior distributions were derived from reversible jump MCMC-search of 30 million iterations using a hyperprior approach. The probability of a multicellular ancestry is shifted towards 1 for each of the three nodes.

At least five reversals to unicellularity occurred in the tree, three of them within clade AC. The first transition occurred on a branch which led to a group of thermophilic cyanobacteria: Acharyochloris sp., Synechococcus lividus C1 and Thermosynechococcus elongatus. Posterior probabilities (PP) and bootstrap values (BV) for this group are 0.99/73%, whereas the sister group within AC is supported by 0.96/66% (PP/BV). The second transition within clade AC led also to a thermophilic cyanobacterium Synechococcus C9. Sister relation of this species to a filamentous thermophilic cyanobacterium is supported by 1.0/99% (PP/BV). The last transition in clade AC occurred within the group including the marine pico-phytoplankton genera Synechococcus and Prochlorococcus. The filamentous Prochlorothrix hollandica is supposed to be the closest relative to the group that includes marine pico-phytoplankton, supported by 1.0/61% (PP/BV). Clade AC has a PP of 0.99, while its BV is below 50%. Although bootstrap support is below 70% for clade AC and some groups within it, posterior probabilities show a very high support (> 0.95). Simulation studies have shown that posterior probabilities approach the actual probability of a clade [6163]. Bootstrapping tends to underestimate the actual probability of a true clade. Although, posterior probabilities tend to be erroneous if the model of evolution is underparameterized, overparameterization has only a minor effect on the posterior probabilities. Therefore, using a complex model of evolution, such as the "general time reversible with gamma distributed rate variation"(GTR+G), is recommended [62, 63]. We used the GTR+G+I model for our analysis, and assume that nodes with a PP higher than 0.95 are reliable.

It is very likely that at least one additional reversal to unicellularity occurred in clade E1, but phylogenetic support is not high enough to locate the exact position of this transition. Similarly, support for the nodes where the other transition to multicellularity within clade E occurred is missing. The exact locations of reversals within clade E therefore are not certain and a scenario where multiple reversals occurred cannot be excluded. In clade E, there is also a reversal to multicellularity observed in Spirulina sp. PCC 6313. The location of this transition is supported by posterior probabilities of 0.99 at two ancestral nodes.

Stucken et al. [64] compared gene sets of multicellular cyanobacteria and found that at least 10 genes are essential for the formation of filaments. Besides genes previously thought to be correlated with heterocyst formation (hetR, patU3 and hetZ) they found seven genes coding for hypothetical proteins. The species they compare are all located within clade E in our tree, most of them being differentiated. Unfortunately no genomes from multicellular species in more basal clades are available at present. But genome projects of Phormidium sp. ISC 31 and Plectonema sp. ISC 33 are presently being conducted If these species turn out to group with Phormidium mucicola IAM M-221 and Plectonema sp. F3 from the basal clades C and AC in our study, this could provide important information on the original metabolic pathways in ancient multicellular cyanobacteria and on possible advantages of multicellularity.

The majority of cyanobacteria living today are described as successful ecological generalists growing under diverse conditions [20]. Our analysis indicates that this diverse range of cyanobacterial morphotypes found in various habitats today —whether multicellular or unicellular— has evolved from multicellular ancestors.

Gaining and losing multicellularity

In eukaryotes, simple multicellular forms build the foundation for the evolution of complex multicellular organisms. Although complex multicellularity exhibiting more than three cell types is presumably missing in prokaryotes, bacteria invented simple multicellular forms possibly more than 1.5 billion years earlier than eukaryotes [2426, 65]. Multicellularity has been described as one of several major transitions that occurred in the history of life. These transitions between different units of selection [66] resulted in changes in the organizational confines of the individual. Maynard Smith and Szathmary [67](1995, p.6) summarize eight major transitions in the evolution of life after which, "entities that were capable of independent replication before the transition can replicate only as part of a larger whole after it". These transitions can create new units of selection at a higher level of complexity [68]. Origin of chromosomes, origin of the eukaryotic cell, origin of multicellular organisms and the origin of eusocial communities are some major transitions that redefine the degree of individuality [66, 67, 69, 70]. Some transitions are thought to be unique, such as the evolution of meiosis or the evolution of the genetic code. Other major transitions occurred several times independently, such as the evolution of eusociality [71, 72] and multicellularity [10, 66, 7375]. There is a tendency to assume that these transitions occur in a progression that leads to an increase in complexity. However, it seems that in cyanobacteria this is not the case. Anatomical complexity has been lost during their evolution several times (Figure 5). In a similar fashion, a complex character such as eusociality has been lost several times in halictid bees [72, 76]. Conversely the phylogeny indicates that multicellularity re-evolved in Spirulina. Regaining complex characters has been observed in other studies as well [7779]. Nonetheless, some studies state that re-evolution of a complex character after a previous loss is not possible [80, 81]. Such studies argue that according to 'Dollo's law', a loss of complexity is irreversible [82], a statement that is not supported in the cyanobacterial case. Repeated transitions in either direction are possible.

Prokaryotic fossil record before the "Great Oxygenation Event": Evidence for multicellular cyanobacteria?

Various claims for life during the early Archean Eon, more than 3.00 billion years ago exist. Most of them from two regions: the Berberton Greenstone Belt, South Africa (around 3.20-3.50 billion years old) and the Pilbara Craton, Western Australia (around 2.90-3.60 billion years old). For some of these "fossils" a biological origin is questioned [26, 27, 83], but for others biogenicity is very likely [23, 25, 26, 8487]. These candidates for early life have clear age constraints and there is no non-biological explanation for these structures. The ages and possible metabolic features of seven fossils of proposed biological origin are plotted in Figure 7 (1-7) [23, 25, 26, 8487]. Some of these fossils are assumed to have been photosynthetic and mat builders, characteristics that can be identified in cyanobacteria as well. One of the oldest fossils recorded, 3.45 billion year old prokaryotic remains found in the Panorama Formation, East Pilbara Craton, Western Australia exhibit a filamentous morphotype and possibly carried out anoxygenic photosynthesis [25, 26].

Figure 7

Timeline with prokaryotic fossil record. Timeline with geological events (A) and prokaryotic fossil record (B). (A) Formation of Earth [118], first evidence of continental crust [119], formation of continents [118], and glaciation events described in the Snowball Earth hypothesis [120]. (B) The oldest conclusive cyanobacterial fossils are found in around 2.15 billion year old rocks. 1-7: Fossils from the Archean Eon [23, 25, 26, 8487]. 8: chroococcacean fossils [24]; 9: oscillatorian fossils [24]. I-V: cyanobacterial fossils [1820]. 10: eukaryotic fossils [65].

Some late Archean fossils show an oscillatorian or chroococcacean morphotype (Figure 7: 8, 9). 2.52 and 2.56 billion year old oscillatorian-like fossils [24, 88, 89] could possibly represent close relatives of cyanobacterial ancestors. 2.72 billion year old filamentous bacteria [24] could potentially represent one of the first multicellular cyanobacteria detected. For single celled forms, 2.56 billion year old unicellular fossils [8992] could likely represent chroococcacean fossils, relatives of ancestral Gloeobacter violaceus or Synechococcus sp. P1 (Figure 7).

The first conclusive cyanobacterial fossils from all five sections have been reported from around 2.15 billion year old rocks. In 1976, Hofmann described Microfossils from stromatolitic dolomite stones in the Kasegalik and McLeary Formations of the Belcher Supergroup in Hudson Bay, Northern Canada. Among these fossils are Halythrix which seems to belong to the order Oscillatoriales (section III), Eosynechococcus and Entophysalis both presumably order Chroococcales (section I) and Myxococcoides fossils (section II). In 1997 similar fossils were described by Amard and Bertrand-Safarti in paleoproterozoic cherty stromatolites from the "Formation C (FC)" of the Franceville Group in Gabon, dating back 2.00 billion years. They also characterized chroococcalean fossils, particularly Eosynechococcus and Tetraphycus, and filamentous bacteria (Gunflinta) which could likely resemble cyanobacteria and Myxococcoides fossils. Furthermore, large microfossils (so called Archaeoellipsoides elongatus), with akinetes similar to the ones from Anabaena-like species were found [4, 19]. Akinetes are resting cells which are only present in differentiated cyanobacteria from sections IV and V. As it has been confirmed in several studies, sections IV and V share a most recent common ancestor [4, 33, 36]. Therefore these fossil akinetes document the existence of differentiated cyanobacteria 2.00 billion years ago. Given that differentiation in cyanobacteria is evolutionary stable only in a multicellular setting [93], this again supports the notion that multicellular species belonging to the cyanobacteria must have existed earlier than 2.0 billion years ago.

Several studies have assessed prokaryotic history using phylogenetic dating methods [50, 52]. In these studies the origin of cyanobacteria has been estimated around the time of the "Great Oxygenation Event" of 2.20-2.45 billion years ago [2, 7]. Other studies have reported elevations of oxygen levels before the great rise of atmospheric oxygen [7, 94]. Using small and large ribosomal subunit sequences, Blank and Sanchez-Baracaldo [39] estimated the origin of cyanobacteria between 2.7 and 3.1 billion years ago. They also try to address the evolution of cyanobacterial traits and assess that multicellular cyanobacteria did not originate before 2.29-2.49 billion years ago. In the study of Blank and Sanchez-Baracaldo [39], a smaller set of cyanobacterial taxa was used, with some basal multicellular species that are present in clade C of our analysis missing. These taxa could have an essential effect on the timing of the first multicellular cyanobacteria. To resolve this issue further dating analyses would be needed. Clearly, as Blank and Sanchez-Baracaldo point out, for such analyses to ultimately resolve the cyanobacterial history, a larger number of cyanobacterial genome data would be needed to represent all the morphological and genetic diversity within this phylum.


Cyanobacteria, photosynthetic prokaryotes, are one of the oldest phyla still alive on this planet. Approximately 2.20-2.45 billion years ago cyanobacteria raised the atmospheric oxygen level and established the basis for the evolution of aerobic respiration [16]. They introduced a dramatic change in the Earth's atmosphere, which might have created possibilities for more complex lifeforms to evolve. Considering the importance of cyanobacteria for the evolution of life, it seems unfortunate that data sets for a representative phylogenomic analysis are not yet available. A coordinated perspective between research groups and a diversified taxon sampling strategy for genome projects would offer the possibility for more comprehensive studies on cyanobacterial evolution. By presenting results obtained from 16S rDNA data analysis here, we hope to boost interest for more extensive genomic studies in this phylum. Phylogenomic approaches would help to further investigate some of the results in the present work.

Multicellular prokaryotic fossils from the Archean Eon are documented [25, 26], and fossil data can support the possibility of multicellular cyanobacteria in the Archean Eon [24, 8890]. Furthermore, studies describe smaller accumulations of oxygen levels around 2.8 to 2.6 billion years ago [7] and around 2.5 billion years ago [94]. Therefore multicellular cyanobacteria could have evolved before the rise of oxygen in the atmosphere. The "Great Oxygenation Event", also referred to as "oxygen crisis", could presumably have marked one of the first mass extinction events during Earth's history. New habitats developing around 2.32 billion years ago, due to a dramatic change of Earth's atmosphere could have triggered cyanobacteria to evolve the variety of morphotypes preserved until today.

In terms of cell types, cyanobacteria reached their maximum morphological complexity around 2.00 billion years ago [95]. By the time eukaryotes evolved, cyanobacteria already exhibited the full range of their morphological diversity. Due to slow evolutionary rates in cyanobacteria, which have been described as "hypobradytelic" [20, 57, 58], extant cyanobacteria that appear to exhibit the same morphotype as in the Precambrian Eon [96] are reminiscent of the idea of "living fossils". However, one should consider the possibility that what may appear as morphological stasis may be due to developmental constraints at the phylum level. Cyanobacteria apparently reached their maximum complexity early in Earth history, but instead of morphological stasis at the species level, our results suggest that they subsequently changed morphotypes several times during their evolution. This allowed for the exploration of diverse morphotypes within their developmental constraints, including the loss and regaining of multicellular growth forms.

Figure 8 summarizes the morphological evolution of the cyanobacteria inferred in this study. All extant cyanobacteria share a most recent common ancestor that was unicellular. Single-celled species at the base of the tree do not seem to have changed much in their morphology and are possibly comparable to ancient cyanobacteria. Aside from Gloeobacter violaceus and Synechococcus P1, which diverged very early, all cyanobacteria living today share multicellular ancestors. Although complex multicellularity is missing in prokaryotes, these simple multicellular forms have evolved several hundred million years before the appearance of eukaryotes, whose fossil record dates back to 1.8-1.3 billion years ago [65]. In agreement with various proposed selective advantages that multicellular growth could confer [97100], the results presented here indicate that the early origin of multicellularity played a key role in the evolutionary radiation that has led to the majority of extant cyanobacteria on the planet.

Figure 8

Schematic illustration of cyanobacterial evolution. Numbers at the nodes indicate Bayesian posterior probabilities (black) and bootstrap values (red) from the phylogenetic analyses. The most recent common ancestor of all cyanobacteria is optimized to have been unicellular. All cyanobacteria derive from a unicellular most recent common ancestor (node 1). The lineage leading to Gloeobacter violaceus diverges very early from the remaining cyanobacteria. Most major clades of cyanobacteria derive from multicellular ancestors (nodes 3-5).


Taxon sampling

A total of 2,065 16S rRNA gene sequences from the phylum cyanobacteria were downloaded from GenBank. Unidentified and uncultured species were excluded. With this large dataset phylogenetic reconstructions were conducted as described in the next section. Aside from cyanobacteria, the dataset included six chloroplast sequences and six eubacterial sequences: Beggiatoa sp., Thiobacillus prosperus, Agrobacterium tumefaciens, Chlorobium sp., Candidatus Chlorothrix halophila and Escherichia coli HS.

From this large tree a subset of 58 cyanobacterial sequences were selected for further analyses. Accession numbers are provided in Table 1. Species from all five sections described by Castenholz et al. [9] were included. Taxa were chosen to represent a 1:1 ratio of unicellular and multicellular species. The final data set contained 22 single-celled taxa from section I, 7 single-celled taxa from section II, 21 multicellular taxa from section III, 5 multicellular, differentiated taxa from section IV and 3 differentiated, branching taxa from section V as described by Castenholz et al. [9].

An outgroup for further analyses was chosen from a set of eubacterial, non-cyanobacterial species whose 16S rRNA gene sequences were downloaded from GenBank (Table 4). Species were sampled to cover a wide range of different phyla. Aside a set of species from phyla represented in the "tree of life" [51], species from additional phyla as described on NCBI - Taxonomy Browser: Bacteria) were selected for analyses.

Table 4 Non-cyanobacterial species used in this study with GenBank accession numbers for 16S rDNA sequences

Phylogenetic analyses

Phylogenetic analyses of all identified cyanobacteria

The 2,065 16S rRNA gene sequences were aligned using the software MAFFT [101] via Cipres Portal [102]. The alignment was corrected manually using BioEdit v7.0.5 [103]. Poorly aligned and duplicated sequences were excluded from the alignment. From the remaining 1,254 sequences (1235 characters) a phylogenetic tree was reconstructed running 10 maximum likelihood analyses as implemented in RAxML v7.0.4 [104]. GTR + G + I (General time reversible model, G: Gamma correction, I: proportion of invariable sites) [105, 106] was used as an evolutionary substitution model. Bootstrap values were calculated from 100 re-samplings of the dataset and plotted on the best maximum likelihood tree using RAxML v7.0.4. The resultant tree (Figure 1; Additional File 6: newick format; Additional File 7: taxon names) was visualised in FigTree v1.3.1 and graphically edited with Adobe Illustrator CS2

Phylogenetic analyses to identify an outgroup

To test different outgroups, phylogenetic trees were reconstructed using all sampled non-cyanobacterial species (Table 4) plus five representative species from the cyanobacterial phylum (Table 1). Sequences were aligned using Clustal-X with default settings [107] and corrected manually. The trees were built using maximum likelihood and Bayesian inference, with and without an outgroup from the kingdom archaea. Fifty separate maximum likelihood searches were conducted using RAxML v7.0.4 software [104], from which the tree with the best log-likelihood was chosen. Bootstrap support for each tree was gathered from 100 re-samplings. Bayesian analyses were conducted with MRBAYES 3.1 [108] using a GTR + G + I evolutionary model with substitution rates, base frequencies, invariable sites and the shape parameter of the gamma distribution estimated by the program. Two Metropolis-coupled Markov Chain Monte Carlo (MC3) searches with four chains, three heated and a cold one, were run. The analyses started with a random tree and was run for 5,000,000 generations. Trees and parameters were sampled every 100th generation. The trees were checked to show a standard deviation of split frequencies below 0.05. The first 3,000,000 generations were excluded as the burn-in.

Additionally phylogenetic analyses were conducted with Bayesian inference, using each of the 22 eubacterial species separately with the sampled cyanobacterial subset (58 taxa). Alignments were built using Clustal-X software with default settings [107] and corrected manually. For each phylogenetic analysis two (MC3) searches were run for 10,000,000 generations using MRBAYES 3.1 [108]. Trees and parameters were sampled every 100th generation. The first 3,000,000 generations being excluded as a burn-in, assuring that the standard deviation of split frequencies were below 0.05 and log-likelihoods of the trees had reached stationarity. Results were compared and Beggiatoa sp. was chosen as an outgroup for further analyses.

Phylogenetic analyses of a cyanobacterial subset

Sequence alignments of the 16S rRNA gene sequences from the cyanobacterial subset and Beggiatoa sp. (59 taxa, 1166 characters) were carried out using Clustal-X with default settings [107] and corrected manually. Whether the cyanobacterial alignment (excluding the outgroup) was substitutionally saturated was tested using the program DAMBE [109, 110]. The information-entropy based index of substitutional saturation [111] was used to analyze our alignment of 16S rRNA gene sequences. The test performs only on a maximum of 32 species. Therefore we sampled from our phylogeny 32 representative sequences that span the whole tree, and performed the test introduced by Xia et al. [111](Table 1 and Additional File 4).

Phylogenetic reconstruction was carried out using Bayesian analysis and maximum likelihood. Maximum likelihood analysis was performed using GARLI 0.96 [112] and Bayesian analysis was conducted with MRBAYES 3.1 [108]. The evolutionary model of nucleotide substitution that best fitted the data was obtained by using the Akaike Information Criterion as implemented in Modeltest 3.5 [113]. The selected model was GTR + G + I. Substitution rates, base frequencies, invariable sites and the shape parameter of the gamma distribution were estimated by the program. Fifty maximum likelihood searches were performed. Bootstrap values were calculated from 500 re-samplings of the data set. The bootstrap values were plotted on the best ML-tree using the program SumTrees [114] (Additional File 3).

Bayesian analysis was conducted running two (MC3) searches, each with four chains, one cold and three heated. Starting with a random tree, analyses were run for 16,616,000 generations each, with trees being sampled every 100th generation. The trees were checked for convergence of parameters (standard deviation of split frequencies below 0.01, effective sample sizes above 200, potential scale reduction factor equal to 1.0) using Tracer v1.4.1 [115] and the program AWTY [116]. Burn-in was set to 3,323,200 generations each, corresponding to the first 20% of the analyses. The average standard deviation of split frequencies was below 0.01 for the remaining 132,929 trees of each run, indicating that steady state of the log-likelihoods was reached.

Ancestral character state reconstruction

Character state reconstructions were performed using maximum parsimony (MP; Additional File 5) and maximum likelihood criteria as implemented in Mesquite 2.71 [60]. 5,000 trees from each MC3 run were randomly chosen from the post burn-in Bayesian sample and combined. Discrete characters were coded into multicellular or unicellular states. The results over 10,000 Bayesian trees were summarized and displayed on the consensus tree of the Bayesian analysis. For maximum likelihood estimates, both the "Markov k-state 1 parameter model" (MK1 model) and "Asymmetrical Markov k-state 2 parameter model" (AsymmMK model) were applied. Rate of change is the only parameter in the MK1 model. The AsymmMK model exhibits two parameters, describing the forward and backward transitions between states. Phylogenetic conservativeness of multicellularity was tested by comparing the observed distribution of parsimony steps across 10,000 randomly chosen trees from the Bayesian analysis against the distribution from 1,000 trees modified from the Bayesian consensus by randomly shuffling the terminal taxa, while keeping the relative proportion of states unaltered. The root was assumed to be at equilibrium. Transition rates for the MK1 and AsymmMK model were estimated by the program. Rates for the latter models presented in Table 2 were estimated for the consensus tree. To explore properties of the data set, character states were additionally reconstructed with manually fixed transition rates (F1-F6; Table 2). The state of the outgroup was excluded from the analyses to avoid biased inferences within the ingroup.

The character states of nodes 3, 4 and 5 of the Bayesian consensus tree were additionally estimated using a reversible jump MCMC search as implemented in BayesTraits [117]. MCMC was run for 30 million iterations, and a burnin set to 50,000. The analysis was run several times with parameters of the evolutionary model being chosen from different prior distributions. In order to determine which model offered the best fitting priors, models were tested using Bayes Factors. A hyperprior approach with mean-values of the exponential priors derived from a uniform distribution between 0 to 10 was determined to fit best the data. The results of the analysis were visualized in Tracer v1.5 [115].


  1. 1.

    Blankenship RE: Molecular Mechanisms of Photosynthesis. 2002, Blackwell Science, Oxford

    Book  Google Scholar 

  2. 2.

    Bekker A, Holland HD, Wang PL, Rumble D, Stein HJ, Hannah JL, Coetzee LL, Beukes NJ: Dating the rise of atmospheric oxygen. Nature. 2004, 427 (6970): 117-120. 10.1038/nature02260.

    CAS  PubMed  Article  Google Scholar 

  3. 3.

    Kopp RE, Kirschvink JL, Hilburn IA, Nash CZ: The paleoproterozoic snowball Earth: A climate disaster triggered by the evolution of oxygenic photosynthesis. Proceedings Of The National Academy Of Sciences Of The United States Of America. 2005, 102 (32): 11131-11136. 10.1073/pnas.0504878102.

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  4. 4.

    Tomitani A, Knoll AH, Cavanaugh CM, Ohno T: The evolutionary diversification of cyanobacteria: Molecular-phylogenetic and paleontological perspectives. Proceedings Of The National Academy Of Sciences Of The United States Of America. 2006, 103 (14): 5442-5447. 10.1073/pnas.0600999103.

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  5. 5.

    Allen JF, Martin W: Evolutionary biology: Out of thin air. Nature. 2007, 445: 610-612. 10.1038/445610a.

    CAS  PubMed  Article  Google Scholar 

  6. 6.

    Rasmussen B, Fletcher IR, Brocks JJ, Kilburn MR: Reassessing the first appearance of eukaryotes and cyanobacteria. Nature. 2008, 455 (7216): 1101-U9. 10.1038/nature07381.

    CAS  PubMed  Article  Google Scholar 

  7. 7.

    Frei R, Gaucher C, Poulton SW, Canfield DE: Fluctuations in Precambrian atmospheric oxygenation recorded by chromium isotopes. Nature. 2009, 461 (7261): 250-U125. 10.1038/nature08266.

    CAS  PubMed  Article  Google Scholar 

  8. 8.

    Rippka R, Deruelles J, Waterbury JB, Herdman M, Stanier RY: Generic Assignments, Strain Histories and Properties of Pure Cultures of Cyanobacteria. Journal Of General Microbiology. 1979, 111: 1-61.

    Google Scholar 

  9. 9.

    Castenholz RW: Bergey's Manual of Systematic Bacteriology: The Archaea and the Deeply Branching and Phototropic Bacteria: Cyanobacteria. 2001, Springer Verlag

    Google Scholar 

  10. 10.

    Bonner JT: The Origin of Multicellularity. Integr Biol. 1998, 1: 28-36.

    Google Scholar 

  11. 11.

    Solari C, Kessler J, Goldstein RE: Motility, mixing, and multicellularity. Genetic Programming and Evolvable Machines. 2007, 8: 115-129. 10.1007/s10710-007-9029-7.

    Article  Google Scholar 

  12. 12.

    Butterfield NJ: Modes of pre-Ediacaran multicellularity. Precambrian Research. 2009, 173 (1-4): 201-211. 10.1016/j.precamres.2009.01.008.

    CAS  Article  Google Scholar 

  13. 13.

    Giddings TJ, Staehelin LA: Observation of Mieroplasmodesmata in both Heterocyst-Forming and Non-Heterocyst Forming Filamentous Cyanobacteria by Freeze-Fracture Electron Microscopy. Archives of Microbiology. 1981, 129: 295-298. 10.1007/BF00414700.

    Article  Google Scholar 

  14. 14.

    Flores E, Herrero A, Wolk CP, Maldener I: Is the periplasm continuous in filamentous multicellular cyanobacteria?. Trends in Microbiology. 2006, 14: 439-443. 10.1016/j.tim.2006.08.007.

    CAS  PubMed  Article  Google Scholar 

  15. 15.

    Mullineaux CW, Mariscal V, Nenninger A, Khanum H, Herrero A, Flores E, Adams DG: Mechanism of intercellular molecular exchange in heterocyst-forming cyanobacteria. European Molecular Biology Organization Journal. 2008, 27: 1299-1308.

    CAS  Article  Google Scholar 

  16. 16.

    Flores E, Herrero A: Compartmentalized function through cell differentiation in filamentous cyanobacteria. Nature Reviews Microbiology. 2010, 8: 39-50. 10.1038/nrmicro2242.

    CAS  PubMed  Article  Google Scholar 

  17. 17.

    Bonner JT: The evolution of complexity by means of natural selection. 1988, Princeton University Press

    Google Scholar 

  18. 18.

    Hofmann HJ: Precambrian Microflora, Belcher Islands, Canada - Significance And Systematics. Journal Of Paleontology. 1976, 50 (6): 1040-1073.

    Google Scholar 

  19. 19.

    Amard B, Bertrand-Sarfati J: Microfossils in 2000 Ma old cherty stromatolites of the Franceville Group, Gabon. Precambrian Research. 1997, 81 (3-4): 197-221. 10.1016/S0301-9268(96)00035-6.

    CAS  Article  Google Scholar 

  20. 20.

    Schopf JW: Disparate rates, differing fates: Tempo and mode of evolution changed from the Precambrian to the Phanerozoic. PNAS. 1994, 91: 6735-6742. 10.1073/pnas.91.15.6735.

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  21. 21.

    Sergeev VN: The distribution of microfossil assemblages in Proterozoic rocks. Precambrian Research. 2009, 173 (1-4): 212-222. 10.1016/j.precamres.2009.04.002.

    CAS  Article  Google Scholar 

  22. 22.

    Walsh MM: Microfossils And Possible Microfossils From The Early Archean Onverwacht Group, Barberton Mountain Land, South-Africa. Precambrian Research. 1992, 54 (2-4): 271-293. 10.1016/0301-9268(92)90074-X.

    CAS  PubMed  Article  Google Scholar 

  23. 23.

    Tice MM, Lowe DR: Photosynthetic microbial mats in the 3,416-Myr-old ocean. Nature. 2004, 431: 549-552. 10.1038/nature02888.

    CAS  PubMed  Article  Google Scholar 

  24. 24.

    Schopf JW, Kudryavtsec AB, Czaja AD, Tripathi AB: Evidence of Archean life: Stromatolites and microfossils. Precambrian Research. 2006, 158: 141-155. 10.1016/j.precamres.2007.04.009.

    Article  CAS  Google Scholar 

  25. 25.

    Westall F, de Vries ST, Nijman W, Rouchon V, Orberger B, Pearson V, Watson J, Verchovsky A, Wright I, Rouzaud JN, Marchesini D, Severine A: The 3.466 Ga "Kitty's Gap Chert," an early Archean microbial ecosystem. GSA Special Papers. 2006, 405: 105-131.

    Google Scholar 

  26. 26.

    Wacey D: Early Life on Earth: A practical Guide. 2009, Springer

    Book  Google Scholar 

  27. 27.

    Schopf JW: Microfossils of the Early Archean Apex Chert: New Evidence of the Antiquity of life. Science. 1993, 260: 640-646. 10.1126/science.260.5108.640.

    CAS  PubMed  Article  Google Scholar 

  28. 28.

    Schopf JW, Kudryavtsev AB, Czaja AD, Tripathi AB: Evidence of archean life: Stromatolites and microfossils. Precambrian Research. 2007, 158 (3-4): 141-155. 10.1016/j.precamres.2007.04.009.

    CAS  Article  Google Scholar 

  29. 29.

    Brasier M, Green O, Lindsay J, Steele A: Earth's oldest (similar to 3.5 Ga) fossils and the 'Early Eden hypothesis': Questioning the evidence. Origins Of Life And Evolution Of The Biosphere. 2004, 34 (1-2): 257-269. 10.1023/B:ORIG.0000009845.62244.d3.

    PubMed  Article  Google Scholar 

  30. 30.

    Brasier M, McLoughlin N, Green O, Wacey D: A fresh look at the fossil evidence for early Archaean cellular life. Phil Trans R Soc B. 2006, 361: 887-902. 10.1098/rstb.2006.1835.

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  31. 31.

    Giovannoni SJ, Turner S, Olsen GJ, Barns S, Lane DJ, Pace NR: Evolutionary Relationships Among Cyanobacteria And Green Chloroplasts. Journal of Bacteriology. 1988, 170: 3584-3592.

    CAS  PubMed  PubMed Central  Google Scholar 

  32. 32.

    Honda D, Yokota A, Sugiyama J: Detection of seven major evolutionary lineages in cyanobacteria based on the 16 S rRNA gene sequence analysis with new sequences of five marine Synechococcus strains. Journal Of Molecular Evolution. 1999, 48 (6): 723-739. 10.1007/PL00006517.

    CAS  PubMed  Article  Google Scholar 

  33. 33.

    Turner S, Pryer KM, Miao VP, Palmer JD: Investigating deep phylogenetic relationships among cyanobacteria and plastids by small submit rRNA sequence analysis. Journal Of Eukaryotic Microbiology. 1999, 46 (4): 327-338. 10.1111/j.1550-7408.1999.tb04612.x.

    CAS  PubMed  Article  Google Scholar 

  34. 34.

    Ishida T, Watanabe MM, Sugiyama J, Yokota A: Evidence for polyphyletic origin of the members of the orders of Oscillatoriales and Pleurocapsales as determined by 16 S rDNA analysis. Fems Microbiology Letters. 2001, 201: 79-82. 10.1111/j.1574-6968.2001.tb10736.x.

    CAS  PubMed  Article  Google Scholar 

  35. 35.

    Garcia-Pichel F, Lopez-Cortes A, Nubel U: Phylogenetic and morphological diversity of cyanobacteria in soil desert crusts from the Colorado Plateau. Applied And Environmental Microbiology. 2001, 67 (4): 1902-1910. 10.1128/AEM.67.4.1902-1910.2001.

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  36. 36.

    Litvaitis MK: A molecular test of cyanobacterial phylogeny: inferences from constraint analyses. Hydrobiologia. 2002, 468: 135-145. 10.1023/A:1015262621848.

    CAS  Article  Google Scholar 

  37. 37.

    Sanchez-Baracaldo P, Hayes PK, Blank CE: Morphological and habitat evolution in the Cyanobacteria using a compartmentalization approach. Geobiology. 2005, 3 (3): 145-165. 10.1111/j.1472-4669.2005.00050.x.

    CAS  Article  Google Scholar 

  38. 38.

    Swingley WD, Blankenship RE, Raymond J: Integrating markov clustering and molecular phylogenetics to reconstruct the cyanobacterial species tree from conserved protein families. Molecular Biology And Evolution. 2008, 25 (4): 643-654. 10.1093/molbev/msn034.

    CAS  PubMed  Article  Google Scholar 

  39. 39.

    Blank C, Sanchez-Baracaldo P: Timing of morphological and ecological innovations in the cyanobacteria - a key to understanding the rise in atmospheric oxygen. Geobiology. 2010, 8: 1-23. 10.1111/j.1472-4669.2009.00220.x.

    CAS  PubMed  Article  Google Scholar 

  40. 40.

    Doolittle WF: Phylogenetic classification and the universal tree. Science. 1999, 284 (5423): 2124-2128. 10.1126/science.284.5423.2124.

    CAS  PubMed  Article  Google Scholar 

  41. 41.

    Suchard MA: Stochastic models for horizontal gene transfer: Taking a random walk through tree space. Genetics. 2005, 170: 419-431. 10.1534/genetics.103.025692.

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  42. 42.

    Hamel L, Zhaxybayeva O, Gogarten JP: PentaPlot: A software tool for the illustration of genome mosaicism. BMC Bioinformatics. 2005, 6: 139-10.1186/1471-2105-6-139.

    PubMed  PubMed Central  Article  CAS  Google Scholar 

  43. 43.

    Hess WR, Rocap G, Ting CS, Larimer F, Stilwagen S, Lamerdin J, Chisholm SW: The photosynthetic apparatus of Prochlorococcus: Insights through comparative genomics. Photosynthesis Research. 2001, 70: 53-71. 10.1023/A:1013835924610.

    CAS  PubMed  Article  Google Scholar 

  44. 44.

    Robertson BR, Tezuka N, Watanabe MM: Phylogenetic analyses of Synechococcus strains (cyanobacteria) using sequences of 16 S rDNA and part of the phycocyanin operon reveal multiple evolutionary lines and reflect phycobilin content. International Journal Of Systematic And Evolutionary Microbiology. 2001, 51: 861-871.

    CAS  PubMed  Article  Google Scholar 

  45. 45.

    Rocap G, Larimer FW, Lamerdin J, Malfatti S, Chain P, Ahlgren NA, Arellano A, Coleman M, Hauser L, Hess WR, Johnson ZI, Land M, Lindell D, Post AF, Regala W, Shah M, Shaw SL, Steglich C, Sullivan MB, Ting CS, Tolonen A, Webb EA, Zinser ER, Chisholm SW: Genome divergence in two Prochlorococcus ecotypes reflects oceanic niche differentiation. Nature. 2003, 424 (6952): 1042-1047. 10.1038/nature01947.

    CAS  PubMed  Article  Google Scholar 

  46. 46.

    Ernst A, Becker S, Wollenzien UIA, Postius C: Ecosystem-dependent adaptive radiations of picocyanobacteria inferred from 16 S rRNA and ITS-1 sequence analysis. Microbiology-Sgm. 2003, 149: 217-228. 10.1099/mic.0.25475-0.

    CAS  Article  Google Scholar 

  47. 47.

    Dufresne A, Ostrowski M, Scanlan DJ, Garczarek L, Mazard S, Palenik BP, Paulsen IT, de Marsac NT, Wincker P, Dossat C, Ferriera S, Johnson J, Post AF, Hess WR, Partensky F: Unraveling the genomic mosaic of a ubiquitous genus of marine cyanobacteria. Genome Biology. 2008, 9 (5): R90.-10.1186/gb-2008-9-5-r90.

    PubMed  PubMed Central  Article  CAS  Google Scholar 

  48. 48.

    Scanlan DJ, Ostrowski M, Mazard S, Dufresne A, Garczarek L, Hess WR, Post AF, Hagemann M, Paulsen I, Partensky F: Ecological Genomics of Marine Picocyanobacteria. Microbiology And Molecular Biology Reviews. 2009, 73 (2): 249-10.1128/MMBR.00035-08.

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  49. 49.

    Brochier C, Bapteste E, Moreira D, Philippe H: Eubacterial phylogeny based on translational apparatus proteins. Trends in Genetics. 2002, 18: 1-4. 10.1016/S0168-9525(01)02522-7.

    CAS  PubMed  Article  Google Scholar 

  50. 50.

    Battistuzzi FU, Feijao A, Hedges SB: A genomic timescale of prokaryote evolution: insights into the origin of methanogenesis, phototrophy, and the colonization of land. BMC Evolutionary Biology. 2004, 4 (44): 1-14.

    Google Scholar 

  51. 51.

    Ciccarelli F, Doerks T, von Mering C, Creevey CJ, Snel B, Bork P: Toward Automatic Reconstruction of a Highly Resolved Tree of Life. Science. 2006, 311: 1283-1286. 10.1126/science.1123061.

    CAS  PubMed  Article  Google Scholar 

  52. 52.

    Battistuzzi FU, Hedges SB: A Major Clade of Prokaryotes with Ancient Adaptations to Life on Land. Mol Biol Evol. 2009, 26 (2): 335-343. 10.1093/molbev/msn247.

    CAS  PubMed  Article  Google Scholar 

  53. 53.

    Delwiche CF, Kuhsel M, Palmer JD: Phylogenetic Analysis Of Tufa Sequences Indicates A Cyanobacterial Origin Of All Plastids. Molecular Phylogenetics And Evolution. 1995, 4 (2): 110-128. 10.1006/mpev.1995.1012.

    CAS  PubMed  Article  Google Scholar 

  54. 54.

    Seo PS, Yokota A: The phylogenetic relationships of cyanobacteria inferred from 16SrRNA, gyrB, rpoC1 and rpoD1 gene sequences. The Journal of General and Applied Microbiology. 2003, 49: 191-203. 10.2323/jgam.49.191.

    CAS  PubMed  Article  Google Scholar 

  55. 55.

    Mangels D, Kruip J, Berry S, Rögner M, Boekema EJ, Koenig F: Photosystem I from the unusual cyanobacterium Gloeobacter violaceus. Photosynthesis Research. 2002, 72: 307-319. 10.1023/A:1019822316789.

    CAS  PubMed  Article  Google Scholar 

  56. 56.

    Nakamura Y, Kaneko T, Sato S, Mimuro M, Myashita H, Tsuchiya T, Sasamoto S, Watanabe A, Kawashima K, Kishida Y, Kiyokawa C, Kohara M, Matsumoto M, Matsuno A, Nakazaki N, Shimpo S, Takeuchi C, Yamada M, Tabata S: Complete Genome Structure of Gloeobacter violaceus PCC7421 a Cyanobacterium that Lacks Thylakoids. DNA Research. 2003, 10: 137-145. 10.1093/dnares/10.4.137.

    CAS  PubMed  Article  Google Scholar 

  57. 57.

    Siefert JL, Fox GE: Phylogenetic mapping of bacterial morphology. Microbiology And Molecular Biology Reviews. 1998, 144: 2803-2808.

    CAS  Google Scholar 

  58. 58.

    Kremer B: Mat-forming coccoid cyanobacteria from early Silurian marine deposits of Sudetes, Poland. Acta Palaeontologica Polonica. 2006, 51: 143-154.

    Google Scholar 

  59. 59.

    Gugger MF, Hoffmann L: Polyphyly of true branching cyanobacteria (Stigonematales). International Journal of Systematic and Evolutionary Microbiology. 2004, 54: 349-357. 10.1099/ijs.0.02744-0.

    CAS  PubMed  Article  Google Scholar 

  60. 60.

    Maddison WP, Maddison DR: Mesquite: a modular system for evolutionary analysis. Version 2.71. 2009, []

    Google Scholar 

  61. 61.

    Erixon P, Svennblad B, Britton T, Oxelman B: Reliability of Bayesian Posterior Probabilities and Bootstrap Frequencies in Phylogenetics. Syst Biol. 2003, 52 (5): 665-673. 10.1080/10635150390235485.

    PubMed  Article  Google Scholar 

  62. 62.

    Huelsenbeck J, Rannala B: Frequentist Properties of Bayesian Posterior Probabilities of Phylogenetic Trees Under Simple and Complex Substitution Models. Syst Biol. 2004, 53 (6): 904-913. 10.1080/10635150490522629.

    PubMed  Article  Google Scholar 

  63. 63.

    Alfaro ME, Holder MT: The Posterior and the Prior in Bayesian Phylogenetics. Annu Rev Ecol Evol Syst. 2006, 37: 19-42. 10.1146/annurev.ecolsys.37.091305.110021.

    Article  Google Scholar 

  64. 64.

    Stucken K, John U, Cembella A, Murillo AA, Soto-Liebe K, Fuentes-Valdés JJ, Friedel M, Plominsky A, Váquez AM, Glöckner G: The Smallest Known Genomes of Multicellular and Toxic Cyanobacteria: Comparison, Minimal Gene Sets for Linked Traits and the Evolutionary Implications. PLoS One. 2010, 5 (2): e9235.-10.1371/journal.pone.0009235.

    PubMed  PubMed Central  Article  CAS  Google Scholar 

  65. 65.

    Knoll AH, Javaux EJ, Hewitt D, Cohen P: Eukaryotic organisms in Proterozoic oceans. Phil Trans R Soc B. 2006, 361: 1023-1038. 10.1098/rstb.2006.1843.

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  66. 66.

    Buss LW: The Evolution of Individuality. 1987, Princeton University Press

    Google Scholar 

  67. 67.

    Maynard Smith J, Szathmáry E: The Major Transitions in Evolution. 1995, W.H. Freeman and Spektrum

    Google Scholar 

  68. 68.

    Buss LW: Slime molds, ascidians, and the utility of evolutionary theory. Proceedings Of The National Academy Of Sciences Of The United States Of America. 1999, 96 (16): 8801-8803. 10.1073/pnas.96.16.8801.

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  69. 69.

    Michod RE: Darwinian Dynamics-Evolutionary Transitions in Fitness and Individuality. 1999, Princeton University Press

    Google Scholar 

  70. 70.

    Tuomi J: Genetic heterogeneity within organisms and the evolution of individuality. Journal Of Evolutionary Biology. 2004, 17 (6): 1182-1183. 10.1111/j.1420-9101.2004.00811.x.

    CAS  PubMed  Article  Google Scholar 

  71. 71.

    Cameron SA, Mardulyn P: Multiple Molecular Data Sets Suggest Independent Origins of Highly Eusocial Behavior in Bees (Hymenoptera: Apinae). Syst Biol. 2001, 50: 194-214. 10.1080/10635150151125851.

    CAS  PubMed  Article  Google Scholar 

  72. 72.

    Danforth BN: Evolution of sociality in a primitively eusocial lineage of bees. PNAS. 2002, 99: 286-290. 10.1073/pnas.012387999.

    CAS  PubMed  Article  Google Scholar 

  73. 73.

    Carroll SB: Chance and necessity: the evolution of morphological complexity and diversity. Nature. 2001, 409: 1102-1109. 10.1038/35059227.

    CAS  PubMed  Article  Google Scholar 

  74. 74.

    King N: The Unicellular Ancestry of Animal Development. Developmental Cell. 2004, 7: 313-325. 10.1016/j.devcel.2004.08.010.

    CAS  PubMed  Article  Google Scholar 

  75. 75.

    Grosberg RK, Strathmann RR: The Evolution of Multicellularity: A Minor Major Transition?. Annu Rev Ecol Evol Syst. 2007, 38: 621-54. 10.1146/annurev.ecolsys.36.102403.114735.

    Article  Google Scholar 

  76. 76.

    Danforth BN, Conway L, Shuqing J: Phylogeny of Eusocial Lasioglossum Reveals Multiple Losses of Eusociality Within a Primitively Eusocial Clade of Bees (Hymenoptera:Halictidae). Syst Biol. 2003, 52: 23-36. 10.1080/10635150390132687.

    PubMed  Article  Google Scholar 

  77. 77.

    Chapman H, Houliston GJ, Robson B, Iline I: A case of reversal: The evolution and maintenance of sexuals from parthenogenetic clones in Hieracium pilosella. International Journal of Plant Science. 2003, 164: 719-728. 10.1086/376819.

    Article  Google Scholar 

  78. 78.

    Whiting MF, Bradler S, Maxwell T: Loss and recovery of wings in stick insects. Nature. 2003, 421: 264-267. 10.1038/nature01313.

    CAS  PubMed  Article  Google Scholar 

  79. 79.

    Domes K, Norton RA, Maraun M, Scheu S: Reevolution of sexuality breaks Dollo's law. PNAS. 2007, 104: 7139-7144. 10.1073/pnas.0700034104.

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  80. 80.

    Zufall RA, Rausher MD: Genetic changes associated with floral adaptation restrict future evolutionary potential. Nature. 2004, 428: 847-850. 10.1038/nature02489.

    CAS  PubMed  Article  Google Scholar 

  81. 81.

    Igic B, Bohs L, Kohn JR: Ancient polymorphism reveals unidirectional breeding system shifts. PNAS. 2006, 103: 1359-1363. 10.1073/pnas.0506283103.

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  82. 82.

    Bull JJ, Charnov EL: On Irreversible Evolution. Evolution. 1985, 39: 1149-1155. 10.2307/2408742.

    Article  Google Scholar 

  83. 83.

    Brasier MD, Green OR, Jephcoat AP, Kleppe AK, Van Kranendonk MJ, Lindsay JF, Steele A, Grassineau NV: Questioning the evidence for Earth's oldest fossils. Nature. 2002, 416: 76-81. 10.1038/416076a.

    PubMed  Article  Google Scholar 

  84. 84.

    Noffke N, Eriksson KA, Hazen RM, Simpson EL: A new window into Early Archean life: Microbial mats in Earth's oldest siliciclastic tidal deposits (3.2 Ga Moodies Group, South Africa). Geology. 2006, 34: 253-256. 10.1130/G22246.1.

    CAS  Article  Google Scholar 

  85. 85.

    Sugitani K, Grey K, Allwood A, Nagaoka T, Mimura K, Minami M, Marshall CP, Van Kranendonk MJ, Walter MR: Diverse microstructures from Archaean chert from the mount goldsworthy-mount grant area, Pilbara Craton, Western Australia: Microfossils, dubiofossils, or pseudofossils?. Precambrian Research. 2007, 158: 228-262. 10.1016/j.precamres.2007.03.006.

    CAS  Article  Google Scholar 

  86. 86.

    Glikson LJ, Duck Mand, Golding ASD, Hofmann Axel, Bolhar R, Webb R, Baiano J, Sly L: Microbial remains in some earliest Earth rocks: Comparison with a potential modern analogue. Precambrian Research. 2008, 164: 187-200. 10.1016/j.precamres.2008.05.002.

    CAS  Article  Google Scholar 

  87. 87.

    Wacey D, Kilburn MR, McLoughlin N, Parnell J, Stoake CA, Grovenor CRM, Brasier MD: Use of NanoSIMS in the search for early life on Earth: ambient inclusion trails in a c. 3400 Ma sandstone. Journal of the Geological Society. 2008, 1: 43-53. 10.1144/0016-76492007-032.

    Article  Google Scholar 

  88. 88.

    Klein C, Beukes NJ, Schopf JW: Filamentous microfossils in the early proterozoic transvaal supergroup: their morphology, significance, and paleoenvironmental setting. Precambrian Research. 1987, 36: 81-94. 10.1016/0301-9268(87)90018-0.

    Article  Google Scholar 

  89. 89.

    Altermann W, Schopf JW: Microfossils from the Neoarchean Campbell Group, Griqualand West Sequence of the Transvaal Supergroup, and their paleoenvironmental and evolutionary implications. Precambrian Research. 1995, 75: 65-90. 10.1016/0301-9268(95)00018-Z.

    CAS  PubMed  Article  Google Scholar 

  90. 90.

    Schopf JW, Packer BM: Early Archean (3.3-Billion to 3.5-Billion-Year-Old) Microfossils from Warrawoona Group, Australia. Science. 1987, 237: 70-73. 10.1126/science.11539686.

    CAS  PubMed  Article  Google Scholar 

  91. 91.

    Knoll AH, Barghoorn ES: Archaean Microfossils Showing Cell Division from the Swaziland System of South Africa. Science. 1977, 198: 396-398. 10.1126/science.198.4315.396.

    CAS  PubMed  Article  Google Scholar 

  92. 92.

    Schopf JW, Barghoorn ES: Alga-Like Fossils from the Early Precambrian of South Africa. Science. 1967, 156: 506-512. 10.1126/science.156.3774.508.

    Article  Google Scholar 

  93. 93.

    Rossetti V, Schirrmeister BE, Bernasconi MV, Bagheri HC: The evolutionary path to terminal differentiation and division of labor in cyanobacteria. Journal of Theoretical Biology. 2010, 262 (1): 23-34. 10.1016/j.jtbi.2009.09.009.

    PubMed  Article  Google Scholar 

  94. 94.

    Anbar AD, Duan Y, Lyons TW, Arnold GL, Kendall B, Creaser RA, Kaufman AJ, Gordon GW, Scott C, Garvin J, Buick R: A Whiff of Oxygen Before the Great Oxidation Event?. Science. 2007, 317: 1903-1906. 10.1126/science.1140325.

    CAS  PubMed  Article  Google Scholar 

  95. 95.

    Butterfield NJ: Macroevolution and macroecology through deep time. Palaeontology. 2007, 50: 41-55. 10.1111/j.1475-4983.2006.00613.x.

    Article  Google Scholar 

  96. 96.

    Damuth JD: Evolution: Tempo and Mode. Encyclopedia of Life Science. 2001, 1-7.

    Google Scholar 

  97. 97.

    Stanley SM: Ecological Theory For Sudden Origin Of Multicellular Life In Late Precambrian - (Adaptive Radiation-Cambrian-Evolution-Paleontology-Predation). Proceedings Of The National Academy Of Sciences Of The United States Of America. 1973, 70 (5): 1486-1489. 10.1073/pnas.70.5.1486.

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  98. 98.

    Boraas ME, Seale DB, Boxhorn JE: Phagotrophy by a flagellate selects for colonial prey: A possible origin of multicellularity. Evolutionary Ecology. 1998, 12 (2): 153-164. 10.1023/A:1006527528063.

    Article  Google Scholar 

  99. 99.

    Kaiser D: Building a multicellular organism. Annual Review Of Genetics. 2001, 35: 103-123. 10.1146/annurev.genet.35.102401.090145.

    CAS  PubMed  Article  Google Scholar 

  100. 100.

    Tannenbaum E: Selective advantage for multicellular replicative strategies: A two-cell example. Physical Review E. 2006, 73: 010904-10.1103/PhysRevE.73.010904.

    Article  CAS  Google Scholar 

  101. 101.

    Katoh K, Misawa K, Kuma K, Miyata T: MAFFT: a novel method for rapid multiple sequence alignment based on fast Fourier transform. Nucleic Acids res. 2002, 30: 3059-3066. 10.1093/nar/gkf436.

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  102. 102.

    Miller M, Holder M, Vos R, Midford P, Liebowitz T, Chan L, Hoover P, Warnow T: The CIPRES Portals. CIPRES. 2009, []

    Google Scholar 

  103. 103.

    Hall T: BioEdit: a user-friendly biological sequence alignment editor and analysis program for Windows 95/97/NT. 1999, Oxford University Press, 41: 95-98.

    Google Scholar 

  104. 104.

    Stamatakis A: RAxML-VI-HPC: maximum likelihood-based phylogenetic analyses with thousands of taxa and mixed models. Bioinformatics. 2006, 22 (21): 2688-2690. 10.1093/bioinformatics/btl446.

    CAS  PubMed  Article  Google Scholar 

  105. 105.

    Lanave C, Preparata G, Saccone C, Serio G: A New Method For Calculating Evolutionary Substitution Rates. Journal Of Molecular Evolution. 1984, 20: 86-93. 10.1007/BF02101990.

    CAS  PubMed  Article  Google Scholar 

  106. 106.

    Rodriguez F, Oliver JL, Marin A, Medina JR: The General Stochastic-Model Of Nucleotide Substitution. Journal Of Theoretical Biology. 1990, 142 (4): 485-501. 10.1016/S0022-5193(05)80104-3.

    CAS  PubMed  Article  Google Scholar 

  107. 107.

    Thompson JD, Gibson TJ, Plewniak F, Jeanmougin F, Higgins DG: The CLUSTAL_X windows interface: flexible strategies for multiple sequence alignment aided by quality analysis tools. Nucleic Acids Research. 1997, 25 (24): 4876-4882. 10.1093/nar/25.24.4876.

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  108. 108.

    Ronquist F, Huelsenbeck JP: MrBayes 3: Bayesian phylogenetic inference under mixed models. Bioinformatics. 2003, 19 (12): 1572-1574. 10.1093/bioinformatics/btg180.

    CAS  PubMed  Article  Google Scholar 

  109. 109.

    Xia X, Xie Z: DAMBE: Data analysis in molecular biology and evolution. Journal of Heredity. 2001, 92: 371-373. 10.1093/jhered/92.4.371.

    CAS  PubMed  Article  Google Scholar 

  110. 110.

    Xia X: Data analysis in molecular biology and evolution. 2001, Kluwer Academic Publishers, Boston

    Google Scholar 

  111. 111.

    Xia X, Xie Z, Salemi M, Chen M, Wang Y: An index of substitution saturation and its application. Molecular Phylogenetics and Evolution. 2003, 26: 1-7. 10.1016/S1055-7903(02)00326-3.

    CAS  PubMed  Article  Google Scholar 

  112. 112.

    Zwickl DJ: Genetic algorithm approaches for the phylogenetic analysis of large biological sequence datasets under the maximum likelihood criterion. PhD-Dissertation. 2006, The University of Texas at Austin

    Google Scholar 

  113. 113.

    Posada D, Crandall KA: MODELTEST: testing the model of DNA substitution. Bioinformatics. 1998, 14 (9): 817-818. 10.1093/bioinformatics/14.9.817.

    CAS  PubMed  Article  Google Scholar 

  114. 114.

    Sukumaran J: SumTrees: Summarization of Split Support on Phylogenetic Trees. Version 1.0.2. DendroPy Phylogenetic Computation Library Version 2.0.3. 2008

    Google Scholar 

  115. 115.

    Rambaut A, Drummond AJ: Tracer v1.4 [online]. 2007, []

    Google Scholar 

  116. 116.

    Wilgenbusch JC, Warren DL, Swofford DL: AWTY: A system for graphical exploration of MCMC convergence in Bayesian phylogenetic inference. 2004, []

    Google Scholar 

  117. 117.

    Pagel M, Meade A, Barker D: Bayesian estimation of ancestral character states on phylogenies. Systematic Biology. 2004, 53: 673-684. 10.1080/10635150490522232.

    PubMed  Article  Google Scholar 

  118. 118.

    Rosing MT, Bird DK, Sleep NH, Glassley W, Albarede F: The rise of continents - An essay on the geologic consequences of photosynthesis. Science direct. 2006, 232: 99-113.

    Google Scholar 

  119. 119.

    Wilde SA, Valley JW, Peck WH, Graham CM: Evidence from detrital zircons for the existence of continental crust and ceans on the Earth 4.4 Gyr ago. Nature. 2001, 409: 175-178. 10.1038/35051550.

    CAS  PubMed  Article  Google Scholar 

  120. 120.

    Evans DA, Beukes NJ, Kirschvink JL: Low-latitude glaciation in the Palaeoproterozoic era. Nature. 1997, 386: 262-266. 10.1038/386262a0.

    CAS  Article  Google Scholar 

Download references


We would like to thank Elena Conti, Brian R. Moore and Jurriaan Vos for helpful comments on an earlier version of our manuscript. Furthermore, we would like to thank Marco Bernasconi whose comments on the final version were of great help, and Jurriaan M. de Vos for help with the software BayesTraits.

Author information



Corresponding author

Correspondence to Bettina E Schirrmeister.

Additional information

Authors' contributions

BES and HCB conceived the study; BES gathered data and conducted analyses; BES, HCB, AA designed research and wrote the paper. All authors read and approved the final manuscript.

Electronic supplementary material

Rooted Bayesian consensus tree of 27 eubacterial species including five cyanobacterial species

Additional file 1: . Bayesian analysis of 16S rRNA gene sequences from 27 Eubacteria, based on GTR+I+G substitution model with an archaean outgroup. Posterior probabilities (black) and bootstrap values (red) from 100 re-samplings are displayed at the nodes. Cyanobacteria (blue-green box) are strongly supported as a monophyletic group with Gloeobacter violaceus being closest to other eubacterial species. (PDF 170 KB)


Additional file 2: Bayesian consensus trees of cyanobacterial subset and different outgroups - newick format. 22 Bayesian consensus trees with posterior probabilities of a cyanobacterial subset (58 taxa) and different eubacterial outgroups, displayed in newick format. Trees were run for 10,000,000 generations using a GTR+I+G substitution model with the first 3,000,000 generations being discarded as a burn-in. (TXT 44 KB)

Maximum likelihood tree of cyanobacterial subset

Additional file 3: . Maximum likelihood analysis of 16S rDNA sequences from 58 cyanobacteria, based on GTR+G+I substitution model, with Beggiatoa sp. as an outgroup. Posterior probabilities (> 0.9) in black and bootstrap values (> 50%) in red are shown at the nodes. Posterior probabilities were calculated from 265,858 trees inferred by Bayesian analysis. Bootstrap values were calculated from 500 re-samplings of the data set. Colors define groups: yellow are single-celled cyanobacteria of section I; orange single-celled from section II; green are multicellular, undifferentiated cyanobacteria from section III; blue are multicellular and differentiated bacteria from section IV; and pink from section V. Sections as described by Castenholz 2001 [9]. AC, B, C, E and E1 denote clades discussed in the text. (PDF 639 KB)


Additional file 4: Results from the test of substitutional saturation. Substitutional saturation of the sequences was tested using DAMBE software. The index of substitutional saturation is smaller than the estimated critical value irrespective of the symmetry of the tree. The sequences are therefore not saturated. (PDF 34 KB)


Additional file 5: Ancestral character state reconstruction using maximum parsimony. Summary of results over 10,000 randomly sampled trees from the Bayesian analysis. Uniquely best states were counted and are shown on the Bayesian consensus tree. Possible states are unicellular (yellow) and multicellular (black). At the nodes, probabilities for each character state are represented with a pie chart. The white part in the pie charts indicates fraction of trees where the node was absent, grey parts describe fraction of trees where both states were equally likely. Nodes where transitions occurred were labelled with an asterisk if they show strong support from the phylogenetic analyses. The maximum parsimony analysis produced a similar result compared to the maximum likelihood analysis. A unicellular ancestry for the most recent common ancestor of all cyanobacteria is supported. Nodes 3, 4 and 5 are most frequently optimized as multicellular. Multicellularity has been estimated for nodes 3 and 4 in 6800 trees and for node 5 in 6900 trees. In contrast, single celled states for these nodes have been reported, for node 3 in 13 out of 10,000 trees and for node 4 and 5 in 14 out of 10,000 trees. Five reversals to unicellularity can be detected and at least one reversal to multicellularity. (PDF 211 KB)


Additional file 6: Phylogenetic tree of cyanobacteria - newick format. Phylogenetic tree of 1,254 cyanobacterial sequences including six chloroplasts and six Eubacteria analyzed using maximum likelihood analysis with a GTR+G+I estimated substitution model, conducted with the software RAxML. (TXT 108 KB)

Taxon names of the phylogenetic tree of cyanobacteria

Additional file 7: . Species names used in the phylogenetic analysis conducted with RAxML software. Taxon names are ordered by sub-groups as in Figure 1. (PDF 94 KB)

Authors’ original submitted files for images

Rights and permissions

This article is published under license to BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Reprints and Permissions

About this article

Cite this article

Schirrmeister, B.E., Antonelli, A. & Bagheri, H.C. The origin of multicellularity in cyanobacteria. BMC Evol Biol 11, 45 (2011).

Download citation


  • Posterior Probability
  • Maximum Likelihood Analysis
  • Thermophilic Cyanobacterium
  • Cyanobacterial Taxon
  • Pilbara Craton