Figures
Abstract
Dinoflagellates are unicellular algae that are ubiquitously abundant in aquatic environments. Species of the genus Symbiodinium form symbiotic relationships with reef-building corals and other marine invertebrates. Despite their ecologic importance, little is known about the genetics of dinoflagellates in general and Symbiodinium in particular. Here, we used 454 sequencing to generate transcriptome data from two Symbiodinium species from different clades (clade A and clade B). With more than 56,000 assembled sequences per species, these data represent the largest transcriptomic resource for dinoflagellates to date. Our results corroborate previous observations that dinoflagellates possess the complete nucleosome machinery. We found a complete set of core histones as well as several H3 variants and H2A.Z in one species. Furthermore, transcriptome analysis points toward a low number of transcription factors in Symbiodinium spp. that also differ in the distribution of DNA-binding domains relative to other eukaryotes. In particular the cold shock domain was predominant among transcription factors. Additionally, we found a high number of antioxidative genes in comparison to non-symbiotic but evolutionary related organisms. These findings might be of relevance in the context of the role that Symbiodinium spp. play as coral symbionts.
Our data represent the most comprehensive dinoflagellate EST data set to date. This study provides a comprehensive resource to further analyze the genetic makeup, metabolic capacities, and gene repertoire of Symbiodinium and dinoflagellates. Overall, our findings indicate that Symbiodinium possesses some unique characteristics, in particular the transcriptional regulation in Symbiodinium may differ from the currently known mechanisms of eukaryotic gene regulation.
Citation: Bayer T, Aranda M, Sunagawa S, Yum LK, DeSalvo MK, Lindquist E, et al. (2012) Symbiodinium Transcriptomes: Genome Insights into the Dinoflagellate Symbionts of Reef-Building Corals. PLoS ONE 7(4): e35269. https://doi.org/10.1371/journal.pone.0035269
Editor: Ahmed Moustafa, American University in Cairo, Egypt
Received: November 26, 2011; Accepted: March 13, 2012; Published: April 18, 2012
This is an open-access article, free of all copyright, and may be freely reproduced, distributed, transmitted, modified, built upon, or otherwise used by anyone for any lawful purpose. The work is made available under the Creative Commons CC0 public domain dedication.
Funding: This study was supported through NSF (National Science Foundation) awards IOS 0644438 and IOS 0926906 (MM), OCE 0424994 (MAC), a KAUST AEA (King Abdullah University of Science and Technology) 3 Joint Collaborative Research award (CRV), and through a Collaborative Travel Fund to TB made by King Abdullah University of Science and Technology. The work conducted by the U.S. Department of Energy Joint Genome Institute is supported by the Office of Science of the U.S. Department of Energy under Contract No. DE-AC02-05CH11231. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
Competing interests: The authors have declared that no competing interests exist.
Introduction
Dinoflagellates are ubiquitous marine and freshwater unicellular eukaryotes. As photosynthetic plankton, they are responsible for much of the primary production of oceans, rivers, and lakes. As photosynthetic marine symbionts, they form mutualistic relationships with reef-building corals and other invertebrates [1]. Approximately half of the 4,000 known dinoflagellate species contain no plastids, and many species are mixotrophic [2]. Dinoflagellates belong to the Alveolata, a large eukaryotic clade that also comprises the ciliates, which are free-living, as well as the Apicomplexans, which all have parasitic lifestyles.
In addition to their ecological diversification, dinoflagellates show some genetic traits that make them distinct from other eukaryotic lineages. In particular, dinoflagellates have extensively methylated nuclear DNA. About 12–70% of thymine bases are replaced by 5-hydroxymethyluracil, and varying levels of cytosine methylation have been observed [3], [4]. Genome sizes are very large and remarkably variable within the group, with estimates ranging from 3–215 gigabases (Gb) in size [5], [6]. The genomic DNA is present in up to several hundred chromosomes per species [7]. Dinoflagellate genomic DNA has been shown to occur in a crystal-like state [8], with chromosomes condensed throughout the cell cycle [9]. Some of these observations initially led authors to conclude that dinoflagellates lacked histones [9]. However, recent genome-enabled studies have confirmed the presence of histones H3 [10], H2A.X [11], and H4 [12] in members of this lineage. Dinoflagellate genomes may host some 40,000–90,000 genes, which might be partly due to high gene copy numbers [13]. Despite the high gene number, dinoflagellate genomes are assumed to consist mostly of non-coding DNA (98–99.9%) [13]. Another unique feature characteristic of the dinoflagellate molecular machinery is the trans-splicing of spliced leader sequences [12], [14]. In this process, a highly conserved spliced leader (SL) is transplanted to the 5′ end of mRNAs. SL trans-splicing acts to convert polycistronic mRNAs to monocistronic mRNAs and has also been suggested to regulate gene expression [15].
Symbiodinium spp. (Alveolata: Dinophycea) – often referred to as zooxanthellae – are a specific group of dinoflagellates that are intracellular symbionts of many marine invertebrates including scleractinian corals. Although initially considered a single symbiotic species, molecular phylogenetics has uncovered major Symbiodinium clades [16] that are separated from each other by tens of millions of years [17]–. Through photosynthesis, Symbiodinium algae supply much of their hosts' dietary needs and in return receive shelter, a light-rich environment, and inorganic nutrients [20]. In most cases this symbiotic relationship is reestablished during each host generation [21]. Recent transcriptome-wide efforts have been mainly devoted towards the understanding of the molecular and cellular processes involved in the onset of symbiosis from the host perspective [22]–[25]. From the symbiont's perspective, a relatively small number of ESTs has been analyzed by Leggat et al. [26] and Voolstra et al. [27]. Voolstra et al. [27] compared orthologous cDNA sequences from cultured and symbiotic species (i.e. Symbiodinium CassKB8 and Symbiodinium C3, respectively), providing some preliminary insight into the genes that might be involved in Symbiodinium symbiosis. In a similar fashion, studies have focused on the biochemical and transcriptomic responses to the breakdown of symbiosis (i.e. coral bleaching) in adult corals [23], [27]–[30]. However, thus far there are no comprehensive Symbiodinium genome-enabled investigations that can shed light onto the complement of genes associated with susceptibility to bleaching.
In this study, we sequenced EST libraries from two Symbiodinium species that are known to establish stable symbioses with coral hosts (clade A: Symbiodinium sp. CassKB8 and clade B: Symbiodinium sp. Mf1.05b). These data represent the largest dinoflagellate EST data set available to date with more than 56,000 assembled transcripts per species. Annotation of these transcripts yielded new insights into the complex gene repertoire of dinoflagellates, and the mechanisms of nuclear organization of DNA and transcriptional regulation among others.
Materials and Methods
Cultures, RNA isolation and sequencing
Two different species of Symbiodinium spp., CassKB8 (clade A) and Mf1.05b (clade B), were exposed to a range of different conditions (heat, cold, light, and dark) for 3–6 days to induce expression of a maximum number of genes. Cultures were grown in f/2 medium [31], for Mf1.05b cultures antibiotics were added to the medium to combat bacterial contamination [32]. ‘Hot’ and ‘cold’ cultures were grown at 30–31°C and 19°C, respectively, all other cultures at 27°C. All treatments were subject to a diurnal light cycle (14:10 hrs) of approximately 50 µmol photons/m2/s, except the ‘dark’ treatment, for which cultures were grown in darkness for 6 days. For the ‘light’ treatment the light intensity was increased to approximately 120 µmol photons/m2/s. Treated Symbiodinium were harvested during the exponential growth phase (approx. 106 cells/mL), pelleted and then snap frozen in liquid nitrogen. Symbiodinium sp. CassKB8 was originally isolated from Cassiopeia sp. in Kaneohe Bay, Hawaii by Robert Kenzie (personal communication). Symbiodinium sp. Mf1.05b was isolated from Montastraea faveolata, Florida Keys by M.A. Coffroth. Frozen pellets were ground into a fine powder using a pre-chilled mortar and pestle, and powder was added directly to Qiazol lysis reagent (Qiagen, Hilden, Germany). Total RNAs were precipitated with isopropanol, and RNA pellets were washed with 80% ethanol and redissolved in water. Total RNAs were cleaned with RNeasy Mini columns (Qiagen) and pooled in equal amounts for each species. Library preparation for sequencing was carried out differently for both strains. For CassKB8 the RNA was used to construct cDNA libraries using the cDNA Rapid Library Preparation Method as outlined in the Roche kit (Roche 454 Life Sciences, Branford, USA), followed by normalization using the protocol provided for the Evrogen Normalization kit (Evrogen, Moscow, Russia). The normalized dscDNA was then used to construct 454 libraries using the 454 library construction protocol provided in the 454 FLX Titanium Roche kit (Roche, Branford, USA) and then sequenced using the 454 GS-FLX platform. For Mf1.05b cDNA was generated using an oligo-dT primer followed by template switching (Clontech, Mountain View, USA) and subsequently normalized using the same kit as above and sequenced as detailed for CassKB8.
Data and Assembly
The reads were assembled using version 3.2.1 of MIRA [33] with settings appropriate for transcriptome assembly (–job = denovo,est,normal,454 COMMON_SETTINGS -GE:not = 8 454_SETTINGS -CL:qc = no:cpat = yes:msvs = yes -AS:mrpc = 1 -OUT:sssip = yes:stsip = yes). Adaptors were searched and marked with SSAHA2 [34], and the locations included in the MIRA input files to enable clipping. As MIRA assembles transcripts (not genes), size sorted contigs and singlets were clustered using the UCLUST algorithm as implemented in USEARCH 4.2.66 [35] in both directions with an identity cutoff of 90% in order to estimate the number of genes (Suppl. Table S1). The cutoff was empirically chosen as a conservative estimate to account for sequencing errors and mRNA editing. In the following, clustered contigs and singlets are referred to as genes. To test the effect of clustering on gene families, all contigs belonging to the actin gene family were determined by searching a full length actin sequence from Symbiodinium (accession no. AB231899, [36]) against all CassKB8 contigs, and comparing to the clustering of these contigs (Suppl. Table S2). All raw reads are available in the NCBI Short Read Archive (SRA) under the accession numbers SRX076710, SRX076709, and SRX076696. The assembled and annotated sequences are available for download at http://medinalab.org/zoox. In most of the cases, we were not able to identify a SL sequence in our dataset. However, PCRs with a SL and gene specific primer for three genes (actin, Glyceraldehyde 3-phosphate dehydrogenase and β-tubulin) showed that the SL sequence is present in all three genes in CassKB8 (data not shown). Absence of the SL from the transcriptome sequences may be a library preparation or sequencing artifact.
Annotation
Assembled transcriptome data were annotated as follows: 1) by BLASTX homology search against protein databases, 2) by mapping to pathways using the KEGG annotation service KAAS [37], and 3) by searching for protein domains with InterProScan [38]. The BLASTX homology search was conducted against the Swissprot, TrEMBL [39] and NCBI nr non-redundant protein databases (all as of May 2011) in that order, and the first hit with an e-value below 10−5 was retained for annotation. For KAAS pathway annotation and analysis, we used the single-directional best hit (SBH) method to query the set of organisms representative for ‘genes’ as suggested on the KAAS website, with the default bitscore threshold of 60. Determination of completeness of the transcriptome data was also based on the KEGG annotation and manual analysis of the pathways and complexes identified. Protein domains were annotated using the InterProScan software in version 4.6 with all possible applications and in all reading frames [38]. The ‘sig’ and ‘SignalPHMM’ databases were excluded from the InterProScan results, as they do not represent functional protein domains.
Codon usage
We searched all contigs and singlets against the NCBI nr database using BLASTX to ensure that only codons in the proper reading frame were used to calculate codon usage statistics. For all calculations we extracted and used only the nucleotide sequences corresponding to the best HSP in hits with an e-value of equal or less than 10−10. This procedure yielded a total of 4,224,266 and 2,525,073 codons for CassKB8 and Mf1.05b, respectively. Transcriptome data were analyzed for codon usage and the effective number of codons (Nc) [40] with the programs cusp and chips from the EMBOSS package [41]. The maximum number for Nc is 61, which indicates uniform codon usage whereas lower values signify codon bias. We analyzed Nc in relation to the GC content of the third codon position (GC3) through an Nc plot (i.e. a plot of Nc versus GC3s for all genes) to determine whether codon usage heterogeneity exists among different genes in our transcriptome data. In order to look at major differences between genes in relation to codon usage, we performed Correspondence Analysis – a multivariate statistic that displays the greatest variance in codon usage in a two dimensional plot. Correspondence analysis of codon usage was calculated with the software CodonW [42]. One group of transcripts formed a distinct cloud of points in this analysis. In order to analyze this group in more detail, we chose a visual cutoff to separate the member transcripts. We summarized the putative functions for these transcripts by clustering at 90% similarity (as described earlier) and by subsequently counting genes with the same annotation. To ensure accurate results, we counted only transcripts with more than 100 analyzed codons.
Histones and Nucleosome-Associated Proteins
Histone and histone-associated genes were identified based on gene annotation. Genes were annotated according to the best annotation hit in the corresponding transcript cluster (Suppl. Table S3). Putative histone transcripts with less than 30 amino acids length were excluded from further analysis. Only full-length amino acid sequences of histones (Suppl. Table S3) were considered for phylogenetic analysis. Histone sequences for different H2A, H3 and H3.3 variants were downloaded from the NCBI databases. We preferentially selected sequences from closer and further related species for which more than one histone variant was present. Sequences were aligned using the MUSCLE [43] implementation in Mega5 v.5.05 with standard settings [44]. Phylogenetic trees were reconstructed using maximum likelihood (ML) and Bayesian analysis. ML analysis was performed using the PhyLM v3.0 software [45] available at the “ATGC South of France bioinformatics platform" (http://www.atgc-montpellier.fr). Analyses were performed using the WAG substitution model (as determined by Mr. Bayes mixed model). Tree improvement was assessed using both, Subtree Pruning and Regrafting topological moves (SPR) and simultaneous Nearest Neighbor Interchanges (NNI) algorithms, branch support was assessed via nonparametric bootstrapping using 1,000 replicates. Bayesian analysis was performed using MrBayes v3.1.2. [46] using the following settings: nchains = 4, one cold and three heated chains, with the exception of codon models were two chains were used; the number of steps = generations was set to 1,000,000 with sampfreq = 100 and burnin = 2,500. Convergence was assessed using Tracer v.1.5 [47] and by examining the PSRF values and standard deviation of split frequencies. The best substitution model was assessed using mixed model as recommended by MrBayes and the WAG model was used for subsequent analysis based on the highest posterior probability.
Transcription factors
We used the comprehensive set of annotated, sequence-specific DNA/RNA binding domains described in [48] to search for transcription factors in our transcriptome data. We included the AP2 domain, which is common in plants, but has recently also been found in apicomplexans [49]. Our analysis was based on Pfam domains with an e-value cutoff of 10−6 as provided by HMMER [50] following the approach of Ryu et al. [51]. All contigs and singlets were translated in all reading frames to obtain all possible peptide sequences using transeq from the EMBOSS package [41]. To estimate transcription factor numbers at the gene level, any domain was counted only once 1) per transcript cluster, and 2) per transcript if the transcript contained multiple domains of the same type. In addition, all dinoflagellate ESTs from the NCBI Genbank dbEST database (as of June 2011) were downloaded and analyzed as described above (total number of sequences: 165,532). Finally, all protein sequences from selected outgroup taxa were included in the analysis: Plasmodium falciparum and P. vivax from PlasmoDB [52], Paramecium tetraurelia from ParameciumDB [53], and Thalassiosira pseudonana, Arabidopsis thaliana, Drosophila melanogaster and human from BioMart [54]. Outgroup protein sequences were analyzed with HMMER as described above.
Antioxidative response
Putative antioxidant genes were identified in a similar manner as the transcription factors. Briefly, we screened our data set for antioxidant-associated genes using a list of pertinent Pfam domains [55] as compiled by Reitzel et al. [56]. We additionally included Pfam motifs for Peroxiredoxin (PF10417), Glutaredoxin2_C (PF04399), Alkylhydroperoxide reductase (PF00578), and exchanged the listed An_peroxidase (PF03098) for peroxidase (PF00141). For outgroup comparisons, we included all protein sequences from Arabidopsis thaliana, Physcomitrella patens, Thalassiosira pseudonana and Phaeodactylum tricornutum available through the BioMart database [54]. To estimate numbers at the gene level, domains were counted as previously described in the transcription factor analysis.
Results
Transcriptome Data Set
We obtained approximately one million reads of around 400 nt in length from each of the Symbiodinium CassKB8 (clade A) and Mf1.05b (clade B) transcriptomes (Table 1). Assembly of the reads yielded 72,152 and 76,284 contigs and singlets for CassKB8 and Mf1.05b, respectively. We clustered all contigs and singlets at 90% identity in order to estimate the true gene number rather than the number of transcripts. This clustering resulted in 57,676 and 56,198 potential genomically encoded genes. The clustering yielded a conservative gene number estimate, as closely related genes from gene families were clustered in one group. For instance, for the actin gene family cluster, 36 contigs clustered into 14 groups with as many as 7 contigs in one group (Suppl. Table S2).
Using BLAST against three protein databases we could only annotate 41% and 31% of all contigs and singlets for CassKB8 and Mf1.05b, respectively. Using KAAS these values were even lower with 15% and 11%. Protein domains could be identified with InterProScan in 34% and 25% of all contigs and singlets (Table 1).
When examining the distribution of hits to the KEGG database in the highest category of the KEGG Brite hierarchy for pathways [57], both transcriptomes showed a similar distribution of genes among categories (Suppl. Table S4). For instance, the highest number of genes had a function in ‘Metabolism’, followed in second place by the ‘Organismal System’ category, and thirdly by the group of genes that are relevant to human diseases. The distribution of genes among these categories and their subcategories is similar to that seen in P. falciparum, P. tetraurelia and A. thaliana (data not shown).
In order to estimate the completeness of our sequenced transcriptomes, we searched the KEGG annotation for components of essential metabolic pathways and protein complexes (Table 2). In addition, we searched for gene families that exist universally in single copy across the tree of life (Suppl. Table S5, [58]). We found the majority of genes for the pathways and complexes analyzed as well as the majority of single copy genes, although the Mf1.05b transcriptome displayed lower gene numbers for the Pentosephosphate pathway, TCA cycle, and the proteasome and spliceosome complexes (Table 2).
Codon Usage
GC content values showed a marked difference between both species. The coding GC content in CassKB8 was about 6% higher than in Mf1.05b (Table 3). In particular, values were much lower than previously reported (∼78%) for the third codon position in the dinoflagellate Alexandrium tamarense [11], [59], but closer to those reported for the dinoflagellate Karenia brevis (53.5%) [60].
The analyzed Symbiodinium species show some codon bias with Nc values of 51.36 for CassKB8 and 55.56 for Mf1.05b, respectively. In comparison, codon bias is higher in A. tamarense with 43.64 [59]. In the Nc plots (Figure 1 A, B), the absence of codon usage bias as a null hypothesis (NcH0) is displayed as a solid curve [40], and genes which lie below this line have a stronger codon bias than expected based purely on their GC3. In both species most genes have an Nc value lower than NcH0, indicating codon bias and that codon usage is not determined by GC content alone (GC3) (Figure 1 A, B).
(A, B) Plots of the effective number of codons (Nc) plotted versus third codon position GC content (GC3) in CassKB8 and Mf1.5b respectively. The red points are the same genes as in C and D, respectively. The yellow line represents the neutral expectation for Nc. (C, D) Correspondence analysis of codon usage. The genes separated from the main cloud are marked red.
The distribution of genes on the two axes on the correspondence plots (Figure 1 C, D) showed one cluster of genes around zero on both axes, and a secondary cluster of genes offset on axis 1. To separate these ‘outlier’ genes, we visually chose a cutoff of < = −0.75 for CassKB8 and < = −0.5 for Mf1.05b, which yielded 270 and 431 genes, respectively. The genes in these separated clusters have much less GC in the third codon base than the majority of genes in both species. Most of these contigs and singlets represented genes encoded by the chloroplast genome, which has been shown to exist in the form of short circular DNA molecules, termed minicircles, in peridinin-containing dinoflagellates such as Symbiodinium [61], [62] (Table 4). In addition to chloroplast genes, the list includes cytochrome oxidase subunit 1, a mitochondrial gene, and single copies of a diverse group of other genes that did not seem to be related to each other in function.
Histones and Nucleosome-Associated Proteins
The absence of histones was in the past perceived as one of the peculiarities of dinoflagellate genetics. Recent analyses of diverse dinoflagellate ESTs, however, revealed nucleosome components, including representatives of the four nucleosome core histones [14]. We have found a total of 20 histone-encoding genes, 53 histone-modifying enzymes as well as several nucleosome- and chromatin-remodeling associated genes in both Symbiodinium transcriptomes (Table 5). Symbiodinium sp. CassKB8 contains several copies of each of the four core histones H2A, H2B, H3, and H4. Histones H2A, H2B, and H3 include members of more than one subfamily, such as orthologs of the minor histone variants H2A.Z as well as putative H3.3 and H3.4 orthologs (Table 5). In Mf1.05b, we found three H2A.Z-like transcripts but no H2A.X ortholog. H3 was represented by two genes similar to the H3.3-like minor histone and a H3-like centromeric protein CSE4. Only one copy of histone H4 and none for H2B were detected in Mf1.05b (Table 5).
Phylogenetic analysis of H2A-like full-length sequences grouped with strong support one of the identified CassKB8 genes with the previously identified dinoflagellate H2A.X sequences from Alexandrium tamarense [11] and Crypthecodinium cohnii [63] (Figure 2A). The classification of this genes as of dinoflagellate origin was further confirmed by the presence of the H2A.X signature motif ‘SEQY‘ in the full-length sequence encoded in the contig kb8_rep_c81. In general H2A.X sequences did not cluster by variant. This was expected since H2A.X genes are known to have arisen multiple times during evolution of the H2A gene family [64], [65]. In contrast to that, the Symbiodinium H2A.Z-like sequences were clearly separated from the H2A.X sequences and formed a group with the H2A.Z sequences from other species, thus reflecting the single evolutionary origin of the H2A.Z protein [64]. The histone H3 family is a diverse histone family [66]. In line with that, we found the highest number of gene copies for H3-like histones. Phylogenetic analysis of the putative Symbiodinium H3 genes places them within well-supported dinoflagellate H3 histone clades (Figure 2B) [64]. However, the putative H3 genes identified here cannot be clearly classified into subfamilies based on phylogenetic grouping since the different variants do not resolve into distinctive groups as is the case for H2A.Z (Figure 2B). This is expected as the different H3 variants evolved multiple times independently in different lineages, including plants, animals, ciliates and apicomplexans [64].
H2A- and H3-like sequences from Symbiodinium sp. CassKB8, Mf1.05b, and other organisms were used to calculate phylogenetic trees. The trees were inferred using contigs and singlets with full-length amino acid sequences of (A) H2A and (B) H3-like genes using Maximum-Likelihood and Bayesian analysis. Bootstrap values and posterior probabilities are provided as ML/MB for nodes with support above 50% or 0.5. The singlet and contig names are provided for Symbiodinium sp. CassKB8 and Mf1.05b sequences (in bold), other taxa are shown as species name followed by GenBank accession number. The H2 tree was rooted for H2A.Bbd sequences whereas the H3 tree was rooted for Homo sapiens H3.
Apart from the nucleosome core histones, we identified a variety of histone-modifying proteins including histone acetyltransferases, deacetylases, methylases, and demethylases as well as several nucleosome assembly and histone binding proteins in both species (Table 5). Furthermore, we found the histone-associated chaperone ASF1 in CassKB8 and the Chromatin assembly factor 1 (CAF1) in Mf1.05, which have important roles in chromatin transactions [67], [68]. We found more histone-modifying genes in Mf1.05b than in CassKB8, 32 and 21 genes, respectively. Histone methylases appear to be the most common type of histone-modifying proteins in both species, followed by deacetylases and demethylases (Table 5).
Transcription factors in Symbiodinium
While histones take part in gene regulation at the genome level, the most important proteins that influence transcription of individual genes are transcription factors (TFs). We found a low number of such domains in Symbiodinium. In the whole dataset, only 156 and 87 genes contained at least one known protein domain for sequence-specific DNA-binding activity in CassKB8 and Mf1.05b, respectively. These numbers correspond to only 0.27% and 0.15% of all genes (as determined from clusters at the 90% similarity level) (Table 6). A similar result was obtained when the same analysis was conducted on the collection of all dinoflagellate sequences available in the Genbank EST database dbEST, with a percentage of 0.29% of all clustered EST sequences containing at least one transcription factor domain.
Not only is the overall number of TF domains low, but the distribution of domains was also different than in other organisms. For instance, Zinc finger C2H2 domain TFs, which make up the largest fraction of TFs in many eukaryotes such as human and Drosophila, were completely absent from the dinoflagellate sequences analyzed here (Suppl. Figure S1). The distribution of the most common TF domains is distinct from the apicomplexans P. falciparum and P. vivax, the ciliate P. tetraurelia, the heterokont diatom T. pseudonana, the plant A. thaliana, and the insect D. melanogaster as well as from human (Figure 3). The most common domain in Symbiodinium was the ‘cold shock factor’ DNA-binding domain, making up more than 60% of the transcription factor domains of CassKB8 and Mf1.05b. This domain is a β-barrel domain present in most organisms from all three domains of life. This type of transcription factor also appeared to be among the most common in all dinoflagellates as assessed from dbEST (Figure 3). This domain also occurs in all non-dinoflagellate species studied, though only a few genes contained it (Suppl. Table S6).
The relative fraction of the most abundant transcription factor domains in the Symbiodinium transcriptomes, all dinoflagellate ESTs from the NCBI dbEST database, and other eukaryotes. Searches were performed by using HMMER to search domain models for DNA binding domains, with an e-value cutoff of < = 1e−6. Domains which make up less than 5% were grouped in the ‘others’ category.
Antioxidative response
Given the importance of Reactive Oxygen Species (ROS) in the bleaching-associated breakdown of the symbiotic relationship between Symbiodinium and their coral host, we screened our data for genes associated with the antioxidative response. We used a Pfam protein domain-based approach to assess the antioxidant gene repertoire in both Symbiodinium species as well as in four photosynthetic outgroup taxa for which whole genome data were available, namely the land plant A. thaliana, the bryophyte P. patens, and the diatoms T. pseudonana and P. tricornutum. We chose plant species for the comparative analysis because land plants are known to possess an efficient antioxidant enzymatic machinery, which allows them to deal with extreme climates and stresses [69], [70]. T. pseudonana and P. tricornutum, in turn, represented outgroup species more closely related to dinoflagellates that share a similar marine lifestyle with dinoflagellates.
The Symbiodinium transcriptomes encoded higher numbers of some proteins involved in the antioxidative response when compared to plants and diatoms, specifically, those containing the Nickel-containing SODs (Sod_Ni), Thioredoxin (Trx), and glutaredoxin 2 (Grx2) domains (Table 7). Interestingly, in contrast to plants, CassKB8 and Mf1.05b possess Sod_Ni, which are common in prokaryotes. Four of the Sod_Ni encoding genes in CassKB8 (kb8_rep_c6308, kb8_rep_c17584, kb8_rep_c1869 and kb8_rep_c6458) and five in Mf1.05b (mf105_rep_c13460, mf105_rep_c40857, mf105_rep_c42288, mf105_rep_c543 and mf105_s69277) were annotated as being Ubiquitin orthologs based on BLASTX. A protein domain analysis confirmed that these genes encoded both a conserved Sod_Ni and an ubiquitin domain. Due to this unexpected result, we searched our sequences against the NCBI nr and dbEST using BLAST to analyze whether this domain composition was restricted to a certain set of species. We found genes encoding both domains in eukaryotic lineages such as the stramenopiles Phaeodactylum tricornutum (XP_002183736), Chaetoceros neogracile (EL622395) and Aureococcus anophagefferens (EGB03009), as well as in different dinoflagellate species including Karlodinium brevis (EX871806), Karlodinium micrum (EC161447), Karlodinium veneficum (GH269044), and Heterocapsa triquetra (EU153190) where these genes appear to be common. The only eukaryotic genes outside the chromalveolates displaying this domain signature were found in Micromonas sp. (XM_002506486, XM_003063226), and none were found in prokaryotes.
The thioredoxin (Trx) superfamily comprises different groups of proteins that share a common structural motif. These include thioredoxins (Trx) and protein disulfide isomerases (PDI) as well as glutathione peroxidases (GSHPx) and glutaredoxins, the last two of which are represented separately in this study and are therefore not addressed as Trx here. Comparison of genes encoding putative Trx domains across the six species analyzed here revealed an unexpected high number of genes in both Symbiodinium species (Table 7). In CassKB8 we identified a total of 106 genes encoding a Trx domain, which is substantially higher than what is found in the plants Arabidopsis thaliana and Physcomitrella patens (79 and 70), while 73 putative Trx genes were identified in Mf1.05b. This result is in stark contrast to the comparably low number in the diatoms Thalassiosira pseudonana and Phaeodactylum tricornutum, where only 55 and 41 Trx domain encoding genes appear to be present in the genomes (Table 7).
Discussion
Assembly and completeness
The sequence data reported in this study comprises the largest transcriptome of dinoflagellates to date, and surpasses the available number of dinoflagellate sequences currently in public databases. The number of genes that can be estimated from the data is around 56,000, more than twice the number of genes predicted for the human genome. However, this number is not too far from the 40,000 estimated genes in Symbiodinium genomes, based on genome size and its correlation with gene number [13]. One caveat with shotgun sequenced transcriptome data is the possibility of assembling fragmented transcripts. These would artificially increase the gene count, a general problem that is hard to evaluate in a transcriptome where most genes do not have orthologs in fully sequenced genomes. However, our gene number estimate is similar to an estimate based on Illumina sequencing data with much higher coverage (unpublished data), which yields more than 43,000 genes of 500 bp and larger. Furthermore, it is known that dinoflagellates have large gene families with some very closely related members [71], [72]. Such closely related genes may be grouped together in the clustering process performed for this study. In the example tested here, the actin gene family in CassKB8 has 36 contigs and singlets as members, which were grouped into only 14 clusters. Thus, this method makes our gene number estimate more conservative.
As found in other dinoflagellate sequencing data sets, the majority of transcripts do not have similarity to sequences in GenBank or KEGG [11], [14], [27], [60]. This novelty could be expected of an organism which is evolutionary distant from most model organisms. Most of the KEGG-based annotations (10–15% of all genes) fall into the metabolic pathways category. The completeness analysis showed that the majority of the standard “housekeeping" genes in the pathways and complexes are present. The difference between the two species, namely the lower coverage in Mf1.05b, probably arose due to the differences in the sequencing read length, which influences the assembly process and may reflect the different sequencing library generation protocols. In addition, all but two of the genes that belong to universal single-copy gene families were found. Ribosomal protein S8 (COG0096) and signal recognition particle GTPase (COG0552) were not identified in either of the two species. Although this absence is not conclusive without the availability of completely sequenced genomes, it will be interesting to see whether and when a lineage-specific loss of these gene families has occurred.
The genus Symbiodinium is comprised of a large number of species encompassed in nine major lineages (clades A–I) [17], [73]. Symbiodinium spp. are crucial components of coral reef ecosystems as endosymbionts of corals and other marine invertebrates. However, few analyses exist which identify genes that might play a role in physiological differences, e.g. susceptibility to bleaching of the different symbiont species or clades [26], [27]. Using two Symbiodinium species, it becomes possible to conduct an evolutionary screen for candidate adaptive genes involved in symbiosis as has been recently conducted for two coral species – i.e. Acropora millepora and A. palmata [74], [75]. Identifying adaptively evolving genes – via the ratio of the relative rates of synonymous and nonsynonymous substitutions (dN/dS) of ortholog genes, [76], [77] can be a powerful strategy to narrow gene lists to a few candidates. However, the synonymous nucleotide changes per synonymous site (dS) we calculated for the two species analyzed here far exceeded 1 (avg dS 26.3 as estimated by PAML, a value that gets increasingly inaccurate if dS>1), indicating that multiple substitutions may have occurred at a single site. For this reason, we did not conduct an evolutionary analysis. Nonetheless, having the benefit of two transcriptome data sets that were sequenced at similar depths, we were able to independently confirm all of our findings in both Symbiodinium species. In this regard, our estimate of gene numbers, the paucity of transcription factors as estimated from DNA binding domains, the presence of full sets of histones, etc. seem to be general hallmarks of Symbiodinium biology rather than clade- or species-specific adaptations. Many of these questions will be answered more definitively in the near future as several whole genome Symbiodinium sequencing projects are currently underway.
Codon usage
We estimated the GC content of the third codon position from a large number of codons in our dataset. GC3 has been found to be a good predictor of the overall genomic GC content [78]. EST data for dinoflagellates in the literature already suggest that GC content in the third codon position is highly variable, from 50% to 77% [11], [59], [60]. It is surprising, however, that we find a large difference of about 14% in GC3 also in two species within the genus Symbiodinium. It is usually assumed that differences in GC content between species stem from genome wide mutational bias [79]. If this is true, and if GC richness is homogeneous across the genome, then it stands to reason that there are indeed different mutational mechanisms at work across even closely related dinoflagellate species.
GC content also influences codon usage bias, in addition to a range of other factors such as mutational bias, selective pressures depending on expression strength, abundance of tRNA genes, and environmental factors, and can also be a means of transcriptional control (see [80] for review). Both Symbiodinium species have a more relaxed codon bias than A. tamarense. Codon usage bias is often coupled with growth rate, as not all tRNAs are present in the cell in equal amounts [81]. Accordingly, the variable codon bias detected between Symbiodinium sp. CassKB8 and Mf1.05b may reflect physiological differences (e.g. growth rate, thermal tolerance).
The effective number of used codons is mostly dependent on the overall GC value in the coding sequences. It is shifted towards lower values in Mf1.05b, as this strain has a lower overall GC content. Overall the picture looks similar to the one found previously for A. tamarense [59], where the majority of genes are below the Nc expected for neutral evolution of codon usage. This suggests that these genes may underlie selective processes that favor certain codons, as is known from highly expressed genes. The correspondence analysis of codon usage (Figure 1 C, D) shows a group of genes that is separated from the main cloud, almost all of these are encoded in the chloroplast and mitochondrial genomes. The chloroplast genome in dinoflagellates deviates from the organization that is found in most plants and algae; many genes seem to have transferred to the nucleus, and the genes that are still present in the chloroplast are encoded on several small DNA molecules, termed minicircles, that contain only one or a few genes (see Lila Koumandou et al. [82] for review). Codon usage in these genes has been found to be different than in nuclear genes, and some contain unusual start codons [62]. Most of the genes that are known to reside on minicircles are grouped together with those genes whose codon usage deviates from the norm. However, none of the chloroplast genes that have been found to be transferred to the nucleus in dinoflagellates are in that group. This supports the data presented in the literature so far, and makes it unlikely that more genes remain encoded in the chloroplast minicircles than currently known for peridinin-containing dinoflagellates, including the Symbiodinium species analyzed here.
Histones and Nucleosome-Associated Proteins
Until now, no complete set of the nucleosome core histones has been identified in a single dinoflagellate species. Here, we report the complete set of nucleosome core histones as well as the minor histones H2A.Z and several H3 variants in a single dinoflagellate species (Symbiodinium sp. CassKB8). Our results indicate that dinoflagellates possess not only a basic nucleosome machinery, but also specialized histones that are known to be involved in transcriptional and epigenetic regulation, e.g. H2A.Z [83]–[85] and H3.3 [64], [86], [87].
In contrast to the histones H2B and H4, the histones H2A and H3 have highly conserved ubiquitously expressed variants with specialized functions. H2A.Z is associated with the promoter region of actively transcribed genes linked to transcriptional competence [83], [84], and is also involved in epigenetic regulation [85]. Studies in A. thaliana have shown that DNA methylation and H2A.Z incorporation are mutually exclusive. DNA methylation plays a pivotal role in establishing and maintaining an inactive state of a gene, suggesting that the transcriptional activity promoted by H2A.Z might be conferred through the inhibition of DNA methylation [85]. This is in agreement with studies on Amphidinium carterae and Symbiodinium microadriaticum, whose genomes appear to be hypermethylated [88]. Furthermore, it has been shown that the methylation status of important photosynthesis genes is correlated with their transcriptional activity [89]. Hence, dinoflagellates might be able to use the nucleosome machinery for transcriptional regulation through the regulation of the methylation status of specific loci. The variation we observed in Symbiodinium H3-like proteins is also suggestive of the role of the nucleosome machinery in transcriptional regulation, with possible subfunctionalization of the multiple variants. Finally, we found that Symbiodinium species contain various genes for the modification of histones including methylation and acetylation as well as orthologs of the histone-specific chaperones ASF1 and CAF1. ASF1 is involved in the modulation of local chromatin structure during gene-selective silencing in Drosophila [67], whereas CAF1 is mainly associated with processes involving DNA, such as DNA replication and DNA repair [68].
Overall, our results provide corroborating evidence for the presence of a functional nucleosome machinery in dinoflagellates. We found specialized histones and histone-associated proteins that are known to be involved in transcriptional and epigenetic regulation. Given that histones appear to be rare in the dinoflagellates nucleus [14], [90], one might speculate that dinoflagellates may employ the nucleosome machinery for transcriptional regulation rather than chromatin packaging. In that case, one would expect that the transcriptional regulation of genes is mainly regulated at the level of accessibility thereby generically switching transcription on or off. Consequently, activated loci would likely display very similar transcription levels. Previous studies have shown that transcriptional levels across many genes indeed appear to be similar [14], [91], [92].
Transcription factors in Symbiodinium
The unusual chromatin structure, low concentration of proteins in the nucleus, and the very large genomes of dinoflagellates raise the question whether gene regulation is realized with the same mechanisms as in other eukaryotes. Transcriptional regulation might play a minor role in dinoflagellates as opposed to other mechanisms of regulation. Here, we analyzed the number and composition of transcription factors in Symbiodinium to get a better understanding of gene regulation. We identified only a small number of proteins with sequence specific nucleic acid binding domains (i.e. putative transcription factors). Transcription factors have been shown to scale with genome size [93], and make up 6–9% of all genes of higher eukaryotic transcriptomes (Suppl. Table S7). The percentages found here for dinoflagellates are much lower than those for other protists such as Plasmodium, even though Plasmodium has a reduced genome due to its parasitic lifestyle.
The assemblage of transcription factors in Symbiodinium seems to be completely different from other eukaryotes; common domains such as zinc fingers, helix loop helix, AP2, or homeobox domains are rare or absent. This is also true for the other dinoflagellates represented in the set of ESTs analyzed here, as the set of transcription factor domains and their abundances are quite similar to those found in Symbiodinium. This low abundance of transcription factors appears to be a genomic signature of the dinoflagellate clade.
A similar conclusion about an apparent low number of transcription factors in P. falciparum and apicomplexans in general [94], [95] was challenged through the discovery of the ApiAP2 family of transcription factors in this group [49]. The 27 members of the ApiAP2 family in P. falciparum fill the gap of the apparent lack of transcription factors, and many have now been investigated in detail and shown to be conserved the Apicomplexa [96]. It seems plausible that dinoflagellates may also contain yet undescribed transcription factor families that would represent part of the “missing" transcriptional regulatory machinery. Thus, it is interesting that more than 60% of the putative transcription factors identified carry a ‘cold shock’ domain (CSD). This domain is not very common in other eukaryotes, suggesting a lineage-specific expansion in dinoflagellates. Such lineage-specific expansions of different transcription factor domains have been found in multiple taxa throughout the tree of life [97], [98]. Originally identified as a reaction to cold shock in E. coli, proteins with CSD domains have now been associated with a wide range of functions. They can act as transcription factors by binding DNA (i.e. Y-box factors), but many interact with RNA rather than DNA. They are involved in regulation of transcription, splicing, and translation, and influence mRNA stability as RNA chaperones (see [99] for review). This observation fits with the notions that 1) regulation in dinoflagellates may take place after transcription, and 2) that RNA editing is widespread [100]. Thus, proteins with cold shock domains may be responsible for much of the transcriptional regulation in dinoflagellates. Considering that Symbiodinium undergoes a dramatic change in its environment and lifestyle upon entering invertebrate hosts, a need for efficient regulation of a large number of genes might be advantageous. However, as all data gathered here are based on Symbiodinium grown in cultures, it is possible that more or different types of transcription factors are expressed in the symbiotic state. As Symbiodinium genomes are currently being sequenced, this question can be conclusively answered in the near future as the genome sequence becomes available.
Antioxidative response
The impact of ROS on the symbiosis of Symbiodinium and its marine invertebrate hosts is likely to affect mechanisms to cope with photosynthesis-generated ROS in order to prevent the breakdown of the symbiotic relationship. Our analysis shows that Symbiodinium also possesses a rich antioxidant gene repertoire, but surprisingly appears to lack or transcribe below detection limit the enzyme catalase (note that catalase activity has been shown by Merle et al. [101]), one of the central enzymes in eukaryotic cellular redox-chemistry. In contrast, catalase was among the most abundant transcripts in Aiptaisa pallida, an anemone host for Symbiodinium [102]. However, since the transcriptome sequences analyzed here were derived from cultured zooxanthellae it cannot be excluded that the catalase gene is only expressed in hospite. One of the main differences between Symbiodinium and diatoms or plants is the presence of several prokaryotic Ni-type SODs in both Symbiodinium species, which were not present in the plant species and were only represented by a single gene in the diatom T. pseudonana. The presence of bacterial proteins is not surprising given that lateral gene transfer between prokaryotes and eukaryotes, especially protists, is common [103], [104]. Furthermore, several genes of bacterial origin have already been identified in Symbiodinium [26]. To our surprise, we found that some of the Ni-type SOD genes identified here also encode an additional ubiquitin domain. The ubiquitin domain is a 76 amino acid domain found in eukaryotes, whereas the SOD_Ni domain is supposedly of prokaryotic origin [105], suggesting that these transcripts might represent fusions of prokaryotic and eukaryotic genes.
In contrast to heterotrophic organisms, plants contain a large family of Trx and Trx-like proteins, although the reasons for their abundance remain unclear [106], [107]. We found that Symbiodinium species appear to possess an unexpectedly high number of Trx domain encoding genes comparable to plants, and in stark contrast to the substantially smaller number found in diatoms. The Trx superfamily proteins fulfill diverse cellular functions. These include the maintenance of cell homeostasis and the regulation of the redox state of the cell [108], [109]. They play key roles in the oxidative stress response [110], [111] and have been shown to be differentially expressed in response to high temperature, salinity, and ultraviolet radiation in corals [28], [112], [113]. Members of these groups are also involved in photosystem repair in response to ROS in plants and algae [114], [115]. Although the function of the large number of Trx containing genes can currently not be assessed, this high abundance might be indicative of a complex redox regulatory system in Symbiodinium as in plants [116]. The general similarity of the antioxidant gene repertoire of Symbiodinium and plants suggests that Symbiodinium might well be adapted to high oxidative stress. The comparably low amount of antioxidant genes found in both diatom species, which supposedly share more similarities with dinoflagellates than plants, raises the question whether Symbiodinium might have evolved a richer repertoire of antioxidant genes as an adaptation to its symbiotic lifestyle. More comprehensive transcriptome studies in other dinoflagellates are necessary to determine whether the features revealed in this study are specific to Symbiodinium or common among dinoflagellates.
This study greatly enhances the available sequence data for dinoflagellates in general, and Symbiodinium in particular. Our data highlight interesting aspects of the genetics of Symbiodinium, and provides the basis for deeper insights into dinoflagellate biology.
Supporting Information
Figure S1.
Relative fraction of all transcription factor domains in the Symbiodinium transcriptomes, all Dinoflagellate ESTs from the NCBI database, and other eukaryotes. Values shown were arcsine transformed. Searches were performed by using HMMER to query the Pfam models for DNA binding domains with an e-value cutoff of 1e−6.
https://doi.org/10.1371/journal.pone.0035269.s001
(PDF)
Table S1.
Cluster numbers and contigs and singlets contained in the clusters.
https://doi.org/10.1371/journal.pone.0035269.s002
(XLS)
Table S2.
Contigs and singlets of the actin gene family and their grouping into contigs (90% similarity).
https://doi.org/10.1371/journal.pone.0035269.s003
(XLS)
Table S4.
Distribution of contigs and singlets among KEGG categories and subcategories.
https://doi.org/10.1371/journal.pone.0035269.s005
(XLS)
Table S5.
Universal single-copy clusters of orthologous groups (COGs) that were used for transcriptome completeness analysis in this study. COGs not identified in Symbiodinium transcriptomes are boldfaced.
https://doi.org/10.1371/journal.pone.0035269.s006
(XLS)
Table S6.
Number of transcription factor domains found in Symbiodinium and other eukaryotes.
https://doi.org/10.1371/journal.pone.0035269.s007
(XLS)
Table S7.
Transcription factor domain counts in Symbiodinium CassKB8 and Mf1.05b, all dinoflagellate ESTs, Plasmodium falciparum and P. vivax, Paramecium tetraurelia, Thalassiosira pseudonana, Arabidopsis thaliana, Drosophila melanogaster and human.
https://doi.org/10.1371/journal.pone.0035269.s008
(XLS)
Acknowledgments
We would like to thank Robert A. Kinzie III for initial donation of the Symbiodinium sp. CassKB8 culture, the Aquarium of Niagara for seawater and three anonymous reviewers for valuable suggestions.
Author Contributions
Conceived and designed the experiments: TB MA SS CRV MM. Performed the experiments: TB MA CRV MM MAC LKY EL. Analyzed the data: TB MA SS CRV. Contributed reagents/materials/analysis tools: MKD MAC. Wrote the paper: TB MA CRV.
References
- 1. Muscatine L, McCloskey LR, Marian RE (1981) Estimating the daily contribution of carbon from zooxanthellae to coral animal respiration. Limnology and Oceanography 26: 601–611.
- 2. Graham LE, Wilcox L (2000) Algae (vol 36, pg 788, 2000). Journal of Phycology 36: 975–975.
- 3. Blank RJ, Huss VAR, Kersten W (1988) Base composition of DNA from symbiotic dinoflagellates: a tool for phylogenetic classification. Archives of Microbiology 149: 515–520.
- 4. Rae PMM (1976) Hydroxymethyluracil in eukaryote DNA: A natural feature of the pyrrophyta (Dinoflagellates). Science 194: 1062–1064.
- 5. Veldhuis MJW, Cucci TL, Sieracki ME (1997) Cellular DNA content of marine phytoplankton using two new fluorochromes: taxonomic and ecological implications. Journal of Phycology 33: 527–541.
- 6. LaJeunesse TC, Lambert G, Andersen RA, Coffroth MA, Galbraith DW (2005) Symbiodinium (pyrrhophyta) genome sizes (DNA content) are smallest among dinoflagellates. Journal of Phycology 41: 880–886.
- 7.
Dodge J (1966) The Dinophyceae. In: Godward MBE, editor. The chromosomes of the algae. New York,: St. Martin's Press.
- 8. Bouligand Y, Norris V (2001) Chromosome separation and segregation in dinoflagellates and bacteria may depend on liquid crystalline states. Biochimie 83: 187–192.
- 9.
Rizzo P (1987) Biochemistry of the dinoflagellate nucleus. In: Taylor F, editor. The Biology of dinoflagellates. Oxford: Blackwell Scientific Publications.
- 10. Okamoto OK, Hastings JW (2003) Novel dinoflagellate clock-related genes identified through microarray analysis. Journal of Phycology 39: 519–526.
- 11. Hackett J, Scheetz T, Yoon H, Soares M, Bonaldo M, et al. (2005) Insights into a dinoflagellate genome through expressed sequence tag analysis. BMC Genomics 6: 80.
- 12. Lin S (2011) Genomic understanding of dinoflagellates. Research in Microbiology 162: 551–569.
- 13. Hou Y, Lin S (2009) Distinct gene number-genome size relationships for eukaryotes and non-eukaryotes: gene content estimation for dinoflagellate genomes. PLoS ONE 4: e6978–e6978.
- 14. Lin S, Zhang H, Zhuang Y, Tran B, Gill J (2010) Spliced leader–based metatranscriptomic analyses lead to recognition of hidden genomic features in dinoflagellates. Proceedings of the National Academy of Sciences 107: 20033–20038.
- 15. Zhang H, Hou Y, Miranda L, Campbell DA, Sturm NR, et al. (2007) Spliced leader RNA trans-splicing in dinoflagellates. Proceedings of the National Academy of Sciences of the United States of America 104: 4618–4623.
- 16. Rowan R, Powers DA (1991) A molecular genetic classification of zooxanthellae and the evolution of animal-algal symbioses. Science 251: 1348–1351.
- 17. Pochon X, Gates RD (2010) A new Symbiodinium clade (Dinophyceae) from soritid foraminifera in Hawai'i. Molecular Phylogenetics and Evolution 56: 492–497.
- 18. Pochon X, LaJeunesse TC, Pawlowski J (2004) Biogeographic partitioning and host specialization among foraminiferan dinoflagellate symbionts (Symbiodinium; Dinophyta). Marine Biology 146: 17–27.
- 19. Lajeunesse TC (2005) “Species" radiations of symbiotic dinoflagellates in the Atlantic and Indo-Pacific since the Miocene-Pliocene transition. Mol Biol Evol 22: 570–581.
- 20. Muscatine L, Falkowski PG, Porter JW, Dubinsky Z (1984) Fate of photosynthetic fixed carbon in light- and shade-adapted colonies of the symbiotic coral Stylophora pistillata. Proceedings of the Royal Society of London Series B Biological Sciences 222: 181–202.
- 21.
Harrison P, Wallace C (1990) Reproduction, dispersal and recruitment of scleractinian corals. In: Dubinsky Z, editor. Ecosystems of the world: coral reefs. Amsterdam; New York: Elsevier.
- 22. Schnitzler CE, Weis VM (2010) Coral larvae exhibit few measurable transcriptional changes during the onset of coral-dinoflagellate endosymbiosis. Marine Genomics 3: 107–116.
- 23. Weis VM (2008) Cellular mechanisms of Cnidarian bleaching: stress causes the collapse of symbiosis. Journal of Experimental Biology 211: 3059.
- 24. Voolstra CR, Schwarz JA, Schnetzer J, Sunagawa S, Desalvo MK, et al. (2009) The host transcriptome remains unaltered during the establishment of coral-algal symbioses. Molecular Ecology 18: 1823–1833.
- 25. Yuyama I, Watanabe T, Takei Y (2011) Profiling differential gene expression of symbiotic and aposymbiotic corals using a high coverage gene expression profiling (HiCEP) analysis. Marine Biotechnology 13: 32–40.
- 26. Leggat W, Hoegh-Guldberg O, Dove S, Yellowlees D (2007) Analysis of an EST library from the dinoflagellate (Symbiodinium sp.) symbiont of reef-building corals. Journal of Phycology 43: 1010–1021.
- 27. Voolstra CR, Sunagawa S, Schwarz JA, Coffroth MA, Yellowlees D, et al. (2009) Evolutionary analysis of orthologous cDNA sequences from cultured and symbiotic dinoflagellate symbionts of reef-building corals (Dinophyceae: Symbiodinium). Comparative Biochemistry and Physiology Part D: Genomics and Proteomics 4: 67–74.
- 28. Desalvo MK, Voolstra CR, Sunagawa S, Schwarz JA, Stillman JH, et al. (2008) Differential gene expression during thermal stress and bleaching in the Caribbean coral Montastraea faveolata. Molecular Ecology 17: 3952–3971.
- 29. Portune KJ, Voolstra CR, Medina M, Szmant AM (2010) Development and heat stress-induced transcriptomic changes during embryogenesis of the scleractinian coral Acropora palmata. Marine Genomics 3: 51–62.
- 30. DeSalvo MK, Sunagawa S, Fisher PL, Voolstra CR, Iglesias-Prieto R, et al. (2010) Coral host transcriptomic states are correlated with Symbiodinium genotypes. Molecular Ecology 19: 1174–1186.
- 31. Guillard RR, Ryther JH (1962) Studies of marine planktonic diatoms. I. Cyclotella nana Hustedt, and Detonula confervacea (cleve) Gran. Can J Microbiol 8: 229–239.
- 32. Polne-Fuller M (1991) A novel technique for preparation of axenic cultures of Symbiodinium (Pyrrophyta) through selective digestion by amoebae. Journal of Phycology 27: 552–554.
- 33. Chevreux B, Pfisterer T, Drescher B, Driesel AJ, Muller WE, et al. (2004) Using the miraEST assembler for reliable and automated mRNA transcript assembly and SNP detection in sequenced ESTs. Genome Res 14: 1147–1159.
- 34. Ning Z, Cox AJ, Mullikin JC (2001) SSAHA: a fast search method for large DNA databases. Genome Res 11: 1725–1729.
- 35. Edgar RC (2010) Search and clustering orders of magnitude faster than BLAST. Bioinformatics 26: 2460–2461.
- 36. Watanabe T, Kii S-i, Tanaka J, Takishita K, Maruyama T (2006) cDNA cloning and phylogenetic and expression analyses of actin in symbiotic dinoflagellates (Symbiodinium spp.). Journal of Applied Phycology 18: 219–225.
- 37. Moriya Y, Itoh M, Okuda S, Yoshizawa AC, Kanehisa M (2007) KAAS: an automatic genome annotation and pathway reconstruction server. Nucleic Acids Research 35: 182–185.
- 38. Zdobnov EM, Apweiler R (2001) InterProScan – an integration platform for the signature-recognition methods in InterPro. Bioinformatics 17: 847–848.
- 39. Boeckmann B, Bairoch A, Apweiler R, Blatter M-C, Estreicher A, et al. (2003) The SWISS-PROT protein knowledgebase and its supplement TrEMBL in 2003. Nucleic Acids Research 31: 365–370.
- 40. Wright F (1990) The ‘effective number of codons’ used in a gene. Gene 87: 23–29.
- 41. Rice P, Longden I, Bleasby A (2000) EMBOSS: the European Molecular Biology Open Software Suite. Trends in Genetics 16: 276–277.
- 42.
Peden JF (1999) (1999) Analysis of Codon Usage: University of Nottingham.
- 43. Edgar RC (2004) MUSCLE: multiple sequence alignment with high accuracy and high throughput. Nucleic Acids Research 32: 1792–1797.
- 44. Tamura K, Peterson D, Peterson N, Stecher G, Nei M, et al. (2011) MEGA5: molecular evolutionary genetics analysis using maximum likelihood, evolutionary distance, and maximum parsimony methods. Molecular Biology and Evolution.
- 45. Guindon S, Dufayard J-F, Lefort V, Anisimova M, Hordijk W, et al. (2010) New algorithms and methods to estimate maximum-likelihood phylogenies: assessing the performance of PhyML 3.0. Systematic Biology 59: 307–321.
- 46. Ronquist F, Huelsenbeck JP (2003) MrBayes 3: bayesian phylogenetic inference under mixed models. Bioinformatics 19: 1572–1574.
- 47. Drummond A, Rambaut A (2007) BEAST: Bayesian evolutionary analysis by sampling trees. BMC Evolutionary Biology 7: 214.
- 48. Wilson D, Charoensawan V, Kummerfeld SK, Teichmann SA (2007) DBD–taxonomically broad transcription factor predictions: new content and functionality. Nucleic Acids Research 36: D88-D92–D88-D92.
- 49. Balaji S, Babu MM, Iyer LM, Aravind L (2005) Discovery of the principal specific transcription factors of Apicomplexa and their implication for the evolution of the AP2-integrase DNA binding domains. Nucleic Acids Research 33: 3994–4006.
- 50. Finn RD, Clements J, Eddy SR (2011) HMMER web server: interactive sequence similarity searching. Nucleic Acids Research 39: W29–W37.
- 51. Ryu T, Mavromatis C, Bayer T, Voolstra C, Ravasi T (2011) Unexpected complexity of the reef-building coral Acropora millepora transcription factor network. BMC Systems Biology 5: 58–58.
- 52. Aurrecoechea C, Brestelli J, Brunk BP, Dommer J, Fischer S, et al. (2009) PlasmoDB: a functional genomic database for malaria parasites. Nucleic Acids Research 37: D539-543-D539-543.
- 53. Arnaiz O, Cain S, Cohen J, Sperling L (2007) ParameciumDB: a community resource that integrates the Paramecium tetraurelia genome sequence with genetic data. Nucleic Acids Research 35: D439-D444-D439-D444.
- 54. Haider S, Ballester B, Smedley D, Zhang J, Rice P, et al. (2009) BioMart Central Portal–unified access to biological data. Nucleic Acids Research 37: W23-27-W23-27.
- 55. Finn RD, Tate J, Mistry J, Coggill PC, Sammut SJ, et al. (2008) The Pfam protein families database. Nucleic Acids Res 36: D281–288.
- 56. Reitzel AM, Sullivan JC, Traylor-Knowles N, Finnerty JR (2008) Genomic survey of candidate stress-response genes in the estuarine anemone Nematostella vectensis. The Biological Bulletin 214: 233–233.
- 57. Kanehisa M, Goto S (2000) KEGG: Kyoto Encyclopedia of Genes and Genomes. Nucleic Acids Research 28: 27–30.
- 58. Ciccarelli FD, Doerks T, von Mering C, Creevey CJ, Snel B, et al. (2006) Toward automatic reconstruction of a highly resolved tree of Life. Science 311: 1283–1287.
- 59. Hsiao Y-Y, Lin C-H, Liu J-K, Wong T-Y, Kuo J (2010) Analysis of codon usage patterns in toxic dinoflagellate Alexandrium tamarense through expressed sequence tag data. Comparative and Functional Genomics 2010: 138538.
- 60. Lidie KB, Ryan JC, Barbier M, Van Dolah FM (2005) Gene expression in Florida red tide dinoflagellate Karenia brevis: analysis of an expressed sequence tag library and development of DNA microarray. Marine Biotechnology 7: 481–493.
- 61. Moore RB, Ferguson KM, Loh WKW, Hoegh-Guldberg O, Carter DA (2003) Highly organized structure in the non-coding region of the psbA minicircle from clade C Symbiodinium. International Journal of Systematic and Evolutionary Microbiology 53: 1725–1734.
- 62. Zhang Z, Green BR, Cavalier-Smith T (1999) Single gene circles in dinoflagellate chloroplast genomes. Nature 400: 155–159.
- 63. Sanchez-Puerta MV, Lippmeier JC, Apt KE, Delwiche CF (2007) Plastid genes in a non-photosynthetic dinoflagellate. Protist 158: 105–117.
- 64. Malik HS, Henikoff S (2003) Phylogenomics of the nucleosome. Nat Struct Mol Biol 10: 882–891.
- 65. Gonzalez-Romero R, Ausio J, Mendez J, Eirin-Lopez JM (2008) Early evolution of histone genes: prevalence of an ‘orphon’ H1 lineage in protostomes and birth-and-death process in the H2A family. Journal of molecular evolution 66: 505–518.
- 66. Pusarla R-H, Bhargava P (2005) Histones in functional diversification. FEBS Journal 272: 5149–5168.
- 67. Moshkin YM, Kan TW, Goodfellow H, Bezstarosti K, Maeda RK, et al. (2009) Histone chaperones ASF1 and NAP1 differentially modulate removal of active histone marks by LID-RPD3 complexes during NOTCH silencing. Molecular Cell 35: 782–793.
- 68. Ramirez-Parra E, Gutierrez C (2007) The many faces of chromatin assembly factor 1. Trends in Plant Science 12: 570–576.
- 69.
Gupta SD (2010) Reactive oxygen species and antioxidants in higher plants. Boca Raton, FL: Science Publishers; Distributed by CRC Press.
- 70. Gill SS, Tuteja N (2010) Reactive oxygen species and antioxidant machinery in abiotic stress tolerance in crop plants. Plant Physiology and Biochemistry 48: 909–930.
- 71. Kim S, Bachvaroff TR, Handy SM, Delwiche CF (2011) Dynamics of actin evolution in dinoflagellates. Mol Biol Evol 28: 1469–1480.
- 72. Moustafa A, Evans AN, Kulis DM, Hackett JD, Erdner DL, et al. (2010) Transcriptome profiling of a toxic dinoflagellate reveals a gene-rich protist and a potential impact on gene expression due to bacterial presence. PLoS ONE 5: e9688.
- 73. LaJeunesse TC (2001) Investigating the biodiversity, ecology, and phylogeny of endosymbiotic dinoflagellates in the genus Symbiodinium using the ITS region: in search of a “species" level marker. Journal of Phycology 37: 866–880.
- 74. Voolstra CR, Sunagawa S, Matz MV, Bayer T, Aranda M, et al. (2011) Rapid evolution of coral proteins responsible for interaction with the environment. PLoS ONE 6: e20392.
- 75. Iguchi A, Shinzato C, Foret S, Miller DJ (2011) Identification of fast-evolving genes in the scleractinian coral Acropora using comparative EST analysis. PLoS ONE 6: e20140.
- 76. Goldman N, Yang Z (1994) A codon-based model of nucleotide substitution for protein-coding DNA sequences. Molecular Biology and Evolution 11: 725–736.
- 77. Nielsen R, Yang Z (1998) Likelihood models for detecting positively selected amino acid sites and applications to the HIV-1 envelope gene. Genetics 148: 929–936.
- 78. Eyre-Walker A, Hurst LD (2001) The evolution of isochores. Nat Rev Genet 2: 549–555.
- 79. Hershberg R, Petrov DA (2008) Selection on Codon Bias. Annual Review of Genetics 42: 287–299.
- 80. Plotkin JB, Kudla G (2011) Synonymous but not the same: the causes and consequences of codon bias. Nat Rev Genet 12: 32–42.
- 81. Dong H, Nilsson L, Kurland CG (1996) Co-variation of tRNA abundance and codon usage in Escherichia coli at different growth rates. Journal of Molecular Biology 260: 649–663.
- 82. Lila Koumandou V, Nisbet RER, Barbrook AC, Howe CJ (2004) Dinoflagellate chloroplasts - where have all the genes gone? Trends in Genetics 20: 261–267.
- 83. Mavrich TN, Ioshikhes IP, Venters BJ, Jiang C, Tomsho LP, et al. (2008) A barrier nucleosome model for statistical positioning of nucleosomes throughout the yeast genome. Genome Res 18: 1073–1083.
- 84. Mavrich TN, Jiang C, Ioshikhes IP, Li X, Venters BJ, et al. (2008) Nucleosome organization in the Drosophila genome. Nature 453: 358–362.
- 85. Zilberman D, Coleman-Derr D, Ballinger T, Henikoff S (2008) Histone H2A.Z and DNA methylation are mutually antagonistic chromatin marks. Nature 456: 125–129.
- 86. Ahmad K, Henikoff S (2002) Histone H3 variants specify modes of chromatin assembly. Proc Natl Acad Sci U S A 99: Suppl 416477–16484.
- 87. Hake SB, Allis CD (2006) Histone H3 variants and their potential role in indexing mammalian genomes: The “H3 barcode hypothesis". Proceedings of the National Academy of Sciences 103: 6428–6435.
- 88. Lohuis MR, Miller DJ (1998) Hypermethylation at CpG-motifs in the dinoflagellates Amphidinium carterae (Dinophyceae) and Symbiodinium microadriaticum (Dinophyceae): evidence from restriction analyses, 5-azacytidine and ethionine treatment. Journal of Phycology 34: 152–159.
- 89. Lohuis MR, Miller DJ (1998) Light-Regulated Transcription of Genes Encoding Peridinin Chlorophyll a Proteins and the Major Intrinsic Light-Harvesting Complex Proteins in the DinoflagellateAmphidinium carterae Hulburt (Dinophycae). Plant Physiology 117: 189–196.
- 90. Kellenberger E, Arnold-Schulz-Gahmen B (1992) Chromatins of low-protein content: special features of their compaction and condensation. FEMS Microbiology Letters 79: 361–370.
- 91. Moustafa A, Evans AN, Kulis DM, Hackett JD, Erdner DL, et al. (2010) Transcriptome profiling of a toxic dinoflagellate reveals a gene-rich protist and a potential impact on gene expression due to bacterial presence. PLoS ONE 5: 9688.
- 92. Erdner D, Anderson D (2006) Global transcriptional profiling of the toxic dinoflagellate Alexandrium fundyense using Massively Parallel Signature Sequencing. BMC Genomics 7: 88.
- 93. van Nimwegen E (2003) Scaling laws in the functional content of genomes. Trends in Genetics 19: 479–484.
- 94. Aravind L, Iyer LM, Wellems TE, Miller LH (2003) Plasmodium biology: genomic gleanings. Cell 115: 771–785.
- 95. Iyer LM, Anantharaman V, Wolf MY, Aravind L (2008) Comparative genomics of transcription factors and chromatin proteins in parasitic protists and other eukaryotes. International Journal for Parasitology 38: 1–31.
- 96. Painter HJ, Campbell TL, Llinás M (2011) The Apicomplexan AP2 family: integral factors regulating Plasmodium development. Molecular and Biochemical Parasitology 176: 1–7.
- 97. Aravind L, Watanabe H, Lipman DJ, Koonin EV (2000) Lineage-specific loss and divergence of functionally linked genes in eukaryotes. Proceedings of the National Academy of Sciences of the United States of America 97: 11319–11324.
- 98. Lespinet O, Wolf YI, Koonin EV, Aravind L (2002) The Role of lineage-specific gene family expansion in the evolution of eukaryotes. Genome Research 12: 1048–1059.
- 99. Mihailovich M, Militti C, Gabaldón T, Gebauer F (2010) Eukaryotic cold shock domain proteins: highly versatile regulators of gene expression. BioEssays 32: 109–118.
- 100.
Lin S, Zhang H, Gray MW (2007) RNA editing in dinoflagellates and its implications for the evolutionary history of the editing machinery. In: Smith H, editor. RNA and DNA Editing: Molecular Mechanisms and Their Integration into Biological Systems: JohnWiley & Sons, Inc. pp. 280–309.
- 101. Merle PL, Sabourault C, Richier S, Allemand D, Furla P (2007) Catalase characterization and implication in bleaching of a symbiotic sea anemone. Free Radic Biol Med 42: 236–246.
- 102. Sunagawa S, Wilson EC, Thaler M, Smith ML, Caruso C, et al. (2009) Generation and analysis of transcriptomic resources for a model system on the rise: the sea anemone Aiptasia pallida and its dinoflagellate endosymbiont. BMC Genomics 10: 258.
- 103. Keeling PJ, Palmer JD (2008) Horizontal gene transfer in eukaryotic evolution. Nat Rev Genet 9: 605–618.
- 104. Andersson JO (2005) Lateral gene transfer in eukaryotes. Cellular and Molecular Life Sciences 62: 1182–1197.
- 105. Schmidt A, Gube M, Schmidt A, Kothe E (2009) In silico analysis of nickel containing superoxide dismutase evolution and regulation. Journal of Basic Microbiology 49: 109–118.
- 106. Hisabori T, Motohashi K, Hosoya-Matsuda N, Ueoka-Nakanishi H, Romano PGN (2007) Towards a functional dissection of thioredoxin networks in plant cells. Photochemistry and Photobiology 83: 145–151.
- 107. Chibani K, Wingsle G, Jacquot J-P, Gelhaye E, Rouhier N (2009) Comparative Genomic Study of the Thioredoxin Family in Photosynthetic Organisms with Emphasis on Populus trichocarpa. Molecular Plant 2: 308–322.
- 108. Eckardt NA (2007) Oxidation Pathways and Plant Development: Crosstalk between Thioredoxin and Glutaredoxin Pathways. The Plant Cell Online 19: 1719–1721.
- 109. Papp E, Nardai G, Söti C, Csermely P (2003) Molecular chaperones, stress proteins and redox homeostasis. BioFactors 17: 249–257.
- 110. Vieira Dos Santos C, Rey P (2006) Plant thioredoxins are key actors in the oxidative stress response. Trends in Plant Science 11: 329–334.
- 111. Niwa T (2007) Protein glutathionylation and oxidative stress. Journal of Chromatography B 855: 59–65.
- 112. Aranda M, Banaszak AT, Bayer T, Luyten JR, Medina M, et al. (2011) Differential sensitivity of coral larvae to natural levels of ultraviolet radiation during the onset of larval competence. Molecular Ecology 20: 2955–2972.
- 113. Edge SE, Morgan MB, Gleason DF, Snell TW (2005) Development of a coral cDNA array to examine gene expression profiles in Montastraea faveolata exposed to environmental stress. Mar Pollut Bull 51: 507–523.
- 114. Nishiyama Y, Allakhverdiev SI, Murata N (2011) Protein synthesis is the primary target of reactive oxygen species in the photoinhibition of photosystem II. Physiologia Plantarum 142: 35–46.
- 115. Kim J, Mayfield SP (1997) Protein Disulfide Isomerase as a Regulator of Chloroplast Translational Activation. Science 278: 1954–1957.
- 116. Dietz K-J, Pfannschmidt T (2011) Novel regulators in photosynthetic redox control of plant metabolism and gene expression. Plant Physiology 155: 1477–1485.
- 117.
Lin S, Zhang H (2008) Dinoflagellate meta-transcriptomics enabled by spliced leader. In: Ho K, Zhou M, Qi Y, editors. Hong Kong: Environmental Publication House Hong Kong. pp. 166–170.