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Appl Environ Microbiol. Jul 2010; 76(13): 4521–4529.
Published online Apr 30, 2010. doi:  10.1128/AEM.02345-09
PMCID: PMC2897438

Gene Expression in Proliferating Cells of the Dinoflagellate Alexandrium catenella (Dinophyceae)[down-pointing small open triangle]

Abstract

Understanding the conditions leading to harmful algal blooms, especially those produced by toxic dinoflagellate species, is important for environmental and health safety. In addition to investigations into the environmental conditions necessary for the formation of toxic blooms, we postulate that investigating gene expression in proliferating cells is essential for understanding bloom dynamics. Expressed sequence tags were produced from cultured cells of the toxic dinoflagellate Alexandrium catenella sampled during the initiation phase of growth using Sanger's method and by 454 pyrosequencing. A significant proportion of identified genes (ca. 25%) represented enzymes and proteins that participate in a variety of cellular regulatory mechanisms that may characterize proliferating cells, e.g., control of the cell cycle and division, regulation of transcription, translation and posttranslational protein modifications, signaling, intracellular trafficking, and transport. All of the several genes selected for gene expression assays due to their involvement in metabolism and the cell cycle were overexpressed during exponential growth. These data will be useful for investigating the mechanisms underlying growth and toxin production in toxic Alexandrium species and for studying and monitoring the development of toxic blooms.

Dinoflagellates are important contributors to primary production in marine coastal systems, either as free-living taxa in the phytoplankton or as symbionts in reef-building corals. This algal class contains the highest number of toxin-producing species among the marine phytoplankton groups (46). Worldwide, the developments of these harmful species, known as harmful algal blooms (HABs), have serious impacts on the exploitation of seafood resources (from natural stocks and aquaculture) and are an important threat to public health (2). Further, the number of toxic events has dramatically increased in the last 4 decades (11).

Alexandrium catenella belongs to the A. tamarense-A. catenella-A. fundyense species complex, which comprises five groups (numbered I to V) defined on the basis of ribosomal DNA (rDNA) sequences (31). Cell chains are formed, generally containing between two and eight cells, when the population is growing. A. catenella produces saxitoxins, a family of alkaloid toxins containing ~20 different molecules. Around the world, large blooms of A. catenella in areas with natural or farmed stocks of shellfish, especially bivalves that filter-feed on these algae, are responsible for toxin accumulation in these shellfish. The intoxication produced in people that consumed these toxic shellfish is called paralytic shellfish poisoning, with respect to the specific neurological symptoms developed. The DNA content per cell in the strain A. tamarense CCMP1598, belonging to group IV as defined by Lilly et al. (31), is estimated to be 103.5 pg (27), corresponding to a genome size of ~96 Gb. As a result, genome sequencing of these species has still not been accomplished. A study investigating the gene content of the genome by transcriptional profiling using massively parallel signature sequencing in a group I strain, A. fundyense GtCA28, found 27,000 unique signatures (10). The first published expression library from the toxic strain A. tamarense CCMP1598 annotated ~20% unique sequences and gave new insights into the peculiar DNA packaging system of dinoflagellates (17, 18). An expressed sequence tag (EST) library from a midexponential culture of the toxic strain A. catenella ACC7 (a group I strain from Chile) led to a focus on the gene expression involved in bioluminescence and photosynthesis (48).

Understanding the causes and environmental conditions leading to toxic algae bloom events is a challenging concern involving many researchers worldwide. For decades, in situ investigations of environmental conditions have focused on describing physical, chemical, and biological proxies during the course of bloom development to understand the mechanisms controlling the changes in cell number at the population level. However, other mechanisms occur at the cellular level that concern cell activation at the initiation of blooms and eventually cell death at the decline of blooms. These cellular mechanisms have been poorly investigated, and we postulated that investigating gene expression will likely help in understanding the dynamics of blooms with respect to prevailing environmental conditions.

As a first approach, we investigated gene expression markers that are related to the proliferating state of dinoflagellate cells. We used a subtracted library between cells in the initiation phase of growth and cells collected in a stationary phase culture to reveal genes that are differentially expressed when the cells activate their metabolism and molecular mechanisms required during the proliferation process. Pyrosequencing using a 454 GS-FLX (Roche) was chosen for providing relatively long reads (200 to 300 bp at that time) and for allowing the identification of sequences generated from organisms lacking a known genome (35). We report here our analysis of these data, focusing on expressed genes possibly related to the proliferating state of A. catenella cells. In addition, expression assays performed on selected metabolism and cell cycle genes demonstrated overexpression during exponential growth.

MATERIALS AND METHODS

Laboratory cultures for library construction.

The A. catenella monoclonal strain ACT03 was isolated from Thau lagoon (French Mediterranean) during a bloom that occurred in 2003. According to its rDNA sequence (32), this strain belongs to group IV of the A. tamarense complex (31), formerly known as the “Temperate Asia” (TA) clade (43). The strain was maintained in a seawater culture medium (ESNW) enriched without silicate (20). The ESNW medium was prepared with aged seawater from the Thau lagoon, the salinity of which was lowered to 36 practical salinity units with distilled water before autoclaving. Cells were grown at 20°C under an irradiance of 100 μmol of photons m−2 s−1 provided by cool-white fluorescent tubes on a 12/12-h light/dark cycle. For cDNA library construction, a culture was brought to stationary phase in ESNW medium. The cells were then inoculated at a one-tenth dilution into new culture medium. Cells, representing the initiation phase of growth, were collected during the light phase at 3 and 5.5 h after inoculation on the first day and on the second day at 20, 21.5, 23, 24.5, and 25.5 h. Cell collection was performed using centrifugation at 3,000 × g at 4°C for 20 min. All cell pellets were pooled for the RNA extraction performed for the library construction. Another culture in stationary phase was collected for use in the subtraction procedure.

RNA extraction and cDNA library construction.

Both initiation-phase and stationary-phase cell pellets were resuspended in lysis buffer (4 M guanidine thiocyanate, 30 mM disodium citrate, 30 mM β-mercaptoethanol [pH 7.0 to 7.5]) and sonicated for 30 s with a 3-mm-diameter probe sonicator (Ultrasonic Processor 75038; Bioblock Scientific, Ilkirch, France). The total RNA was then isolated by using a phase separation procedure after sequential addition of 1 volume of buffer-saturated phenol and 1/5 volume of chloroform-isoamyl alcohol (24:1 [vol/vol]) (33). The total RNA extract was further purified by using PureLink Micro-to-Midi cartridges (Invitrogen). cDNA synthesis and the library subtraction procedure were performed by Evrogen Lab, Ltd. (Moscow, Russia). Double-stranded cDNA was prepared by using the SMART procedure (52). Subtraction was performed by using the suppression subtractive hybridization (SSH) method (9; see also http://www.evrogen.com/technologies/SSH.shtml). The initiation-phase culture was assigned as the “tester”, and the stationary-phase culture was assigned as the “driver.” The subtractive hybridization was performed by mixing 30 ng of the tester with 1,000 ng of the driver.

Sequencing of cDNA libraries.

A sample of the PCR products resulting from the SSH procedure was cloned into the pCR4-TOPO plasmid vector by using a PCR cloning kit for sequencing (Invitrogen, Carlsbad, CA). Transformed Escherichia coli colonies were grown overnight at 37°C in Terrific Broth medium (catalog no. T9179; Sigma-Aldrich) before plasmid isolation using a PureLink 96-well plasmid purification kit (Invitrogen). The sequencing of plasmid inserts was performed by Macrogen, Inc. (Seoul, South Korea), on an ABI3770 automated sequencer. A total of 16 96-well plasmid plates were sequenced, of which ~85% of sequences were usable. Subsequently, pyrosequencing was performed on SSH-PCR products by Eurofins MWG Biotech (Martinsried, Germany), using a GS-FLX machine (454 Life Sciences; Roche Diagnostics), which resulted in ~72,000 sequences.

Bioinformatics analyses.

EST sequences were trimmed to remove contaminants (vector arms and SSH adapters), low-quality sequences, and low-complexity sequences using the SeqClean program downloaded from the Gene Index Project website (http://compbio.dfci.harvard.edu/tgi/software). Passing EST sequences were assembled by using the TGICL program (39), and the consensus sequences of the resulting contigs were used for further analysis. Consensus sequences and EST singletons were searched against the GenBank nonredundant protein database (GenPept) with the TBLASTX program (1) using an E-value threshold of 1e−5. A BLAST search against the rRNA database was performed to identify putative rRNA sequences, including nuclear and organelle rRNA from A. catenella and bacterial and eukaryotic contaminants (e.g., putative parasites or symbionts). Accordingly, 77 Alexandrium or dinoflagellate rRNA sequences (contigs or singletons) were removed from the analyzed sequence data set, while no other known putative eukaryotic rRNA sequences were detected. Phylogenetic analyses were performed with the maximum-likelihood tree reconstruction approach of the PhyML program (16) using the phylogeny.fr web tool (8). The sequencing data were submitted to the NCBI Short Read Archive under accession number SRP000647.

Growth experiment with gene expression assays.

An ACT03 culture was grown in ESNW medium to stationary phase with a cell density of 1.4 × 104 cells ml−1 for use as an inoculum for this experiment. This culture was then diluted to 103 cells ml−1 in 40 ml in three flasks (EasyFlask 25 with filter cap; Nunc) containing fresh ESNW medium ~1 h after the onset of light phase. Triplicate cultures were grown for 2 weeks in the conditions described above. Samples were taken daily in the middle of light phase (experimental midday) starting on the inoculation day (i.e., ~5 h after inoculation, day 0, corresponding to early lag phase). Samples were fixed in 2% (final) formaldehyde for direct cell counts under the microscope of single cells and two-cell and four-cell chains. The growth rate was calculated as k (number of divisions per day [div day−1]) using the following equation, according to the method of Guillard (13): k = (ln Nt2 − ln Nt1)/(δt·ln2), where Nt2 and Nt1 are the cell concentrations at times t2 and t1, respectively, and δt is the period of time (in days) between days t1 and t2.

During growth, the volume of samples taken for gene expression assays was adjusted to contain <104 cells. Cells were pelleted by centrifugation (12,000 × g, 4°C) for 5 min, rinsed with 180 μl of phosphate-buffered saline, and centrifuged again. Lost cells in both supernatants were counted for a better estimation of the number of cells contained in each sample. Lysis and reverse transcription were performed by using a SideStep QPCR cDNA synthesis kit (Stratagene), according to the manufacturer's instructions. Briefly, pelleted cells were immediately resuspended in 100 μl of SideStep lysis buffer and sonicated for 40 s on ice using a UP 100H ultrasonic processor (Hielscher Ultrasonics, Germany) with the time cycle set at 0.8 and a 60% power amplitude and equipped with a 0.5-mm-diameter Sonotrode (MS0.5). Lysate samples were then stored at −20°C until the end of the culture experiment. Reverse transcription was performed in 20-μl reactions with a mixture of the provided oligo(dT) primer and 10 ng of the 5.8S rRNA F-primer/μl (Table (Table1)1) and 2 μl of lysate sample. Reverse transcription reactions were diluted 40-fold with DNase-free water before real-time quantitative PCR (qPCR) assays. These cDNA samples were stored at −20°C and subsequently used for all qPCR assays.

TABLE 1.
Primers and annealing temperatures used in SYBR qPCR assays

Several genes were selected for gene expression assays due to their involvement in metabolism and the cell cycle. Expression of 5.8S rRNA, a constituent of ribosomes responsible for protein translation, was considered as reflecting the whole-cell metabolism activity. Based on our expression library analysis (see Discussion), of the 12 protein-coding genes selected for designing qPCR gene expression assays, only 7 resulted in functional qPCR assays. Two metabolic genes are involved in inorganic carbon concentration and fixation: a δ-carbonic anhydrase (DCA) and form II of RuBisCO (ribulose-1,5-bisphosphate carboxylase oxygenase, i.e., rbcL2). A putative cellulase was selected as possibly involved in cell division. Proliferating cell nuclear antigen (PCNA) was selected to represent DNA replication. Three transcription factors were selected to represent the regulation of gene expression: two of them were homologs of TATA box-binding proteins RuvB-like (RVBL) and RuvB-like2 (RVBL2), and the third was a homolog of a Tubby-like protein (TUBL). Primer sequences were designed by using Primer3 software (42) and BLAST analyses (1) to confirm their specificity with respect to other known Alexandrium genes. Primer pairs, annealing temperatures, and amplicon sizes are provided in Table Table1.1. A Stratagene Mx3000P instrument and MxPro QPCR v4.00 software were used for PCR amplifications and for the detection of amplified PCR products. qPCR was performed in triplicate for each assay using SYBR Premix Ex Taq Perfect real-time reagents (Takara, Japan), with ROX as a passive reference and 2 μl of template (purified PCR products as standards or diluted reverse transcription reaction for experimental assays), in a final volume of 20 μl. PCRs were performed according to the reagent manufacturer's instructions for the instrument. All qPCR assays were followed by a dissociation curve analysis to ensure the single PCR products matched with the standard products. The calculation of template amounts in samples was performed using cycle threshold (CT) values with respect to standard curves established for each targeted gene. Calculated numbers of cDNA copies were normalized to the cell number in each sample. For each targeted gene, the expression level for the first time point (midday of inoculation day) was set to 1, and relative expression ratios were calculated in each sample with respect to this reference value. Control qPCR assays were performed on lysate samples without reverse transcription for verification that genomic DNA copies were negligible (either not detectable or in low quantity).

RESULTS

Analysis of expression libraries.

The expression library was obtained from a culture sampled during the initiation phase of growth (lag phase) in the light phase. The statistics of the sequencing data after cleaning, assembly, and annotation are summarized in Table Table2.2. TBLASTX analysis returned a relatively small percentage (24.3%) of clustered sequences matching known or hypothetical proteins in GenPept, and one-third of sequences had a matching homolog (BLASTN) in the deposited Alexandrium EST data. The complete list of putative proteins is provided as supplementary material (see Table S1 in the supplemental material). Altogether, 46.5% of our clustered cDNA sequences appeared to have homologous sequences in either protein or nucleotide databases. Among the gene clusters with matches in GenPept, 75% of them contained fewer than five sequences (see Fig. S1 in the supplemental material), suggesting that the subtraction method allowed a good equalization of the cDNA population. Among the recovered sequences matching sequences within diverse taxonomic groups, only 24% were similar to known dinoflagellate proteins (Fig. (Fig.1).1). A high number (27%) matched bacterial protein sequences, while ~50 different putative bacterial 16S rRNA sequences (~60 sequences in the data set) were detected; most of these putative rRNA sequences referred to uncultured bacteria (60%). A noticeable number of sequences appeared related to proteins from phycodnaviruses.

FIG. 1.
Distribution of matching hits among taxonomic groups for the 5,153 protein-coding sequences. Protozoa included Ciliophora and Apicomplexa (sister clades of Dinophyceae within Alveolata), Acanthamoeba, Diplomonadida, Entamoebidae, Granuloreticulosea, Mycetozoa, ...
TABLE 2.
Statistics of gene sequences obtained from clustered EST data

The analysis of functional gene categories focused mainly on the different genes identified across eukaryotic organisms (Fig. (Fig.2).2). We scored the novelty features of eukaryotic genes and classified them into large functional categories in dinoflagellates and other photosynthetic and nonphotosynthetic taxa (see Fig. S2 in the supplemental material). One-third (33.4%) of our annotated A. catenella sequences had dinoflagellate homologs in protein databases. The largest proportion of these genes was related to photosynthesis and, to a lesser extent, metabolism, respiration, and various regulatory processes (e.g., cell cycle, transcription, translation, and posttranslational modifications). Conversely, fewer genes were known in dinoflagellates in functions related to cell structure, transport, and trafficking.

FIG. 2.
Distribution of A. catenella cDNA clusters encoding eukaryotic proteins into functional categories. The 3,765 transcripts identified as encoding known or hypothetical proteins in eukaryotes and the corresponding 2,002 genes were classified into functional ...

Among the most highly represented genes in our sequence data set, the majority (33 of 38) matched dinoflagellates (Table (Table3),3), and many were involved in photosynthesis and metabolism. Genes related to the cell division cycle were involved either in the cytoskeleton or DNA synthesis. The last group included genes encoding proteins involved in translation and posttranslational processing of proteins. The two genes responsible for bioluminescence were also very prominent.

TABLE 3.
Most abundant eukaryotic protein-coding gene sequences in our data seta

Variation in expression of selected genes during exponential growth.

Exponential growth phase occurred between day 3 and day 8 (Fig. (Fig.33 A), whereas the mother culture remained stationary for several days after the inoculum samples were taken (data not shown). The daily growth rate reached its maximum average value (0.41 div day−1) during the fifth culture day (Fig. (Fig.3A).3A). During exponential growth, the proportion of cells forming two-cell or four-cell chains increased until Day 7 (Fig. (Fig.3B).3B). The number of chains started decreasing 1 day before the end of exponential growth, and cultures gradually returned mostly to single cells toward the end of the experiment (nearly reaching the stationary phase). At midday on the first day, 5 h after inoculation (time zero, Fig. Fig.4),4), gene expression was increased in the new cultures compared to stationary-phase cells in the mother culture. For the eight target genes, relative gene expression per cell apparently decreased for 2 to 3 days, between day 1 and day 2 or 3 depending on the gene, and then peaked on day 4, the day before the maximum growth rate period. The level of expression varied among the targeted genes in the range of 1.1- to 4.1-fold with respect to the inoculation day. The highest variation was scored for the 5.8S rRNA (Fig. (Fig.4A).4A). Regarding CO2 assimilation, the variation in DCA expression (inorganic carbon-concentrating mechanism to fuel RuBisCO with CO2) was ~2-fold higher than for rbcL2, which exhibited the smallest variations among the selected target genes during growth (Fig. 4C and D). After the mid-exponential growth phase, relative expression gradually decreased for all eight target genes.

FIG. 3.
Growth curve and variation in growth rate (dashed line) (A) and proportion (B) of single cells and chain-forming cells in ACT03 cultures used for the gene expression assays. Error bars represent the standard deviations of triplicate experiments.
FIG. 4.
Variation in relative gene expression of eight selected genes during the growth of triplicate ACT03 cultures, as shown in Fig. Fig.3.3. All measurements were performed at experimental midday. The relative change in gene expression was normalized ...

DISCUSSION

Putative genes specifically overexpressed in proliferating dinoflagellate cells.

A large proportion of clustered sequences was related to general processes involved in cell dynamics by participation in cellular regulatory processes, such as transcription, synthesis and modifications of proteins, and the control of the cell division cycle (Fig. (Fig.2),2), the most notable of which are highlighted below.

Transcriptional regulation and RNA processing.

Only a few transcriptional regulators have been described thus far (14, 15, 38). We identified numerous novel dinoflagellate genes involved in transcriptional and posttranscriptional regulations. For example, putative transcription factors included homologs for a forkhead box protein (FOXL1), multiprotein bridging factor type 1 (MBF1), and RAP2.4. Two identified TATA box-binding protein interacting proteins (TBP-IP; RuvB-like 1 and 2) and one of the two identified Tubby-like protein homologs (transcription factors involved in signaling [4]) were overexpressed during exponential growth (RVBL, RVBL2, and TUBL in Fig. 4G, H, and F, respectively).

RNA processing has been recently demonstrated in dinoflagellates (30, 51). We identified homologs of proteins involved in splicing, e.g., splicing coactivator subunit SRm300, U5 snRNP-specific protein (Prp8-binding), and U4/U6-associated splicing factor PRP4. Several identified proteins may play a critical role in RNA silencing, including homologs of a silent information regulator-2 homolog, TAR RNA binding protein 1, and DRB4. These features are indicative of a complex mechanism of posttranscriptional regulation.

Protein synthesis and translational regulation.

The mechanism of translational regulation in dinoflagellates has been evidenced for awhile (36). Together with tRNA synthetases and ribosomal proteins, ubiquitous eukaryotic initiation, elongation, and release factors were well represented in our data set (Table (Table3),3), including elongation factor 3 (EF-3) reported here for the first time in dinoflagellates, and translation initiation factor 5A (eIF-5A), which might also be involved in the G1/S transition of the cell cycle (6). These were expressed along with mitochondrial or plastid elongation factors (EF-Tu, EF-Ts, and EF-G).

Mechanisms responsible for posttranslational protein modifications were mainly represented by ubiquitous folding proteins, e.g., chaperones (Hsp70 and Hsp90 families) and peptidyl-prolyl cis-trans isomerases. Conversely, the expression of genes involved in protein degradation by ubiquitination or proteasome formation suggests that intense protein turnover is involved in the dynamics of cellular and metabolic processes. Enzymes and proteins that have a role in regulating the activity of functional proteins and possibly in silencing and signaling processes were found, including numerous protein kinases and phosphatases, methyltransferases, several thioredoxin homologs, and a farnesyltransferase.

Transport, trafficking, and signaling.

Identified transporters were mainly involved in membrane ion transit, one-third of them being ATP-binding cassette proteins (ABC transporters). Trafficking proteins were mainly involved in vesicle transport, such as small GTPases of the Rab family and ADP-ribosylation factors. Signaling mainly involved GTP-binding proteins (G proteins). In Alveolata, G proteins are involved in sensory/mechanical signal transduction in dinoflagellates (e.g., the response to shear stress triggering bioluminescence [7]) and in phototransduction in ciliates (45). Calmodulin and calmodulin-related proteins were also highly represented in our library. These proteins participate in transcriptional regulation by acting on transcription factors, especially during responses to environmental changes (23-24), and are involved in a wide variety of cell processes by stimulating calmodulin-dependent enzymes (also present in our data set) (41).

Cell division.

Numerous transcripts related to DNA replication, e.g., ribonucleoside reductase (RNR) and PCNA (an auxiliary protein of DNA polymerase that operates during S phase) (Table (Table3),3), reflected the proliferation state of analyzed cells (5, 21). The latter (PCNA) was overexpressed during exponential growth (Fig. (Fig.4B).4B). Furthermore, many transcripts encoded Rad24, which functions in DNA damage checkpoint control (Table (Table3).3). The expression of DNA packaging proteins, which are linked to DNA duplication and transcriptional regulation, predominantly concerned a histonelike protein (Table (Table3),3), as previously reported in Alexandrium (17, 47), whereas histone H2A.X was rarely expressed (17). We also identified a potential homolog of an H1-type linker histone.

Among genes whose expression might be related to the cell cycle, especially for the synthesis of cell wall components required before division, we identified an orthologous sequence of the alveolin protein family (12), which is specific for membranous sacs (alveoli) subtending the plasma membrane of Alveolata. Interestingly, a new cellulase previously unknown in photosynthetic organisms (Table (Table3)3) exhibited increased expression during the exponential growth phase (Fig. (Fig.4E).4E). We hypothesize that this cellulase is involved in cell partitioning during mitosis for “opening” the theca (the peculiar cell wall of many dinoflagellates), which is made of cellulosic plates. Transcripts encoding proteins that are involved in mitosis included caltractin (also known as centrin), which localizes in the centriole and flagellar bodies and is involved in the regulation of duplication and segregation of centrosomes (3, 22, 26), and homologs of kinesinlike motor proteins associated with the mitotic spindle (44).

Genes related to photosynthesis.

Most cDNA fragments matched dinoflagellate homologs (Table (Table3);3); however, several photosystem proteins and chlorophyll synthesis enzymes had their best match in cyanobacteria and other photosynthetic eukaryotes (see Table S1 in the supplemental material). The light-harvesting complex protein family contained the highest number of unique gene sequences, suggesting a large diversity of isoforms. All enzymes of the Calvin cycle, responsible for carbon fixation, were highly expressed in the initiation phase of growth (Table (Table3).3). Upstream of the photosynthesis process per se in the process of carbon fixation, we detected three different forms of DCA. In addition to the DCA form detected in group IV A. tamarense CCMP1598 (34) and those similar to form 2 of δ-CA described in Lingulodinium polyedrum (28), the last form, never before reported in a dinoflagellate, was overexpressed during exponential growth (Fig. (Fig.4D).4D). Antioxidative enzymes responsible for protection against reactive oxygen species (ROS) included catalase and Cu/Zn superoxide dismutase, the latter possibly being operational in the plastid stroma against ROS produced during photosynthesis (50).

Cellular metabolism genes.

Two enzymes of the S-adenosylmethionine (SAM) cycle, involved in methylation, were highly represented: S-adenosyl-homocysteine hydrolase (SAHH) and SAM synthetase (SAM-S). SAHH was related to the early G1 phase of the cell cycle of A. fundyense (47), whereas paralytic shellfish toxin production was correlated with SAM-S and SAHH expression (strain CAWD44, group IV) (19). In dinoflagellates, many toxins and bioactive compounds are polyketides, whose biosynthesis is mediated by polyketide synthase (PKS) enzymes (40). A dinoflagellate PKS-like protein and homologs of type I PKS were detected in our library. The prominent expression of luciferase and luciferin-binding protein, the only two proteins required for bioluminescence in dinoflagellates (49), suggests that proliferating Alexandrium cells produce intense bioluminescence at night.

Variation in gene expression during exponential growth.

In the growth experiment, the small increase in cell concentration observed on day 2 (consistently with the appearance of two-cell chains) may correspond to the division of a small cohort of cells whose cell cycle was in G2 phase in the inoculum mother culture. Conversely, it is suggested that most other cells were in G1 phase (but not all at the same point) in the mother culture at the time of inoculation. For the eight analyzed genes, the higher expression levels taken as references at midday on the inoculation day compared to those measured on day 2 and day 3, suggest that a rapid induction of transcription occurred in the hours following inoculation. Circadian changes in gene expression may also explain this increase, given that some genes are mostly transcribed around midday (25). However, this early peak of expression was apparently followed by a gap in the following 2 days, possibly corresponding to the period necessary to complete a cell cycle (about one division every 3 days, as seen later in the exponential growth phase). The small increase in cell number due to division of the first cohort of cells also contributed to this decrease of averaged gene expression per cell as measured in the cell population. The surge in 5.8S rRNA synthesis in early exponential growth phase reflected a massive production of new ribosomes required for protein synthesis. The sharp peak of DCA expression in the early exponential growth phase is consistent with observations showing increased carbonic anhydrase activity in zooxanthellae after several days in culture (29). Conversely, small variations in rbcL2 expression suggest a longer lifetime (i.e., a moderate turnover) of RuBisCO, a finding consistent with the lack of clear circadian changes in the amount of RuBisCO in L. polyedrum (37). On the whole, variations in gene expression during exponential growth were in the same range (i.e., 2- to 3-fold variations) as circadian gene expression (38). Interestingly, during exponential growth, the peak of gene expression on day 4 preceded the period of highest growth rate and the occurrence of four-cell chains. Hence, in monitoring field studies, A. catenella chains may indicate an exponentially growing bloom. However, more importantly, being able to detect sharply increased gene expression might help in anticipating bloom formation and narrowing the period of time to focus on finding the triggering environmental conditions. We have clearly identified five genes whose expression can be used to monitor the rapid growth occurring at the onset of HABs, which will provide invaluable tools for identification of environmental conditions that trigger these phenomena.

Conclusion.

This high-throughput sequencing study makes a substantial step toward understanding the transcribed gene repertoire of A. catenella, a toxic dinoflagellate harboring a massive genome of ~96 Gb for which whole-genome sequencing is still not possible using today's next generation sequencing technologies. Many newly identified protein-coding genes are involved in the cell cycle and division machinery and in multiple regulatory processes supporting cellular dynamics. Of the genes that may characterize proliferating dinoflagellate cells, three transcription factors, a cellulase, and a δ-carbonic anhydrase overexpressed during early exponential growth phase might be used as markers of proliferating Alexandrium cells. These data will be useful for the development of molecular biology tools (e.g., microarrays) to investigate the mechanisms underlying the growth and toxicity of Alexandrium species and for in situ investigation of toxic blooms.

Supplementary Material

[Supplemental material]

Acknowledgments

This research was supported by grants from the Agence Nationale de la Recherche (ANR-05-BLAN-0219 XPressFlorAl and ANR-06-BLAN-0397 GenoSynTox) and from the National Program Ecosphère Continentale et Côtière (EC2CO-PNEC). Support was also received through Ifremer (ALCAT program), the cluster Infrastructures en Biologie Santé et Agronomie, and the Centre National de la Recherche Scientifique (CNRS).

Footnotes

[down-pointing small open triangle]Published ahead of print on 30 April 2010.

Supplemental material for this article may be found at http://aem.asm.org/.

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