MICROARRAY ANALYSIS OF DIURNAL- AND CIRCADIAN-REGULATED GENES IN THE FLORIDA RED-TIDE DINOFLAGELLATE...

MICROARRAY ANALYSIS OF DIURNAL- AND CIRCADIAN-REGULATED GENES INTHE FLORIDA RED-TIDE DINOFLAGELLATE KARENIA BREVIS (DINOPHYCEAE)1

Frances M. Van Dolah2, Kristy B. Lidie, Jeanine S. Morey, Stephanie A. Brunelle, James C. Ryan, Emily A. Monroe,

and Bennie L. Haynes

Marine Biotoxins Program, NOAA Center for Coastal Environmental Health and Biomolecular Research, Charleston,

South Carolina 29412, USA

The photoperiod plays a central role in regulatingthe physiology and behavior of photosynthetic phy-toplankton, and many of these processes are con-trolled by an underlying circadian rhythm. Indinoflagellates, circadian rhythms have been shownto depend largely on posttranscriptional regulation.However, the extent to which dinoflagellates modu-late transcript levels to regulate gene expression inresponse to light and dark has not been addressed.Here we utilized an oligonucleotide microarray con-taining probes to 4629 unique genes from theFlorida red-tide dinoflagellate Karenia brevis(C. C. Davis) G. Hansen et Moestrup to characterizeglobal gene expression patterns over a 24 h day incells exposed to a 16:8 light:dark cycle (LD treat-ment) and cells under constant light (LL treatment).We determined that 9.8% of the queried genes weredifferentially expressed during LD, while 3% ofgenes were differentially expressed over the 24 hday when exposed to LL. Most genes exhibitedeither peak or minimum expression in early darkphase. Of the 453 differentially expressed genes inLD, 104 were assigned predicted annotations basedon BLASTX searches. Few of the identifiable genesthat were differentially expressed in LD appear tobe under circadian control, as their cyclical expres-sion did not persist in LL. Analysis of coordinatelyexpressed genes revealed several novel insights intodinoflagellate gene expression, including an unusu-ally high representation of genes involved in post-transcriptional processing of RNA and proteinturnover, the regulation of PSII genes in responseto light and dark, and the absence of transcriptionalregulation of cell-cycle genes.

Key index words: cell cycle; circadian; dinoflagel-late; diurnal; gene expression; microarray

Abbreviations: LD treatment, 16:8 light:dark cycle;LL treatment, constant light; QRT-PCR, quantita-tive real-time PCR

The toxic dinoflagellate Karenia brevis is respon-sible for red tides in the Gulf of Mexico that causeextensive marine animal mortalities and human ill-ness through the production of neurotoxic brevetox-ins (Van Dolah 2000). Understanding themechanisms that control the growth of K. brevis andpermit it to maintain dominance in the water col-umn over many months is therefore of interest. Manyphysiological and behavioral functions in phyto-plankton are modulated by the photoperiod, eitherdirectly in response to light or indirectly by entrain-ment of an underlying circadian rhythm. In dinoflag-ellates, circadian-controlled processes includephotosynthetic oxygen evolution, carbon fixation,bioluminescence, nutrient acquisition, cell-cycle pro-gression, and vertical migration behavior driven bydaytime phototaxis and nighttime chemotaxis (Su-zuki and Johnson 2001). In K. brevis, circadianrhythms are known to control cell division and verti-cal migration (Kamykowski et al. 1998, Van Dolahand Leighfield 1999, Brunelle et al. 2007). As bothcell division and vertical migration behavior areprocesses central to formation of blooms of K. brevis,the current study was undertaken to gain insight intothe gene expression that underlies these processes.

Circadian rhythms are self-sustaining oscillationswhose period length approximates the 24 h day.These rhythms are generated by a central oscillator,which is entrained to the photoperiod by light-dependent input pathways that remain poorlydefined. In most photosynthetic organisms, circa-dian output is exerted primarily by modulatinglevels of mRNA transcripts of genes involved incircadian-controlled biochemical pathways(McClung 2001, Young and McKay 2001). However,in the dinoflagellate Lingulodinium polyedrum(F. Stein) J. D. Dodge, the well-studied circadianrhythm of bioluminescence depends on changes inprotein translation rates in the absence of changesin mRNA levels (Morse et al. 1989, Mittag et al.1998). Bioluminescence depends on the coordinatedexpression of the enzyme luciferase and luciferin-binding protein (lbp), which binds the substrate

1Received 5 October 2006. Accepted 22 February 2007.2Author for correspondence: e-mail [email protected].

J. Phycol. 43, 741–752 (2007)� 2007 Phycological Society of AmericaNo Claim to original US government worksDOI: 10.1111/j.1529-8817.2007.00354.x

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luciferin and prevents it from interacting with lucif-erase. Their differential expression over circadiantime is controlled by an RNA-binding protein(circadian-controlled translational repressor, CCTR)that binds to the 3¢ untranslated region (UTR) oflbp mRNA, preventing its translation during the day(Mittag 1996). The RNA-binding activity of CCTR isitself regulated in a circadian manner, with itshighest levels occurring during the day. Thus, asCCTR’s RNA-binding activity decreases at night,the translation of luciferase and lbp increases.Translational control of circadian gene expressionin dinoflagellates has also been shown for GAPDH(Fagan et al. 1999), peridinin-chl a-binding pro-tein (Le et al. 2001), and superoxide dismutase(Okamoto et al. 2001)—proteins whose expressionspeak during the day. In the case of GAPDH, theprecise translational step modulated appears to beelongation, as GAPDH mRNA is present in the poly-some fraction even at a time of day when its synthe-sis is halted (Fagan et al. 1999).

Circadian rhythms in dinoflagellates can also begenerated posttranslationally, as is seen in therhythm of carbon fixation (Nassoury et al. 2001).RUBISCO carries out the reversible, rate-limitingstep of carbon fixation. However, neither the activitynor the expression level of RUBISCO changes overcircadian time. Rather, the localization of RUBISCOwithin the chloroplast changes from the pyrenoidsduring the peak of CO2 fixation to a diffuse dis-tribution during the peak of oxygen evolution.Circadian-controlled changes in enzyme localizationthus serve to separate these two incompatible activit-ies within the chloroplast without altering RUBISCOexpression levels.

The extensive work on the above rhythms in di-noflagellates has led to the overall consensus thatcircadian gene expression is controlled posttran-scriptionally in dinoflagellates. However, the analysisof global transcript profiles of Pyrocystis lunula(F. Schutt) F. Schutt (Okamoto and Hastings 2003)suggests that in fact differential transcript levels arefound for �3% of the genes queried over circadiantime. This is within the range (2%–8%) ofcircadian-controlled genes observed in other eukary-otes (Harmer et al. 2000, Schaffer et al. 2001,Duffield et al. 2002).

The cell-division cycle is under circadian controlin all dinoflagellates studied. The mechanismsregulating this rhythm are of particular interestbecause of their key role in regulating the rate ofcell division during the growth phase of bloom for-mation. In eukaryotes, the cell cycle is driven pri-marily by changes in the transcript abundance ofcyclin, the regulatory subunit of the cyclin-depend-ent kinase (CDK) complexes that control entry intoS phase and mitosis. At S-phase entry, the activationof CDK is achieved by the binding of newly expressedcyclin, followed by changes in phosphorylation ofboth proteins. Activated CDK then orchestrates the

onset of DNA synthesis by inducing the transcrip-tion of a suite of S phase–specific genes throughactivation of the E2F transcription factor. Given theprecedence for post-transcriptional control of circa-dian processes in dinoflagellates, the mechanismsgenerating the cell-cycle rhythm are also of interestbecause post-transcriptional control of cell-cyclegenes in dinoflagellates would represent a novelmechanism among eukaryotes. However, to date,the lack of identified cell-cycle genes in dinoflagel-lates has prevented studies on the mechanisms of itscircadian control.

The current project uses an oligonucleotidemicroarray containing probes to 4629 uniqueK. brevis genes to characterize global gene expres-sion patterns over a 24 h day in cells exposed to a16:8 light:dark (L:D) cycle (LD treatment) andcells under constant light (LL treatment). Wefound that 9.8% of genes were differentiallyexpressed during the L:D cycle, while 3% of geneswere differentially expressed over the 24 h daywhen exposed to constant light. The differentiallyexpressed genes represent a wide array of physiolo-gical processes, both those known previously torespond to the photoperiod and novel genes notpreviously identified in dinoflagellates and whoseroles remain unidentified. The percentage of genesresponding to both the photoperiod and circadianclock is similar to that observed in other photo-synthetic organisms that use transcriptional controlto regulate their circadian processes. However, asmicroarray analysis measures relative RNA abun-dance, the degree to which changes in gene-speci-fic rates of transcription versus changes in mRNAstability are responsible for the observed patterns isnot yet known. Possible mechanisms that may medi-ate changes in transcript stability are discussed inlight of recent insights gained into dinoflagellategenome biology.

MATERIALS AND METHODS

Culture conditions. Karenia brevis (Wilson isolate) was main-tained in autoclaved, 20 lm filtered seawater (36&) enrichedwith f ⁄ 2 media (Guillard 1973), modified with the use of ferricsequestrene in place of EDTAÆNa2 and FeCl3Æ6H20 and theaddition of 0.01 lM selenous acid. Experimental cultures weregrown in 1 L glass bottles at 25�C ± 1�C with a 16:8 L:D cycleand cool-white light at 150lE Æ m)2 Æ s)1, determined using aBiospherical Instruments (San Diego, CA, USA) QSL-100 lightmeter.

Diel study design. Twenty 900 mL batch cultures were grownto mid-log phase. At that time, 10 cultures were transferred to24 h light (the LL treatment) for 2 d prior to sampling, whilethe remaining 10 cultures were maintained on 16:8 L:D. Onthe sampling day, duplicate cultures from the LD and LL setswere harvested at circadian times (CT) 2, 6, 10, 18, and 22 h(i.e., the time in hours past the time the lights turned on in theLD set). Ten milliliters of culture was collected for cell-cycleanalysis. The remaining culture was used for RNA isolation.Following RNA labeling (details below), the global transcriptprofile of each culture was compared with that in one CT2culture, which served as the experimental reference. Biological

742 FRANCES M. VAN DOLAH ET AL.

replicates at each time point were run with dyes reversed toaccount for potential dye bias.

RNA isolation. Karenia brevis cells were harvested by centrif-ugation at 600g for 10 min. Total RNA was prepared byresuspending the cell pellet in 1 mL Tri-Reagent (MolecularResearch Center Inc., Cincinnati, OH, USA) and processingaccording to the manufacturer’s protocol. Following precipita-tion of the RNA, the pellet was resuspended and furtherprocessed using a Qiagen RNeasy column (Valencia, CA, USA)for removal of residual salts, DNA, and protein. Total RNA wasquantified by UV spectroscopy and qualified on an AgilentBioanalyzer (Valencia, CA, USA). Only samples yielding high-quality RNA were used for microarray.

Cell-cycle analysis. At each experimental time point, 10 mLof culture from each bottle was fixed with 2% gluteraldehydeand stored at 4�C for at least 12 h. Cells were thencentrifuged for 3 min at 700g and resuspended in –20�Cmethanol for a minimum of 4 h. The cells were thencentrifuged for 3 min at 700g, and their DNA stained by theaddition of 10 lg Æ mL)1 propidium iodide (PI; Sigma,St. Louis, MO, USA) in PBS containing 10 mg Æ mL)1 RNase(Sigma) and 0.5% Tween 20. The DNA analysis was per-formed on an Epics MXL4 flow cytometer (Beckman Coulter,Miami, FL, USA) using a 5 W argon laser with a 488 nmexcitation wavelength and 635 nm emission wavelength.Multicycle software (Phoenix Flow Systems, San Diego, CA,USA) was used to estimate the percentages of cells in eachstage of the cell cycle.

Microarray analysis. An 8.4 K feature K. brevis oligonucleo-tide microarray described by Lidie et al. (2005) was used forthese studies, using a two-color protocol. Total RNA (500 ng)from each culture was labeled with Cy3 or Cy5 dye using anAgilent low-input linear RNA amplification kit (Agilent, PaloAlto, CA) according to the manufacturer’s recommendations.Following cleanup, labeled cRNA was quantified by UVspectroscopy, and 500 ng each of Cy3- and Cy5-labeled targetswas hybridized to the array for 17 h at 60�C. After hybridiza-tion, arrays were washed consecutively in solutions of 6X SSPEand 0.005% N-laurylsarcosine, and 0.06X SSPE and 0.005%N-laurylsarcosine for 1 min each at room temperature, followedby a 30 s wash in a stabilization and drying solution (Agilent).Microarrays were imaged using an Agilent microarray scanner.Images were extracted with Agilent Feature Extractor versionA7.5.1, and, using a rank consistency filter, features weresubjected to a combination linear ⁄ LOWESS normalizationalgorithm. The normalized array data were analyzed usingRosetta Luminator 2.0 gene expression analysis system (RosettaInformatics, Seattle, WA, USA). Expression ratios calculatedindependently from duplicate probes had an average coeffi-cient of variation (CV) of 4.5% and a median CV of 2.2%. Foranalysis, however, an average intensity was calculated forduplicate probes, and the variability between probe replicateswas reflected in the P-value. A composite array was generatedfrom the two dye-swapped biological replicate arrays at eachtime point. For the generation of composite arrays, a weightedaverage was calculated for each feature based on the Rosettaerror model designed for the Agilent platform, in whichfeature quality (P-value) is taken into consideration. Thecomposite arrays were then used for a trend analysis to viewthe expression pattern of genes throughout the time course.Only features with absolute differential expression ‡1.7-fold inat least one time point and a P-value £0.0001 in at least onetime point were used for trending. These data were thenclustered by two independent methods: a hierarchical cluster-ing algorithm using average link for the heuristic criterion andEuclidean distance as similarity metric, and K-means clusteringusing a Euclidean distance metric. The number of clusters usedfor K-means was determined using a Gap statistic (Acuity 4.0,http://www.moleculardevices.com).

Quantitative real-time PCR (QRT-PCR). All primers used arelisted in Table S2. The optimum annealing temperature foreach primer set was determined prior to the analysis ofexperimental samples. The specificity of each primer set andmolecular weight of the amplicon were monitored by melting-curve analysis and further verified by analysis using an AgilentBioanalyzer 2100. The efficiency of each primer set wasdetermined using gene-specific plasmid DNA and K. brevis RTproduct to construct a standard curve. The efficiency of theprimer set was calculated using the following equation:

ð10ð�1=�mÞ � 1Þ � 100 ð1Þ

where m is equal to the slope of the standard curve. Onlyprimers with efficiencies of 90%–110% were used, whichallowed relative expression levels between time points to becalculated as fold-changes, where each PCR cycle represents a2-fold change.

For analysis of experimental samples, 1 lg of total RNA wasreverse-transcribed (Ambion RETROscript kit, Austin, TX,USA) with oligo(dT) primers for a two-step QRT-PCR assay.All real-time assays were performed on an Applied Biosystems7500 Real-Time system (Foster City, CA, USA). A samplevolume of 25 lL was used for all assays, which contained a 1Xfinal concentration of ABI SYBR green (Applied Biosystems)PCR master mix, nuclease-free water, gene-specific primers(400 nM final concentration), and 1 lL of reverse-transcribedsample. Assays were run using the following protocol: 95�C for15 min, then 40 cycles of 94�C for 15 s, gene-specific annealingtemperature (58�C–62�C) for 40 s, and 72�C for 1 min,followed by a gradual increase in temperature from 60�C to95�C during the dissociation (melt) stage. Duplicate reverse-transcription reactions were each run in duplicate QRT-PCRanalyses of normalization genes and cell-cycle genes. Singlereverse transcriptions were run in triplicate QRT-PCR reactionsfor validation of LD and LL changers.

For normalization purposes, QRT-PCR analyses were per-formed on one gene from the LD data set and two genes fromthe LL data set that showed little change but significantP-values in microarray analyses. Prior to statistical analysis,QRT-PCR results were normalized using the DCt method (Livakand Schmittgen 2001).

Statistical analysis was performed using a Kruskal–Wallisnonparametric test or a one-way analysis of variance (ANOVA)in JMP version 5.1.2 (SAS Institute Inc., Pacific Grove, CA,USA). Correlation between microarray and QRT-PCR resultswere calculated using Spearman’s Rho in JMP version 5.1.2.Spearman’s Rho, the rank-based, nonparametric equivalent ofthe more commonly used Pearson’s correlation calculation,was used because the data were nonnormally distributed(Shapiro–Wilk test). The average log ratio of replicate QRT-PCR analyses and the weighted average of duplicate microar-rays were used in correlation analyses.

RESULTS

We employed a DNA microarray containing 60-mer oligonucleotide probes to 4629 unique K. brevisgenes to identify genes with transcripts under pho-toperiodic or circadian control. The experimentaldesign is summarized in Figure 1. For identificationof genes under photoperiodic control, cultures weremaintained on a 16:8 L:D cycle. The RNA was isola-ted from duplicate log-phase cultures at circadiantimes (CT) 2, 6, 10, 18, and 22 h of a 24 h day(where CT0 is the time the lights come on), andexpression profiles from duplicate cultures at each

DIURNAL AND CIRCADIAN GENE EXPRESSION IN KARENIA BREVIS 743

time point were compared with the expression pro-file in one CT2 sample, which served as reference,using two-color arrays with Cy3- and Cy5-labeledRNA. To identify the genes under circadian control,parallel cultures were transferred to 24 h light andmaintained for 2 d prior to sampling. The RNA wasthen isolated from duplicate cultures at the sametime points as in the LD treatment, and theirexpression profiles were compared with the expres-sion in one CT2 LL reference culture. Biologicalreplicates from both LD and LL series were ana-

lyzed with the fluorescent labels reversed to accountfor potential dye bias. Replicate arrays at each timepoint were combined to generate a composite arrayin which the data for each feature represented aweighted average of the replicates based on featurequality. Raw intensity plots and MA plots of theerror-modeled data for each composite array maybe seen in Figures S1 and S2. The composite arrayswere used for trend analysis to determine theexpression pattern of genes throughout the timecourse. Only features with absolute differentialexpression ‡1.7-fold in at least one time point and aP-value £ 0.0001 were used for trending.

Genes differentially expressed over the photoperiod. Atotal of 458 gene transcripts (9.8%) were differen-tially expressed over a 16:8 L:D cycle using thecriteria outlined above. A complete gene list ofdifferentially expressed genes may be found inTable S2. Hierarchical clustering of these revealedtwo major trends (Fig. 2a): 355 (77%) wereup-regulated during the day (red), relative to theirexpression at CT2, with a peak late in the light orearly in the dark phase; the remaining 103 genes(22%) were down-regulated during the day (green),with a minimum expression level occurring early inthe dark. These patterns of expression were simi-larly observed using K-means clustering (Fig. 2b),which further distinguished the up-regulated genesinto two clusters based on their magnitude ofchange or the time of their peak in expression. Thefirst cluster (red) contained 146 genes that gener-ally peaked in expression at CT18, while the second(dark red) contained 209 genes that generally rosemore slowly to a peak at CT18 or 22, but with asmaller amplitude of change. The down-regulatedcluster (cluster 3, green) contained 103 genes that

LD

LL

Sampling Day

Day 1 Day 2 Day 3

1062 18 22

1062 18 22

CT (hours)

Fig. 1. Experimental design. Log-phase cells were eithermaintained in a 16:8 light:dark cycle (LD treatment) or trans-ferred to constant light (LL treatment) for 2 d before samplingfor total RNA at five time points during the 24 h circadian day.Global transcript profiles were then compared at each time pointto that at circadian time (CT) 2 in the respective treatment (LDor LL).

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Fig. 2. Differential expressionof genes in LD treatment. (a)Heat map generated by hierarchi-cal clustering identifies overallup-regulated (red) and down-regulated (green) patterns ofgene expression. (b) K-meansclustering independently definesthree main clusters, with two up-regulated clusters (red and darkred) distinguished primarily byamplitude of change. One down-regulated gene cluster is green.


were generally at minimum expression levels atCT18.

Of the 146 transcripts in cluster 1 (red), 116 lacksignificant similarity to known genes in the Gen-Bank nr data base, and 11 had similarity to con-served proteins of unknown function. This is notunexpected, as only 29% of all ESTs in the K. brevislibrary have identity to known genes (Lidie et al.2005). The greatest change in transcript expressionwas an 8.4-fold increase between CT2 and CT18(protein phosphatase 1). The 18 genes with similar-ity to known genes (expected value £ 10)4) repre-sent several cellular processes, includingcarbohydrate and lipid metabolism, transcrip-tion ⁄ translation, and cell structure ⁄ motility, as wellas an array of signaling and chaperone functions(Table S1). Genes involved in carbohydrate metabo-lism included isocitrate dehydrogenase, the rate-limiting enzyme for the TCA cycle, and the cytosolicform of GAPDH (GapC2), a key enzyme in glycoly-sis. Also in this group were ATP citrate lyase andacetyl CoA acetyl transferase, enzymes responsiblefor two sequential steps early in the fatty acid bio-synthesis pathway. Genes involved in RNA process-ing included a nuclear splicing factor and apentatricopeptide repeat (PPR)–containing protein.The PPR repeat motifs are unique to a gene familyinvolved in posttranscriptional chloroplast mRNAprocessing (Small and Peeters 2000).

Cluster 2 (dark red) contained 133 genes, with44 genes that have assignable identity (Table S1).This cluster differed from cluster 1 primarily inhaving a lower amplitude of expression change overthe diel cycle and wider variation in the time ofpeak expression. The major processes representedincluded RNA processing, chaperones, cell struc-ture ⁄ motility, and signaling-pathway components.However, the members of these groups differedfrom those in cluster 1. Genes involved in RNA-processing were largely involved in translational con-trol or RNA stability. These included translation ini-tiation factor 5A. Several of the RNA-processinggenes in this cluster appeared to be associated withorganellar processes, including a chloroplast-RNA-processing protein (crp1) homolog and an uniden-tified PPR-repeat-containing gene, both probablyinvolved in chloroplast translational control. Alsoincluded in this group was a DEAD box RNA heli-case involved in mRNA degradation. Chaper-one ⁄ stress proteins included several dnaJ andhsp90s family members, absent from cluster 1, aswell as two petidylprolyl isomerases, which, based ontheir similarity matches on GenBank, may beinvolved in protein translocation into the chloro-plast. The antioxidant glutathione-S-transferase wasalso present in this cluster. Genes involved in cellstructure ⁄ motility included two kinesins with homol-ogy to spindle- and chromosome-associated kinesins,and actinfilin, all of which are involved in cytoskele-ton restructuring. Also in this group is extensin, a

Ser-(Hyp)4-rich membrane glycoprotein. Theincreased expression of these genes may be relevantto the cytoskeletal restructuring as K. brevis cellsexpand in volume during the light (Van Dolah andLeighfield 1999) and ⁄ or as they proceed throughthe cell cycle in a diel-dependent manner (seebelow).

Cluster 3 (green) contained transcripts thatdecrease in expression during the day, with minimalexpression at CT18. The composition of this clusterwas distinct in that many of the gene processesincluded are associated with chloroplast and mitoch-ondrial function, with the major groups represent-ing photosynthesis, oxidative phosphorylation,chaperones, and carbohydrate metabolism(Table S1). Whereas the expression of cytosolicGAPDH increased over the diel cycle with maximalexpression at CT18 (cluster 1), the nuclear-encoded,chloroplast-localized GAPDH C1-fd and a mitoch-ondrial localized GAPDH-triose phosphate iso-merase fusion protein, initially identified in diatoms(Liand et al. 2000), were down-regulated with min-imal expression at CT18. Several PSII genes andthose involved in photosynthetic electron transportwere among the most highly differentially expressedgenes in this cluster. Chaperones present in thiscluster included three unique peptidyl prolyl isom-erases of unknown function or location, whereas theHSP family members found in clusters 1 and 2 areabsent. Genes involved in RNA processing arehighly represented. Among these, and unique tothis down-regulated cluster, were two potentialhomologs to nocturnin, a circadian-regulated mRNAdeadenylase first described in the retina and respon-sible for posttranscriptional control of circadiangene expression. Another down-regulated geneinvolved in RNA decay was a RENT (regulator ofnonsense transcripts) homolog.

Genes differentially expressed in 24 h light. Exposureto 24 h light resulted in significantly fewer differen-tially expressed genes, with 153 genes (3.3%) dis-playing differential expression at the transcript levelrelative to CT2. In addition, in LL, all genes dis-played lower amplitude of change than observed inLD. This damping of response in 24 h light likelyreflects the absence of a dark cue, resulting in eleva-ted (or depressed) expression levels at CT2 relativeto the same genes in LD. Genes differentiallyexpressed in LD that remained so in constant lightare candidates for circadian-controlled processes.Twenty percent (33) of the differentially expressedgenes in LL fell into this category.

Hierarchical and K-means clustering identifiedthree main trends in constant light (Fig. 3, a andb). The first cluster (K-means red) contained tran-scripts that were up-regulated during the day withmaximal expression at CT18. This cluster contained39 genes, of which 17 (43%) were members of theup-regulated clusters 1 or 2 in the LD experiment.However, of these, only actinfilin was among the


identifiable genes up-regulated in the photoperiodexperiment.

Cluster 2 (dark green) was a set of genes whosepattern differed from any observed in the LD study.In this group, expression was lower at CT6 than atCT2, but returned to levels at or above those at CT2as the 24 h day proceeded, reaching maximumexpression during the subjective night. Of the 32genes in this cluster, 5 (15.6%) were present in clus-ters 1 or 2 (up-regulated) in the LD study, nonewith known identity. One gene from the down-regulated LD cluster 3, F1 ATPase, shifted toexpress its minimal transcript level at CT6 under24 h light.

Cluster 3 (green) was composed of 82 genes thatwere minimally expressed at CT18. Eleven (13.4%)were also present in the down-regulated cluster inLL. Of these, cytochrome C oxidase was the onlyidentifiable gene. Significantly, the photosystemgenes that made up some of the most differentiallyexpressed genes under photoperiodic control aremissing, indicating a lack of differential expressionunder continuous light.

Expression of cell-cycle genes under LD and LL. Previ-ous diel studies have shown that the cell cycle inK. brevis is under circadian control and that a lightsignal at the onset of day provides the cue thatserves to entrain this rhythm to the photoperiod,leading to S-phase entry several hours after the begin-ning of the light phase (Van Dolah and Leighfield1999). Given the central role of CDK-dependenttranscriptional activation in controlling the onset ofDNA synthesis and entry into mitosis in higher euk-aryotes, we were interested in the transcriptionalbehavior of cell-cycle genes identified in theK. brevis cDNA library (Table 1). Both LD and LL

populations displayed phased cell division asassessed by flow cytometry of cells stained with theDNA fluorochrome, propidium iodide (Fig. 4). InLD, approximately 50% of the cells proceededthrough the cell cycle, with a temporal progressiontypical of that previously observed in K. brevis (VanDolah and Leighfield 1999, Brunelle et al. 2007).S-phase cells first appeared between CT6 and 10,peaking at CT14, and a maximum of G2 + M-phasecells appeared at CT18–22. Following 3 d in con-stant light, the percentage of cycling cells increasedto approximately 70%. Both S phase and mitosisshifted forward, with S phase starting prior to CT6(20% of cells are in S phase at CT6) and the peakof G2 + M occurring as early as CT14.

There was little change in expression of any ofthe S-phase genes in either LD (Fig. 5a) or LL

2 6 10 18 22Time (h)

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of genes in LL treatment. (a)Heat map generated by hierarchi-cal clustering identifies overallup-regulated (red) and down-regulated (green) patterns ofgene expression. (b) K-meansclustering independently definesthree main clusters, one up-regulated (red) and two down-regulated clusters (green anddark green).

Table 1. Cell-cycle genes identified in Karenia brevis.

ID Probe GenBank best hit E-value

S phaseDNA ligase noaa_2476 XP222537 1.00E-24PCNA noaa_4167 AA014679 4.00E-109Rep C noaa_3136 AAH71335 2.00E-48Rep A noaa_1138 CAA47665 6.00E-34RnR noaa_0343 XP780425 1.00E-16

M phasecdc2 CDK noaa_3947 AD21952 1.00E-57cdc27 APC noaa_4467 AAM91580 1.00E-18cdk7 CAK noaa_2911 AAH05298 1.00E-34NIMA kinase noaa_0329 AAQ64683.1 2.00E-04

PCNA, proliferating cell nuclear antigen; Rep C, replicationfactor C; Rep A, replication protein A; RnR, ribonucleo-tide reductase; CDK, cyclin-dependent kinase; APC, anaphase-promoting complex; CAK, cyclin-dependent kinase activatingkinase; NIMA, ‘‘never in mitosis.’’


(Fig. 5b). In LD, replication protein A increased inexpression by 1.88-fold, but not until CT18, wellbeyond the peak of S phase. However, this expres-sion change was not supported by QRT-PCR analysis(Fig. 5c). In LL, when 70% of the cells were cycling,no significant changes in expression of any of theS phase–specific genes were observed by microarray(Fig. 5d). Similarly, all changes reported by QRT-PCR were below the <1.7-fold significance cutoff.

Two genes involved in progression into mitosis,NIMA kinase (‘‘never in mitosis’’) and Cdc2 kinase,appeared to be differentially expressed in LD basedon microarray analysis (Fig. 5a). However, the NIMAand Cdc2 expression patterns in LD were not sup-ported by QRT-PCR analysis (Fig. 5c). In LL, no dif-ferential expression of mitosis-specific genes wasapparent by microarray or QRT-PCR (Fig. 5, b andd) even though �70% of cells were cycling.

Validation of microarray results by QRT-PCR. To con-firm the gene expression data obtained using themicroarray, the expression levels of 10 of the mosthighly up-regulated or down-regulated genes in LDand ⁄ or LL were selected for confirmation byQRT-PCR over the time course (Fig. 6). The overallpattern of up-regulation or down-regulation wasconsistent between the two methods, although thedegree of change reported by QRT-PCR was smallerthan that reported by the array for the up-regulatedgenes. The use of the error-weighted average in the

generation of the composite arrays may contributeto this overestimation (J. C. Ryan and F. M. VanDolah, unpublished). Nonetheless, significant corre-lation (q = 0.681, P < 0.0001, n = 70) was foundbetween microarray and QRT-PCR data. Whengenes were filtered for the 1.7-fold change criteriarequired for the trend analysis, correlationincreased to 0.813 (Spearman’s Rho, P < 0.0001,n = 33). This difference reflects the variability inher-ent in both the microarray and QRT-PCR methodswhen measuring small changes in expression and isthe basis for the selection of the 1.7-fold signifi-cance cutoff using the 60-mer oligonucleotidemicroarray platform and QRT-PCR using SYBRgreen (Morey et al. 2006). Overall, the genesassayed by QRT-PCR exhibited correlations similarto those obtained using 60-mer oligonucleotide mic-roarrays to mouse and human (Ryan et al. 2005;Morey et al. 2006).

DISCUSSION

This study identifies genes whose transcript abun-dance is modulated by the photoperiod or by circa-dian mechanisms in the dinoflagellate K. brevis. Toour knowledge, this is the first report of photoperio-dic regulation of global transcript levels in a unicel-lular alga. We observed that 9.8% of the 4629 geneson the microarray varied in their transcript levelsduring the photoperiod, with the highest differen-tial expression generally occurring early in the darkphase. The functions of cycling genes were diverseand were represented by genes involved in mRNAand protein processing and degradation, photosyn-thesis, cellular structure and motility, signaling path-ways and trafficking, and various metabolicpathways. The proportion of genes showing differ-ential expression is consistent with a microarraystudy carried out in Arabidopsis, in which 11% oftranscripts were under photoperiodic control(Schaffer et al. 2001). When K. brevis was main-tained in constant light, the number of genes differ-entially expressed over the 24 h day decreased to3.3%, and their amplitude of change wasdiminished. Genes in LD that continued to displaydifferential expression in LL are candidates forcircadian-regulated processes. In P. lunula, the onlyother dinoflagellate examined by global transcriptanalysis for circadian gene expression, 3% of thegenes were similarly differentially expressed over cir-cadian time (Okamoto and Hastings 2003). Studieson Arabidopsis (Schaffer et al. 2001) and Chlamydo-monas (Kucho et al. 2005) found 2% and 2.6% ofthe genes, respectively, to respond in a circadianmanner.

Prevalence of genes involved in RNA and proteinprocessing. Among the identifiable genes differen-tially expressed in LD, 17% were genes involved inRNA posttranscriptional processing and turnover.This percentage is much higher than that seen in

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the diurnal-controlled genes in Arabidopsis (�4% ofidentifiable differentially expressed genes; suppl.materials B in Schaffer et al. 2001). Another 16% ofthe differentially expressed genes in K. brevis wereinvolved in protein processing, including chaper-ones (10%) and proteases (6%), compared with 3%chaperones and < 1% proteases in Arabidopsis. Thismay point to a higher level of posttranscriptionalprocessing in the dinoflagellate than in higherplants, as suggested by previous studies on dinoflag-ellate circadian expression.

Absence of transcriptionally controlled oxidative stressgenes in LD. Photosynthetic organisms experienceoxidative stress on a diel basis due to the high levelsof oxygen present in the illuminated chloroplast.Indeed, in Arabidopsis, 5% of the differentiallyexpressed transcripts are involved in oxidative stress(suppl. materials B in Schaffer et al. 2001). How-ever, in K. brevis, we observed only one potentialantioxidant gene, glutathione-S-transferase, amongthe differentially expressed genes in LD, showingmaximal expression in late light to early darkphase. Probes for several antioxidant genes werepresent on the array, including a Cu ⁄ Zn superoxide

dismutase, catalase ⁄ peroxidase, thioredoxin, nucle-oredoxin, glutaredoxin, and glutathione peroxidase.In natural sunlight, K. brevis undergoes diurnal vari-ations in photosynthetic efficiency, in vivo fluores-cence and pigment composition, and activation ofthe photoprotective xanthophyll cycle (Evens et al.2001). It is possible that in the low-light conditionsK. brevis tolerates in laboratory cultures, such diur-nal stress is not experienced, and therefore anti-oxidant mechanisms are not activated. However, inthe dinoflagellate L. polyedrum grown under150 lE Æ m)2 Æ s)1, typically used for laboratory cul-tures of dinoflagellates, robust diurnal rhythms ofactivity of the antioxidant Fe-superoxide dismutaseare reported (Okamoto et al. 2001) that were notaccompanied by changes in gene transcription.Thus, the absence of transcriptionally regulatedantioxidant mechanisms may suggest that thesegenes are regulated posttranscriptionally to allowchanges appropriate to photosynthetic oxidativechallenges.

Regulation of photosynthesis by light and dark.Although a circadian rhythm of photosynthesis hasnot been formally demonstrated in Karenia brevis,

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Fig. 5. Microarray and QRT-PCR expression analysis of cell-cycle genes for the LD and LL treatments. Dotted lines indicate 1.7-foldchange. CT, circadian time; PCNA, proliferating cell nuclear antigen; Rep C, replication factor C; Rep A, replication protein A; RnR, ribo-nucleoside reductase; cdc2, cyclin-dependent kinase (=CDK1); CAK, cyclin-dependent kinase activating kinase, or CDK7; NIMA, NIMA kin-ase ‘‘never in mitosis.’’


circadian photosynthesis rhythms are well estab-lished in the peridinin dinoflagellates L. polyedrumand Pryocystis fusiformis (Wyville-Thomson ex Haeckel)V. H. Blackman. Okamoto and Hastings (2003)found light-harvesting proteins, chl a ⁄ b–binding pro-tein, fucoxanthin-chl-binding protein, and peridinin-chl a-binding protein among the circadian-regulatedgenes in Pyrocystis. The first two, although amongthe highest expressed genes in the K. brevis library(Lidie et al. 2005), were omitted from the K. brevisarray and were therefore not queried by this study.The third gene is absent from K. brevis, as this spe-cies is a member of the fucoxanthin-containingdinoflagellate clade, which does not express peridi-nin and appears to possess a markedly differentchloroplast structure from that of peridinin dino-flagellates (Ishida and Greene 2002, Yoon et al.2005, Nosenko et al. 2006).

Our study revealed changes in transcripts associ-ated with photosynthesis that appear to be undercontrol of light and dark. The most striking is thepresence of PSII genes in LD cluster 3 and theirabsence from the LL differentially expressed geneset. Their presence in the LD set was somewhat sur-prising because these are chloroplast-encoded genesand were not expected to be present in a cDNA lib-rary made from polyadenylated mRNA (the sourceof probes for the microarray). However, in thechloroplast, polyadenylation is used to target tran-scripts for degradation (Komine et al. 2000). There-

fore, their apparent decrease in expression on thearray may, in fact, represent a posttranscriptionalprocess of message stabilization, resulting indecreased polyadenylation of PSII genes during thedark phase of the diel cycle. Previous studies inLingulodinium (=Gonyaulax) observed psbA (PSII32 kDa protein) to be regulated solely by light ⁄ darkand not by circadian mechanisms (Wang et al.2005), where steady-state levels of psbA protein areachieved by an increase in both protein synthesisand degradation during the light, and increased sta-bility during the dark. In Lingulodinium, psbAmRNA levels remained unchanged over the dielcycle as assessed by Northern blotting. Our resultsmay point to increased mRNA stability during thedark as an additional mechanism to achieve con-stant levels of PSII protein expression throughoutthe photoperiod. In this regard, the absence of PSIgenes from both the library and the microarray maysuggest that the posttranscriptional regulation ofPSI and PSII genes differs. Consistent with thisnotion, Samuelsson et al. (1983) determined thatchanges in electron flow through PSII aloneaccount for the circadian rhythm of photosynthesisin Lingulodinium, while flow through PSI remainsconstant.

Polyketide synthase transcripts do not change in expressionover the diel cycle. Toxin biosynthesis in synchronouscultures of a number of toxic dinoflagellate specieshas been shown to correlate with specific cell-cycle

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Fig. 6. The QRT-PCR confirmation of gene expression results reported by microarray. The largest changers from the LD treatment up-regulated cluster 1 and down-regulated cluster 3 were assayed. CT, circadian time.


stages and ⁄ or diel phases, including saxitoxins inAlexandrium fundyense Balech (Taroncher-Oldenburgand Anderson 2000) and dinophysis toxins in Proro-centrum lima (Ehrenb.) J. D. Dodge (Pan et al. 1998),which are produced primarily in G1, correspondingto the light phase. In contrast, the production ofspirolides by Alexandrium ostenfeldii (Paulsen) Balechet Tangen appears to be limited to G2 + M, whichoccurs in the dark (John et al. 2001). In the prymne-siophyte Chrysochromulina polylepis Manton et Parke,polyketide synthase (PKS) gene expression appearsto correlate with its diel-dependent toxicity (Johnet al. 2004). Putative PKS genes have been identifiedin P. lima (Snyder et al. 2003) and A. ostenfeldii(Cembella et al. 2004), and surprisingly, a PKSgene was recently reported in a gene cluster respon-sible for saxitoxin biosynthesis in cyanobacteria(Kellerman et al. 2006). However, the regulation ofPKS gene expression and their role in toxin bio-synthesis remains poorly studied in dinoflagellates.

Karenia brevis produces polyether brevetoxins,neurotoxins that are responsible for its adversehealth effects on marine animals and humans. TypeI modular PKS genes have been identified in K. bre-vis using degenerate PCR (Snyder et al. 2005) andhigh-throughput cDNA library screening (Monroeet al. 2005). The K. brevis microarray containsprobes to six unique PKS genes. None of thesegenes showed significant changes in expression overthe diel cycle. Diel changes in brevetoxin expressionlevels have not been demonstrated in K. brevis, poss-ibly because of the lack of fully synchronous cul-tures. Nonetheless, in light of the diel-dependenttoxicity observed in other dinoflagellates producingpolyether toxins, the absence of differential PKSexpression over the diel cycle is noteworthy andmay provide some insight into its regulation.

Cell-cycle genes do not appear to be transcriptionallyregulated. Because the cell cycle in K. brevis is undercircadian control, we were interested to determineif cell-cycle genes, like many other circadian-controlled processes in dinoflagellates, are controlledpost transcriptionally, or if, as in most eukaryotes,they are regulated at the transcriptional level. TheK. brevis microarray contains probes specific for twocyclin-dependent kinases, cdc2 (=CDK1) and CAK(cyclin-dependent kinase activating kinase, orCDK7). In lower eukaryotes, CDK1 is responsible forboth G1 ⁄ S and G2 ⁄ M transitions as the catalytic sub-unit of CDK ⁄ cyclin complexes that contain eitherG1- or M-specific cyclins as their regulatory subunits.The CAK is the catalytic subunit of the CDK-activa-ting kinase, whose partner is cyclin H. The CAK isresponsible for activating CDK1 by phosphorylation,thus controlling G1 ⁄ S and G2 ⁄ M transitionsupstream of CDK1, in concert with cyclin binding toCDK1. Through this activity, CAK appears to controlthe expression of most or all members of a cluster ofcell-cycle-regulated genes in fission yeast (Fisher2005).

Probes for four S phase–specific genes involvedin carrying out the DNA replication process are onthe microarray: ribonucleoside reductase (RnR),proliferating cell nuclear antigen (PCNA), replica-tion factor C (Rep C), and replication protein A(Rep A). In higher eukaryotes, the expression ofthis suite of genes is controlled by transcription fac-tors activated by cyclin ⁄ CDK at the onset of S phase(Spellman et al. 1998, Kalma et al. 2001). There-fore, their expression is likely a downstream targetof the diel cues that entrain the circadian cell-cyclerhythm and relay signals to activate the K. brevis cell-cycle machinery. RnR carries out one of the key pro-cesses required early in S phase, the biosynthesis ofdeoxynucleotides from ribonucleotides. The forma-tion of replication forks requires the binding of RepA, which unwinds dsDNA and stabilizes it in thesingle-strand conformation, thereby providing accessto DNA polymerase. Both Rep C and PCNA arerequired for processive DNA synthesis.

The genes included on the K. brevis microarrayinvolved in controlling M-phase entry include ahomolog to NIMA kinase, which is a substrate ofCDK at the G2 ⁄ M transition and is required forspindle formation and spindle-pole maturation(Graillert et al. 2004). Also on the array is a mem-ber of the anaphase-promoting complex (APC),cdc27, a large E3 ubiquitin ligase that mediates theexit from mitosis.

We observed little change in expression of genesspecific to either S phase or M phase, in either LD(50% of cells cycling) or LL (70% cycling). Thissuggests that K. brevis has evolved a posttranscrip-tional mechanism for regulating expression of thesegenes or that their variation in transcript levels issmall enough to be hidden by the 30% noncyclingcells in LL. In the fission yeast, CAK, CDK1, RnR,Rep A, NIMA, and APC complex members wereamong 407 genes periodically expressed in a cell-cycle-dependent manner (Rustici et al. 2004), withfold-changes >2.5. Of these, RnR, Rep A, and NIMAare among the 40 core cell-cycle genes whosecell-cycle-dependent transcription is conservedbetween two genes in widely divergent yeasts,Schizosaccharomyces pombe and Saccharomyces cerevisiae(Rustici et al. 2004). However, PCNA recently char-acterized in a dinoflagellate, Pfiesteria piscicida Steid.et J. M. Burkh., did not show differential expressionbetween exponential growth and stationary phase atthe transcript level and showed little change at theprotein level (Zhang et al. 2006). Similarly, a cyclinB was found to be present at all cell-cycle stages inK. brevis (Barbier et al. 2003), suggesting that itsregulation may lie in translational and ⁄ or posttrans-lational mechanisms.

Differential transcript profiles may be generated by post-transcriptional processes. The extent to which differ-ential gene expression was observed in response tothe photoperiod and circadian clock was similar tothat observed in a higher plant (Arabidopsis) and


green alga (Chlamydomonas). This finding was atfirst glance somewhat surprising given the existingliterature on circadian rhythms in dinoflagellatesthat suggests a predominance of posttranscriptionalcontrol of gene expression. However, changes intranscript abundance reported by microarrays mayreflect either changes in the gene-specific rate oftranscription or changes in mRNA stability overthe time course. The extent to which these twoprocesses are responsible for the results of the cur-rent study is unknown, as little work has been car-ried out on the regulation of gene expression indinoflagellates outside of the circadian-rhythmstudies. Transcription initiation is the most import-ant control point for gene expression in mostorganisms, both prokaryotic and eukaryotic. How-ever, differential gene expression can be achievedsolely by posttranscriptional mechanisms, as dem-onstrated by the kinetoplastid parasites Trypanosomabruceii and Leishmania major (Clayton 2002). In thekinetoplastids, transcription by RNA polymerase II(Pol II), responsible for expression of protein-encoding genes in eukaryotes, does not appear tobe temporally regulated. Instead, constitutive Pol IItranscription leads to the synthesis of polycistronicmRNAs that are processed to yield capped, polya-denylated mRNAs by means of a trans-splicingmechanism (Palenchar and Bellofatto 2006, Stoveret al. 2006). The recent identification of a similartrans-splicing mechanism in K. brevis (K. B. Lidieand F. M. Van Dolah, unpublished) and severalother dinoflagellates (Zhang et al. 2007) suggeststhat dinoflagellates may also carry out Pol II tran-scription constitutively. In kinetoplastids, the abun-dance of specific mRNAs is regulated by sequenceswithin the 3¢ UTR that determine their stabilityand may also affect translation efficiency (Clayton2002). Similar sequences appear to be present inthe 3¢ UTR of the lbp gene transcript in L. polye-drum to facilitate binding of the CCTR proteinthat appears to act as a clock-controlled transla-tional repressor (Mittag 1996). Thus, it is possiblethat K. brevis has evolved similar mechanisms thatmay be responsible for some or all of the differen-tial expression of genes examined in the currentstudy.

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Supplementary material

The following supplementary material is avail-able for this article:

Figure S1. MA plots and intensity plots of com-posite arrays generated at each time point in theLD time course.

Figure S2. MA plots and intensity plots of com-posite arrays generated at each time point in theLL time course.

Table S1. Primer sets used for QRT-PCR.

Table S2. Genes differentially expressed overthe diurnal cycle (>1.7-fold, P < 0.0001), deter-mined by K-means clustering. Amplitudeexpressed as fold-change.

This material is available as part of the onlinearticle from: http://www.blackwell-synergy.com/doi/abs/10.1111/j.1529-8817.2007.00354.x (Thislink will take you to the article abstract).

Please note: Blackwell publishing is notresponsible for the content or functionality ofany supplementary materials supplied by theauthors. Any queries (other than missing mater-ial) should be directed to the correspondingauthor for the article.


MICROARRAY ANALYSIS OF DIURNAL- AND CIRCADIAN-REGULATED GENES IN THE FLORIDA RED-TIDE DINOFLAGELLATE...

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