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J Bacteriol. Mar 2005; 187(6): 2190–2199.
PMCID: PMC1064041

Global Analysis of Circadian Expression in the Cyanobacterium Synechocystis sp. Strain PCC 6803


Cyanobacteria are the only bacterial species found to have a circadian clock. We used DNA microarrays to examine circadian expression patterns in the cyanobacterium Synechocystis sp. strain PCC 6803. Our analysis identified 54 (2%) and 237 (9%) genes that exhibited circadian rhythms under stringent and relaxed filtering conditions, respectively. The expression of most cycling genes peaked around the time of transition from subjective day to night, suggesting that the main role of the circadian clock in Synechocystis is to adjust the physiological state of the cell to the upcoming night environment. There were several chromosomal regions where neighboring genes were expressed with similar circadian patterns. The physiological functions of the cycling genes were diverse and included a wide variety of metabolic pathways, membrane transport, and signal transduction. Genes involved in respiration and poly(3-hydroxyalkanoate) synthesis showed coordinated circadian expression, suggesting that the regulation is important for the supply of energy and carbon source in the night. Genes involved in transcription and translation also followed circadian cycling patterns. These genes may be important for output of the rhythmic information generated by the circadian clock. Our findings provided critical insights into the importance of the circadian clock on cellular physiology and the mechanism of clock-controlled gene regulation.

Circadian rhythm is a self-sustaining oscillation whose period length coincides with the 24-h day-night cycle. Many biological activities show circadian patterns, allowing organisms to adapt to daily fluctuations in the environment. Circadian rhythms are widespread and involve functions as diverse as human sleep-wake cycles and cyanobacterial nitrogen fixation. The molecular basis of circadian rhythms involves negative and positive feedback regulation of clock genes (16).

Cyanobacteria are the only bacterial species found to have a circadian clock. Three clock genes, kaiA, kaiB, and kaiC, have been identified in Synechococcus sp. strain PCC 7942 (25). kaiB and kaiC form an operon and are coordinately transcribed, while kaiA is transcribed independently. All of the kai genes show circadian rhythms of expression. Continuous overexpression of kaiC represses expression of kaiBC, but overexpression of kaiA enhances expression of kaiBC. This suggests that kaiC is regulated by a negative feedback autoregulatory loop and that KaiA activates kaiBC expression, thus sustaining the cyclical expression of kaiBC (25).

In cyanobacteria, activities as diverse as cell division, amino acid uptake, nitrogen fixation, respiration, and carbohydrate synthesis are under circadian control (18), but a clear mechanistic link between physiological rhythms and the regulation of output genes is still lacking. Promoter trap analyses were performed with two cyanobacterial species, Synechococcus sp. strain PCC 7942 (33) and Synechocystis sp. strain PCC 6803 (4). The percentage of rhythmic clones was lower in Synechocystis organisms (77%) than in Synechococcus organisms (~100%), and the amplitude of the rhythms was lower in Synechocystis than in Synechococcus organisms. Most rhythmic clones showed similar phases of oscillation: they showed peak expression at the end of the subjective night in Synechocystis and at the end of the subjective day in Synechococcus. However, these attempts to identify circadian output genes have had limited success, at least in part because the cloning and sequencing of candidate clock-controlled genes is labor-intensive. A different, more recent approach with DNA microarray technology has been used successfully to detect transcripts with circadian expression patterns in eukaryotic organisms, including mice (1, 54, 63, 66), rats (15, 19), Drosophila melanogaster (11, 32, 39, 67), Arabidopsis thaliana (21, 60), Neurospora crassa (13, 51), and the dinoflagellate Pyrocystis lunula (53). In Arabidopsis, Harmer et al. (21) identified hundreds of novel circadian-rhythm-regulated genes (6% of those tested) that play a role in physiological processes as diverse as photosynthesis; photoprotective pigment production; cold resistance; carbon, nitrogen, and sulfur metabolism; and cell elongation. They also found a highly conserved promoter motif required for circadian control of gene expression.

Synechocystis sp. strain PCC 6803 carries out light-activated heterotrophic growth in glucose-supplemented medium in darkness (2). The genomic sequence of this Synechocystis strain is complete (30), and a genome-wide DNA microarray representing most chromosomal genes is available (23, 35, 40, 71). The dnaK gene of Synechocystis exhibits a bona fide circadian rhythm under both continuous light (3) and dark (5) conditions. Three genes expressed with circadian rhythms were also identified by promoter trap analysis (4). In the present study, we performed a genome-wide DNA microarray analysis to identify genes in Synechocystis that exhibit circadian expression patterns. The results provided critical insights into the importance of the circadian clock in cellular physiology and the mechanism of clock-controlled gene regulation.


Overall experimental design of microarray analysis.

We isolated RNA samples from two independent cyanobacterial cultures (two biological replicates). Each RNA sample was used for three independent microarray experiments (three technical replicates). The microarray used in this study contained duplicate spots per gene. Thus, a maximum of six data points per gene was obtained for each time point of a biological replicate (i.e., three technical replicates × two spots). Each biological replicate was treated independently with the same procedure until the final step of the cycling gene identification (evaluation of reproducibility of rhythmicity and phase [see below]).

Strain and culture conditions.

Cells of Synechocystis sp. strain PCC 6803 carrying the bacterial luciferase gene luxAB (7) fused to an 805-bp dnaK promoter sequence were cultured in BG-11 medium (55) at 30°C under 91 μmol of white light illumination m−2 s−1 with bubbling of air and stirring. The optical density of the culture at 730 nm was maintained at approximately 0.35 by dilution with fresh BG-11 medium. To entrain the circadian clock, the culture was placed in darkness for 12 h and then kept in constant light conditions. Aliquots were taken every 4 h for 2 days (12 time points). Physiological states of the cells, such as growth rate, show circadian rhythms under the conditions. We did not use a regimen of 12 h of light and 12 h of dark for microarray analysis because genes that oscillate in response to light are detected. The harvested cells were frozen in liquid nitrogen and stored at −80°C until used for RNA isolation. The entrainment of the circadian clock was confirmed by bioluminescence measurements (3). Total RNA was isolated by the hot-phenol method (9) or the acid phenol-guanidinium thiocyanate-chloroform method (12) and then purified by the SV total RNA isolation system (Promega, Wis.).

Hybridization and scanning of DNA microarrays.

DNA microarray analysis was performed with CyanoCHIP, version 1.6 (TaKaRa, Ohtsu, Japan). The microarray contained 3,070 Synechocystis chromosomal genes and several control DNAs. Fluorescently labeled cDNA was prepared from 5 μg of total RNA by using the RNA fluorescence labeling core kit with Moloney murine leukemia virus reverse transcriptase (TaKaRa). Cy3-dUTP or Cy5-dUTP (Amersham, Little Chalfont, United Kingdom) was incorporated during synthesis of the first cDNA strand with a random primer. Human transferrin receptor (TFR) control RNA (TaKaRa) was added to the labeling reaction mixtures to validate the accuracy of the experiments (see Results). Cy3-labeled test cDNA was synthesized with total RNA from cells harvested at each time point. Cy5-labeled reference cDNA was synthesized with a mixture of total RNA sample from all time points. These cDNAs were competitively hybridized on a microarray. Hybridization was carried out for 16 h at 65°C in 20 μl of 6× SSC (1× SSC is 0.15 M NaCl plus 0.015 M sodium citrate), 0.2% sodium dodecyl sulfate (SDS), 5× Denhardt's solution (57), and 100 ng of denatured salmon sperm DNA/μl. After hybridization, the microarrays were washed with 2× SSC-0.2% SDS once at 55°C for 5 min and twice at 65°C for 5 min and then rinsed with 0.05× SSC at room temperature. The washed microarrays were dried by centrifugation. Fluorescence images of Cy3 and Cy5 were obtained with a GenePix 4000B scanner (Axon Instruments, Union City, Calif.). Each microarray was scanned twice. The second scan was performed with lower photomultiplier tube gain to avoid signal saturation. Data obtained from the both scans were analyzed independently, and genes scored as circadian in either of the scans were finally selected.

Data analysis.

The signal intensity of each spot and its local background were determined with GenePix Pro software (versions 4.1 and 5.0; Axon Instruments). The net signal intensity was calculated by subtraction of the median signal intensity of all pixels within the local background area from the median signal intensity of all pixels within the spot area. Correct recognition of all spot areas by the automatic alignment function of the GenePix Pro was confirmed visually. Spots meeting any of the following criteria were flagged and not used for subsequent data analysis: (i) the GenePix Pro did not find the spot area automatically, (ii) the net signal intensity was ≤0, (iii) the percentage of saturated pixels in the spot area was ≥25, and (iv) severe noise was present. Biases in signal intensity between the two fluorescent dye channels in a microarray were normalized by locally weighted linear regression analysis (lowess normalization) (70) using MIDAS (freely available from http://www.tigr.org/software/tm4/midas.html). For all normalization, the smoothing parameter was set to 0.33. The normalized data will be available at http://www.genome.ad.jp/kegg/expression/. The relative expression level of a gene at a time point was calculated as log2 (Cy3/Cy5), where Cy3 and Cy5 were normalized signal intensities from test and reference cDNA. The means of the relative expression levels from the three technical replicates were calculated for each biological replicate and used in the subsequent analysis. Genes carrying fewer than two unflagged data at any time point were removed from the analysis. We calculated the ratio of net signal intensity to the background standard deviation of all spots for each gene, and genes with an average ratio of ≥2.5 were considered detectably expressed (2,648 genes). Genes that did not satisfy this criterion were not analyzed further.

Identification of cycling genes.

Rhythmicity in temporal expression data was analyzed by the modified Cosiner method (52), which is based on cosine curve fitting developed for analysis of biological rhythms (50). Briefly, we let y1, y2, …, yi, …, y12 be the mean relative expression levels at time points t1, t2, …, ti, …, t12 for a gene. First, we performed linear regression analysis with the data set of (y1, y2, …, yi, …, y12) and (t1, t2, …, ti, …, t12) by the least-squares method and obtained a regression line where f(t) = αt + β. The f(t) is defined as a trend of the temporal expression data. In most cases, temporal expression data have a trend (i.e., α or β is not 0), which prevents correct cosine curve fitting. Therefore, we used detrended data [yi = yif(ti)] in the subsequent cosine curve fitting. We fit the detrended data to 241 cosine curves of Fj(t) (j = 1 to 241) with a series of period lengths (Tj) (12 to 36 h at 0.1-h intervals) by the Fourier transformation method by using the following equations:

equation M1

equation M2

equation M3

where ϕ is acrophase, which is defined as tan−1(b/a), and n is 12. To evaluate the residual of the data set from the fitted cosine curve, we calculated an error factor (Ef) for each Fj(t) as follows:

equation M4

Fj(t) with the minimum Ef value was considered to be the best-fitting cosine curve, and then the amplitude (equation M5) and the peak expression times (ϕTj/2π) were calculated. The amplitude represents the ratio of the peak value to the mean value of the oscillation. The peak expression time was multiplied by 24/Tj to convert it to circadian time. This procedure was applied to every gene on the microarray.

Cycling genes were objectively selected by the following successive filtering steps. Genes with a period length of less than 18 h or more than 26.8 h were rejected. Genes whose expression data deviated greatly from the best fitting cosine curve (Ef > 0.2) were rejected. The difference in expression levels between peak and trough time points were evaluated for significance by the Student t test (58). Type I errors (false positives) were controlled by the method of Holm (24). If the differences were not significant (P > 0.05) on either day of the experiment, the genes were rejected. Finally, genes that in both biological replicates satisfied all three criteria and had ≤4 h of difference in phase were judged as cycling genes.

Northern blot analysis.

Total RNA was separated by electrophoresis on a denaturing agarose gel containing formaldehyde and blotted to a BIODYNE B nylon membrane (Pall, East Hill, N.Y.) (57). A DNA fragment spotted on the microarray was used as a probe for each gene. Probes were labeled with [α-32P]dCTP by using the random primer DNA labeling kit, version 2.0 (TaKaRa). Membranes were hybridized in ExpressHyb solution (Clontech, Palo Alto, Calif.) at 68°C for 1 h and then washed in 0.1× SSC-0.1% SDS at 65°C for 1 h. The signal intensity was quantified with a BAS2000 image analyzer (Fujifilm, Tokyo,Japan).

Identification of chromosomal regions containing genes with similar circadian expression patterns.

Genes carrying fewer than two unflagged data at any time point were excluded from the analysis. There are genes whose sequences physically overlap in the Synechocystis genome. Because the overlapping genes could show an artificially high correlation in their expression patterns (cDNA from both genes could hybridize to the probes corresponding to a single gene), we identified all pairs of overlapping genes in the genome and removed the smaller of the two from the analysis. The final data sets contained 2,470 and 2,875 genes for the biological replicates. These genes were sorted by their position on the genome. We searched the sorted data sets for groups of neighboring genes in which the correlation coefficient of the mean relative expression levels for 12 time points were >0.7 for all possible pairs of genes. Among them, we selected groups of genes satisfying the following criteria: (i) formation of the group was reproduced in the two biological replicates, (ii) the group contained more than two independent transcription units (i.e., operon or singly transcribed gene), and (iii) the group contained at least 1 of 54 cycling genes that were identified under stringent filtering condition. We consulted a prediction for organization of the transcription units on the Synechocystis genome (44). Accuracy of the prediction was confirmed by Northern blot analysis for four cycling gene clusters.


Identification of genes showing circadian oscillation by DNA microarray.

We analyzed circadian patterns of expression by using a DNA microarray carrying probes for 3,070 chromosomal Synechocystis genes. This approach detects changes in the amount of accumulated mRNA. Circadian regulation may also operate at the level of protein synthesis, but that cannot be detected by this technique. Synechocystis has 397 episomal genes on four plasmids (29) that are not considered in the present analysis. Thus, the microarray probes encompassed 84% of all genes and 94% of chromosomal genes.

Synechocystis cells were entrained by a single 12-h dark incubation and then placed under continuous light conditions. Cells were collected from two independent cultures (two biological replicates) at 4-h intervals over 2 days (12 time points). Total RNA was isolated from each aliquot of cells, and three independent series of microarray experiments were carried out per RNA sample (three technical replicates). The microarray contained eight human TFR DNA spots as a control. We added human TFR RNA to each labeling reaction mixture so that the amount of TFR RNA followed a circadian pattern. Signals obtained from the control spots showed exactly the expected rhythm (correlation coefficient, >0.9). This result validated the accuracy of the microarray experiments and normalization procedure.

To identify circadian expression patterns, we fitted the temporal expression data for each gene to a cosine curve (see Materials and Methods for details). We considered genes satisfying the following five criteria as under circadian regulation: (i) the period length of the best fitting cosine curve was between 18 and 26.8 h (because the circadian period is 22.4 ± 0.4 h in Synechocystis grown under constant light conditions [3] and the time resolution in our experiment was 4 h), (ii) the temporal expression data fitted the cosine curve precisely (Ef ≤ 0.2) (see Materials and Methods), (iii) the difference between expression at the peak and trough time points was statistically significant on both days of the experiment, (iv) criteria i, ii, and iii were satisfied in both biological replicate experiments, and (v) the phase difference between the biological replicates was less than 4 h. These filtering steps identified 54 cycling genes (Fig. (Fig.1;1; Table Table1)1) (see Fig. S1 in the supplemental material). Figure Figure11 (see also Fig. S1 in the supplemental material) shows results from the two biological replicates and indicates that expression patterns of the cycling genes were highly reproduced. We performed Northern blot analysis for seven genes that displayed different amplitudes of oscillation, and rhythmicity with a similar pattern to microarray analysis was confirmed (see Fig. S2 in the supplemental material). This result validated the experimental strategy adopted in this study.

FIG. 1.
An overview of Synechocystis cycling genes. (A) Expression profile of cycling genes. Relative expression at each time point was normalized to the mean expression at all time points and represented by color scale. White and gray boxes represent subjective ...
Genes exhibiting circadian rhythm in Synechocystis

The kaiABC genes are the central circadian oscillator genes in cyanobacteria (25). The Synechocystis genome encodes one kaiA gene, three kaiB genes, and three kaiC genes. We have shown by bioluminescent analysis that kaiA (slr0756), kaiB3 (sll0486), kaiC1 (slr0758), and kaiC3 (slr1942) show circadian rhythm (K. Okamoto and M. Ishiura, unpublished data). However, only a kaiA gene was identified as circadian in our microarray analysis. Although the expression of the other kai genes seemed circadian (Fig. (Fig.2),2), our stringent filtering criteria rejected them. When criterion iii was omitted (hereafter referred to as relaxed filtering condition), an additional 183 genes were identified (see Table S1 in the supplemental material). These genes were considered to be cycling genes with a lower amplitude of oscillation or relatively large standard deviations of measurements. The kaiC1 and kaiC3 genes were included in the group. Thus, the 54 cycling genes identified under the stringent filtering condition were not inclusive, and the Synechocystis genome probably contains additional cycling genes.

FIG. 2.
Expression profiles of kai genes. Red, kaiA (slr0756); black, kaiB3 (sll0486); orange, kaiC1 (slr0758); blue, kaiC3 (slr1942). A representative result from two biological replicates is shown. The vertical axis shows the relative expression at each time ...

The expression profiles of the 54 cycling genes are displayed in Fig. Fig.1A1A (see also Fig S1A in the supplemental material). Expression peaked at various circadian times (CTs) (Fig. (Fig.1B;1B; see Fig. S1B in the supplemental material) but mostly at the time of transition from subjective day to night (i.e., CT8 to 16).

Distribution of similar circadian expression patterns on the Synechocystis chromosome.

In the chloroplast of the green alga Chlamydomonas reinhardtii, DNA superhelicities at two chromosomal regions fluctuate under continuous light conditions preceded by 12-h light-12-h dark cycles, and the fluctuation correlates highly with the mRNA levels of genes contained in those regions (56). Drosophila has many chromosomal regions where neighboring genes are expressed with similar circadian patterns (67). These observations suggest that changes in local chromosomal structure affect the transcriptional activity of the genes contained there and determine their circadian expression patterns. A similar hypothesis is also proposed for Synechococcus (41, 45, 49, 68). To test the possibility of such regulation in the Synechocystis genome, we examined the similarity of circadian expression patterns among neighboring genes. In Synechocystis, we expected neighboring genes to show similar expression patterns because they are cotranscribed from an operon. We therefore searched for chromosomal regions where more than two transcription units (operons or singly transcribed genes) showed similar circadian expression patterns (see Materials and Methods for details). We identified 12 such chromosomal regions that contained an average of 2.5 genes. Two typical examples are shown in Fig. Fig.3,3, in which genes transcribed in opposite directions were expressed with strikingly similar rhythmic patterns. The 12 regions covered 18 (33%) of the 54 cycling genes that were identified under stringent filtering conditions and 23 (10%) of the 237 cycling genes that were identified under relaxed filtering conditions. Among the 18 cycling genes, the numbers of genes that peaked within CT0 to -4, CT4 to -8, CT8 to -12, CT12 to -16, CT16 to -20, and CT20 to -24 were 2, 4, 6, 5, 1, and 0, respectively. These results indicate that clustering of genes with similar circadian expression patterns does occur in the Synechocystis chromosome, but it is limited to narrow regions.

FIG. 3.
(A, B, and C) Structure of cycling gene clusters slr0772-slr0773-sll0772 (A), slr0572-sll0543 (B), and sll0062-slr0058 (C). Arrows indicate transcription units and their direction of transcription. Black and gray boxes represent cycling genes that were ...

Physiological functions influenced by circadian rhythms.

Sixty-seven percent of the 54 cycling genes were assigned predicted functions. In considering the possible importance of the circadian oscillations of those genes on cellular physiology, we used a gene catalog list based on the PATHWAY database in KEGG (28) (http://www.genome.ad.jp/dbget-bin/get_htext?Synechocystis.kegg). We found at least one cycling gene in 13 of 19 (68%) functional categories (Table (Table2),2), including various metabolisms, membrane transport, transcription, translation, and signal transduction, suggesting that circadian rhythms influence a wide variety of cellular functions. A large number of genes involved in carbohydrate and energy metabolism showed circadian oscillations (Table (Table2),2), suggesting important roles of the regulation for the cyanobacterial physiology.

Functional classification of cycling genes

Circadian rhythms in genes associated with respiration and poly(3-hydroxyalkanoate) synthesis.

Most genes involved in carbohydrate and energy metabolism were associated with respiration. The main electron donor in the respiratory electron transport chain in cyanobacteria is NADPH, which is produced by the pentose phosphate cycle (8). This is because cyanobacteria possess an incomplete tricarboxylic acid cycle lacking α-ketoglutarate dehydrogenase (62). Four enzymes in the pentose phosphate cycle exhibited circadian expression patterns: 6-phosphogluconolactonase (Glc, sll1479), transaldolase (TalB, slr1793), and the rate-limiting NADPH-producing enzymes glucose-6-phosphate 1-dehydrogenase (Zwf, slr1843) and 6-phosphogluconate dehydrogenase (Gnd, sll0329) (Fig. 4A and B). Four genes encoding components of the respiratory electron transport chain were also cycling (Fig. 4A and C), including HoxE (sll1220), a subunit of the bidirectional hydrogenase which appears to function in the NAD(P)H dehydrogenase complex (6), and three subunits of the cytochrome c oxidase complex (slr1136, slr1137, and slr1138). The final step of respiration is ATP synthesis by use of energy stored in the electrochemical proton gradient (Fig. (Fig.4A).4A). A subunit c of ATP synthase (ssl2615), which is a transmembrane H+ carrier, was under circadian regulation (Fig. (Fig.4D).4D). In addition, four respiratory genes were scored as circadian under relaxed filtering conditions (see Table S1 in the supplemental material). All cycling genes involved in respiration showed peak expression around the time of transition from subjective day to night.

FIG. 4.
Circadian expression of genes associated with respiration. (A) An overview of respiration in cyanobacteria. Boxes and a circle represent protein components of the electron transport chain. Thick gray arrows indicate electron flow. For the pentose phosphate ...

Cyanobacteria lack the cytochrome bc1 complex that is generally used in the respiratory electron transport chain and instead use the cytochrome b6f complex, which functions in the photosynthetic electron transport chain (61) (Fig. (Fig.4A).4A). Thus, cyanobacteria use the cytochrome b6f complex in both respiration and photosynthesis. In contrast to other genes involved in respiration, genes encoding subunits of the cytochrome b6f complex did not exhibit significant circadian oscillation (Fig. (Fig.4E).4E). This expression pattern would reflect the constant use of the cytochrome b6f complex, which is required for photosynthesis during the day and respiration during the night.

We observed another example of coregulation of genes in the same biological pathway in the synthesis of poly(3-hydroxyalkanoate) (PHA) (Fig. (Fig.5).5). Cyanobacteria accumulate PHA in the cell as a carbon and energy reserve (22). Transcription of two genes involved in PHA synthesis, such as acetyl coenzyme A acetyltransferase (slr1993) and PHA synthase (slr1829) (Fig. (Fig.5A),5A), exhibited circadian rhythms with peak expression at the end of the subjective day (Fig. (Fig.5B).5B). This result suggests the circadian clock control of PHA synthesis.

FIG. 5.
Circadian regulation in the PHA synthesis pathway. (A) PHA biosynthetic pathway. Enzymes showing circadian rhythm are boxed. CoA, coenzyme A. (B) Expression profiles of cycling genes associated with PHA biosynthesis. Open circles and closed squares represent ...

Circadian rhythms in genes associated with transcription and translation.

We found that several genes associated with transcription were controlled by the circadian clock. They contained genes encoding the sigma subunit of RNA polymerase and a response regulator containing a DNA-binding domain. Cyanobacterial RNA polymerase has a core complex composed of α, β, β′, and γ subunits (14) that binds DNA nonspecifically and does not efficiently initiate transcription. When associated with a single sigma subunit (sigma factor), the core component acquires promoter binding specificity and initiates transcription efficiently (10). Two sigma factors exhibited circadian rhythm: one was sll1689, which showed a robust oscillation (Fig. (Fig.6),6), and the other was sll0687, which oscillated with a lower amplitude and was detected only under relaxed filtering conditions (see Table S1 in the supplemental material). The sll1689 gene is required for viability of the cells after long periods of nitrogen starvation (47). The two-component signal transduction system consists of two types of signal transducers, sensory kinase and response regulator. Typically, the sensory kinase transfers a phosphoryl group to the response regulator and the phosphorylated response regulator controls transcription of the target genes. We found that three response regulators containing a DNA-binding domain (43) were expressed with circadian rhythm: sll1330, which was identified under stringent filtering conditions (Fig. (Fig.6),6), and slr0312 and slr0947, which were identified under relaxed filtering conditions (see Table S1 in the supplemental material).

FIG. 6.
Expression profiles of genes associated with transcription and translation, including a sigma factor (closed triangle, sll1689), a response regulator with a DNA-binding domain (open circle, sll1330), and prolyl-tRNA synthetase (open square, sll1425). ...

We also found that genes encoding prolyl-tRNA synthetase (sll1425) (Fig. (Fig.6)6) exhibited circadian rhythm. In addition, seven genes associated with various steps of translation such as aminoacyl-tRNA synthesis and elongation and termination of the polypeptide chain were identified as circadian under relaxed filtering conditions (see Table S1 in the supplemental material). Expression of all of these genes was strikingly coregulated and peaked at early subjective day.


The goal of this study was to comprehensively analyze temporal changes in amounts of accumulated mRNA and to identify genes showing circadian rhythm in Synechocystis. We used a DNA microarray that provided nearly complete coverage of the Synechocystis genome. Similar analyses have been performed with other eukaryotic organisms, including mammals (1, 15, 19, 54, 63, 66), a fly (11, 32, 39, 67), a fungus (13, 51), a higher plant (21, 60), and a dinoflagellate (53). However, this is the first genome-wide investigation of circadian gene expression in a bacterial species. We applied two filtering conditions to microarray data and identified 237 cycling genes (54 genes under stringent conditions and an additional 183 genes under relaxed conditions). Thus, ~9% of 2,648 detectably expressed genes were estimated to show circadian rhythm. The result differs from that of an earlier study of circadian gene expression of Synechocystis that used a promoter trap strategy. A screen for circadian expression that used the bacterial luxAB luciferase reporter genes showed that approximately 80% of 72 bioluminescence-positive clones exhibited circadian rhythms (4). The discrepancy could be mainly due to the relatively lower sensitivity of the microarray to the kinetics of gene expression changes. The time resolution of the microarray experiments in the present study was 4 h (the interval between time points), while it was less than 1 h in the promoter trap analysis (4). In addition, measurements of expression levels obtained from microarray analysis contain relatively large experimental errors. Since most bioluminescent clones obtained by the promoter trap analysis exhibited rhythms with very low amplitude in Synechocystis (S. Aoki and M. Ishiura, unpublished data), microarray analysis probably could not detect such rhythms, thus underestimating the number of genes with circadian expression. It should also be pointed out that the microarray method monitors the amount of accumulated mRNA, while the promoter trap method monitors transcriptional activity. Therefore, if the lifetime of a transcript is very long, circadian rhythms in the amount of the accumulated transcript may not be detected by microarray analysis even though transcriptional activity of the gene exhibits circadian rhythm. In support of the hypothesis, Gutiérrez et al. reported that clock-controlled genes detected by microarray analysis were enriched in the population of unstable transcripts of Arabidopsis (20).

We identified cycling genes with various phases of oscillation (Fig. (Fig.1;1; see Fig. S1 in the supplemental material). The majority of them showed peak expression at the time of transition from subjective day to night, suggesting that the main role of the circadian clock in Synechocystis is to adjust the physiological state of the cell for the upcoming night environment. These included genes involved in respiration (Fig. (Fig.4)4) and PHA synthesis (Fig. (Fig.5).5). Circadian regulation of these genes would help supply energy and a carbon source in the night. Mitsui et al. reported that circadian rhythm in respiratory oxygen uptake peaked during subjective night in the cyanobacterium Synechococcus sp. strain Miami (42). The transcriptional coregulation of respiratory genes would contribute to the stimulation of respiratory activity in cyanobacteria. In Arabidopsis, many photosynthetic genes followed a circadian rhythm peaking in expression around midday (21). In Synechocystis, in contrast, most photosynthetic genes showed unstable rhythms that were not reproducible in the two biological replicate experiments (data not shown).

We found 12 chromosomal regions where neighboring genes were expressed with similar circadian patterns (Fig. (Fig.3).3). Such clusters of cycling genes spanned relatively narrow chromosomal regions that contained an average of 2.5 genes and covered 10 to 33% of cycling genes. These results suggest that changes in local chromosomal structure may play a significant role, but it may not be a dominant determinant of circadian expression patterns for the level of mRNA accumulation.

Our microarray analysis revealed that several genes associated with transcription, such as sigma factors and response regulators, showed circadian patterns of expression (Fig. (Fig.6).6). Circadian oscillation of sigma factors and their involvement in clock-controlled gene regulation were also found in Synechococcus (48, 65). The involvement of response regulators in the circadian clock was reported in a higher plant. Arabidopsis has five pseudo-response regulator genes, APRR1/TOC1, APRR3, APRR5, APRR7, and APRR9, that are circadianly expressed (38), and loss of function and constitutive expression of these genes affected properties of the circadian rhythm such as period length, amplitude, and phase (17, 26, 27, 34, 36, 37, 46, 59, 64, 69). The circadian rhythm generated by the central oscillator kaiABC genes is output to downstream target genes by a largely unknown molecular mechanism. The quantitative changes in these gene products would play a significant role in the transcriptional regulation of downstream genes in Synechocystis. We also found that many genes involved in translation showed a coregulated circadian rhythm with peak expression at early subjective day. This result suggests that circadian control of protein synthesis may operate in the cyanobacterial cell and that it is enhanced during daylight periods. In support of this hypothesis, previous studies with Chlamydomonas showed that the rate of chloroplast protein synthesis fluctuated during a light-dark cycle and peaked in the light period (31).

Targeted regulation of rate-limiting enzymes is clearly an efficient mechanism of modulating activity of an entire metabolic pathway. In Synechocystis, three rate-limiting enzymes in the sugar metabolic pathway were expressed with a robust circadian rhythm: phosphofructokinase (sll1196), glucose-6-phosphate 1-dehydrogenase (slr1843), and 6-phosphogluconate dehydrogenase (sll0329) (Table (Table11 and Fig. Fig.4).4). In addition, two rate-limiting enzymes, including glucokinase (sll0593) and pyruvate kinase (sll1275), were also identified as cycling under relaxed filtering conditions (see Table S1 in the supplemental material). Circadian regulation of the rate-limiting enzymes in sugar metabolism also occurs in Arabidopsis (21), Drosophila (11), and mice (54), although the regulated enzymes differ among species. The fact that circadian regulation of sugar metabolism has been conserved from bacteria to animals suggests that it plays a critical role in survival.

Supplementary Material

[Supplemental material]


We thank Hiroyuki Honda, Shigeru Itoh, Takahisa Yamato, and Kunio Ihara for useful suggestions and discussions. We thank Miriam Bloom (SciWrite Biomedical Writing and Editing Services) for professional editing.

This work was supported by grants from the Japanese Ministry of Education, Science and Culture (MEXT), “Program for Promotion of Basic Research Activities for Innovative Biosciences (PROBRAIN)” promoted by BRAIN, “Research for the Future Novel Gene Function Involved in Higher-Order Regulation of Nutrition-Storage in Plants” promoted by the Japan Society for the Promotion of Science, “Ground-Based Research Announcement for Space Utilization” promoted by the Japan Space Forum, “National Project on Protein Structural and Functional Analyses” promoted by MEXT, and “Joint-Project for Leading Science and Technology” promoted by the Aichi Science and Technology Foundation to M.I. M.I. was also supported by a 21st Century COE grant from MEXT.


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


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