Dawn- and dusk-phased circadian transcription rhythms coordinate anabolic and catabolic functions in Neurospora
© Sancar et al.; licensee BioMed Central. 2015
- Received: 13 October 2014
- Accepted: 3 February 2015
- Published: 24 February 2015
Circadian clocks control rhythmic expression of a large number of genes in coordination with the 24 hour day-night cycle. The mechanisms generating circadian rhythms, their amplitude and circadian phase are dependent on a transcriptional network of immense complexity. Moreover, the contribution of post-transcriptional mechanisms in generating rhythms in RNA abundance is not known.
Here, we analyzed the clock-controlled transcriptome of Neurospora crassa together with temporal profiles of elongating RNA polymerase II. Our data indicate that transcription contributes to the rhythmic expression of the vast majority of clock-controlled genes (ccgs) in Neurospora. The ccgs accumulate in two main clusters with peak transcription and expression levels either at dawn or dusk. Dawn-phased genes are predominantly involved in catabolic and dusk-phased genes in anabolic processes, indicating a clock-controlled temporal separation of the physiology of Neurospora. Genes whose expression is strongly dependent on the core circadian activator WCC fall mainly into the dawn-phased cluster while rhythmic genes regulated by the glucose-dependent repressor CSP1 fall predominantly into the dusk-phased cluster. Surprisingly, the number of rhythmic transcripts increases about twofold in the absence of CSP1, indicating that rhythmic expression of many genes is attenuated by the activity of CSP1.
The data indicate that the vast majority of transcript rhythms in Neurospora are generated by dawn and dusk specific transcription. Our observations suggest a substantial plasticity of the circadian transcriptome with respect to the number of rhythmic genes as well as amplitude and phase of the expression rhythms and emphasize a major role of the circadian clock in the temporal organization of metabolism and physiology.
Circadian clocks are molecular oscillators that coordinate metabolism, physiology and behavior of organisms with daily environmental changes [1-3]. In eukaryotes, the robustness of circadian oscillations is dependent on cell-autonomous interconnected transcriptional-translational feedback loops. These circadian oscillators drive rhythmic expression of clock-controlled genes (ccgs) in various organisms [4-7]. In mammals, the circadian clock coordinates metabolic pathways, such as glycolysis, gluconeogenesis, fatty acid oxidation and xenobiotic detoxification [8-11]. Disruption of the circadian oscillator in mammals is associated with metabolic pathologies, premature aging and cancer [12-14]. In plants, misalignment of the circadian clock with the external light–dark cycle results in lower chlorophyll production and slower growth [15,16]. The mechanisms generating circadian expression rhythms and circadian phase are complex. It is therefore important to identify in a comprehensive manner the genes that are controlled by the circadian clock and understand the molecular mechanisms underlying rhythmic gene expression. Gene expression analyses in a variety of organisms suggested that 2% to 15% of their transcriptomes are expressed in a circadian fashion with different phases throughout the day [2,17-19]. Circadian chromatin modifications and transcribing RNA polymerase II (RNAPII) profiles indicate a crucial role of circadian transcription in the orchestration of rhythmic gene expression [6,20]. Moreover, recent genome-wide studies in animals suggest that post-transcriptional processes contribute substantially to the generation of rhythmic transcript levels in addition to rhythmic transcription [4-6,21].
The white collar complex (WCC) is the core transcription activator of the circadian oscillator of Neurospora crassa [22,23]. It is composed of two GATA type transcription factors, white collar-1 (WC1) and white collar-2 (WC2) [24,25]. WC1 is the main blue-light photo-receptor of Neurospora [26,27]. WCC is activated by light and required for the synchronization of the circadian clock with exogenous light–dark cycles [24,27]. Most ccgs identified previously had maximum expression levels around dawn, that is, at a time when the WCC is highly active [28-30] and also at dusk . We, in collaboration with colleagues, recently presented evidence that the WCC controls expression of about 24 transcription factors , which have the potential to transduce circadian information to downstream genes. Such second tier circadian transcription factors may generate different phases of circadian gene expression. In particular, the transcription repressor CSP1, which is rhythmically expressed with morning-specific peaks in abundance and repressing activity, has the potential to modulate rhythmic expression of several target genes with an evening-specific phase [33,34].
Here, we have compared rhythmic transcript abundance and transcription profiles of transcribing RNAPII to determine the circadian transcriptome of Neurospora and assess the contribution of transcription versus post-transcriptional processes to gene expression rhythms controlled by the circadian clock. By frequent sampling we have obtained detailed phase information. Moreover, we analyzed the roles of WCC and CSP1 on circadian gene expression rhythms.
Transcription-based rhythmic gene expression in two circadian phases
A total of 345 rhythmically transcribed genes (RNAPII-S2P rhythm, P <0.05) did not show corresponding in phase transcript abundance rhythms (Group 2) (see Additional file 5: Table S4). Heat-map analysis suggested that the transcript levels of these genes increase with a substantial delay after the transcription rhythm (RNAPII-S2P profile). Hence, these RNAs could have a long half-life resulting in a delayed phase and a blunted abundance rhythm . Moreover, RNAPII-S2P amplitude of the oscillations of these genes was lower compared to genes identified by both methods (Figure 2B).
Finally, 170 genes with significant RNA abundance rhythms did not exhibit rhythmic RNAPII-S2P occupancy profiles (Group 3) (see Additional file 6: Table S5). This class of genes is potentially interesting since the RNA abundance rhythms might be based on hitherto unknown post-transcriptional mechanisms that are controlled by the circadian clock. However, the average RNAPII-S2P read coverage of these genes was low in comparison to genes that have significant profiles in RNAPII-S2P occupancy and RNA abundance (Figure 2C) (P <10−4). Thus, we cannot rigorously exclude transcription based expression rhythms of these genes. Together, the data suggest that expression of ccgs in Neurospora is predominantly associated with rhythmic transcription. Transcription independent generation of circadian transcript abundance rhythms, for example, on the level of rhythmic RNA turnover, may thus not be a major pathway used by the circadian clock.
A very recent RNA-seq analysis of three replicate time-courses by Hurley et al.  identified 872 genes with circadian RNA abundances rhythms. Of these 872 genes, 697 were expressed under our growth conditions and 327 of them were assigned as rhythmic in our study (see Additional file 7: Figure S2A, Additional file 4: Table S3). The remaining 370 genes were expressed at low levels under our conditions and oscillated with low amplitude in our and the Hurley et al. study (see Additional file 7: Figure S2B-D). A comparison of RNA abundance rhythms and RNAPII-S2P profiles of the rhythmic genes identified by both studies suggests that these genes are controlled on the level of transcription (see Additional file 7: Figure S2E).
Hurley et al. found among 187 luciferase reporters two genes (ccg1 and ccg9) with rhythmic RNA but no luciferase rhythm of the corresponding reporter gene (compare Additional file 8: Figure S6 and Additional file 9: Table S10 in Hurley et al.), suggesting a posttranscriptional regulation. We analyzed the RNAPII-S2P profiles of these genes by ChIP-seq and ChIP-PCR and found that both genes are rhythmically transcribed (see Additional file 7: Figure S2F and G).
WCC and CSP1 are major determinants of circadian phase
We then analyzed the expression phases of ccgs that might be indirectly induced by the CSP1 repressor (303 genes) or indirectly repressed by the WCC activator (191 genes) (see Additional file 12: Figure S3). These ccgs were also expressed in either a morning- or evening-specific manner. The 191 ccgs that were upregulated in ∆wc2 were preferentially dusk-phased (Figure 3A and Additional file 12: Figure S3A) (see Additional file 10: Table S6) while the 303 ccgs upregulated in a CSP1 overexpressing strain (csp1 OE ) were mainly dawn-phased (Figure 3B and Additional file 12: Figure S3B) (see Additional file 11: Table S7). Together, WCC and CSP1 appear to regulate, directly or indirectly, at least 1,137 of the 1,407 genes (approximately 80%) that were rhythmically transcribed in wt. Indeed, we found that 457 of 1,407 rhythmically expressed genes have CSP1 binding sites in their upstream regions suggesting a direct regulation by CSP1 (Additional file 11: Table S7). Hence, WCC and CSP1 are major determinants of clock-controlled transcription and circadian phase.
We have previously shown that CSP1 inhibits wc1 transcription and thereby regulates WCC expression levels . Therefore, we also analyzed the expression of frq, which is a direct target of WCC. In ∆csp1 the levels of frq RNA were slightly elevated and the expression phase was slightly advanced (Figure 4D), consistent with the shorter period rhythm observed in ∆csp1 in high glucose conditions .
Interestingly, we identified 1,867 genes that were rhythmically expressed in ∆csp1 (P <0.05) but not in wt (see Additional file 14: Figure S4B and Additional file 15: Table S9). The expression rhythms of these ∆csp1-specific ccgs also were clustered in two phases. About 2/3 of the ∆csp1-specific ccgs were expressed with a dusk- and 1/3 with a dawn-specific phase. Intriguingly, the dusk- and dawn-phased ∆csp1-specific ccgs also clustered in two groups in wt, displaying rather complex temporal expression profiles (see Additional file 14: Figure S4B). The data suggest that expression of these genes is clock-controlled. However, under the conditions analyzed, that is, 2% glucose-containing medium, their circadian regulation in wt appears to be blunted by the glucose-dependent activity of CSP1. Examples of these ∆csp1-specific genes are shown in Additional file 14: Figure S4C.
Together, the data suggest that CSP1 regulates the phase and amplitude of both dusk-phased and dawn-phased rhythmic genes. The over-expression of CSP1 indicates that dusk-phased genes are repressed by CSP1 in the subjective morning whereas dawn-phased genes are indirectly activated via unknown pathways. Rhythmic expression of only a fraction of dusk-phased genes is affected in a ∆csp1 strain, suggesting that other transcription factors (TFs) contribute to evening-specific gene expression. The increased accumulation of WCC in ∆csp1  could additionally affect expression of dawn- and dusk-phased ccgs, suggesting a further mechanism by which CSP1 may affect circadian gene expression.
Temporal separation of biological functions by the circadian clock
Examples of dawn-phased genes involved in cell rescue and defense are shown in Figure 6C. The dawn-phased expression of heat-shock factors may contribute to the adaptation of Neurospora to higher temperature expected during the day. In order to prevent desiccation during the day fungi (as well as bacteria and plants) synthesize trehalose. Neurospora can synthesize trehalose via a one-step pathway by the trehalose synthase encoded by the morning-specific clock-controlled gene-9 (ccg-9)  and via a two-step reaction requiring α, α-trehalose phosphate synthase and trehalose phosphatase (see Additional file 16: Figure S5B). The corresponding genes are rhythmically expressed with a morning-specific phase (Figure 6C, Additional file 1: Table S1 and Additional file 3: Table S2). It is also noteworthy that the Neurospora lysozyme gene (lyz) was rhythmically expressed with peak levels at dawn (Figure 6C). Although the function of lysozymes in fungi is not well characterized, a potential anti-microbial activity might preferentially be required during the day, when bacterial growth is supported by elevated temperature.
A recent study revealed a role of the circadian clock in the coordination of ribosome biogenesis in mammals . In Neurospora, the genes rpc-19, rpb-6 and rpa-12, which encode subunits of RNA polymerase I, were rhythmically expressed with a dusk-phased peak (Figure 6D). Moreover, rpf-2 and dbp-8, genes involved in rRNA maturation and ribosome assembly, were rhythmically expressed in an evening-specific manner. The data suggest that the circadian clock of Neurospora might affect ribosome biogenesis and protein translation.
Genes involved in DNA replication and cell division were also enriched among the dusk-phased circadian genes (Figure 6E). For example, mcm-3, mcm4 and mcm-5 encoding subunits of DNA replication licensing factor, required to initiate DNA replication in eukaryotes , were rhythmically expressed with a peak around dusk. The other subunits, mcm-2, 6, and 7, did not qualify as rhythmic genes by our criteria but appeared to be expressed with low-amplitude dusk-phased rhythms (see Additional file 8: Figure S6A). In addition rpa-1 and rpa-2, encoding the subunits of hetero-trimeric replication protein A, also showed circadian expression with a similar peak time with mcm genes. (Figure 6E and Additional file 8: Figure S6B). Furthermore, rfc-2, which encodes a subunit of the hetero-pentameric clamp loader complex, was expressed with a dusk-phased rhythm. The genes encoding the remaining subunits of the clamp loader were potentially also expressed in an evening specific manner (see Additional file 8: Figure S6C). Moreover, the genes encoding the core histones hH2A, hH2B, hH3 and hH4 were expressed at higher levels during dusk (Figure 6E, Additional file 8: Figure S6D). Finally, the gene encoding for the reverse transcriptase subunit of telomerase was robustly rhythmic with an evening specific phase (Figure 6E). The data strongly suggest that genes required for various aspects of cell division and growth are expressed in an evening-specific manner under the control of the circadian clock of Neurospora.
Together, the bi-phasic clustering of ccgs and the pronounced separation of the corresponding gene functions suggest a global and comprehensive temporal coordination of gene expression by the circadian clock to support rhythmic growth of Neurospora.
Our data suggest that clusters of functionally related genes are expressed in a morning- or evening-specific manner under the control of the Neurospora clock.
In this study we analyzed circadian gene expression in Neurospora on a genome-wide level to reveal organizational principles of clock-regulation. Temporal profiles of the circadian transcriptome and elongating RNAPII revealed 912 and 1,372 genes with significant rhythms, respectively. The two hour experimental sampling frequency (12 time points) provided rather reliable amplitude and phase information even from a single replicate. A ‘false discovery’ estimation based on randomly shuffled data indicated that the confidence for detecting clock-controlled genes increases with sequence coverage and amplitude of the RNAPII-S2P ChIP-seq and RNA-seq. Based on the false discovery estimation many sequencing replicates would be required to reliably identify all lowly expressed clock-controlled genes. However, assuming that such genes are rhythmically regulated in similar fashion as highly expressed genes, a complete list of such genes per se may not be of interest.
We show that the RNA abundance and transcription rhythms clustered mainly in two phases with peak levels around dawn and dusk. At least 1,407 genes appear to be transcribed in circadian fashion by our criteria (same phase of RNAPII and RNA abundance rhythms and at least one rhythm with a P-value <0.05). Another 345 genes are rhythmically transcribed but may encode rather stable transcripts that accumulate with a delayed phase and blunted amplitude. The abundance levels of 170 transcripts cycled in circadian fashion without an apparent rhythm of transcribing RNAPII. Rhythmic expression of these genes could potentially be regulated by post-transcriptional clock-controlled mechanisms. However, these genes showed very low RNAPII occupancy and low RNA expression levels so that the absence of an apparent transcription rhythm might, in many cases, be due to detection problems. Hence, the vast majority of rhythmically expressed genes of Neurospora appear to be controlled on the level of rhythmic transcription. Clock-dependent posttranscriptional mechanisms could in principle synergistically contribute to cycling RNA abundance levels to support rhythmic transcription. However, circadian expression of only a very small fraction of rhythmic genes might be exclusively controlled on a post-transcriptional level.
A recent analysis of clock-regulated genes in Neurospora suggested post-transcriptional regulation of circadian mRNA abundance rhythms based on the absence of a strong correlation between endogenous transcript abundance rhythms (RNA-seq) and the presumed transcriptional activity of 187 luciferase reporter genes . However, the luciferase reporters contained complete gene-specific 5’ UTRs and 3’ UTRs (500 bp), which usually contain regulatory elements for posttranscriptional control [49-52] and were thus not suited to report promoter activity. The observed differences between mRNA and luc expression were likely due to growth conditions suppressing and supporting rhythmic conidiation, respectively.
In contrast to Neurospora, genome-wide analyses of circadian gene expression rhythms in mice and flies suggested a substantial contribution of posttranscriptional mechanisms [4,5,21]. Although the distribution of expression phases of ccgs was more diverse in mice and flies [4-7] than in Neurospora, a recent analysis suggested dawn and dusk as rush hours for circadian gene expression in various mouse tissues and organs .
Surprisingly, the number of rhythmically expressed genes increased more than twofold in the absence of CSP1. This increase may reflect the interconnection between circadian gene expression and metabolism, since CSP1 expression is dependent on the glucose concentration in the medium . The circadian transcriptomes of wt and Δcsp1 were determined from cultures grown in high glucose medium, that is, under conditions when CSP1 is active. Under these conditions Δcsp1-specific ccgs might be repressed in wt and/or their rhythm might be blunted beyond the limit of detection. The rhythmic RNA abundances of the previously identified CSP1 target genes remained rhythmic in Δcsp1 but showed phase shifts towards morning indicating that unknown TFs rhythmically activate their transcription.
The data strongly suggest that the complexity of the apparent circadian transcriptome is dependent on growth conditions. Recently, a similar phenomenon has been observed in mammals: feeding mice with a high-fat diet (HFD) resulted in phase advances or even loss of transcript rhythms, in addition to the appearance of new oscillating transcripts . The appearance of new rhythmic transcripts was dependent on increased PPARγ activity. Integration of nutrient sensing TFs, such as CSP1 in Neurospora and PPARγ in mammals, into the core circadian gene network could modulate circadian output according to the needs of the cell under various nutrient conditions. The rich medium and constant conditions generally used to analyze circadian rhythms in Neurospora or mammalian cell culture may specifically disfavor and even suppress circadian gene expression rhythms, resulting in detection of a rather low number of ccgs.
An overruling distinction of dusk- and dawn-phased genes became evident when we analyzed the functional gene categories enriched in these two groups. Dawn-phased gene expression supports catabolic reactions whereas expression of dusk-phased genes supports anabolic reactions. This temporal separation of physiology could confer efficient utilization of available energy sources. Thus, during the day, when forward growth of Neurospora is fast but little biomass is produced, it may occupy and assess its environment and mobilize available resources (secretion of cellulases and degrading enzymes). During the day Neurospora seems to specifically protect itself from desiccation (synthesis of trehalose and glycerol), high temperature (heat shock proteins) and bacterial competitors (lysozyme). Towards the end of the day and during the night, when the environment becomes less hostile, genes required for protein synthesis (rRNA synthesis and ribosome assembly), DNA replication and cell division  (histones and DNA replication regulatory proteins) are upregulated to initiate cell growth and production of biomass in accordance with the previous assessment of available resources.
Number, identity, phase and amplitude of rhythmically expressed genes, that is, the complexity of the circadian transcriptome, are likely a property of a complex metabolic network and depend on environmental conditions. The intimate link of circadian clocks with metabolism seems to be conserved in cyanobacteria, plants and animals [15,56-60]. Coordination of catabolic and anabolic function might thus be the major role of circadian clocks.
Neurospora strains and culture conditions
Neurospora strains wt (FGSC#2489), ∆csp1 (FGSC#11348) and ∆wc2 (FGSC#11124) were acquired from Fungal Genetics Stock Center (FGSC, Manhattan, KS, USA). Vogel's medium (1 X) supplemented with 2% glucose, 0.5% L-arginine and 10 ng/ml biotin was used as a standard growth medium. For the circadian time course experiments, 200 ml medium in 500 ml flasks were inoculated with approximately 106 conidia and grown in light for six hours for conidial germination. Afterwards cultures were entrained in 11 hour/11 hour light/dark cycles for 2 days and released to constant dark for 22 hours. Cultures were transferred in a staggered manner, so harvesting of the cultures was performed within 10 hours.
Cultures were grown in Vogel’s medium with 2% Avicel as the carbon source for the cellulase assay. During harvesting, cultures were washed with three culture volumes of cold water to minimize extracellular cellulase contamination. Due to the adherence of Neurospora to Avicel we can measure extracellular cellulase that is trapped in the Avicel/Neurospora mesh in addition to intracellular cellulase in the secretory pathway. Extraction buffer (50 mM Tris–HCl pH 7.5, 150 mM NaCl, 1.5 mM MgCl2, 0.1% NP-40, 1 mM phenylmethylsulfonyl fluoride (PMSF), 1 μg/ml leupeptin, 1 μg/ml pepstatin A) was mixed with 500 μl ground mycelia and incubated on ice for approximately 30 minutes with frequent vortexing. The cell homogenate was centrifuged at 20,000 g at 4°C for 30 minutes. Protein concentration was determined by NanoDrop 1000 spectrometer. Cellulase activity was calculated by using Azo-CM-Cellulose (Megazyme, Bray, Ireland) according to the manufacturer's protocol with minor modifications. Briefly, 120 μg total protein extract diluted in 200 μl 100 mM NaOAc, pH 4.6 was mixed with 200 μl Azo-CM-Cellulose and incubated at 40°C for one hour with constant shaking. A 1 ml precipitation solution (300 mM NaOAc, 20 mM ZnAc, pH 5, 75% EtOH) was added to the protein-Azo-CM-Cellulose mixture and centrifuged at 10,000 g at room temperature for 10 minutes. The color of the supernatant was measured at 590 nm with a Jenway 6320D spectrophotometer. To generate the standard curve for cellulase activity (see Additional file 17: Figure S7C), cellulase from Aspergillus niger (1U/μg) (Sigma-Aldrich Chemie Gmbh Munich, Germany) was used.
RNA was extracted with peqGOLD TriFAST (peqLab, Erlangen, Germany) according to the manufacturer’s protocol. RNA was dissolved in 70 μl nuclease free water with 80u Ribolock RNAse inhibitor (ThermoScientific, Waltham, MA US). For the cDNA preparation Maxima First Strand cDNA Synthesis Kit (ThermoScientific, Waltham, MA US) was used. Transcript levels were analyzed by quantitative real-time PCR in 96-well plates with the StepOnePlus Real-Time PCR System (Life Technologies, NY, USA) using TaqMan Gene Expression Master Mix (Life Technologies, NY, USA). Primers and probes are listed in Additional file 18: Table S11. rRNA was used for normalization.
ChIP was performed as described previously  by using specific antibody for serine-2 phosphorylated RNAPII C-terminal tail. Polyclonal anti-rabbit RNAPII Ser2-P was raised against the peptide (pS)PTSPSY(pS)PTSPSC. Primers and probes used for ChIP-PCRs are listed in Additional file 18: Table S11. actin gene (ncu04173) was used for normalization.
RNA sequencing and ChIP sequencing
cDNA was prepared by using NEBNext® Ultra RNA Prep kit with NEBNext® Multiplex oligos according to the manufacturer’s instructions. ChIP DNA libraries were prepared with NEBNext® ChIP-Seq Library Prep Reagent Set for Illumina® with NEBNext® Multiplex oligos. A 2100 Bioanalyzer was used to check the size and the quality of the libraries. Un-paired sequencing with 50 bp reads was performed with a HiSeq 2000 at GeneCore EMBL Heidelberg for RNA-seq and by the BGI, Hong Kong, for ChIP-seq. Individual sequence reads for each run are available in the Sequence Read Archive (SRA) database under the study name PRJNA248256. Accession numbers for experiments and number of sequence reads are listed in Additional file 19: Table S12.
High-throughput data analysis
Raw sequence reads were mapped to the Neurospora crassa genome (NC10) using Bowtie , where parameters were set to allow maximum three mismatches and suppress alignments which mapped to more than one location. Gene expression was quantified by the number of reads falling into the annotated exons. For analysis of the RNAPII-S2P ChIP-seq the reads that fall to the 500 bp window upstream of gene end position were counted. Normalization was carried out using the size factor formula as described .
Differential gene expression analysis
Identification of rhythmic RNA and RNAPII-S2P profiles
The ARSER  program was used to identify rhythmic RNA and RNAPII-S2P profiles. Period length was set between 18 to 26 hours. In order to detect rhythmic expression of genes that have very high expression levels in light (0 time point), we ran ARSER twice with and without the 0 time point. All genes that have ARSER detected RNA and/or RNAPII-S2P profiles were selected for further analysis. The ARSER output is shown in Additional file 3: Table S2. Independent phase and amplitude determination was performed by fitting the data to a sine wave using a negative binomial generalized linear model. The ‘glm.nb/function from MASS package of R was used and the period length was set as 22 hours.
False discovery rate analysis
Where, f bg(Amplitude), f ob(Amplitude) are the estimated occurrence of rhythmic genes against the amplitude using the fitted exponential decay function, and Weight (Amplitude) is introduced to remove the bias for the smaller number of occurrence when the amplitude is high. FDR according to Coverage is computed by using the same formula.
We thank David Ibberson (CellNetworks deep-sequencing core facility) for RNA-seq and ChIP-seq library preparations. MB is an investigator of CellNetworks. This work was supported by grants of the Deutsche Forschungsgemeinschaft: GRK1188 and SFB1036.
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