1. Department of Biology,
2. Program in Computational Biology and Bioinformatics,
3. Department of Computer Science,
4. Department of Physics, and,
5. Department of Statistical Science, Duke University, Durham, North Carolina 27708, USA

Correspondence to: Steven B. Haase¹ Correspondence and requests for materials should be addressed to S.B.H. (Email: shaase@duke.edu).

The biochemical oscillator controlling periodic events during the cell cycle is centred on the activity of CDKs (reviewed in ref. 5). The cyclin–CDK oscillator governs the major events of the cell cycle, and in embryonic systems this oscillator functions in the absence of transcription, relying only on maternal stockpiles of messenger RNAs and proteins. CDKs are also thought to act as the central oscillator in somatic cells and yeast, and directed studies suggest that they are important for controlling the temporally ordered programme of transcription (reviewed in refs 4, 6). However, systems-level analyses using high-throughput technologies^{1, 2, 7, 8} have suggested alternative models for the regulation of periodic transcription during the yeast cell cycle^{1, 3, 9}. By correlating genome-wide transcription data with global transcription factor binding data, models have been constructed in which periodic transcription is an emergent property of a transcription factor network^{1, 3, 9}. In these networks, transcription factors expressed in one cell-cycle phase bind to the promoters of genes encoding transcription factors that function in a subsequent phase. Thus, the temporal programme of transcription could be controlled by sequential waves of transcription factor expression, even in the absence of extrinsic control by cyclin–CDK complexes.

The validity and relevance of the hypotheses regarding intrinsically oscillatory networks of transcription factors remain uncertain, because for the limited number of periodic genes that have been dissected in detail, periodic transcription was found to be governed by CDKs (reviewed in ref. 4). We therefore sought to determine to what extent CDKs and transcription factor networks contribute to global regulation of the cell-cycle transcription programme. To this end, we investigated the dynamics of genome-wide transcription in budding yeast cells disrupted for all S-phase and mitotic cyclins (clb1,2,3,4,5,6). These cyclin-mutant cells are unable to replicate DNA, to separate SPBs, to undergo isotropic bud growth or to complete nuclear division, indicating that they are devoid of functional Clb–CDK complexes^{10, 11, 12}. So, by conventional cell-cycle measures, clb1,2,3,4,5,6 cells arrest at the G1/S border. We have shown previously that clb1,2,3,4,5,6 cells trigger G1 events cyclically¹⁰, including the activation of G1-specific transcription and bud emergence. Nevertheless, if Clb–CDK activities are essential for triggering the transcriptional programme, then periodic expression of S-phase-specific and G2/M-specific genes should not be observed.

We examined global transcription dynamics in synchronized populations of both wild-type cells and cyclin-mutant cells. Synchronous populations of early G1 cells were collected by centrifugal elutriation. Cell aliquots were then harvested at 16-min intervals for 270 min (equivalent to about two cell cycles in the wild type and about 1.5 cell cycles in the cyclin mutant). Transcript levels were measured genome-wide for each time point with the use of Yeast 2.0 oligonucleotide arrays. Results from two independent experiments each for both wild-type and cyclin-mutant cells were highly reproducible, with adjusted r² values of 0.995 and 0.989, respectively (Supplementary Fig. 1). All statistical analyses were performed with replicate data sets; however, to facilitate illustration, single data sets were used for all graphical representations.

To identify periodically transcribed genes, we applied a modification of a method developed previously¹³ to data acquired from our wild-type cells. We established a set of 1,271 genes that were transcribed periodically (Fig. 1a and Supplementary Table 1). This set of periodic genes shares 510 and 577 genes with those sets previously identified as periodic by Spellman et al.² and Pramila et al.¹, respectively (Supplementary Fig. 2), with 440 consensus periodic genes identified by all three studies (Supplementary Table 2). We then examined the transcriptional dynamics of our set of 1,271 periodic genes in the cyclin mutant (Fig. 1b). The behaviour of many genes changed significantly in the cyclin mutant, supporting previous findings. However, despite the fact that cyclin-mutant cells arrest at the G1/S border, a large fraction of periodic genes in all cell-cycle phases continued to be expressed on schedule (Fig. 1b). Similar cyclin-dependent and cyclin-independent behaviours are also observed in the set of 440 consensus periodic genes (Supplementary Fig. 3).

Figure 1: Dynamics of periodic transcripts in wild-type and cyclin-mutant cells.

Heat maps depicting mRNA levels of periodic genes for wild-type (a) and cyclin-mutant (b) cells. Each row in a and b represents data for the same gene (Supplementary Table 1). Transcript levels are expressed as a log₂-fold change relative to mean expression. Transcript levels at each point in the time series were mapped onto a cell-cycle timeline (see Methods). The S and G2/M phases of the cyclin-mutant timeline are shaded, indicating that, by conventional definitions, cyclin-mutant cells arrest at the G1/S-phase border.

High resolution image and legend (156K)

Using absolute change and Pearson correlation analyses (see Supplementary Information), we determined that 833 of the periodic genes showed changes in expression behaviour in the cyclin mutant and are therefore likely to be directly or indirectly regulated by B-cyclin–CDK.

Our genome-level experiments accurately reproduced previous findings on several well-studied B-cyclin–CDK-regulated genes (Fig. 2). We observed that a subset of late G1 transcripts (SBF-regulated genes such as CLN2 but not MBF-regulated genes such as RNR1) were not fully repressed (Fig. 2a, b) as expected in mitotic cyclin-mutant cells^{14, 15}. A subset of M/G1 transcripts (including SIC1 and NIS1) are targets of the transcription factors Swi5 and Ace2, which are normally excluded from the nucleus by CDK phosphorylation until late mitosis^{16, 17, 18, 19}. SIC1 and NIS1 were expressed earlier in the cyclin mutant (Fig. 2c, d), presumably because nuclear exclusion of Swi5 and Ace2 is lost in cyclin-mutant cells. The modest degree of shift in the timing of SIC1 and NIS1 transcription probably reflects the fact that SWI5 and ACE2 transcripts do not accumulate to maximal levels in cyclin-mutant cells as expected for Clb2-cluster genes (including CDC20) (Fig. 2e, f)^{14, 20, 21}. Although a significant fraction of periodic genes showed changes in the amplitude of expression (increased or decreased), a statistical analysis of the dynamic range of expression across all periodic genes revealed that most genes in cyclin-mutant cells show only modest changes, if any, in comparison with wild-type cells (Supplementary Fig. 4).

Figure 2: Transcription dynamics of established cyclin–CDK-regulated genes.

Absolute transcript levels (dChip-normalized Affymetrix intensity units/1,000) are shown for the genes CLN2 (a) and RNR1 (b), which are regulated by SBF and MBF, respectively; the Ace2/Swi5-regulated genes SIC1 (c) and NIS1 (d); and the Clb2-cluster genes CDC20 (e) and ACE2 (f). Solid lines, wild-type cells; dashed lines, cyclin-mutant cells.

High resolution image and legend (105K)

To identify new subsets of co-regulated genes on the basis of transcriptional behaviours observed in both wild-type and cyclin-mutant cells, we employed the affinity propagation algorithm²², first to cluster genes based on expression in wild-type cells, and then to subcluster genes on the basis of their behaviour in cyclin-mutant cells (Fig. 3 and Supplementary Fig. 5). Of the 833 cyclin-regulated genes, 513 were assigned to 30 discrete clusters showing similar behaviours in wild-type cells (Fig. 3a and Supplementary Fig. 6), and these were then subclustered into 56 clusters on the basis of their transcription profiles in cyclin-mutant cells (Fig. 3b and Supplementary Table 3). Using data from global transcription factor localization studies²³, we identified subsets of transcription factors that may regulate these subclusters with the use of over-representation analyses (Fig. 3 and Supplementary Table 4). On the basis of their association with the promoters of genes in cyclin-regulated subclusters, these factors are likely to be directly or indirectly regulated by cyclins. Consistent with this hypothesis are previous demonstrations that several of these factors are CDK targets^{14, 15, 18, 19, 24, 25, 26, 27, 28, 29}. These findings lay the groundwork for elucidating the full range of mechanisms by which cyclin–CDKs regulate transcription during the cell cycle.

Figure 3: Genes showing altered behaviours in cyclin-mutant cells.

a, Clusters of genes with similar expression patterns in wild-type cells. b, Subclusters of genes with similarly altered expression patterns in cyclin-mutant cells. Each row in a and b represents data for the same gene (Supplementary Table 1). Transcript levels are depicted as in Fig. 1. Up to five over-represented transcription factors for each cluster are shown (see Methods and Supplementary Table 4 for complete lists).

High resolution image and legend (217K)

Of the genes identified as periodic in wild-type cells, 882 continued to be expressed on schedule in cyclin-mutant cells despite cell-cycle 'arrest' at the G1/S border (Fig. 4a, b). Some of these genes (450 in total) showed minor changes in transcript behaviour but continued to be expressed at the proper time, as shown above for ACE2. Therefore some genes that were cyclin-regulated are also included in the set of genes that maintain periodicity. Nevertheless, a statistical analysis of the dynamic range of expression of these genes in wild-type and cyclin-mutant cells indicates that the amplitude changes for most of these genes are quite modest (see Supplementary Figs 7–9). The finding that nearly 70% of the genes identified as periodic in wild-type cells are still expressed on schedule in cyclin-mutant cells demonstrates the existence of a cyclin–CDK-independent mechanism that regulates temporal transcription dynamics during the cell cycle. This observation is supported by the analysis of the set of 440 consensus periodic genes, the bulk of which maintain periodicity in cyclin-mutant cells (Supplementary Fig. 10).

Figure 4: The periodic transcription programme is largely intact in cyclin-mutant cells that arrest at the G1/S border.

a, b, Genes maintaining periodic expression in cyclin-mutant cells (a) show similar dynamics in wild-type cells (b). Each row in a and b represents the same gene (Supplementary Table 1). Transcript levels are depicted as in Fig. 1. c, Synchronously updating boolean network model. Transcription factors are arranged on the basis of the time of peak transcript levels in cyclin-mutant cells. Arrows indicate transcription factor/promoter interaction. Activating interactions, outer rings; repressive interactions, inner rings. Colouring indicates activity in one of five successive states; SBF and YHP1 are active in two states (Supplementary Table 6).

High resolution image and legend (161K)

In principle, a transcription network defined by sequential waves of expression of transcription factors^{1, 3, 9} might function independently of any extrinsic control by CDKs. To determine whether a transcription network could account for cyclin–CDK-independent periodic transcription, we constructed a synchronously updating boolean network model and determined that such a model can indeed explain the periodic expression patterns we observed in cyclin-mutant cells (Fig. 4c). Transcription factors that maintained periodicity in the cyclin mutant were placed on a circularized cell-cycle timeline on the basis of their peak time of transcription in the cyclin mutant. Connections were drawn on the basis of documented physical interactions^{23, 30} (Supplementary Table 5) between a transcription factor and the promoter region of a gene encoding a transcription factor expressed subsequently (see Supplementary Information). The architecture of the network in cyclin-mutant cells is virtually identical to that in wild-type cells (Supplementary Fig. 11) and is also remarkably similar to models based on wild-type expression data from previous studies^{1, 3, 9}.

When the network is endowed with boolean logic functions (Supplementary Table 6a), synchronous updating of the model leads to a cycle that produces successive waves of transcription by progressing through five distinct states before returning to the initial state (Supplementary Fig. 12a, b). Thus, the model functions as an oscillator and produces a correctly sequenced temporal programme of transcription.

To examine the robustness of the network oscillator, we evaluated outcomes when initializing the network from all possible starting states. More than 80% of the 512 starting states entered the oscillatory cycle depicted in Fig. 4c, with the remainder terminating in a steady state in which all genes were transcriptionally inactive (Supplementary Table 6b, c). We also examined whether the oscillations were sensitive to the choice of the boolean logic functions assigned to nodes with multiple inputs, specifically the activating inputs to Cln3 and SFF, and the repressors of SBF and Cln3. For most of the logic functions, the predominant outcome was again the oscillatory cycle depicted in Fig. 4c, but in some cases the model entered two qualitatively similar cycles (Supplementary Fig. 12c, d, and Supplementary Table 6), with the remainder again terminating in a transcriptionally inactive steady state. Several boolean logic functions were found to produce the same cycles (Supplementary Table 6b), so the model cannot precisely determine the true logic of the network connections. Nevertheless, the fact that the model can produce qualitatively similar cycles, and that these cycles can be reached from many initial states, suggests that robust oscillation is an emergent property of the network architecture.

Previous studies proposed that a cyclin–CDK-independent oscillator could trigger some periodic events, including bud emergence¹⁰. The robust oscillating character of our model indicates that a transcription factor network may function as this cyclin–CDK-independent oscillator. Because cyclin genes are themselves among the periodic genes targeted by this network, and because cyclin–CDKs can, in turn, influence the behaviour of transcription factors in the network, precise cell-cycle control could be achieved by coupling a transcription factor network oscillator with the cyclin–CDK oscillator. The existence of coupled oscillators could explain why the cell cycle is so robust to significant perturbations in gene expression or cyclin–CDK activity.

Our findings also indicate that the properly scheduled expression of genes required for cell-cycle regulated processes such as DNA synthesis and mitosis is not sufficient for triggering these events. The execution of cell-cycle events in wild-type cells is likely to require both properly timed transcription and post-transcriptional modifications mediated by CDKs.

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Supplementary Information

Supplementary information accompanies this paper.

Acknowledgements

We thank D. Lew and L. Simmons Kovacs for discussions and critical reading of the manuscript, and P. Benfey for helpful discussions and support. Financial support was provided by the American Cancer Society (to S.B.H.), the Alfred P. Sloan Foundation (to A.J.H.), the National Science Foundation (to A.J.H. and J.E.S.S.) and the National Institutes of Health (to S.B.H., A.J.H. and J.E.S.S.).

Author Contributions D.A.O., C.Y.L. and S.B.H. designed and performed the experiments. J.Y.W. provided technical expertise. D.A.O., C.Y.L., A.B., E.S.I. and A.J.H. performed the computational analyses, with contributions from J.E.S.S. and S.B.H. to the boolean model. D.A.O. and S.B.H. prepared the manuscript with contributions from C.Y.L., A.B., E.S.I. and A.J.H.

Author Information

The microarray data discussed in this publication have been deposited in NCBI's Gene Expression Omnibus (GEO, http://www.ncbi.nlm.nih.gov/geo/) and are accessible through GEO series accession number GSE8799.

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