Reconciling conflicting models for global control of cell-cycle transcription

Abstract

Models for the control of global cell-cycle transcription have advanced from a CDK-APC/C oscillator, a transcription factor (TF) network, to coupled CDK-APC/C and TF networks. Nonetheless, current models were challenged by a recent study that concluded that the cell-cycle transcriptional program is primarily controlled by a CDK-APC/C oscillator in budding yeast. Here we report an analysis of the transcriptome dynamics in cyclin mutant cells that were not queried in the previous study. We find that B-cyclin oscillation is not essential for control of phase-specific transcription. Using a mathematical model, we demonstrate that the function of network TFs can be retained in the face of significant reductions in transcript levels. Finally, we show that cells arrested at mitotic exit with non-oscillating levels of B-cyclins continue to cycle transcriptionally. Taken together, these findings support a critical role of a TF network and a requirement for CDK activities that need not be periodic.

Keywords: Cell-cycle transcription; mathematical modeling; time-series transcriptomics; transcriptional network.

Figure 1.

Models for the global control of cell-cycle transcription. (A) The CDK-APC/C network functions as an autonomous oscillator and drives the cell-cycle transcriptional program. (B) The network TFs drive the cell-cycle transcriptional program without CDK-APC/C input. (C) The TF network and CDK-APC/C network can function independently, but are coupled to drive the cell-cycle transcriptional program. (D) CDK-APC/C and TF networks are highly connected and act as a single network to control the cell-cycle transcriptional program. In models (B)-(D), periodic input from CDK-APC/C is not required for oscillations of the transcriptional program.

Figure 2.

A large program of cell-cycle transcription persists in cells lacking B-cyclins. (A) Cartoon line graphs illustrating the levels of G1 cyclin-CDKs (green) and B-cyclin-CDKs (red) in the time-course experiments from Rahi et al. (top panels) and Orlando et al. (bottom panels). (B) Venn diagrams showing the relationships of sets of cell-cycle genes reported previously^, and those examined by Rahi et al. (C) Heat maps showing transcript dynamics of 881 cell-cycle genes (in the same order) in CLB control and clb△ mutant datasets from Orlando et al. (bottom panels) and Rahi et al. (top panels). In all experiments, early G1 cells were released into the cell cycle (with the CLB expression shut-off for B-cyclin mutants) for time-series gene expression profiling. Transcript levels are depicted as fold change versus mean in each individual dataset. Gene lists and corresponding microarray probes can be found in Table S2. See also Figures S1 and S2.

Figure 3.

Phase-specific transcription of cell-cycle gene clusters in cells lacking B-cyclins. Line graphs showing transcript dynamics of selected genes in the indicated gene clusters for the CLB control and clbΔ mutant data sets from Orlando et al. and Rahi et al. Early G1 cells were released into the cell cycle (with the CLB expression shut-off for B-cyclin mutants) for time-series gene expression profiling. CLN and CLB levels are as shown in Figure 2A. Transcript dynamics are plotted as fold change vs. mean as used in Figure 2C. See also Figure S3 and S4.

Figure 4.

Evidence for serial activation of network TFs in cells lacking B-cyclins. (A) Diagram of the network TFs model proposed by Orlando et al. SIC1 is an output normally activated by Swi5 during mitotic exit. See Table S3 for edge evidence. (B)(C) Line graphs showing the absolute transcript levels (arbitrary units) of the network TFs components in the CLB control and clbΔ mutant data sets from Orlando et al. and Rahi et al.

Figure 5.

A quantitative model demonstrates the robust activation of SIC1 by low-amplitude SWI5 oscillation. (A) Network topology used for quantitative modeling of transcriptional regulation of SIC1 (see Document S1 for explicit description of equations). (B) Line graphs of selected variables generated by numerical simulation of mathematical model in (A) for a specific choice of parameters, ${\Theta }^{*}$, along with scatter plots of CLB2, SWI5, and SIC1 levels in the RNA-seq data (in FPKM values) from Rahi et al. See Document S1 for the parameter values in ${\Theta }^{*}$. (C) Contour plots of the logarithm of the local-minimum-normalized model error over 2 2-dimensional regions of parameters space, centered at ${\Theta }^{*}$. In particular we plot $\log \left(\mathcal{L}\left(\text{Θ}\right)/\mathcal{L}\left({\text{Θ}}^{*}\right)\right)$, where $\mathcal{L}\left(\text{Θ}\right)$ is the objective function defined in Document S1, as we independently vary several parameters in a neighborhood of ${\Theta }^{*}$. See also Figure S5.

Figure 6.

The cell-cycle transcriptional program in mutants defective in mitotic exit. (A) Simplified diagram of part of the CDK-APC/C network (black) controlling progression through mitosis and their established input into network TFs (colored). See Table S3 for edge evidence. (B) Line graphs showing transcript dynamics of canonical targets of network TFs in indicated strains. In all experiments, early G1 cells were released for time-series gene expression profiling by microarray. Transcript levels are depicted as percentage of maximal level in corresponding wild-type controls at the same temperature from previous studies.^, Results of the wild-type control from Orlando et al. are shown. (C) Heat maps showing transcript dynamics of 249 periodic genes in indicated strains. The wild-type data from Orlando et al. are shown. Transcript levels were measured by microarray analysis and are depicted as fold change vs. mean in individual data sets. See also Figures S6 and S7.

Figure 7.

Integrated network model for the control of the cell-cycle transcriptional program in budding yeast. (A) Network diagram incorporating components of the CDK-APC/C model proposed by Rahi et al. and the TF network model proposed by Orlando et al. Nodes are ordered horizontally by their approximate time of activation during the cell cycle. See Table S3 for edge evidence. (B) Functional outcomes of the cell-cycle-transcriptional program during different CDK-APC/C perturbations with either high or low CDK activities.