Global control of cell-cycle transcription by coupled CDK and network oscillators

Abstract

A significant fraction of the Saccharomyces cerevisiae genome is transcribed periodically during the cell division cycle1,2, suggesting that properly timed gene expression is important for regulating cell cycle events. Genomic analyses of transcription factor localization and expression dynamics suggest that a network of sequentially expressed transcription factors could control the temporal program of transcription during the cell cycle3. However, directed studies interrogating small numbers of genes indicate that their periodic transcription is governed by the activity of cyclin-dependent kinases (CDKs)4. To determine the extent to which the global cell cycle transcription program is controlled by cyclin/CDK complexes, we examined genome-wide transcription dynamics in budding yeast mutant cells that do not express S-phase and mitotic cyclins. Here we show that a significant fraction of periodic genes were aberrantly expressed in the cyclin mutant. Surprisingly, although cells lacking cyclins are blocked at the G1/S border, nearly 70% of periodic genes continued to be expressed periodically and on schedule. Our findings reveal that while CDKs play a role in the regulation of cell cycle transcription, they are not solely responsible for establishing the global periodic transcription program. We propose that periodic transcription is an emergent property of a transcription factor network that can function as a cell cycle oscillator independent of, and in tandem with, the CDK oscillator.The biochemical oscillator controlling periodic events during the cell cycle is centered on the activity of cyclin-dependent kinases (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 mRNAs and proteins. CDKs are also thought to act as the central oscillator in somatic cells and yeast, and directed

Branching process deconvolution algorithm reveals a detailed cell-cycle transcription program

“…Reassured by the performance of our algorithm on budding index data, we jointly learned deconvolved transcription profiles from two replicate cell-cycle time-course microarray experiments in budding yeast (4). Our decision to keep G1 distinct from DG1 allowed us to capture possibly different transcription programs for mother and daughter cells during G1.…”

“…This set includes 73% of the 1,271 periodic genes identified in ref. 4, 69% of the 895 recallable periodic genes identified in ref. 3, 76% of the 709 recallable periodic genes identified in ref.…”

“…These data differed from ours in a number of important respects. The cells were synchronized using a temperature-sensitive cdc28-13 mutant strain rather than by elutriation; the data were from a single time-series experiment rather than replicate time-series experiments; and the markers that were collected to characterize synchrony loss in the population were different: Orlando et al (4,18) collected budding index plus flow cytometric measurements of DNA content, whereas Granovskaia et al used indices of the fraction of cells with buds, with nuclei across the bud neck, and with divided nuclei.…”

“…To effect this synchrony, cells are arrested or selected at one stage of the cell cycle and then released to progress through subsequent division cycles. Molecular species can then be measured in the population at various time points after release (1)(2)(3)(4)(5).…”

Autonomous Boolean modelling of developmental gene regulatory networks

“…For example, the sea urchin endomesoderm network database, represented in BioTapestry [1,33], allows us to see the times when a gene changes state and when its targets change state. Timeresolved expression datasets are also available for other systems showing genes that switch ON and OFF in response to their regulators [34,35].…”