Transcription network and cyclin/CDKs: The yin and yang of cell cycle oscillators

Kovacs, Laura A. Simmons; Orlando, David A.; Haase, Steven B.

doi:10.4161/cc.7.17.6515

Cited by 40 publications

(18 citation statements)

References 36 publications

(43 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Many of the decisive regulatory events occur by post translational modifications of pre-existing proteins (Pagliuca et al, 2011), but underlying this regulatory level is also synchronized de novo production of cell cycle regulated components. A large number of genes have been reported to be timely transcribed as part of cell cycle progression (Sun et al, 2007; Simmons Kovacs et al, 2008; Muller and Engeland, 2010). Here, we have taken advantage of emerging technology to study gene expression profiles in single cells of different cell cycle phases and of different cell sizes.…”

Section: Discussionmentioning

confidence: 99%

Cell Cycle and Cell Size Dependent Gene Expression Reveals Distinct Subpopulations at Single-Cell Level

et al. 2017

View full text Add to dashboard Cite

Cell proliferation includes a series of events that is tightly regulated by several checkpoints and layers of control mechanisms. Most studies have been performed on large cell populations, but detailed understanding of cell dynamics and heterogeneity requires single-cell analysis. Here, we used quantitative real-time PCR, profiling the expression of 93 genes in single-cells from three different cell lines. Individual unsynchronized cells from three different cell lines were collected in different cell cycle phases (G0/G1 – S – G2/M) with variable cell sizes. We found that the total transcript level per cell and the expression of most individual genes correlated with progression through the cell cycle, but not with cell size. By applying the random forests algorithm, a supervised machine learning approach, we show how a multi-gene signature that classifies individual cells into their correct cell cycle phase and cell size can be generated. To identify the most predictive genes we used a variable selection strategy. Detailed analysis of cell cycle predictive genes allowed us to define subpopulations with distinct gene expression profiles and to calculate a cell cycle index that illustrates the transition of cells between cell cycle phases. In conclusion, we provide useful experimental approaches and bioinformatics to identify informative and predictive genes at the single-cell level, which opens up new means to describe and understand cell proliferation and subpopulation dynamics.

show abstract

Section: Discussionmentioning

confidence: 99%

Cell Cycle and Cell Size Dependent Gene Expression Reveals Distinct Subpopulations at Single-Cell Level

et al. 2017

View full text Add to dashboard Cite

show abstract

“…However, the manner by which the regulatory signaling cascade induced by the cell wall integrity checkpoint feeds into the complex transcriptional network of cell cycle progression has not been fully investigated. Genome-wide transcriptional studies have revealed the transcription cascade of cell cycle-related transcription factors (6)(7)(8)(9). Studies have shown major transcription factors regulating each other at specific cell cycle stages, such as Ace2/Swi5 (M-to G 1 -phase transition), Swi4/Swi6 and Swi6/Mbp1 (late G 1 -to Sphase transition), Hcm1 (late S phase), and Ndd1/Fkh2 (M phase).…”

mentioning

confidence: 99%

The Late S-Phase Transcription Factor Hcm1 Is Regulated through Phosphorylation by the Cell Wall Integrity Checkpoint

Negishi

Veis

Hollenstein

et al. 2016

Molecular and Cellular Biology

View full text Add to dashboard Cite

The cell wall integrity (CWI) checkpoint in the budding yeast Saccharomyces cerevisiae coordinates cell wall construction and cell cycle progression. In this study, we showed that the regulation of Hcm1, a late-S-phase transcription factor, arrests the cell cycle via the cell wall integrity checkpoint. Although the HCM1 mRNA level remained unaffected when the cell wall integrity checkpoint was induced, the protein level decreased. The overproduction of Hcm1 resulted in the failure of the cell wall integrity checkpoint. We identified 39 Hcm1 phosphorylation sites, including 26 novel sites, by tandem mass spectrometry analysis. A mutational analysis revealed that phosphorylation of Hcm1 at S61, S65, and S66 is required for the proper onset of the cell wall integrity checkpoint by regulating the timely decrease in its protein level. Hyperactivation of the CWI mitogen-activated protein kinase (MAPK) signaling pathway significantly reduced the Hcm1 protein level, and the deletion of CWI MAPK Slt2 resulted in a failure to decrease Hcm1 protein levels in response to stress, suggesting that phosphorylation is regulated by CWI MAPK. In conclusion, we suggest that Hcm1 is regulated negatively by the cell wall integrity checkpoint through timely phosphorylation and degradation under stress to properly control budding yeast proliferation. Critical events during cell cycle progression, such as DNA replication and mitosis, are regulated by cell cycle checkpoints to ensure the proper completion of cell division. The cell cycle in the budding yeast Saccharomyces cerevisiae is coordinated with bud growth to secure the morphological space for cell division (1). One of the cell cycle checkpoints required to monitor budding morphology is the morphogenesis checkpoint (2), and the other is the cell wall integrity (CWI) checkpoint (3). They function independently to coordinate bud growth and cell wall construction, respectively, with cell cycle progression (4). Without a supply of cell wall materials for bud growth, the budding yeast cell cycle is arrested before entry into M phase and cannot proceed to mitosis (3).Compared to the morphogenesis checkpoint that regulates Swe1 phosphorylation and its degradation to activate cyclin-dependent kinase (CDK) (5), the primary function of the cell wall integrity checkpoint is inhibiting cell cycle progression to mitosis, which is achieved essentially by downregulating M-phase cyclin CLB2 (3, 4). During functions of the active cell wall integrity checkpoint, cells accumulate at the small budded state. Budding starts as cells proceed to S phase; thus, the checkpoint must be activated during S phase to prevent later cell cycle events. However, the manner by which the regulatory signaling cascade induced by the cell wall integrity checkpoint feeds into the complex transcriptional network of cell cycle progression has not been fully investigated. Genome-wide transcriptional studies have revealed the transcription cascade of cell cycle-related transcription factors (6-9). Studies have shown major tr...

show abstract

“…The cell cycle is regulated by cyclins/CDK complexes, which sequentially activate and inhibit each other, creating a periodicity which is the clock of the cell. Recently sequential waves of transcriptional activation independent of cyclins activation have been discovered [42,43], but they are not taken into account in the model. It anyway serves as an ideal starting point for the the performance analysis of our analysis, since the data generating network is explicitly known and can be compared to our inferred regulatory interactions.…”

Section: Resultsmentioning

confidence: 99%

Inference of sparse combinatorial-control networks from gene-expression data: a message passing approach

et al. 2010

View full text Add to dashboard Cite

BackgroundTranscriptional gene regulation is one of the most important mechanisms in controlling many essential cellular processes, including cell development, cell-cycle control, and the cellular response to variations in environmental conditions. Genes are regulated by transcription factors and other genes/proteins via a complex interconnection network. Such regulatory links may be predicted using microarray expression data, but most regulation models suppose transcription factor independence, which leads to spurious links when many genes have highly correlated expression levels.ResultsWe propose a new algorithm to infer combinatorial control networks from gene-expression data. Based on a simple model of combinatorial gene regulation, it includes a message-passing approach which avoids explicit sampling over putative gene-regulatory networks. This algorithm is shown to recover the structure of a simple artificial cell-cycle network model for baker's yeast. It is then applied to a large-scale yeast gene expression dataset in order to identify combinatorial regulations, and to a data set of direct medical interest, namely the Pleiotropic Drug Resistance (PDR) network.ConclusionsThe algorithm we designed is able to recover biologically meaningful interactions, as shown by recent experimental results [1]. Moreover, new cases of combinatorial control are predicted, showing how simple models taking this phenomenon into account can lead to informative predictions and allow to extract more putative regulatory interactions from microarray databases.

show abstract

Transcription network and cyclin/CDKs: The yin and yang of cell cycle oscillators

Cited by 40 publications

References 36 publications

Cell Cycle and Cell Size Dependent Gene Expression Reveals Distinct Subpopulations at Single-Cell Level

Cell Cycle and Cell Size Dependent Gene Expression Reveals Distinct Subpopulations at Single-Cell Level

The Late S-Phase Transcription Factor Hcm1 Is Regulated through Phosphorylation by the Cell Wall Integrity Checkpoint

Inference of sparse combinatorial-control networks from gene-expression data: a message passing approach

Contact Info

Product

Resources

About