Reconciling conflicting models for global control of cell-cycle transcription

Cho, Chun-Yi; Motta, Francis C.; Kelliher, Christina M.; Deckard, Anastasia; Haase, Steven B.

doi:10.1080/15384101.2017.1367073

Cited by 17 publications

(25 citation statements)

References 78 publications

(214 reference statements)

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“…Cell cycle-arrested budding yeast lacking multiple mitotic and S-phase cyclins continued to show periodic activation of subsequent G1-specific events that were essential for cell-cycle progression (Haase and Reed, 1999). Comprehensive microarray studies of budding yeast in aerobic continuous culture revealed that more than half of genes in the budding yeast genome are expressed periodically, in accordance with respiratory oscillations (Spellman et al, 1998; Klevecz et al, 2004; Tu et al, 2005; Cho et al, 2017) and with cell-cycle progression (Cho et al, 1998; Spellman et al, 1998; Klevecz et al, 2004; Tu et al, 2005). These results are consistent with a model in which genome-wide transcriptional oscillation is independent of the CDK oscillator and coupled with other oscillators.…”

Section: Building a Unified Model Of The Quantitative Studies For Celmentioning

confidence: 99%

Quantitative Studies for Cell-Division Cycle Control

Arata

Takagi

2019

Front. Physiol.

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The cell-division cycle (CDC) is driven by cyclin-dependent kinases (CDKs). Mathematical models based on molecular networks, as revealed by molecular and genetic studies, have reproduced the oscillatory behavior of CDK activity. Thus, one basic system for representing the CDC is a biochemical oscillator (CDK oscillator). However, genetically clonal cells divide with marked variability in their total duration of a single CDC round, exhibiting non-Gaussian statistical distributions. Therefore, the CDK oscillator model does not account for the statistical nature of cell-cycle control. Herein, we review quantitative studies of the statistical properties of the CDC. Over the past 70 years, studies have shown that the CDC is driven by a cluster of molecular oscillators. The CDK oscillator is coupled to transcriptional and mitochondrial metabolic oscillators, which cause deterministic chaotic dynamics for the CDC. Recent studies in animal embryos have raised the possibility that the dynamics of molecular oscillators underlying CDC control are affected by allometric volume scaling among the cellular compartments. Considering these studies, we discuss the idea that a cluster of molecular oscillators embedded in different cellular compartments coordinates cellular physiology and geometry for successful cell divisions.

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Section: Building a Unified Model Of The Quantitative Studies For Celmentioning

confidence: 99%

Quantitative Studies for Cell-Division Cycle Control

Arata

Takagi

2019

Front. Physiol.

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“…Of these, ChIP, Deletion, and PWM1 have significantly fewer TF-target interactions with each other than expected categories by Spellman et al [63]. Although multiple transcriptome studies of the yeast cell cycle have been characterized since, we use the Spellman et al definition because it provides a clear distinction between the phases of the cell cycles which remains in common use [10,12,21,28,51,54,59,60]. The Spellman definition of cell-cycle genes includes five phases of expression, G1, S, S/G2, G2/M, and M/G1, consisting of 71-300 genes based on the timing of peak expression that corresponds to different cell cycle phases ( Fig.…”

Section: Recovering Phase-specific Expression During S Cerevisiae Cementioning

confidence: 99%

Improved recovery of cell-cycle gene expression in Saccharomyces cerevisiae from regulatory interactions in multiple omics data

2020

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Background: Gene expression is regulated by DNA-binding transcription factors (TFs). Together with their target genes, these factors and their interactions collectively form a gene regulatory network (GRN), which is responsible for producing patterns of transcription, including cyclical processes such as genome replication and cell division. However, identifying how this network regulates the timing of these patterns, including important interactions and regulatory motifs, remains a challenging task. Results: We employed four in vivo and in vitro regulatory data sets to investigate the regulatory basis of expression timing and phase-specific patterns cell-cycle expression in Saccharomyces cerevisiae. Specifically, we considered interactions based on direct binding between TF and target gene, indirect effects of TF deletion on gene expression, and computational inference. We found that the source of regulatory information significantly impacts the accuracy and completeness of recovering known cell-cycle expressed genes. The best approach involved combining TF-target and TF-TF interactions features from multiple datasets in a single model. In addition, TFs important to multiple phases of cell-cycle expression also have the greatest impact on individual phases. Important TFs regulating a cell-cycle phase also tend to form modules in the GRN, including two sub-modules composed entirely of unannotated cell-cycle regulators (STE12-TEC1 and RAP1-HAP1-MSN4). Conclusion: Our findings illustrate the importance of integrating both multiple omics data and regulatory motifs in order to understand the significance regulatory interactions involved in timing gene expression. This integrated approached allowed us to recover both known cell-cycles interactions and the overall pattern of phase-specific expression across the cell-cycle better than any single data set. Likewise, by looking at regulatory motifs in the form of TF-TF interactions, we identified sets of TFs whose co-regulation of target genes was important for cell-cycle expression, even when regulation by individual TFs was not. Overall, this demonstrates the power of integrating multiple data sets and models of interaction in order to understand the regulatory basis of established biological processes and their associated gene regulatory networks.

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“…1, center). 2 Tests of these models have aimed to block oscillations in CDK activity and cell-cycle progression by holding cells at either low CDK activity 3,4 or high CDK activity 5,6 , followed by examining the effects on cell cycle transcription. Surprisingly, blocks at either low or high CDK activity failed to impede certain transcriptional oscillations, and demonstrated that much of the periodic transcriptional program could be uncoupled from cell-cycle progression.…”

mentioning

confidence: 99%

“…To address this contradiction directly, Cho et al analyzed data published by both groups. 6 Even with distinctions in experimental approach and variations in the engineered yeast strains, the two groups provide remarkably reproducible and complementary data. While it is tempting to speculate that subtle variations appeared due to differences in strains, experimental protocol, or quantitative analysis, Cho et al provide compelling explanations for variations and provide a new set of experimental evidence underscoring a model in which CDKs are part of a transcriptional oscillator, but that periodic input from CDK is not required to drive global transcriptional oscillations.…”

mentioning

confidence: 99%