Experimental testing of a new integrated model of the budding yeast S<scp>tart</scp>transition

Adames, Neil R.; Schuck, P. Logan; Chen, Katherine; Murali, T. M.; Tyson, John J.; Peccoud, Jean

doi:10.1091/mbc.e15-06-0358

Cited by 29 publications

(35 citation statements)

References 100 publications

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“…As expected, the cln1D strain exhibited a glycerol-specific large phenotype, whereas the cln2D strain exhibited a large phenotype on both glucose and glycerol medium ( Figure 5A). We observed only a subtle size increase for the mbp1D strain in glucose medium, in agreement with previous reports (Flick et al, 1998;Bean et al, 2005;Adames et al, 2015). In glycerol medium, mbp1D cells exhibited a large size phenotype when compared with the WT control (Figure 5A).…”

Section: Sbf and Mbf Concentrations Are Upregulated In Glycerol Mediumsupporting

confidence: 93%

G1/S Transcription Factor Copy Number Is a Growth-Dependent Determinant of Cell Cycle Commitment in Yeast

et al. 2018

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To understand how commitment to cell division in late G1 phase (Start) is controlled by growth and nutrients in budding yeast, we determined the absolute concentrations of the G1/S transcription factors SBF (composed of Swi4 and Swi6) and MBF (composed of Mbp1 and Swi6), the transcriptional repressor Whi5, and the G1 cyclins, Cln1 and Cln2, in single live yeast cells using scanning number and brightness (sN&B) microscopy. In rich medium, Whi5, Mbp1, and Swi6 concentrations were independent of cell size, whereas Swi4 concentration doubled in G1 phase, leading to a size-dependent decrease in the Whi5/Swi4 ratio. In small cells, SBF and MBF copy numbers were insufficient to saturate target G1/S promoters, but this restriction diminished as cells grew in size. In poor medium, SBF and MBF subunits, as well as Cln1, were elevated, consistent with a smaller cell size at Start. A mathematical model constrained by sN&B data suggested that size- and nutrient-dependent occupancy of G1/S promoters by SBF/MBF helps set the cell size threshold for Start activation.

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Section: Sbf and Mbf Concentrations Are Upregulated In Glycerol Mediumsupporting

confidence: 93%

G1/S Transcription Factor Copy Number Is a Growth-Dependent Determinant of Cell Cycle Commitment in Yeast

et al. 2018

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“…The Whi5-Nsr1-GFP chimera was present at a slightly lower concentration, 80-100nM ( Figure 6A), close to endogenous Nsr1 nuclear levels in nitrogenlimited media (60-80nM, see Figure 2C). This slight decrease in Whi5 abundance was unlikely to explain the small size of cells expressing Whi5-Nsr1-GFP since cell size is not strongly sensitive to WHI5 gene dosage (45). We confirmed that hemizygous WHI5/whi5 diploid cells were only marginally smaller than WT diploid cells ( Figure S5E) and WHI5 overexpression only causes a 20-30% increase in mode size ( Figure 3C), in agreement with previous results (45).…”

Section: Nsr1 Contributes To Bypass Whi5 Inhibition Of Sbfsupporting

confidence: 92%

A nitrogen source-regulated microprotein confers an alternative mechanism of G1/S transcriptional activation in budding yeast

Tollis

Singh

Thattikota

et al. 2020

Preprint

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Commitment to cell division at the end of G1 phase, termed Start in the budding yeast Saccharomyces cerevisiae, is strongly influenced by nutrient availability. To identify new dominant activators of Start that might operate under different nutrient conditions, we screened a genome-wide ORF overexpression library for genes that bypass a Start arrest caused by absence of the G1 cyclin Cln3 and the transcriptional activator Bck2. We recovered a hypothetical gene YLR053c, renamed NSR1 for Nitrogen-responsive Start Regulator 1, which encodes a poorly characterized 108 amino acid microprotein. Endogenous Nsr1 was nuclear-localized, restricted to poor nitrogen conditions, induced upon mTORC1 inhibition, and cell cycle-regulated with a peak at Start. NSR1 interacted genetically with SWI4 and SWI6, which encode the master G1/S transcription factor complex SBF. Correspondingly, Nsr1 physically interacted with Swi4 and Swi6and was localized to G1/S promoter DNA. Nsr1 exhibited inherent transactivation activity and fusion of Nsr1 to the SBF inhibitor Whi5 was sufficient to suppress other Start defects. Nsr1 appears to be a recently evolved microprotein that rewires the G1/S transcriptional machinery under poor nutrient conditions. Author SummaryUnicellular microorganisms must adapt to ever-changing nutrient conditions and hence must adjust cell growth and proliferation to maximize fitness. In the budding yeast Saccharomyces cerevisiae, commitment to cell division, termed Start, is heavily influenced by nutrient availability. The mechanisms of Start activation under conditions of nutrient limitation are less well characterized than under nutrient excess. To identify potential new Start regulators specific to poor nutrient environments, we screened for genes able to bypass a genetic Start arrest caused by loss of the G1 cyclin Cln3 and the transcriptional activator Bck2. This screen uncovered YLR053c, which we renamed NSR1 for Nitrogen-responsive Start Regulator. Sequence analysis across yeast species indicated that Nsr1 is a recently-evolved microprotein. We showed that NSR1 is nutrient-and cell cycle-regulated, and directly binds the main G1/S transcription factor complex SBF. We demonstrated that Nsr1 has an intrinsic trans-activation activity and provided genetic evidence to suggest that Nsr1 can bypass the requirement for normal Cln3-dependent 3 activation of SBF. These results uncover a new mechanism of Start activation and demonstrate how microproteins can rapidly emerge to rewire fundamental cellular processes.

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“…First, because of the central roles played by SBF, MBF and Cln3 in the START transition of the budding yeast cell cycle, we addressed our new results suggesting a viable phenotype for swi4D cln3D double-mutant cells in opposition to previous reports that swi4D cln3D is a synthetic lethal strain 33 . To 'rescue' swi4D cln3D cells, we significantly increased the activation of MBF (Swi6:Mbp1) by Bck2 (the only activator of MBF in the absence of Cln3), while simultaneously increasing the inactivation of MBF by Clb2 and decreasing slightly the activation of MBF by Cln3, in order to keep the level of MBF activity similar to that of the previous model, thus minimizing the perturbations to all other mutants that were previously explained by the model.…”

Section: Comparing the Results Of Our Screen With Previously Reportedmentioning

confidence: 90%

“…Ideally, one should combine top-down and bottom-up data, but huge discrepancies of scale between these two data types present barriers to integrating and understanding the hypotheses derived from each approach 11-17 .To mitigate these problems, many researchers, including ourselves, have developed detailed mathematical models that integrate top-down and bottom-up approaches in order to describe the molecular mechanisms that underlie cell cycle regulation in budding yeast 4, 17-22 . The governing equations of the model are simulated on a computer, and the model (the 'wiring diagram' of molecular interactions) is adjusted until it generates dynamic behaviors that reflect the documented molecular changes and general network behaviors observed in cells (e.g., cell viability, timing of cell cycle events, cell size at birth, response to DNA damage or chromosome misalignment at mitosis) [23][24][25][26] . Often, the documented data is missing detailed molecular information, such as protein concentrations and rate constants of crucial reactions, but fitting the model (i.e., fine-tuning the parameter values) to extensive sets of phenotypic data usually introduces strong constraints on these unknown parameters 19, 27 .…”

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confidence: 99%

Genetic interactions derived from high-throughput phenotyping of 7,350 yeast cell cycle mutants

Gallegos

Adames²,

Rogers³

et al. 2019

Preprint

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Over the last 30 years, computational biologists have developed increasingly realistic mathematical models of the regulatory networks controlling the division of eukaryotic cells. These models capture data resulting from two complementary experimental approaches: lowthroughput experiments aimed at extensively characterizing the functions of small numbers of genes, and large-scale genetic interaction screens that provide a systems-level perspective on the cell division process. The former is insufficient to capture the interconnectivity of the genetic control network, while the latter is fraught with irreproducibility issues. Here, we describe a hybrid approach in which the genetic interactions between 36 cell-cycle genes are quantitatively estimated by high-throughput phenotyping with an unprecedented number of biological replicates. Using this approach, we identify a subset of high-confidence genetic interactions, which we use to refine a previously published mathematical model of the cell cycle. We also present a quantitative dataset of the growth rate of these mutants under six different media conditions in order to inform future cell cycle models. Author SummaryThe process of cell division, also called the cell cycle, is controlled by a highly complex network of interconnected genes. If this process goes awry, diseases such as cancer can result. In order to unravel the complex interactions within the cell cycle control network, computational biologists have developed mathematical models that describe how different cell cycle genes are related. These models are built using large datasets describing the effect of mutating one or more genes within the network. In this manuscript, we present a novel method for producing such datasets. Using our method, we generate 7,350 yeast mutants to explore the interactions between key cell cycle genes. We measure the effect of the mutations by monitoring the growth rate of the yeast mutants under different environmental conditions. We use our mutants to revise an existing model of the yeast cell cycle and present a dataset of ~44,000 gene by environment combinations as a resource to the yeast genetics and modeling communities.Eukaryotic cells grow and divide using a highly conserved and integrated network of positive and negative controls that ensure genomic integrity and maintain cell size within reasonable bounds. Proper control of the cell division cycle is essential for competitive fitness, embryonic development and maturation, and tissue homeostasis. Failure in these control mechanisms may result in cell death, developmental defects, tissue dysplasia, or cancers. One of the foremost model organisms for unraveling the molecular mechanisms of cell cycle control is the budding yeast Saccharomyces cerevisiae. Several hundred yeast mutants, generated in dozens of research laboratories over the past 40 years, have led to the discovery and characterization of many genes and proteins that regulate progression through the cell cycle 1 . Because of the intense labor involved in t...

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Experimental testing of a new integrated model of the budding yeast Starttransition

Cited by 29 publications

References 100 publications

G1/S Transcription Factor Copy Number Is a Growth-Dependent Determinant of Cell Cycle Commitment in Yeast

G1/S Transcription Factor Copy Number Is a Growth-Dependent Determinant of Cell Cycle Commitment in Yeast

A nitrogen source-regulated microprotein confers an alternative mechanism of G1/S transcriptional activation in budding yeast

Genetic interactions derived from high-throughput phenotyping of 7,350 yeast cell cycle mutants

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