Yeast Cell Cycle Research Articles

A continuous-time stochastic Boolean model provides a quantitative description of the budding yeast cell cycle

The cell division cycle is regulated by a complex network of interacting genes and proteins. The control system has been modeled in many ways, from qualitative Boolean switching-networks to quantitative differential equations and highly detailed stochastic simulations. Here we develop a continuous-time stochastic model using seven Boolean variables to represent the activities of major regulators of the budding yeast cell cycle plus one continuous variable representing cell growth. The Boolean variables are updated asynchronously by logical rules based on known biochemistry of the cell-cycle control system using Gillespie’s stochastic simulation algorithm. Time and cell size are updated continuously. By simulating a population of yeast cells, we calculate statistical properties of cell cycle progression that can be compared directly to experimental measurements. Perturbations of the normal sequence of events indicate that the cell cycle is 91% robust to random ‘flips’ of the Boolean variables, but 9% of the perturbations induce lethal mistakes in cell cycle progression. This simple, hybrid Boolean model gives a good account of the growth and division of budding yeast cells, suggesting that this modeling approach may be as accurate as detailed reaction-kinetic modeling with considerably less demands on estimating rate constants.

TORC1 and PKA activity towards ribosome biogenesis oscillates in synchrony with the budding yeast cell cycle.

Recent studies have revealed that the growth rate of budding yeast and mammalian cells varies during the cell cycle. By linking a multitude of signals to cell growth, the highly conserved target of rapamycin complex 1 (TORC1) and protein kinase A (PKA) pathways are prime candidates for mediating the dynamic coupling between growth and division. However, measurements of TORC1 and PKA activity during the cell cycle are still lacking. By following the localization dynamics of two TORC1 and PKA targets via time-lapse microscopy in hundreds of yeast (Saccharomyces cerevisiae) cells, we found that the activity of these pathways towards ribosome biogenesis fluctuates in synchrony with the cell cycle even under constant external conditions. Analysis of the effects of mutations of upstream TORC1 and PKA regulators suggests that internal metabolic signals partially mediate these activity changes. Our study reveals a new aspect of TORC1 and PKA signaling, which will be important for understanding growth regulation during the cell cycle.

Flow cytometric resolution of yeast is affected by enzymatic treatment and culture media.

Saccharomyces cerevisiae is an important model organism and a typical fungal representative for studies of eukaryotes. The cell cycle of yeast can be analyzed by flow cytometry, and refining the cytometric resolution of the cell cycle with this technique is important. Such refinement is potentially influenced by multiple factors, including enzymatic treatment and variations in the culture media used, although this has been subject to only limited investigation. Here, we examined the effect of different enzymatic pre‐treatments and various media on cytometric resolution. We show that cytometric resolution is significantly altered by both enzymatic conditions and the media used. Culture media with different amino nitrogen concentrations potentially impact the protein content in the yeast cell wall, which may affect the permeability of the cell wall and alter cytometric resolution. The present study provides beneficial technical information about the influence of media and enzymes on the cytometric resolution of the yeast cell cycle and most likely other fungi, which should be considered in future research.

High-resolution mass measurements of single budding yeast reveal linear growth segments

The regulation of cell growth has fundamental physiological, biotechnological and medical implications. However, methods that can continuously monitor individual cells at sufficient mass and time resolution hardly exist. Particularly, detecting the mass of individual microbial cells, which are much smaller than mammalian cells, remains challenging. Here, we modify a previously described cell balance (‘picobalance’) to monitor the proliferation of single cells of the budding yeast, Saccharomyces cerevisiae, under culture conditions in real time. Combined with optical microscopy to monitor the yeast morphology and cell cycle phase, the picobalance approaches a total mass resolution of 0.45 pg. Our results show that single budding yeast cells (S/G2/M phase) increase total mass in multiple linear segments sequentially, switching their growth rates. The growth rates weakly correlate with the cell mass of the growth segments, and the duration of each growth segment correlates negatively with cell mass. We envision that our technology will be useful for direct, accurate monitoring of the growth of single cells throughout their cycle.

A nonlinear correlation measure with applications to gene expression data.

Nonlinear correlation exists in many types of biomedical data. Several types of pairwise gene expression in humans and other organisms show nonlinear correlation across time, e.g., genes involved in human T helper (Th17) cells differentiation, which motivated this study. The proposed procedure, called Kernelized correlation (Kc), first transforms nonlinear data on the plane via a function (kernel, usually nonlinear) to a high-dimensional (Hilbert) space. Next, we plug the transformed data into a classical correlation coefficient, e.g., Pearson's correlation coefficient (r), to yield a nonlinear correlation measure. The algorithm to compute Kc is developed and the R code is provided online. In three simulated nonlinear cases, when noise in data is moderate, Kc with the RBF kernel (Kc-RBF) outperforms Pearson's r and the well-known distance correlation (dCor). However, when noise in data is low, Pearson's r and dCor perform slightly better than (equivalently to) Kc-RBF in Case 1 and 3 (in Case 2); Kendall's tau performs worse than the aforementioned measures in all cases. In Application 1 to discover genes involved in the early Th17 cell differentiation, Kc is shown to detect the nonlinear correlations of four genes with IL17A (a known marker gene), while dCor detects nonlinear correlations of two pairs, and DESeq fails in all these pairs. Next, Kc outperforms Pearson's and dCor, in estimating the nonlinear correlation of negatively correlated gene pairs in yeast cell cycle regulation. In conclusion, Kc is a simple and competent procedure to measure pairwise nonlinear correlations.

A yeast cell cycle model integrating stress, signaling, and physiology.

The cell division cycle in eukaryotic cells is a series of highly coordinated molecular interactions that ensure that cell growth, duplication of genetic material, and actual cell division are precisely orchestrated to give rise to two viable progeny cells. Moreover, the cell cycle machinery is responsible for incorporating information about external cues or internal processes that the cell must keep track of to ensure a coordinated, timely progression of all related processes. This is most pronounced in multicellular organisms, but also a cardinal feature in model organisms such as baker's yeast. The complex and integrative behavior is difficult to grasp and requires mathematical modeling to fully understand the quantitative interplay of the single components within the entire system. Here, we present a self-oscillating mathematical model of the yeast cell cycle that comprises all major cyclins and their main regulators. Furthermore, it accounts for the regulation of the cell cycle machinery by a series of external stimuli such as mating pheromones and changes in osmotic pressure or nutrient quality. We demonstrate how the external perturbations modify the dynamics of cell cycle components and how the cell cycle resumes after adaptation to or relief from stress.

LncRNAs of Saccharomyces cerevisiae bypass the cell cycle arrest imposed by ethanol stress

Ethanol alters many subsystems of Saccharomyces cerevisiae, including the cell cycle. Two ethanol-responsive lncRNAs in yeast interact with cell cycle proteins, and here, we investigated the role of these RNAs in cell cycle. Our network dynamic modeling showed that higher and lower ethanol-tolerant strains undergo cell cycle arrest in mitosis and G1 phases, respectively, during ethanol stress. The higher population rebound of the lower ethanol-tolerant phenotype after stress relief responds to the late phase arrest. We found that the lncRNA lnc9136 of SEY6210 (a lower ethanol-tolerant strain) induces cells to skip mitosis arrest. Simulating an overexpression of lnc9136 and analyzing CRISPR–Cas9 mutants lacking this lncRNA suggest that lnc9136 induces a regular cell cycle even under ethanol stress, indirectly regulating Swe1p and Clb1/2 by binding to Gin4p and Hsl1p. Notably, lnc10883 of BY4742 (a higher ethanol-tolerant strain) does not prevent G1 arrest in this strain under ethanol stress. However, lnc19883 circumvents DNA and spindle damage checkpoints, maintaining a functional cell cycle by interacting with Mec1p or Bub1p even in the presence of DNA/spindle damage. Overall, we present the first evidence of direct roles for lncRNAs in regulating yeast cell cycle proteins, the dynamics of this system in different ethanol-tolerant phenotypes, and a new yeast cell cycle model.

Dynamical modeling for non-Gaussian data with high-dimensional sparse ordinary differential equations

Ordinary differential equations (ODE) have been widely used for modeling dynamical complex systems. For high-dimensional ODE models where the number of differential equations is large, it remains challenging to estimate the ODE parameters and to identify the sparse structure of the ODE models. Most existing methods exploit the least-square based approach and are only applicable to Gaussian observations. However, as discrete data are ubiquitous in applications, it is of practical importance to develop dynamic modeling for non-Gaussian observations. New methods and algorithms are developed for both parameter estimation and sparse structure identification in high-dimensional linear ODE systems. First, the high-dimensional generalized profiling method is proposed as a likelihood-based approach with ODE fidelity and sparsity-inducing regularization, along with efficient computation based on parameter cascading. Second, two versions of the two-step collocation methods are extended to the non-Gaussian set-up by incorporating the iteratively reweighted least squares technique. Simulations show that the profiling procedure has excellent performance in latent process and derivative fitting and ODE parameter estimation, while the two-step collocation approach excels in identifying the sparse structure of the ODE system. The usefulness of the proposed methods is also demonstrated by analyzing three real datasets from Google trends, stock market sectors, and yeast cell cycle studies.

Mitochondrial transport, partitioning, and quality control at the heart of cell proliferation and fate acquisition.

Mitochondria are essential to cell homeostasis, and alterations in mitochondrial distribution, segregation, and turnover have been linked to complex pathologies such as neurodegenerative diseases and cancer. Understanding how these functions are coordinated in specific cell types is a major challenge to discover how mitochondria globally shape cell functionality. In this review, we first describe how mitochondrial transport and dynamics are regulated throughout the cell cycle in yeast and in mammals. Second, we explore the functional consequences of mitochondrial transport and partitioning on cell proliferation, fate acquisition, and stemness and on the way cells adapt their metabolism. Finally, we focus on how mitochondrial clearance programs represent a further layer of complexity for cell differentiation or in the maintenance of stemness. Defining how mitochondrial transport, dynamics, and clearance are mutually orchestrated in specific cell types may help our understanding of how cells can transition from a physiological to a pathological state.

Unveiling Forkhead-mediated regulation of yeast cell cycle and metabolic networks

Transcription factors are regulators of the cell’s genomic landscape. By switching single genes or entire molecular pathways on or off, transcription factors modulate the precise timing of their activation. The Forkhead (Fkh) transcription factors are evolutionarily conserved to regulate organismal physiology and cell division. In addition to molecular biology and biochemical efforts, genome-wide studies have been conducted to characterize the genomic landscape potentially regulated by Forkheads in eukaryotes. Here, we discuss and interpret findings reported in six genome-wide Chromatin ImmunoPrecipitation (ChIP) studies, with a particular focus on ChIP-chip and ChIP-exo. We highlight their power and challenges to address Forkhead-mediated regulation of the cellular landscape in budding yeast. Expression changes of the targets identified in the binding assays are investigated by taking expression data for Forkhead deletion and overexpression into account. Forkheads are revealed as regulators of the metabolic network through which cell cycle dynamics may be temporally coordinated further, in addition to their well-known role as regulators of the gene cluster responsible for cell division.

A peculiar cell cycle arrest at g2/m stage during the stationary phase of growth in the wine yeast Hanseniaspora vineae.

Yeasts of the genus Hanseniaspora gained notoriety in the last years due to their contribution to wine quality, and their loss of several genes, mainly related to DNA repair and cell cycle processes. Based on genomic data from many members of this genus, they have been classified in two well defined clades: the "faster-evolving linage" (FEL) and the "slower-evolving lineage" (SEL). In this context, we had detected that H. vineae exhibited a rapid loss of cell viability in some conditions during the stationary phase compared to H. uvarum and S. cerevisiae. The present work aimed to evaluate the viability and cell cycle progression of representatives of Hanseniaspora species along their growth in an aerobic and discontinuous system. Cell growth, viability and DNA content were determined by turbidity, Trypan Blue staining, and flow cytometry, respectively. Results showed that H. uvarum and H. opuntiae (representing FEL group), and H. osmophila (SEL group) exhibited a typical G1/G0 (1C DNA) arrest during the stationary phase, as S. cerevisiae. Conversely, the three strains studied here of H. vineae (SEL group) arrested at G2/M stages of cell cycle (2C DNA), and lost viability rapidly when enter the stationary phase. These results showed that H. vineae have a unique cell cycle behavior that will contribute as a new eukaryotic model for future studies of genetic determinants of yeast cell cycle control and progression.

Robust approach for variable selection with high dimensional longitudinal data analysis.

This article proposes a new robust smooth-threshold estimating equation to select important variables and automatically estimate parameters for high dimensional longitudinal data. A novel working correlation matrix is proposed to capture correlations within the same subject. The proposed procedure works well when the number of covariates increases as the number of subjects n increases. The proposed estimates are competitive with the estimates obtained with the true correlation structure, especially when the data are contaminated. Moreover, the proposed method is robust against outliers in the response variables and/or covariates. Furthermore, the oracle properties for robust smooth-threshold estimating equations under "large n, diverging " are established under some regularity conditions. Extensive simulation studies and a yeast cell cycle data are used to evaluate the performance of the proposed method, and results show that the proposed method is competitive with existing robust variable selection procedures.

Boolean factor graph model for biological systems: the yeast cell-cycle network

BackgroundThe desire to understand genomic functions and the behavior of complex gene regulatory networks has recently been a major research focus in systems biology. As a result, a plethora of computational and modeling tools have been proposed to identify and infer interactions among biological entities. Here, we consider the general question of the effect of perturbation on the global dynamical network behavior as well as error propagation in biological networks to incite research pertaining to intervention strategies.ResultsThis paper introduces a computational framework that combines the formulation of Boolean networks and factor graphs to explore the global dynamical features of biological systems. A message-passing algorithm is proposed for this formalism to evolve network states as messages in the graph. In addition, the mathematical formulation allows us to describe the dynamics and behavior of error propagation in gene regulatory networks by conducting a density evolution (DE) analysis. The model is applied to assess the network state progression and the impact of gene deletion in the budding yeast cell cycle. Simulation results show that our model predictions match published experimental data. Also, our findings reveal that the sample yeast cell-cycle network is not only robust but also consistent with real high-throughput expression data. Finally, our DE analysis serves as a tool to find the optimal values of network parameters for resilience against perturbations, especially in the inference of genetic graphs.ConclusionOur computational framework provides a useful graphical model and analytical tools to study biological networks. It can be a powerful tool to predict the consequences of gene deletions before conducting wet bench experiments because it proves to be a quick route to predicting biologically relevant dynamic properties without tunable kinetic parameters.

Cell Length Growth in the Fission Yeast Cell Cycle: Is It (Bi)linear or (Bi)exponential?

Fission yeast is commonly used as a model organism in eukaryotic cell growth studies. To describe the cells’ length growth patterns during the mitotic cycle, different models have been proposed previously as linear, exponential, bilinear and biexponential ones. The task of discriminating among these patterns is still challenging. Here, we have analyzed 298 individual cells altogether, namely from three different steady-state cultures (wild-type, wee1-50 mutant and pom1Δ mutant). We have concluded that in 190 cases (63.8%) the bilinear model was more adequate than either the linear or the exponential ones. These 190 cells were further examined by separately analyzing the linear segments of the best fitted bilinear models. Linear and exponential functions have been fitted to these growth segments to determine whether the previously fitted bilinear functions were really correct. The majority of these growth segments were found to be linear; nonetheless, a significant number of exponential ones were also detected. However, exponential ones occurred mainly in cases of rather short segments (<40 min), where there were not enough data for an accurate model fitting. By contrast, in long enough growth segments (≥40 min), linear patterns highly dominated over exponential ones, verifying that overall growth is probably bilinear.

Forced apart: a microtubule-based mechanism for equidistant positioning of multiple nuclei in single cells

Cells can position multiple copies of components like carboxysomes, nucleoids, and nuclei at regular intervals. By controlling positions, cells, for example, ensure equal partitioning of organelles over daughter cells and, in the case of nuclei, control cell sizes during cellularization. Mechanisms that generate regular patterns are as yet poorly understood. We used fission yeast cell cycle mutants to investigate the dispersion of multiple nuclei by microtubule-generated forces in single cells. After removing internuclear attractive forces by microtubule-based molecular motors, we observed the establishment of regular patterns of nuclei. Based on live-cell imaging, we hypothesized that microtubule growth within internuclear spaces pushes neighbouring nuclei apart. In the proposed mechanism, which was validated by stochastic simulations, the repulsive force weakens with increasing separation because stochastic shortening events limit the extent over which microtubules generate forces. Our results, therefore, demonstrate how cells can exploit the dynamics of microtubule growth for the equidistant positioning of organelles.

Using Budding Yeast to Identify Molecules That Block Cancer Cell 'Mitotic Slippage' Only in the Presence of Mitotic Poisons.

Research on the budding yeast Saccharomyces cerevisiae has yielded fundamental discoveries on highly conserved biological pathways and yeast remains the best-studied eukaryotic cell in the world. Studies on the mitotic cell cycle and the discovery of cell cycle checkpoints in budding yeast has led to a detailed, although incomplete, understanding of eukaryotic cell cycle progression. In multicellular eukaryotic organisms, uncontrolled aberrant cell division is the defining feature of cancer. Some of the most successful classes of anti-cancer chemotherapeutic agents are mitotic poisons. Mitotic poisons are thought to function by inducing a mitotic spindle checkpoint-dependent cell cycle arrest, via the assembly of the highly conserved mitotic checkpoint complex (MCC), leading to apoptosis. Even in the presence of mitotic poisons, some cancer cells continue cell division via ‘mitotic slippage’, which may correlate with a cancer becoming refractory to mitotic poison chemotherapeutic treatments. In this review, knowledge about budding yeast cell cycle control is explored to suggest novel potential drug targets, namely, specific regions in the highly conserved anaphase-promoting complex/cyclosome (APC/C) subunits Apc1 and/or Apc5, and in a specific N-terminal region in the APC/C co-factor cell division cycle 20 (Cdc20), which may yield molecules which block ‘mitotic slippage’ only in the presence of mitotic poisons.

Predicting with the quantify intensities of transcription factor-target genes binding using random forest technique

With the rapid development of technology, this development led to the emergence of microarray technology. It has the effect of studying the levels of gene expression in a way that makes it easier for researchers to observe the expression levels of millions of genes at the same time in a single experiment. Development also helped in the emergence of powerful tools to identify interactions between target genes and regulatory factors. The main aim of this study is to build models to predicate the relationship (Interaction) between Transcription Factors (TFs) proteins and target genes by selecting the subset of important genes (Relevant genes) from original dataset. The proposed methodology comprises into three major stages: the genes selection, merge datasets and the prediction stage. The process of reducing the computational space of gene data has been accomplished by using proposed mutual information method for genes selection based on the data of gene expression. In the prediction, the proposed prediction regression techniques are utilized to predict with binding rate between single TF-target gene. It has been compared the efficiency of two different proposed regression techniques including: Linear Regression and Random Forest Regression. Two available data sets have been utilized to achieve the objectives of this study: Gene’s expression data of Yeast Cell Cycle dataset and Transcription Factors dataset. The evaluation of predictions performance has been performed depending on two performance prediction measures (Root Mean Square Error (RMSE) and Mean Absolute Error (MAE) with (10) Folds-Cross Validation.

Introducing the novel Cytoscape app TimeNexus to analyze time-series data using temporal MultiLayer Networks (tMLNs)

Integrating -omics data with biological networks such as protein–protein interaction networks is a popular and useful approach to interpret expression changes of genes in changing conditions, and to identify relevant cellular pathways, active subnetworks or network communities. Yet, most -omics data integration tools are restricted to static networks and therefore cannot easily be used for analyzing time-series data. Determining regulations or exploring the network structure over time requires time-dependent networks which incorporate time as one component in their structure. Here, we present a method to project time-series data on sequential layers of a multilayer network, thus creating a temporal multilayer network (tMLN). We implemented this method as a Cytoscape app we named TimeNexus. TimeNexus allows to easily create, manage and visualize temporal multilayer networks starting from a combination of node and edge tables carrying the information on the temporal network structure. To allow further analysis of the tMLN, TimeNexus creates and passes on regular Cytoscape networks in form of static versions of the tMLN in three different ways: (i) over the entire set of layers, (ii) over two consecutive layers at a time, (iii) or on one single layer at a time. We combined TimeNexus with the Cytoscape apps PathLinker and AnatApp/ANAT to extract active subnetworks from tMLNs. To test the usability of our app, we applied TimeNexus together with PathLinker or ANAT on temporal expression data of the yeast cell cycle and were able to identify active subnetworks relevant for different cell cycle phases. We furthermore used TimeNexus on our own temporal expression data from a mouse pain assay inducing hindpaw inflammation and detected active subnetworks relevant for an inflammatory response to injury, including immune response, cell stress response and regulation of apoptosis. TimeNexus is freely available from the Cytoscape app store at https://apps.cytoscape.org/apps/TimeNexus.

A cytofluorimetric analysis of a Saccharomyces cerevisiae population cultured in a fed-batch bioreactor.

The yeast Saccharomyces cerevisiae is a reference model system and one of the widely used microorganisms in many biotechnological processes. In industrial yeast applications, combined strategies aim to maximize biomass/product yield, with the fed-batch culture being one of the most frequently used. Flow cytometry (FCM) is widely applied in biotechnological processes and represents a key methodology to monitor cell population dynamics. We propose here an application of FCM in the analysis of yeast cell cycle along the time course of a typical S. cerevisiae fed-batch culture. We used two different dyes, SYTOX Green and SYBR Green, with the aim to better define each stage of cell cycle during S. cerevisiae fed-batch culture. The results provide novel insights in the use of FCM cell cycle analysis for the real-time monitoring of S. cerevisiae bioprocesses.

Essential Oils of Rosmarinus officinalis L., Cymbopogon citratus (DC.) Stapf., and the phyto-compounds, delta-carene and alpha-pinene mediate cell cycle arrest at G2/M transition in budding yeast Saccharomyces cerevisiae

Ethnopharmacological relevance: Rosmarinus officinalis L. (Lamiaceae) is widely used in folk and traditional medicine for inflammation-related diseases due to its anti-oxidant and wound healing properties. Cymbopogon citratus (DC.) Stapf. is a widely distributed perennial herb belonging to the Poaceae family and has been extensively consumed for its traditional and ethnomedicinal, cosmetic, and nutritional effects for centuries.Aim of the study: Budding yeast is a valuable system to study cell-cycle regulation, which is the underlying defect in cancer cells. The aim of the present study was to characterize the antiproliferative effect of essential oil of Rosmarinus officinalis L. (rosemary) and Cymbopogon citratus (DC.) Stapf. (lemon grass) and their effect on cell cycle of Saccharomyces cerevisiae.Materials and methods: Rosmarinus officinalis L. and Cymbopogon citratus (DC.) Stapf. essential oil was extracted from leaves and characterized by GC–MS, TLC and FTIR. The antiproliferative activity of essential oils and their major compounds, delta-carene and α-pinene was studied by liquid growth assay. Microscopic examination of yeast cells was done to determine the budding index. Flowcytometry analysis of DNA content was analyzed to confirm the G2/M arrest of cell cycle of budding yeast.Results: Both the essential oils mediated a dose-dependent inhibition of yeast growth in liquid assays. Analysis of budding index and DNA content by flowcytometry revealed that R. officinalis L. and C. citratus (DC.) Stapf. essential oils mediate the arrest of yeast cells at G2/M phase of cell cycle. GC/MS analysis of R. officinalis oil revealed the presence of two major compounds, delta-carene (7.75%), and α-pinene (15.1%). Both delta-carene and α-pinene showed antiproliferative activity (92% and 95% of growth inhibition, respectively) and caused the arrest of yeast cell cycle at G2/M phase.Conclusions: The essential oils of R. officinalis and C. citratus, delta-carene and α-pinene can be validated in animal model for cell cycle inhibition and cancer therapeutics.