The effective application of a discrete transition model to explore cell-cycle regulation in yeast

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.

Ethanol is a cell stressor in yeast that dampen ethanol production. LncRNAs are RNAs that control many cellular processes. Computational simulations allow us to study the dynamism of cell systems. Therefore, we built a computational model of the yeast cell cycle to investigate how cells respond to ethanol stress. Simulations showed that ethanol stress or spindle damage arrests the cell cycle. Furthermore, the performance of higher and lower ethanol-tolerant strains in poststress recovery growth seems to be related to the cell cycle phase in which cells are stalled. However, two lncRNAs maintain the activity of the cell cycle even in yeast cells under these stresses by repressing specific cell cycle proteins. Finally, this model facilitates analyses of the yeast cell cycle for applied or basic science purposes.

A Note on GRegNetSim: A Tool for the Discrete Simulation and Analysis of Genetic Regulatory Networks

Discrete simulations of genetic regulatory networks were used to study subsystems of yeast successfully. However, implementations of existing models underlying these simulations do not support a graphic interface, and require computations necessary to analyze their results to be done manually. Furthermore, differences between existing models suggest that an enriched model, encompassing both existing models, is needed. We developed a software tool, GRegNetSim, that allows the end-user to describe genetic regulatory networks graphically. The user can specify various transition functions at different nodes of the network, supporting, for example, threshold and gradient effects, and then apply the network to a variety of inputs. GRegNetSim displays the relationship between the inputs and the mode of behavior of the network in a graphic form that is easy to interpret. Furthermore, it can automatically extract statistical data necessary to analyze the simulations. The discrete simulations performed by GRegNetSim can be used to elucidate and predict the behavior, structure and properties of genetic regulatory networks in a unified manner. GRegNetSim is implemented as a Cytoscape App. Installation files, examples and source code, along with a detailed user guide, are freely available at https://sites.google.com/site/gregnetsim/.

Interactions between the circadian clock and TGF-β signaling pathway in zebrafish

Background

TGF-β signaling is a cellular pathway that functions in most cells and has been shown to play a role in multiple processes, such as the immune response, cell differentiation and proliferation. Recent evidence suggests a possible interaction between TGF-β signaling and the molecular circadian oscillator. The current study aims to characterize this interaction in the zebrafish at the molecular and behavioral levels, taking advantage of the early development of a functional circadian clock and the availability of light-entrainable clock-containing cell lines.

Results

Smad3a, a TGF-β signaling-related gene, exhibited a circadian expression pattern throughout the brain of zebrafish larvae. Both pharmacological inhibition and indirect activation of TGF-β signaling in zebrafish Pac-2 cells caused a concentration dependent disruption of rhythmic promoter activity of the core clock gene Per1b. Inhibition of TGF-β signaling in intact zebrafish larvae caused a phase delay in the rhythmic expression of Per1b mRNA. TGF-β inhibition also reversibly disrupted, phase delayed and increased the period of circadian rhythms of locomotor activity in zebrafish larvae.

Conclusions

The current research provides evidence for an interaction between the TGF-β signaling pathway and the circadian clock system at the molecular and behavioral levels, and points to the importance of TGF-β signaling for normal circadian clock function. Future examination of this interaction should contribute to a better understanding of its underlying mechanisms and its influence on a variety of cellular processes including the cell cycle, with possible implications for cancer development and progression.

Advances and challenges in logical modeling of cell cycle regulation: perspective for multi-scale, integrative yeast cell models

The eukaryotic cell cycle is robustly designed, with interacting molecules organized within a definite topology that ensures temporal precision of its phase transitions. Its underlying dynamics are regulated by molecular switches, for which remarkable insights have been provided by genetic and molecular biology efforts. In a number of cases, this information has been made predictive, through computational models. These models have allowed for the identification of novel molecular mechanisms, later validated experimentally. Logical modeling represents one of the youngest approaches to address cell cycle regulation. We summarize the advances that this type of modeling has achieved to reproduce and predict cell cycle dynamics. Furthermore, we present the challenge that this type of modeling is now ready to tackle: its integration with intracellular networks, and its formalisms, to understand crosstalks underlying systems level properties, ultimate aim of multi-scale models. Specifically, we discuss and illustrate how such an integration may be realized, by integrating a minimal logical model of the cell cycle with a metabolic network.

BioNSi: A Discrete Biological Network Simulator Tool

Modeling and simulation of biological networks is an effective and widely used research methodology. The Biological Network Simulator (BioNSi) is a tool for modeling biological networks and simulating their discrete-time dynamics, implemented as a Cytoscape App. BioNSi includes a visual representation of the network that enables researchers to construct, set the parameters, and observe network behavior under various conditions. To construct a network instance in BioNSi, only partial, qualitative biological data suffices. The tool is aimed for use by experimental biologists and requires no prior computational or mathematical expertise. BioNSi is freely available at http://bionsi.wix.com/bionsi , where a complete user guide and a step-by-step manual can also be found.