A steady-state modeling approach to validate an in vivo mechanism of the GAL regulatory network in Saccharomyces cerevisiae

Positive Effect of Green Photo-Selective Filter on Graft Union Formation in Tomatoes

This study investigated the effects of green and red photo-selective filters (shade nets) on the process of graft union formation (healing and acclimation) in grafted tomato plants. The research evaluated oxidative stress, physiological characteristics, and anatomical development of graft unions. Plants were subjected to green-netting, red-netting, and no-netting treatments for 28 days, starting 4 days after grafting. Markers of oxidative stress, including reactive oxygen species (ROS), superoxide dismutase (SOD), peroxidase (POD), and malondialdehyde (MDA), as well as protein concentration of SOD/POD enzyme-enriched extracts, were quantified. The anatomical development of the graft unions was examined using microscopy. The results demonstrated that the red and green photo-selective filters increased ROS production by 5% and 4% after 3 days of exposure, by 58% and 14% after 7 days, and by 30% and 13% after 14 days in comparison to the control treatment. The increase in ROS activates the defense mechanism, enhancing the activity of SOD and POD enzymes. In terms of anatomy, the green netting resulted in enhanced cell proliferation and early differentiation of vascular tissue cells. Notably, at the 28-day mark, when the plants were ready for transplanting, the green-net treatment showed a reduction in lipid peroxidation damage and increases of 20% and 54% in dry weight compared with the control and red-net treatments, respectively. Finally, our results suggest that the use of a green photo-selective filter has a positive effect on oxidative stress, anatomical development, and overall growth of grafted tomato plants during the process of graft union formation.

The yeast galactose network as a quantitative model for cellular memory

Recent experiments have revealed surprising behavior in the yeast galactose (GAL) pathway, one of the preeminent systems for studying gene regulation. Under certain circumstances, yeast cells display memory of their prior nutrient environments. We distinguish two kinds of cellular memory discovered by quantitative investigations of the GAL network and present a conceptual framework for interpreting new experiments and current ideas on GAL memory. Reinduction memory occurs when cells respond transcriptionally to one environment, shut down the response during several generations in a second environment, then respond faster and with less cell-to-cell variation when returned to the first environment. Persistent memory describes a long-term, arguably stable response in which cells adopt a bimodal or unimodal distribution of induction levels depending on their preceding environment. Deep knowledge of how the yeast GAL pathway responds to different sugar environments has enabled rapid progress in uncovering the mechanisms behind GAL memory, which include cytoplasmic inheritance of inducer proteins and positive feedback loops among regulatory genes. This network of genes, long used to study gene regulation, is now emerging as a model system for cellular memory.

Steady state analysis of the genetic regulatory network incorporating underlying molecular mechanisms for anaerobic metabolism in Escherichia coli

A Gene Regulatory Network (GRN) represents complex connections between genes in a cell which interact with each other through their RNA and protein expression products, thereby determining the expression levels of mRNA and proteins required for functioning of the cell. Microarray experiments yield the log fold change in mRNA abundance and quantify the expression levels for a GRN at the genome level. While Boolean or Bayesian modeling along with expression and location data are useful in analyzing microarray data, they lack underlying mechanistic details present in GRNs. Our objective is to understand the role of molecular mechanisms in quantifying a GRN. To that effect, we analyze under steady state, the complete GRN for the central metabolic pathway during anaerobiosis in Escherichia coli. We simulate the microarray experiments using a steady state gene expression simulator (SSGES) that models molecular mechanistic details such as dimerization, multiple-site binding, auto-regulation and feedback. Given a GRN, the SSGES provided the log fold change in mRNA expression values as the output, which can be compared to data from microarray experiments. We predict the log fold changes for mutants obtained by knocking out crucial transcriptional regulators such as FNR (F), ArcA (A), IHFA-B (I) and DpiA (D) and observe a high degree of correlation with previously reported experimental data. We also predict the microarray expression values for hitherto unknown combinations of deletion mutants. We hierarchically cluster the predicted log fold change values for these mutants and postulate that E. coli has evolved from a predominantly lactate secreting (FAID mutant) into a mixed acid secreting phenotype as seen in the wild type (WT) during anaerobiosis. Upon simulating a model without incorporating the mechanistic details, not only the correlation with the experimental data reduced considerably, but also the clustering of expression data indicated WT to be closer to the quadruple mutant FAID. This clearly demonstrates the significance of incorporating mechanistic data while quantifying the expression profile of a GRN which can help in predicting the effect of a gene mutant and understanding the evolution of transcriptional control.

Growth-related model of the GAL system in Saccharomyces cerevisiae predicts behaviour of several mutant strains

The genetic regulatory network responds dynamically to perturbations in the intracellular and extracellular environments of an organism. The GAL system in the yeast Saccharomyces cerevisiae has evolved to utilise galactose as an alternative carbon and energy source, in the absence of glucose in the environment. This work contains a modified dynamic model for GAL system in S. cerevisiae, which includes a novel mechanism for Gal3p activation upon induction with galactose. The modification enables the model to simulate the experimental observation that in absence of galactose, oversynthesis of Gal3p can also induce the GAL system. Subsequently, the model is related to growth on galactose and glucose in a structured manner. The growth-related models are validated with experimental data for growth on individual substrates as well as mixed substrates. Finally, the model is tested for its prediction of a variety of known mutant behaviours. The exercise shows that the authors' model with a single set of parameters is able to capture the rich behaviour of the GAL system in S. cerevisiae. [Includes supplementary material].

Mathematical model of GAL regulon dynamics in Saccharomyces cerevisiae

Genetic switches are prevalent in nature and provide cells with a strategy to adapt to changing environments. The GAL switch is an intriguing example which is not understood in all detail. The GAL switch allows organisms to metabolize galactose, and controls whether the machinery responsible for the galactose metabolism is turned on or off. Currently, it is not known exactly how the galactose signal is sensed by the transcriptional machinery. Here we utilize quantitative tools to understand the S. cerevisiae cell response to galactose challenge, and to analyze the plausible molecular mechanisms underlying its operation. We work at a population level to develop a dynamic model based on the interplay of the key regulatory proteins Gal4p, Gal80p, and Gal3p. To our knowledge, the model presented here is the first to reproduce qualitatively the bistable network behavior found experimentally. Given the current understanding of the GAL circuit induction (Wightman et al., 2008; Jiang et al., 2009), we propose that the most likely in vivo mechanism leading to the transcriptional activation of the GAL genes is the physical interaction between galactose-activated Gal3p and Gal80p, with the complex Gal3p-Gal80p remaining bound at the GAL promoters. Our mathematical model is in agreement with the flow cytometry profiles of wild type, gal3Δ and gal80Δ mutant strains from Acar et al. (2005), and involves a fraction of actively transcribing cells with the same qualitative features as in the data set collected by Acar et al. (2010). Furthermore, the computational modeling provides an explanation for the contradictory results obtained by independent laboratories when tackling experimentally the issue of binary versus graded response to galactose induction.

Evolutionary aspects of a genetic network: studying the lactose/galactose regulon of Kluyveromyces lactis

The budding yeast Kluyveromyces lactis has diverged from the Saccharomyces lineage before the whole-genome duplication and its genome sequence reveals lower redundancy of many genes. Moreover, it shows lower preference for fermentative carbon metabolism and a broader substrate spectrum making it a particularly rewarding system for comparative and evolutionary studies of carbon-regulated genetic networks. The lactose/galactose regulon of K. lactis, which is regulated by the prototypic transcription activator Gal4 exemplifies important aspects of network evolution when compared with the model GAL regulon of Saccharomyces cerevisiae. Differences in physiology relate to different subcellular compartmentation of regulatory components and, importantly, to quantitative differences in protein-protein interactions rather than major differences in network architecture. Here, we introduce genetic and biochemical tools to study K. lactis in general and the lactose/galactose regulon in particular. We present methods to quantify relevant protein-protein interactions in that network and to visualize such differences in simple plate assays allowing for genetic approaches in further studies.

Steady state approach to model gene regulatory networks—Simulation of microarray experiments

Genetic regulatory networks (GRN) represent complex interactions between genes brought about through proteins that they code for. Quantification of expression levels in GRN either through experiments or theoretical modeling is a challenging task. Recently, microarray experiments have gained importance in evaluating GRN at the genome level. Microarray experiments yield log fold change in mRNA abundance which is helpful in deciphering connectivity in GRN. Current approaches such as data mining, Boolean or Bayesian modeling and combined use of expression and location data are useful in analyzing microarray data. However, these methodologies lack underlying mechanistic details present in GRN. We present here a steady state gene expression simulator (SSGES) which sets up steady state equations and simulates the response for a given network structure of a GRN. SSGES includes mechanistic details such as stoichiometry, protein-DNA and protein-protein interactions, translocation of regulatory proteins and autoregulation. SSGES can be used to simulate the response of a GRN in terms of fractional transcription and protein expression. SSGES can also be used to generate log fold change in mRNA abundance and protein expression implying that it is useful to simulate microarray type experiments. We have demonstrated these capabilities of SSGES by modeling the steady state response of GAL regulatory system in Saccharomyces cerevisiae. We have demonstrated that the predicted data qualitatively matched the microarray data obtained experimentally by Ideker et al. [Ideker, T., Thorsson, V., Ranish, J.A., Christmas, R., Buhler, J., Eng, J.K., Bumgarner, R., Goodlett, D.R., Aebersold, R., Hood, L., 2001. Integrated genomic and proteomic analyses of a systematically perturbed metabolic network. Science 292, 929-934]. SSGES is available from authors upon request.

The Galactose Switch in Kluyveromyces lactis Depends on Nuclear Competition between Gal4 and Gal1 for Gal80 Binding

The Gal4 protein represents a universally functional transcription activator, which in yeast is regulated by protein-protein interaction of its transcription activation domain with the inhibitor Gal80. Gal80 inhibition is relieved via galactose-mediated Gal80-Gal1-Gal3 interaction. The Gal4-Gal80-Gal1/3 regulatory module is conserved between Saccharomyces cerevisiae and Kluyveromyces lactis. Here we demonstrate that K. lactis Gal80 (KlGal80) is a nuclear protein independent of the Gal4 activity status, whereas KlGal1 is detected throughout the entire cell, which implies that KlGal80 and KlGal1 interact in the nucleus. Consistently KlGal1 accumulates in the nucleus upon KlGAL80 overexpression. Furthermore, we show that the KlGal80-KlGal1 interaction blocks the galactokinase activity of KlGal1 and is incompatible with KlGal80-KlGal4-AD interaction. Thus, we propose that dissociation of KlGal80 from the AD forms the basis of KlGal4 activation in K. lactis. Quantitation of the dissociation constants for the KlGal80 complexes gives a much lower affinity for KlGal1 as compared with Gal4. Mathematical modeling shows that with these affinities a switch based on competition between Gal1 and Gal4 for Gal80 binding is nevertheless efficient provided two monomeric Gal1 molecules interact with dimeric Gal80. Consistent with such a mechanism, analysis of the sedimentation behavior by analytical ultracentrifugation demonstrates the formation of a heterotetrameric KlGal80-KlGal1 complex of 2:2 stoichiometry.

Dual feedback loops in the GAL regulon suppress cellular heterogeneity in yeast

Transcriptional noise is known to be an important cause of cellular heterogeneity and phenotypic variation. The extent to which molecular interaction networks may have evolved to either filter or exploit transcriptional noise is a much debated question. The yeast genetic network regulating galactose metabolism involves two proteins, Gal3p and Gal80p, that feed back positively and negatively, respectively, on GAL gene expression. Using kinetic modeling and experimental validation, we demonstrate that these feedback interactions together are important for (i) controlling the cell-to-cell variability of GAL gene expression and (ii) ensuring that cells rapidly switch to an induced state for galactose uptake.

A comparative analysis of the GAL genetic switch between not-so-distant cousins: Saccharomyces cerevisiae versus Kluyveromyces lactis

Despite their close phylogenetic relationship, Kluyveromyces lactis and Saccharomyces cerevisiae have adapted their carbon utilization systems to different environments. Although they share identities in the arrangement, sequence and functionality of their GAL gene set, both yeasts have evolved important differences in the GAL genetic switch in accordance to their relative preference for the utilization of galactose as a carbon source. This review provides a comparative overview of the GAL-specific regulatory network in S. cerevisiae and K. lactis, discusses the latest models proposed to explain the transduction of the galactose signal, and describes some of the particularities that both microorganisms display in their regulatory response to different carbon sources. Emphasis is placed on the potential for improved strategies in biotechnological applications using yeasts.