A systems-level analysis of perfect adaptation in yeast osmoregulation

available in PMC 2011 June 7.Negative feedback can serve many different cellular functions, including noise reduction in transcriptional networks and the creation of circadian oscillations. However, only one special type of negative feedback (“integral feedback”) ensures perfect adaptation, where steady-state output is independent of steady-state input. Here we quantitatively measure single-cell dynamics in the Saccharomyces cerevisiae hyperosmotic shock network, which regulates membrane turgor pressure. Importantly, we find that the nuclear enrichment of the MAP kinase Hog1 perfectly adapts to changes in external osmolarity, a feature robust to signaling fidelity and operating with very low noise. By monitoring multiple system quantities (e.g., cell volume, Hog1, glycerol) and using varied input waveforms (e.g., steps and ramps), we assess in a minimally invasive manner the network location of the mechanism responsible for perfect adaptation. We conclude that the system contains only one effective integrating mechanism, which requires Hog1 kinase activity and regulates glycerol synthesis but not leakage.National Science Foundation (U.S.) (Graduate Research Fellowship)Massachusetts Institute of Technology (MIT-Merck Graduate Fellowship)National Institutes of Health (U.S.) (NIH grant R01-GM068957)National Institutes of Health (U.S.) (NIH grant 5 R90 DK071511-01

Insights into the biochemical strategies of adaptation to heat stress in the hyperthermophilic archaeon Pyrococcus furiosus

Organisms that thrive optimally at temperatures above 80°C are called hyperthermophiles. These prokaryotes have been isolated from a variety of hot environments, such as marine geothermal areas, hence they are usually slightly halophilic. Like other halophiles, marine hyperthermophiles have to cope with fluctuations in the salinity of the external medium and generally use low-molecular mass organic compounds to adjust cell turgor pressure. These compounds can accumulate to high levels without interfering with cell metabolism, thereby deserving the designation of compatible solutes. Curiously, the accumulation of compatible solutes also occurs in response to supraoptimal temperatures.(...)Fundação para a Ciência e a Tecnologi

Quantitative description of ion transport via plasma membrane of yeast and small cells

Modeling of ion transport via plasma membrane needs identification and quantitative understanding of the involved processes. Brief characterization of main ion transport systems of a yeast cell (Pma1, Ena1, TOK1, Nha1, Trk1, Trk2, non-selective cation conductance) and determining the exact number of molecules of each transporter per a typical cell allow us to predict the corresponding ion flows. In this review a comparison of ion transport in small yeast cell and several animal cell types is provided. The importance of cell volume to surface ratio is emphasized. The role of cell wall and lipid rafts is discussed in respect to required increase in spatial and temporal resolution of measurements. Conclusions are formulated to describe specific features of ion transport in a yeast cell. Potential directions of future research are outlined based on the assumptions.Comment: 22 pages, 6 figures, 1 tabl

Automated Ensemble Modeling with modelMaGe: Analyzing Feedback Mechanisms in the Sho1 Branch of the HOG Pathway

In systems biology uncertainty about biological processes translates into alternative mathematical model candidates. Here, the goal is to generate, fit and discriminate several candidate models that represent different hypotheses for feedback mechanisms responsible for downregulating the response of the Sho1 branch of the yeast high osmolarity glycerol (HOG) signaling pathway after initial stimulation. Implementing and testing these candidate models by hand is a tedious and error-prone task. Therefore, we automatically generated a set of candidate models of the Sho1 branch with the tool modelMaGe. These candidate models are automatically documented, can readily be simulated and fitted automatically to data. A ranking of the models with respect to parsimonious data representation is provided, enabling discrimination between candidate models and the biological hypotheses underlying them. We conclude that a previously published model fitted spurious effects in the data. Moreover, the discrimination analysis suggests that the reported data does not support the conclusion that a desensitization mechanism leads to the rapid attenuation of Hog1 signaling in the Sho1 branch of the HOG pathway. The data rather supports a model where an integrator feedback shuts down the pathway. This conclusion is also supported by dedicated experiments that can exclusively be predicted by those models including an integrator feedback

A systematic approach to detecting transcription factors in response to environmental stresses

Abstract Background Eukaryotic cells have developed mechanisms to respond to external environmental or physiological changes (stresses). In order to increase the activities of stress-protection functions in response to an environmental change, the internal cell mechanisms need to induce certain specific gene expression patterns and pathways by changing the expression levels of specific transcription factors (TFs). The conventional methods to find these specific TFs and their interactivities are slow and laborious. In this study, a novel efficient method is proposed to detect the TFs and their interactivities that regulate yeast genes that respond to any specific environment change. Results For each gene expressed in a specific environmental condition, a dynamic regulatory model is constructed in which the coefficients of the model represent the transcriptional activities and interactivities of the corresponding TFs. The proposed method requires only microarray data and information of all TFs that bind to the gene but it has superior resolution than the current methods. Our method not only can find stress-specific TFs but also can predict their regulatory strengths and interactivities. Moreover, TFs can be ranked, so that we can identify the major TFs to a stress. Similarly, it can rank the interactions between TFs and identify the major cooperative TF pairs. In addition, the cross-talks and interactivities among different stress-induced pathways are specified by the proposed scheme to gain much insight into protective mechanisms of yeast under different environmental stresses. Conclusion In this study, we find significant stress-specific and cell cycle-controlled TFs via constructing a transcriptional dynamic model to regulate the expression profiles of genes under different environmental conditions through microarray data. We have applied this TF activity and interactivity detection method to many stress conditions, including hyper- and hypo- osmotic shock, heat shock, hydrogen peroxide and cell cycle, because the available expression time profiles for these conditions are long enough. Especially, we find significant TFs and cooperative TFs responding to environmental changes. Our method may also be applicable to other stresses if the gene expression profiles have been examined for a sufficiently long time.</p

Mining for genotype-phenotype relations in Saccharomyces using partial least squares

<p>Abstract</p> <p>Background</p> <p>Multivariate approaches are important due to their versatility and applications in many fields as it provides decisive advantages over univariate analysis in many ways. Genome wide association studies are rapidly emerging, but approaches in hand pay less attention to multivariate relation between genotype and phenotype. We introduce a methodology based on a BLAST approach for extracting information from genomic sequences and Soft- Thresholding Partial Least Squares (ST-PLS) for mapping genotype-phenotype relations.</p> <p>Results</p> <p>Applying this methodology to an extensive data set for the model yeast <it>Saccharomyces cerevisiae</it>, we found that the relationship between genotype-phenotype involves surprisingly few genes in the sense that an overwhelmingly large fraction of the phenotypic variation can be explained by variation in less than 1% of the full gene reference set containing 5791 genes. These phenotype influencing genes were evolving 20% faster than non-influential genes and were unevenly distributed over cellular functions, with strong enrichments in functions such as cellular respiration and transposition. These genes were also enriched with known paralogs, stop codon variations and copy number variations, suggesting that such molecular adjustments have had a disproportionate influence on <it>Saccharomyces </it>yeasts recent adaptation to environmental changes in its ecological niche.</p> <p>Conclusions</p> <p>BLAST and PLS based multivariate approach derived results that adhere to the known yeast phylogeny and gene ontology and thus verify that the methodology extracts a set of fast evolving genes that capture the phylogeny of the yeast strains. The approach is worth pursuing, and future investigations should be made to improve the computations of genotype signals as well as variable selection procedure within the PLS framework.</p

Design, Synthesis, and Characterization of a Highly Effective Hog1 Inhibitor: A Powerful Tool for Analyzing MAP Kinase Signaling in Yeast

The Saccharomyces cerevisiae High-Osmolarity Glycerol (HOG) pathway is a conserved mitogen-activated protein kinase (MAPK) signal transduction system that often serves as a model to analyze systems level properties of MAPK signaling. Hog1, the MAPK of the HOG-pathway, can be activated by various environmental cues and it controls transcription, translation, transport, and cell cycle adaptations in response to stress conditions. A powerful means to study signaling in living cells is to use kinase inhibitors; however, no inhibitor targeting wild-type Hog1 exists to date. Herein, we describe the design, synthesis, and biological application of small molecule inhibitors that are cell-permeable, fast-acting, and highly efficient against wild-type Hog1. These compounds are potent inhibitors of Hog1 kinase activity both in vitro and in vivo. Next, we use these novel inhibitors to pinpoint the time of Hog1 action during recovery from G1 checkpoint arrest, providing further evidence for a specific role of Hog1 in regulating cell cycle resumption during arsenite stress. Hence, we describe a novel tool for chemical genetic analysis of MAPK signaling and provide novel insights into Hog1 action

The Adaptive Potential of the Middle Domain of Yeast Hsp90

The distribution of fitness effects (DFEs) of new mutations across different environments quantifies the potential for adaptation in a given environment and its cost in others. So far, results regarding the cost of adaptation across environments have been mixed, and most studies have sampled random mutations across different genes. Here, we quantify systematically how costs of adaptation vary along a large stretch of protein sequence by studying the distribution of fitness effects of the same approximately 2,300 amino-acid changing mutations obtained from deep mutational scanning of 119 amino acids in the middle domain of the heat shock protein Hsp90 in five environments. This region is known to be important for client binding, stabilization of the Hsp90 dimer, stabilization of the N-terminal-Middle and Middle-C-terminal interdomains, and regulation of ATPase-chaperone activity. Interestingly, we find that fitness correlates well across diverse stressful environments, with the exception of one environment, diamide. Consistent with this result, we find little cost of adaptation; on average only one in seven beneficial mutations is deleterious in another environment. We identify a hotspot of beneficial mutations in a region of the protein that is located within an allosteric center. The identified protein regions that are enriched in beneficial, deleterious, and costly mutations coincide with residues that are involved in the stabilization of Hsp90 interdomains and stabilization of client-binding interfaces, or residues that are involved in ATPase-chaperone activity of Hsp90. Thus, our study yields information regarding the role and adaptive potential of a protein sequence that complements and extends known structural information

Eisosomes Provide Membrane Reservoirs for Rapid Expansion of the Yeast Plasma Membrane

Cell surface area rapidly increases during mechanical and hypoosmotic stresses. Such expansion of the plasma membrane requires \u27membrane reservoirs\u27 that provide surface area and buffer membrane tension, but the sources of this membrane remain poorly understood. In principle, the flattening of invaginations and buds within the plasma membrane could provide this additional surface area, as recently shown for caveolae in animal cells. Here, we used microfluidics to study the rapid expansion of the yeast plasma membrane in protoplasts, which lack the rigid cell wall. To survive hypoosmotic stress, yeast cell protoplasts required eisosomes, protein-based structures that generate long invaginations at the plasma membrane. Both budding yeast and fission yeast protoplasts lacking eisosomes were unable to expand like wild-type protoplasts during hypoosmotic stress, and subsequently lysed. By performing quantitative fluorescence microscopy on single protoplasts, we also found that eisosomes disassembled as surface area increased. During this process, invaginations generated by eisosomes at the plasma membrane became flattened, as visualized by scanning electron microscopy. We propose that eisosomes serve as tension-dependent membrane reservoirs for expansion of yeast cells in an analogous manner to caveolae in animal cells

A systematic approach to detecting transcription factors in response to environmental stresses

[[abstract]]Background Eukaryotic cells have developed mechanisms to respond to external environmental or physiological changes (stresses). In order to increase the activities of stress-protection functions in response to an environmental change, the internal cell mechanisms need to induce certain specific gene expression patterns and pathways by changing the expression levels of specific transcription factors (TFs). The conventional methods to find these specific TFs and their interactivities are slow and laborious. In this study, a novel efficient method is proposed to detect the TFs and their interactivities that regulate yeast genes that respond to any specific environment change. Results For each gene expressed in a specific environmental condition, a dynamic regulatory model is constructed in which the coefficients of the model represent the transcriptional activities and interactivities of the corresponding TFs. The proposed method requires only microarray data and information of all TFs that bind to the gene but it has superior resolution than the current methods. Our method not only can find stress-specific TFs but also can predict their regulatory strengths and interactivities. Moreover, TFs can be ranked, so that we can identify the major TFs to a stress. Similarly, it can rank the interactions between TFs and identify the major cooperative TF pairs. In addition, the cross-talks and interactivities among different stress-induced pathways are specified by the proposed scheme to gain much insight into protective mechanisms of yeast under different environmental stresses. Conclusion In this study, we find significant stress-specific and cell cycle-controlled TFs via constructing a transcriptional dynamic model to regulate the expression profiles of genes under different environmental conditions through microarray data. We have applied this TF activity and interactivity detection method to many stress conditions, including hyper- and hypo- osmotic shock, heat shock, hydrogen peroxide and cell cycle, because the available expression time profiles for these conditions are long enough. Especially, we find significant TFs and cooperative TFs responding to environmental changes. Our method may also be applicable to other stresses if the gene expression profiles have been examined for a sufficiently long time.[[fileno]]2030106030033[[department]]電機工程學