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1.
Mol Syst Biol ; 14(4): e7390, 2018 04 04.
Article in English | MEDLINE | ID: mdl-29618636

ABSTRACT

Populations of isogenic cells often respond coherently to signals, despite differences in protein abundance and cell state. Previously, we uncovered processes in the Saccharomyces cerevisiae pheromone response system (PRS) that reduced cell-to-cell variability in signal strength and cellular response. Here, we screened 1,141 non-essential genes to identify 50 "variability genes". Most had distinct, separable effects on strength and variability of the PRS, defining these quantities as genetically distinct "axes" of system behavior. Three genes affected cytoplasmic microtubule function: BIM1, GIM2, and GIM4 We used genetic and chemical perturbations to show that, without microtubules, PRS output is reduced but variability is unaffected, while, when microtubules are present but their function is perturbed, output is sometimes lowered, but its variability is always high. The increased variability caused by microtubule perturbations required the PRS MAP kinase Fus3 and a process at or upstream of Ste5, the membrane-localized scaffold to which Fus3 must bind to be activated. Visualization of Ste5 localization dynamics demonstrated that perturbing microtubules destabilized Ste5 at the membrane signaling site. The fact that such microtubule perturbations cause aberrant fate and polarity decisions in mammals suggests that microtubule-dependent signal stabilization might also operate throughout metazoans.


Subject(s)
MAP Kinase Signaling System/genetics , Microtubule Proteins/genetics , Microtubules/genetics , Single-Cell Analysis , Adaptor Proteins, Signal Transducing/genetics , Cell Cycle Proteins/genetics , Microtubules/metabolism , Mitogen-Activated Protein Kinases/genetics , Pheromones/genetics , Saccharomyces cerevisiae/genetics , Saccharomyces cerevisiae Proteins/genetics , Signal Transduction/genetics
2.
Cell Syst ; 3(5): 444-455.e2, 2016 11 23.
Article in English | MEDLINE | ID: mdl-27894998

ABSTRACT

Many cell signaling systems, including the yeast pheromone response system, exhibit "dose-response alignment" (DoRA), in which output of one or more downstream steps closely matches the fraction of occupied receptors. DoRA can improve the fidelity of transmitted dose information. Here, we searched systematically for biochemical network topologies that produced DoRA. Most networks, including many containing feedback and feedforward loops, could not produce DoRA. However, networks including "push-pull" mechanisms, in which the active form of a signaling species stimulates downstream activity and the nominally inactive form reduces downstream activity, enabled perfect DoRA. Networks containing feedbacks enabled DoRA, but only if they also compared feedback to input and adjusted output to match. Our results establish push-pull as a non-feedback mechanism to align output with variable input and maximize information transfer in signaling systems. They also suggest genetic approaches to determine whether particular signaling systems use feedback or push-pull control.


Subject(s)
Signal Transduction , Computer Simulation , Feedback, Physiological , Saccharomyces cerevisiae
3.
Proc Natl Acad Sci U S A ; 109(10): 3874-8, 2012 Mar 06.
Article in English | MEDLINE | ID: mdl-22355134

ABSTRACT

Organismal fitness depends on the ability of gene networks to function robustly in the face of environmental and genetic perturbations. Understanding the mechanisms of this stability is one of the key aims of modern systems biology. Dissecting the basis of robustness to mutation has proven a particular challenge, with most experimental models relying on artificial DNA sequence variants engineered in the laboratory. In this work, we hypothesized that negative regulatory feedback could stabilize gene expression against the disruptions that arise from natural genetic variation. We screened yeast transcription factors for feedback and used the results to establish ROX1 (Repressor of hypOXia) as a model system for the study of feedback in circuit behaviors and its impact across genetically heterogeneous populations. Mutagenesis experiments revealed the mechanism of Rox1 as a direct transcriptional repressor at its own gene, enabling a regulatory program of rapid induction during environmental change that reached a plateau of moderate steady-state expression. Additionally, in a given environmental condition, Rox1 levels varied widely across genetically distinct strains; the ROX1 feedback loop regulated this variation, in that the range of expression levels across genetic backgrounds showed greater spread in ROX1 feedback mutants than among strains with the ROX1 feedback loop intact. Our findings indicate that the ROX1 feedback circuit is tuned to respond to perturbations arising from natural genetic variation in addition to its role in induction behavior. We suggest that regulatory feedback may be an important element of the network architectures that confer mutational robustness across biology.


Subject(s)
DNA Mutational Analysis , Repressor Proteins/genetics , Saccharomyces cerevisiae Proteins/genetics , Saccharomyces cerevisiae/genetics , Computational Biology/methods , Flow Cytometry , Genes, Fungal , Genetic Variation , Genomics , Green Fluorescent Proteins/metabolism , Hypoxia , Microscopy, Fluorescence/methods , Models, Genetic , Open Reading Frames , Saccharomyces cerevisiae/metabolism , Sequence Analysis, DNA , Transcription Factors/metabolism
4.
Proc Natl Acad Sci U S A ; 108(50): 20265-70, 2011 Dec 13.
Article in English | MEDLINE | ID: mdl-22114196

ABSTRACT

Although the proteins comprising many signaling systems are known, less is known about their numbers per cell. Existing measurements often vary by more than 10-fold. Here, we devised improved quantification methods to measure protein abundances in the Saccharomyces cerevisiae pheromone response pathway, an archetypical signaling system. These methods limited variation between independent measurements of protein abundance to a factor of two. We used these measurements together with quantitative models to identify and investigate behaviors of the pheromone response system sensitive to precise abundances. The difference between the maximum and basal signaling output (dynamic range) of the pheromone response MAPK cascade was strongly sensitive to the abundance of Ste5, the MAPK scaffold protein, and absolute system output depended on the amount of Fus3, the MAPK. Additional analysis and experiment suggest that scaffold abundance sets a tradeoff between maximum system output and system dynamic range, a prediction supported by recent experiments.


Subject(s)
Saccharomyces cerevisiae Proteins/metabolism , Saccharomyces cerevisiae/metabolism , Signal Transduction , Systems Biology , Fluorescence , Immunoblotting , MAP Kinase Signaling System , Models, Biological , Pheromones/metabolism
5.
Nature ; 456(7223): 755-61, 2008 Dec 11.
Article in English | MEDLINE | ID: mdl-19079053

ABSTRACT

Haploid Saccharomyces cerevisiae yeast cells use a prototypic cell signalling system to transmit information about the extracellular concentration of mating pheromone secreted by potential mating partners. The ability of cells to respond distinguishably to different pheromone concentrations depends on how much information about pheromone concentration the system can transmit. Here we show that the mitogen-activated protein kinase Fus3 mediates fast-acting negative feedback that adjusts the dose response of the downstream system response to match the dose response of receptor-ligand binding. This 'dose-response alignment', defined by a linear relationship between receptor occupancy and downstream response, can improve the fidelity of information transmission by making downstream responses corresponding to different receptor occupancies more distinguishable and reducing amplification of stochastic noise during signal transmission. We also show that one target of the feedback is a previously uncharacterized signal-promoting function of the regulator of G-protein signalling protein Sst2. Our work suggests that negative feedback is a general mechanism used in signalling systems to align dose responses and thereby increase the fidelity of information transmission.


Subject(s)
Feedback, Physiological/physiology , Mitogen-Activated Protein Kinases/metabolism , Pheromones/metabolism , Saccharomyces cerevisiae Proteins/metabolism , Saccharomyces cerevisiae/physiology , Signal Transduction , Dose-Response Relationship, Drug , GTPase-Activating Proteins/metabolism , Pheromones/pharmacology , Protein Binding , Saccharomyces cerevisiae/drug effects , Saccharomyces cerevisiae/metabolism , Signal Transduction/drug effects
6.
Nat Methods ; 4(2): 175-81, 2007 Feb.
Article in English | MEDLINE | ID: mdl-17237792

ABSTRACT

Microscope-based cytometry provides a powerful means to study cells in high throughput. Here we present a set of refined methods for making sensitive measurements of large numbers of individual Saccharomyces cerevisiae cells over time. The set consists of relatively simple 'wet' methods, microscope procedures, open-source software tools and statistical routines. This combination is very sensitive, allowing detection and measurement of fewer than 350 fluorescent protein molecules per living yeast cell. These methods enabled new protocols, including 'snapshot' protocols to calculate rates of maturation and degradation of molecular species, including a GFP derivative and a native mRNA, in unperturbed, exponentially growing yeast cells. Owing to their sensitivity, accuracy and ability to track changes in individual cells over time, these microscope methods may complement flow-cytometric measurements for studies of the quantitative physiology of cellular systems.


Subject(s)
Image Cytometry/methods , Microscopy, Fluorescence/methods , Proteins/analysis , Saccharomyces cerevisiae Proteins/analysis , Saccharomyces cerevisiae/chemistry , Flow Cytometry , Fluorescence , Green Fluorescent Proteins/analysis , HL-60 Cells , Humans , Proteins/metabolism , RNA Stability , Saccharomyces cerevisiae/cytology , Saccharomyces cerevisiae/growth & development , Saccharomyces cerevisiae Proteins/metabolism , Sensitivity and Specificity , Time Factors
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