17 research outputs found
Recommended from our members
Robust network structure of the Sln1-Ypd1-Ssk1 three-component phospho-relay prevents unintended activation of the HOG MAPK pathway in Saccharomyces cerevisiae
Background: The yeast Saccharomyces cerevisiae relies on the high-osmolarity glycerol (HOG) signaling pathway to respond to increases in external osmolarity. The HOG pathway is rapidly activated under conditions of elevated osmolarity and regulates transcriptional and metabolic changes within the cell. Under normal growth conditions, however, a three-component phospho-relay consisting of the histidine kinase Sln1, the transfer protein Ypd1, and the response regulator Ssk1 represses HOG pathway activity by phosphorylation of Ssk1. This inhibition of the HOG pathway is essential for cellular fitness in normal osmolarity. Nevertheless, the extent to and mechanisms by which inhibition is robust to fluctuations in the concentrations of the phospho-relay components has received little attention. Results: We established that the Sln1-Ypd1-Ssk1 phospho-relay is robust—it is able to maintain inhibition of the HOG pathway even after significant changes in the levels of its three components. We then developed a biochemically realistic mathematical model of the phospho-relay, which suggested that robustness is due to buffering by a large excess pool of Ypd1. We confirmed experimentally that depletion of the Ypd1 pool results in inappropriate activation of the HOG pathway. Conclusions: We identified buffering by an intermediate component in excess as a novel mechanism through which a phospho-relay can achieve robustness. This buffering requires multiple components and is therefore unavailable to two-component systems, suggesting one important advantage of multi-component relays. Electronic supplementary material The online version of this article (doi:10.1186/s12918-015-0158-y) contains supplementary material, which is available to authorized users
Recommended from our members
Noise and interlocking signaling pathways promote distinct transcription factor dynamics in response to different stresses
All cells perceive and respond to environmental stresses through elaborate stress-sensing networks. Yeast cells sense stress through diverse signaling pathways that converge on the transcription factors Msn2 and Msn4, which respond by initiating rapid, idiosyncratic cycles into and out of the nucleus. To understand the role of Msn2/4 nuclear localization dynamics, we combined time-lapse studies of Msn2-GFP localization in living cells with computational modeling of stress-sensing signaling networks. We find that several signaling
pathways, including Ras/protein kinase A, AMP-activated kinase, the high-osmolarity response mitogen-activated protein kinase pathway, and protein phosphatase 1, regulate activation of Msn2 in distinct ways in response to different stresses. Moreover, we find that bursts of nuclear localization elicit a more robust transcriptional response than does sustained nuclear localization. Using stochastic modeling, we reproduce in silico the responses of Msn2 to different stresses, and demonstrate that bursts of localization arise from noise in the signaling pathways amplified by the small number of Msn2 molecules in the cell. This noise imparts diverse behaviors to genetically identical cells, allowing cell populations to “hedge their bets” in responding to an uncertain future, and to balance growth and survival in an unpredictable environment
Recommended from our members
Visualization and Analysis of mRNA Molecules Using Fluorescence <em>In Situ</em> Hybridization in <em>Saccharomyces cerevisiae</em>
The Fluorescence in situ Hybridization (FISH) method allows one to detect nucleic acids in the native cellular environment. Here we provide a protocol for using FISH to quantify the number of mRNAs in single yeast cells. Cells can be grown in any condition of interest and then fixed and made permeable. Subsequently, multiple single-stranded deoxyoligonucleotides conjugated to fluorescent dyes are used to label and visualize
mRNAs. Diffraction-limited fluorescence from single mRNA molecules is quantified using a spot-detection algorithm to identify and count the number of mRNAs per cell. While the more standard quantification methods of northern blots, RT-PCR and gene expression microarrays provide information on average mRNAs in the bulk population, FISH facilitates both the counting and localization of these mRNAs in single cells at single-molecule resolution
Single-cell RNA sequencing reveals intrinsic and extrinsic regulatory heterogeneity in yeast responding to stress
<div><p>From bacteria to humans, individual cells within isogenic populations can show significant variation in stress tolerance, but the nature of this heterogeneity is not clear. To investigate this, we used single-cell RNA sequencing to quantify transcript heterogeneity in single <i>Saccharomyces cerevisiae</i> cells treated with and without salt stress to explore population variation and identify cellular covariates that influence the stress-responsive transcriptome. Leveraging the extensive knowledge of yeast transcriptional regulation, we uncovered significant regulatory variation in individual yeast cells, both before and after stress. We also discovered that a subset of cells appears to decouple expression of ribosomal protein genes from the environmental stress response in a manner partly correlated with the cell cycle but unrelated to the yeast ultradian metabolic cycle. Live-cell imaging of cells expressing pairs of fluorescent regulators, including the transcription factor Msn2 with Dot6, Sfp1, or MAP kinase Hog1, revealed both coordinated and decoupled nucleocytoplasmic shuttling. Together with transcriptomic analysis, our results suggest that cells maintain a cellular filter against decoupled bursts of transcription factor activation but mount a stress response upon coordinated regulation, even in a subset of unstressed cells.</p></div
RP transcripts show low variation in abundance across cells.
<p>The mean and variance of transcript read counts per mRNA length (“length-norm”) was plotted for each mRNA from unstressed (left) or stressed (right) cells. (A,C) highlight RP transcripts and (B,D) highlight iESR and RiBi transcript against all other mRNAs (grey points). Plots are zoomed to capture most points. iESR, induced-Environmental Stress Response; RiBi, ribosome biogenesis; RP, ribosomal protein.</p
The influence of cell-cycle phase on ESR activation.
<p>Cycling genes used for classification were identified by clustering the scRNA-seq data [<a href="http://www.plosbiology.org/article/info:doi/10.1371/journal.pbio.2004050#pbio.2004050.ref054" target="_blank">54</a>] and then selecting clusters enriched for cell-cycle markers (<a href="http://www.plosbiology.org/article/info:doi/10.1371/journal.pbio.2004050#pbio.2004050.s003" target="_blank">S3 Table</a>, see <a href="http://www.plosbiology.org/article/info:doi/10.1371/journal.pbio.2004050#sec013" target="_blank">Methods</a>). (A) Cells (columns) were clustered based on the centroid expression pattern of genes within each group (rows) and manually classified into and sorted within designated groups (A, grey bins, <a href="http://www.plosbiology.org/article/info:doi/10.1371/journal.pbio.2004050#pbio.2004050.s009" target="_blank">S9 Table</a>). Stressed and unstressed cells are annotated by the purple/orange vector (A, bottom row). (B) The percentage of cells in each cell-cycle phase. Cell phases are listed in <a href="http://www.plosbiology.org/article/info:doi/10.1371/journal.pbio.2004050#pbio.2004050.s005" target="_blank">S5 Table</a>. (C) Boxplots (without whiskers) of all iESR (red) or RP (blue) genes from cells in that phase. Significance was assessed by Welch <i>t</i> test on the pooled RP or iESR genes from cells within a given phase compared to all other cells; unstressed and stressed cells were analyzed separately. Note only one cell was classified as G1/S after stress. ESR, Environmental Stress Response; iESR, induced-Environmental Stress Response; RP, ribosomal protein; scRNA-seq, single-cell RNA sequencing.</p
Transcript detection rate correlates with functional class.
<p>The fraction of cells in which each mRNA was detected was plotted against the mean length-normalized read count for that transcript, calculated from cells in which the transcript was measured, in (A) unstressed or (B) stressed cells. Listed <i>p</i>-values and arrows (where significant) indicate if the detection rate was higher or lower than randomly sampled genes. Plots are zoomed in to show transcripts whose mean read count is below 1.0; most transcripts above this range are detected in all cells, not shown. iESR, induced-Environmental Stress Response; RiBi, ribosome biogenesis; RP, ribosomal protein.</p
Stress-activated regulators show both coordinated and decoupled nuclear localization.
<p>(A) Distribution of nuclear/cytoplasmic signal for paired factors in individual cells before and after NaCl treatment (average <i>n</i> = 676 cells per time point). Data from two biological replicates were very similar and combined (<a href="http://www.plosbiology.org/article/info:doi/10.1371/journal.pbio.2004050#pbio.2004050.s011" target="_blank">S11 Table</a>). (B) Median ratios from (A) plotted over time; the Msn2 plot combines measurements from all three strains. (C) Nuclear TF signals (see <a href="http://www.plosbiology.org/article/info:doi/10.1371/journal.pbio.2004050#sec013" target="_blank">Methods</a>) of Dot6-GFP (left) and Msn2-mCherry (right) expressed in the same cells over time, before stress and after NaCl addition at 81 min (arrows). Each row aligned across all plots represents a different cell, and each column represents a different time point. Red plots show traces of nuclear localization according to the key (see <a href="http://www.plosbiology.org/article/info:doi/10.1371/journal.pbio.2004050#sec013" target="_blank">Methods</a>), and corresponding grey-scale plots show quantitative measurements only for time points called as peaks. Colored boxes above the plots indicate 80 min before stress (grey box), 30 min after NaCl treatment (dark red box), and beyond 30 min after NaCl treatment (pink box). Data are available in <a href="http://www.plosbiology.org/article/info:doi/10.1371/journal.pbio.2004050#pbio.2004050.s012" target="_blank">S12 Table</a>. (D) Correlation between Dot6-GFP and Msn2-mCherry traces for each temporal phase, according to the key. (E) Representative traces from (C), where called peaks (colored according to key) are indicated with asterisks. TF, transcription factor.</p
Quantitative variation in ESR activation across cells.
<p>(A) Mean-centered log<sub>2</sub>(read counts) for ESR gene groups before and after stress. Each row represents a transcript and each column is an individual cell, with expression values according to the key; white indicates no detected transcript. (B) The average mean-centered log<sub>2</sub> values for a given ESR gene group as measured in one cell was plotted against the average mean-centered log<sub>2</sub> values for a second ESR gene group as measured in the same cell. Correlations for unstressed (orange) and stressed (purple) cells are indicated on each plot. (C) Boxplots (without whiskers) of mean-centered log<sub>2</sub>(read counts) of RP and iESR transcripts in individual cells, sorted by iESR-group median. Arrows indicate unstressed cells with unusually low RP transcript abundances (FDR < 0.05, see Quantitative variation in ESR expression) and asterisks indicate those cells that also had high median iESR log<sub>2</sub> values. ESR, Environmental Stress Response; FDR, false discovery rate; iESR, induced-Environmental Stress Response; RiBi, ribosome biogenesis; RP, ribosomal protein.</p
Single-molecule FISH confirms differences in detection rate at several transcripts.
<p>Distributions of (A) length-normalized read counts measured by scRNA-seq and (B) mRNA molecules per cell measured by single-molecule FISH, for <i>PPT1</i>, <i>RLP7</i>, and <i>SES1</i> as a control. Note only part of the <i>SES1</i> distribution is shown. Median counts in cells with a measurement and detection rate (percentage of cells with a measurement) are listed below the figure. Data are available in <a href="http://www.plosbiology.org/article/info:doi/10.1371/journal.pbio.2004050#pbio.2004050.s010" target="_blank">S10 Table</a>. scRNA-seq, single-cell RNA sequencing.</p