Systems Level Analysis of the Yeast Osmo-Stat

Abstract

Adaptation is an important property of living organisms enabling them to cope with environmental stress and maintaining homeostasis. Adaptation is mediated by signaling pathways responding to different stimuli. Those signaling pathways might communicate in order to orchestrate the cellular response to multiple simultaneous stimuli, a phenomenon called crosstalk. Here, we investigate possible mechanisms of crosstalk between the High Osmolarity Glycerol (HOG) and the Cell Wall Integrity (CWI) pathways in yeast, which mediate adaptation to hyper- and hypo-osmotic challenges, respectively. We combine ensemble modeling with experimental investigations to test in quantitative terms different hypotheses about the crosstalk of the HOG and the CWI pathways. Our analyses indicate that for the conditions studied i) the CWI pathway activation employs an adaptive mechanism with a variable volume-dependent threshold, in contrast to the HOG pathway, whose activation relies on a fixed volume-dependent threshold, ii) there is no or little direct crosstalk between the HOG and CWI pathways, and iii) its mainly the HOG alone mediating adaptation of cellular osmotic pressure for both hyper- as well as hypo-osmotic stress. Thus, by iteratively combining mathematical modeling with experimentation we achieved a better understanding of regulatory mechanisms of yeast osmo-homeostasis and formulated new hypotheses about osmo-sensing.

Figure 1. Hyper-hypo-shock concept and generic signal functions.

(a–c) Conceptual illustration of the experimental setup for external osmolarity (solid line), and corresponding theoretical internal glycerol (dashed line) and volume (dotted line). (a) Single hyper-shock of 0.8 M sorbitol induces a sudden volume decrease with subsequent volume recovery as internal glycerol increases. (b) Single hyper-osmotic shock of 0.8 M sorbitol induces a sudden volume decrease with subsequent volume recovery as internal glycerol increases. Subsequent decrease to 0.27 M external sorbitol induces a sudden volume increase. Shift to 0.27 M sorbitol does not induce a hypo-osmotic shock, as the sudden volume increase does not exceed initial volume. (c) Single hyper-osmotic shock of 0.8 M sorbitol induces a sudden volume decrease with subsequent volume recovery as internal glycerol increases. Subsequent decrease to 0.27 M external sorbitol induces a sudden volume increase. Shift to 0.27 M sorbitol induces a hypo-shock, because the sudden volume increase exceeds initial volume. (d) Wiring schemes of the signaling module of the HOG and the CWI pathways with corresponding steady states as a function of volume. The parameters k₀ and k₂, defining the volume threshold upon which pathway activation is triggered, are the rate constants for reactions v₀ and v₂, respectively. Solid and dashed lines indicate Hog1 and Slt2 activation respectively. The dotted line indicates dynamic threshold regulation via a hypothetical sensitizing entity. MM: Michaelis-Menten kinetics. Mathematical details are provided in the Supplementary Material.

Figure 2. Alternative model structures.

Dashed lines indicate alternative components defining different model candidates. The best ranked model included the dark dashed lines. Three different sources of variation were implemented, each of them could adopt two different setups, and consequently 8 different model combinations were achievable: (a) Activated Hog1 inhibits the Slt2 activation/phosphorylation (reaction v₁₂), (b) Activated Slt2 inhibits the Hog1 activation/phosphorylation through (reaction v₃) and (c) a Slt2PP-dependent Sensitizer decreases CWISignal (v₁₁). We primarily started with four models and included the 3^rd source of variation “c” later on, as explained in the text. Gly, Osm, and So represent Glycerol, Osmotic pressure, and Sorbitol, respectively.

Figure 3. Reproduction of experimental data dedicated for parameter estimation using model with sensitized negative feedback.

Relative Hog1 and Slt2 phosphorylation data and relative single cell volume measurements, used for models parameters estimation, are plotted versus time. Simulations were done using the best ranked model from the ensemble of models with sensitized negative feedback. Solid lines show model simulations and filled circles (•) show the experimental data (Mean ± SD (n = 3)). (a) Comparison between Hog1 phosphorylation data and respective simulation for 0.8 M sorbitol shock only (NoHYPOS-Exp) and 4′, 14′ and 30′ hypo-shock experiments using the best ranked model (4 minHYPOS, 14 minHYPOS, 30 minHYPOS, respectively). (b) Comparison between Slt2 phosphorylation data and its simulation for 0.8 M sorbitol shock only, 4′, 14′, 30′ hypo-shock using best ranked model. The selected model can reproduce the 4′ Slt2 activation. (c) Comparison between the relative value of single cell volume measurement and its simulation upon 0.8 M sorbitol shock. The same color code was used for panel a,b.

Figure 4. Reproduction of experimental data dedicated for prediction using model with sensitized negative feedback.

Relative Hog1 and Slt2 phosphorylation data and relative value of cellular glycerol measurements, used for prediction, are plotted versus time. Simulations were done using the best ranked model from the ensemble of models with sensitized negative feedback. Solid lines show model simulations and filled circles (•) show the experimental data (Mean ± SD (n = 3)). (a) Comparison between Hog1 phosphorylation data and its simulation for 0.8 M sorbitol shock with subsequent dilution to 0.27 M sorbitol at 45″, 90″ and 45′ (45SecHYPO-Exp, 90SecHYPO-Exp, 45 minHYPO-Exp) and 0.8 M sorbitol shock with subsequent dilution to 0.5 and 0.4 M sorbitol at 4′ (4 min0.5HYPO-Exp, 4 min0.4HYPO-Ex) using the best ranked model. (b) Comparison between Slt2 phosphorylation data and its simulation for 0.8 M sorbitol shock with subsequent dilution to 0.27 M sorbitol at 45″, 90″ and 45′ and 0.8 M sorbitol shock with subsequent dilution to 0.5 and 0.4 M sorbitol at 4′ using the best ranked model. (c) Comparison between the relative value of intracellular glycerol content for 0.8 M sorbitol shock and its simulation. We used same color code for panel a,b.

Figure 5. Experimental validation of sensitizer.

Relative Hog1 and Slt2 phosphorylation data, used for further validation of model with sensitized negative feedback, are plotted versus time. Lines, except the vertical dash dot line, show model simulations and filled circles (•) show the experimental data (Mean ± SD (n = 3)). (a/d) Comparison between Hog1/Slt2 phosphorylation data and its simulation for 0.8 M sorbitol shock with subsequent dilution to 0.27 M sorbitol at 14′ in fps1-Δ1 mutant (Fully open Fps1). (b/e) Comparison between Hog1/Slt2 phosphorylation data and its simulation for a sorbitol shock of 0.4 with subsequent dilution to 0.27 M at 400″, respectively. (c/f) Comparison between Hog1/Slt2 phosphorylation data and its simulation for 0.8M sorbitol shock with subsequent dilution to 0.27 M at 14′, respectively.

Figure 6. Experimental validation for no/weak Slt2 mediated Fps1 dephosphorylation.

Relative Slt2 and Hog1 phosphorylation data, used for experimental validation of the model predictions, are plotted versus time. Solid lines show model simulations and cross marks (x) and filled circles (•) show the experimental data (Mean ± SD (n = 3)) for control and calcofluor exposed cells respectively. (a) The relative Slt2 phosphorylation/activation data under calcofluor exposure were used to calibrate the new module’s parameters. The model simulation shows Slt2 activation upon calcofluor exposure two hours following 0.8 M of sorbitol shock. (b) The normalized cellular glycerol level is plotted for control and calcofluor exposed cells at the time of calcofluor stress, t = 0, and 60 minutes after calcofluor exposure. The data do not show intracellular glycerol level decrease in calcofluor exposed cells. (c) Comparison of the relative Hog1 phosphorylation data with model simulation validates model prediction regarding no Hog1 activation upon calcofluor exposure two hours following 0.8 M of sorbitol shock.