Modeling Calcium Signaling in S. cerevisiae Highlights the Role and Regulation of the Calmodulin-Calcineurin Pathway in Response to Hypotonic Shock

Calcium homeostasis and signaling processes in Saccharomyces cerevisiae, as well as in any eukaryotic organism, depend on various transporters and channels located on both the plasma and intracellular membranes. The activity of these proteins is regulated by a number of feedback mechanisms that act through the calmodulin-calcineurin pathway. When exposed to hypotonic shock (HTS), yeast cells respond with an increased cytosolic calcium transient, which seems to be conditioned by the opening of stretch-activated channels. To better understand the role of each channel and transporter involved in the generation and recovery of the calcium transient-and of their feedback regulations-we defined and analyzed a mathematical model of the calcium signaling response to HTS in yeast cells. The model was validated by comparing the simulation outcomes with calcium concentration variations before and during the HTS response, which were observed experimentally in both wild-type and mutant strains. Our results show that calcium normally enters the cell through the High Affinity Calcium influx System and mechanosensitive channels. The increase of the plasma membrane tension, caused by HTS, boosts the opening probability of mechanosensitive channels. This event causes a sudden calcium pulse that is rapidly dissipated by the activity of the vacuolar transporter Pmc1. According to model simulations, the role of another vacuolar transporter, Vcx1, is instead marginal, unless calcineurin is inhibited or removed. Our results also suggest that the mechanosensitive channels are subject to a calcium-dependent feedback inhibition, possibly involving calmodulin. Noteworthy, the model predictions are in accordance with literature results concerning some aspects of calcium homeostasis and signaling that were not specifically addressed within the model itself, suggesting that it actually depicts all the main cellular components and interactions that constitute the HTS calcium pathway, and thus can correctly reproduce the shaping of the calcium signature by calmodulin- and calcineurin-dependent complex regulations. The model predictions also allowed to provide an interpretation of different regulatory schemes involved in calcium handling in both wild-type and mutants yeast strains. The model could be easily extended to represent different calcium signals in other eukaryotic cells

Differential scaling between G1 protein production and cell size dynamics promotes commitment to the cell division cycle in budding yeast

In the unicellular eukaryote Saccharomyces cerevisiae, Cln3-cyclin-dependent kinase activity enables Start, the irreversible commitment to the cell division cycle. However, the concentration of Cln3 has been paradoxically considered to remain constant during G1, due to the presumed scaling of its production rate with cell size dynamics. Measuring metabolic and biosynthetic activity during cell cycle progression in single cells, we found that cells exhibit pulses in their protein production rate. Rather than scaling with cell size dynamics, these pulses follow the intrinsic metabolic dynamics, peaking around Start. Using a viral-based bicistronic construct and targeted proteomics to measure Cln3 at the single-cell and population levels, we show that the differential scaling between protein production and cell size leads to a temporal increase in Cln3 concentration, and passage through Start. This differential scaling causes Start in both daughter and mother cells across growth conditions. Thus, uncoupling between two fundamental physiological parameters drives cell cycle commitment.status: publishe

Differential scaling between G1 protein production and cell size dynamics promotes commitment to the cell division cycle in budding yeast

In the unicellular eukaryote Saccharomyces cerevisiae, Cln3-cyclin-dependent kinase activity enables Start, the irreversible commitment to the cell division cycle. However, the concentration of Cln3 has been paradoxically considered to remain constant during G1, due to the presumed scaling of its production rate with cell size dynamics. Measuring metabolic and biosynthetic activity during cell cycle progression in single cells, we found that cells exhibit pulses in their protein production rate. Rather than scaling with cell size dynamics, these pulses follow the intrinsic metabolic dynamics, peaking around Start. Using a viral-based bicistronic construct and targeted proteomics to measure Cln3 at the single-cell and population levels, we show that the differential scaling between protein production and cell size leads to a temporal increase in Cln3 concentration, and passage through Start. This differential scaling causes Start in both daughter and mother cells across growth conditions. Thus, uncoupling between two fundamental physiological parameters drives cell cycle commitment

Dataset Figure_5: Whi5-GFP intensity versus size and time in a synchronous G1 population

Whi5-GFP intensity as a function of time for the different FOV of elutriated cells. This dataset contains: 1. Raw TIF_images_NADH – These are autofluorescence images taken at each time point for the purposes of calculating cell size. Time points were every 10 minutes except at 30 minutes which had to be discarded due to poor focus. 2. Raw TIF_images_WHI5 – These raw image files correspond to images of Whi5-GFP excited at 1000 nm fpr all 11 time points of different FOV obtained at 3 different z positions (1-3) (0, -0.5 um, + 0.5 um). Whi5-GFP intensity values for each nucleus were taken for the z-position that gave the highest intensity for each nucleus. 3. NADHDATA_with_time plot.xlsx is the analysis of the raw images of auto-fluorescence exciting at 750 nm. The only relevant information for the Figure is in Colume C sheet 1. It is the cell area in total pixels. 4. Whi5Data_BestFocusPlanes_All_FOV.xlsx is the analysis of the Whi5-GFP images for Whi5-GFP intensity vs time and size for each time point (which corresponds to a different FOV)

Dataset Figure_4: Nuclear Whi5-GFP intensity versus time from repeated imaging of the same individual cells

Whi5-GFP intensity as a function of time for the same FOVs. Only small daughter cells in the asynchronous population at time point 0 were quantified as a function of time. The folder 'Image Files' contains raw image files for Whi5-GFP for FOV1,3,4,5 & 6 at each of the 6 time points (0, 20, 40, 60, 80, 100 min). The folder 'Excel_analysis_output' contains the output files for all five FOV (1,3,4,5,6) for 1. fovX_t0 or time 100_whi5_nadh_*.xlsx – at time 0 and time 100 minutes. These files correspond to the analysis of the background auto-fluorescence excited at 750 nm used to calculate size. The only relevant information from these analyses is the Cyto size (fL) column in tab 3 of each file 2. fovX_tX_whi5_*.xlsx Analysis of all time points and all FOV for Whi5-GFP intensities

Dataset Figure_6: Cln3 levels pulse prior to Start

Data and Matlab script 1. FLmean_Cln3_GLU: CSV file containing mean GFP concentration from Cln3-P2A-GFP for single daughter cells grown in glucose. Each cell is followed from birth until a few minutes after bud appearance. Data is organized by columns (one column corresponds to one cell). The first column is the vector of measurement time points (in minutes). 2. Vol_Cln3_GLU: CSV file containing cell volume for each of the daughter cells described above. Data is organized by columns (one column corresponds to one cell). The first column is the vector of measurement time points (in minutes). 3. bud_times: Excel file containing the time of bud appearance for each of the daughter cells described above 4. GP_FLtot_Cln3_GLU_example: Matlab script used to perform Gaussian process regression (see description in Methods) on the total GFP fluorescence for each of the daughter cells described above. The script produces single-cell Cln3 abundance data used to generate Fig. 5c of the main text and Extended Data Figure 4H. NOTE: in order to run, the Matlab script requires the installation of the GPML Matlab toolbox (freely available at http://www.gaussianprocess.org/gpml/code/matlab/doc/)

Dataset Figure_2: Determination of nuclear Whi5 concentration by widefield fluorescence microscopy is confounded by non-proportional scaling of the nuclear-to-cell volume

This dataset contain the data used to determine the nuclear to whole-cell volume for the Nup133-yeGFP strain

Dataset Figure_1: Determination of Whi5 concentration with widefield fluorescence microscopy may be confounded by photobleaching

This dataset contains all data and scripts used to generate figure 1, "Determination of Whi5 concentration with widefield fluorescence microscopy may be confounded by photobleaching". - The ImageJ macro ("Macro_bleaching.ijm") is used to background correct the unprocessed bleaching movies, which are stored in the folders "Bleaching GFP (movies)" and "Bleaching RFP (movies)" respectively. - The Python notebook ("Fig1_data_analysis.ipynb") contains all code needed to analyze the bleaching movies and generate figure 1