contains code and data accompanying

Altered expression response upon repeated gene repression in single yeast cells

Lea Schuh^1,2,3,4, Igor Kukhtevich³, Poonam Bheda³, Melanie Schulz^1,2, Maria Bordukova^1,2, Robert Schneider^3,5,6,*, Carsten Marr^1,2,*

_{^{1Institute of Computational Biology, Helmholtz Zentrum München - German Research Center for Environmental Health, 85764 Neuherberg, Germany
2Institute of AI for Health, Helmholtz Zentrum München - German Research Center for Environmental Health, 85764 Neuherberg, Germany
3Institute of Functional Epigenetics, Helmholtz Zentrum München - German Research Center for Environmental Health, 85764 Neuherberg, Germany
4Department of Mathematics, Technical University of Munich, Garching, 85748, Germany
5German Center for Diabetes Research (DZD), 85764 Neuherberg, Germany
6Faculty of Biology, Ludwig-Maximilians University of Munich, 82152 Planegg-Martinsried, Germany
*correspondence: [email protected], [email protected]}}

Cells must continuously adjust to changing environments and, thus, have evolved mechanisms allowing them to respond to repeated stimuli. While faster gene induction upon a repeated stimulus is known as reinduction memory, responses to repeated repression have been less studied so far. Here, we studied gene repression across repeated carbon source shifts in over 1,500 single Saccharomyces cerevisiae cells. By monitoring the expression of a carbon source-responsive gene, galactokinase 1 (Gal1), and fitting a mathematical model to the single-cell data, we observed a faster response upon repeated repressions at the population level. Exploiting our single-cell data and quantitative modeling approach, we discovered that the faster response is mediated by a shortened repression response delay, the estimated time between carbon source shift and Gal1 protein production termination. Interestingly, we can exclude two alternative hypotheses, i) stronger dilution because of e.g., increased proliferation, and ii) a larger fraction of repressing cells upon repeated repressions. Collectively, our study provides a quantitative description of repression kinetics in single cells and allows us to pinpoint potential mechanisms underlying a faster response upon repeated repression. The computational results of our study can serve as the starting point for experimental follow-up studies.

Required software and toolboxes:

• MATLAB (R2017b)

The analysis was performed on a macOS Big Sur version 11.4.

Here you can find the list of all folders contained in this repository:

• Data
• Figures
• Functions
• Results
• Segmentation
• Tools

The important scripts are (i) main_script.m for running the workflow containing the data preprocessing, parameter estimation and statistical analysis (see Workflow) and (ii) plotFigureX.m to plot the single figure panels of figure X (see Figures).

The raw MATLAB structures containing the information extracted from segmenting single budding yeast cells can be found in the folder Segmentation, where the file name ExpX_posY_Z_A summarizes: X - experiment name, Y - position, Z - strain, A - strain name.

Single cell information important for our analysis (namely cell ID, mother cell ID, detection frame, relative GFP intensities per time and cell area per time) was extracted and summarized in MATLAB structures named SExpX_posY_Z_A according to their raw MATLAB files in Segmentation. The MATLAB structure containing the total GFP of all computed non-dividing cells, can also be found in the folder Data.

The workflow to the whole manuscript can be found in the script main_script.m.

plotFigureX.m scripts recreate all figure panels of figure X. The created figure files as pdfs are saved to and can be found in the folder Figures. You can also find the full figures in this folder.

All functions used by the main_script.m can be found here. More information on the functions can be found in the scripts themselves eg. input, output.

Both toolboxes, PESTO - Parameter EStimation TOolbox - and phyloCell, used in this analyis can be found here.

All outputs generated by the main_script.m apart from the data structures are saved here eg. estimated parameters for each total GFP trace for repressions 1 and 2 as well as the outputs of the statistical analysis and simulation study.