The MYpop toolbox: Putting yeast stress responses in cellular context on single cell and population scales

Extreme Osmotolerance and Halotolerance in Food-Relevant Yeasts and the Role of Glycerol-Dependent Cell Individuality

Osmotolerance or halotolerance are used to describe resistance to sugars and salt, or only salt, respectively. Here, a comprehensive screen of more than 600 different yeast isolates revealed that osmosensitive species were equally affected by NaCl and glucose. However, the relative toxicity of salt became increasingly prominent in more osmoresistant species. We confirmed that growth inhibition by glucose in a laboratory strain of Saccharomyces cerevisiae occurred at a lower water activity (A_w) than by salt (NaCl), and pre-growth in high levels of glucose or salt gave enhanced cross-resistance to either. Salt toxicity was largely due to osmotic stress but with an additive enhancement due to effects of the relevant cation. Almost all of the yeast isolates from the screen were also noted to exhibit hetero-resistance to both salt and sugar, whereby high concentrations restricted growth to a small minority of cells within the clonal populations. Rare resistant colonies required growth for up to 28 days to become visible. This cell individuality was more marked with salt than sugar, a possible further reflection of the ion toxicity effect. In both cases, heteroresistance in S. cerevisiae was strikingly dependent on the GPD1 gene product, important for glycerol synthesis. In contrast, a tps1Δ deletant impaired for trehalose showed altered MIC but no change in heteroresistance. Effects on heteroresistance were evident in chronic (but not acute) salt or glucose stress, particularly relevant to growth on low A_w foods. The study reports diverse osmotolerance and halotolerance phenotypes and heteroresistance across an extensive panel of yeast isolates, and indicates that Gpd1-dependent glycerol synthesis is a key determinant enabling growth of rare yeast subpopulations at low A_w, brought about by glucose and in particular salt.

Transcriptional timing and noise of yeast cell cycle regulators-a single cell and single molecule approach

Gene expression is a stochastic process and its appropriate regulation is critical for cell cycle progression. Cellular stress response necessitates expression reprogramming and cell cycle arrest. While previous studies are mostly based on bulk experiments influenced by synchronization effects or lack temporal distribution, time-resolved methods on single cells are needed to understand eukaryotic cell cycle in context of noisy gene expression and external perturbations. Using smFISH, microscopy and morphological markers, we monitored mRNA abundances over cell cycle phases and calculated transcriptional noise for SIC1, CLN2, and CLB5, the main G1/S transition regulators in budding yeast. We employed mathematical modeling for in silico synchronization and for derivation of time-courses from single cell data. This approach disclosed detailed quantitative insights into transcriptional regulation with and without stress, not available from bulk experiments before. First, besides the main peak in G1 we found an upshift of CLN2 and CLB5 expression in late mitosis. Second, all three genes showed basal expression throughout cell cycle enlightening that transcription is not divided in on and off but rather in high and low phases. Finally, exposing cells to osmotic stress revealed different periods of transcriptional inhibition for CLN2 and CLB5 and the impact of stress on cell cycle phase duration. Combining experimental and computational approaches allowed us to precisely assess cell cycle progression timing, as well as gene expression dynamics.

Complexity-aware simple modeling

Mathematical models continue to be essential for deepening our understanding of biology. On one extreme, simple or small-scale models help delineate general biological principles. However, the parsimony of detail in these models as well as their assumption of modularity and insulation make them inaccurate for describing quantitative features. On the other extreme, large-scale and detailed models can quantitatively recapitulate a phenotype of interest, but have to rely on many unknown parameters, making them often difficult to parse mechanistically and to use for extracting general principles. We discuss some examples of a new approach-complexity-aware simple modeling-that can bridge the gap between the small-scale and large-scale approaches.