A living vector field reveals constraints on galactose network induction in yeast

Abstract

When a cell encounters a new environment, its transcriptional response can be constrained by its history. For example, yeast cells in galactose induce GAL genes with a speed and unanimity that depends on previous nutrient conditions. Cellular memory of longterm glucose exposure delays GAL induction and makes it highly variable with in a cell population, while other nutrient histories lead to rapid, uniform responses. To investigate how cell-level gene expression dynamics produce population-level phenotypes, we built living vector fields from thousands of single-cell time courses of the proteins Gal3p and Gal1p as cells switched to galactose from various nutrient histories. We show that, after sustained glucose exposure, the lack of these GAL transducers leads to induction delays that are long but also variable; that cellular resources constrain induction; and that bimodally distributed expression levels arise from lineage selection-a subpopulation of cells induces more quickly and outcompetes the rest. Our results illuminate cellular memory in this important model system and illustrate how resources and randomness interact to shape the response of a population to a new environment.

Assigning function to natural allelic variation via dynamic modeling of gene network induction

“…The single‐cell value of this lag time is directly correlated with the number of potential inducer proteins (Gal1p and Galp3p) present in the cell just before induction (Appendix Fig S2). This is in very good agreement with recent single‐cell experiments on galactose induction (Stockwell & Rifkin, ). Note that a binary response is not necessarily a signature of steady‐state bistability (Hermsen et al , ) but may represent a transient regime converging to a monostable ON state at equilibrium (see Appendix Text S1).…”

“…Cells with few inducers (as compared to repressors) display a lag time before responding, leading to a binary response pattern at the population level. This prediction from our model is fully consistent with recent experiments that tracked the induction of the network at the single-cell level and showed that the initial concentrations of Gal1p and Gal3p are predictive of the transient bimodal response (Stockwell & Rifkin, 2017). We also observed that feedback loops were important to control the strength of cell-to-cell variability before induction (Gal80-mediated negative feedback) and the duration of lag times (Gal3/Gal1-mediated positive feedbacks), which agrees with the previous observation that disabling the Gal80p and Gal3p feedback loops can transform a gradual response into a binary one (Ramsey et al, 2006).…”

“…Fourth, while we based our approach on cell population distributions, tracking the response dynamics of individual cells over time is also possible (Stockwell & Rifkin, 2017) and can provide more information on the network response. In other contexts, such methods had been very useful to infer parameters associated with individual cells (Llamosi et al, 2016).…”

Assigning function to natural allelic variation via dynamic modeling of gene network induction

“…Cells with few inducers (as compared to repressors) display a lag time before responding, leading to a binary response pattern at the population level. This prediction from our model is fully consistent with recent experiments that tracked the induction of the network at the single-cell level and showed that the initial concentrations of Gal1p and Gal3p are predictive of the transient bimodal response 25 . We also observed that feedback loops were important to control the strength of cell-to-cell variability before induction (Gal80-mediated negative feedback) and the duration of lag times (Gal3/Gal1-mediated positive feedbacks), which agrees with the previous observation that disabling the Gal80p and Gal3p feedback loops can transform a gradual response into a binary one 14 .…”

“…There are multiple ways that a GAL3 variant could affect the dynamics of induction: by modifying the production or degradation rates of the Gal3p protein or of its messenger RNA, by changing the affinity of Gal3p to galactose or ATP, by changing the capacity of Gal3p to dimerize, by changing the nucleocytoplasmic ratio of Gal3p molecules, or by changing the affinity of Gal3p to Gal80p. A GAL3 variant may also affect the background expression level of Gal3p prior to stimulation, which is known to be critical for network memory of prior stimulations 25 . Thus, it is difficult to predict the functional consequence of sequence variation in GAL3 .…”