Fine-Tuning of Energy Levels Regulates SUC2 via a SNF1-Dependent Feedback Loop

Nutrient sensing pathways are playing an important role in cellular response to different energy levels. In budding yeast, Saccharomyces cerevisiae, the sucrose non-fermenting protein kinase complex SNF1 is a master regulator of energy homeostasis. It is affected by multiple inputs, among which energy levels is the most prominent. Cells which are exposed to a switch in carbon source availability display a change in the gene expression machinery. It has been shown that the magnitude of the change varies from cell to cell. In a glucose rich environment Snf1/Mig1 pathway represses the expression of its downstream target, such as SUC2. However, upon glucose depletion SNF1 is activated which leads to an increase in SUC2 expression. Our single cell experiments indicate that upon starvation, gene expression pattern of SUC2 shows rapid increase followed by a decrease to initial state with high cell-to-cell variability. The mechanism behind this behavior is currently unknown. In this work we study the long-term behavior of the Snf1/Mig1 pathway upon glucose starvation with a microfluidics and non-linear mixed effect modeling approach. We show a negative feedback mechanism, involving Snf1 and Reg1, which reduces SUC2 expression after the initial strong activation. Snf1 kinase activity plays a key role in this feedback mechanism. Our systems biology approach proposes a negative feedback mechanism that works through the SNF1 complex and is controlled by energy levels. We further show that Reg1 likely is involved in the negative feedback mechanism

Modelling of glucose repression signalling in yeast Saccharomyces cerevisiae

Saccharomyces cerevisiae has a sophisticated signalling system that plays a crucial role in cellular adaptation to changing environments. The SNF1 pathway regulates energy homeostasis upon glucose derepression; hence, it plays an important role in various processes, such as metabolism, cell cycle and autophagy. To unravel its behaviour, SNF1 signalling has been extensively studied. However, the pathway components are strongly interconnected and inconstant; therefore, elucidating its dynamic behaviour based on experimental data only is challenging. To tackle this complexity, systems biology approaches have been successfully employed. This review summarizes the progress, advantages and disadvantages of the available mathematical modelling frameworks covering Boolean, dynamic kinetic, single-cell models, which have been used to study processes and phenomena ranging from crosstalks to sources of cell-to-cell variability in the context of SNF1 signalling. Based on the lessons from existing models, we further discuss how to develop a consensus dynamic mechanistic model of the entire SNF1 pathway that can provide novel insights into the dynamics of nutrient signalling

Towards mapping the 3D genome through high speed single-molecule tracking of functional transcription factors in single living cells

How genomic DNA is organized in the nucleus is a long-standing question. We describe a single-molecule bioimaging method utilizing super-localization precision coupled to fully quantitative image analysis tools, towards determining snapshots of parts of the 3D genome architecture of model eukaryote budding yeast Saccharomyces cerevisiae with exceptional millisecond time resolution. We employ astigmatism imaging to enable robust extraction of 3D position data on genomically encoded fluorescent protein reporters that bind to DNA. Our relatively straightforward method enables snippets of 3D architectures of likely single genome conformations to be resolved captured via DNA-sequence specific binding proteins in single functional living cells

Correlating single-molecule characteristics of the yeast aquaglyceroporin Fps1 with environmental perturbations directly in living cells

Membrane proteins play key roles at the interface between the cell and its environment by mediating selective import and export of molecules via plasma membrane channels. Despite a multitude of studies on transmembrane channels, understanding of their dynamics directly within living systems is limited. To address this, we correlated molecular scale information from living cells with real time changes to their microenvironment. We employed super-resolved millisecond fluorescence microscopy with a single-molecule sensitivity, to track labelled molecules of interest in real time. We use as example the aquaglyceroporin Fps1 in the yeast Saccharomyces cerevisiae to dissect and correlate its stoichiometry and molecular turnover kinetics with various extracellular conditions. We show that Fps1 resides in multi tetrameric clusters while hyperosmotic and oxidative stress conditions cause Fps1 reorganization. Moreover, we demonstrate that rapid exposure to hydrogen peroxide causes Fps1 degradation. In this way we shed new light on aspects of architecture and dynamics of glycerol-permeable plasma membrane channels

Correlative single-molecule fluorescence barcoding of gene regulation in Saccharomyces cerevisiae

Most cells adapt to their environment by switching combinations of genes on and off through a complex interplay of transcription factor proteins (TFs). The mechanisms by which TFs respond to signals, move into the nucleus and find specific binding sites in target genes is still largely unknown. Single-molecule fluorescence microscopes, which can image single TFs in live cells, have begun to elucidate the problem. Here, we show that different environmental signals, in this case carbon sources, yield a unique single-molecule fluorescence pattern of foci of a key metabolic regulating transcription factor, Mig1, in the nucleus of the budding yeast, Saccharomyces cerevisiae. This pattern serves as a ‘barcode’ of the gene regulatory state of the cells which can be correlated with cell growth characteristics and other biological function

Scalable and flexible inference framework for stochastic dynamic single-cell models

Understanding the inherited nature of how biological processes dynamically change over time and exhibit intra- and inter-individual variability, due to the different responses to environmental stimuli and when interacting with other processes, has been a major focus of systems biology. The rise of single-cell fluorescent microscopy has enabled the study of those phenomena. The analysis of single-cell data with mechanistic models offers an invaluable tool to describe dynamic cellular processes and to rationalise cell-to-cell variability within the population. However, extracting mechanistic information from single-cell data has proven difficult. This requires statistical methods to infer unknown model parameters from dynamic, multi-individual data accounting for heterogeneity caused by both intrinsic (e.g. variations in chemical reactions) and extrinsic (e.g. variability in protein concentrations) noise. Although several inference methods exist, the availability of efficient, general and accessible methods that facilitate modelling of single-cell data, remains lacking. Here we present a scalable and flexible framework for Bayesian inference in state-space mixed-effects single-cell models with stochastic dynamic. Our approach infers model parameters when intrinsic noise is modelled by either exact or approximate stochastic simulators, and when extrinsic noise is modelled by either time-varying, or time-constant parameters that vary between cells. We demonstrate the relevance of our approach by studying how cell-to-cell variation in carbon source utilisation affects heterogeneity in the budding yeast Saccharomyces cerevisiae SNF1 nutrient sensing pathway. We identify hexokinase activity as a source of extrinsic noise and deduce that sugar availability dictates cell-to-cell variability