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

Persson, Sebastian; Welkenhuysen, Niek; Shashkova, Sviatlana; Cvijović, Marija

doi:10.3389/fphys.2020.00954

Cited by 10 publications

(7 citation statements)

References 76 publications

(124 reference statements)

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“…Its multiplicative nature [ 27 ] makes the log-normal distribution a common choice for modelling parameters believed to vary between cells [ 6 ]. We also correlated the rate parameters to emulate that protein synthesis and degradation rates might co-vary [ 5 , 28 ] (additional setup details are in Section D in S1 Text ).…”

Section: Resultsmentioning

confidence: 99%

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

et al. 2022

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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.

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Section: Resultsmentioning

confidence: 99%

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

et al. 2022

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“…Non-linear mixed-effects modelling is typically used for longitudinal data exhibiting both within and between-subject variability (Davidian and Giltinan 2003). This method has been widely used in pharmacokinetics and pharmacodynamic studies (Lavielle and Mentré 2007; Sissoko et al 2016), but in recent years it is exploited in single-cell time-lapse data facilitating our understanding of cell-to-cell variability (Almquist et al 2015; Llamosi et al 2016; Persson et al 2020; Welkenhuysen et al 2017). When analyzing fluorescence measurements of a tagged protein in single cells over time, the observed intensity will differ between measurements even if the cells are in a steady-state due to the measurement error.…”

Section: Resultsmentioning

confidence: 99%

Localization and phosphorylation in the Snf1 network is controlled by two independent pathways

Österberg

Welkenhuysen

Persson

et al. 2021

Preprint

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AMPK/SNF1 is the master regulator of energy homeostasis in eukaryotic cells and has a key role in glucose de-repression. If glucose becomes depleted, Snf1 is phosphorylated and activated. Activation of Snf1 is required but is not sufficient for mediating glucose de-repression indicating a second glucose-regulated step that adjusts the Snf1 pathway. To elucidate this regulation, we further explore the spatial dynamics of Snf1 and Mig1 and how they are controlled by concentrations of hexose sugars. We utilize fluorescence recovery after photobleaching (FRAP) to study the movement of Snf1 and how it responds to external glucose concentrations. We show that the Snf1 pathway reacts both to the presence and to the absolute concentration of glucose. Furthermore, we identify a negative feedback loop regulating Snf1 activity. We also show that Mig1 localization correlates with the Snf1 phosphorylation pattern and not with the Mig1 phosphorylation pattern, suggesting that inactivation of Snf1 has a more pronounced effect on the localization of Mig1 than on the phosphorylation of Mig1. Our data offer insight into the true complexity of regulation of this central signaling pathway by one signal (glucose depletion) interpreted by the cell in different ways.

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“… 2015 , Persson et al . 2020 ) and sources of cell heterogeneity for the SNF1 pathway (Welkenhuysen et al . 2017 , Persson et al .…”

Section: Single-cell Dynamic Modelling—a Tale Of Individual Cellsmentioning

confidence: 99%

Modelling of glucose repression signalling in yeast Saccharomyces cerevisiae

Persson

Shashkova

Österberg

et al. 2022

FEMS Yeast Research

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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, plays an important role in various processes, such as metabolism, cell cycle, 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 summarises 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.

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

Cited by 10 publications

References 76 publications

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

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

Localization and phosphorylation in the Snf1 network is controlled by two independent pathways

Modelling of glucose repression signalling in yeast Saccharomyces cerevisiae

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