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Single-cell study links metabolism with nutrient signaling and reveals sources of variability

Journal article
Authors Niek Welkenhuysen
Johannes Borgqvist
Mattias Backman
Loubna Bendrioua
Mattias Goksör
Caroline B. Adiels
Marija Cvijovic
Stefan Hohmann
Published in Bmc Systems Biology
Volume 11
Issue 59
ISSN 1752-0509
Publication year 2017
Published at Department of Mathematical Sciences
Department of Chemistry and Molecular Biology
Department of Physics (GU)
Language en
Keywords Microfluidics systems, Glucose uptake, Non-linear mixed effect modelling, Dynamical modelling, activated protein-kinase, saccharomyces-cerevisiae, glucose repression, hexose transporters, regulates phosphorylation, glycolytic flux, hexokinase 2, snf1 kinase, yeast-cells, complex, Mathematical & Computational Biology
Subject categories Bioinformatics and Systems Biology, Bioinformatics (Computational Biology)


Background: The yeast AMPK/SNF1 pathway is best known for its role in glucose de/repression. When glucose becomes limited, the Snf1 kinase is activated and phosphorylates the transcriptional repressor Mig1, which is then exported from the nucleus. The exact mechanism how the Snf1-Mig1 pathway is regulated is not entirely elucidated. Results: Glucose uptake through the low affinity transporter Hxt1 results in nuclear accumulation of Mig1 in response to all glucose concentrations upshift, however with increasing glucose concentration the nuclear localization of Mig1 is more intense. Strains expressing Hxt7 display a constant response to all glucose concentration upshifts. We show that differences in amount of hexose transporter molecules in the cell could cause cell-to-cell variability in the Mig1-Snf1 system. We further apply mathematical modelling to our data, both general deterministic and a nonlinear mixed effect model. Our model suggests a presently unrecognized regulatory step of the Snf1-Mig1 pathway at the level of Mig1 dephosphorylation. Model predictions point to parameters involved in the transport of Mig1 in and out of the nucleus as a majorsource of cell to cell variability. Conclusions: With this modelling approach we have been able to suggest steps that contribute to the cell-to-cell variability. Our data indicate a close link between the glucose uptake rate, which determines the glycolytic rate, and the activity of the Snf1/Mig1 system. This study hence establishes a close relation between metabolism and signalling.

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