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Schuh, L. ; Kukhtevich, I. ; Bheda, P. ; Schulz, M. ; Bordukova, M. ; Schneider, R. ; Marr, C.

Altered expression response upon repeated gene repression in single yeast cells.

PLoS Comput. Biol. 18:e1010640 (2022)
Verlagsversion Forschungsdaten DOI PMC
Open Access Gold
Creative Commons Lizenzvertrag
Cells must continuously adjust to changing environments and, thus, have evolved mechanisms allowing them to respond to repeated stimuli. While faster gene induction upon a repeated stimulus is known as reinduction memory, responses to repeated repression have been less studied so far. Here, we studied gene repression across repeated carbon source shifts in over 1,500 single Saccharomyces cerevisiae cells. By monitoring the expression of a carbon source-responsive gene, galactokinase 1 (Gal1), and fitting a mathematical model to the single-cell data, we observed a faster response upon repeated repressions at the population level. Exploiting our single-cell data and quantitative modeling approach, we discovered that the faster response is mediated by a shortened repression response delay, the estimated time between carbon source shift and Gal1 protein production termination. Interestingly, we can exclude two alternative hypotheses, i) stronger dilution because of e.g., increased proliferation, and ii) a larger fraction of repressing cells upon repeated repressions. Collectively, our study provides a quantitative description of repression kinetics in single cells and allows us to pinpoint potential mechanisms underlying a faster response upon repeated repression. The computational results of our study can serve as the starting point for experimental follow-up studies.
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Publikationstyp Artikel: Journalartikel
Dokumenttyp Wissenschaftlicher Artikel
Schlagwörter Green Fluorescent Protein; Transcriptional Memory; Parameter-estimation; Gal Genes; Model; Methylation; Galactose; Mechanism; Glucose
Sprache englisch
Veröffentlichungsjahr 2022
HGF-Berichtsjahr 2022
ISSN (print) / ISBN 1553-734X
e-ISSN 1553-7358
Zeitschrift PLoS Computational Biology
Quellenangaben Band: 18, Heft: 10, Seiten: , Artikelnummer: e1010640 Supplement: ,
Verlag Public Library of Science (PLoS)
Verlagsort 1160 Battery Street, Ste 100, San Francisco, Ca 94111 Usa
Begutachtungsstatus Peer reviewed
Institut(e) Institute of AI for Health (AIH)
Institute of Functional Epigenetics (IFE)
Institute of Computational Biology (ICB)
POF Topic(s) 30205 - Bioengineering and Digital Health
30203 - Molecular Targets and Therapies
Forschungsfeld(er) Enabling and Novel Technologies
Helmholtz Diabetes Center
PSP-Element(e) G-540007-001
G-502800-001
G-503800-001
Förderungen European Research Council (ERC)
Deutsche Forschungsgemeinschaft (DFG, German Research Foundation)
European Research Council (ERC) under the European Union
Bundesministerium fur Bildung und Forschung (project TIDY)
DOI 10.1371/journal.pcbi.1010640
WOS ID 000925007200034
Scopus ID 85141890633
PubMed ID 36256678
Erfassungsdatum 2022-11-25
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